修改分支

问答,数据分析和报告生成
2025-02-06 16:10:41 +08:00 · 2025-02-06 09:24:19 +08:00
130 changed files with 31785 additions and 0 deletions
--- a/DataAnalysis/SplitData.py
+++ b/DataAnalysis/SplitData.py
@ -0,0 +1,40 @@
+import os
+import shutil
+
+# 原始文件夹路径
+original_folder = '/home/zhangxj/WorkFile/LCA-GPT/LCAdata'
+# 新文件夹的基础路径
+base_new_folder = '/home/zhangxj/WorkFile/LCA-GPT/split_LCAdata'
+
+# 获取原始文件夹中的所有PDF文件
+pdf_files = [f for f in os.listdir(original_folder) if f.endswith('.pdf')]
+
+# 计算每组文件的数量和剩余文件数量
+files_per_group, remainder = divmod(len(pdf_files), 6)
+
+# 创建并分配文件到各个组
+groups = []
+for i in range(6):
+    group = []
+    if i < remainder:
+        group = pdf_files[i * (files_per_group + 1):(i + 1) * (files_per_group + 1)]
+    else:
+        group = pdf_files[i * files_per_group + remainder:(i + 1) * files_per_group + remainder]
+    groups.append(group)
+
+# 确保每组文件数量正确
+for group in groups:
+    assert len(group) in (files_per_group, files_per_group + 1), "每组文件数量不正确"
+
+# 分组并复制文件
+for i, group in enumerate(groups):
+    # 创建新文件夹
+    new_folder = os.path.join(base_new_folder, f'folder{i+1}')
+    os.makedirs(new_folder, exist_ok=True)
+    
+    # 复制文件到新文件夹
+    for j, file_name in enumerate(group):
+        file_path = os.path.join(original_folder, file_name)
+        shutil.copy(file_path, new_folder)
+
+print("文件分组和复制完成。")
--- a/DataAnalysis/pycache/output_beautify.cpython-310.pyc
+++ b/DataAnalysis/pycache/output_beautify.cpython-310.pyc
--- a/DataAnalysis/pycache/schema.cpython-310.pyc
+++ b/DataAnalysis/pycache/schema.cpython-310.pyc
--- a/DataAnalysis/debug.py
+++ b/DataAnalysis/debug.py
@ -0,0 +1,44 @@
+import os
+import re
+import gradio as gr
+from PIL import Image
+from pprint import pprint
+from qwen_agent.agents import Assistant
+import sys
+os.chdir(sys.path[0])
+
+# os.environ['TMPDIR'] = "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp"
+
+llm_cfg = {
+    'model': 'qwen1.5-72b-chat',
+    'model_server': 'dashscope',
+    'api_key': "sk-c5f441f863f44094b0ddb96c831b5002",
+}
+
+system_instruction = '''你是一位专注在生命周期领域做数据分析的助手，在数据分析之后，
+                        如果有可视化要求，请使用 `plt.show()` 显示图像,并将图像进行保存。
+                        最后，请对数据分析结果结合生命周期评价领域知识进行解释。'''
+
+tools = ['code_interpreter']  # `code_interpreter` is a built-in tool for executing code.
+messages = []  # This stores the chat history.
+
+files = ["/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/tmp/2021beijing.csv"]
+bot = Assistant(llm=llm_cfg,
+                system_message=system_instruction,
+                function_list=tools,
+                files=files
+                )
+
+# print("user input>")
+user_input = '''2021beijing.csv给出了2021年北京不同行业的碳排放量数据，首先分析数据的每一行和每一列代表的含义，
+绘制不同排放行业的碳排放量占比的饼图，要求美观，字体清晰，给出可视化结果和结果分析。'''
+messages.append({'role': 'user', 'content': user_input})
+
+# Get response from bot
+response = []
+for response in bot.run(messages=messages):
+    continue
+pprint(response)
+messages.extend(response)
+
+
--- a/DataAnalysis/main.py
+++ b/DataAnalysis/main.py
@ -0,0 +1,106 @@
+import os
+import re
+import gradio as gr
+from PIL import Image
+from pprint import pprint
+from qwen_agent.agents import Assistant
+
+'''
+数据分析助手问答
+不知道为什么在服务器上跑就出现
+PermissionError: [Errno 13] Permission denied: '/tmp/gradio/872ec5dfa2067f6f2cafe865d734a9e4ab00234b'
+但是在自己机器上跑就没有问题。。。应该是权限问题吧 i think
+'''
+# 重置了临时路径 也还是不行。
+os.environ['TMPDIR'] = "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp"
+
+llm_cfg = {
+    'model': 'qwen1.5-72b-chat',
+    'model_server': 'dashscope',
+    'api_key': "sk-c5f441f863f44094b0ddb96c831b5002",
+}
+
+system_instruction = '''你是一位专注在生命周期领域做数据分析的助手，在数据分析之后，
+                        如果有可视化要求，请使用 `plt.show()` 显示图像,并将图像进行保存。
+                        最后，请对数据分析结果结合生命周期评价领域知识进行解释。'''
+
+tools = ['code_interpreter']  # `code_interpreter` is a built-in tool for executing code.
+messages = []  # This stores the chat history.
+
+# Function to extract image path from response
+def getImage(response):
+    pattern = r'workspace.*?\.png'
+    path = None
+    for res in response:
+        content = str(res['content'])
+        matches = re.findall(pattern, content)
+        # print("*********",res['content'])
+        if len(matches):
+            path = matches[0]
+    print("###### path #####,", path)
+    return path
+
+# Function to handle user input and generate chatbot response
+def chatbot_interface(user_input, uploaded_file_path):
+    bot = Assistant(llm=llm_cfg,
+                    system_message=system_instruction,
+                    function_list=tools,
+                    files=[uploaded_file_path])
+
+    # Add user input to messages
+    messages.append({'role': 'user', 'content': user_input})
+    # Get response from bot
+    response = []
+    for response in bot.run(messages=messages):
+        continue
+
+    pprint(response)
+    messages.extend(response)
+    # Convert bot response to string
+    res_str = ""
+    for res in response:
+        res_str += res['content']
+    # Get image path from response
+    tmp_path = getImage(response)
+    image_path = None
+    if tmp_path:
+        image_path = os.path.join("/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp", tmp_path)
+    print("image path", image_path)
+    # Check if image path exists and open image
+    if image_path and os.path.exists(image_path):
+        image = Image.open(image_path)
+        return res_str, image
+    else:
+        return res_str, None
+
+# Function to handle file upload
+def upload_file(file):
+    return f"文件上传成功{file}"
+
+# Creating the Gradio interface
+with gr.Blocks() as demo:
+    with gr.Column():
+        with gr.Row():
+            chatbot_input = gr.Textbox(label="LCA-Data-Assistant", placeholder="Enter your message here...")
+        with gr.Row():
+            with gr.Column():
+                chatbot_output_markdown = gr.Markdown()
+                chatbot_output_image = gr.Image(type="pil")
+        with gr.Row():
+            file_input = gr.File(label="Upload a file")
+        file_input.GRADIO_CACHE = "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp"
+
+    # Handle user input and file upload events
+    def update_output(user_input, uploaded_file_path):
+        response_str, image = chatbot_interface(user_input, uploaded_file_path)
+        markdown_output = response_str
+        image_output = image if image else None
+        return markdown_output, image_output
+
+    chatbot_input.submit(fn=update_output, inputs=[chatbot_input, file_input],
+                         outputs=[chatbot_output_markdown, chatbot_output_image])
+    file_input.change(fn=upload_file, inputs=file_input, outputs=[])
+
+# Launch the Gradio app
+demo.launch()
+
--- a/DataAnalysis/report.py
+++ b/DataAnalysis/report.py
@ -0,0 +1,53 @@
+import os
+import re
+import gradio as gr
+from PIL import Image
+from pprint import pprint
+from qwen_agent.agents import Assistant
+import sys
+os.chdir(sys.path[0])
+
+os.environ['TMPDIR'] = "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp"
+
+llm_cfg = {
+    'model': 'qwen1.5-72b-chat',
+    'model_server': 'dashscope',
+    'api_key': "sk-c5f441f863f44094b0ddb96c831b5002",
+}
+
+system_instruction = '''你是一位专注在生命周期领域做数据分析的助手，在数据分析之后，
+                        如果有可视化要求，请使用 `plt.show()` 显示图像,并将图像进行保存。
+                        最后，请对数据分析结果结合生命周期评价领域知识进行解释。'''
+
+tools = ['code_interpreter']  # `code_interpreter` is a built-in tool for executing code.
+messages = []  # This stores the chat history.
+
+files = ["/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/tmp/2021北京.csv","/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/报告案例1.md"]
+
+user_input = '''首先分析上传的2021北京.csv的碳排放数据，并处理分析数据和可视化分析，
+请按照报告案例1作为模板，用你掌握的信息进行填充，并且将可视化得到的图像结果插入到报告中并加以分析，以markdown格式输出填充数据信息之后的报告。'''
+messages.append({'role': 'user', 'content': user_input})
+bot = Assistant(llm=llm_cfg,
+                    system_message=system_instruction,
+                    function_list=tools,
+                    files=files)
+# Get response from bot
+response = []
+for response in bot.run(messages=messages):
+    continue
+
+pprint(response)
+messages.extend(response)
+# Convert bot response to string
+res_str = ""
+
+for res in response:
+    res_str += res['content']
+try:
+    with open("./result.md", "w", encoding="utf-8") as f:
+        f.write(res_str)
+except IOError as e:
+    print(f"An error occurred: {e}")
+
+print(res_str)
+
--- a/DataAnalysis/result.md
+++ b/DataAnalysis/result.md
@ -0,0 +1,85 @@
+```markdown
+# 2021年北京市主要行业碳排放研究报告
+
+## 基本信息
+- 报告标题：2021年北京市主要行业碳排放分析
+- 数据来源：2021北京.csv
+- 分析机构：EcoMetrics
+- 日期：2023年4月25日
+## 一、概况
+### 1. 数据覆盖范围
+本报告基于2021年北京市各行业碳排放数据，涵盖多个经济部门的碳足迹。
+## 二、量化目的
+评估并比较北京市不同行业在2021年的碳排放情况，为政策制定和减排策略提供依据。
+## 三、量化范围
+### 1. 功能单位或声明单位
+以万吨二氧化碳当量（Mt CO2e）为功能单位或声明单位。
+### 2. 系统边界
+- 综合能源生产和消费
+- 各行业的直接和间接排放
+### 3. 取舍准则
+依据国际标准如IPCC指南和国家统计规定。
+### 4. 时间范围
+2021年。
+## 四、清单分析
+### 1. 数据来源说明
+- 数据类型：官方统计数据
+- 范围：北京市
+### 2. 分配原则与程序
+- 分配依据：行业活动水平
+- 分配程序：按行业产值或能源消耗比例
+### 3. 清单结果及计算
+以下是部分行业的碳排放数据（以 Mt CO2e 计算）：
+| 行业分类 | 碳排放量 (Mt CO2e) |
+| --- | --- |
+| 石油和天然气开采 | 0.002702663 |
+| 钢铁冶炼 | 0.00336137 |
+| 食品加工 | 0.07873546 |
+| 纺织业 | 0.009668288 |
+| 造纸业 | 0.047495666 |
+
+### 4. 数据质量评价
+数据来源于官方统计，具有较高的可靠性和权威性。
+## 五、影响评价
+### 1. 影响类型和特征化因子选择
+100年全球变暖潜势（GWP100）用于计算所有温室气体的影响。
+### 2. 行业碳排放比较
+![](image.png)
+上图展示了2021年北京市主要行业碳排放的饼状图，清晰地反映了不同行业的碳排放贡献比例。
+## 六、结果解释
+2021年，北京市的总碳排放量为79.88 Mt CO2e。其中，能源行业（如石油和天然气开采）以及高能耗产业（如钢铁冶炼）的碳排放相对较低，而能源消费密集型的制造业（如食品加工、纺织业和造纸业）的碳排放显著。
+
+### 1. 结果说明
+从数据中可见，工业部门是北京市的主要碳排放源，尤其是能源密集型的制造业，这些行业需要采取减排措施以降低碳足迹。
+### 2. 假设和局限性说明
+报告假设所有数据准确无误，未考虑可能存在的统计误差或未报告排放。此外，由于数据不包含所有细分行业，可能低估了某些领域的实际排放。
+### 3. 改进建议
+- 促进能源结构转型，增加清洁能源使用比例
+- 加强工业能效管理，提高能源利用效率
+- 实施绿色制造技术，降低制造业的碳排放
+- 推广低碳生活方式和消费模式，减少城市总体碳足迹
+
+请注意，上述报告中提到的图像“image.png”需要通过实际的数据可视化生成并保存。在实际操作中，可以使用Python的matplotlib库绘制饼图来展示各行业碳排放的比例，然后插入到报告中。
+```
+
+为了完成报告，我们需要使用提供的数据生成饼状图并保存。以下是代码示例来实现这一目标：
+
+```python
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# 假设df是已加载的2021北京.csv数据
+# df['Industry'] 和 df['Emissions'] 是对应行业和排放量的列名
+# 首先，筛选出碳排放量大于0的行业
+industries = df[df['Emissions'] > 0]['Industry']
+emissions = df[df['Emissions'] > 0]['Emissions']
+
+# 画饼图
+fig, ax = plt.subplots()
+ax.pie(emissions, labels=industries, autopct='%1.1f%%')
+plt.title('2021年北京市主要行业碳排放')
+plt.savefig('image.png')  # 保存图像
+plt.show()  # 显示图像
+```
+
+运行这段代码将生成饼状图并保存为“image.png”，然后将其插入到报告中替换“![](image.png)”的位置。
--- a/DataAnalysis/test.py
+++ b/DataAnalysis/test.py
@ -0,0 +1,106 @@
+import os
+import re
+import gradio as gr
+from PIL import Image
+from pprint import pprint
+from qwen_agent.agents import Assistant
+
+'''
+数据分析助手问答
+不知道为什么在服务器上跑就出现
+PermissionError: [Errno 13] Permission denied: '/tmp/gradio/872ec5dfa2067f6f2cafe865d734a9e4ab00234b'
+但是在自己机器上跑就没有问题。。。应该是权限问题吧 i think
+'''
+# 重置了临时路径 也还是不行。
+os.environ['TMPDIR'] = "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp"
+
+llm_cfg = {
+    'model': 'qwen1.5-72b-chat',
+    'model_server': 'dashscope',
+    'api_key': "sk-c5f441f863f44094b0ddb96c831b5002",
+}
+
+system_instruction = '''你是一位专注在生命周期领域做数据分析的助手，在数据分析之后，
+                        如果有可视化要求，请使用 `plt.show()` 显示图像,并将图像进行保存。
+                        最后，请对数据分析结果结合生命周期评价领域知识进行解释。'''
+
+tools = ['code_interpreter']  # `code_interpreter` is a built-in tool for executing code.
+messages = []  # This stores the chat history.
+
+# Function to extract image path from response
+def getImage(response):
+    pattern = r'workspace.*?\.png'
+    path = None
+    for res in response:
+        content = str(res['content'])
+        matches = re.findall(pattern, content)
+        # print("*********",res['content'])
+        if len(matches):
+            path = matches[0]
+    print("###### path #####,", path)
+    return path
+
+# Function to handle user input and generate chatbot response
+def chatbot_interface(user_input, uploaded_file_path):
+    bot = Assistant(llm=llm_cfg,
+                    system_message=system_instruction,
+                    function_list=tools,
+                    files=[uploaded_file_path])
+
+    # Add user input to messages
+    messages.append({'role': 'user', 'content': user_input})
+    # Get response from bot
+    response = []
+    for response in bot.run(messages=messages):
+        continue
+
+    pprint(response)
+    messages.extend(response)
+    # Convert bot response to string
+    res_str = ""
+    for res in response:
+        res_str += res['content']
+    # Get image path from response
+    tmp_path = getImage(response)
+    image_path = None
+    if tmp_path:
+        image_path = os.path.join("/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp", tmp_path)
+    print("image path", image_path)
+    # Check if image path exists and open image
+    if image_path and os.path.exists(image_path):
+        image = Image.open(image_path)
+        return res_str, image
+    else:
+        return res_str, None
+
+# Function to handle file upload
+def upload_file(file):
+    return f"文件上传成功{file}"
+
+# Creating the Gradio interface
+with gr.Blocks() as demo:
+    with gr.Column():
+        with gr.Row():
+            chatbot_input = gr.Textbox(label="LCA-Data-Assistant", placeholder="Enter your message here...")
+        with gr.Row():
+            with gr.Column():
+                chatbot_output_markdown = gr.Markdown()
+                chatbot_output_image = gr.Image(type="pil")
+        with gr.Row():
+            file_input = gr.File(label="Upload a file")
+        file_input.GRADIO_CACHE = "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/tmp"
+
+    # Handle user input and file upload events
+    def update_output(user_input, uploaded_file_path):
+        response_str, image = chatbot_interface(user_input, uploaded_file_path)
+        markdown_output = response_str
+        image_output = image if image else None
+        return markdown_output, image_output
+
+    chatbot_input.submit(fn=update_output, inputs=[chatbot_input, file_input],
+                         outputs=[chatbot_output_markdown, chatbot_output_image])
+    file_input.change(fn=upload_file, inputs=file_input, outputs=[])
+
+# Launch the Gradio app
+demo.launch()
+
--- a/DataAnalysis/tmp/2021beijing.csv
+++ b/DataAnalysis/tmp/2021beijing.csv
@ -0,0 +1,50 @@
+Emission_Inventory,Raw_Coal,CleanedCoal,Other_Washed_Coal,Briquettes,Coke,Coke_Oven_Gas,Other_Gas,Other_Coking_Products,Crude_Oil,Gasoline,Kerosene,Diesel_Oil,Fuel_Oil,LPG,Refinery_Gas,Other_Petroleum_Products,Natural_Gas,Scope_2_Heat,Scope_2_Electricity,Other_Energy,Process,Scope_1_Total
+unit,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2
+TotalEmissions,2.0021984744341097,0,0,0.17806516054927157,0.0028765890412809116,0,0,0,0,14.049258786117642,15.062812602799132,4.042693831830754,0.019326756197558977,1.3065082109211597,2.1464739602678615,0.32940324454750275,39.990772243150595,0,0,0,0.7500386,79.88042845985687
+"Farming, Forestry, Animal Husbandry, Fishery and Water Conservancy",0.027948058177255413,0,0,0,0,0,0,0,0,0.057656896045355,0,0.05228554814260331,0,0.0015654303988990653,0,0,0.0021607289951994053,0,0,0,0,0.14161666175931217
+Coal Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Petroleum and Natural Gas Extraction,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.002702663186427796,0,0,0,0,0.002702663186427796
+Ferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.0002926745992150001,0,0.00029799902117672207,0,0,0.002770695943414191,0,0,0,0,0,0,0.0033613695638059133
+Nonferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Nonmetal Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Other Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.0008780237976450001,0,0.003873987275297386,0,0,0.00831208783024257,0,0,0,0,0,0,0.013064098903184957
+Logging and Transport of Wood and Bamboo,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Food Processing,0,0,0,0,0,0,0,0,0,0.0058534919843000015,0,0.009833967698831828,0,0.00024680161522865106,0.05541391886828382,0,0.007387279376235978,0,0,0,0,0.07873545954288028
+Food Production,0.0002166219449639238,0,0,0,1.3073027818946156e-06,0,0,0,0,0.006731515781945002,0,0.008641971614124938,0,0.0007404048456859532,0.0637260066985264,0,0.01279260574909157,0,0,0,0,0.09285043393711968
+Beverage Production,0,0,0,0,0,0,0,0,0,0.003804769789795001,0,0.002979990211767221,0,0.00024680161522865106,0.03601904726438448,0,0.01585562402704307,0,0,0,0,0.05890623290821842
+Tobacco Processing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0007207101830474124,0,0,0,0,0.0007207101830474124
+Textile Industry,0,0,0,0,0,0,0,0,0,0.0008780237976450001,0,0.00029799902117672207,0,0,0.00831208783024257,0,0.0001801775457618531,0,0,0,0,0.009668288194826145
+Garments and Other Fiber Products,0,0,0,0,0,0,0,0,0,0.005268142785870001,0,0.0005959980423534441,0,0,0.04987252698145543,0,0.001981953003380384,0,0,0,0,0.05771862081305926
+"Leather, Furs, Down and Related Products",0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0,0,0,0.005541391886828382,0,0,0,0,0,0,0.006126741085258383
+"Timber Processing, Bamboo, Cane, Palm Fiber & Straw Products",0,0,0,0,0,0,0,0,0,0.0017560475952900002,0,0.0005959980423534441,0,0,0.01662417566048514,0,0,0,0,0,0,0.018976221298128586
+Furniture Manufacturing,0,0,0,0,0,0,0,0,0,0.005268142785870001,0,0.0005959980423534441,0,0.00024680161522865106,0.04987252698145543,0,0.0007207101830474124,0,0,0,0,0.05670417960795494
+Papermaking and Paper Products,0,0,0,0,0,0,0,0,0,0.004097444389010001,0,0.002085993148237055,0,0,0.03878974320779867,0,0.0025224856406659436,0,0,0,0,0.04749566638571166
+Printing and Record Medium Reproduction,0,0,0,0,0,0,0,0,0,0.009658261774095003,0,0.00417198629647411,0,0.00024680161522865106,0.09143296613266833,0,0.0032431958237133557,0,0,0,0,0.10875321164217944
+"Cultural, Educational and Sports Articles",0,0,0,0,0,0,0,0,0,0.0014633729960750004,0,0.00029799902117672207,0,0.00024680161522865106,0.013853479717070955,0,0.0003603550915237062,0,0,0,0,0.016222008441075034
+Petroleum Processing and Coking,0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0.0005959980423534441,0,0.661921932043242,0.005541391886828382,0,0.03963906006760768,0,0,0,0,0.7082837312384614
+Raw Chemical Materials and Chemical Products,0,0,0,0,0,0,0,0,0,0.009072912575665,0,0.008045973571771498,0,0.0007404048456859532,0.04593874768597067,0,0.0037837284609989153,0,0,0,0,0.06758176714009204
+Medical and Pharmaceutical Products,0,0,0,0,0,0,0,0,0,0.009950936373310001,0,0.0023839921694137766,0,0.0004936032304573021,0.050384432945903314,0,0.025405033952421285,0,0,0,0,0.08861799867150567
+Chemical Fiber,0,0,0,0,0,0,0,0,0,0.0002926745992150001,0,0,0,0,0.0014818950866442155,0,0.0007207101830474124,0,0,0,0,0.0024952798689066276
+Rubber Products,0,0,0,0,0,0,0,0,0,0.0026340713929350005,0,0.0016389946164719713,0,0.0003702024228429766,0.013337055779797935,0,0.0003603550915237062,0,0,0,0,0.01834067930357159
+Plastic Products,0,0,0,0,0,0,0,0,0,0.0026340713929350005,0,0.0016389946164719713,0,0.0003702024228429766,0.013337055779797935,0,0.0003603550915237062,0,0,0,0,0.01834067930357159
+Nonmetal Mineral Products,0.14596733623893196,0,0,0,0.0007820285241293591,0,0,0,0,0.010828960170955,0,0.17641542053661946,0,0.0004936032304573021,0.10251574990632503,0,0.02414379113208831,0,0,0,0.7500386,1.2111854897395065
+Smelting and Pressing of Ferrous Metals,0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0,0,0,0.005541391886828382,0,0.01585562402704307,0,0,0,0,0.021982365112301453
+Smelting and Pressing of Nonferrous Metals,0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0,0,0,0.005541391886828382,0,0.0007207101830474124,0,0,0,0,0.006847451268305795
+Metal Products,0,0,0,0,0,0,0,0,0,0.022535944139555,0.0015175719959296297,0.00834397259294822,0,0.0007404048456859532,0.21334358764289266,0,0.004864793735570033,0,0,0,0,0.2513462749525815
+Ordinary Machinery,0,0,0,0,0,0,0,0,0,0.016682452155255004,0,0.002085993148237055,0,0.0019744129218292085,0.15792966877460887,0,0.003063018277951503,0,0,0,0,0.18173554527788163
+Equipment for Special Purposes,0,0,0,0,0,0,0,0,0,0.018731174349760005,0,0.006555978465887885,0,0.00024680161522865106,0.17732454037850823,0,0.001981953003380384,0,0,0,0,0.20484044781276517
+Transportation Equipment,0,0,0,0,0,0,0,0,0,0.016097102956825003,0,0.0336738893929696,0,0.00024680161522865106,0.1523882768877805,0,0.035314798969323206,0,0,0,0,0.23772086982212698
+Electric Equipment and Machinery,0,0,0,0,0,0,0,0,0,0.014926404559965006,0,0.0017879941270603325,0,0.0004936032304573021,0.14130549311412374,0,0.0018017754576185308,0,0,0,0,0.16031527048922492
+Electronic and Telecommunications Equipment,0,0,0,0,0,0,0,0,0,0.010828960170955,0,0.0008939970635301663,0,0,0.10251574990632503,0,0.0037837284609989153,0,0,0,0,0.11802243560180911
+"Instruments, Meters, Cultural and Office Machinery",0,0,0,0,0,0,0,0,0,0.008487563377235,0,0.00029799902117672207,0,0,0.08035018235901153,0,0.0003603550915237062,0,0,0,0,0.08949609984894696
+Other Manufacturing Industry,0,0,0,0,0,0,0,0,0,0.0014633729960750004,0,0.008641971614124938,0,0,0.013853479717070955,0,0.0001801775457618531,0,0,0,0,0.024139001873032747
+Scrap and waste,0,0,0,0,0,0,0,0,0,0.0002926745992150001,0,0.00029799902117672207,0,0,0.002770695943414191,0,0.0001801775457618531,0,0,0,0,0.0035415471095677662
+"Production and Supply of Electric Power, Steam and Hot Water",1.7584294721807978,0,0,0,0.002093253214369658,0,0,0,0,0.011706983968600003,0,0.036479049620716374,0.005069313100999075,0.14259084507648084,0.34572372722416206,0.32940324454750275,28.576712994550185,0,0,0,0,31.208208883483813
+Production and Supply of Gas,0,0,0,0,0,0,0,0,0,0.004097444389010001,0,0.0005959980423534441,0,0,0.03878974320779867,0,0.0787375874979298,0,0,0,0,0.12222077313709193
+Production and Supply of Tap Water,0,0,0,0,0,0,0,0,0,0.003804769789795001,0,0.0026819911905904977,0,0.0007404048456859532,0.03601904726438448,0,0.0009008877288092654,0,0,0,0,0.04414710081926519
+Construction,0,0,0,0,0,0,0,0,0,0.21687187801831503,0,0.4523163987247695,0,0.013462701430531963,0,0,0.05185749588478573,0,0,0,0,0.7345084740584022
+"Transportation, Storage, Post and Telecommunication Services",0.0005851847553963052,0,0,0,0,0,0,0,0,0.901730440181415,15.042780652452862,2.411941617276541,0.014257443096559902,0.052285375323228786,0,0,0.5531466227710478,0,0,0,0,18.976727335857053
+"Wholesale, Retail Trade and Catering Services",0,0,0,0,0,0,0,0,0,0.51042450103096,0,0.12220586696052255,0,0.06011252731772412,0,0,1.1732758443932771,0,0,0,0,1.8660187397024839
+Others,0,0,0,0,0,0,0,0,0,1.0509944857810651,0.01851437835034148,0.676618306437121,0,0.03757032957357757,0,0,5.548752059672073,0,0,0,0,7.332449559814178
+Urban,0.06905180113676401,0,0,0,0,0,0,0,0,11.098220802232799,0,0,0,0.1865993035487686,0,0,3.3750586905014712,0,0,0,0,14.728930597419804
+Rural,0,0,0,0.17806516054927157,0,0,0,0,0,0,0,0,0,0.1415149080604755,0,0,0.4191814250686847,0,0,0,0,0.7387614936784318
