代码定期提交

dimaoshibie/segformer.py

@@ -34,30 +34,29 @@ class SegFormer_Segmentation(object):
 
         # 颜色设置
         self.colors = [
-            (0, 0, 0),  # Background (黑色)                背景
+            (0, 0, 0),  # Background (黑色)
-            (252, 250, 205),  # Cropland (淡黄色)          农田
+            (252, 250, 205),  # Cropland (淡黄色)
-            (0, 123, 79),  # Forest (深绿色)               森林
+            (0, 123, 79),  # Forest (深绿色)
-            (157, 221, 106),  # Grass (浅绿色)             草地
+            (157, 221, 106),  # Grass (浅绿色)
-            (77, 208, 159),  # Shrub (浅蓝绿色)            灌木
+            (77, 208, 159),  # Shrub (浅蓝绿色)
-            (111, 208, 242),  # Wetland (浅蓝色)           湿地
+            (111, 208, 242),  # Wetland (浅蓝色)
-            (10, 78, 151),  # Water (深蓝色)               水
+            (10, 78, 151),  # Water (深蓝色)
-            (92, 106, 55),  # Tundra (土黄色)              苔原
+            (92, 106, 55),  # Tundra (土黄色)
-            (155, 36, 22),  # Impervious surface (红色)    不透明水面
+            (155, 36, 22),  # Impervious surface (红色)
-            (205, 205, 205),  # Bareland (灰色)            裸地
+            (205, 205, 205),  # Bareland (灰色)
-            (211, 242, 255)  # Ice/snow (浅天蓝色)          冰雪
+            (211, 242, 255)  # Ice/snow (浅天蓝色)
         ]
 
         # 如果类别数小于等于11，使用预设颜色；否则动态生成颜色
         if self.num_classes <= 11:  # 10个标签 + 背景
             self.colors = self.colors[:self.num_classes]
-        else: # 随机生成颜色
+        else:
             hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
             self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
             self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
 
         # 初始化模型
         self.generate()
-        # 打印参数配置
         show_config(**self._defaults)
 
     def generate(self, onnx=False):
@@ -67,8 +66,8 @@ class SegFormer_Segmentation(object):
         """
         self.net = SegFormer(num_classes=self.num_classes, phi=self.phi, pretrained=False)
         device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        self.net.load_state_dict(torch.load(self.model_path, map_location=device)) # 加载权重
+        self.net.load_state_dict(torch.load(self.model_path, map_location=device))
-        self.net = self.net.eval() # 评估模式
+        self.net = self.net.eval()
         print('{} model, and classes loaded.'.format(self.model_path))
         if not onnx:
             if self.cuda:
@@ -123,7 +122,7 @@ class SegFormer_Segmentation(object):
             # 混合原图和分割图
             seg_img = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr, [-1])], [orininal_h, orininal_w, -1])
             image = Image.fromarray(np.uint8(seg_img))
-            image = Image.blend(old_img, image, 1)
+            image = Image.blend(old_img, image, 1.0)
         elif self.mix_type == 1:
             # 仅显示分割图
             seg_img = np.reshape(np.array(self.colors, np.uint8)[np.reshape(pr, [-1])], [orininal_h, orininal_w, -1])

fast_api_run.py

@@ -68,8 +68,8 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m
 
 # 模型调用
 """地貌识别"""
-# # 地貌识别 - 图形分割
+# 地貌识别 - 图形分割
-# # path 这里需要每次上传，出现一个uuid命名的文件夹，防止文件冲突  eg:D:\\project\\ai_station\\wudingpv\\tmp\\99f06853-4788-4608-8884-2a3b7bc33768
+# path 这里需要每次上传，出现一个uuid命名的文件夹，防止文件冲突  eg:D:\\project\\ai_station\\wudingpv\\tmp\\99f06853-4788-4608-8884-2a3b7bc33768
 # path = "/home/xiazj/ai-station/ai_station_merge/tmp/dimaoshibie/crop_9_14.png"
 # result = model_deal.dimaoshibie_pic(dimaoshibie_SegFormer,path,param.dmsb_count,param.dmsb_name_classes)
 # print(result)
@@ -82,7 +82,7 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m
 
 
 """屋顶识别"""
-# # 屋顶识别- 图形分割
+# 屋顶识别- 图形分割
 # path = "/home/xiazj/ai-station/ai_station_merge/tmp/wuding/99f06853-4788-4608-8884-2a3b7bc32131/taihuyuan_7-8.png"
 # result = model_deal.roof_pic(roof_model,path,param.wdpv_palette)
 # print(result)
@@ -97,12 +97,12 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m
 
 
 """ 光伏识别 """  # 公用一个接口即可,url进行区别
-# # 光伏识别- 图形分割
+# 光伏识别- 图形分割
 # path = "/home/xiazj/ai-station/ai_station_merge/tmp/pv/99f06853-4788-4608-8884-2a3b7bc32131/taihuyuan_7-8.png"
 # result = model_deal.roof_pic(pv_model,path,param.wdpv_palette)
 # print(result)
 
-# 光伏识别 - 像素面积计算 
+# # 光伏识别 - 像素面积计算 
 # path = "/home/xiazj/ai-station/ai_station_merge/tmp/pv/99f06853-4788-4608-8884-2a3b7bc32131/taihuyuan_7-8.png"
 # scale_m = 0.92*0.92 # Z18代表的，像素大小
 # result = model_deal.roof_area(path,scale_m,param.wdpv_colors)
@@ -111,7 +111,7 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m
 
 
 """ 屋顶光伏识别 """
-# 屋顶光伏识别- 图形分割
+# # 屋顶光伏识别- 图形分割
 # path = "/home/xiazj/ai-station/ai_station_merge/tmp/wudingpv/99f06853-4788-4608-8884-2a3b7bc32131/taihuyuan_7-8.png"
 # result = model_deal.roofpv_pic(roof_model,pv_model,path,param.wdpv_palette)
 # file_list = result['reason']
@@ -138,18 +138,21 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m
 
 # 3. 上传数据的预测
 # 返回pred_data，true_data，通过时间戳为x轴，进行展示结果即可,没有的话就是没有，有的话就是有
-# test_data_path = "/home/xiazj/ai-station/ai_station_merge/tmp/guangfufadian/99f06853-4788-4608-8884-2a3b7bc32131/run_test.csv"
+test_data_path = "/home/xiazj/ai-station-code/guangfufadian/datasets/run_test.csv"
 # predictions = pvfd_model.run_inference(test_data_path)
+# print(len(predictions))
 # predictions = np.array(predictions).flatten()
 # pred_data, true_data = prepare_data.result_merge_guangfufadian(test_data_path,predictions)
 # print(pred_data)
 # print(true_data)
-
+# start_time = '2019-05-23 03:15:00'
+# end_time = '2019-05-25 03:30:00'
+# print(model_deal.start_pvelectric_predict_endpoint(pvfd_model,test_data_path,start_time,end_time,True))
 
 
 """风力发电预测 """
-# # 1. 读取训练文件，截取部分数据，并针对点击列进行数据前端展示
+# 1. 读取训练文件，截取部分数据，并针对点击列进行数据前端展示
-# # 展示某一列，并进行返回
+# 展示某一列，并进行返回
 # tmp = prepare_data.show_data_windfd("Wind speed at height of 10 meters (m/s)")
 # print(tmp)
 