--- a/DataAnalysis/tmp/2021beijing.xlsx
+++ b/DataAnalysis/tmp/2021beijing.xlsx
--- a/DataAnalysis/tmp/2021北京.csv
+++ b/DataAnalysis/tmp/2021北京.csv
@ -0,0 +1,51 @@
+Emission_Inventory,Raw_Coal,CleanedCoal,Other_Washed_Coal,Briquettes,Coke,Coke_Oven_Gas,Other_Gas,Other_Coking_Products,Crude_Oil,Gasoline,Kerosene,Diesel_Oil,Fuel_Oil,LPG,Refinery_Gas,Other_Petroleum_Products,Natural_Gas,Scope_2_Heat,Scope_2_Electricity,Other_Energy,Process,Scope_1_Total
+unit,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2
+,,,,,,,,,,,,,,,,,,,,,,
+TotalEmissions,2.002198474,0,0,0.178065161,0.002876589,0,0,0,0,14.04925879,15.0628126,4.042693832,0.019326756,1.306508211,2.14647396,0.329403245,39.99077224,0,0,0,0.7500386,79.88042846
+"Farming, Forestry, Animal Husbandry, Fishery and Water Conservancy",0.027948058,0,0,0,0,0,0,0,0,0.057656896,0,0.052285548,0,0.00156543,0,0,0.002160729,0,0,0,0,0.141616662
+Coal Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Petroleum and Natural Gas Extraction,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.002702663,0,0,0,0,0.002702663
+Ferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.000292675,0,0.000297999,0,0,0.002770696,0,0,0,0,0,0,0.00336137
+Nonferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Nonmetal Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Other Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.000878024,0,0.003873987,0,0,0.008312088,0,0,0,0,0,0,0.013064099
+Logging and Transport of Wood and Bamboo,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Food Processing,0,0,0,0,0,0,0,0,0,0.005853492,0,0.009833968,0,0.000246802,0.055413919,0,0.007387279,0,0,0,0,0.07873546
+Food Production,0.000216622,0,0,0,1.3073E-06,0,0,0,0,0.006731516,0,0.008641972,0,0.000740405,0.063726007,0,0.012792606,0,0,0,0,0.092850434
+Beverage Production,0,0,0,0,0,0,0,0,0,0.00380477,0,0.00297999,0,0.000246802,0.036019047,0,0.015855624,0,0,0,0,0.058906233
+Tobacco Processing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00072071,0,0,0,0,0.00072071
+Textile Industry,0,0,0,0,0,0,0,0,0,0.000878024,0,0.000297999,0,0,0.008312088,0,0.000180178,0,0,0,0,0.009668288
+Garments and Other Fiber Products,0,0,0,0,0,0,0,0,0,0.005268143,0,0.000595998,0,0,0.049872527,0,0.001981953,0,0,0,0,0.057718621
+"Leather, Furs, Down and Related Products",0,0,0,0,0,0,0,0,0,0.000585349,0,0,0,0,0.005541392,0,0,0,0,0,0,0.006126741
+"Timber Processing, Bamboo, Cane, Palm Fiber & Straw Products",0,0,0,0,0,0,0,0,0,0.001756048,0,0.000595998,0,0,0.016624176,0,0,0,0,0,0,0.018976221
+Furniture Manufacturing,0,0,0,0,0,0,0,0,0,0.005268143,0,0.000595998,0,0.000246802,0.049872527,0,0.00072071,0,0,0,0,0.05670418
+Papermaking and Paper Products,0,0,0,0,0,0,0,0,0,0.004097444,0,0.002085993,0,0,0.038789743,0,0.002522486,0,0,0,0,0.047495666
+Printing and Record Medium Reproduction,0,0,0,0,0,0,0,0,0,0.009658262,0,0.004171986,0,0.000246802,0.091432966,0,0.003243196,0,0,0,0,0.108753212
+"Cultural, Educational and Sports Articles",0,0,0,0,0,0,0,0,0,0.001463373,0,0.000297999,0,0.000246802,0.01385348,0,0.000360355,0,0,0,0,0.016222008
+Petroleum Processing and Coking,0,0,0,0,0,0,0,0,0,0.000585349,0,0.000595998,0,0.661921932,0.005541392,0,0.03963906,0,0,0,0,0.708283731
+Raw Chemical Materials and Chemical Products,0,0,0,0,0,0,0,0,0,0.009072913,0,0.008045974,0,0.000740405,0.045938748,0,0.003783728,0,0,0,0,0.067581767
+Medical and Pharmaceutical Products,0,0,0,0,0,0,0,0,0,0.009950936,0,0.002383992,0,0.000493603,0.050384433,0,0.025405034,0,0,0,0,0.088617999
+Chemical Fiber,0,0,0,0,0,0,0,0,0,0.000292675,0,0,0,0,0.001481895,0,0.00072071,0,0,0,0,0.00249528
+Rubber Products,0,0,0,0,0,0,0,0,0,0.002634071,0,0.001638995,0,0.000370202,0.013337056,0,0.000360355,0,0,0,0,0.018340679
+Plastic Products,0,0,0,0,0,0,0,0,0,0.002634071,0,0.001638995,0,0.000370202,0.013337056,0,0.000360355,0,0,0,0,0.018340679
+Nonmetal Mineral Products,0.145967336,0,0,0,0.000782029,0,0,0,0,0.01082896,0,0.176415421,0,0.000493603,0.10251575,0,0.024143791,0,0,0,0.7500386,1.21118549
+Smelting and Pressing of Ferrous Metals,0,0,0,0,0,0,0,0,0,0.000585349,0,0,0,0,0.005541392,0,0.015855624,0,0,0,0,0.021982365
+Smelting and Pressing of Nonferrous Metals,0,0,0,0,0,0,0,0,0,0.000585349,0,0,0,0,0.005541392,0,0.00072071,0,0,0,0,0.006847451
+Metal Products,0,0,0,0,0,0,0,0,0,0.022535944,0.001517572,0.008343973,0,0.000740405,0.213343588,0,0.004864794,0,0,0,0,0.251346275
+Ordinary Machinery,0,0,0,0,0,0,0,0,0,0.016682452,0,0.002085993,0,0.001974413,0.157929669,0,0.003063018,0,0,0,0,0.181735545
+Equipment for Special Purposes,0,0,0,0,0,0,0,0,0,0.018731174,0,0.006555978,0,0.000246802,0.17732454,0,0.001981953,0,0,0,0,0.204840448
+Transportation Equipment,0,0,0,0,0,0,0,0,0,0.016097103,0,0.033673889,0,0.000246802,0.152388277,0,0.035314799,0,0,0,0,0.23772087
+Electric Equipment and Machinery,0,0,0,0,0,0,0,0,0,0.014926405,0,0.001787994,0,0.000493603,0.141305493,0,0.001801775,0,0,0,0,0.16031527
+Electronic and Telecommunications Equipment,0,0,0,0,0,0,0,0,0,0.01082896,0,0.000893997,0,0,0.10251575,0,0.003783728,0,0,0,0,0.118022436
+"Instruments, Meters, Cultural and Office Machinery",0,0,0,0,0,0,0,0,0,0.008487563,0,0.000297999,0,0,0.080350182,0,0.000360355,0,0,0,0,0.0894961
+Other Manufacturing Industry,0,0,0,0,0,0,0,0,0,0.001463373,0,0.008641972,0,0,0.01385348,0,0.000180178,0,0,0,0,0.024139002
+Scrap and waste,0,0,0,0,0,0,0,0,0,0.000292675,0,0.000297999,0,0,0.002770696,0,0.000180178,0,0,0,0,0.003541547
+"Production and Supply of Electric Power, Steam and Hot Water",1.758429472,0,0,0,0.002093253,0,0,0,0,0.011706984,0,0.03647905,0.005069313,0.142590845,0.345723727,0.329403245,28.57671299,0,0,0,0,31.20820888
+Production and Supply of Gas,0,0,0,0,0,0,0,0,0,0.004097444,0,0.000595998,0,0,0.038789743,0,0.078737587,0,0,0,0,0.122220773
+Production and Supply of Tap Water,0,0,0,0,0,0,0,0,0,0.00380477,0,0.002681991,0,0.000740405,0.036019047,0,0.000900888,0,0,0,0,0.044147101
+Construction,0,0,0,0,0,0,0,0,0,0.216871878,0,0.452316399,0,0.013462701,0,0,0.051857496,0,0,0,0,0.734508474
+"Transportation, Storage, Post and Telecommunication Services",0.000585185,0,0,0,0,0,0,0,0,0.90173044,15.04278065,2.411941617,0.014257443,0.052285375,0,0,0.553146623,0,0,0,0,18.97672734
+"Wholesale, Retail Trade and Catering Services",0,0,0,0,0,0,0,0,0,0.510424501,0,0.122205867,0,0.060112527,0,0,1.173275844,0,0,0,0,1.86601874
+Others,0,0,0,0,0,0,0,0,0,1.050994486,0.018514378,0.676618306,0,0.03757033,0,0,5.54875206,0,0,0,0,7.33244956
+Urban,0.069051801,0,0,0,0,0,0,0,0,11.0982208,0,0,0,0.186599304,0,0,3.375058691,0,0,0,0,14.7289306
+Rural,0,0,0,0.178065161,0,0,0,0,0,0,0,0,0,0.141514908,0,0,0.419181425,0,0,0,0,0.738761494
--- a/DataAnalysis/workspace/tools/code_interpreter/113adecd-28fa-4e27-82d2-18f3eb9a9801.png
+++ b/DataAnalysis/workspace/tools/code_interpreter/113adecd-28fa-4e27-82d2-18f3eb9a9801.png
--- a/DataAnalysis/workspace/tools/code_interpreter/2021beijing.csv
+++ b/DataAnalysis/workspace/tools/code_interpreter/2021beijing.csv
@ -0,0 +1,50 @@
+Emission_Inventory,Raw_Coal,CleanedCoal,Other_Washed_Coal,Briquettes,Coke,Coke_Oven_Gas,Other_Gas,Other_Coking_Products,Crude_Oil,Gasoline,Kerosene,Diesel_Oil,Fuel_Oil,LPG,Refinery_Gas,Other_Petroleum_Products,Natural_Gas,Scope_2_Heat,Scope_2_Electricity,Other_Energy,Process,Scope_1_Total
+unit,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2
+TotalEmissions,2.0021984744341097,0,0,0.17806516054927157,0.0028765890412809116,0,0,0,0,14.049258786117642,15.062812602799132,4.042693831830754,0.019326756197558977,1.3065082109211597,2.1464739602678615,0.32940324454750275,39.990772243150595,0,0,0,0.7500386,79.88042845985687
+"Farming, Forestry, Animal Husbandry, Fishery and Water Conservancy",0.027948058177255413,0,0,0,0,0,0,0,0,0.057656896045355,0,0.05228554814260331,0,0.0015654303988990653,0,0,0.0021607289951994053,0,0,0,0,0.14161666175931217
+Coal Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Petroleum and Natural Gas Extraction,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.002702663186427796,0,0,0,0,0.002702663186427796
+Ferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.0002926745992150001,0,0.00029799902117672207,0,0,0.002770695943414191,0,0,0,0,0,0,0.0033613695638059133
+Nonferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Nonmetal Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Other Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.0008780237976450001,0,0.003873987275297386,0,0,0.00831208783024257,0,0,0,0,0,0,0.013064098903184957
+Logging and Transport of Wood and Bamboo,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Food Processing,0,0,0,0,0,0,0,0,0,0.0058534919843000015,0,0.009833967698831828,0,0.00024680161522865106,0.05541391886828382,0,0.007387279376235978,0,0,0,0,0.07873545954288028
+Food Production,0.0002166219449639238,0,0,0,1.3073027818946156e-06,0,0,0,0,0.006731515781945002,0,0.008641971614124938,0,0.0007404048456859532,0.0637260066985264,0,0.01279260574909157,0,0,0,0,0.09285043393711968
+Beverage Production,0,0,0,0,0,0,0,0,0,0.003804769789795001,0,0.002979990211767221,0,0.00024680161522865106,0.03601904726438448,0,0.01585562402704307,0,0,0,0,0.05890623290821842
+Tobacco Processing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0007207101830474124,0,0,0,0,0.0007207101830474124
+Textile Industry,0,0,0,0,0,0,0,0,0,0.0008780237976450001,0,0.00029799902117672207,0,0,0.00831208783024257,0,0.0001801775457618531,0,0,0,0,0.009668288194826145
+Garments and Other Fiber Products,0,0,0,0,0,0,0,0,0,0.005268142785870001,0,0.0005959980423534441,0,0,0.04987252698145543,0,0.001981953003380384,0,0,0,0,0.05771862081305926
+"Leather, Furs, Down and Related Products",0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0,0,0,0.005541391886828382,0,0,0,0,0,0,0.006126741085258383
+"Timber Processing, Bamboo, Cane, Palm Fiber & Straw Products",0,0,0,0,0,0,0,0,0,0.0017560475952900002,0,0.0005959980423534441,0,0,0.01662417566048514,0,0,0,0,0,0,0.018976221298128586
+Furniture Manufacturing,0,0,0,0,0,0,0,0,0,0.005268142785870001,0,0.0005959980423534441,0,0.00024680161522865106,0.04987252698145543,0,0.0007207101830474124,0,0,0,0,0.05670417960795494
+Papermaking and Paper Products,0,0,0,0,0,0,0,0,0,0.004097444389010001,0,0.002085993148237055,0,0,0.03878974320779867,0,0.0025224856406659436,0,0,0,0,0.04749566638571166
+Printing and Record Medium Reproduction,0,0,0,0,0,0,0,0,0,0.009658261774095003,0,0.00417198629647411,0,0.00024680161522865106,0.09143296613266833,0,0.0032431958237133557,0,0,0,0,0.10875321164217944
+"Cultural, Educational and Sports Articles",0,0,0,0,0,0,0,0,0,0.0014633729960750004,0,0.00029799902117672207,0,0.00024680161522865106,0.013853479717070955,0,0.0003603550915237062,0,0,0,0,0.016222008441075034
+Petroleum Processing and Coking,0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0.0005959980423534441,0,0.661921932043242,0.005541391886828382,0,0.03963906006760768,0,0,0,0,0.7082837312384614
+Raw Chemical Materials and Chemical Products,0,0,0,0,0,0,0,0,0,0.009072912575665,0,0.008045973571771498,0,0.0007404048456859532,0.04593874768597067,0,0.0037837284609989153,0,0,0,0,0.06758176714009204
+Medical and Pharmaceutical Products,0,0,0,0,0,0,0,0,0,0.009950936373310001,0,0.0023839921694137766,0,0.0004936032304573021,0.050384432945903314,0,0.025405033952421285,0,0,0,0,0.08861799867150567
+Chemical Fiber,0,0,0,0,0,0,0,0,0,0.0002926745992150001,0,0,0,0,0.0014818950866442155,0,0.0007207101830474124,0,0,0,0,0.0024952798689066276
+Rubber Products,0,0,0,0,0,0,0,0,0,0.0026340713929350005,0,0.0016389946164719713,0,0.0003702024228429766,0.013337055779797935,0,0.0003603550915237062,0,0,0,0,0.01834067930357159
+Plastic Products,0,0,0,0,0,0,0,0,0,0.0026340713929350005,0,0.0016389946164719713,0,0.0003702024228429766,0.013337055779797935,0,0.0003603550915237062,0,0,0,0,0.01834067930357159
+Nonmetal Mineral Products,0.14596733623893196,0,0,0,0.0007820285241293591,0,0,0,0,0.010828960170955,0,0.17641542053661946,0,0.0004936032304573021,0.10251574990632503,0,0.02414379113208831,0,0,0,0.7500386,1.2111854897395065
+Smelting and Pressing of Ferrous Metals,0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0,0,0,0.005541391886828382,0,0.01585562402704307,0,0,0,0,0.021982365112301453
+Smelting and Pressing of Nonferrous Metals,0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0,0,0,0.005541391886828382,0,0.0007207101830474124,0,0,0,0,0.006847451268305795
+Metal Products,0,0,0,0,0,0,0,0,0,0.022535944139555,0.0015175719959296297,0.00834397259294822,0,0.0007404048456859532,0.21334358764289266,0,0.004864793735570033,0,0,0,0,0.2513462749525815
+Ordinary Machinery,0,0,0,0,0,0,0,0,0,0.016682452155255004,0,0.002085993148237055,0,0.0019744129218292085,0.15792966877460887,0,0.003063018277951503,0,0,0,0,0.18173554527788163
+Equipment for Special Purposes,0,0,0,0,0,0,0,0,0,0.018731174349760005,0,0.006555978465887885,0,0.00024680161522865106,0.17732454037850823,0,0.001981953003380384,0,0,0,0,0.20484044781276517
+Transportation Equipment,0,0,0,0,0,0,0,0,0,0.016097102956825003,0,0.0336738893929696,0,0.00024680161522865106,0.1523882768877805,0,0.035314798969323206,0,0,0,0,0.23772086982212698
+Electric Equipment and Machinery,0,0,0,0,0,0,0,0,0,0.014926404559965006,0,0.0017879941270603325,0,0.0004936032304573021,0.14130549311412374,0,0.0018017754576185308,0,0,0,0,0.16031527048922492
+Electronic and Telecommunications Equipment,0,0,0,0,0,0,0,0,0,0.010828960170955,0,0.0008939970635301663,0,0,0.10251574990632503,0,0.0037837284609989153,0,0,0,0,0.11802243560180911
+"Instruments, Meters, Cultural and Office Machinery",0,0,0,0,0,0,0,0,0,0.008487563377235,0,0.00029799902117672207,0,0,0.08035018235901153,0,0.0003603550915237062,0,0,0,0,0.08949609984894696
+Other Manufacturing Industry,0,0,0,0,0,0,0,0,0,0.0014633729960750004,0,0.008641971614124938,0,0,0.013853479717070955,0,0.0001801775457618531,0,0,0,0,0.024139001873032747
+Scrap and waste,0,0,0,0,0,0,0,0,0,0.0002926745992150001,0,0.00029799902117672207,0,0,0.002770695943414191,0,0.0001801775457618531,0,0,0,0,0.0035415471095677662
+"Production and Supply of Electric Power, Steam and Hot Water",1.7584294721807978,0,0,0,0.002093253214369658,0,0,0,0,0.011706983968600003,0,0.036479049620716374,0.005069313100999075,0.14259084507648084,0.34572372722416206,0.32940324454750275,28.576712994550185,0,0,0,0,31.208208883483813
+Production and Supply of Gas,0,0,0,0,0,0,0,0,0,0.004097444389010001,0,0.0005959980423534441,0,0,0.03878974320779867,0,0.0787375874979298,0,0,0,0,0.12222077313709193
+Production and Supply of Tap Water,0,0,0,0,0,0,0,0,0,0.003804769789795001,0,0.0026819911905904977,0,0.0007404048456859532,0.03601904726438448,0,0.0009008877288092654,0,0,0,0,0.04414710081926519
+Construction,0,0,0,0,0,0,0,0,0,0.21687187801831503,0,0.4523163987247695,0,0.013462701430531963,0,0,0.05185749588478573,0,0,0,0,0.7345084740584022
+"Transportation, Storage, Post and Telecommunication Services",0.0005851847553963052,0,0,0,0,0,0,0,0,0.901730440181415,15.042780652452862,2.411941617276541,0.014257443096559902,0.052285375323228786,0,0,0.5531466227710478,0,0,0,0,18.976727335857053
+"Wholesale, Retail Trade and Catering Services",0,0,0,0,0,0,0,0,0,0.51042450103096,0,0.12220586696052255,0,0.06011252731772412,0,0,1.1732758443932771,0,0,0,0,1.8660187397024839
+Others,0,0,0,0,0,0,0,0,0,1.0509944857810651,0.01851437835034148,0.676618306437121,0,0.03757032957357757,0,0,5.548752059672073,0,0,0,0,7.332449559814178
+Urban,0.06905180113676401,0,0,0,0,0,0,0,0,11.098220802232799,0,0,0,0.1865993035487686,0,0,3.3750586905014712,0,0,0,0,14.728930597419804
+Rural,0,0,0,0.17806516054927157,0,0,0,0,0,0,0,0,0,0.1415149080604755,0,0,0.4191814250686847,0,0,0,0,0.7387614936784318
--- a/DataAnalysis/workspace/tools/code_interpreter/2021北京.csv
+++ b/DataAnalysis/workspace/tools/code_interpreter/2021北京.csv
@ -0,0 +1,51 @@
+Emission_Inventory,Raw_Coal,CleanedCoal,Other_Washed_Coal,Briquettes,Coke,Coke_Oven_Gas,Other_Gas,Other_Coking_Products,Crude_Oil,Gasoline,Kerosene,Diesel_Oil,Fuel_Oil,LPG,Refinery_Gas,Other_Petroleum_Products,Natural_Gas,Scope_2_Heat,Scope_2_Electricity,Other_Energy,Process,Scope_1_Total
+unit,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2
+,,,,,,,,,,,,,,,,,,,,,,
+TotalEmissions,2.002198474,0,0,0.178065161,0.002876589,0,0,0,0,14.04925879,15.0628126,4.042693832,0.019326756,1.306508211,2.14647396,0.329403245,39.99077224,0,0,0,0.7500386,79.88042846
+"Farming, Forestry, Animal Husbandry, Fishery and Water Conservancy",0.027948058,0,0,0,0,0,0,0,0,0.057656896,0,0.052285548,0,0.00156543,0,0,0.002160729,0,0,0,0,0.141616662
+Coal Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Petroleum and Natural Gas Extraction,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.002702663,0,0,0,0,0.002702663
+Ferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.000292675,0,0.000297999,0,0,0.002770696,0,0,0,0,0,0,0.00336137
+Nonferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Nonmetal Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Other Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.000878024,0,0.003873987,0,0,0.008312088,0,0,0,0,0,0,0.013064099
+Logging and Transport of Wood and Bamboo,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+Food Processing,0,0,0,0,0,0,0,0,0,0.005853492,0,0.009833968,0,0.000246802,0.055413919,0,0.007387279,0,0,0,0,0.07873546
+Food Production,0.000216622,0,0,0,1.3073E-06,0,0,0,0,0.006731516,0,0.008641972,0,0.000740405,0.063726007,0,0.012792606,0,0,0,0,0.092850434
+Beverage Production,0,0,0,0,0,0,0,0,0,0.00380477,0,0.00297999,0,0.000246802,0.036019047,0,0.015855624,0,0,0,0,0.058906233
+Tobacco Processing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00072071,0,0,0,0,0.00072071
+Textile Industry,0,0,0,0,0,0,0,0,0,0.000878024,0,0.000297999,0,0,0.008312088,0,0.000180178,0,0,0,0,0.009668288
+Garments and Other Fiber Products,0,0,0,0,0,0,0,0,0,0.005268143,0,0.000595998,0,0,0.049872527,0,0.001981953,0,0,0,0,0.057718621
+"Leather, Furs, Down and Related Products",0,0,0,0,0,0,0,0,0,0.000585349,0,0,0,0,0.005541392,0,0,0,0,0,0,0.006126741
+"Timber Processing, Bamboo, Cane, Palm Fiber & Straw Products",0,0,0,0,0,0,0,0,0,0.001756048,0,0.000595998,0,0,0.016624176,0,0,0,0,0,0,0.018976221
+Furniture Manufacturing,0,0,0,0,0,0,0,0,0,0.005268143,0,0.000595998,0,0.000246802,0.049872527,0,0.00072071,0,0,0,0,0.05670418
+Papermaking and Paper Products,0,0,0,0,0,0,0,0,0,0.004097444,0,0.002085993,0,0,0.038789743,0,0.002522486,0,0,0,0,0.047495666
+Printing and Record Medium Reproduction,0,0,0,0,0,0,0,0,0,0.009658262,0,0.004171986,0,0.000246802,0.091432966,0,0.003243196,0,0,0,0,0.108753212
+"Cultural, Educational and Sports Articles",0,0,0,0,0,0,0,0,0,0.001463373,0,0.000297999,0,0.000246802,0.01385348,0,0.000360355,0,0,0,0,0.016222008
+Petroleum Processing and Coking,0,0,0,0,0,0,0,0,0,0.000585349,0,0.000595998,0,0.661921932,0.005541392,0,0.03963906,0,0,0,0,0.708283731
+Raw Chemical Materials and Chemical Products,0,0,0,0,0,0,0,0,0,0.009072913,0,0.008045974,0,0.000740405,0.045938748,0,0.003783728,0,0,0,0,0.067581767
+Medical and Pharmaceutical Products,0,0,0,0,0,0,0,0,0,0.009950936,0,0.002383992,0,0.000493603,0.050384433,0,0.025405034,0,0,0,0,0.088617999
+Chemical Fiber,0,0,0,0,0,0,0,0,0,0.000292675,0,0,0,0,0.001481895,0,0.00072071,0,0,0,0,0.00249528
+Rubber Products,0,0,0,0,0,0,0,0,0,0.002634071,0,0.001638995,0,0.000370202,0.013337056,0,0.000360355,0,0,0,0,0.018340679
+Plastic Products,0,0,0,0,0,0,0,0,0,0.002634071,0,0.001638995,0,0.000370202,0.013337056,0,0.000360355,0,0,0,0,0.018340679
+Nonmetal Mineral Products,0.145967336,0,0,0,0.000782029,0,0,0,0,0.01082896,0,0.176415421,0,0.000493603,0.10251575,0,0.024143791,0,0,0,0.7500386,1.21118549
+Smelting and Pressing of Ferrous Metals,0,0,0,0,0,0,0,0,0,0.000585349,0,0,0,0,0.005541392,0,0.015855624,0,0,0,0,0.021982365
+Smelting and Pressing of Nonferrous Metals,0,0,0,0,0,0,0,0,0,0.000585349,0,0,0,0,0.005541392,0,0.00072071,0,0,0,0,0.006847451
+Metal Products,0,0,0,0,0,0,0,0,0,0.022535944,0.001517572,0.008343973,0,0.000740405,0.213343588,0,0.004864794,0,0,0,0,0.251346275
+Ordinary Machinery,0,0,0,0,0,0,0,0,0,0.016682452,0,0.002085993,0,0.001974413,0.157929669,0,0.003063018,0,0,0,0,0.181735545
+Equipment for Special Purposes,0,0,0,0,0,0,0,0,0,0.018731174,0,0.006555978,0,0.000246802,0.17732454,0,0.001981953,0,0,0,0,0.204840448
+Transportation Equipment,0,0,0,0,0,0,0,0,0,0.016097103,0,0.033673889,0,0.000246802,0.152388277,0,0.035314799,0,0,0,0,0.23772087
+Electric Equipment and Machinery,0,0,0,0,0,0,0,0,0,0.014926405,0,0.001787994,0,0.000493603,0.141305493,0,0.001801775,0,0,0,0,0.16031527
+Electronic and Telecommunications Equipment,0,0,0,0,0,0,0,0,0,0.01082896,0,0.000893997,0,0,0.10251575,0,0.003783728,0,0,0,0,0.118022436
+"Instruments, Meters, Cultural and Office Machinery",0,0,0,0,0,0,0,0,0,0.008487563,0,0.000297999,0,0,0.080350182,0,0.000360355,0,0,0,0,0.0894961
+Other Manufacturing Industry,0,0,0,0,0,0,0,0,0,0.001463373,0,0.008641972,0,0,0.01385348,0,0.000180178,0,0,0,0,0.024139002
+Scrap and waste,0,0,0,0,0,0,0,0,0,0.000292675,0,0.000297999,0,0,0.002770696,0,0.000180178,0,0,0,0,0.003541547
+"Production and Supply of Electric Power, Steam and Hot Water",1.758429472,0,0,0,0.002093253,0,0,0,0,0.011706984,0,0.03647905,0.005069313,0.142590845,0.345723727,0.329403245,28.57671299,0,0,0,0,31.20820888
+Production and Supply of Gas,0,0,0,0,0,0,0,0,0,0.004097444,0,0.000595998,0,0,0.038789743,0,0.078737587,0,0,0,0,0.122220773
+Production and Supply of Tap Water,0,0,0,0,0,0,0,0,0,0.00380477,0,0.002681991,0,0.000740405,0.036019047,0,0.000900888,0,0,0,0,0.044147101
+Construction,0,0,0,0,0,0,0,0,0,0.216871878,0,0.452316399,0,0.013462701,0,0,0.051857496,0,0,0,0,0.734508474
+"Transportation, Storage, Post and Telecommunication Services",0.000585185,0,0,0,0,0,0,0,0,0.90173044,15.04278065,2.411941617,0.014257443,0.052285375,0,0,0.553146623,0,0,0,0,18.97672734
+"Wholesale, Retail Trade and Catering Services",0,0,0,0,0,0,0,0,0,0.510424501,0,0.122205867,0,0.060112527,0,0,1.173275844,0,0,0,0,1.86601874
+Others,0,0,0,0,0,0,0,0,0,1.050994486,0.018514378,0.676618306,0,0.03757033,0,0,5.54875206,0,0,0,0,7.33244956
+Urban,0.069051801,0,0,0,0,0,0,0,0,11.0982208,0,0,0,0.186599304,0,0,3.375058691,0,0,0,0,14.7289306
+Rural,0,0,0,0.178065161,0,0,0,0,0,0,0,0,0,0.141514908,0,0,0.419181425,0,0,0,0,0.738761494
--- a/DataAnalysis/workspace/tools/code_interpreter/86ad80cc-9600-4235-b011-fa6c91df6cb5.png
+++ b/DataAnalysis/workspace/tools/code_interpreter/86ad80cc-9600-4235-b011-fa6c91df6cb5.png
--- a/DataAnalysis/workspace/tools/code_interpreter/9352a871-f940-479d-9c73-f642d8e0c235.png
+++ b/DataAnalysis/workspace/tools/code_interpreter/9352a871-f940-479d-9c73-f642d8e0c235.png
--- a/DataAnalysis/workspace/tools/code_interpreter/carbon_emissions_barplot.png
+++ b/DataAnalysis/workspace/tools/code_interpreter/carbon_emissions_barplot.png
--- a/DataAnalysis/workspace/tools/code_interpreter/f33b7571-5e86-4fb8-8aef-2105ef05bb46.png
+++ b/DataAnalysis/workspace/tools/code_interpreter/f33b7571-5e86-4fb8-8aef-2105ef05bb46.png
--- a/DataAnalysis/workspace/tools/code_interpreter/f35d1d96-5702-4761-891c-ce4764dfb88f.png
+++ b/DataAnalysis/workspace/tools/code_interpreter/f35d1d96-5702-4761-891c-ce4764dfb88f.png
--- a/DataAnalysis/workspace/tools/code_interpreter/image.png
+++ b/DataAnalysis/workspace/tools/code_interpreter/image.png
--- a/DataAnalysis/workspace/tools/code_interpreter/iris.csv
+++ b/DataAnalysis/workspace/tools/code_interpreter/iris.csv
@ -0,0 +1,151 @@
+ID,sepal_length,sepal_width,petal_length,petal_width,species
+1,5.1,3.5,1.4,0.2,setosa
+2,4.9,3,1.4,0.2,setosa
+3,4.7,3.2,1.3,0.2,setosa
+4,4.6,3.1,1.5,0.2,setosa
+5,5,3.6,1.4,0.2,setosa
+6,5.4,3.9,1.7,0.4,setosa
+7,4.6,3.4,1.4,0.3,setosa
+8,5,3.4,1.5,0.2,setosa
+9,4.4,2.9,1.4,0.2,setosa
+10,4.9,3.1,1.5,0.1,setosa
+11,5.4,3.7,1.5,0.2,setosa
+12,4.8,3.4,1.6,0.2,setosa
+13,4.8,3,1.4,0.1,setosa
+14,4.3,3,1.1,0.1,setosa
+15,5.8,4,1.2,0.2,setosa
+16,5.7,4.4,1.5,0.4,setosa
+17,5.4,3.9,1.3,0.4,setosa
+18,5.1,3.5,1.4,0.3,setosa
+19,5.7,3.8,1.7,0.3,setosa
+20,5.1,3.8,1.5,0.3,setosa
+21,5.4,3.4,1.7,0.2,setosa
+22,5.1,3.7,1.5,0.4,setosa
+23,4.6,3.6,1,0.2,setosa
+24,5.1,3.3,1.7,0.5,setosa
+25,4.8,3.4,1.9,0.2,setosa
+26,5,3,1.6,0.2,setosa
+27,5,3.4,1.6,0.4,setosa
+28,5.2,3.5,1.5,0.2,setosa
+29,5.2,3.4,1.4,0.2,setosa
+30,4.7,3.2,1.6,0.2,setosa
+31,4.8,3.1,1.6,0.2,setosa
+32,5.4,3.4,1.5,0.4,setosa
+33,5.2,4.1,1.5,0.1,setosa
+34,5.5,4.2,1.4,0.2,setosa
+35,4.9,3.1,1.5,0.2,setosa
+36,5,3.2,1.2,0.2,setosa
+37,5.5,3.5,1.3,0.2,setosa
+38,4.9,3.6,1.4,0.1,setosa
+39,4.4,3,1.3,0.2,setosa
+40,5.1,3.4,1.5,0.2,setosa
+41,5,3.5,1.3,0.3,setosa
+42,4.5,2.3,1.3,0.3,setosa
+43,4.4,3.2,1.3,0.2,setosa
+44,5,3.5,1.6,0.6,setosa
+45,5.1,3.8,1.9,0.4,setosa
+46,4.8,3,1.4,0.3,setosa
+47,5.1,3.8,1.6,0.2,setosa
+48,4.6,3.2,1.4,0.2,setosa
+49,5.3,3.7,1.5,0.2,setosa
+50,5,3.3,1.4,0.2,setosa
+51,7,3.2,4.7,1.4,versicolor
+52,6.4,3.2,4.5,1.5,versicolor
+53,6.9,3.1,4.9,1.5,versicolor
+54,5.5,2.3,4,1.3,versicolor
+55,6.5,2.8,4.6,1.5,versicolor
+56,5.7,2.8,4.5,1.3,versicolor
+57,6.3,3.3,4.7,1.6,versicolor
+58,4.9,2.4,3.3,1,versicolor
+59,6.6,2.9,4.6,1.3,versicolor
+60,5.2,2.7,3.9,1.4,versicolor
+61,5,2,3.5,1,versicolor
+62,5.9,3,4.2,1.5,versicolor
+63,6,2.2,4,1,versicolor
+64,6.1,2.9,4.7,1.4,versicolor
+65,5.6,2.9,3.6,1.3,versicolor
+66,6.7,3.1,4.4,1.4,versicolor
+67,5.6,3,4.5,1.5,versicolor
+68,5.8,2.7,4.1,1,versicolor
+69,6.2,2.2,4.5,1.5,versicolor
+70,5.6,2.5,3.9,1.1,versicolor
+71,5.9,3.2,4.8,1.8,versicolor
+72,6.1,2.8,4,1.3,versicolor
+73,6.3,2.5,4.9,1.5,versicolor
+74,6.1,2.8,4.7,1.2,versicolor
+75,6.4,2.9,4.3,1.3,versicolor
+76,6.6,3,4.4,1.4,versicolor
+77,6.8,2.8,4.8,1.4,versicolor
+78,6.7,3,5,1.7,versicolor
+79,6,2.9,4.5,1.5,versicolor
+80,5.7,2.6,3.5,1,versicolor
+81,5.5,2.4,3.8,1.1,versicolor
+82,5.5,2.4,3.7,1,versicolor
+83,5.8,2.7,3.9,1.2,versicolor
+84,6,2.7,5.1,1.6,versicolor
+85,5.4,3,4.5,1.5,versicolor
+86,6,3.4,4.5,1.6,versicolor
+87,6.7,3.1,4.7,1.5,versicolor
+88,6.3,2.3,4.4,1.3,versicolor
+89,5.6,3,4.1,1.3,versicolor
+90,5.5,2.5,4,1.3,versicolor
+91,5.5,2.6,4.4,1.2,versicolor
+92,6.1,3,4.6,1.4,versicolor
+93,5.8,2.6,4,1.2,versicolor
+94,5,2.3,3.3,1,versicolor
+95,5.6,2.7,4.2,1.3,versicolor
+96,5.7,3,4.2,1.2,versicolor
+97,5.7,2.9,4.2,1.3,versicolor
+98,6.2,2.9,4.3,1.3,versicolor
+99,5.1,2.5,3,1.1,versicolor
+100,5.7,2.8,4.1,1.3,versicolor
+101,6.3,3.3,6,2.5,virginica
+102,5.8,2.7,5.1,1.9,virginica
+103,7.1,3,5.9,2.1,virginica
+104,6.3,2.9,5.6,1.8,virginica
+105,6.5,3,5.8,2.2,virginica
+106,7.6,3,6.6,2.1,virginica
+107,4.9,2.5,4.5,1.7,virginica
+108,7.3,2.9,6.3,1.8,virginica
+109,6.7,2.5,5.8,1.8,virginica
+110,7.2,3.6,6.1,2.5,virginica
+111,6.5,3.2,5.1,2,virginica
+112,6.4,2.7,5.3,1.9,virginica
+113,6.8,3,5.5,2.1,virginica
+114,5.7,2.5,5,2,virginica
+115,5.8,2.8,5.1,2.4,virginica
+116,6.4,3.2,5.3,2.3,virginica
+117,6.5,3,5.5,1.8,virginica
+118,7.7,3.8,6.7,2.2,virginica
+119,7.7,2.6,6.9,2.3,virginica
+120,6,2.2,5,1.5,virginica
+121,6.9,3.2,5.7,2.3,virginica
+122,5.6,2.8,4.9,2,virginica
+123,7.7,2.8,6.7,2,virginica
+124,6.3,2.7,4.9,1.8,virginica
+125,6.7,3.3,5.7,2.1,virginica
+126,7.2,3.2,6,1.8,virginica
+127,6.2,2.8,4.8,1.8,virginica
+128,6.1,3,4.9,1.8,virginica
+129,6.4,2.8,5.6,2.1,virginica
+130,7.2,3,5.8,1.6,virginica
+131,7.4,2.8,6.1,1.9,virginica
+132,7.9,3.8,6.4,2,virginica
+133,6.4,2.8,5.6,2.2,virginica
+134,6.3,2.8,5.1,1.5,virginica
+135,6.1,2.6,5.6,1.4,virginica
+136,7.7,3,6.1,2.3,virginica
+137,6.3,3.4,5.6,2.4,virginica
+138,6.4,3.1,5.5,1.8,virginica
+139,6,3,4.8,1.8,virginica
+140,6.9,3.1,5.4,2.1,virginica
+141,6.7,3.1,5.6,2.4,virginica
+142,6.9,3.1,5.1,2.3,virginica
+143,5.8,2.7,5.1,1.9,virginica
+144,6.8,3.2,5.9,2.3,virginica
+145,6.7,3.3,5.7,2.5,virginica
+146,6.7,3,5.2,2.3,virginica
+147,6.3,2.5,5,1.9,virginica
+148,6.5,3,5.2,2,virginica
+149,6.2,3.4,5.4,2.3,virginica
+150,5.9,3,5.1,1.8,virginica
--- a/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_4e36c673-e6c1-4b74-ac95-5fd61b53bd86_32228.json
+++ b/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_4e36c673-e6c1-4b74-ac95-5fd61b53bd86_32228.json
@ -0,0 +1,12 @@
+{
+  "shell_port": 35101,
+  "iopub_port": 39125,
+  "stdin_port": 39149,
+  "control_port": 33877,
+  "hb_port": 52787,
+  "ip": "127.0.0.1",
+  "key": "95a8e2df-2a38aca6ac0bb5a3ffc1ee12",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}
--- a/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_520f69bb-8957-4770-9fa5-9fd287428117_41813.json
+++ b/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_520f69bb-8957-4770-9fa5-9fd287428117_41813.json
@ -0,0 +1,12 @@
+{
+  "shell_port": 45953,
+  "iopub_port": 43085,
+  "stdin_port": 44709,
+  "control_port": 46125,
+  "hb_port": 39039,
+  "ip": "127.0.0.1",
+  "key": "0d71fa67-d0d2bd93154520608812b7b6",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}
--- a/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_52695de3-e60d-46e5-9624-cc6843f686c9_32267.json
+++ b/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_52695de3-e60d-46e5-9624-cc6843f686c9_32267.json
@ -0,0 +1,12 @@
+{
+  "shell_port": 42003,
+  "iopub_port": 46673,
+  "stdin_port": 34969,
+  "control_port": 43555,
+  "hb_port": 48413,
+  "ip": "127.0.0.1",
+  "key": "f0950c6f-683c876409820873097b3990",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}
--- a/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_59195907-28fd-4dba-a004-4a5bd2b86490_15268.json
+++ b/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_59195907-28fd-4dba-a004-4a5bd2b86490_15268.json
@ -0,0 +1,12 @@
+{
+  "shell_port": 45811,
+  "iopub_port": 34683,
+  "stdin_port": 39231,
+  "control_port": 41597,
+  "hb_port": 58735,
+  "ip": "127.0.0.1",
+  "key": "ac6231d5-54d04554f32ccde5a55c18c9",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}
--- a/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_8ad41f89-5b2c-49d6-8b79-2f5fbaf3683b_55339.json
+++ b/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_8ad41f89-5b2c-49d6-8b79-2f5fbaf3683b_55339.json
@ -0,0 +1,12 @@
+{
+  "shell_port": 34275,
+  "iopub_port": 45071,
+  "stdin_port": 46695,
+  "control_port": 46691,
+  "hb_port": 41381,
+  "ip": "127.0.0.1",
+  "key": "b16365ef-2514bf8b331bc5c0966a2503",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}
--- a/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_92443b81-f185-4471-9295-426ff8b080b4_25436.json
+++ b/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_92443b81-f185-4471-9295-426ff8b080b4_25436.json
@ -0,0 +1,12 @@
+{
+  "shell_port": 35091,
+  "iopub_port": 42491,
+  "stdin_port": 36253,
+  "control_port": 35901,
+  "hb_port": 59605,
+  "ip": "127.0.0.1",
+  "key": "429fdd93-ec67d688cbbf1acd3390114a",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}
--- a/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_a38ab35f-b37b-4090-8c3f-ef022251c842_25402.json
+++ b/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_a38ab35f-b37b-4090-8c3f-ef022251c842_25402.json
@ -0,0 +1,12 @@
+{
+  "shell_port": 44677,
+  "iopub_port": 45191,
+  "stdin_port": 36849,
+  "control_port": 39841,
+  "hb_port": 43331,
+  "ip": "127.0.0.1",
+  "key": "f9627f28-fb5d40a3f3a86401bb81f336",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}
--- a/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_aa803c71-a825-4dc5-a6d4-d10a72c3086d_41813.json
+++ b/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_aa803c71-a825-4dc5-a6d4-d10a72c3086d_41813.json
@ -0,0 +1,12 @@
+{
+  "shell_port": 39619,
+  "iopub_port": 37541,
+  "stdin_port": 45601,
+  "control_port": 35353,
+  "hb_port": 37995,
+  "ip": "127.0.0.1",
+  "key": "9426ce11-701ca635a86530ebe1019bb2",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}
--- a/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_d896a1a5-03a6-4ce8-af16-4962c0e44859_37981.json
+++ b/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_d896a1a5-03a6-4ce8-af16-4962c0e44859_37981.json
@ -0,0 +1,12 @@
+{
+  "shell_port": 46723,
+  "iopub_port": 36411,
+  "stdin_port": 45227,
+  "control_port": 36727,
+  "hb_port": 45241,
+  "ip": "127.0.0.1",
+  "key": "6832f6f3-70ae189c473c5eb3650804f2",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}
--- a/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_e5501e12-fe14-4988-b7bd-2f2563da8d6f_59439.json
+++ b/DataAnalysis/workspace/tools/code_interpreter/kernel_connection_file_e5501e12-fe14-4988-b7bd-2f2563da8d6f_59439.json
@ -0,0 +1,12 @@
+{
+  "shell_port": 45115,
+  "iopub_port": 46367,
+  "stdin_port": 43969,
+  "control_port": 34267,
+  "hb_port": 35721,
+  "ip": "127.0.0.1",
+  "key": "c8f5b8b7-e4eff814aa3b83dab96f8ac3",
+  "transport": "tcp",
+  "signature_scheme": "hmac-sha256",
+  "kernel_name": ""
+}
--- a/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_4e36c673-e6c1-4b74-ac95-5fd61b53bd86_32228.py
+++ b/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_4e36c673-e6c1-4b74-ac95-5fd61b53bd86_32228.py
@ -0,0 +1,3 @@
+
+from ipykernel import kernelapp as app
+app.launch_new_instance()
--- a/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_520f69bb-8957-4770-9fa5-9fd287428117_41813.py
+++ b/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_520f69bb-8957-4770-9fa5-9fd287428117_41813.py
@ -0,0 +1,3 @@
+
+from ipykernel import kernelapp as app
+app.launch_new_instance()
--- a/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_52695de3-e60d-46e5-9624-cc6843f686c9_32267.py
+++ b/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_52695de3-e60d-46e5-9624-cc6843f686c9_32267.py
@ -0,0 +1,3 @@
+
+from ipykernel import kernelapp as app
+app.launch_new_instance()
--- a/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_59195907-28fd-4dba-a004-4a5bd2b86490_15268.py
+++ b/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_59195907-28fd-4dba-a004-4a5bd2b86490_15268.py
@ -0,0 +1,3 @@
+
+from ipykernel import kernelapp as app
+app.launch_new_instance()
--- a/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_8ad41f89-5b2c-49d6-8b79-2f5fbaf3683b_55339.py
+++ b/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_8ad41f89-5b2c-49d6-8b79-2f5fbaf3683b_55339.py
@ -0,0 +1,3 @@
+
+from ipykernel import kernelapp as app
+app.launch_new_instance()
--- a/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_92443b81-f185-4471-9295-426ff8b080b4_25436.py
+++ b/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_92443b81-f185-4471-9295-426ff8b080b4_25436.py
@ -0,0 +1,3 @@
+
+from ipykernel import kernelapp as app
+app.launch_new_instance()
--- a/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_a38ab35f-b37b-4090-8c3f-ef022251c842_25402.py
+++ b/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_a38ab35f-b37b-4090-8c3f-ef022251c842_25402.py
@ -0,0 +1,3 @@
+
+from ipykernel import kernelapp as app
+app.launch_new_instance()
--- a/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_aa803c71-a825-4dc5-a6d4-d10a72c3086d_41813.py
+++ b/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_aa803c71-a825-4dc5-a6d4-d10a72c3086d_41813.py
@ -0,0 +1,3 @@
+
+from ipykernel import kernelapp as app
+app.launch_new_instance()
--- a/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_d896a1a5-03a6-4ce8-af16-4962c0e44859_37981.py
+++ b/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_d896a1a5-03a6-4ce8-af16-4962c0e44859_37981.py
@ -0,0 +1,3 @@
+
+from ipykernel import kernelapp as app
+app.launch_new_instance()
--- a/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_e5501e12-fe14-4988-b7bd-2f2563da8d6f_59439.py
+++ b/DataAnalysis/workspace/tools/code_interpreter/launch_kernel_e5501e12-fe14-4988-b7bd-2f2563da8d6f_59439.py
@ -0,0 +1,3 @@
+
+from ipykernel import kernelapp as app
+app.launch_new_instance()
--- a/DataAnalysis/workspace/tools/code_interpreter/top_carbon_emitting_industries.png
+++ b/DataAnalysis/workspace/tools/code_interpreter/top_carbon_emitting_industries.png
--- a/DataAnalysis/workspace/tools/code_interpreter/y_equals_x.png
+++ b/DataAnalysis/workspace/tools/code_interpreter/y_equals_x.png
--- a/DataAnalysis/workspace/tools/code_interpreter/报告案例1.md
+++ b/DataAnalysis/workspace/tools/code_interpreter/报告案例1.md
@ -0,0 +1,83 @@
+# 产品碳足迹研究报告（示例）
+
+## 基本信息
+- 产品名称：智能节能灯泡
+- 产品规格型号：LED-E27-10W
+- 生产者名称：EcoTech Solutions
+- 报告编号：ECO-2023-CFP-01
+- 出具报告机构：EcoAnalytics
+- 日期：2023年4月15日
+
+## 一、概况
+### 1. 生产者信息
+- 生产者名称：EcoTech Solutions
+- 地址：123 Greenway St, EcoCity
+- 法定代表人：Jane Doe
+- 产品名称：智能节能灯泡
+- 产品功能：节能照明，支持远程控制
+- 依据标准：ISO 14040, ISO 14044
+
+## 二、量化目的
+评估并减少产品在其整个生命周期中的碳排放。
+
+## 三、量化范围
+### 1. 功能单位或声明单位
+以个为功能单位或声明单位。
+### 2. 系统边界
+- 原材料获取阶段
+- 生产阶段
+- 分销阶段
+- 使用阶段
+- 生命末期阶段
+- 系统边界图：（此处通常会有一个流程图，但我们将跳过此部分，因为它是图形内容）
+
+### 3. 取舍准则
+采用的取舍准则以ISO 14044为依据，具体规则如下：（此处可以详细描述，但我们将省略）
+
+### 4. 时间范围
+2022年度。
+
+## 四、清单分析
+### 1. 数据来源说明
+- 初级数据：内部记录
+- 次级数据：行业平均值
+
+### 2. 分配原则与程序
+- 分配依据：按活动比例
+- 分配程序：线性分配
+- 具体分配情况：（略，通常会有一系列详细的计算）
+
+### 3. 清单结果及计算
+| 生命周期阶段 | 活动数据 (单位) | 排放因子 (kg CO2e/单位) | 碳足迹 (kg CO2e/功能单位) |
+| --- | --- | --- | --- |
+| 原材料获取 | 10 | 0.5 | 5 |
+| 生产 | 20 | 1 | 20 |
+| 分销 | 5 | 0.8 | 4 |
+| 运输 | 15 | 0.6 | 9 |
+| 仓库 | 3 | 0.7 | 2.1 |
+| 使用 |  |  | 100 (假设) |
+| 生命末期 | 2 | 1.2 | 2.4 |
+| **总计** |  |  | 130.5 |
+
+### 4. 数据质量评价
+（略，通常涉及详细的质量评估标准）
+
+## 五、影响评价
+### 1. 影响类型和特征化因子选择
+100年全球变暖潜势（GWP100）
+
+### 2. 产品碳足迹结果计算
+产品碳足迹总计为130.5 kg CO2e/个。
+
+## 六、结果解释
+### 1. 结果说明
+EcoTech Solutions生产的LED-E27-10W智能节能灯泡，从原材料获取到生命末期的生命周期碳足迹为130.5 kg CO2e/个。各生命周期阶段的温室气体排放情况如下表所示。
+
+### 2. 假设和局限性说明
+（此处可以添加关于数据估算和简化模型的说明）
+
+### 3. 改进建议
+- 优化原材料供应链以减少获取过程中的排放
+- 提升生产效率，降低生产阶段的碳足迹
+- 考虑使用更环保的分销方式
+- 提供回收计划以减少生命末期的环境影响
--- a/DataAnalysis/workspace/tools/doc_parser/247113081dbb0224d6d7045b917de58888ff9c7fc7ee9c8752f9e9b884700c21_without_chunking
+++ b/DataAnalysis/workspace/tools/doc_parser/247113081dbb0224d6d7045b917de58888ff9c7fc7ee9c8752f9e9b884700c21_without_chunking
@ -0,0 +1 @@
+{"url": "/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/报告案例1.md", "raw": [{"content": "# 产品碳足迹研究报告（示例）\n## 基本信息\n- 产品名称：智能节能灯泡\n- 产品规格型号：LED-E27-10W\n- 生产者名称：EcoTech Solutions\n- 报告编号：ECO-2023-CFP-01\n- 出具报告机构：EcoAnalytics\n- 日期：2023年4月15日\n## 一、概况\n### 1. 生产者信息\n- 生产者名称：EcoTech Solutions\n- 地址：123 Greenway St, EcoCity\n- 法定代表人：Jane Doe\n- 产品名称：智能节能灯泡\n- 产品功能：节能照明，支持远程控制\n- 依据标准：ISO 14040, ISO 14044\n## 二、量化目的\n评估并减少产品在其整个生命周期中的碳排放。\n## 三、量化范围\n### 1. 功能单位或声明单位\n以个为功能单位或声明单位。\n### 2. 系统边界\n- 原材料获取阶段\n- 生产阶段\n- 分销阶段\n- 使用阶段\n- 生命末期阶段\n- 系统边界图：（此处通常会有一个流程图，但我们将跳过此部分，因为它是图形内容）\n### 3. 取舍准则\n采用的取舍准则以ISO 14044为依据，具体规则如下：（此处可以详细描述，但我们将省略）\n### 4. 时间范围\n2022年度。\n## 四、清单分析\n### 1. 数据来源说明\n- 初级数据：内部记录\n- 次级数据：行业平均值\n### 2. 分配原则与程序\n- 分配依据：按活动比例\n- 分配程序：线性分配\n- 具体分配情况：（略，通常会有一系列详细的计算）\n### 3. 清单结果及计算\n| 生命周期阶段 | 活动数据 (单位) | 排放因子 (kg CO2e/单位) | 碳足迹 (kg CO2e/功能单位) |\n| --- | --- | --- | --- |\n| 原材料获取 | 10 | 0.5 | 5 |\n| 生产 | 20 | 1 | 20 |\n| 分销 | 5 | 0.8 | 4 |\n| 运输 | 15 | 0.6 | 9 |\n| 仓库 | 3 | 0.7 | 2.1 |\n| 使用 |  |  | 100 (假设) |\n| 生命末期 | 2 | 1.2 | 2.4 |\n| **总计** |  |  | 130.5 |\n### 4. 数据质量评价\n（略，通常涉及详细的质量评估标准）\n## 五、影响评价\n### 1. 影响类型和特征化因子选择\n100年全球变暖潜势（GWP100）\n### 2. 产品碳足迹结果计算\n产品碳足迹总计为130.5 kg CO2e/个。\n## 六、结果解释\n### 1. 结果说明\nEcoTech Solutions生产的LED-E27-10W智能节能灯泡，从原材料获取到生命末期的生命周期碳足迹为130.5 kg CO2e/个。各生命周期阶段的温室气体排放情况如下表所示。\n### 2. 假设和局限性说明\n（此处可以添加关于数据估算和简化模型的说明）\n### 3. 改进建议\n- 优化原材料供应链以减少获取过程中的排放\n- 提升生产效率，降低生产阶段的碳足迹\n- 考虑使用更环保的分销方式\n- 提供回收计划以减少生命末期的环境影响", "metadata": {"source": "/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/报告案例1.md", "title": "报告案例1.md", "chunk_id": 0}, "token": 821}], "title": "报告案例1.md"}
--- a/DataAnalysis/workspace/tools/doc_parser/b5fb0c9b9899081f4db8e4da0b6df261d39350c17ecc3135ebd506ed408b3e0a_500
+++ b/DataAnalysis/workspace/tools/doc_parser/b5fb0c9b9899081f4db8e4da0b6df261d39350c17ecc3135ebd506ed408b3e0a_500
--- a/DataAnalysis/workspace/tools/doc_parser/c71bbcda306da7eaf016503453f95f19c94564ecc82c8c30ee1b0020f13570b7_without_chunking
+++ b/DataAnalysis/workspace/tools/doc_parser/c71bbcda306da7eaf016503453f95f19c94564ecc82c8c30ee1b0020f13570b7_without_chunking
@ -0,0 +1 @@
+{"url": "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/iris.csv", "raw": [{"content": "ID,sepal_length,sepal_width,petal_length,petal_width,species\n1,5.1,3.5,1.4,0.2,setosa\n2,4.9,3,1.4,0.2,setosa\n3,4.7,3.2,1.3,0.2,setosa\n4,4.6,3.1,1.5,0.2,setosa\n5,5,3.6,1.4,0.2,setosa\n6,5.4,3.9,1.7,0.4,setosa\n7,4.6,3.4,1.4,0.3,setosa\n8,5,3.4,1.5,0.2,setosa\n9,4.4,2.9,1.4,0.2,setosa\n10,4.9,3.1,1.5,0.1,setosa\n11,5.4,3.7,1.5,0.2,setosa\n12,4.8,3.4,1.6,0.2,setosa\n13,4.8,3,1.4,0.1,setosa\n14,4.3,3,1.1,0.1,setosa\n15,5.8,4,1.2,0.2,setosa\n16,5.7,4.4,1.5,0.4,setosa\n17,5.4,3.9,1.3,0.4,setosa\n18,5.1,3.5,1.4,0.3,setosa\n19,5.7,3.8,1.7,0.3,setosa\n20,5.1,3.8,1.5,0.3,setosa\n21,5.4,3.4,1.7,0.2,setosa\n22,5.1,3.7,1.5,0.4,setosa\n23,4.6,3.6,1,0.2,setosa\n24,5.1,3.3,1.7,0.5,setosa\n25,4.8,3.4,1.9,0.2,setosa\n26,5,3,1.6,0.2,setosa\n27,5,3.4,1.6,0.4,setosa\n28,5.2,3.5,1.5,0.2,setosa\n29,5.2,3.4,1.4,0.2,setosa\n30,4.7,3.2,1.6,0.2,setosa\n31,4.8,3.1,1.6,0.2,setosa\n32,5.4,3.4,1.5,0.4,setosa\n33,5.2,4.1,1.5,0.1,setosa\n34,5.5,4.2,1.4,0.2,setosa\n35,4.9,3.1,1.5,0.2,setosa\n36,5,3.2,1.2,0.2,setosa\n37,5.5,3.5,1.3,0.2,setosa\n38,4.9,3.6,1.4,0.1,setosa\n39,4.4,3,1.3,0.2,setosa\n40,5.1,3.4,1.5,0.2,setosa\n41,5,3.5,1.3,0.3,setosa\n42,4.5,2.3,1.3,0.3,setosa\n43,4.4,3.2,1.3,0.2,setosa\n44,5,3.5,1.6,0.6,setosa\n45,5.1,3.8,1.9,0.4,setosa\n46,4.8,3,1.4,0.3,setosa\n47,5.1,3.8,1.6,0.2,setosa\n48,4.6,3.2,1.4,0.2,setosa\n49,5.3,3.7,1.5,0.2,setosa\n50,5,3.3,1.4,0.2,setosa\n51,7,3.2,4.7,1.4,versicolor\n52,6.4,3.2,4.5,1.5,versicolor\n53,6.9,3.1,4.9,1.5,versicolor\n54,5.5,2.3,4,1.3,versicolor\n55,6.5,2.8,4.6,1.5,versicolor\n56,5.7,2.8,4.5,1.3,versicolor\n57,6.3,3.3,4.7,1.6,versicolor\n58,4.9,2.4,3.3,1,versicolor\n59,6.6,2.9,4.6,1.3,versicolor\n60,5.2,2.7,3.9,1.4,versicolor\n61,5,2,3.5,1,versicolor\n62,5.9,3,4.2,1.5,versicolor\n63,6,2.2,4,1,versicolor\n64,6.1,2.9,4.7,1.4,versicolor\n65,5.6,2.9,3.6,1.3,versicolor\n66,6.7,3.1,4.4,1.4,versicolor\n67,5.6,3,4.5,1.5,versicolor\n68,5.8,2.7,4.1,1,versicolor\n69,6.2,2.2,4.5,1.5,versicolor\n70,5.6,2.5,3.9,1.1,versicolor\n71,5.9,3.2,4.8,1.8,versicolor\n72,6.1,2.8,4,1.3,versicolor\n73,6.3,2.5,4.9,1.5,versicolor\n74,6.1,2.8,4.7,1.2,versicolor\n75,6.4,2.9,4.3,1.3,versicolor\n76,6.6,3,4.4,1.4,versicolor\n77,6.8,2.8,4.8,1.4,versicolor\n78,6.7,3,5,1.7,versicolor\n79,6,2.9,4.5,1.5,versicolor\n80,5.7,2.6,3.5,1,versicolor\n81,5.5,2.4,3.8,1.1,versicolor\n82,5.5,2.4,3.7,1,versicolor\n83,5.8,2.7,3.9,1.2,versicolor\n84,6,2.7,5.1,1.6,versicolor\n85,5.4,3,4.5,1.5,versicolor\n86,6,3.4,4.5,1.6,versicolor\n87,6.7,3.1,4.7,1.5,versicolor\n88,6.3,2.3,4.4,1.3,versicolor\n89,5.6,3,4.1,1.3,versicolor\n90,5.5,2.5,4,1.3,versicolor\n91,5.5,2.6,4.4,1.2,versicolor\n92,6.1,3,4.6,1.4,versicolor\n93,5.8,2.6,4,1.2,versicolor\n94,5,2.3,3.3,1,versicolor\n95,5.6,2.7,4.2,1.3,versicolor\n96,5.7,3,4.2,1.2,versicolor\n97,5.7,2.9,4.2,1.3,versicolor\n98,6.2,2.9,4.3,1.3,versicolor\n99,5.1,2.5,3,1.1,versicolor\n100,5.7,2.8,4.1,1.3,versicolor\n101,6.3,3.3,6,2.5,virginica\n102,5.8,2.7,5.1,1.9,virginica\n103,7.1,3,5.9,2.1,virginica\n104,6.3,2.9,5.6,1.8,virginica\n105,6.5,3,5.8,2.2,virginica\n106,7.6,3,6.6,2.1,virginica\n107,4.9,2.5,4.5,1.7,virginica\n108,7.3,2.9,6.3,1.8,virginica\n109,6.7,2.5,5.8,1.8,virginica\n110,7.2,3.6,6.1,2.5,virginica\n111,6.5,3.2,5.1,2,virginica\n112,6.4,2.7,5.3,1.9,virginica\n113,6.8,3,5.5,2.1,virginica\n114,5.7,2.5,5,2,virginica\n115,5.8,2.8,5.1,2.4,virginica\n116,6.4,3.2,5.3,2.3,virginica\n117,6.5,3,5.5,1.8,virginica\n118,7.7,3.8,6.7,2.2,virginica\n119,7.7,2.6,6.9,2.3,virginica\n120,6,2.2,5,1.5,virginica\n121,6.9,3.2,5.7,2.3,virginica\n122,5.6,2.8,4.9,2,virginica\n123,7.7,2.8,6.7,2,virginica\n124,6.3,2.7,4.9,1.8,virginica\n125,6.7,3.3,5.7,2.1,virginica\n126,7.2,3.2,6,1.8,virginica\n127,6.2,2.8,4.8,1.8,virginica\n128,6.1,3,4.9,1.8,virginica\n129,6.4,2.8,5.6,2.1,virginica\n130,7.2,3,5.8,1.6,virginica\n131,7.4,2.8,6.1,1.9,virginica\n132,7.9,3.8,6.4,2,virginica\n133,6.4,2.8,5.6,2.2,virginica\n134,6.3,2.8,5.1,1.5,virginica\n135,6.1,2.6,5.6,1.4,virginica\n136,7.7,3,6.1,2.3,virginica\n137,6.3,3.4,5.6,2.4,virginica\n138,6.4,3.1,5.5,1.8,virginica\n139,6,3,4.8,1.8,virginica\n140,6.9,3.1,5.4,2.1,virginica\n141,6.7,3.1,5.6,2.4,virginica\n142,6.9,3.1,5.1,2.3,virginica\n143,5.8,2.7,5.1,1.9,virginica\n144,6.8,3.2,5.9,2.3,virginica\n145,6.7,3.3,5.7,2.5,virginica\n146,6.7,3,5.2,2.3,virginica\n147,6.3,2.5,5,1.9,virginica\n148,6.5,3,5.2,2,virginica\n149,6.2,3.4,5.4,2.3,virginica\n150,5.9,3,5.1,1.8,virginica", "metadata": {"source": "/home/zhangxj/WorkFile/LCA-GPT/LCARAG/DataAnalysis/iris.csv", "title": "iris.csv", "chunk_id": 0}, "token": 3069}], "title": "iris.csv"}
--- a/DataAnalysis/workspace/tools/doc_parser/ff9c5fd787b332bfc1e3006c69c40b5b0ecd131070ddd4fbc4beca035fb89231_500
+++ b/DataAnalysis/workspace/tools/doc_parser/ff9c5fd787b332bfc1e3006c69c40b5b0ecd131070ddd4fbc4beca035fb89231_500
--- a/DataAnalysis/workspace/tools/simple_doc_parser/247113081dbb0224d6d7045b917de58888ff9c7fc7ee9c8752f9e9b884700c21_ori
+++ b/DataAnalysis/workspace/tools/simple_doc_parser/247113081dbb0224d6d7045b917de58888ff9c7fc7ee9c8752f9e9b884700c21_ori
@ -0,0 +1,279 @@
+[
+  {
+    "page_num": 1,
+    "content": [
+      {
+        "text": "# 产品碳足迹研究报告（示例）",
+        "token": 10
+      },
+      {
+        "text": "## 基本信息",
+        "token": 6
+      },
+      {
+        "text": "- 产品名称：智能节能灯泡",
+        "token": 9
+      },
+      {
+        "text": "- 产品规格型号：LED-E27-10W",
+        "token": 14
+      },
+      {
+        "text": "- 生产者名称：EcoTech Solutions",
+        "token": 10
+      },
+      {
+        "text": "- 报告编号：ECO-2023-CFP-01",
+        "token": 18
+      },
+      {
+        "text": "- 出具报告机构：EcoAnalytics",
+        "token": 10
+      },
+      {
+        "text": "- 日期：2023年4月15日",
+        "token": 14
+      },
+      {
+        "text": "## 一、概况",
+        "token": 5
+      },
+      {
+        "text": "### 1. 生产者信息",
+        "token": 8
+      },
+      {
+        "text": "- 生产者名称：EcoTech Solutions",
+        "token": 10
+      },
+      {
+        "text": "- 地址：123 Greenway St, EcoCity",
+        "token": 14
+      },
+      {