@@ -160,51 +163,52 @@ ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_m
 
 
 # # 3. 上传数据的预测
-# test_data_path = "/home/xiazj/ai-station/ai_station_merge/tmp/fenglifadian/99f06853-4788-4608-8884-2a3b7bc32131/Wind_farm_test.csv"
+test_data_path = "/home/xiazj/ai-station-code/fenglifadian/datasets/Wind_farm_test.csv"
-# predictions = windfd_model.run_inference(test_data_path)
+predictions = windfd_model.run_inference(test_data_path)
-# predictions = np.array(predictions).flatten()
+predictions = np.array(predictions).flatten()
-# pred_data, true_data = prepare_data.result_merge_fenglifadian(test_data_path,predictions)
+print(len(predictions))
-# print(pred_data)
+pred_data, true_data = prepare_data.result_merge_fenglifadian(test_data_path,predictions)
-# print(true_data)
+print(pred_data)
+print(true_data)
 
 
 """甲烷产量预测"""
-#1、读取训练文件，截取部分数据，并针对点击列进行数据前端展示
+# 1、读取训练文件，截取部分数据，并针对点击列进行数据前端展示
 # test_data_path = "/home/xiazj/ai-station/ai_station_merge/tmp/jiawanyuce/jiawan_test.csv"
 # tmp = prepare_data.show_data_jiawanyuce("X_ch")
 # print(tmp)
 
-# #2、进行预测，预测接口
+# 2、进行预测，预测接口
-# test_data_path = "/home/xiazj/ai-station/ai_station_merge/tmp/jiawanyuce/jiawan_test.csv"
+# test_data_path = "/home/xiazj/ai-station-code/jiawanyuce/data/jiawan_test.csv"
-# tmp = model_deal.start_predict_endpoint(ch4_model_flow,ch4_model_gas,test_data_path,"2023-01-01 02:30:00","2023-01-03 02:30:00",2,False)
+# tmp = model_deal.start_predict_endpoint(ch4_model_flow,ch4_model_gas,test_data_path,"2023-01-01 02:30:00","2023-01-03 02:30:00",2,True)
 # print(tmp)
 
-""" 煤热解 """
+# """ 煤热解 """
-meirejie_test_content = {
+# meirejie_test_content = {
-    'A':    11.92,
+#     'A':    11.92,
-    'V' :	51.16,
+#     'V' :	51.16,
-    'FC':	48.84,
+#     'FC':	48.84,
-    'C':	83.22 ,
+#     'C':	83.22 ,
-    'H':	3.89,
+#     'H':	3.89,
-    'N':	2.72,
+#     'N':	2.72,
-    'S':	0.45,
+#     'S':	0.45,
-    'O':20.21,
+#     'O':20.21,
-    'H/C':0.56,
+#     'H/C':0.56,
-    'O/C':0.18,
+#     'O/C':0.18,
-    'N/C':0.03,
+#     'N/C':0.03,
-    'Rt':40.00,
+#     'Rt':40.00,
-    'Hr':5.00,
+#     'Hr':5.00,
-    'dp':0.20,
+#     'dp':0.20,
-    'T':510.00
+#     'T':510.00
-}
+# }
-meirejie_test_content = pd.DataFrame([meirejie_test_content])  
+# meirejie_test_content = pd.DataFrame([meirejie_test_content])  
-meirejie_result_content = {
+# meirejie_result_content = {
-    'Tar':11.71,
+#     'Tar':11.71,
-    'Gas':10.81 ,
+#     'Gas':10.81 ,
-    'Char':75.31,
+#     'Char':75.31,
-    'Water':2.17
+#     'Water':2.17
-}
+# }
-#1、 tar 焦油
+# #1、 tar 焦油
 #1.1 单独预测
 # tar_result = model_deal.pred_single_tar(meirejie_test_content)
 # print(tar_result)
@@ -245,28 +249,28 @@ meirejie_result_content = {
 
 
 
-"""煤基碳材料"""
+# """煤基碳材料"""
-meijiegou_test_content = {
+# meijiegou_test_content = {
-    'A':    10.43,
+#     'A':    10.43,
-    'VM' :	35.88,
+#     'VM' :	35.88,
-    'K/C':	4,
+#     'K/C':	4,
-    'MM':	0 ,
+#     'MM':	0 ,
-    'AT':	800,
+#     'AT':	800,
-    'At':	1,
+#     'At':	1,
-    'Rt':	5
+#     'Rt':	5
-}
+# }
-# meijiegou_test_content = pd.DataFrame([meijiegou_test_content])  
+# # meijiegou_test_content = pd.DataFrame([meijiegou_test_content])  
 
-meicailiao_test_content = {
+# meicailiao_test_content = {
-    "SSA":1141.8,
+#     "SSA":1141.8,
-    "TPV":0.46,
+#     "TPV":0.46,
-    "N":1.74,
+#     "N":1.74,
-    "O":3.84,
+#     "O":3.84,
-    "ID/IG":1.102,
+#     "ID/IG":1.102,
-    "J":0.5
+#     "J":0.5
-}
+# }
 
-meicailiao_test_content = pd.DataFrame([meicailiao_test_content])  
+# meicailiao_test_content = pd.DataFrame([meicailiao_test_content])  
 
 # 活性炭总表面积预测分析 #
 # # # 1、 单独预测
@@ -324,17 +328,18 @@ meicailiao_test_content = pd.DataFrame([meicailiao_test_content])
 #               "KC_min" : None, "KC_max": None, "KC_step": None,"MM_min" : None, "MM_max": None, "MM_step": None,
 #               "AT_min" : None, "AT_max": None, "AT_step": None,"At_min" : None, "At_max": None, "At_step": None,
 #               "Rt_min" : None, "Rt_max": None, "Rt_step": None}
-params = {'A_min': 4.0, 'A_max': 48.0, 'A_step': 4.0, 'VM_min': 5.0, 'VM_max': 50.0, 'VM_step': 5.0, 'KC_min': 1.0, 'KC_max': 4.0, 'KC_step': 0.5, 'MM_min': 0.0, 'MM_max': 1.0, 'MM_step': 1.0, 'AT_min': 600.0, 'AT_max': 900.0, 'AT_step': 50.0, 'At_min': 0.5, 'At_max': 2.0, 'At_step': 0.5, 'Rt_min': 5.0, 'Rt_max': 10.0, 'Rt_step': 5.0}
+# params = {'A_min': 4.0, 'A_max': 48.0, 'A_step': 4.0, 'VM_min': 5.0, 'VM_max': 50.0, 'VM_step': 5.0, 'KC_min': 1.0, 'KC_max': 4.0, 'KC_step': 0.5, 'MM_min': 0.0, 'MM_max': 1.0, 'MM_step': 1.0, 'AT_min': 600.0, 'AT_max': 900.0, 'AT_step': 50.0, 'At_min': 0.5, 'At_max': 2.0, 'At_step': 0.5, 'Rt_min': 5.0, 'Rt_max': 10.0, 'Rt_step': 5.0}
-params = prepare_data.get_params(params)
+# params = prepare_data.get_params(params)
-pred_data = prepare_data.create_pred_data(params) 
+# pred_data = prepare_data.create_pred_data(params) 
-result = model_deal.pred_func("xgb",pred_data)
+# result = model_deal.pred_func("xgb",pred_data)
-sorted_result = result.sort_values(by=['SSA', 'TPV'], ascending=[False, False])
+# sorted_result = result.sort_values(by=['SSA', 'TPV'], ascending=[False, False])
-# 保留条数
+# sorted_result.to_csv('/home/xiazj/ai-station-code/meijitancailiao/data/moni.csv')
-num = 6
+# # 保留条数
-if num is None:
+# num = 6
-    print(sorted_result.head()) # 返回全部
+# if num is None:
-else:
+#     print(sorted_result.head()) # 返回全部
-    print(sorted_result.head(6)) # 返回所需条数
+# else:
+#     print(sorted_result.head(6)) # 返回所需条数
 