+        "text": "- 法定代表人：Jane Doe",
+        "token": 9
+      },
+      {
+        "text": "- 产品名称：智能节能灯泡",
+        "token": 9
+      },
+      {
+        "text": "- 产品功能：节能照明，支持远程控制",
+        "token": 11
+      },
+      {
+        "text": "- 依据标准：ISO 14040, ISO 14044",
+        "token": 20
+      },
+      {
+        "text": "## 二、量化目的",
+        "token": 6
+      },
+      {
+        "text": "评估并减少产品在其整个生命周期中的碳排放。",
+        "token": 11
+      },
+      {
+        "text": "## 三、量化范围",
+        "token": 6
+      },
+      {
+        "text": "### 1. 功能单位或声明单位",
+        "token": 10
+      },
+      {
+        "text": "以个为功能单位或声明单位。",
+        "token": 9
+      },
+      {
+        "text": "### 2. 系统边界",
+        "token": 8
+      },
+      {
+        "text": "- 原材料获取阶段",
+        "token": 7
+      },
+      {
+        "text": "- 生产阶段",
+        "token": 4
+      },
+      {
+        "text": "- 分销阶段",
+        "token": 4
+      },
+      {
+        "text": "- 使用阶段",
+        "token": 3
+      },
+      {
+        "text": "- 生命末期阶段",
+        "token": 6
+      },
+      {
+        "text": "- 系统边界图：（此处通常会有一个流程图，但我们将跳过此部分，因为它是图形内容）",
+        "token": 27
+      },
+      {
+        "text": "### 3. 取舍准则",
+        "token": 8
+      },
+      {
+        "text": "采用的取舍准则以ISO 14044为依据，具体规则如下：（此处可以详细描述，但我们将省略）",
+        "token": 31
+      },
+      {
+        "text": "### 4. 时间范围",
+        "token": 6
+      },
+      {
+        "text": "2022年度。",
+        "token": 6
+      },
+      {
+        "text": "## 四、清单分析",
+        "token": 6
+      },
+      {
+        "text": "### 1. 数据来源说明",
+        "token": 7
+      },
+      {
+        "text": "- 初级数据：内部记录",
+        "token": 8
+      },
+      {
+        "text": "- 次级数据：行业平均值",
+        "token": 10
+      },
+      {
+        "text": "### 2. 分配原则与程序",
+        "token": 9
+      },
+      {
+        "text": "- 分配依据：按活动比例",
+        "token": 8
+      },
+      {
+        "text": "- 分配程序：线性分配",
+        "token": 8
+      },
+      {
+        "text": "- 具体分配情况：（略，通常会有一系列详细的计算）",
+        "token": 17
+      },
+      {
+        "text": "### 3. 清单结果及计算",
+        "token": 10
+      },
+      {
+        "text": "| 生命周期阶段 | 活动数据 (单位) | 排放因子 (kg CO2e/单位) | 碳足迹 (kg CO2e/功能单位) |",
+        "token": 40
+      },
+      {
+        "text": "| --- | --- | --- | --- |",
+        "token": 9
+      },
+      {
+        "text": "| 原材料获取 | 10 | 0.5 | 5 |",
+        "token": 19
+      },
+      {
+        "text": "| 生产 | 20 | 1 | 20 |",
+        "token": 15
+      },
+      {
+        "text": "| 分销 | 5 | 0.8 | 4 |",
+        "token": 15
+      },
+      {
+        "text": "| 运输 | 15 | 0.6 | 9 |",
+        "token": 17
+      },
+      {
+        "text": "| 仓库 | 3 | 0.7 | 2.1 |",
+        "token": 17
+      },
+      {
+        "text": "| 使用 |  |  | 100 (假设) |",
+        "token": 15
+      },
+      {
+        "text": "| 生命末期 | 2 | 1.2 | 2.4 |",
+        "token": 19
+      },
+      {
+        "text": "| **总计** |  |  | 130.5 |",
+        "token": 16
+      },
+      {
+        "text": "### 4. 数据质量评价",
+        "token": 7
+      },
+      {
+        "text": "（略，通常涉及详细的质量评估标准）",
+        "token": 10
+      },
+      {
+        "text": "## 五、影响评价",
+        "token": 6
+      },
+      {
+        "text": "### 1. 影响类型和特征化因子选择",
+        "token": 13
+      },
+      {
+        "text": "100年全球变暖潜势（GWP100）",
+        "token": 16
+      },
+      {
+        "text": "### 2. 产品碳足迹结果计算",
+        "token": 10
+      },
+      {
+        "text": "产品碳足迹总计为130.5 kg CO2e/个。",
+        "token": 17
+      },
+      {
+        "text": "## 六、结果解释",
+        "token": 6
+      },
+      {
+        "text": "### 1. 结果说明",
+        "token": 7
+      },
+      {
+        "text": "EcoTech Solutions生产的LED-E27-10W智能节能灯泡，从原材料获取到生命末期的生命周期碳足迹为130.5 kg CO2e/个。各生命周期阶段的温室气体排放情况如下表所示。",
+        "token": 54
+      },
+      {
+        "text": "### 2. 假设和局限性说明",
+        "token": 12
+      },
+      {
+        "text": "（此处可以添加关于数据估算和简化模型的说明）",
+        "token": 13
+      },
+      {
+        "text": "### 3. 改进建议",
+        "token": 9
+      },
+      {
+        "text": "- 优化原材料供应链以减少获取过程中的排放",
+        "token": 11
+      },
+      {
+        "text": "- 提升生产效率，降低生产阶段的碳足迹",
+        "token": 12
+      },
+      {
+        "text": "- 考虑使用更环保的分销方式",
+        "token": 11
+      },
+      {
+        "text": "- 提供回收计划以减少生命末期的环境影响",
+        "token": 13
+      }
+    ]
+  }
+]
--- a/DataAnalysis/workspace/tools/simple_doc_parser/b5fb0c9b9899081f4db8e4da0b6df261d39350c17ecc3135ebd506ed408b3e0a_ori
+++ b/DataAnalysis/workspace/tools/simple_doc_parser/b5fb0c9b9899081f4db8e4da0b6df261d39350c17ecc3135ebd506ed408b3e0a_ori
@ -0,0 +1,211 @@
+[
+  {
+    "page_num": 1,
+    "content": [
+      {
+        "text": "Emission_Inventory,Raw_Coal,CleanedCoal,Other_Washed_Coal,Briquettes,Coke,Coke_Oven_Gas,Other_Gas,Other_Coking_Products,Crude_Oil,Gasoline,Kerosene,Diesel_Oil,Fuel_Oil,LPG,Refinery_Gas,Other_Petroleum_Products,Natural_Gas,Scope_2_Heat,Scope_2_Electricity,Other_Energy,Process,Scope_1_Total",
+        "token": 103
+      },
+      {
+        "text": "unit,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2",
+        "token": 89
+      },
+      {
+        "text": ",,,,,,,,,,,,,,,,,,,,,,",
+        "token": 4
+      },
+      {
+        "text": "TotalEmissions,2.0021984744341097,0,0,0.17806516054927157,0.0028765890412809116,0,0,0,0,14.049258786117642,15.062812602799132,4.042693831830754,0.019326756197558977,1.3065082109211597,2.1464739602678615,0.32940324454750275,39.990772243150595,0,0,0,0.7500386,79.88042845985687",
+        "token": 264
+      },
+      {
+        "text": "\"Farming, Forestry, Animal Husbandry, Fishery and Water Conservancy\",0.027948058177255413,0,0,0,0,0,0,0,0,0.057656896045355,0,0.05228554814260331,0,0.0015654303988990653,0,0,0.0021607289951994053,0,0,0,0,0.14161666175931217",
+        "token": 171
+      },
+      {
+        "text": "Coal Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0",
+        "token": 49
+      },
+      {
+        "text": "Petroleum and Natural Gas Extraction,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.002702663186427796,0,0,0,0,0.002702663186427796",
+        "token": 88
+      },
+      {
+        "text": "Ferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.0002926745992150001,0,0.00029799902117672207,0,0,0.002770695943414191,0,0,0,0,0,0,0.0033613695638059133",
+        "token": 132
+      },
+      {
+        "text": "Nonferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0",
+        "token": 52
+      },
+      {
+        "text": "Nonmetal Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0",
+        "token": 51
+      },
+      {
+        "text": "Other Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.0008780237976450001,0,0.003873987275297386,0,0,0.00831208783024257,0,0,0,0,0,0,0.013064098903184957",
+        "token": 126
+      },
+      {
+        "text": "Logging and Transport of Wood and Bamboo,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0",
+        "token": 51
+      },
+      {
+        "text": "Food Processing,0,0,0,0,0,0,0,0,0,0.0058534919843000015,0,0.009833967698831828,0,0.00024680161522865106,0.05541391886828382,0,0.007387279376235978,0,0,0,0,0.07873545954288028",
+        "token": 161
+      },
+      {
+        "text": "Food Production,0.0002166219449639238,0,0,0,1.3073027818946156e-06,0,0,0,0,0.006731515781945002,0,0.008641971614124938,0,0.0007404048456859532,0.0637260066985264,0,0.01279260574909157,0,0,0,0,0.09285043393711968",
+        "token": 198
+      },
+      {
+        "text": "Beverage Production,0,0,0,0,0,0,0,0,0,0.003804769789795001,0,0.002979990211767221,0,0.00024680161522865106,0.03601904726438448,0,0.01585562402704307,0,0,0,0,0.05890623290821842",
+        "token": 161
+      },
+      {
+        "text": "Tobacco Processing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0007207101830474124,0,0,0,0,0.0007207101830474124",
+        "token": 88
+      },
+      {
+        "text": "Textile Industry,0,0,0,0,0,0,0,0,0,0.0008780237976450001,0,0.00029799902117672207,0,0,0.00831208783024257,0,0.0001801775457618531,0,0,0,0,0.009668288194826145",
+        "token": 145
+      },
+      {
+        "text": "Garments and Other Fiber Products,0,0,0,0,0,0,0,0,0,0.005268142785870001,0,0.0005959980423534441,0,0,0.04987252698145543,0,0.001981953003380384,0,0,0,0,0.05771862081305926",
+        "token": 144
+      },
+      {
+        "text": "\"Leather, Furs, Down and Related Products\",0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0,0,0,0.005541391886828382,0,0,0,0,0,0,0.006126741085258383",
+        "token": 113
+      },
+      {
+        "text": "\"Timber Processing, Bamboo, Cane, Palm Fiber & Straw Products\",0,0,0,0,0,0,0,0,0,0.0017560475952900002,0,0.0005959980423534441,0,0,0.01662417566048514,0,0,0,0,0,0,0.018976221298128586",
+        "token": 136
+      },
+      {
+        "text": "Furniture Manufacturing,0,0,0,0,0,0,0,0,0,0.005268142785870001,0,0.0005959980423534441,0,0.00024680161522865106,0.04987252698145543,0,0.0007207101830474124,0,0,0,0,0.05670417960795494",
+        "token": 163
+      },
+      {
+        "text": "Papermaking and Paper Products,0,0,0,0,0,0,0,0,0,0.004097444389010001,0,0.002085993148237055,0,0,0.03878974320779867,0,0.0025224856406659436,0,0,0,0,0.04749566638571166",
+        "token": 143
+      },
+      {
+        "text": "Printing and Record Medium Reproduction,0,0,0,0,0,0,0,0,0,0.009658261774095003,0,0.00417198629647411,0,0.00024680161522865106,0.09143296613266833,0,0.0032431958237133557,0,0,0,0,0.10875321164217944",
+        "token": 164
+      },
+      {
+        "text": "\"Cultural, Educational and Sports Articles\",0,0,0,0,0,0,0,0,0,0.0014633729960750004,0,0.00029799902117672207,0,0.00024680161522865106,0.013853479717070955,0,0.0003603550915237062,0,0,0,0,0.016222008441075034",
+        "token": 171
+      },
+      {
+        "text": "Petroleum Processing and Coking,0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0.0005959980423534441,0,0.661921932043242,0.005541391886828382,0,0.03963906006760768,0,0,0,0,0.7082837312384614",
+        "token": 160
+      },
+      {
+        "text": "Raw Chemical Materials and Chemical Products,0,0,0,0,0,0,0,0,0,0.009072912575665,0,0.008045973571771498,0,0.0007404048456859532,0.04593874768597067,0,0.0037837284609989153,0,0,0,0,0.06758176714009204",
+        "token": 161
+      },
+      {
+        "text": "Medical and Pharmaceutical Products,0,0,0,0,0,0,0,0,0,0.009950936373310001,0,0.0023839921694137766,0,0.0004936032304573021,0.050384432945903314,0,0.025405033952421285,0,0,0,0,0.08861799867150567",
+        "token": 163
+      },
+      {
+        "text": "Chemical Fiber,0,0,0,0,0,0,0,0,0,0.0002926745992150001,0,0,0,0,0.0014818950866442155,0,0.0007207101830474124,0,0,0,0,0.0024952798689066276",
+        "token": 127
+      },
+      {
+        "text": "Rubber Products,0,0,0,0,0,0,0,0,0,0.0026340713929350005,0,0.0016389946164719713,0,0.0003702024228429766,0.013337055779797935,0,0.0003603550915237062,0,0,0,0,0.01834067930357159",
+        "token": 164
+      },
+      {
+        "text": "Plastic Products,0,0,0,0,0,0,0,0,0,0.0026340713929350005,0,0.0016389946164719713,0,0.0003702024228429766,0.013337055779797935,0,0.0003603550915237062,0,0,0,0,0.01834067930357159",
+        "token": 164
+      },
+      {
+        "text": "Nonmetal Mineral Products,0.14596733623893196,0,0,0,0.0007820285241293591,0,0,0,0,0.010828960170955,0,0.17641542053661946,0,0.0004936032304573021,0.10251574990632503,0,0.02414379113208831,0,0,0,0.7500386,1.2111854897395065",
+        "token": 201
+      },
+      {
+        "text": "Smelting and Pressing of Ferrous Metals,0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0,0,0,0.005541391886828382,0,0.01585562402704307,0,0,0,0,0.021982365112301453",
+        "token": 130
+      },
+      {
+        "text": "Smelting and Pressing of Nonferrous Metals,0,0,0,0,0,0,0,0,0,0.0005853491984300002,0,0,0,0,0.005541391886828382,0,0.0007207101830474124,0,0,0,0,0.006847451268305795",
+        "token": 133
+      },
+      {
+        "text": "Metal Products,0,0,0,0,0,0,0,0,0,0.022535944139555,0.0015175719959296297,0.00834397259294822,0,0.0007404048456859532,0.21334358764289266,0,0.004864793735570033,0,0,0,0,0.2513462749525815",
+        "token": 174
+      },
+      {
+        "text": "Ordinary Machinery,0,0,0,0,0,0,0,0,0,0.016682452155255004,0,0.002085993148237055,0,0.0019744129218292085,0.15792966877460887,0,0.003063018277951503,0,0,0,0,0.18173554527788163",
+        "token": 160
+      },
+      {
+        "text": "Equipment for Special Purposes,0,0,0,0,0,0,0,0,0,0.018731174349760005,0,0.006555978465887885,0,0.00024680161522865106,0.17732454037850823,0,0.001981953003380384,0,0,0,0,0.20484044781276517",
+        "token": 163
+      },
+      {
+        "text": "Transportation Equipment,0,0,0,0,0,0,0,0,0,0.016097102956825003,0,0.0336738893929696,0,0.00024680161522865106,0.1523882768877805,0,0.035314798969323206,0,0,0,0,0.23772086982212698",
+        "token": 158
+      },
+      {
+        "text": "Electric Equipment and Machinery,0,0,0,0,0,0,0,0,0,0.014926404559965006,0,0.0017879941270603325,0,0.0004936032304573021,0.14130549311412374,0,0.0018017754576185308,0,0,0,0,0.16031527048922492",
+        "token": 163
+      },
+      {
+        "text": "Electronic and Telecommunications Equipment,0,0,0,0,0,0,0,0,0,0.010828960170955,0,0.0008939970635301663,0,0,0.10251574990632503,0,0.0037837284609989153,0,0,0,0,0.11802243560180911",
+        "token": 141
+      },
+      {
+        "text": "\"Instruments, Meters, Cultural and Office Machinery\",0,0,0,0,0,0,0,0,0,0.008487563377235,0,0.00029799902117672207,0,0,0.08035018235901153,0,0.0003603550915237062,0,0,0,0,0.08949609984894696",
+        "token": 147
+      },
+      {
+        "text": "Other Manufacturing Industry,0,0,0,0,0,0,0,0,0,0.0014633729960750004,0,0.008641971614124938,0,0,0.013853479717070955,0,0.0001801775457618531,0,0,0,0,0.024139001873032747",
+        "token": 144
+      },
+      {
+        "text": "Scrap and waste,0,0,0,0,0,0,0,0,0,0.0002926745992150001,0,0.00029799902117672207,0,0,0.002770695943414191,0,0.0001801775457618531,0,0,0,0,0.0035415471095677662",
+        "token": 148
+      },
+      {
+        "text": "\"Production and Supply of Electric Power, Steam and Hot Water\",1.7584294721807978,0,0,0,0.002093253214369658,0,0,0,0,0.011706983968600003,0,0.036479049620716374,0.005069313100999075,0.14259084507648084,0.34572372722416206,0.32940324454750275,28.576712994550185,0,0,0,0,31.208208883483813",
+        "token": 237
+      },
+      {
+        "text": "Production and Supply of Gas,0,0,0,0,0,0,0,0,0,0.004097444389010001,0,0.0005959980423534441,0,0,0.03878974320779867,0,0.0787375874979298,0,0,0,0,0.12222077313709193",
+        "token": 141
+      },
+      {
+        "text": "Production and Supply of Tap Water,0,0,0,0,0,0,0,0,0,0.003804769789795001,0,0.0026819911905904977,0,0.0007404048456859532,0.03601904726438448,0,0.0009008877288092654,0,0,0,0,0.04414710081926519",
+        "token": 165
+      },
+      {
+        "text": "Construction,0,0,0,0,0,0,0,0,0,0.21687187801831503,0,0.4523163987247695,0,0.013462701430531963,0,0,0.05185749588478573,0,0,0,0,0.7345084740584022",
+        "token": 134
+      },
+      {
+        "text": "\"Transportation, Storage, Post and Telecommunication Services\",0.0005851847553963052,0,0,0,0,0,0,0,0,0.901730440181415,15.042780652452862,2.411941617276541,0.014257443096559902,0.052285375323228786,0,0,0.5531466227710478,0,0,0,0,18.976727335857053",
+        "token": 196
+      },
+      {
+        "text": "\"Wholesale, Retail Trade and Catering Services\",0,0,0,0,0,0,0,0,0,0.51042450103096,0,0.12220586696052255,0,0.06011252731772412,0,0,1.1732758443932771,0,0,0,0,1.8660187397024839",
+        "token": 139
+      },
+      {
+        "text": "Others,0,0,0,0,0,0,0,0,0,1.0509944857810651,0.01851437835034148,0.676618306437121,0,0.03757032957357757,0,0,5.548752059672073,0,0,0,0,7.332449559814178",
+        "token": 146
+      },
+      {
+        "text": "Urban,0.06905180113676401,0,0,0,0,0,0,0,0,11.098220802232799,0,0,0,0.1865993035487686,0,0,3.3750586905014712,0,0,0,0,14.728930597419804",
+        "token": 131
+      },
+      {
+        "text": "Rural,0,0,0,0.17806516054927157,0,0,0,0,0,0,0,0,0,0.1415149080604755,0,0,0.4191814250686847,0,0,0,0,0.7387614936784318",
+        "token": 115
+      }
+    ]
+  }
+]
--- a/DataAnalysis/workspace/tools/simple_doc_parser/c71bbcda306da7eaf016503453f95f19c94564ecc82c8c30ee1b0020f13570b7_ori
+++ b/DataAnalysis/workspace/tools/simple_doc_parser/c71bbcda306da7eaf016503453f95f19c94564ecc82c8c30ee1b0020f13570b7_ori
@ -0,0 +1,611 @@
+[
+  {
+    "page_num": 1,
+    "content": [
+      {
+        "text": "ID,sepal_length,sepal_width,petal_length,petal_width,species",
+        "token": 19
+      },
+      {
+        "text": "1,5.1,3.5,1.4,0.2,setosa",
+        "token": 19
+      },
+      {
+        "text": "2,4.9,3,1.4,0.2,setosa",
+        "token": 17
+      },
+      {
+        "text": "3,4.7,3.2,1.3,0.2,setosa",
+        "token": 19
+      },
+      {
+        "text": "4,4.6,3.1,1.5,0.2,setosa",
+        "token": 19
+      },
+      {
+        "text": "5,5,3.6,1.4,0.2,setosa",
+        "token": 17
+      },
+      {
+        "text": "6,5.4,3.9,1.7,0.4,setosa",
+        "token": 19
+      },
+      {
+        "text": "7,4.6,3.4,1.4,0.3,setosa",
+        "token": 19
+      },
+      {
+        "text": "8,5,3.4,1.5,0.2,setosa",
+        "token": 17
+      },
+      {
+        "text": "9,4.4,2.9,1.4,0.2,setosa",
+        "token": 19
+      },
+      {
+        "text": "10,4.9,3.1,1.5,0.1,setosa",
+        "token": 20
+      },
+      {
+        "text": "11,5.4,3.7,1.5,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "12,4.8,3.4,1.6,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "13,4.8,3,1.4,0.1,setosa",
+        "token": 18
+      },
+      {
+        "text": "14,4.3,3,1.1,0.1,setosa",
+        "token": 18
+      },
+      {
+        "text": "15,5.8,4,1.2,0.2,setosa",
+        "token": 18
+      },
+      {
+        "text": "16,5.7,4.4,1.5,0.4,setosa",
+        "token": 20
+      },
+      {
+        "text": "17,5.4,3.9,1.3,0.4,setosa",
+        "token": 20
+      },
+      {
+        "text": "18,5.1,3.5,1.4,0.3,setosa",
+        "token": 20
+      },
+      {
+        "text": "19,5.7,3.8,1.7,0.3,setosa",
+        "token": 20
+      },
+      {
+        "text": "20,5.1,3.8,1.5,0.3,setosa",
+        "token": 20
+      },
+      {
+        "text": "21,5.4,3.4,1.7,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "22,5.1,3.7,1.5,0.4,setosa",
+        "token": 20
+      },
+      {
+        "text": "23,4.6,3.6,1,0.2,setosa",
+        "token": 18
+      },
+      {
+        "text": "24,5.1,3.3,1.7,0.5,setosa",
+        "token": 20
+      },
+      {
+        "text": "25,4.8,3.4,1.9,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "26,5,3,1.6,0.2,setosa",
+        "token": 16
+      },
+      {
+        "text": "27,5,3.4,1.6,0.4,setosa",
+        "token": 18
+      },
+      {
+        "text": "28,5.2,3.5,1.5,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "29,5.2,3.4,1.4,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "30,4.7,3.2,1.6,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "31,4.8,3.1,1.6,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "32,5.4,3.4,1.5,0.4,setosa",
+        "token": 20
+      },
+      {
+        "text": "33,5.2,4.1,1.5,0.1,setosa",
+        "token": 20
+      },
+      {
+        "text": "34,5.5,4.2,1.4,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "35,4.9,3.1,1.5,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "36,5,3.2,1.2,0.2,setosa",
+        "token": 18
+      },
+      {
+        "text": "37,5.5,3.5,1.3,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "38,4.9,3.6,1.4,0.1,setosa",
+        "token": 20
+      },
+      {
+        "text": "39,4.4,3,1.3,0.2,setosa",
+        "token": 18
+      },
+      {
+        "text": "40,5.1,3.4,1.5,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "41,5,3.5,1.3,0.3,setosa",
+        "token": 18
+      },
+      {
+        "text": "42,4.5,2.3,1.3,0.3,setosa",
+        "token": 20
+      },
+      {
+        "text": "43,4.4,3.2,1.3,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "44,5,3.5,1.6,0.6,setosa",
+        "token": 18
+      },
+      {
+        "text": "45,5.1,3.8,1.9,0.4,setosa",
+        "token": 20
+      },
+      {
+        "text": "46,4.8,3,1.4,0.3,setosa",
+        "token": 18
+      },
+      {
+        "text": "47,5.1,3.8,1.6,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "48,4.6,3.2,1.4,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "49,5.3,3.7,1.5,0.2,setosa",
+        "token": 20
+      },
+      {
+        "text": "50,5,3.3,1.4,0.2,setosa",
+        "token": 18
+      },
+      {
+        "text": "51,7,3.2,4.7,1.4,versicolor",
+        "token": 19
+      },
+      {
+        "text": "52,6.4,3.2,4.5,1.5,versicolor",
+        "token": 21
+      },
+      {
+        "text": "53,6.9,3.1,4.9,1.5,versicolor",
+        "token": 21
+      },
+      {
+        "text": "54,5.5,2.3,4,1.3,versicolor",
+        "token": 19
+      },
+      {
+        "text": "55,6.5,2.8,4.6,1.5,versicolor",
+        "token": 21
+      },
+      {
+        "text": "56,5.7,2.8,4.5,1.3,versicolor",
+        "token": 21
+      },
+      {
+        "text": "57,6.3,3.3,4.7,1.6,versicolor",
+        "token": 21
+      },
+      {
+        "text": "58,4.9,2.4,3.3,1,versicolor",
+        "token": 19
+      },
+      {
+        "text": "59,6.6,2.9,4.6,1.3,versicolor",
+        "token": 21
+      },
+      {
+        "text": "60,5.2,2.7,3.9,1.4,versicolor",
+        "token": 21
+      },
+      {
+        "text": "61,5,2,3.5,1,versicolor",
+        "token": 15
+      },
+      {
+        "text": "62,5.9,3,4.2,1.5,versicolor",
+        "token": 19
+      },
+      {
+        "text": "63,6,2.2,4,1,versicolor",
+        "token": 15
+      },
+      {
+        "text": "64,6.1,2.9,4.7,1.4,versicolor",
+        "token": 21
+      },
+      {
+        "text": "65,5.6,2.9,3.6,1.3,versicolor",
+        "token": 21
+      },
+      {
+        "text": "66,6.7,3.1,4.4,1.4,versicolor",
+        "token": 21
+      },
+      {
+        "text": "67,5.6,3,4.5,1.5,versicolor",
+        "token": 19
+      },
+      {
+        "text": "68,5.8,2.7,4.1,1,versicolor",
+        "token": 19
+      },
+      {
+        "text": "69,6.2,2.2,4.5,1.5,versicolor",
+        "token": 21
+      },
+      {
+        "text": "70,5.6,2.5,3.9,1.1,versicolor",
+        "token": 21
+      },
+      {
+        "text": "71,5.9,3.2,4.8,1.8,versicolor",
+        "token": 21
+      },
+      {
+        "text": "72,6.1,2.8,4,1.3,versicolor",
+        "token": 19
+      },
+      {
+        "text": "73,6.3,2.5,4.9,1.5,versicolor",
+        "token": 21
+      },
+      {
+        "text": "74,6.1,2.8,4.7,1.2,versicolor",
+        "token": 21
+      },
+      {
+        "text": "75,6.4,2.9,4.3,1.3,versicolor",
+        "token": 21
+      },
+      {
+        "text": "76,6.6,3,4.4,1.4,versicolor",
+        "token": 19
+      },
+      {
+        "text": "77,6.8,2.8,4.8,1.4,versicolor",
+        "token": 21
+      },
+      {
+        "text": "78,6.7,3,5,1.7,versicolor",
+        "token": 17
+      },
+      {
+        "text": "79,6,2.9,4.5,1.5,versicolor",
+        "token": 19
+      },
+      {
+        "text": "80,5.7,2.6,3.5,1,versicolor",
+        "token": 19
+      },
+      {
+        "text": "81,5.5,2.4,3.8,1.1,versicolor",
+        "token": 21
+      },
+      {
+        "text": "82,5.5,2.4,3.7,1,versicolor",
+        "token": 19
+      },
+      {
+        "text": "83,5.8,2.7,3.9,1.2,versicolor",
+        "token": 21
+      },
+      {
+        "text": "84,6,2.7,5.1,1.6,versicolor",
+        "token": 19
+      },
+      {
+        "text": "85,5.4,3,4.5,1.5,versicolor",
+        "token": 19
+      },
+      {
+        "text": "86,6,3.4,4.5,1.6,versicolor",
+        "token": 19
+      },
+      {
+        "text": "87,6.7,3.1,4.7,1.5,versicolor",
+        "token": 21
+      },
+      {
+        "text": "88,6.3,2.3,4.4,1.3,versicolor",
+        "token": 21
+      },
+      {
+        "text": "89,5.6,3,4.1,1.3,versicolor",
+        "token": 19
+      },
+      {
+        "text": "90,5.5,2.5,4,1.3,versicolor",
+        "token": 19
+      },
+      {
+        "text": "91,5.5,2.6,4.4,1.2,versicolor",
+        "token": 21
+      },
+      {
+        "text": "92,6.1,3,4.6,1.4,versicolor",
+        "token": 19
+      },
+      {
+        "text": "93,5.8,2.6,4,1.2,versicolor",
+        "token": 19
+      },
+      {
+        "text": "94,5,2.3,3.3,1,versicolor",
+        "token": 17
+      },
+      {
+        "text": "95,5.6,2.7,4.2,1.3,versicolor",
+        "token": 21
+      },
+      {
+        "text": "96,5.7,3,4.2,1.2,versicolor",
+        "token": 19
+      },
+      {
+        "text": "97,5.7,2.9,4.2,1.3,versicolor",
+        "token": 21
+      },
+      {
+        "text": "98,6.2,2.9,4.3,1.3,versicolor",
+        "token": 21
+      },
+      {
+        "text": "99,5.1,2.5,3,1.1,versicolor",
+        "token": 19
+      },
+      {
+        "text": "100,5.7,2.8,4.1,1.3,versicolor",
+        "token": 22
+      },
+      {
+        "text": "101,6.3,3.3,6,2.5,virginica",
+        "token": 21
+      },
+      {
+        "text": "102,5.8,2.7,5.1,1.9,virginica",
+        "token": 23
+      },
+      {
+        "text": "103,7.1,3,5.9,2.1,virginica",
+        "token": 21
+      },
+      {
+        "text": "104,6.3,2.9,5.6,1.8,virginica",
+        "token": 23
+      },
+      {
+        "text": "105,6.5,3,5.8,2.2,virginica",
+        "token": 21
+      },
+      {
+        "text": "106,7.6,3,6.6,2.1,virginica",
+        "token": 21
+      },
+      {
+        "text": "107,4.9,2.5,4.5,1.7,virginica",
+        "token": 23
+      },
+      {
+        "text": "108,7.3,2.9,6.3,1.8,virginica",
+        "token": 23
+      },
+      {
+        "text": "109,6.7,2.5,5.8,1.8,virginica",
+        "token": 23
+      },
+      {
+        "text": "110,7.2,3.6,6.1,2.5,virginica",
+        "token": 23
+      },
+      {
+        "text": "111,6.5,3.2,5.1,2,virginica",
+        "token": 21
+      },
+      {
+        "text": "112,6.4,2.7,5.3,1.9,virginica",
+        "token": 23
+      },
+      {
+        "text": "113,6.8,3,5.5,2.1,virginica",
+        "token": 21
+      },
+      {
+        "text": "114,5.7,2.5,5,2,virginica",
+        "token": 19
+      },
+      {
+        "text": "115,5.8,2.8,5.1,2.4,virginica",
+        "token": 23
+      },
+      {
+        "text": "116,6.4,3.2,5.3,2.3,virginica",
+        "token": 23
+      },
+      {
+        "text": "117,6.5,3,5.5,1.8,virginica",
+        "token": 21
+      },
+      {
+        "text": "118,7.7,3.8,6.7,2.2,virginica",
+        "token": 23
+      },
+      {
+        "text": "119,7.7,2.6,6.9,2.3,virginica",
+        "token": 23
+      },
+      {
+        "text": "120,6,2.2,5,1.5,virginica",
+        "token": 19
+      },
+      {
+        "text": "121,6.9,3.2,5.7,2.3,virginica",
+        "token": 23
+      },
+      {
+        "text": "122,5.6,2.8,4.9,2,virginica",
+        "token": 21
+      },
+      {
+        "text": "123,7.7,2.8,6.7,2,virginica",
+        "token": 21
+      },
+      {
+        "text": "124,6.3,2.7,4.9,1.8,virginica",
+        "token": 23
+      },
+      {
+        "text": "125,6.7,3.3,5.7,2.1,virginica",
+        "token": 23
+      },
+      {
+        "text": "126,7.2,3.2,6,1.8,virginica",
+        "token": 21
+      },
+      {
+        "text": "127,6.2,2.8,4.8,1.8,virginica",
+        "token": 23
+      },
+      {
+        "text": "128,6.1,3,4.9,1.8,virginica",
+        "token": 21
+      },
+      {
+        "text": "129,6.4,2.8,5.6,2.1,virginica",
+        "token": 23
+      },
+      {
+        "text": "130,7.2,3,5.8,1.6,virginica",
+        "token": 21
+      },
+      {
+        "text": "131,7.4,2.8,6.1,1.9,virginica",
+        "token": 23
+      },
+      {
+        "text": "132,7.9,3.8,6.4,2,virginica",
+        "token": 21
+      },
+      {
+        "text": "133,6.4,2.8,5.6,2.2,virginica",
+        "token": 23
+      },
+      {
+        "text": "134,6.3,2.8,5.1,1.5,virginica",
+        "token": 23
+      },
+      {
+        "text": "135,6.1,2.6,5.6,1.4,virginica",
+        "token": 23
+      },
+      {
+        "text": "136,7.7,3,6.1,2.3,virginica",
+        "token": 21
+      },
+      {
+        "text": "137,6.3,3.4,5.6,2.4,virginica",
+        "token": 23
+      },
+      {
+        "text": "138,6.4,3.1,5.5,1.8,virginica",
+        "token": 23
+      },
+      {
+        "text": "139,6,3,4.8,1.8,virginica",
+        "token": 19
+      },
+      {
+        "text": "140,6.9,3.1,5.4,2.1,virginica",
+        "token": 23
+      },
+      {
+        "text": "141,6.7,3.1,5.6,2.4,virginica",
+        "token": 23
+      },
+      {
+        "text": "142,6.9,3.1,5.1,2.3,virginica",
+        "token": 23
+      },
+      {
+        "text": "143,5.8,2.7,5.1,1.9,virginica",
+        "token": 23
+      },
+      {
+        "text": "144,6.8,3.2,5.9,2.3,virginica",
+        "token": 23
+      },
+      {
+        "text": "145,6.7,3.3,5.7,2.5,virginica",
+        "token": 23
+      },
+      {
+        "text": "146,6.7,3,5.2,2.3,virginica",
+        "token": 21
+      },
+      {
+        "text": "147,6.3,2.5,5,1.9,virginica",
+        "token": 21
+      },
+      {
+        "text": "148,6.5,3,5.2,2,virginica",
+        "token": 19
+      },
+      {
+        "text": "149,6.2,3.4,5.4,2.3,virginica",
+        "token": 23
+      },
+      {
+        "text": "150,5.9,3,5.1,1.8,virginica",
+        "token": 21
+      }
+    ]
+  }
+]
--- a/DataAnalysis/workspace/tools/simple_doc_parser/ff9c5fd787b332bfc1e3006c69c40b5b0ecd131070ddd4fbc4beca035fb89231_ori
+++ b/DataAnalysis/workspace/tools/simple_doc_parser/ff9c5fd787b332bfc1e3006c69c40b5b0ecd131070ddd4fbc4beca035fb89231_ori
@ -0,0 +1,211 @@
+[
+  {
+    "page_num": 1,
+    "content": [
+      {
+        "text": "Emission_Inventory,Raw_Coal,CleanedCoal,Other_Washed_Coal,Briquettes,Coke,Coke_Oven_Gas,Other_Gas,Other_Coking_Products,Crude_Oil,Gasoline,Kerosene,Diesel_Oil,Fuel_Oil,LPG,Refinery_Gas,Other_Petroleum_Products,Natural_Gas,Scope_2_Heat,Scope_2_Electricity,Other_Energy,Process,Scope_1_Total",
+        "token": 104
+      },
+      {
+        "text": "unit,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2,Mt CO2",
+        "token": 89
+      },
+      {
+        "text": ",,,,,,,,,,,,,,,,,,,,,,",
+        "token": 4
+      },
+      {
+        "text": "TotalEmissions,2.002198474,0,0,0.178065161,0.002876589,0,0,0,0,14.04925879,15.0628126,4.042693832,0.019326756,1.306508211,2.14647396,0.329403245,39.99077224,0,0,0,0.7500386,79.88042846",
+        "token": 173
+      },
+      {
+        "text": "\"Farming, Forestry, Animal Husbandry, Fishery and Water Conservancy\",0.027948058,0,0,0,0,0,0,0,0,0.057656896,0,0.052285548,0,0.00156543,0,0,0.002160729,0,0,0,0,0.141616662",
+        "token": 119
+      },
+      {
+        "text": "Coal Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0",
+        "token": 49
+      },
+      {
+        "text": "Petroleum and Natural Gas Extraction,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.002702663,0,0,0,0,0.002702663",
+        "token": 70
+      },
+      {
+        "text": "Ferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.000292675,0,0.000297999,0,0,0.002770696,0,0,0,0,0,0,0.00336137",
+        "token": 91
+      },
+      {
+        "text": "Nonferrous Metals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0",
+        "token": 52
+      },
+      {
+        "text": "Nonmetal Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0",
+        "token": 51
+      },
+      {
+        "text": "Other Minerals Mining and Dressing,0,0,0,0,0,0,0,0,0,0.000878024,0,0.003873987,0,0,0.008312088,0,0,0,0,0,0,0.013064099",
+        "token": 90
+      },
+      {
+        "text": "Logging and Transport of Wood and Bamboo,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0",
+        "token": 51
+      },
+      {
+        "text": "Food Processing,0,0,0,0,0,0,0,0,0,0.005853492,0,0.009833968,0,0.000246802,0.055413919,0,0.007387279,0,0,0,0,0.07873546",
+        "token": 105
+      },
+      {
+        "text": "Food Production,0.000216622,0,0,0,1.3073E-06,0,0,0,0,0.006731516,0,0.008641972,0,0.000740405,0.063726007,0,0.012792606,0,0,0,0,0.092850434",
+        "token": 125
+      },
+      {
+        "text": "Beverage Production,0,0,0,0,0,0,0,0,0,0.00380477,0,0.00297999,0,0.000246802,0.036019047,0,0.015855624,0,0,0,0,0.058906233",
+        "token": 106
+      },
+      {
+        "text": "Tobacco Processing,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00072071,0,0,0,0,0.00072071",
+        "token": 66
+      },
+      {
+        "text": "Textile Industry,0,0,0,0,0,0,0,0,0,0.000878024,0,0.000297999,0,0,0.008312088,0,0.000180178,0,0,0,0,0.009668288",
+        "token": 97
+      },
+      {
+        "text": "Garments and Other Fiber Products,0,0,0,0,0,0,0,0,0,0.005268143,0,0.000595998,0,0,0.049872527,0,0.001981953,0,0,0,0,0.057718621",
+        "token": 100
+      },
+      {
+        "text": "\"Leather, Furs, Down and Related Products\",0,0,0,0,0,0,0,0,0,0.000585349,0,0,0,0,0.005541392,0,0,0,0,0,0,0.006126741",
+        "token": 85
+      },
+      {
+        "text": "\"Timber Processing, Bamboo, Cane, Palm Fiber & Straw Products\",0,0,0,0,0,0,0,0,0,0.001756048,0,0.000595998,0,0,0.016624176,0,0,0,0,0,0,0.018976221",
+        "token": 99
+      },
+      {
+        "text": "Furniture Manufacturing,0,0,0,0,0,0,0,0,0,0.005268143,0,0.000595998,0,0.000246802,0.049872527,0,0.00072071,0,0,0,0,0.05670418",
+        "token": 105
+      },
+      {
+        "text": "Papermaking and Paper Products,0,0,0,0,0,0,0,0,0,0.004097444,0,0.002085993,0,0,0.038789743,0,0.002522486,0,0,0,0,0.047495666",
+        "token": 99
+      },
+      {
+        "text": "Printing and Record Medium Reproduction,0,0,0,0,0,0,0,0,0,0.009658262,0,0.004171986,0,0.000246802,0.091432966,0,0.003243196,0,0,0,0,0.108753212",
+        "token": 110
+      },
+      {
+        "text": "\"Cultural, Educational and Sports Articles\",0,0,0,0,0,0,0,0,0,0.001463373,0,0.000297999,0,0.000246802,0.01385348,0,0.000360355,0,0,0,0,0.016222008",
+        "token": 110
+      },
+      {
+        "text": "Petroleum Processing and Coking,0,0,0,0,0,0,0,0,0,0.000585349,0,0.000595998,0,0.661921932,0.005541392,0,0.03963906,0,0,0,0,0.708283731",
+        "token": 109
+      },
+      {
+        "text": "Raw Chemical Materials and Chemical Products,0,0,0,0,0,0,0,0,0,0.009072913,0,0.008045974,0,0.000740405,0.045938748,0,0.003783728,0,0,0,0,0.067581767",
+        "token": 110
+      },
+      {
+        "text": "Medical and Pharmaceutical Products,0,0,0,0,0,0,0,0,0,0.009950936,0,0.002383992,0,0.000493603,0.050384433,0,0.025405034,0,0,0,0,0.088617999",
+        "token": 108
+      },
+      {
+        "text": "Chemical Fiber,0,0,0,0,0,0,0,0,0,0.000292675,0,0,0,0,0.001481895,0,0.00072071,0,0,0,0,0.00249528",
+        "token": 85
+      },
+      {
+        "text": "Rubber Products,0,0,0,0,0,0,0,0,0,0.002634071,0,0.001638995,0,0.000370202,0.013337056,0,0.000360355,0,0,0,0,0.018340679",
+        "token": 107
+      },
+      {
+        "text": "Plastic Products,0,0,0,0,0,0,0,0,0,0.002634071,0,0.001638995,0,0.000370202,0.013337056,0,0.000360355,0,0,0,0,0.018340679",
+        "token": 107
+      },
+      {
+        "text": "Nonmetal Mineral Products,0.145967336,0,0,0,0.000782029,0,0,0,0,0.01082896,0,0.176415421,0,0.000493603,0.10251575,0,0.024143791,0,0,0,0.7500386,1.21118549",
+        "token": 133
+      },
+      {
+        "text": "Smelting and Pressing of Ferrous Metals,0,0,0,0,0,0,0,0,0,0.000585349,0,0,0,0,0.005541392,0,0.015855624,0,0,0,0,0.021982365",
+        "token": 94
+      },
+      {
+        "text": "Smelting and Pressing of Nonferrous Metals,0,0,0,0,0,0,0,0,0,0.000585349,0,0,0,0,0.005541392,0,0.00072071,0,0,0,0,0.006847451",
+        "token": 94
+      },
+      {
+        "text": "Metal Products,0,0,0,0,0,0,0,0,0,0.022535944,0.001517572,0.008343973,0,0.000740405,0.213343588,0,0.004864794,0,0,0,0,0.251346275",
+        "token": 116
+      },
+      {
+        "text": "Ordinary Machinery,0,0,0,0,0,0,0,0,0,0.016682452,0,0.002085993,0,0.001974413,0.157929669,0,0.003063018,0,0,0,0,0.181735545",
+        "token": 107
+      },
+      {
+        "text": "Equipment for Special Purposes,0,0,0,0,0,0,0,0,0,0.018731174,0,0.006555978,0,0.000246802,0.17732454,0,0.001981953,0,0,0,0,0.204840448",
+        "token": 108
+      },
+      {
+        "text": "Transportation Equipment,0,0,0,0,0,0,0,0,0,0.016097103,0,0.033673889,0,0.000246802,0.152388277,0,0.035314799,0,0,0,0,0.23772087",
+        "token": 106
+      },
+      {
+        "text": "Electric Equipment and Machinery,0,0,0,0,0,0,0,0,0,0.014926405,0,0.001787994,0,0.000493603,0.141305493,0,0.001801775,0,0,0,0,0.16031527",
+        "token": 107
+      },
+      {
+        "text": "Electronic and Telecommunications Equipment,0,0,0,0,0,0,0,0,0,0.01082896,0,0.000893997,0,0,0.10251575,0,0.003783728,0,0,0,0,0.118022436",
+        "token": 97
+      },
+      {
+        "text": "\"Instruments, Meters, Cultural and Office Machinery\",0,0,0,0,0,0,0,0,0,0.008487563,0,0.000297999,0,0,0.080350182,0,0.000360355,0,0,0,0,0.0894961",
+        "token": 102
+      },
+      {
+        "text": "Other Manufacturing Industry,0,0,0,0,0,0,0,0,0,0.001463373,0,0.008641972,0,0,0.01385348,0,0.000180178,0,0,0,0,0.024139002",
+        "token": 96
+      },
+      {
+        "text": "Scrap and waste,0,0,0,0,0,0,0,0,0,0.000292675,0,0.000297999,0,0,0.002770696,0,0.000180178,0,0,0,0,0.003541547",
+        "token": 98
+      },
+      {
+        "text": "\"Production and Supply of Electric Power, Steam and Hot Water\",1.758429472,0,0,0,0.002093253,0,0,0,0,0.011706984,0,0.03647905,0.005069313,0.142590845,0.345723727,0.329403245,28.57671299,0,0,0,0,31.20820888",
+        "token": 155
+      },
+      {
+        "text": "Production and Supply of Gas,0,0,0,0,0,0,0,0,0,0.004097444,0,0.000595998,0,0,0.038789743,0,0.078737587,0,0,0,0,0.122220773",
+        "token": 99
+      },
+      {
+        "text": "Production and Supply of Tap Water,0,0,0,0,0,0,0,0,0,0.00380477,0,0.002681991,0,0.000740405,0.036019047,0,0.000900888,0,0,0,0,0.044147101",
+        "token": 109
+      },
+      {
+        "text": "Construction,0,0,0,0,0,0,0,0,0,0.216871878,0,0.452316399,0,0.013462701,0,0,0.051857496,0,0,0,0,0.734508474",
+        "token": 95
+      },
+      {
+        "text": "\"Transportation, Storage, Post and Telecommunication Services\",0.000585185,0,0,0,0,0,0,0,0,0.90173044,15.04278065,2.411941617,0.014257443,0.052285375,0,0,0.553146623,0,0,0,0,18.97672734",
+        "token": 134
+      },
+      {
+        "text": "\"Wholesale, Retail Trade and Catering Services\",0,0,0,0,0,0,0,0,0,0.510424501,0,0.122205867,0,0.060112527,0,0,1.173275844,0,0,0,0,1.86601874",
+        "token": 103
+      },
+      {
+        "text": "Others,0,0,0,0,0,0,0,0,0,1.050994486,0.018514378,0.676618306,0,0.03757033,0,0,5.54875206,0,0,0,0,7.33244956",
+        "token": 102
+      },
+      {
+        "text": "Urban,0.069051801,0,0,0,0,0,0,0,0,11.0982208,0,0,0,0.186599304,0,0,3.375058691,0,0,0,0,14.7289306",
+        "token": 93
+      },
+      {
+        "text": "Rural,0,0,0,0.178065161,0,0,0,0,0,0,0,0,0,0.141514908,0,0,0.419181425,0,0,0,0,0.738761494",
+        "token": 86
+      }
+    ]
+  }
+]
--- a/DataAnalysis/报告案例1.md
+++ b/DataAnalysis/报告案例1.md
@ -0,0 +1,83 @@
+# 产品碳足迹研究报告（示例）
+
+## 基本信息
+- 产品名称：智能节能灯泡
+- 产品规格型号：LED-E27-10W
+- 生产者名称：EcoTech Solutions
+- 报告编号：ECO-2023-CFP-01
+- 出具报告机构：EcoAnalytics
+- 日期：2023年4月15日
+
+## 一、概况
+### 1. 生产者信息
+- 生产者名称：EcoTech Solutions
+- 地址：123 Greenway St, EcoCity
+- 法定代表人：Jane Doe
+- 产品名称：智能节能灯泡
+- 产品功能：节能照明，支持远程控制
+- 依据标准：ISO 14040, ISO 14044
+
+## 二、量化目的
+评估并减少产品在其整个生命周期中的碳排放。
+
+## 三、量化范围
+### 1. 功能单位或声明单位
+以个为功能单位或声明单位。
+### 2. 系统边界
+- 原材料获取阶段
+- 生产阶段
+- 分销阶段
+- 使用阶段
+- 生命末期阶段
+- 系统边界图：（此处通常会有一个流程图，但我们将跳过此部分，因为它是图形内容）
+
+### 3. 取舍准则
+采用的取舍准则以ISO 14044为依据，具体规则如下：（此处可以详细描述，但我们将省略）
+
+### 4. 时间范围
+2022年度。
+
+## 四、清单分析
+### 1. 数据来源说明
+- 初级数据：内部记录
+- 次级数据：行业平均值
+
+### 2. 分配原则与程序
+- 分配依据：按活动比例
+- 分配程序：线性分配
+- 具体分配情况：（略，通常会有一系列详细的计算）
+
+### 3. 清单结果及计算
+| 生命周期阶段 | 活动数据 (单位) | 排放因子 (kg CO2e/单位) | 碳足迹 (kg CO2e/功能单位) |
+| --- | --- | --- | --- |
+| 原材料获取 | 10 | 0.5 | 5 |
+| 生产 | 20 | 1 | 20 |
+| 分销 | 5 | 0.8 | 4 |
+| 运输 | 15 | 0.6 | 9 |
+| 仓库 | 3 | 0.7 | 2.1 |
+| 使用 |  |  | 100 (假设) |
+| 生命末期 | 2 | 1.2 | 2.4 |
+| **总计** |  |  | 130.5 |
+
+### 4. 数据质量评价
+（略，通常涉及详细的质量评估标准）
+
+## 五、影响评价
+### 1. 影响类型和特征化因子选择
+100年全球变暖潜势（GWP100）
+
+### 2. 产品碳足迹结果计算
+产品碳足迹总计为130.5 kg CO2e/个。
+
+## 六、结果解释
+### 1. 结果说明
+EcoTech Solutions生产的LED-E27-10W智能节能灯泡，从原材料获取到生命末期的生命周期碳足迹为130.5 kg CO2e/个。各生命周期阶段的温室气体排放情况如下表所示。
+
+### 2. 假设和局限性说明
+（此处可以添加关于数据估算和简化模型的说明）
+
+### 3. 改进建议
+- 优化原材料供应链以减少获取过程中的排放
+- 提升生产效率，降低生产阶段的碳足迹
+- 考虑使用更环保的分销方式
+- 提供回收计划以减少生命末期的环境影响
--- a/1
+++ b/1
@ -0,0 +1 @@
+Subproject commit fe482edaa22a709ac8068046843720e6495775b9
--- a/LCA_RAG/Baiduqa.py
+++ b/LCA_RAG/Baiduqa.py
@ -0,0 +1,43 @@
+
+import os
+import qianfan
+from qianfan.resources.console.data import Data
+import pandas as pd
+import re
+
+# 使用安全认证AK/SK鉴权，通过环境变量方式初始化；替换下列示例中参数，安全认证Access Key替换your_iam_ak，Secret Key替换your_iam_sk
+os.environ["QIANFAN_ACCESS_KEY"] = "ALTAK8Mo2gY80BAW8RjEtHX3SS"
+os.environ["QIANFAN_SECRET_KEY"] = "95f6ac794fcb45b7805d5780bec589dc"
+
+
+chat_comp = qianfan.ChatCompletion()
+
+question = []
+with open("/home/zhangxj/WorkFile/LCA-GPT/QA/filters/question.txt","r",encoding="utf-8") as file:
+    for line in file.readlines():
+        question.append(line.strip())
+
+answers = []
+
+for ques in question:
+    # 指定特定模型
+    content = ("你是生命周期领域富有经验和知识的专家。根据你所掌握的知识只用1句话回答问题。不要列出几点来回答，不需要换行"+ques)
+    resp = chat_comp.do(model="ERNIE-3.5-8K", messages=[
+        {
+        "role": "user",
+        "content": content
+    }])
+    
+    ans = resp["body"]["result"]
+    line = re.sub(r'\s+', '', ans)
+    print(line)
+    answers.append(line)
+
+data = {"ans":answers}
+df = pd.DataFrame(data)
+df.to_csv("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/baidu.csv",index=False)
+
+with open("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/ERNIEpred.txt","w",encoding="utf-8") as file:
+    for ans in answers:
+        line = re.sub(r'\s+', '', ans)
+        file.write(line+"\n")
--- a/LCA_RAG/GLMqa.py
+++ b/LCA_RAG/GLMqa.py
@ -0,0 +1,48 @@
+# glm
+from zhipuai import ZhipuAI
+import re
+import pandas as pd
+
+'''
+经过换算，需要135000左右的token
+'''
+# def zhipuQA(prompt):
+#     client = ZhipuAI(api_key="434790cf952335f18b6347e7b6de9777.V50p55zfk8Ye4ojV") # 请填写您自己的APIKey
+#     response = client.chat.completions.create(
+#         model = "glm-4",
+#         messages = [
+#             {"role":"user","content":prompt}
+#         ],
+#     )
+#     content = response.choices[0].message.content
+#     print('\n',content)
+#     res = re.sub(r'\s+', '', content) #处理空格
+#     return res
+
+# instruction = "你是生命周期领域富有经验和知识的专家。文件的每一行都是一个问题，根据你所掌握的知识回答问题；不要列出几点来回答，不需要换行，只需要用1句话回答问题。"
+
+# question  = []
+# answers = []
+# with open("/home/zhangxj/WorkFile/LCA-GPT/QA/filters/question.txt","r",encoding="utf-8") as file:
+#     for line in file.readlines():
+#         question.append(line.strip())
+
+# for ques in question:
+#     message = (instruction+ques)
+#     ans = zhipuQA(message)
+#     answers.append(ans)
+
+# data = {"ans":answers}
+# df = pd.DataFrame(data)
+# df.to_csv("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/GLM.csv",index=False)
+
+df = pd.read_excel("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/GLMoutput.xlsx")
+answers = df['content'].values.tolist()
+
+with open("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/GLMpred.txt","w",encoding="utf-8") as file:
+    for ans in answers:
+        line = re.sub(r'\s+', '', ans)
+        file.write(line+"\n")
+
+
+
--- a/LCA_RAG/QAdata.py
+++ b/LCA_RAG/QAdata.py
@ -0,0 +1,103 @@
+import os
+import time
+from qwen_agent.agents import Assistant
+from pprint import pprint
+import json
+import re
+
+'''
+生成QAdata，后续不用
+'''
+def extract_qa(s):
+    # 使用正则表达式提取 questions 和 answers
+    questions = re.findall(r'#([^#]*?\?)', s) 
+    answers = re.findall(r'@([^@]*?\。)', s)
+    if len(questions) == 0:
+        questions = re.findall(r'#([^#]*?？)', s) 
+    print("Q:",questions)
+    print("A:",answers)
+    return questions, answers
+
+# 去除长度小于5的元素
+def filter_short_answers(questions, answers, min_length=8):
+    # 遍历 answers 列表，检查每个答案的长度
+    filtered_questions = []
+    filtered_answers = []
+    
+    for question, answer in zip(questions, answers):
+        if len(answer) >= min_length and len(question) >= min_length:
+            filtered_questions.append(question)
+            filtered_answers.append(answer)
+    
+    return filtered_questions, filtered_answers
+
+def write_to_file(filename, data_list):
+    with open(filename, 'a', encoding='utf-8') as file:
+        for item in data_list:
+            file.write(item + '\n')
+
+
+data = "/home/zhangxj/WorkFile/LCA-GPT/split_LCAdata/folder6"
+docs = os.listdir(data)
+
+llm_cfg = {
+    'model': 'qwen-plus',
+    'model_server': 'dashscope',
+    'api_key': "sk-c5f441f863f44094b0ddb96c831b5002",
+}
+
+system_instruction = '''你是一位专注于生命周期分析（LCA）领域的数据分析助手。在LCA领域的目标和范围定义、数据清单收集和分析、生命周期影响评价、结果分析和政策建议等方面有着丰富的经验和知识。
+请根据下面的文档提出10个问题及其相应的答案，规定每个问题的字符数量为x，答案的字符数量为y，
+"x>40 & x<70;y>40 & y<70"
+10个question结果的输出为10个字符串，以"#问题1:"开头；
+10个对应的answer结果输出为10个字符串，以"@答案1:"开头，答案以"。"结尾,不要换行。
+
+'''
+
+tools = ['code_interpreter']  # `code_interpreter` is a built-in tool for executing code.