 
 """数据界面获取"""

fenglifadian/model_base.py

@@ -60,22 +60,20 @@ class ModelInference:
     数据长度满足192，截取前192
     数据长度不满足192，前拼接满足数量级
     """
-    def load_pred_data(self,data_path):
+    def load_pred_data(self,data):
-
-        data = pd.read_csv(data_path)
         raw_data = data.values
         length = raw_data.shape[0]
         # 顺序，必须一致才能进行下一步
-        check_list = ['date', 'Wind speed at height of 10 meters (m/s)',
+        check_list = ['date', 'WS_10',
-       'Wind direction at height of 10 meters (?)',
+       'WD_10',
-       'Wind speed at height of 30 meters (m/s)',
+       'WS_30',
-       'Wind direction at height of 30 meters (?)',
+       'WD_30',
-       'Wind speed at height of 50 meters (m/s)',
+       'WS_50',
-       'Wind direction at height of 50 meters (?)',
+       'WD_50',
-       'Wind speed - at the height of wheel hub (m/s)',
+       'WS_hub',
-       'Wind speed - at the height of wheel hub (?)',
+       'WD_hub',
-       'Air temperature  (буC) ', 'Atmosphere (hpa)', 'Relative humidity (%)',
+       'Air temperature', 'Atmosphere', 'Relative humidity',
-       'Power(MW)']
+       'Power']
         columns_match = list(data.columns) == check_list
         if columns_match:
             if length > fenglifadian_args.in_len:
@@ -90,10 +88,10 @@ class ModelInference:
                 input_data = np.array(input_data)
                 input_data = input_data[:fenglifadian_args.in_len, 1:].astype(np.float32)
                 input_tensor = torch.tensor(input_data, dtype=torch.float32).unsqueeze(0)  # 增加batch维度
-            return input_tensor
+            return {'status':True, 'reason':input_tensor }
         else:
             print("文件不匹配，请检查上传文件与模版是否一致")
-            return None
+            return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'}
 
     # 数据归一化
     def preprocess_data(self, input_tensor):
@@ -106,11 +104,14 @@ class ModelInference:
         # return predictions.cpu().numpy()  # 返回 NumPy 数组
         return predictions
     # 推理过程
-    def run_inference(self, file_path):
+    def run_inference(self, data):
-        raw_data = self.load_pred_data(file_path)
+        raw_data = self.load_pred_data(data)
-        input_tensor = self.preprocess_data(raw_data)
+        if raw_data['status'] == False:
-        predictions = self.predict(input_tensor)
+            return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'}
-        predictions = self.scaler.inverse_transform(predictions)
+        else:
-        predictions = predictions.squeeze(0)
+            input_tensor = self.preprocess_data(raw_data['reason'])
-        predictions = predictions.cpu().numpy()
+            predictions = self.predict(input_tensor)
-        return predictions[:,-1:]
+            predictions = self.scaler.inverse_transform(predictions)
+            predictions = predictions.squeeze(0)
+            predictions = predictions.cpu().numpy()
+            return {'status':True, 'reason':predictions[:,-1:]}

guangfufadian/datasets/run_test.csv

@@ -1,289 +1,482 @@
 date_time,nwp_globalirrad,nwp_directirrad,nwp_temperature,nwp_humidity,nwp_windspeed,nwp_winddirection,nwp_pressure,lmd_totalirrad,lmd_diffuseirrad,lmd_temperature,lmd_pressure,lmd_winddirection,lmd_windspeed,power
-2019-05-23 03:15:00,0.0,0.0,20.98,17.05,7.09,268.51,947.19,0,0,24.1,946.700012,258,3.4,0.0
+5/23/2019 3:15,0,0,20.98,17.05,7.09,268.51,947.19,0,0,24.1,946.700012,258,3.4,0
-2019-05-23 03:30:00,0.0,0.0,21.12,16.75,7.05,269.86,947.05,0,0,23.9,946.799988,253,3.3,0.0
+5/23/2019 3:30,0,0,21.12,16.75,7.05,269.86,947.05,0,0,23.9,946.799988,253,3.3,0
-2019-05-23 03:45:00,0.0,0.0,21.27,16.43,6.99,271.05,947.15,0,0,23.9,946.599976,278,3.2,0.0
+5/23/2019 3:45,0,0,21.27,16.43,6.99,271.05,947.15,0,0,23.9,946.599976,278,3.2,0
-2019-05-23 04:00:00,0.0,0.0,21.39,16.14,6.91,272.47,947.27,0,0,24.299999,946.599976,275,4.5,0.0
+5/23/2019 4:00,0,0,21.39,16.14,6.91,272.47,947.27,0,0,24.299999,946.599976,275,4.5,0
-2019-05-23 04:15:00,0.0,0.0,21.42,15.93,6.84,273.84,947.27,0,0,23.6,946.5,250,5.1,0.0
+5/23/2019 4:15,0,0,21.42,15.93,6.84,273.84,947.27,0,0,23.6,946.5,250,5.1,0
-2019-05-23 04:30:00,0.0,0.0,21.35,15.85,6.78,273.76,947.3,0,0,23.299999,946.599976,185,2.5,0.0
+5/23/2019 4:30,0,0,21.35,15.85,6.78,273.76,947.3,0,0,23.299999,946.599976,185,2.5,0
-2019-05-23 04:45:00,0.0,0.0,21.2,15.86,6.73,272.42,947.47,0,0,23.4,946.799988,278,5.0,0.0
+5/23/2019 4:45,0,0,21.2,15.86,6.73,272.42,947.47,0,0,23.4,946.799988,278,5,0
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+5/28/2019 1:15,0,0,14.83,33.93,4.38,295,958.58,0,0,18,957.5,8,0.5,0
+5/28/2019 1:30,0,0,14.61,34.36,4.45,294.89,958.65,0,0,18,957.5,345,1.3,0
+5/28/2019 1:45,0,0,14.36,34.87,4.49,293.92,958.78,0,0,17.799999,957.599976,28,1.4,0
+5/28/2019 2:00,0,0,14.18,35.17,4.52,293.25,958.77,0,0,18,957.400024,33,0.6,0
+5/28/2019 2:15,0,0,13.89,35.69,4.54,292.06,958.71,0,0,17.9,957.5,216,0,0
+5/28/2019 2:30,0,0,13.68,35.99,4.62,288.97,958.91,0,0,17.700001,957.400024,331,0.4,0
+5/28/2019 2:45,0,0,13.59,35.93,4.81,286.17,959.03,0,0,17.9,957.400024,205,0.2,0
+5/28/2019 3:00,0,0,13.61,35.67,5.05,283.86,959.17,0,0,17.6,957.299988,56,0.1,0
+5/28/2019 3:15,0,0,13.72,35.32,5.28,282.53,959.17,0,0,17.5,957.299988,244,0.1,0

guangfufadian/model_base.py

@@ -60,9 +60,7 @@ class ModelInference:
     数据长度满足192，截取前192
     数据长度不满足192，前拼接满足数量级
     """
-    def load_pred_data(self,data_path):
+    def load_pred_data(self,data):
-
-        data = pd.read_csv(data_path)
         raw_data = data.values
         length = raw_data.shape[0]
         # 顺序，必须一致才能进行下一步
@@ -84,10 +82,10 @@ class ModelInference:
                 input_data = np.array(input_data)
                 input_data = input_data[:guangfufadian_args.in_len, 1:].astype(np.float32)
                 input_tensor = torch.tensor(input_data, dtype=torch.float32).unsqueeze(0)  # 增加batch维度
-            return input_tensor
+            return {'status':True, 'reason':input_tensor }
         else:
             print("文件不匹配，请检查上传文件与模版是否一致")
-            return None
+            return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'}
 
     # 数据归一化
     def preprocess_data(self, input_tensor):
@@ -100,11 +98,14 @@ class ModelInference:
         # return predictions.cpu().numpy()  # 返回 NumPy 数组
         return predictions
     # 推理过程
-    def run_inference(self, file_path):
+    def run_inference(self, data):
-        raw_data = self.load_pred_data(file_path)
+        raw_data = self.load_pred_data(data)
-        input_tensor = self.preprocess_data(raw_data)
+        if raw_data['status'] == False:
-        predictions = self.predict(input_tensor)
+            return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'}
-        predictions = self.scaler.inverse_transform(predictions)
+        else:
-        predictions = predictions.squeeze(0)
+            input_tensor = self.preprocess_data(raw_data['reason'])
-        predictions = predictions.cpu().numpy()
+            predictions = self.predict(input_tensor)
-        return predictions[:,-1:]
+            predictions = self.scaler.inverse_transform(predictions)
+            predictions = predictions.squeeze(0)
+            predictions = predictions.cpu().numpy()
+            return {'status':True, 'reason':predictions[:,-1:]}

meijitancailiao/meishuxing_SSA.py

@@ -5,7 +5,7 @@ import numpy as np
 import xgboost as xgb
 from sklearn.svm import SVR
 from sklearn.tree import DecisionTreeRegressor
-from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor
+from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor,GradientBoostingRegressor
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import StandardScaler, MinMaxScaler,RobustScaler
 from sklearn.linear_model import LinearRegression,Ridge,LogisticRegression, Lasso, ElasticNet
@@ -634,60 +634,101 @@ XGB
 """
 
 # 创建Pipeline
-base_estimator = DecisionTreeRegressor(max_depth=3)
+# base_estimator = DecisionTreeRegressor(max_depth=3)
+# pipeline = Pipeline([
+#     ('scaler', StandardScaler()),  # 可选
+#     ('xgb', xgb.XGBRegressor(objective='reg:squarederror', 
+#                             random_state=42,
+#                             n_jobs=-1))
+# ])
+# # 定义超参数网格
+# param_grid = {
+#     'xgb__n_estimators': np.arange(1,150),  # 树的数量
+#     # 'xgb__max_depth': [3, 4, 5],  # 树的最大深度
+#     # 'xgb__learning_rate': [0.01, 0.05, 0.1],  # 学习率
+#     # 'xgb__subsample': [0.6, 0.8, 1.0],  # 样本采样比例
+#     # 'xgb__colsample_bytree': [0.6, 0.8, 1.0],  # 特征采样比例
+#     #  'xgb__gamma': [0, 0.1, 0.2],  # 最小分裂损失
+#     #  'xgb__reg_alpha': [0, 0.1, 1],  # L1正则化
+#     #  'xgb__reg_lambda': [0.1, 1, 10]  # L2正则化  
+# }
+
+# # 设置5折交叉验证
+# kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+# # 网格搜索（优化超参数）
+# grid_search = GridSearchCV(
+#     pipeline, 
+#     param_grid, 
+#     cv=kf, 
+#     scoring='neg_mean_squared_error',  # 最小化MSE
+#     n_jobs=-1,  # 使用所有CPU核心
+#     verbose=2   # 打印进度
+# )
+
+# # 训练模型
+# print("开始网格搜索优化...")
+# grid_search.fit(train_x, train_y)
+# print("\n最佳参数组合:", grid_search.best_params_)
+
+# # 使用最佳模型进行预测
+# best_model = grid_search.best_estimator_
+
+# # 保存模型
+# dump(best_model, './model/SSA_XGB.joblib')
+
+# pred_y = best_model.predict(valid_x)
+
+# # 验证
+# acc = evaluate_model_accuracy(pred_y,valid_y)
+# print(acc)
+# # 画图保存
+# draw_picture(valid_y,pred_y,acc,'./pic/ssa','XGB')
+
+# # 加载模型
+# loaded_pipeline = load('./model/SSA_XGB.joblib')
+
+# # 使用模型预测
+# pred_y = loaded_pipeline.predict(valid_x)
+# print(pred_y)
+
+
+
+"""GBDT"""
 pipeline = Pipeline([
-    ('scaler', StandardScaler()),  # 可选
+     ('scaler', StandardScaler()),  # 可替换为MinMaxScaler()
-    ('xgb', xgb.XGBRegressor(objective='reg:squarederror', 
+     ('gbdt', GradientBoostingRegressor(random_state=42))
-                            random_state=42,
+ ])
-                            n_jobs=-1))
-])
-# 定义超参数网格
 param_grid = {
-    'xgb__n_estimators': np.arange(1,150),  # 树的数量
+    'gbdt__n_estimators': [20, 30, 40, 50, 60, 70, 80,100],       # 树的数量
-    # 'xgb__max_depth': [3, 4, 5],  # 树的最大深度
+    'gbdt__learning_rate': [0.01, 0.1, 0.2],    # 学习率
-    # 'xgb__learning_rate': [0.01, 0.05, 0.1],  # 学习率
+    'gbdt__max_depth': [3, 5, 7],               # 树的最大深度
-    # 'xgb__subsample': [0.6, 0.8, 1.0],  # 样本采样比例
+    'gbdt__min_samples_split': [2, 5],          # 分裂所需最小样本数
-    # 'xgb__colsample_bytree': [0.6, 0.8, 1.0],  # 特征采样比例
+    'gbdt__min_samples_leaf': [1, 2,3,4,5],           # 叶节点最小样本数
-    #  'xgb__gamma': [0, 0.1, 0.2],  # 最小分裂损失
+    'gbdt__max_features': ['sqrt', 'log2'],     # 特征选择方式
-    #  'xgb__reg_alpha': [0, 0.1, 1],  # L1正则化
-    #  'xgb__reg_lambda': [0.1, 1, 10]  # L2正则化  
 }
-
+ # 设置K折交叉验证和网格搜索
-# 设置5折交叉验证
 kf = KFold(n_splits=5, shuffle=True, random_state=42)
-
+grid_search = GridSearchCV(pipeline, param_grid, cv=kf, scoring='neg_mean_squared_error', 
-# 网格搜索（优化超参数）
+                           n_jobs=-1, verbose=2)
-grid_search = GridSearchCV(
-    pipeline, 
-    param_grid, 
-    cv=kf, 
-    scoring='neg_mean_squared_error',  # 最小化MSE
-    n_jobs=-1,  # 使用所有CPU核心
-    verbose=2   # 打印进度
-)
 
 # 训练模型
-print("开始网格搜索优化...")
+print("开始网格搜索...")
 grid_search.fit(train_x, train_y)
+
 print("\n最佳参数组合:", grid_search.best_params_)
 
-# 使用最佳模型进行预测
+#使用最佳模型进行预测
 best_model = grid_search.best_estimator_
 
-# 保存模型
+dump(best_model, './model/SSA_GDBT.joblib')
-dump(best_model, './model/SSA_XGB.joblib')
 
 pred_y = best_model.predict(valid_x)
-
-# 验证
 acc = evaluate_model_accuracy(pred_y,valid_y)
+print(pred_y)
+print("\n")
 print(acc)
-# 画图保存
+draw_picture(valid_y,pred_y,acc,'./pic/SSA','GDBT')
-draw_picture(valid_y,pred_y,acc,'./pic/ssa','XGB')
+loaded_pipeline = load('./model/SSA_GDBT.joblib')
-
-# 加载模型
-loaded_pipeline = load('./model/SSA_XGB.joblib')
-
-# 使用模型预测
 pred_y = loaded_pipeline.predict(valid_x)
 print(pred_y)

run.py

@@ -1,13 +1,14 @@
 import sys
 
-from fastapi import FastAPI,File, UploadFile,Form
+from fastapi import FastAPI,File, UploadFile,Form,Query
 from fastapi.staticfiles import StaticFiles
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import FileResponse
 import sys
 import os
 import shutil
 from pydantic import BaseModel, validator
 from typing import List
-from fastapi import FastAPI, HTTPException
 import asyncio
 import pandas as pd
 import numpy as np
@@ -29,11 +30,52 @@ from work_util.logger import logger
 import joblib
 import mysql.connector
 import uuid
+import json
+import zipfile
+from pathlib import Path
 
 version = f"{sys.version_info.major}.{sys.version_info.minor}"
 
 app = FastAPI()
 