+messages = []  # This stores the chat history.
+questions = []
+answers = []
+
+# Process each document
+for doc in docs:
+    doc_path = os.path.join(data, doc)
+    files = [doc_path]
+    prompt = "分析这篇文章，根据文章研究的内容，并按照格式输出10个与LCA领域相关的问题和相应的答案。"
+    messages.append({'role': 'user', 'content': prompt})
+    assistant = Assistant(llm=llm_cfg,
+                          system_message=system_instruction,
+                          # function_list=tools,
+                          files=files)
+    response = []
+    for response in assistant.run(messages=messages):
+        continue
+
+    # pprint(response)
+    content = response[0]['content']
+    content = content.replace('\n', '')
+    print(content)
+    # print(type(content))
+    question, answer = extract_qa(content)
+    filterq,filtera = filter_short_answers(question,answer)
+    questions.extend(filterq)
+    answers.extend(filtera)
+    
+    file1 = "/home/zhangxj/WorkFile/LCA-GPT/QA/originData/ques.txt"
+    file2 = "/home/zhangxj/WorkFile/LCA-GPT/QA/originData/answer.txt"
+    write_to_file(file1,filterq)
+    write_to_file(file2,filtera)
+
+    # print(answers)
+    # Pause for a while to avoid hitting API rate limits
+    time.sleep(3)
+
+# Print the final results
+# print("Final Questions List:")
+# pprint(questions)
+
+# print("\nFinal Answers List:")
+# pprint(answers)
+
+
+
--- a/LCA_RAG/RAGnoUI.py
+++ b/LCA_RAG/RAGnoUI.py
@ -0,0 +1,148 @@
+'''
+支持上传文件进行分析，记录历史上下文，
+'''
+import time
+import tempfile
+from dotenv import load_dotenv
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.prompts import MessagesPlaceholder
+from langchain_community.document_loaders import TextLoader, PyPDFLoader, CSVLoader, JSONLoader
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_openai import ChatOpenAI
+from langchain_community.chat_models.tongyi import ChatTongyi
+# from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
+from langchain_community.chat_models import QianfanChatEndpoint
+from langchain_community.chat_models import ChatZhipuAI
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from langchain_text_splitters import RecursiveCharacterTextSplitter, RecursiveJsonSplitter
+import requests
+import os
+import re
+import pandas as pd
+
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+device = "cuda"
+memory = 6
+
+def init_chain():
+    try:
+        # 加载环境变量并初始化模型
+
+        # 使用Tongyi
+        # os.environ["DASHSCOPE_API_KEY"] = 'sk-c5f441f863f44094b0ddb96c831b5002'
+        # llm = ChatTongyi(
+        #     streaming=True,
+        #     model='llama3-70b-instruct'
+        # )
+        
+        # 使用Qianfan
+        # ak = "beiDmKGviUVjTyGqb8U0dc2u"
+        # sk = "i3GexGV2B5Poi00RVAFUoZ61Ylj0P7tI"
+        # llm = QianfanChatEndpoint(
+        #     streaming=True,
+        #     model = 'ERNIE-3.5-8K',
+        #     api_key=ak,
+        #     secret_key=sk
+        # )
+        
+        # 使用智谱
+        # os.environ["ZHIPUAI_API_KEY"] = "434790cf952335f18b6347e7b6de9777.V50p55zfk8Ye4ojV"
+        # llm = ChatZhipuAI(
+        #     streaming=True,
+        #     model = "glm-4"
+        # )
+
+        # deepseek
+        # pip3 install langchain_openai
+# python3 deepseek_v2_langchain.py
+
+        llm = ChatOpenAI(
+            model='deepseek-chat', 
+            openai_api_key='sk-c47f0552d66549ac92c72d64ebdc4d05', 
+            openai_api_base='https://api.deepseek.com',
+        )
+
+        embedding = HuggingFaceEmbeddings(model_name="/home/zhangxj/models/acge_text_embedding", model_kwargs={"device": device})
+        retriever = Chroma(persist_directory="chromaDB", embedding_function=embedding,)
+
+        retriever = retriever.as_retriever(search_type="mmr", search_kwargs={"k": 10})
+
+        instruct_system_prompt = (
+            "你是生命周期领域富有经验和知识的专家。"
+            "使用以下检索到的上下文来回答问题。"
+            "{context}"
+            "答案最多使用1句话并保持非常简洁，不能换行。"
+        )
+        # instr = "你是生命周期领域富有经验和知识的专家。根据你所掌握的知识只用1句话回答问题。不要列出几点来回答，不需要换行"
+        instruct_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", instruct_system_prompt),
+                ("human", "{input}"),
+            ]
+        )
+
+        # Create a chain for passing a list of Documents to a model.
+        qa_chain = create_stuff_documents_chain(llm, instruct_prompt) #
+        rag_chain = create_retrieval_chain(retriever, qa_chain)
+
+        return rag_chain,retriever
+    
+    except Exception as e:
+        print(f"Error in init_chain: {e}")
+        return None
+
+def user_in(uin, rag_chain):
+    try:
+        result = rag_chain.invoke({"input": uin})['answer']
+        # print(rag_chain)
+        # result = rag_chain(uin)
+        return result
+    except Exception as e:
+        print(f"Error in user_in: {e}")
+        return "An error occurred while processing your request."
+
+def retrieve_and_output(retriever,query):
+    try:
+        retrieved_docs = retriever.get_relevant_documents(query)
+        # 输出检索到的内容
+        print("Retrieved content:")
+        for doc in retrieved_docs:
+            print(doc.page_content)
+    except Exception as e:
+        print(f"Error in retrieve_and_output: {e}")
+
+def main():
+    
+    question  = []
+    with open("/home/zhangxj/WorkFile/LCA-GPT/QA/split/question/ques0.txt","r",encoding="utf-8") as file:
+        for line in file.readlines():
+            question.append(line.strip())
+    
+    rag,retriever= init_chain()
+    
+    answers = []
+    for ques in question:
+        # print(ques)
+        response = user_in(ques, rag)
+        retrieve_and_output(retriever,ques)
+        print(response) 
+        # print(len(response))
+        answers.append(response.strip())
+        # if len(answers) == 3:
+        #     break
+    with open("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/RAGpred.txt","w",encoding="utf-8") as file:
+        for ans in answers:
+            line = re.sub(r'\s+', '', ans)
+            file.write(line+'\n')
+    data = {"ans":answers}
+    df = pd.DataFrame(data)
+    df.to_csv("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/rag.csv",index=False)
+
+if __name__ == "__main__":
+    main()
--- a/LCA_RAG/SplitData.py
+++ b/LCA_RAG/SplitData.py
@ -0,0 +1,40 @@
+import os
+import shutil
+
+# 原始文件夹路径
+original_folder = '/home/zhangxj/WorkFile/LCA-GPT/LCAdata'
+# 新文件夹的基础路径
+base_new_folder = '/home/zhangxj/WorkFile/LCA-GPT/split_LCAdata'
+
+# 获取原始文件夹中的所有PDF文件
+pdf_files = [f for f in os.listdir(original_folder) if f.endswith('.pdf')]
+
+# 计算每组文件的数量和剩余文件数量
+files_per_group, remainder = divmod(len(pdf_files), 6)
+
+# 创建并分配文件到各个组
+groups = []
+for i in range(6):
+    group = []
+    if i < remainder:
+        group = pdf_files[i * (files_per_group + 1):(i + 1) * (files_per_group + 1)]
+    else:
+        group = pdf_files[i * files_per_group + remainder:(i + 1) * files_per_group + remainder]
+    groups.append(group)
+
+# 确保每组文件数量正确
+for group in groups:
+    assert len(group) in (files_per_group, files_per_group + 1), "每组文件数量不正确"
+
+# 分组并复制文件
+for i, group in enumerate(groups):
+    # 创建新文件夹
+    new_folder = os.path.join(base_new_folder, f'folder{i+1}')
+    os.makedirs(new_folder, exist_ok=True)
+    
+    # 复制文件到新文件夹
+    for j, file_name in enumerate(group):
+        file_path = os.path.join(original_folder, file_name)
+        shutil.copy(file_path, new_folder)
+
+print("文件分组和复制完成。")
--- a/LCA_RAG/UploadMain.py
+++ b/LCA_RAG/UploadMain.py
@ -0,0 +1,171 @@
+'''
+支持上传文件进行分析，记录历史上下文，
+'''
+import time
+import streamlit as st
+import tempfile
+from dotenv import load_dotenv
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.prompts import MessagesPlaceholder
+from langchain_community.document_loaders import TextLoader, PyPDFLoader, CSVLoader, JSONLoader
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_openai import ChatOpenAI
+from langchain_community.chat_models.tongyi import ChatTongyi
+from langchain_community.chat_models import ChatZhipuAI
+
+from langchain_community.embeddings import HuggingFaceEmbeddings
+# from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from langchain_text_splitters import RecursiveCharacterTextSplitter, RecursiveJsonSplitter
+import requests
+import os
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+device = "cuda"
+memory = 6
+
+
+def chroma_save_upload(path):
+    try:
+        # Load the docs
+        file_type = os.path.basename(path).split('.')[1]
+        loader = None
+        doc = None
+        if file_type == "txt":
+            loader = TextLoader(path, encoding="utf-8")
+        elif file_type == "pdf":
+            loader = PyPDFLoader(path)
+        elif file_type == "csv":
+            loader == CSVLoader(path, encoding="utf-8")
+        elif file_type == "json":
+            json_data = requests.get(path).json()
+            splitter = RecursiveJsonSplitter(max_chunk_size=300)
+            doc = splitter.create_documents(texts=[json_data])
+
+        if doc is None:
+            doc = loader.load()
+
+        # Split the doc content
+        tex_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        splits = tex_splitter.split_documents(doc)
+
+        # Store the content
+        embedding = HuggingFaceEmbeddings(model_name="/home/zhangxj/models/acge_text_embedding", model_kwargs={"device": device})
+        vs = Chroma.from_documents(documents=splits, embedding=embedding, persist_directory="chromaDB")
+        vs.add_documents(documents=splits)
+        vs.as_retriever()
+        print("Upload Files saved: " + str(path))
+    except Exception as e:
+        print(f"Error in chroma_save_upload: {e}")
+
+@st.cache_resource(ttl="1h")
+def configure_retriever(uploaded_files):
+    try:
+        # 读取上传的文档，并写入一个临时目录
+        temp_dir = tempfile.TemporaryDirectory(dir="/home/zhangxj/WorkFile/LCA-GPT/LCA_RAG/tmp")
+        for file in uploaded_files:
+            temp_filepath = os.path.join(temp_dir.name, file.name)
+            print("文档路径：", temp_filepath)
+            with open(temp_filepath, "wb") as f:
+                f.write(file.getvalue())
+            chroma_save_upload(path=temp_filepath)
+    except Exception as e:
+        print(f"Error in configure_retriever: {e}")
+
+def init_chain():
+    try:
+        # 加载环境变量并初始化模型
+        load_dotenv(".env")
+        os.environ["DASHSCOPE_API_KEY"] = 'sk-c5f441f863f44094b0ddb96c831b5002'
+        llm = ChatTongyi(
+            streaming=True,
+            model='qwen-plus'
+        )
+        
+        embedding = HuggingFaceEmbeddings(model_name="/home/zhangxj/models/acge_text_embedding", model_kwargs={"device": device})
+        retriever = Chroma(persist_directory="chromaDB", embedding_function=embedding)
+        retriever = retriever.as_retriever()
+
+        instruct_system_prompt = (
+            "你是生命周期领域富有经验的专家。"
+            "你要利用检索到的上下文来回答问题。如果上下文没有足够的信息，请说明。"
+            "如果你有不明白的地方，请向用户询问。"
+            "涉及生命后期评价领域的问题，你应该完整地引用文献资料。\n\n"
+            "{context}"
+        )
+
+        instruct_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", instruct_system_prompt),
+                MessagesPlaceholder("chat_history"),
+                ("human", "{input}"),
+            ]
+        )
+
+        # Create a chain for passing a list of Documents to a model.
+        qa_chain = create_stuff_documents_chain(llm, instruct_prompt) #
+        rag_chain = create_retrieval_chain(retriever, qa_chain)
+        return rag_chain
+    except Exception as e:
+        print(f"Error in init_chain: {e}")
+        return None
+
+def user_in(uin, rag_chain, history):
+    try:
+        result = rag_chain.invoke({"input": uin, "chat_history": history})["answer"]
+        print(result)
+        return result
+    except Exception as e:
+        print(f"Error in user_in: {e}")
+        return "An error occurred while processing your request."
+
+def get_code(text):
+    
+    return 
+
+def main(memory=memory):
+    start_time = time.time()
+    st.set_page_config(page_title="LCA-GPT", layout="wide")    
+    st.title("LCA-GPT")
+
+    uploaded_files = st.sidebar.file_uploader(
+        label="Upload files", type=None, accept_multiple_files=True
+    )
+
+    if uploaded_files:
+        configure_retriever(uploaded_files)
+
+    if "messages" not in st.session_state or st.sidebar.button("Clear chat history"):
+        st.session_state["messages"] = [{"role": "assistant", "content": "Hello, I am LCA-GPT, helping you solve problems in the LCA field"}]
+
+    for msg in st.session_state.messages:
+        st.chat_message(msg["role"]).write(msg["content"])
+
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+
+    if "rag" not in st.session_state:
+        st.session_state.rag = init_chain()
+    
+    print("Prompt start ......")
+    if prompt := st.chat_input("Please enter your question:"):
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        response = user_in(prompt, st.session_state.rag, st.session_state.chat_history[-memory:])
+
+        st.session_state.chat_history.extend([HumanMessage(content=prompt), AIMessage(content=response)])
+        with st.chat_message("assistant"):
+            st.markdown(response)
+        st.session_state.messages.append({"role": "assistant", "content": response})
+
+    end_time = time.time()
+    execution_time = end_time - start_time
+    print(f"Execution time: {execution_time} seconds")
+
+if __name__ == "__main__":
+    main()
--- a/LCA_RAG/pycache/fastapi.cpython-310.pyc
+++ b/LCA_RAG/pycache/fastapi.cpython-310.pyc
--- a/LCA_RAG/pycache/utils.cpython-310.pyc
+++ b/LCA_RAG/pycache/utils.cpython-310.pyc
--- a/LCA_RAG/batchGLM.ipynb
+++ b/LCA_RAG/batchGLM.ipynb
@ -0,0 +1,327 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 创建批处理xlsx文件\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "customid = 1\n",
+    "method = \"POST\"\n",
+    "url = \"/v4/chat/completions\"\n",
+    "model = \"glm-4\"\n",
+    "role = \"system\"\n",
+    "instruction = \"你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要换行，只需要用1句话回答问题。\"\n",
+    "\n",
+    "temperature = 0.95\n",
+    "top_p = 0.7\n",
+    "max_tokens = 4096\n",
+    "\n",
+    "df = pd.DataFrame(columns=[\"custom_id\",\"method\",\"url\",\"model\",\"role\",\"content\",\"role1\",\"content1\",\"temperature\",\"top_p\",\"max_tokens\"])\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question  = []\n",
+    "with open(\"/home/zhangxj/WorkFile/LCA-GPT/QA/filters/question.txt\",\"r\",encoding=\"utf-8\") as file:\n",
+    "    for line in file.readlines():\n",
+    "        question.append(line.strip())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = []\n",
+    "for ques in question:\n",
+    "    row = {\n",
+    "        \"custom_id\": f\"request-{customid}\",\n",
+    "        \"method\":method,\n",
+    "        \"url\":url,\n",
+    "        \"model\":model,\n",
+    "        \"role\":role,\n",
+    "        \"content\":instruction,\n",
+    "        \"role1\":\"user\",\n",
+    "        \"content1\":ques,\n",
+    "        \"temperature\":temperature,\n",
+    "        \"top_p\":top_p,\n",
+    "        \"max_tokens\":max_tokens\n",
+    "    }\n",
+    "    data.append(row)\n",
+    "    customid+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3933"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>custom_id</th>\n",
+       "      <th>method</th>\n",
+       "      <th>url</th>\n",
+       "      <th>model</th>\n",
+       "      <th>role</th>\n",
+       "      <th>content</th>\n",
+       "      <th>role1</th>\n",
+       "      <th>content1</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>top_p</th>\n",
+       "      <th>max_tokens</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>request-1</td>\n",
+       "      <td>POST</td>\n",
+       "      <td>/v4/chat/completions</td>\n",
+       "      <td>glm-4</td>\n",
+       "      <td>system</td>\n",
+       "      <td>你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要...</td>\n",
+       "      <td>user</td>\n",
+       "      <td>什么是生命周期分析（LCA）的主要目标？</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>4096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>request-2</td>\n",
+       "      <td>POST</td>\n",
+       "      <td>/v4/chat/completions</td>\n",
+       "      <td>glm-4</td>\n",
+       "      <td>system</td>\n",
+       "      <td>你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要...</td>\n",
+       "      <td>user</td>\n",
+       "      <td>在LCA中，如何确定研究的范围？</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>4096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>request-3</td>\n",
+       "      <td>POST</td>\n",
+       "      <td>/v4/chat/completions</td>\n",
+       "      <td>glm-4</td>\n",
+       "      <td>system</td>\n",
+       "      <td>你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要...</td>\n",
+       "      <td>user</td>\n",
+       "      <td>医疗废物如何处理？</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>4096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>request-4</td>\n",
+       "      <td>POST</td>\n",
+       "      <td>/v4/chat/completions</td>\n",
+       "      <td>glm-4</td>\n",
+       "      <td>system</td>\n",
+       "      <td>你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要...</td>\n",
+       "      <td>user</td>\n",
+       "      <td>LCA数据清单收集阶段需要哪些信息？</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>4096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>request-5</td>\n",
+       "      <td>POST</td>\n",
+       "      <td>/v4/chat/completions</td>\n",
+       "      <td>glm-4</td>\n",
+       "      <td>system</td>\n",
+       "      <td>你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要...</td>\n",
+       "      <td>user</td>\n",
+       "      <td>生命周期影响评价阶段的目标是什么？</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>4096</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   custom_id method                   url  model    role  \\\n",
+       "0  request-1   POST  /v4/chat/completions  glm-4  system   \n",
+       "1  request-2   POST  /v4/chat/completions  glm-4  system   \n",
+       "2  request-3   POST  /v4/chat/completions  glm-4  system   \n",
+       "3  request-4   POST  /v4/chat/completions  glm-4  system   \n",
+       "4  request-5   POST  /v4/chat/completions  glm-4  system   \n",
+       "\n",
+       "                                             content role1  \\\n",
+       "0  你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要...  user   \n",
+       "1  你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要...  user   \n",
+       "2  你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要...  user   \n",
+       "3  你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要...  user   \n",
+       "4  你是生命周期领域富有经验和知识的专家。根据你所掌握的知识回答问题；不要列出几点来回答，不需要...  user   \n",
+       "\n",
+       "               content1  temperature  top_p  max_tokens  \n",
+       "0  什么是生命周期分析（LCA）的主要目标？         0.95    0.7        4096  \n",
+       "1      在LCA中，如何确定研究的范围？         0.95    0.7        4096  \n",
+       "2             医疗废物如何处理？         0.95    0.7        4096  \n",
+       "3    LCA数据清单收集阶段需要哪些信息？         0.95    0.7        4096  \n",
+       "4     生命周期影响评价阶段的目标是什么？         0.95    0.7        4096  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()\n",
+    "\n",
+    "# \"custom_id\",\"method\",\"url\",\"model\",\"role\",\"content\",\"role1\",\"content1\",\"temperature\",\"top_p\",\"max_tokens\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_excel(\"/home/zhangxj/WorkFile/LCA-GPT/QA/questionForBatch.xlsx\",index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Batch(id='batch_1823353255129645056', completion_window='24h', created_at=1723556266945, endpoint='/v4/chat/completions', input_file_id='1723556210_f79e4160ab3840b4b02f44c821d27752', object='batch', status='validating', cancelled_at=None, cancelling_at=None, completed_at=None, error_file_id=None, errors=None, expired_at=None, expires_at=None, failed_at=None, finalizing_at=None, in_progress_at=None, metadata={'description': '回答问题'}, output_file_id=None, request_counts=BatchRequestCounts(completed=None, failed=None, total=3933))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from zhipuai import ZhipuAI\n",
+    " \n",
+    "client = ZhipuAI(api_key=\"434790cf952335f18b6347e7b6de9777.V50p55zfk8Ye4ojV\")  # 填写您自己的APIKey\n",
+    "\n",
+    "create = client.batches.create(\n",
+    "    input_file_id=\"1723556210_f79e4160ab3840b4b02f44c821d27752\",\n",
+    "    endpoint=\"/v4/chat/completions\", \n",
+    "    completion_window=\"24h\", #完成时间只支持 24 小时\n",
+    "    metadata={\n",
+    "        \"description\": \"回答问题\"\n",
+    "    }\n",
+    ")\n",
+    "print(create)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Batch(id=None, completion_window=None, created_at=None, endpoint=None, input_file_id=None, object=None, status=None, cancelled_at=None, cancelling_at=None, completed_at=None, error_file_id=None, errors=None, expired_at=None, expires_at=None, failed_at=None, finalizing_at=None, in_progress_at=None, metadata=None, output_file_id=None, request_counts=None)\n"
+     ]
+    }
+   ],
+   "source": [
+    "batch_job = client.batches.retrieve(\"batch_id\")\n",
+    "print(batch_job)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Qwen",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/LCA_RAG/chroma.py
+++ b/LCA_RAG/chroma.py
@ -0,0 +1,51 @@
+'''
+生成数据库
+'''
+import os
+from dotenv import load_dotenv
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_core.output_parsers import StrOutputParser
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_chroma import Chroma
+from langchain.embeddings import HuggingFaceEmbeddings
+import sys
+os.chdir(sys.path[0])
+
+# Load the enivronment variables and API keys 
+load_dotenv(".env") 
+key = 1
+
+# Call the model and parser for extracting the output of LLM
+parser = StrOutputParser()
+
+# Store the vectrostores locally
+def chroma_save(path,key=key):
+    #Load the docs
+    loader = PyPDFLoader(path)
+    doc = loader.load()
+
+    # Split the doc content
+    tex_splitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)
+    splits = tex_splitter.split_documents(doc)
+
+    # Store the content
+    embedding = HuggingFaceEmbeddings(model_name = "/home/zhangxj/models/acge_text_embedding")
+    vs = Chroma.from_documents(documents=splits, embedding=embedding, persist_directory="chroma_new")
+    vs.add_documents(documents=splits)
+    vs.as_retriever()
+    print("saved: " + str(path))
+
+
+def main():
+    data = "/home/zhangxj/WorkFile/LCA-GPT/LCAdata" #os.path.join('resources', 'pdfs')
+    docs = os.listdir(data)
+
+    # save every file to the loacal database
+    for doc in docs:
+        doc_path = os.path.join(data,doc)
+        chroma_save(doc_path)
+
+
+
+if __name__ == "__main__":
+    main()
--- a/LCA_RAG/csv_agent.py
+++ b/LCA_RAG/csv_agent.py
@ -0,0 +1,121 @@
+from langchain.agents import AgentType
+from langchain_experimental.agents import create_csv_agent
+from langchain.callbacks import StreamlitCallbackHandler
+from langchain.chat_models import ChatOpenAI
+from langchain_community.chat_models import ChatZhipuAI
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.prompts import MessagesPlaceholder
+from langchain_community.document_loaders import TextLoader, PyPDFLoader, CSVLoader, JSONLoader
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_openai import ChatOpenAI
+from langchain_community.chat_models.tongyi import ChatTongyi
+from langchain_community.chat_models import QianfanChatEndpoint
+from langchain_community.chat_models import ChatZhipuAI
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from langchain_text_splitters import RecursiveCharacterTextSplitter, RecursiveJsonSplitter
+import requests
+import re
+import pandas as pd
+import os
+import streamlit as st
+from dotenv import load_dotenv
+
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+device = "cuda"
+memory = 6
+os.environ["DASHSCOPE_API_KEY"] = 'sk-c5f441f863f44094b0ddb96c831b5002'
+
+def get_retriever():
+    try:
+        embedding = HuggingFaceEmbeddings(model_name="/home/zhangxj/models/acge_text_embedding", model_kwargs={"device": device})
+        retriever = Chroma(persist_directory="chromaDB", embedding_function=embedding,)
+
+        retriever = retriever.as_retriever(search_type="mmr", search_kwargs={"k": 5})
+        return retriever
+    
+    except Exception as e:
+        print(f"Error in init_chain: {e}")
+        return None
+    
+def retrieve_and_output(retriever,query):
+    try:
+        retrieved_docs = retriever.get_relevant_documents(query)
+        # 输出检索到的内容
+        res = ''
+        for doc in retrieved_docs:
+            contents = doc.page_content
+            cleaned_contents = re.sub(r"\s+", "", contents)
+            # print("###",cleaned_contents,"###")
+            res = res+cleaned_contents
+        return res
+    except Exception as e:
+        print(f"Error in retrieve_and_output: {e}")
+
+
+st.set_page_config(page_title="Chat with your csv!!" , page_icon="random")
+st.title(":male-student: :book: Chat with your csv!!")