+param = params.ModelParams()
+def get_roof_model():
+    model_roof =  roof_resUnetpamcarb()
+    model_path_roof = os.path.join(current_dir,'wudingpv/models/roof_best.pth')
+    model_dict_roof = torch.load(model_path_roof, map_location=torch.device('cpu'))
+    model_roof.load_state_dict(model_dict_roof['net'])
+    logger.info("屋顶识别权重加载成功")
+    model_roof.eval()
+    model_roof.cuda()
+    return model_roof
+
+
+def get_pv_model():
+    model_roof =  roof_resUnetpamcarb()
+    model_path_roof = os.path.join(current_dir,'wudingpv/models/pv_best.pth')
+    model_dict_roof = torch.load(model_path_roof, map_location=torch.device('cpu'))
+    model_roof.load_state_dict(model_dict_roof['net'])
+    logger.info("屋顶识别权重加载成功")
+    model_roof.eval()
+    model_roof.cuda()
+    return model_roof
+
+# 模型实例
+dimaoshibie_SegFormer = segformer.SegFormer_Segmentation()
+# 模型实例
+dimaoshibie_SegFormer = segformer.SegFormer_Segmentation()
+roof_model = get_roof_model()
+pv_model = get_pv_model()
+ch4_model_flow = joblib.load(os.path.join(current_dir,'jiawanyuce/liuliang_model/xgb_model_liuliang.pkl'))
+ch4_model_gas = joblib.load(os.path.join(current_dir,'jiawanyuce/qixiangnongdu_model/xgb_model_qixiangnongdu.pkl'))
+
+pvfd_param = guangfufadian_model_base.guangfufadian_Args()
+pvfd_model_path = os.path.join(pvfd_param.checkpoints,'Crossformer_station08_il192_ol96_sl6_win2_fa10_dm256_nh4_el3_itr0/checkpoint.pth')  # 修改为实际模型路径
+pvfd_model = guangfufadian_model_base.ModelInference(pvfd_model_path, pvfd_param)
+
+windfd_args = fenglifadian_model_base.fenglifadian_Args()
+windfd_model_path = os.path.join(windfd_args.checkpoints,'Crossformer_Wind_farm_il192_ol12_sl6_win2_fa10_dm256_nh4_el3_itr0/checkpoint.pth')  # 修改为实际模型路径
+windfd_model = fenglifadian_model_base.ModelInference(windfd_model_path, windfd_args)
 
 # 将 /root/app 目录挂载为静态文件
 app.mount("/files", StaticFiles(directory="/root/app"), name="files")
@@ -46,7 +88,7 @@ async def read_root():
 
 
 # 获取数据界面资源信息
-@app.get("/api/data_source/show")
+@app.get("/ai-station-api/data_source/show")
 async def get_data_source_info():
     sql = "SELECT id,application_name, task_type, sample_name, img_url, time, download_url FROM data_samples"
     data = data_util.fetch_data(sql)
@@ -64,7 +106,7 @@ async def get_data_source_info():
 
 
 # 获取应用界面资源信息
-@app.get("/api/app_source/show")
+@app.get("/ai-station-api/app_source/show")
 async def get_app_source_info():
     sql = "SELECT id,application_name, task_type, sample_name, img_url, time FROM app_samples"
     data = data_util.fetch_data(sql)
@@ -83,7 +125,7 @@ async def get_app_source_info():
 
 
 # 获取煤基碳材料界面资源信息
-@app.get("/api/mjtcl_source/show")
+@app.get("/ai-station-api/mjtcl_source/show")
 async def get_mjtcl_source_info():
     sql = "SELECT id,application_name, task_type, sample_name, img_url, time FROM meijitancailiao_samples"
     data = data_util.fetch_data(sql)
@@ -101,7 +143,7 @@ async def get_mjtcl_source_info():
 
 
 # 获取煤热解界面资源信息
-@app.get("/api/mrj_source/show")
+@app.get("/ai-station-api/mrj_source/show")
 async def get_mrj_source_info():
     sql = "SELECT id,application_name, task_type, sample_name, img_url, time FROM meirejie_samples"
     data = data_util.fetch_data(sql)
@@ -121,12 +163,12 @@ async def get_mrj_source_info():
 """煤基碳材料输入特征说明接口列表 
 type = {zongkongtiji,zongbiaomianji,tancailiao,meiliqing,moniqi}
 """
-@app.get("/api/mjt_feature/show")
+@app.get("/ai-station-api/mjt_feature/show")
 async def get_mjt_feature_info(type:str = None):
     # if type in ["tancailiao","meiliqing"]:
     #     sql = "SELECT type, chinese_name, col_name, data_type, unit FROM meijitancailiao_features where use_type = %s;"
     # else:
-    sql = "SELECT type, chinese_name, col_name, data_type, unit, data_scale FROM meijitancailiao_features where use_type = %s;"
+    sql = "SELECT type, chinese_name, col_name, data_type, unit, data_scale,best_data FROM meijitancailiao_features where use_type = %s;"
     data = data_util.fetch_data_with_param(sql,(type,))
     if data is None:
         return {
@@ -144,7 +186,7 @@ async def get_mjt_feature_info(type:str = None):
 """煤热解材料输入特征说明接口列表
 type = {tar,char,gas,water}
 """
-@app.get("/api/mrj_feature/show")
+@app.get("/ai-station-api/mrj_feature/show")
 async def get_mrj_feature_info(type:str = None):
     sql = "SELECT type, chinese_name, col_name, data_type, unit, data_scale FROM meirejie_features where use_type = %s;"
     data = data_util.fetch_data_with_param(sql,(type,))
@@ -160,7 +202,7 @@ async def get_mrj_feature_info(type:str = None):
             "data":data}
     
 #========================================煤基碳材料 ==================================
-@app.post("/api/mjt/predict")
+@app.post("/ai-station-api/mjt/predict")
 async def mjt_models_predict(content: post_model.Zongbiaomianji):
     # 处理接收到的字典数据
     meijiegou_test_content = pd.DataFrame([content.model_dump()])
@@ -185,7 +227,7 @@ async def mjt_models_predict(content: post_model.Zongbiaomianji):
 """
 模型综合分析
 """
-@app.post("/api/mjt_models_ssa/predict")
+@app.post("/ai-station-api/mjt_models_ssa/predict")
 async def mjt_models_predict_ssa(content: post_model.Zongbiaomianji):
     # 处理接收到的字典数据
     meijiegou_test_content = pd.DataFrame([content.model_dump()])
@@ -209,7 +251,7 @@ async def mjt_models_predict_ssa(content: post_model.Zongbiaomianji):
 """
 上传文件
 """
-@app.post("/api/document/upload")
+@app.post("/ai-station-api/document/upload")
 async def upload_file(file: UploadFile = File(...),type: str = Form(...), ):
     if not file.filename.endswith('.csv'):
         raise HTTPException(status_code=400, detail="文件类型有误，必须是 CSV 文件")
@@ -228,7 +270,7 @@ async def upload_file(file: UploadFile = File(...),type: str = Form(...), ):
 """
 批量预测接口
 """
-@app.get("/api/mjt_multi_ssa/predict")
+@app.get("/ai-station-api/mjt_multi_ssa/predict")
 async def mjt_multi_ssa_pred(model:str = None, path:str = None):
     data = model_deal.get_excel_ssa(model, path)
     return {"success":True,
@@ -241,7 +283,7 @@ async def mjt_multi_ssa_pred(model:str = None, path:str = None):
 """
 模型综合分析
 """
-@app.post("/api/mjt_models_tpv/predict")
+@app.post("/ai-station-api/mjt_models_tpv/predict")
 async def mjt_models_predict_tpv(content: post_model.Zongbiaomianji):
     # 处理接收到的字典数据
     meijiegou_test_content = pd.DataFrame([content.model_dump()])
@@ -263,7 +305,7 @@ async def mjt_models_predict_tpv(content: post_model.Zongbiaomianji):
 """
 批量预测接口
 """
-@app.get("/api/mjt_multi_tpv/predict")
+@app.get("/ai-station-api/mjt_multi_tpv/predict")
 async def mjt_multi_tpv_pred(model:str = None, path:str = None):
     data = model_deal.get_excel_tpv(model, path)
     return {"success":True,
@@ -271,7 +313,7 @@ async def mjt_multi_tpv_pred(model:str = None, path:str = None):
             "data":data}
 
 #==========================煤基碳材料-煤炭材料应用细节接口==================================
-@app.post("/api/mjt_models_meitan/predict")
+@app.post("/ai-station-api/mjt_models_meitan/predict")
 async def mjt_models_predict_meitan(content: post_model.Meitan):
     # 处理接收到的字典数据
     meijiegou_test_content = pd.DataFrame([content.model_dump()])
@@ -294,7 +336,7 @@ async def mjt_models_predict_meitan(content: post_model.Meitan):
 """
 批量预测接口
 """
-@app.get("/api/mjt_multi_meitan/predict")
+@app.get("/ai-station-api/mjt_multi_meitan/predict")
 async def mjt_multi_meitan_pred(model:str = None, path:str = None):
     data = model_deal.get_excel_meitan(model, path)
     return {"success":True,
@@ -303,7 +345,7 @@ async def mjt_multi_meitan_pred(model:str = None, path:str = None):
 
 
 #==========================煤基碳材料-煤沥青应用细节接口==================================
-@app.post("/api/mjt_models_meiliqing/predict")
+@app.post("/ai-station-api/mjt_models_meiliqing/predict")
 async def mjt_models_predict_meiliqing(content: post_model.Meitan):
     # 处理接收到的字典数据
     meijiegou_test_content = pd.DataFrame([content.model_dump()])
@@ -326,7 +368,7 @@ async def mjt_models_predict_meiliqing(content: post_model.Meitan):
 """
 批量预测接口
 """
-@app.get("/api/mjt_multi_meiliqing/predict")
+@app.get("/ai-station-api/mjt_multi_meiliqing/predict")
 async def mjt_multi_meiliqing_pred(model:str = None, path:str = None):
     data = model_deal.get_excel_meiliqing(model, path)
     return {"success":True,
@@ -337,7 +379,8 @@ async def mjt_multi_meiliqing_pred(model:str = None, path:str = None):
 