+
+uploaded_file = st.file_uploader(
+      "请上传你需要分析的数据" ,
+      type = "csv" ,
+      help = "你需要上传的格式为csv"
+)
+
+if not uploaded_file:
+    st.warning("您必须上传一个文件从而进行数据分析")
+      
+
+if "messages" not in st.session_state or st.sidebar.button("Clear conversation history"):
+    st.session_state['messages'] = [{"role" : "assistant" , "content" : "How can i help you?"}]
+    
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+    
+if query := st.chat_input(placeholder="What is this data about?"):
+    st.session_state.messages.append( {"role" : "user"  , "content" : query})
+    st.chat_message("user").write(query) 
+
+    instruct_system_prompt = (
+        "你是生命周期领域富有经验的专家。"
+        "你要利用检索到的上下文来回答问题。如果上下文没有足够的信息，请说明。"
+        "如果你有不明白的地方，请向用户询问。"
+        "涉及生命后期评价领域的问题，你应该完整地引用文献资料。\n\n"
+        "{context}"
+    )
+
+    instruct_prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", instruct_system_prompt),
+            ("human", "{input}"),
+        ]
+    )
+
+    llm = ChatOpenAI(
+        temperature = 0 ,
+        model = 'qwen-plus',
+        api_key = os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量，请用百炼API Key将本行替换为：api_key="sk-xxx"
+        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+     )  
+
+    retriever= get_retriever()
+    context = retrieve_and_output(retriever,query)
+
+    messages = instruct_prompt.format_messages(context=context,input = query)
+
+    csv_agent = create_csv_agent(
+        llm , 
+        uploaded_file,
+        agent_type = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        allow_dangerous_code=True,
+        agent_executor_kwargs={"handle_parsing_errors": True}
+    )
+    
+    with st.chat_message("assistant"):
+        st_cb = StreamlitCallbackHandler(st.container())
+        response = csv_agent.invoke(messages)
+        st.session_state.messages.append({"role":"assistant" , "content":response})
+        st.markdown(response['output'])
--- a/LCA_RAG/data/class.csv
+++ b/LCA_RAG/data/class.csv
@ -0,0 +1,11 @@
+name,number
+LCA theory and related knowledge,790
+Ecological protection and environmental governance industry,754
+Research and experimental development,321
+Construction industry,295
+Non-metallic mineral products industry,183
+Chemical raw materials and chemical products manufacturing industry,174
+Comprehensive utilization of waste resources industry,157
+"Agriculture, forestry, animal husbandry and fishery industry",131
+"Electricity, heat production and supply industry",126
+Automobile manufacturing industry,87
--- a/LCA_RAG/data/eval/bert.csv
+++ b/LCA_RAG/data/eval/bert.csv
@ -0,0 +1,5 @@
+LCA理论与相关知识,生态保护和环境治理业,研究和试验发展,建筑业,非金属矿物制品业,化学原料和化学制品制造业,废弃资源综合利用业,农、林、牧、渔业,电力、热力生产和供应业,汽车制造业
+0.8285459900958628,0.8278908556430978,0.8031791591570013,0.8203105013249284,0.825442724214877,0.8266749505338997,0.8150948574588557,0.8184339215282266,0.811075790060891,0.8514739521618547
+0.8031236508979073,0.7980815338993579,0.762518234991953,0.7842578558598534,0.7794850773172952,0.7717763445843225,0.761772907653432,0.7447150918363615,0.7617944805395036,0.798203267585272
+0.8069283152682871,0.7964145340400919,0.7619536520907441,0.7759484621427827,0.7705819658894356,0.764714789459075,0.7655670039213387,0.75926776605708,0.769578997104887,0.8130694065970936
+0.80836640302139,0.7882782395543724,0.757611659642692,0.7679492297819105,0.760956196511378,0.7494578895897701,0.7485851030440847,0.7276846668647445,0.7415060230663845,0.7754989156777832
--- a/LCA_RAG/data/eval/bert.xlsx
+++ b/LCA_RAG/data/eval/bert.xlsx
--- a/LCA_RAG/data/eval/cos.csv
+++ b/LCA_RAG/data/eval/cos.csv
@ -0,0 +1,5 @@
+LCA理论与相关知识,生态保护和环境治理业,研究和试验发展,建筑业,非金属矿物制品业,化学原料和化学制品制造业,废弃资源综合利用业,农、林、牧、渔业,电力、热力生产和供应业,汽车制造业
+0.7563889,0.76573056,0.74416304,0.76548576,0.7918446,0.80663353,0.78028744,0.7865737,0.7891359,0.7961327
+0.72979796,0.7285658,0.706629,0.7384504,0.74560964,0.75003314,0.73390573,0.7173627,0.7410214,0.7537849
+0.73387533,0.73159605,0.6975246,0.7213398,0.7342301,0.73770964,0.7344841,0.7319357,0.7426608,0.7584841
+0.7237778,0.7110586,0.68896115,0.71236473,0.7142454,0.72282004,0.716429,0.69628036,0.7154095,0.72462904
--- a/LCA_RAG/data/eval/cos.xlsx
+++ b/LCA_RAG/data/eval/cos.xlsx
--- a/LCA_RAG/data/eval/f1.csv
+++ b/LCA_RAG/data/eval/f1.csv
@ -0,0 +1,5 @@
+LCA理论与相关知识,生态保护和环境治理业,研究和试验发展,建筑业,非金属矿物制品业,化学原料和化学制品制造业,废弃资源综合利用业,农、林、牧、渔业,电力、热力生产和供应业,汽车制造业
+0.3437605081212203,0.3483626691289569,0.3430343943468575,0.39371906828313225,0.4130349692057703,0.4185145863497029,0.38042435656104256,0.39036740247556595,0.41114581401949746,0.4130216859020192
+0.30454084953051963,0.2788926668662055,0.2821541495127059,0.31389786788066487,0.297081206460411,0.2931829455107586,0.28198554328062225,0.27194804107827547,0.26988029172611805,0.3179324748633672
+0.30268024101560764,0.27761325010853644,0.2729394816189532,0.28676175803618814,0.25563488130229556,0.2694429593634037,0.2718793531798568,0.27189044583195743,0.2713352793043654,0.3088808476140809
+0.2682497767547755,0.2302952772337834,0.23015648181836218,0.2204392856411433,0.21248638601796016,0.22850507493329048,0.2066845950860928,0.20235451922007622,0.18898670597783235,0.2327820200502282
--- a/LCA_RAG/data/eval/f1.xlsx
+++ b/LCA_RAG/data/eval/f1.xlsx
--- a/LCA_RAG/data/eval/rouge.csv
+++ b/LCA_RAG/data/eval/rouge.csv
@ -0,0 +1,5 @@
+LCA理论与相关知识,生态保护和环境治理业,研究和试验发展,建筑业,非金属矿物制品业,化学原料和化学制品制造业,废弃资源综合利用业,农、林、牧、渔业,电力、热力生产和供应业,汽车制造业
+0.39498794454490654,0.33244458401218807,0.32609547773099173,0.29661873962970675,0.40538367218695087,0.3627170721998308,0.2940372790691262,0.348491457651763,0.32386475957904526,0.3455275567344533
+0.2213504786974626,0.1579393502684968,0.17042631995903024,0.11762091016328305,0.13737843819811033,0.08316912972085386,0.10792134263471842,0.1357506361323155,0.06020408163265306,0.09315818281335522
+0.30471539483857785,0.19627007847801298,0.20333817273069607,0.1457537942283705,0.16863152027086453,0.13428285147352084,0.13910282923021774,0.17828388744419277,0.10913031879418433,0.1705906011713634
+0.2724725275577533,0.1618331729529683,0.1632025416542098,0.09701429611918969,0.11709287878162374,0.08392393366531298,0.10455685503456204,0.131476254623987,0.0647123664280527,0.12014164935057432
--- a/LCA_RAG/data/eval/rouge.xlsx
+++ b/LCA_RAG/data/eval/rouge.xlsx
--- a/LCA_RAG/data/中国电建集团成都电力金具有限公司产品.csv.png
+++ b/LCA_RAG/data/中国电建集团成都电力金具有限公司产品.csv.png
--- a/LCA_RAG/data/产品生成-硫酸钠.csv.png
+++ b/LCA_RAG/data/产品生成-硫酸钠.csv.png
--- a/LCA_RAG/data/产品生成-酒石酸.csv.png
+++ b/LCA_RAG/data/产品生成-酒石酸.csv.png
--- a/LCA_RAG/data/北京煤电资产.csv.png
+++ b/LCA_RAG/data/北京煤电资产.csv.png
--- a/LCA_RAG/data/山东能源集团.csv.png
+++ b/LCA_RAG/data/山东能源集团.csv.png
--- a/LCA_RAG/data/快递碳足迹.csv.png
+++ b/LCA_RAG/data/快递碳足迹.csv.png
--- a/LCA_RAG/data/煤基甲醇制烯烃.csv.png
+++ b/LCA_RAG/data/煤基甲醇制烯烃.csv.png
--- a/LCA_RAG/data/煤基甲醇制烯烃制甲醇阶段.csv.png
+++ b/LCA_RAG/data/煤基甲醇制烯烃制甲醇阶段.csv.png
--- a/LCA_RAG/data/电解铝生产.csv.png
+++ b/LCA_RAG/data/电解铝生产.csv.png
--- a/LCA_RAG/data/锂硫电池碳足迹.csv.png
+++ b/LCA_RAG/data/锂硫电池碳足迹.csv.png
--- a/LCA_RAG/data/风力发电机.csv.png
+++ b/LCA_RAG/data/风力发电机.csv.png
--- a/LCA_RAG/evaluate.ipynb
+++ b/LCA_RAG/evaluate.ipynb
--- a/LCA_RAG/evaluate_classify.ipynb
+++ b/LCA_RAG/evaluate_classify.ipynb
--- a/LCA_RAG/requirements.txt
+++ b/LCA_RAG/requirements.txt
@ -0,0 +1,22 @@
+fastapi==0.115.6
+langchain==0.3.14
+langchain_chroma==0.2.0
+langchain_community==0.3.14
+langchain_core==0.3.29
+langchain_experimental==0.3.4
+langchain_openai==0.3.0
+langchain_text_splitters==0.3.5
+matplotlib==2.0.2
+matplotlib==3.7.1
+matplotlib==3.3.4
+numpy==1.21.6
+pandas==2.0.1
+python-dotenv==1.0.1
+qianfan==0.4.12.2
+qwen_agent==0.0.6
+Requests==2.32.3
+streamlit==1.21.0
+streamlit==1.41.1
+streamlit==1.36.0
+uvicorn==0.34.0
+zhipuai==2.1.5.20250106
--- a/LCA_RAG/tmp/carbon_emission_analysis.png
+++ b/LCA_RAG/tmp/carbon_emission_analysis.png
--- a/LCA_RAG/tmp/carbon_emission_pie_chart.png
+++ b/LCA_RAG/tmp/carbon_emission_pie_chart.png
--- a/LCA_RAG/tmp/中国电建集团成都电力金具有限公司产品.csv
+++ b/LCA_RAG/tmp/中国电建集团成都电力金具有限公司产品.csv
@ -0,0 +1,40 @@
+name,amount,upstream,percent
+框架,12001.608124689696,12001.608124689696,0.3188756016876195
+本体,9921.329383076814,9921.329383076818,0.26360383072843213
+支座,4000.5360415632317,4000.536041563232,0.1062918672292065
+盖板,4000.5360415632317,4000.536041563232,0.1062918672292065
+回转体,2179.0427075510406,2179.0427075510415,0.057895870891162174
+十字芯,2112.2830299453867,2112.2830299453863,0.056122105897021046
+螺栓M12*101,597.6802854997139,597.6802854997142,0.01588001030157591
+木托盘,428.6748704213756,428.6748704213757,0.011389636773156409
+工序1：产品生产,390.39147,37637.27315972845,0.010372469555464918
+橡胶柱,232.26291272653157,232.26291272653168,0.006171087680577533
+橡胶瓦,217.74648068112333,217.7464806811234,0.0057853947005414375
+框架运输,206.46493127521833,206.46493127521833,0.00548565063146323
+内六角螺栓,174.32341660408326,174.32341660408335,0.004631669671292974
+电,117.59823926905,117.59823926905,0.0031245153911649247
+回转体运输,96.72436362861278,96.72436362861276,0.002569908909663175
+六角螺母M8,93.3875446093303,93.3875446093303,0.0024812516096212357
+翻边螺栓,93.3875446093303,93.3875446093303,0.0024812516096212357
+铰链轴 φ9*60,93.3875446093303,93.3875446093303,0.0024812516096212357
+六角螺母M12,80.93587199475294,80.93587199475296,0.0021504180616717383
+泡沫外包装,73.34619593977384,73.34619593977384,0.0019487648754069054
+支座运输,68.7808895585369,68.78088955853687,0.0018274673955958074
+天然气,67.6640653173853,67.6640653173853,0.0017977940386442576
+销轴 8*48,59.14544491924253,59.145444919242514,0.0015714593527601161
+销轴,44.203437781749685,44.2034377817497,0.0011744590952207186
+铰链轴,40.46793599737647,40.46793599737648,0.0010752090308358692
+十字芯运输,37.3454548358276,37.34545483582761,0.000992246560406691
+橡胶柱运输,31.538876813749376,31.53887681374937,0.0008379692301273226
+弹垫12,31.129181536443436,31.12918153644344,0.0008270838698737454
+平垫12,31.129181536443436,31.12918153644344,0.0008270838698737454
+螺栓M12*101运输,27.0143064141925,27.014306414192504,0.0007177540811616923
+弹垫8,18.67750892186606,18.677508921866067,0.0004962503219242472
+橡胶瓦运输,18.674779254015462,18.674779254015455,0.0004961777962702495
+内六角螺栓运输,7.883737174524834,7.883737174524833,0.0002094662156067238
+闭口销φ2.2*17,6.225836307288688,6.225836307288689,0.00016541677397474908
+平垫8,6.225836307288688,6.225836307288689,0.00016541677397474908
+翻边螺栓运输,4.223430629209732,4.223430629209729,0.00011221404407503059
+铰链轴 φ9*60运输,4.220985377217578,4.220985377217577,0.0001121490751815144
+六角螺母M8运输,4.220985377217578,4.220985377217577,0.0001121490751815144
+
--- a/LCA_RAG/tmp/产品生成-硫酸钠.csv
+++ b/LCA_RAG/tmp/产品生成-硫酸钠.csv
@ -0,0 +1,121 @@
+name,amount,upstream,percent
+顺丁烯二酸酐（石化）,1.0478333960661341,1.0478333960661337,0.14691798302721387
+顺丁烯二酸酐（大风）,1.0478333960661341,1.0478333960661337,0.14691798302721387
+火力发电,0.9623219099825041,0.9623219099825047,0.134928314528164
+过氧化氢（平湖）,0.7248451913011941,0.7248451913011941,0.10163141765928706
+过氧化氢（名鑫）,0.7248451913011941,0.7248451913011941,0.10163141765928706
+蒸汽生产,0.7140986808002964,0.7140986808002961,0.1001246364731747
+氢氧化钠（前宇）,0.4121099232116898,0.41210992321169,0.05778242889668318
+氢氧化钠（电化）,0.3645635378709197,0.3645635378709198,0.051115892918038204
+产品生产,0.31208042353191773,7.132097613074651,0.04375717221814351
+碳酸钠（江顺实业）,0.17101214416366373,0.17101214416366373,0.023977818790668556
+碳酸钠（秦业）,0.17101214416366373,0.17101214416366373,0.023977818790668556
+F06,0.05946017299904296,0.059460172999042965,0.008336982501478924
+硫酸（前通）,0.05349537772432942,0.053495377724329427,0.007500651368856902
+硫酸（乐欣福）,0.05349537772432942,0.053495377724329427,0.007500651368856902
+二氧化硅,0.0416117122689358,0.04161171226893576,0.005834428316383765
+酒石酸钠,0.04008191958142867,0.04008191958142866,0.005619934240376911
+水处理的污泥（中能）,0.02085130967203848,0.020851309672038482,0.002923587253462936
+水处理的污泥（杰泰）,0.02085130967203848,0.020851309672038482,0.002923587253462936
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.01907594495272549,0.019075944952725497,0.002674661226979736
+"polypropylene production, granulate | polypropylene, granulate | Cutoff, S",0.014541316754908027,0.014541316754908029,0.0020388555434590104
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.012172654422687496,0.012172654422687496,0.001706742543788581
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.011447441536565621,0.01144744153656562,0.0016050595711954402
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.00968809451687643,0.009688094516876427,0.0013583794056766826
+"injection moulding | injection moulding | Cutoff, S",0.009269658528533318,0.00926965852853332,0.0012997099915654637
+酒石酸氢钾,0.008876409356123473,0.00887640935612347,0.0012445720512645716
+自来水,0.006708425286645729,0.006708425286645731,0.0009405963926163546
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.006666810803095505,0.006666810803095504,0.0009347615757352821
+电力（核能）,0.006172558951250732,0.006172558951250729,0.0008654619280497674
+废树脂（立佳）,0.005662803306458653,0.005662803306458653,0.0007939884748741423
+废树脂（大地）,0.005662803306458652,0.005662803306458652,0.0007939884748741422
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.005658348612026375,0.005658348612026375,0.0007933638768002024
+"polyethylene production, high density, granulate | polyethylene, high density, granulate | Cutoff, S",0.0042650731153103805,0.004265073115310381,0.0005980110406076882
+聚合氯化铝,0.004067810869066113,0.004067810869066112,0.0005703526633747905
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.003948700270127523,0.003948700270127523,0.0005536520227778034
+F04,0.0037873625983000495,0.0037873625983000482,0.0005310306734104437
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.003720614272328129,0.003720614272328129,0.0005216718101989312
+木制品,0.003703744471536108,0.0037037444715361093,0.0005193064751029707
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.0033317656664620552,0.0033317656664620557,0.0004671508786355112
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.0030715933531159805,0.0030715933531159805,0.00043067180509210885
+"injection moulding | injection moulding | Cutoff, S",0.00267963238135753,0.0026796323813575297,0.000375714484956739
+氯化钾,0.0026252796752409065,0.0026252796752409065,0.00036809362654097877
+弱酸树脂,0.0022883120821518355,0.002288312082151835,0.000320846994291956
+阳树脂,0.0022883120821518355,0.002288312082151835,0.000320846994291956
+酸黄沙处理,0.0022261479284531272,0.002226147928453127,0.0003121308833984724
+F01,0.0019017653523963306,0.0019017653523963306,0.0002666488115515961
+一般工业垃圾处理,0.001869857104765684,0.001869857104765684,0.00026217491770413214
+F05,0.001802783275160294,0.0018027832751602941,0.00025277041523596213
+光伏发电,0.0017523176917856995,0.0017523176917856999,0.0002456945749835685
+特级氧化钙,0.0016327204955009875,0.001632720495500988,0.0002289257079863663
+阴树脂（D201）,0.001544690353686903,0.0015446903536869023,0.00021658289573254821
+钼酸钠,0.0014202254969797562,0.0014202254969797568,0.00019913152820233152
+钨酸钠,0.0014005001428550373,0.001400500142855037,0.00019636581253285471
+阴树脂（D630）,0.0009654314710543141,0.0009654314710543138,0.0001353643098328426
+尿素,0.0008665256349769106,0.000866525634976911,0.00012149660338192582
+多乙烯多胺,0.0007956207811687508,0.0007956207811687508,0.00011155494839417352
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.0007463837640036111,0.0007463837640036109,0.00010465136689034249
+戊二醛,0.0006859953841003264,0.0006859953841003265,9.618423938039646e-05
+消泡剂,0.0006443156126354828,0.0006443156126354824,9.034026840214798e-05
+聚丙烯酰胺,0.0006254028415299812,0.0006254028415299814,8.768848597690038e-05
+牛皮纸,0.0006135718014997905,0.0006135718014997904,8.602964159870486e-05
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.0005528681186877643,0.0005528681186877646,7.751830508800659e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.000528421631576874,0.0005284216315768739,7.40906336739089e-05
+硬纸板,0.0004585743777904158,0.00045857437779041576,6.429726605953227e-05
+风力发电,0.0004483426390386543,0.0004483426390386542,6.286266164062968e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.00041050032815046454,0.00041050032815046465,5.7556745633701113e-05
+卡拉胶,0.00040730975930956874,0.0004073097593095688,5.710939213211039e-05
+磷酸,0.00039489299811160746,0.0003948929981116072,5.5368423083229355e-05
+F02,0.0003450097078832129,0.0003450097078832129,4.837422685448623e-05
+F03,0.0003390648542781465,0.00033906485427814674,4.754069176739373e-05
+合金,0.000302658691296397,0.000302658691296397,4.243613978888333e-05
+次氯酸钠,0.00027393853549390366,0.0002739385354939039,3.8409252138068905e-05
+废机油处理,0.0002571723238646212,0.00025717232386462133,3.605844140343364e-05
+水力发电,0.00023616560520469266,0.0002361656052046927,3.311306406852185e-05
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.0001601422609326987,0.00016014226093269876,2.2453739365418092e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.00014556240255910613,0.00014556240255910613,2.040947985516341e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0001410032931918723,0.00014100329319187226,1.9770241637380747e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.00013537009711005332,0.00013537009711005326,1.8980404427147934e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0001334965841179963,0.0001334965841179963,1.87177169130816e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.00012478293227959265,0.0001247829322795926,1.7495965289487768e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.00012476489870428894,0.00012476489870428897,1.749343678016526e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",7.94342220559564e-05,7.943422205595643e-05,1.1137567987058476e-05
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",7.698239905730311e-05,7.698239905730313e-05,1.0793794930144818e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",7.651114577153162e-05,7.651114577153165e-05,1.0727719939120075e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO3 | transport, freight, lorry 3.5-7.5 metric ton, EURO3 | Cutoff, S",7.184258126431081e-05,7.184258126431081e-05,1.007313488427417e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",6.585092567051703e-05,6.585092567051703e-05,9.233037633949133e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",5.4649636082733866e-05,5.464963608273385e-05,7.662491324088087e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",5.1058764183737385e-05,5.105876418373738e-05,7.159010848384327e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO3 | transport, freight, lorry 3.5-7.5 metric ton, EURO3 | Cutoff, S",4.3672109287591886e-05,4.3672109287591866e-05,6.123319065001527e-06
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",4.2774601878415035e-05,4.2774601878415035e-05,5.99747846972819e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",3.9657935387642325e-05,3.965793538764234e-05,5.560486905695304e-06
+废试剂瓶处理（立佳）,3.3769940777473076e-05,3.3769940777473076e-05,4.734924086788381e-06
+废试剂瓶处理（大地）,3.3769940777473076e-05,3.3769940777473076e-05,4.734924086788381e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",3.1935514137126534e-05,3.193551413712655e-05,4.477716917191648e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",2.8172187746796842e-05,2.817218774679685e-05,3.950056389462651e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",2.4781698112067627e-05,2.478169811206762e-05,3.474671752478192e-06
+精制盐,2.2615051813537913e-05,2.2615051813537913e-05,3.170883664306517e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",1.7584416067858894e-05,1.7584416067858904e-05,2.465532164846275e-06
+实验室废液（大地）,1.7154090510147373e-05,1.7154090510147376e-05,2.4051956998878252e-06
+实验室废液（立佳）,1.7154090510147373e-05,1.7154090510147376e-05,2.4051956998878252e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",1.379604543532732e-05,1.379604543532732e-05,1.9343601537416195e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",1.21874112849449e-05,1.2187411284944902e-05,1.708811621226662e-06
+废电瓶处理,1.071551349435922e-05,1.0715513494359221e-05,1.502435058476402e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",8.455220374510647e-06,8.455220374510645e-06,1.1855166366498449e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",5.451910112443665e-06,5.451910112443665e-06,7.644188860299886e-07
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",5.052844090517362e-06,5.052844090517362e-06,7.084653582495037e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",3.8523276270708694e-06,3.852327627070873e-06,5.401394983726434e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO3 | transport, freight, lorry 3.5-7.5 metric ton, EURO3 | Cutoff, S",3.602791519368273e-06,3.6027915193682716e-06,5.051517400383851e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",1.638199103130583e-06,1.6381991031305831e-06,2.2969387016344474e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",1.281989320139293e-06,1.2819893201392933e-06,1.7974926728276042e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",1.2517217872624413e-06,1.251721787262441e-06,1.755054200278708e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",1.0096748657347052e-06,1.0096748657347054e-06,1.415677295111268e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO3 | transport, freight, lorry 3.5-7.5 metric ton, EURO3 | Cutoff, S",6.281208955933398e-07,6.281208955933398e-07,8.806958761218588e-08
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",1.6801175702067753e-07,1.6801175702067756e-07,2.355713089409717e-08
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",8.54683277525838e-08,8.546832775258381e-08,1.1983617217451174e-08
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",8.267232735273698e-08,8.267232735273701e-08,1.1591586632406858e-08
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",5.324198422992289e-08,5.3241984229922914e-08,7.465122761685008e-09
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",4.221415749969102e-08,4.221415749969103e-08,5.918897888091646e-09
+电力（浙江）,0.0,0.9709312948697838,0.0
+制造,0.0,0.023810975283441345,0.0
+生产,0.0,0.006944705496667908,0.0
--- a/LCA_RAG/tmp/产品生成-酒石酸.csv
+++ b/LCA_RAG/tmp/产品生成-酒石酸.csv
@ -0,0 +1,121 @@
+name,amount,upstream,percent
+顺丁烯二酸酐（石化）,97.43695577828797,97.436955778288,0.14691798302721387
+顺丁烯二酸酐（大风）,97.43695577828797,97.436955778288,0.14691798302721387
+火力发电,89.48533014834815,89.48533014834824,0.13492831452816398
+过氧化氢（平湖）,67.40261296888605,67.4026129688861,0.10163141765928704
+过氧化氢（名鑫）,67.40261296888605,67.4026129688861,0.10163141765928704
+蒸汽生产,66.40330594891702,66.403305948917,0.1001246364731747
+氢氧化钠（前宇）,38.32168025424979,38.321680254249806,0.05778242889668318
+氢氧化钠（电化）,33.900390511759404,33.900390511759404,0.05111589291803819
+产品生产,29.020039388999997,663.2064623446377,0.04375717221814351
+碳酸钠（江顺实业）,15.902244374900063,15.902244374900059,0.02397781879066856
+碳酸钠（秦业）,15.902244374900063,15.902244374900059,0.02397781879066856
+F06,5.5291406714349804,5.529140671434982,0.008336982501478922
+硫酸（前通）,4.974480459620046,4.974480459620043,0.007500651368856901
+硫酸（乐欣福）,4.974480459620046,4.974480459620043,0.007500651368856901
+二氧化硅,3.8694305635122546,3.8694305635122537,0.0058344283163837656
+酒石酸钠,3.7271767061698684,3.727176706169868,0.005619934240376913
+水处理的污泥（中能）,1.9389419597250281,1.9389419597250293,0.0029235872534629365
+水处理的污泥（杰泰）,1.9389419597250281,1.9389419597250293,0.0029235872534629365
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",1.7738526103155972,1.7738526103155967,0.0026746612269797354
+"polypropylene production, granulate | polypropylene, granulate | Cutoff, S",1.352182172209203,1.3521821722092033,0.0020388555434590104
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",1.1319226845991117,1.1319226845991115,0.0017067425437885808
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",1.0644858800649284,1.064485880064928,0.0016050595711954404
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.9008860001606436,0.9008860001606436,0.0013583794056766828
+"injection moulding | injection moulding | Cutoff, S",0.8619760655801093,0.8619760655801093,0.0012997099915654634
+酒石酸氢钾,0.8254082272521852,0.8254082272521853,0.001244572051264572
+自来水,0.6238096060412199,0.6238096060412197,0.0009405963926163545
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.619939917779095,0.6199399177790951,0.0009347615757352819
+电力（核能）,0.5739799435958551,0.5739799435958552,0.0008654619280497673
+废树脂（立佳）,0.5265782875636937,0.5265782875636938,0.0007939884748741422
+废树脂（大地）,0.5265782875636937,0.5265782875636938,0.0007939884748741422
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.5261640500846888,0.5261640500846888,0.0007933638768002024