 
 #=============================煤基炭材料-制备模拟器================================================
-@app.post("/api/mnq_model/analysis")
+# 正向模拟器
+@app.post("/ai-station-api/mnq_model/analysis")
 async def mnq_model_predict(return_count: int,  # 返回条数
     model_choice: str,   # 模型选择标记
     form_data: post_model.FormData # 表单数据 
@@ -347,21 +390,622 @@ async def mnq_model_predict(return_count: int,  # 返回条数
     pred_data = prepare_data.create_pred_data(params) 
     result = model_deal.pred_func(model_choice,pred_data)
     sorted_result = result.sort_values(by=['SSA', 'TPV'], ascending=[False, False])
+    upload_dir  = os.path.join(current_dir,'tmp','moniqi', str(uuid.uuid4()))
+    if not os.path.exists(upload_dir):
+        os.makedirs(upload_dir )
+    # 将文件保存到指定目录
+    file_name = model_choice + "_moniqi.csv"
+    file_location = os.path.join(upload_dir , file_name)
+    sorted_result.to_csv(file_location, index = False)
     # 保留条数
     if return_count is None:
         return {"success":True,
             "msg":"获取信息成功",
-            "data":{"result": sorted_result.to_json(orient='index')}}
+            "data":{"result": sorted_result.to_dict(orient='records'), "path":file_location}}
+    else:
+        data = sorted_result.iloc[:return_count]
+        # logger.info(data.to_dict(orient="records"))
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":{"result":data.to_dict(orient="records"),  "path":file_location}
+        }
+
+# 反向模拟器
+@app.get("/ai-station-api/mnq_model/reverse_analysis")
+async def mjt_multi_meiliqing_pred(model:str = None, path:str = None, type:int = 1, scale:float=None, num:int=5):
+    result = prepare_data.moniqi_data_prepare(model,path,type,scale,num)
+    if result['status'] == False:
+        return  {
+            "success":False,
+            "msg":result['reason'],
+            "data":None
+        }
+    else:
+        return {
+            "success":True,
+            "msg":"获取信息成功",
+            "data":{"data": result['reason']}
+        }
+
+
+#================================ 地貌识别 ====================================================
+"""
+上传图片 , type = dimaoshibie
+"""
+@app.post("/ai-station-api/image/upload")
+async def upload_image(file: UploadFile = File(...),type: str = Form(...), ):
+    if not data_util.allowed_file(file.filename):
+        return  {
+            "success":False,
+            "msg":"图片必须以 '.jpg', '.jpeg', '.png', '.tif' 结尾",
+            "data":None
+        }
+    if file.size > param.MAX_FILE_SIZE:
+        return {
+            "success":False,
+            "msg":"图片大小不能大于100MB",
+            "data":None
+        }
+    upload_dir  = os.path.join(current_dir,'tmp',type, str(uuid.uuid4()))
+    if not os.path.exists(upload_dir):
+        os.makedirs(upload_dir )
+    # 将文件保存到指定目录
+    file_location = os.path.join(upload_dir , file.filename)
+    with open(file_location, "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
+    file_location = model_deal.get_pic_url(file_location)
+    return {"success":True,
+            "msg":"获取信息成功",
+            "data":{"location": file_location}}
+
+"""
+图片地貌识别
+"""
+@app.get("/ai-station-api/dmsb_image/analysis")
+async def dmsb_image_analysis(path:str = None):
+    path = model_deal.get_pic_path(path)
+    result = model_deal.dimaoshibie_pic(dimaoshibie_SegFormer,path,param.dmsb_count,param.dmsb_name_classes)
+    if result['status'] == False:
+        return {"success":False,
+                "msg":result['reason'],
+                "data":None}
+    else:
+        path = result['reason']
+        path= model_deal.get_pic_url(path[0]) # 是一个列表
+        return {"success":True,
+                "msg":"获取信息成功",
+                "data":{"result": path}}
+
+
+"""
+图片地貌 - 分割结果计算 ,图片demo的是z17
+"""
+@app.get("/ai-station-api/dmsb_image/calculate")
+async def dmsb_image_calculate(path:str = None, scale:float = 1.92*1.92):
+    path = model_deal.get_pic_path(path)
+    result = model_deal.dimaoshibie_area(path,scale,param.dmsb_colors)
+    logger.info(result)
+    if result['status'] == True:
+        res = result['reason']
+        translated_dict = {param.dmsb_type[key]: value for key, value in res.items()}
+        # json_data =  json.dumps(translated_dict)
+        return {"success":True,
+                "msg":"获取信息成功",
+                "data":{"result": translated_dict}}
+    else:
+        return {"success":False,
+                "msg":result['reason'],
+                "data":None}
+
+
+"""
+下载
+"""
+@app.get("/ai-station-api/download")
+async def download_zip(path:str = None):
+    path = model_deal.get_pic_path(path)
+    zip_filename = "download.zip"
+    zip_filepath = Path(zip_filename)
+    dir_path = os.path.dirname(path)
+    # 创建 ZIP 文件
+    try:
+        with zipfile.ZipFile(zip_filepath, 'w') as zip_file:
+            # 遍历文件夹中的所有文件
+            for foldername, subfolders, filenames in os.walk(dir_path):
+                for filename in filenames:
+                    file_path = os.path.join(foldername, filename)
+                    # 将文件写入 ZIP 文件
+                    zip_file.write(file_path, os.path.relpath(file_path, dir_path))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error creating zip file: {str(e)}")
+
+    # 返回 ZIP 文件作为响应
+    return FileResponse(zip_filepath, media_type='application/zip', filename=zip_filename)
+#============================屋顶识别======================================
+"""
+上传图片  type = roof
+"""
+
+"""
+图片分析
+"""
+@app.get("/ai-station-api/roof_image/analysis")
+async def roof_image_analysis(path:str = None):
+    path = model_deal.get_pic_path(path)
+    result = model_deal.roof_pic(roof_model,path,param.wdpv_palette)
+    
+    if result['status'] == False:
+        return {"success":False,
+                "msg":result['reason'],
+                "data":None}
+    else:
+        path = result['reason']
+        path= model_deal.get_pic_url(path[0])
+        return {"success":True,
+                "msg":"获取信息成功",
+                "data":{"result": path}}
+
+
+"""
+屋顶面积 图 - 分割结果计算 ,图片demo的是z18
+"""
+@app.get("/ai-station-api/roof_image/calculate")
+async def roof_image_calculate(path:str = None, scale:float = 0.92*0.92):
+    path = model_deal.get_pic_path(path)
+    result = model_deal.roof_area(path,scale,param.wdpv_colors)
+    logger.info(result)
+    if result['status'] == True:
+        res = result['reason']
+        translated_dict = {param.wd_type[key]: value for key, value in res.items()}
+        del translated_dict['其他']
+        return {"success":True,
+                "msg":"获取信息成功",
+                "data":{"result": translated_dict}}
+    else:
+        return {"success":False,
+                "msg":result['reason'],
+                "data":None}
+
+
+
+#============================光伏识别======================================
+"""
+上传图片  type = pv
+"""
+"""
+图片分析
+"""
+@app.get("/ai-station-api/pv_image/analysis")
+async def pv_image_analysis(path:str = None):
+    path = model_deal.get_pic_path(path)
+    result = model_deal.roof_pic(pv_model,path,param.wdpv_palette)
+    if result['status'] == False:
+        return {"success":False,
+                "msg":result['reason'],
+                "data":None}
+    else:
+        path = result['reason']
+        path= model_deal.get_pic_url(path[0])
+        return {"success":True,
+                "msg":"获取信息成功",
+                "data":{"result": path}}
+
+"""
+光伏面积 图 - 分割结果计算 ,图片demo的是z18
+"""
+@app.get("/ai-station-api/pv_image/calculate")
+async def pv_image_calculate(path:str = None, scale:float = 0.92*0.92):
+    path = model_deal.get_pic_path(path)
+    result = model_deal.roof_area(path,scale,param.wdpv_colors)
+    logger.info(result)
+    if result['status'] == True:
+        res = result['reason']
+        translated_dict = {param.pv_type[key]: value for key, value in res.items()}
+        del translated_dict['其他']
+        return {"success":True,
+                "msg":"获取信息成功",
+                "data":{"result": translated_dict}}
+    else:
+        return {"success":False,
+                "msg":result['reason'],
+                "data":None}
+
+#============================屋顶光伏识别======================================
+"""
+上传图片  type = roofpv
+"""
+
+"""
+图片分析
+"""
+@app.get("/ai-station-api/roofpv_image/analysis")
+async def roofpv_image_analysis(path:str = None):
+    path = model_deal.get_pic_path(path)
+    result = model_deal.roofpv_pic(roof_model,pv_model,path,param.wdpv_palette)
+    if result['status'] == False:
+        return {"success":False,
+                "msg":result['reason'],
+                "data":None}
+    else:
+        file_list = result['reason']
+        final_path = prepare_data.merge_binary(file_list)
+        final_path = model_deal.get_pic_url(final_path) 
+        return {"success":True,
+                "msg":"获取信息成功",
+                "data":{"result": final_path}}
+
+
+"""
+光伏面积 图 - 分割结果计算 ,图片demo的是z18
+"""
+@app.get("/ai-station-api/roofpv_image/calculate")
+async def roofpv_image_calculate(path:str = None, scale:float = 0.92*0.92):
+    path = model_deal.get_pic_path(path)
+    result = model_deal.roof_area_roofpv(path,scale,param.wdpv_colors)
+    logger.info(result)
+    if result['status'] == True:
+        res = result['reason']
+        translated_dict = {param.wdpv_type[key]: value for key, value in res.items()}
+        del translated_dict['其他']
+        return {"success":True,
+                "msg":"获取信息成功",
+                "data":{"result": translated_dict}}
+    else:
+        return {"success":False,
+                "msg":result['reason'],
+                "data":None}
+
+# ====================================时序预测类============================================
+
+# ====================================甲烷预测==============================================
+
+"""
+返回显示列表
+"""
+@app.get("/ai-station-api/ch4_features/show")
+async def get_ch4_features_info():
+    sql = "SELECT chinese_name, col_name,type,data_type,unit,data_sample FROM ch4_features;"
+    data = data_util.fetch_data(sql)
+    if data is None:
+        return {
+            "success":False,
+            "msg":"获取信息列表失败",
+            "data":None
+        }
     else:
         return {"success":True,
             "msg":"获取信息成功",
-            "data":{"result": sorted_result.head(return_count).to_json(orient='index')}}
+            "data":data}
 
 
-#===================================================================================================
+
+@app.get("/ai-station-api/ch4_pic_table/show")
+async def get_ch4_pic_table_info():
+    sql = "SELECT chinese_name, col_name FROM ch4_features where col_name != 'date_time';"
+    data = data_util.fetch_data(sql)
+    if data is None:
+        return {
+            "success":False,
+            "msg":"获取信息列表失败",
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data}
 
 
-# @app.post("/api/items/")
+"""
+针对列表，返回数据，供画图
+"""
+@app.get("/ai-station-api/ch4_feature_pic/select")
+async def get_ch4_feature_pic_select(type:str = None, path:str =None):
+    if type == 'date_time':
+        return {
+            "success":False,
+            "msg":"时间列无法展示，请重新选择",
+            "data":None
+        }
+    data = prepare_data.show_data_jiawanyuce(type,path)
+    if data is None:
+        return {
+            "success":False,
+            "msg":"获取信息列表失败",
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data}
+
+"""
+上传数据分页展示
+"""
+@app.get("/ai-station-api/ch4_data/show")
+async def get_ch4_data(path:str = None, page: int = Query(1, ge=1), page_size: int = Query(10, ge=1)):
+    data = prepare_data.get_jiawanyuce_data(path)
+    total_items = len(data)  # 获取总条数
+    total_pages = (total_items + page_size - 1) // page_size  # 计算总页数
+    # 检查请求的页码是否超出范围
+    if page > total_pages:
+        raise HTTPException(status_code=404, detail="Page not found")
+    # 计算起始和结束索引
+    start_idx = (page - 1) * page_size
+    end_idx = start_idx + page_size
+
+    # 获取当前页的数据
+    items = data.iloc[start_idx:end_idx].to_dict(orient="records")
+
+    # 返回数据，包括总条数、总页数和当前页的数据
+    return {
+        "success":True,
+        "msg":"获取信息成功",
+        "data":{
+        "total_items": total_items,
+        "total_pages": total_pages,
+        "current_page": page,
+        "page_size": page_size,
+        "items": items
+        }
+    }
+
+"""
+csv上传
+"""
+# @app.post("/ai-station-api/document/upload")   type = "ch4"
+
+"""
+预测, type 
+"""        
+@app.get("/ai-station-api/ch4_data/predict")
+async def get_ch4_predict(path:str=None,start_time:str=None, end_time:str = None,type:int=1,is_show:bool=True):
+    data = model_deal.start_predict_endpoint(ch4_model_flow,ch4_model_gas,path,start_time,end_time,type,is_show)
+    if data['status'] ==False:
+        return {
+            "success":False,
+            "msg":data['reason'],
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data['reason']}
+
+
+#========================================光伏预测==========================================================
+"""
+返回显示列表
+"""
+@app.get("/ai-station-api/pvelectric_features/show")
+async def get_pvelectric_features_info():
+    sql = "SELECT chinese_name, col_name,type,data_type,unit,data_sample FROM pv_electric_features;"
+    data = data_util.fetch_data(sql)
+    if data is None:
+        return {
+            "success":False,
+            "msg":"获取信息列表失败",
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data}
+
+
+@app.get("/ai-station-api/pvelectric_pic_table/show")
+async def get_pvelectric_pic_table_info():
+    sql = "SELECT chinese_name, col_name FROM pv_electric_features where col_name != 'date_time';"
+    data = data_util.fetch_data(sql)
+    if data is None:
+        return {
+            "success":False,
+            "msg":"获取信息列表失败",
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data}
+
+
+"""
+针对列表，返回数据，供画图
+"""
+@app.get("/ai-station-api/pvelectric_feature_pic/select")
+async def get_pvelectric_feature_pic_select(type:str = None, path:str =None):
+    if type == 'date_time':
+        return {
+            "success":False,
+            "msg":"时间列无法展示，请重新选择",
+            "data":None
+        }
+    data = prepare_data.show_data_pvfd(path,type)
+    if data is None:
+        return {
+            "success":False,
+            "msg":"获取信息列表失败",
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data}
+
+
+"""
+上传数据分页展示
+"""
+@app.get("/ai-station-api/pvelectric_data/show")
+async def get_pvelectric_data(path:str = None, page: int = Query(1, ge=1), page_size: int = Query(10, ge=1)):
+    data = prepare_data.show_testdata_pvfd(path)
+    total_items = len(data)  # 获取总条数
+    total_pages = (total_items + page_size - 1) // page_size  # 计算总页数
+    # 检查请求的页码是否超出范围
+    if page > total_pages:
+        raise HTTPException(status_code=404, detail="Page not found")
+    # 计算起始和结束索引
+    start_idx = (page - 1) * page_size
+    end_idx = start_idx + page_size
+
+    # 获取当前页的数据
+    items = data.iloc[start_idx:end_idx].to_dict(orient="records")
+
+    # 返回数据，包括总条数、总页数和当前页的数据
+    return {
+        "success":True,
+        "msg":"获取信息成功",
+        "data":{
+        "total_items": total_items,
+        "total_pages": total_pages,
+        "current_page": page,
+        "page_size": page_size,
+        "items": items
+        }
+    }
+
+"""
+csv上传
+"""
+# @app.post("/ai-station-api/document/upload")   type = "pvelectric"
+
+"""
+预测, type 
+"""        
+@app.get("/ai-station-api/pvelectric_data/predict")
+async def get_pvelectri_predict(path:str=None,start_time:str=None, end_time:str = None,is_show:bool=True):
+    data = model_deal.start_pvelectric_predict_endpoint(pvfd_model,path,start_time,end_time,is_show)
+    if data['status'] ==False:
+        return {
+            "success":False,
+            "msg":data['reason'],
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data['reason']}
+
+
+#========================================风力发电预测==========================================================
+"""
+返回显示列表
+"""
+@app.get("/ai-station-api/wind_electric_features/show")
+async def get_wind_electric_features_info():
+    sql = "SELECT chinese_name, col_name,type,data_type,unit,data_sample FROM wind_electric_features;"
+    data = data_util.fetch_data(sql)
+    if data is None:
+        return {
+            "success":False,
+            "msg":"获取信息列表失败",
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data}
+
+
+@app.get("/ai-station-api/wind_electric_pic_table/show")
+async def get_wind_electric_pic_table_info():
+    sql = "SELECT chinese_name, col_name FROM wind_electric_features where col_name != 'date';"
+    data = data_util.fetch_data(sql)
+    if data is None:
+        return {
+            "success":False,
+            "msg":"获取信息列表失败",
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data}
+
+
+"""
+针对列表，返回数据，供画图
+"""
+@app.get("/ai-station-api/wind_electric_feature_pic/select")
+async def get_wind_electric_feature_pic_select(type:str = None, path:str =None):
+    if type == 'date':
+        return {
+            "success":False,
+            "msg":"时间列无法展示，请重新选择",
+            "data":None
+        }
+    data = prepare_data.show_data_windfd(path,type)
+    if data is None:
+        return {
+            "success":False,
+            "msg":"获取信息列表失败",
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data}
+
+
+"""
+上传数据分页展示
+"""
+@app.get("/ai-station-api/wind_electric_data/show")
+async def get_wind_electric_data(path:str = None, page: int = Query(1, ge=1), page_size: int = Query(10, ge=1)):
+    data = prepare_data.show_testdata_windfd(path)
+    total_items = len(data)  # 获取总条数
+    total_pages = (total_items + page_size - 1) // page_size  # 计算总页数
+    # 检查请求的页码是否超出范围
+    if page > total_pages:
+        raise HTTPException(status_code=404, detail="Page not found")
+    # 计算起始和结束索引
+    start_idx = (page - 1) * page_size
+    end_idx = start_idx + page_size
+
+    # 获取当前页的数据
+    items = data.iloc[start_idx:end_idx].to_dict(orient="records")
+
+    # 返回数据，包括总条数、总页数和当前页的数据
+    return {
+        "success":True,
+        "msg":"获取信息成功",
+        "data":{
+        "total_items": total_items,
+        "total_pages": total_pages,
+        "current_page": page,
+        "page_size": page_size,
+        "items": items
+        }
+    }
+
+"""
+csv上传
+"""
+# @app.post("/ai-station-api/document/upload")   type = "wind_electric"
+
+"""
+预测, type 
+"""        
+@app.get("/ai-station-api/wind_electric_data/predict")
+async def get_wind_electri_predict(path:str=None,start_time:str=None, end_time:str = None,is_show:bool=True):
+    data = model_deal.start_wind_electric_predict_endpoint(windfd_model,path,start_time,end_time,is_show)
+    if data['status'] ==False:
+        return {
+            "success":False,
+            "msg":data['reason'],
+            "data":None
+        }
+    else:
+        return {"success":True,
+            "msg":"获取信息成功",
+            "data":data['reason']}
+
+
+
+
+
+
+
+# @app.post("/ai-station-api/items/")
 # async def create_item(item: post_model.Zongbiaomianji):
 #     try:
 #         data = {