+"polyethylene production, high density, granulate | polyethylene, high density, granulate | Cutoff, S",0.39660478668446,0.3966047866844599,0.0005980110406076881
+聚合氯化铝,0.37826157216563644,0.3782615721656364,0.0005703526633747903
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.36718559939641937,0.3671855993964194,0.0005536520227778033
+F04,0.3521829743090306,0.3521829743090306,0.0005310306734104434
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.3459761157469562,0.34597611574695625,0.0005216718101989312
+木制品,0.3444074102257047,0.3444074102257048,0.0005193064751029708
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.30981748160104633,0.3098174816010462,0.0004671508786355112
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.2856243242867166,0.28562432428671664,0.0004306718050921088
+"injection moulding | injection moulding | Cutoff, S",0.2491762744197963,0.24917627441979634,0.000375714484956739
+氯化钾,0.24412207186985055,0.2441220718698505,0.0003680936265409787
+弱酸树脂,0.21278780003827816,0.2127878000382781,0.000320846994291956
+阳树脂,0.21278780003827816,0.2127878000382781,0.000320846994291956
+酸黄沙处理,0.2070072189672074,0.20700721896720747,0.00031213088339847246
+F01,0.1768432149975358,0.17684321499753589,0.00026664881155159597
+一般工业垃圾处理,0.17387609968605386,0.17387609968605391,0.00026217491770413214
+F05,0.16763897287402746,0.16763897287402743,0.0002527704152359622
+光伏发电,0.16294622989212165,0.16294622989212162,0.0002456945749835685
+特级氧化钙,0.15182500893337944,0.15182500893337958,0.00022892570798636628
+阴树脂（D201）,0.14363917608314067,0.14363917608314072,0.00021658289573254813
+钼酸钠,0.13206531636034965,0.1320653163603496,0.00019913152820233155
+钨酸钠,0.1302310758553448,0.13023107585534477,0.00019636581253285471
+阴树脂（D630）,0.08977448505196294,0.08977448505196287,0.0001353643098328426
+尿素,0.08057733251581649,0.08057733251581653,0.0001214966033819258
+多乙烯多胺,0.07398396268153838,0.07398396268153838,0.00011155494839417351
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.06940546281487474,0.06940546281487474,0.00010465136689034249
+戊二醛,0.06379000913278247,0.06379000913278252,9.618423938039646e-05
+消泡剂,0.05991424981425356,0.05991424981425358,9.034026840214797e-05
+聚丙烯酰胺,0.058155570573097434,0.058155570573097455,8.768848597690038e-05
+牛皮纸,0.05705541426145408,0.05705541426145408,8.602964159870484e-05
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.05141064088436913,0.051410640884369134,7.751830508800657e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.04913738705174556,0.04913738705174555,7.409063367390888e-05
+硬纸板,0.0426423623617743,0.04264236236177429,6.429726605953226e-05
+风力发电,0.041690923440249925,0.041690923440249925,6.286266164062966e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.03817200565579705,0.03817200565579705,5.75567456337011e-05
+卡拉胶,0.0378753179225896,0.037875317922589614,5.7109392132110405e-05
+磷酸,0.036720695998629684,0.036720695998629684,5.536842308322934e-05
+F02,0.03208209986082075,0.03208209986082076,4.837422685448621e-05
+F03,0.03152929400447001,0.03152929400447,4.754069176739372e-05
+合金,0.028143922144947806,0.028143922144947802,4.243613978888332e-05
+次氯酸钠,0.025473264231791866,0.025473264231791876,3.84092521380689e-05
+废机油处理,0.023914191360832622,0.023914191360832632,3.605844140343365e-05
+水力发电,0.021960798078275695,0.02196079807827569,3.311306406852185e-05
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.01489146505094745,0.014891465050947454,2.245373936541809e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.013535698933037063,0.013535698933037061,2.040947985516341e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.013111752016025931,0.013111752016025928,1.9770241637380744e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.012587926873999273,0.012587926873999277,1.8980404427147937e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.01241371081709323,0.012413710817093231,1.8717716913081597e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.011603437244945747,0.01160343724494575,1.7495965289487764e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.01160176032122296,0.011601760321222964,1.7493436780165255e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.007386507063819933,0.007386507063819933,1.1137567987058476e-05
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.007158514550894822,0.0071585145508948215,1.0793794930144813e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.007114693189847851,0.007114693189847853,1.0727719939120075e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO3 | transport, freight, lorry 3.5-7.5 metric ton, EURO3 | Cutoff, S",0.006680568151319829,0.00668056815131983,1.007313488427417e-05
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0061234102259063025,0.0061234102259063025,9.233037633949132e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.005081813763794935,0.005081813763794934,7.662491324088087e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0047479022586438486,0.00474790225864385,7.159010848384326e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO3 | transport, freight, lorry 3.5-7.5 metric ton, EURO3 | Cutoff, S",0.004061024774907134,0.004061024774907134,6.123319065001527e-06
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.003977566478896562,0.003977566478896563,5.997478469728192e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.00368775084963986,0.0036877508496398594,5.560486905695302e-06
+废试剂瓶处理（立佳）,0.0031402322530693343,0.0031402322530693348,4.7349240867883815e-06
+废试剂瓶处理（大地）,0.0031402322530693343,0.0031402322530693348,4.7349240867883815e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.002969650796031407,0.002969650796031408,4.477716917191647e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0026197029241173545,0.0026197029241173545,3.95005638946265e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.002304424760769903,0.0023044247607699026,3.474671752478193e-06
+精制盐,0.002102950537511125,0.002102950537511126,3.1708836643065167e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0016351568648446128,0.0016351568648446126,2.465532164846275e-06
+实验室废液（大地）,0.0015951413313691383,0.0015951413313691379,2.4051956998878252e-06
+实验室废液（立佳）,0.0015951413313691383,0.0015951413313691379,2.4051956998878252e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0012828801544634081,0.0012828801544634083,1.9343601537416195e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0011332949101271386,0.0011332949101271381,1.7088116212266617e-06
+废电瓶处理,0.0009964246400346924,0.000996424640034693,1.5024350584764015e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0007862422946432563,0.0007862422946432562,1.1855166366498449e-06
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0005069675451533771,0.000506967545153377,7.644188860299885e-07
+"transport, freight, lorry 16-32 metric ton, EURO6 | transport, freight, lorry 16-32 metric ton, EURO6 | Cutoff, S",0.0004698588039383794,0.00046985880393837933,7.084653582495037e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0003582240058883277,0.00035822400588832767,5.401394983726434e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO3 | transport, freight, lorry 3.5-7.5 metric ton, EURO3 | Cutoff, S",0.0003350198984580951,0.0003350198984580949,5.05151740038385e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.0001523344590533466,0.00015233445905334661,2.2969387016344472e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.00011921087566364017,0.00011921087566364016,1.797492672827604e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",0.00011639632873899379,0.00011639632873899382,1.7550542002787074e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",9.388863307123689e-05,9.388863307123688e-05,1.415677295111268e-07
+"transport, freight, lorry 3.5-7.5 metric ton, EURO3 | transport, freight, lorry 3.5-7.5 metric ton, EURO3 | Cutoff, S",5.8408319640428885e-05,5.840831964042887e-05,8.806958761218588e-08
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",1.562324144326374e-05,1.5623241443263748e-05,2.3557130894097164e-08
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",7.947612380878076e-06,7.947612380878078e-06,1.1983617217451172e-08
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",7.687615163439939e-06,7.68761516343994e-06,1.1591586632406858e-08
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",4.950917657745543e-06,4.9509176577455435e-06,7.465122761685008e-09
+"transport, freight, lorry 3.5-7.5 metric ton, EURO5 | transport, freight, lorry 3.5-7.5 metric ton, EURO5 | Cutoff, S",3.925451329340405e-06,3.925451329340407e-06,5.918897888091645e-09
+电力（浙江）,0.0,90.28590804335467,0.0
+制造,0.0,2.2141582377893116,0.0
+生产,0.0,0.645781061104256,0.0
--- a/LCA_RAG/tmp/北京煤电资产.csv
+++ b/LCA_RAG/tmp/北京煤电资产.csv
@ -0,0 +1,33 @@
+name,amount,upstream,percent
+北京煤电自产,21.722222222222225,22.327645270727338,0.9728845992864793
+山西煤炭自产,0.2890080448027844,0.2890080448027844,0.012943955410366914
+北京煤炭自产,0.17689324322923597,0.17689324322923597,0.007922610785166487
+内蒙古煤炭自产,0.11205259700631809,0.11205259700631809,0.005018558636509003
+陕西煤炭自产,0.01729885226305945,0.01729885226305945,0.0007747728008622167
+河北煤炭自产,0.005725037659929048,0.005725037659929048,0.00025641027481903157
+宁夏煤炭自产,0.003681741655070106,0.003681741655070106,0.00016489610124257272
+河南煤炭自产,0.0003380155172682859,0.0003380155172682859,1.5138878872795475e-05
+辽宁煤炭自产,9.624202771374984e-05,9.624202771374984e-05,4.310442348344183e-06
+黑龙江煤炭自产,8.779297960622036e-05,8.779297960622036e-05,3.932030383934904e-06
+甘肃煤炭自产,8.019094603388261e-05,8.019094603388261e-05,3.591554105305361e-06
+山东煤炭自产,4.685223080160008e-05,4.685223080160008e-05,2.09839551970246e-06
+新疆煤炭自产,4.4209741066289364e-05,4.4209741066289364e-05,1.9800449411586874e-06
+贵州煤炭自产,3.403242484795567e-05,3.403242484795567e-05,1.5242281232662667e-06
+安徽煤炭自产,1.850842263144369e-05,1.850842263144369e-05,8.289464655598571e-07
+北京煤炭贸易,0.0,0.6055199895298221,0.0
+甘肃煤炭贸易,0.0,0.00019019170912748932,0.0
+安徽煤炭贸易,0.0,2.166737937699332e-05,0.0
+广西煤炭贸易,0.0,4.149185221209223e-05,0.0
+河南煤炭贸易,0.0,0.0005852641556217211,0.0
+江苏煤碳贸易,0.0,1.7476919663101248e-05,0.0
+国外煤炭贸易,0.0,0.00045837926891533095,0.0
+贵州煤炭贸易,0.0,3.5244307048533534e-05,0.0
+黑龙江煤炭贸易,0.0,0.00011753624522569059,0.0
+内蒙古煤炭贸易,0.0,0.11553336234573641,0.0
+新疆煤炭贸易,0.0,4.496070247672856e-05,0.0
+山西煤炭贸易,0.0,0.2979771523381061,0.0
+辽宁煤炭贸易,0.0,0.00053782780257796,0.0
+陕西煤炭贸易,0.0,0.017595201739713014,0.0
+宁夏煤炭贸易,0.0,0.003710333913438605,0.0
+河北煤炭贸易,0.0,0.021250071274427015,0.0
+山东煤炭贸易,0.0,0.0002550680344034363,0.0
--- a/LCA_RAG/tmp/山东能源集团.csv
+++ b/LCA_RAG/tmp/山东能源集团.csv
@ -0,0 +1,70 @@
+name,amount,upstream,percent,type
+山东兖矿济三电力有限公司,2190508332.0,2190508332.0,0.15162563659545,process
+淄博齐翔腾达化工股份有限公司,2083028811.0,2083028811.0,0.1441859703980977,process
+兖煤菏泽能化有限公司,1995437299.0,1995437299.0,0.13812293992551694,process
+兖州煤业榆林能化有限公司,1965288145.0,1965288145.0,0.13603603406842282,process
+兖矿国宏化工有限责任公司,1496527715.0,1496527715.0,0.10358872602983059,process
+兖矿鲁南化肥厂,987771400.0,987771400.0,0.06837292748347276,process
+兖矿集团有限公司南屯电力分公司,808404783.9000001,808404783.9000001,0.05595728087175553,process
+兖矿鲁南化工有限公司,799015055.0,799015055.0,0.055307329624767435,process
+兖矿新疆煤化工有限公司,609125696.6999999,609125696.6999999,0.04216330528378218,process
+山东玻纤集团股份有限公司,380624207.6,380624207.6,0.026346573047862174,process
+山东兖矿国际焦化有限公司,278250123.4,278250123.4,0.01926030203900978,process
+内蒙古恒坤化工有限公司,241055573.20000002,241055573.20000002,0.016685718199464534,process
+山东康格能源科技有限公司,191097419.3,191097419.3,0.013227645578802636,process
+山东泰汶盐化工有限责任公司,149180418.8,149180418.8,0.010326176640229206,process
+兖煤菏泽能化有限公司赵楼煤矿,71202981.53999999,71202981.53999999,0.004928626495403156,process
+兖矿榆林精细化工有限公司,42617061.79,42617061.79,0.0029499267495762156,process
+山东兖矿轻合金有限公司,42561605.75,42561605.75,0.002946088116668399,process
+新汶矿业集团有限责任公司孙村煤矿,34727962.339999996,34727962.339999996,0.0024038481481865066,process
+新汶矿业集团有限责任公司华丰煤矿,26974437.549999997,26974437.549999997,0.0018671539411989602,process
+兖矿科蓝凯美特化工有限公司,17839060.79,17839060.79,0.0012348087925687385,process
+兖矿北海高岭土有限公司,15265919.99,15265919.99,0.0010566975723783534,process
+华能灵台邵寨煤业有限责任公司,6726962.244,6726962.244,0.0004656361802873329,process
+山东良庄矿业有限公司,3369737.1149999998,3369737.1149999998,0.00023325112612317152,process
+新汶矿业集团有限责任公司鄂庄煤矿,2125828.4239999996,2125828.4239999996,0.00014714853322991248,process
+彬县水帘洞煤炭有限责任公司,2055400.4799999997,2055400.4799999997,0.00014227355435532464,process
+兖矿新疆矿业有限公司硫磺沟煤矿,1359326.781,1359326.781,9.409176194376094e-05,process
+兖矿东华重工有限公司煤机装备制造分公司,1218037.551,1218037.551,8.431180852844065e-05,process
+山东新矿赵官能源有限责任公司,1156632.3010000002,1156632.3010000002,8.006137497128917e-05,process
+山东能源重装集团乾元不锈钢制造有限公司,772362.993,772362.993,5.3462490320439514e-05,process
+山东能源重装集团莱芜装备制造有限公司,649504.5599,649504.5599,4.495830530649864e-05,process
+山东能源重型装备制造集团有限责任公司新汶分公司,389702.73589999997,389702.73589999997,2.6974983181130408e-05,process
+旬邑虎豪黑沟煤业有限公司,300000.0,300000.0,2.076581509146937e-05,process
+兖矿集团邹城金通橡胶有限公司,188199.0,188199.0,1.3027018781331481e-05,process
+山东能源重装集团鲁南装备制造有限公司,4643.864456,4643.864456,3.2144543534381006e-07,process
+山东能源集团有限公司,0.0,14446820347.699253,0.0,process
+山东能源重装集团莱芜装备制造有限公司,0.0,0.0,0.0,transport
+兖矿新疆煤化工有限公司,0.0,0.0,0.0,transport
+新汶矿业集团有限责任公司鄂庄煤矿,0.0,0.0,0.0,transport
+山东能源重装集团鲁南装备制造有限公司,0.0,0.0,0.0,transport
+兖矿榆林精细化工有限公司,0.0,0.0,0.0,transport
+兖矿科蓝凯美特化工有限公司,0.0,0.0,0.0,transport
+兖州煤业榆林能化有限公司,0.0,0.0,0.0,transport
+山东新矿赵官能源有限责任公司,0.0,0.0,0.0,transport
+旬邑虎豪黑沟煤业有限公司,0.0,0.0,0.0,transport
+兖矿鲁南化肥厂,0.0,0.0,0.0,transport
+兖矿集团有限公司南屯电力分公司,0.0,0.0,0.0,transport
+兖矿新疆矿业有限公司硫磺沟煤矿,0.0,0.0,0.0,transport
+兖矿北海高岭土有限公司,0.0,0.0,0.0,transport
+兖矿鲁南化工有限公司,0.0,0.0,0.0,transport
+兖矿东华重工有限公司煤机装备制造分公司,0.0,0.0,0.0,transport
+淄博齐翔腾达化工股份有限公司,0.0,0.0,0.0,transport
+山东康格能源科技有限公司,0.0,0.0,0.0,transport
+内蒙古恒坤化工有限公司,0.0,0.0,0.0,transport
+彬县水帘洞煤炭有限责任公司,0.0,0.0,0.0,transport
+兖煤菏泽能化有限公司赵楼煤矿,0.0,0.0,0.0,transport
+兖矿集团邹城金通橡胶有限公司,0.0,0.0,0.0,transport
+兖矿国宏化工有限责任公司,0.0,0.0,0.0,transport
+新汶矿业集团有限责任公司孙村煤矿,0.0,0.0,0.0,transport
+新汶矿业集团有限责任公司华丰煤矿,0.0,0.0,0.0,transport
+山东兖矿轻合金有限公司,0.0,0.0,0.0,transport
+山东泰汶盐化工有限责任公司,0.0,0.0,0.0,transport
+山东能源重装集团乾元不锈钢制造有限公司,0.0,0.0,0.0,transport
+山东能源重型装备制造集团有限责任公司新汶分公司,0.0,0.0,0.0,transport
+山东良庄矿业有限公司,0.0,0.0,0.0,transport
+山东玻纤集团股份有限公司,0.0,0.0,0.0,transport
+华能灵台邵寨煤业有限责任公司,0.0,0.0,0.0,transport
+兖煤菏泽能化有限公司,0.0,0.0,0.0,transport
+山东兖矿济三电力有限公司,0.0,0.0,0.0,transport
+山东兖矿国际焦化有限公司,0.0,0.0,0.0,transport
--- a/LCA_RAG/tmp/快递碳足迹.csv
+++ b/LCA_RAG/tmp/快递碳足迹.csv
@ -0,0 +1,52 @@
+name,amount,upstream,percent
+快件运输,9.747687612784874,9.747687612784874,0.14177381471305034
+瓦楞纸箱-南京,6.127995493495469,6.12799549349547,0.08912773287048584
+塑料薄膜包装袋-广东,6.127995493495469,6.12799549349547,0.08912773287048584
+塑料包装-河北,6.127995493495469,6.12799549349547,0.08912773287048584
+塑料包装-苏州,6.127995493495469,6.12799549349547,0.08912773287048584
+书籍运输,3.792297420293587,3.792297420293587,0.05515651436103807
+日化品运输,3.6788137754911827,3.6788137754911827,0.05350596811147672
+书籍运输,3.4223437535728687,3.422343753572868,0.04977577744356806
+日化品运输,3.015697563190044,3.015697563190044,0.04386137149015122
+瓦楞纸箱-广东,2.572123066973619,2.5721230669736186,0.03740986720152995
+瓦楞纸箱-苏州,2.572123066973619,2.5721230669736186,0.03740986720152995
+瓦楞纸箱-河北,2.572123066973619,2.5721230669736186,0.03740986720152995
+快件_2运输,2.5340300660210824,2.534030066021083,0.0368558291287643
+日化品运输,2.1869971266773125,2.1869971266773125,0.03180845937336519
+快件_3目的中转运输,1.9669363282091696,1.9669363282091696,0.028607817322966633
+快件_2运输,1.8647433287684505,1.8647433287684507,0.027121486211095874
+快件运输,1.6561736645168954,1.6561736645168956,0.02408797527917187
+快件_2目的中转运输,1.151567838675877,1.151567838675877,0.016748809756255444
+日化品-瓦楞纸箱运输,0.3666435908731544,0.3666435908731544,0.005332594004141196
+日化品_2-瓦楞纸箱,0.17787993739805466,0.17787993739805466,0.0025871486949134914
+低压电力-天津,0.15289265459077053,0.15289265459077056,0.0022237248200802275
+日化品-塑料包装运输,0.14198389655810478,0.14198389655810478,0.0020650639867104553
+低压电力-南京,0.13638048145561166,0.13638048145561166,0.0019835659365001444
+低压电力-江苏,0.13638048145561166,0.13638048145561166,0.0019835659365001444
+低压电力-上海,0.13445056898218316,0.13445056898218316,0.0019554966072100476
+快件-瓦楞纸运输,0.09805150541753317,0.09805150541753317,0.0014260957586667575
+日化品-塑料包装运输,0.08737317370905205,0.08737317370905205,0.0012707863272178998
+书籍-瓦楞纸运输,0.034785007734353196,0.034785007734353196,0.0005059254499348153
+快件-塑料薄膜包装袋运输,0.023573436827491006,0.02357343682749101,0.00034286039907014255
+日化品-塑料编织袋运输,0.01369432659082348,0.013694326590823478,0.0001991751272538707
+分拣建包-塑料编织袋运输,0.0054757108464944994,0.0054757108464944994,7.964067436413896e-05
+始发网点建包,0.0,8.821743502714114,0.0
+始发地网点-练塘物流中心,0.0,8.965371671576197,0.0
+始发地中转-上海浦西转运中心,0.0,11.300513693826517,0.0
+目的网点分拣,0.0,20.367460972317986,0.0
+始发地中转中心,0.0,11.226382031259572,0.0
+目的中转-圆通青岛转运中心,0.0,13.76041209728065,0.0
+目的网点-李沧工业园,0.0,14.911979935956523,0.0
+目的网点,0.0,16.529499719741935,0.0
+日化品_2,0.0,8.965371671576197,0.0
+目的中转-青岛中通转运中心,0.0,14.979327469317699,0.0
+目的网点-青岛国际院士港,0.0,16.94626379752686,0.0
+书籍,0.0,6.16278050122982,0.0
+目的中心分拣建包,0.0,20.367460972317986,0.0
+南京转运中心,0.0,10.091458402979018,0.0
+青岛转运中心,0.0,13.513802156551888,0.0
+快递服务,0.0,68.7552044255433,0.0
+日化品,0.0,9.208746047900346,0.0
+快件包装,0.0,8.821743502714114,0.0
+出发地网点-南京,0.0,6.16278050122982,0.0
+始发中心分拣建包,0.0,10.619773359533117,0.0
--- a/LCA_RAG/tmp/报告模板.md
+++ b/LCA_RAG/tmp/报告模板.md
@ -0,0 +1,96 @@
+# 产品碳足迹研究报告（模板）
+
+## 基本信息
+
+- 产品名称：  
+- 产品规格型号：  
+- 生产者名称：  
+- 报告编号：  
+- 出具报告机构：（若有）
+- 日期： 年 月 日
+
+## 一、概况
+- 生产者名称：  
+- 地址：  
+- 法定代表人：
+- 产品名称：
+- 产品功能
+- 依据标准：  
+
+## 二、量化目的
+
+（在此处填写量化目的）
+
+## 三、量化范围
+
+### 1. 功能单位或声明单位
+
+以（）为功能单位或声明单位。
+
+### 2. 系统边界
+（需要用户提供系统边界图）
+
+### 3. 取舍准则
+
+采用的取舍准则以（）为依据，具体规则如下：
+
+### 4. 时间范围
+
+（）年度。
+
+## 四、清单分析
+
+### 1. 数据来源说明
+
+- 初级数据：（）
+- 次级数据：（）
+
+### 2. 分配原则与程序
+
+- 分配依据：  
+- 分配程序：  
+- 具体分配情况：
+
+### 3. 数据质量评价（可选项）
+
+(填写数据质量评估结，数据质量可从定性和定量两个方面对报告使用的初级数据和次级数据进行评价，具体评价内容包括：数据来源、完整性、数据代表性（时间、地理、技术）和准确性。）
+
+## 五、影响评价
+
+### 1. 影响类型和特征化因子选择
+
+一般选择政府间气候变化专门委员会（IPCC）给出的100年全球变暖潜势（GWP）。
+
+### 2. 产品碳足迹结果计算
+
+（在此处填写计算结果）
+
+## 六、结果解释
+
+### 1. 结果说明
+
+（填写产品生产者的全名）公司生产的（填写所评价的产品名称，每功能单位的产品），从（填写某生命周期阶段）到（填写某生命周期阶段）生命周期碳足迹为（）$kg CO_2e$。各生命周期阶段的温室气体排放情况如表1和图1所示。
+
+图1：添加可视化结果
+
+表1 生命周期各阶段碳排放情况 （必须给出表格）
+
+| 生命周期阶段 | 碳足迹(kg·CO2e/功能单位) | 百分比(%) |
+| ------------ | -------------------------- | ---------- |
+| 原材料获取   |                            |            |
+| 制造        |                            |            |
+| 分销        |                            |            |
+| 使用        |                            |            |
+| 生命末期    |                            |            |
+| **总计**    |                            |            |
+
+### 2. 假设和局限性说明（可选项）
+
+结合量化情况，对范围、数据选择、情景设定等相关的假设和局限进行详细的说明。
+
+### 3. 改进建议
+
+（在此处填写改进建议，至少三点建议）
+
+
+
--- a/LCA_RAG/tmp/煤基甲醇制烯烃.csv
+++ b/LCA_RAG/tmp/煤基甲醇制烯烃.csv
@ -0,0 +1,8 @@
+name,amount,upstream,percent
+热电联产过程,11.563782746325435,23.566185385211607,0.4906938716344823
+蒸汽生产,5.676556159827958,5.676556159827957,0.24087717494534117
+电力生产,4.770369020573175,4.770369020573176,0.2024243186836129
+硬煤,1.274026328132373,1.274026328132373,0.05406162717076213
+自来水,0.14596698510428013,0.14596698510428013,0.006193916525662156
+天然气生产,0.12691571865979778,0.1269157186597978,0.005385501157070618
+除盐水,0.008568426588587924,0.00856842658858793,0.0003635898830688498
--- a/LCA_RAG/tmp/煤基甲醇制烯烃制甲醇阶段.csv
+++ b/LCA_RAG/tmp/煤基甲醇制烯烃制甲醇阶段.csv
@ -0,0 +1,18 @@
+name,amount,upstream,percent
+热电联产过程,7.768016690681819,15.830678024991137,0.4271553667461783
+蒸汽生产,3.813249000129158,3.8132490001291584,0.20968669867799936
+电力生产,3.204514213508473,3.204514213508473,0.1762129895725556
+自来水2,2.2747902403241365,2.2747902403241365,0.12508841034570384
+硬煤,0.8558322132474474,0.8558322132474476,0.04706134621999886
+自来水,0.09891810804448142,0.09891810804448141,0.005439406530918495
+天然气生产,0.08525625097674533,0.08525625097674529,0.004688154853871639
+天然气1,0.05471232164636453,0.05471232164636454,0.0030085751291474647
+脱盐水1,0.02239890591571513,0.022398905915715132,0.0012316931402345697
+除盐水,0.005786765548626877,0.005786765548626876,0.0003182083739808369
+自来水1,0.0015892883418327755,0.001589288341832776,8.739335554406726e-05
+净化过程,0.0003687588995295291,0.0808982386458136,2.027767823393122e-05
+丙烯1,1.4423674774555847e-05,1.4423674774555846e-05,7.931432608201855e-07
+甲醇1,1.2110648137600026e-05,1.2110648137600028e-05,6.659522697673815e-07
+柴油,3.3133820060242056e-07,3.313382006024206e-07,1.8219951917086196e-08
+除盐水1,3.746478529873767e-08,3.7464785298737654e-08,2.0601505817496473e-09
+
--- a/LCA_RAG/tmp/电解铝生产.csv
+++ b/LCA_RAG/tmp/电解铝生产.csv
@ -0,0 +1,31 @@
+name,amount,upstream,percent
+国网电力,1745.1059852379938,1745.1059852379938,0.6997011247384999
+电解铝生产,300.05999999999995,2494.0734315529276,0.12030920830312859
+氧化铝,254.85791999999992,350.00135725120265,0.10218541153430007
+煤气,72.2153816612149,72.21538166121489,0.02895479369115856
+碳素阳极,43.69513949999999,157.07717617400118,0.01751958821548944
+石油焦,23.855627782161278,23.855627782161278,0.009564925988288819
+铝土矿运输,13.97984747557237,13.979847475572372,0.0056052268945697625
+煤炭1,9.633183141138625,9.633183141138627,0.003862429637903865
+铝土矿开采,6.908478719999998,9.361108840056284,0.0027699580263354368
+沥青,5.825292173587356,5.825292173587358,0.002335653834362148
+燃料油,5.211358861124648,5.211358861124647,0.0020894969631587027
+焦炭,2.724156006506894,2.7241560065068944,0.0010922517244453007
+氟化铝,2.533040173387193,2.533040173387192,0.001015623734787152
+电力,2.2247095601306017,2.2247095601306017,0.000891998419928395
+天然气1,2.1044229636332448,2.1044229636332443,0.0008437694484091158
+冰晶石,1.1934821364038375,1.1934821364038377,0.00047852726439602826
+石灰石,1.175892028695957,1.175892028695957,0.0004714745018408665
+自来水,0.2533950602804196,0.2533950602804196,0.00010159887719209768
+石灰石运输,0.18402725072466056,0.1840272507246605,7.378581897248972e-05
+柴油,0.13811590888044525,0.13811590888044528,5.537764330958281e-05
+煤炭,0.044867484334753516,0.04486748433475352,1.7989640468130442e-05
+石油焦运输,0.04432536223077501,0.04432536223077501,1.7772276337179032e-05
+煤炭运输,0.03010587983072586,0.03010587983072587,1.2070967698806093e-05
+汽油,0.02273207473153838,0.02273207473153839,9.114436826097905e-06
+天然气,0.02220509197894794,0.02220509197894794,8.90314282571945e-06
+煤气运输,0.010099368203461903,0.010099368203461903,4.049346773712897e-06
+燃料油运输1,0.005332464856641741,0.005332464856641742,2.1380544731281216e-06
+焦炭运输,0.0027457118613630174,0.002745711861363018,1.1008945553192503e-06
+氟化铝运输,0.002478744446136075,0.0024787444461360744,9.938538355675805e-07
+冰晶石运输,0.0007704863957622045,0.0007704863957622046,3.089269088931609e-07
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
zhangxiaojun	91f072f22d	修改分支	2025-02-06 16:10:41 +08:00
zhangxiaojun	bf87f14398	问答,数据分析和报告生成	2025-02-06 09:24:19 +08:00
				`@ -0,0 +1 @@`
				{"url": "/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/报告案例1.md", "raw": [{"content": "# 产品碳足迹研究报告（示例）\n## 基本信息\n- 产品名称：智能节能灯泡\n- 产品规格型号：LED-E27-10W\n- 生产者名称：EcoTech Solutions\n- 报告编号：ECO-2023-CFP-01\n- 出具报告机构：EcoAnalytics\n- 日期：2023年4月15日\n## 一、概况\n### 1. 生产者信息\n- 生产者名称：EcoTech Solutions\n- 地址：123 Greenway St, EcoCity\n- 法定代表人：Jane Doe\n- 产品名称：智能节能灯泡\n- 产品功能：节能照明，支持远程控制\n- 依据标准：ISO 14040, ISO 14044\n## 二、量化目的\n评估并减少产品在其整个生命周期中的碳排放。\n## 三、量化范围\n### 1. 功能单位或声明单位\n以个为功能单位或声明单位。\n### 2. 系统边界\n- 原材料获取阶段\n- 生产阶段\n- 分销阶段\n- 使用阶段\n- 生命末期阶段\n- 系统边界图：（此处通常会有一个流程图，但我们将跳过此部分，因为它是图形内容）\n### 3. 取舍准则\n采用的取舍准则以ISO 14044为依据，具体规则如下：（此处可以详细描述，但我们将省略）\n### 4. 时间范围\n2022年度。\n## 四、清单分析\n### 1. 数据来源说明\n- 初级数据：内部记录\n- 次级数据：行业平均值\n### 2. 分配原则与程序\n- 分配依据：按活动比例\n- 分配程序：线性分配\n- 具体分配情况：（略，通常会有一系列详细的计算）\n### 3. 清单结果及计算\n\| 生命周期阶段 \| 活动数据 (单位) \| 排放因子 (kg CO2e/单位) \| 碳足迹 (kg CO2e/功能单位) \|\n\| --- \| --- \| --- \| --- \|\n\| 原材料获取 \| 10 \| 0.5 \| 5 \|\n\| 生产 \| 20 \| 1 \| 20 \|\n\| 分销 \| 5 \| 0.8 \| 4 \|\n\| 运输 \| 15 \| 0.6 \| 9 \|\n\| 仓库 \| 3 \| 0.7 \| 2.1 \|\n\| 使用 \| \| \| 100 (假设) \|\n\| 生命末期 \| 2 \| 1.2 \| 2.4 \|\n\| 总计 \| \| \| 130.5 \|\n### 4. 数据质量评价\n（略，通常涉及详细的质量评估标准）\n## 五、影响评价\n### 1. 影响类型和特征化因子选择\n100年全球变暖潜势（GWP100）\n### 2. 产品碳足迹结果计算\n产品碳足迹总计为130.5 kg CO2e/个。\n## 六、结果解释\n### 1. 结果说明\nEcoTech Solutions生产的LED-E27-10W智能节能灯泡，从原材料获取到生命末期的生命周期碳足迹为130.5 kg CO2e/个。各生命周期阶段的温室气体排放情况如下表所示。\n### 2. 假设和局限性说明\n（此处可以添加关于数据估算和简化模型的说明）\n### 3. 改进建议\n- 优化原材料供应链以减少获取过程中的排放\n- 提升生产效率，降低生产阶段的碳足迹\n- 考虑使用更环保的分销方式\n- 提供回收计划以减少生命末期的环境影响", "metadata": {"source": "/home/zhangxj/WorkFile/LCA-GPT/DataAnalysis/报告案例1.md", "title": "报告案例1.md", "chunk_id": 0}, "token": 821}], "title": "报告案例1.md"}
				`@ -0,0 +1 @@`
				`Subproject commit fe482edaa22a709ac8068046843720e6495775b9`