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
-            (252, 250, 205),  # Cropland (淡黄色)          农田
-            (0, 123, 79),  # Forest (深绿色)               森林
-            (157, 221, 106),  # Grass (浅绿色)             草地
-            (77, 208, 159),  # Shrub (浅蓝绿色)            灌木
-            (111, 208, 242),  # Wetland (浅蓝色)           湿地
-            (10, 78, 151),  # Water (深蓝色)               水
-            (92, 106, 55),  # Tundra (土黄色)              苔原
-            (155, 36, 22),  # Impervious surface (红色)    不透明水面
-            (205, 205, 205),  # Bareland (灰色)            裸地
-            (211, 242, 255)  # Ice/snow (浅天蓝色)          冰雪
+            (0, 0, 0),  # Background (黑色)
+            (252, 250, 205),  # Cropland (淡黄色)
+            (0, 123, 79),  # Forest (深绿色)
+            (157, 221, 106),  # Grass (浅绿色)
+            (77, 208, 159),  # Shrub (浅蓝绿色)
+            (111, 208, 242),  # Wetland (浅蓝色)
+            (10, 78, 151),  # Water (深蓝色)
+            (92, 106, 55),  # Tundra (土黄色)
+            (155, 36, 22),  # Impervious surface (红色)
+            (205, 205, 205),  # Bareland (灰色)
+            (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 = self.net.eval() # 评估模式
+        self.net.load_state_dict(torch.load(self.model_path, map_location=device))
+        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
-# # 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
+# 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 = "/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
-# # 展示某一列，并进行返回
+# 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
 # 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"
-# predictions = windfd_model.run_inference(test_data_path)
-# predictions = np.array(predictions).flatten()
-# pred_data, true_data = prepare_data.result_merge_fenglifadian(test_data_path,predictions)
-# print(pred_data)
-# print(true_data)
+test_data_path = "/home/xiazj/ai-station-code/fenglifadian/datasets/Wind_farm_test.csv"
+predictions = windfd_model.run_inference(test_data_path)
+predictions = np.array(predictions).flatten()
+print(len(predictions))
+pred_data, true_data = prepare_data.result_merge_fenglifadian(test_data_path,predictions)
+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
-# test_data_path = "/home/xiazj/ai-station/ai_station_merge/tmp/jiawanyuce/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)
+# 2、进行预测，预测接口
+# 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,True)
 # print(tmp)
 
-""" 煤热解 """
-meirejie_test_content = {
-    'A':    11.92,
-    'V' :	51.16,
-    'FC':	48.84,
-    'C':	83.22 ,
-    'H':	3.89,
-    'N':	2.72,
-    'S':	0.45,
-    'O':20.21,
-    'H/C':0.56,
-    'O/C':0.18,
-    'N/C':0.03,
-    'Rt':40.00,
-    'Hr':5.00,
-    'dp':0.20,
-    'T':510.00
-}
-meirejie_test_content = pd.DataFrame([meirejie_test_content])  
-meirejie_result_content = {
-    'Tar':11.71,
-    'Gas':10.81 ,
-    'Char':75.31,
-    'Water':2.17
-}
-#1、 tar 焦油
+# """ 煤热解 """
+# meirejie_test_content = {
+#     'A':    11.92,
+#     'V' :	51.16,
+#     'FC':	48.84,
+#     'C':	83.22 ,
+#     'H':	3.89,
+#     'N':	2.72,
+#     'S':	0.45,
+#     'O':20.21,
+#     'H/C':0.56,
+#     'O/C':0.18,
+#     'N/C':0.03,
+#     'Rt':40.00,
+#     'Hr':5.00,
+#     'dp':0.20,
+#     'T':510.00
+# }
+# meirejie_test_content = pd.DataFrame([meirejie_test_content])  
+# meirejie_result_content = {
+#     'Tar':11.71,
+#     'Gas':10.81 ,
+#     'Char':75.31,
+#     'Water':2.17
+# }
+# #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"""
-meijiegou_test_content = {
-    'A':    10.43,
-    'VM' :	35.88,
-    'K/C':	4,
-    'MM':	0 ,
-    'AT':	800,
-    'At':	1,
-    'Rt':	5
-}
-# meijiegou_test_content = pd.DataFrame([meijiegou_test_content])  
+# """煤基碳材料"""
+# meijiegou_test_content = {
+#     'A':    10.43,
+#     'VM' :	35.88,
+#     'K/C':	4,
+#     'MM':	0 ,
+#     'AT':	800,
+#     'At':	1,
+#     'Rt':	5
+# }
+# # meijiegou_test_content = pd.DataFrame([meijiegou_test_content])  
 
-meicailiao_test_content = {
-    "SSA":1141.8,
-    "TPV":0.46,
-    "N":1.74,
-    "O":3.84,
-    "ID/IG":1.102,
-    "J":0.5
-}
+# meicailiao_test_content = {
+#     "SSA":1141.8,
+#     "TPV":0.46,
+#     "N":1.74,
+#     "O":3.84,
+#     "ID/IG":1.102,
+#     "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 = prepare_data.get_params(params)
-pred_data = prepare_data.create_pred_data(params) 
-result = model_deal.pred_func("xgb",pred_data)
-sorted_result = result.sort_values(by=['SSA', 'TPV'], ascending=[False, False])
-# 保留条数
-num = 6
-if num is None:
-    print(sorted_result.head()) # 返回全部
-else:
-    print(sorted_result.head(6)) # 返回所需条数
+# 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)
+# pred_data = prepare_data.create_pred_data(params) 
+# result = model_deal.pred_func("xgb",pred_data)
+# sorted_result = result.sort_values(by=['SSA', 'TPV'], ascending=[False, False])
+# sorted_result.to_csv('/home/xiazj/ai-station-code/meijitancailiao/data/moni.csv')
+# # 保留条数
+# num = 6
+# if num is None:
+#     print(sorted_result.head()) # 返回全部
+# 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):
-
-        data = pd.read_csv(data_path)
+    def load_pred_data(self,data):
         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)',
-       'Wind direction at height of 10 meters (?)',
-       'Wind speed at height of 30 meters (m/s)',
-       'Wind direction at height of 30 meters (?)',
-       'Wind speed at height of 50 meters (m/s)',
-       'Wind direction at height of 50 meters (?)',
-       'Wind speed - at the height of wheel hub (m/s)',
-       'Wind speed - at the height of wheel hub (?)',
-       'Air temperature  (буC) ', 'Atmosphere (hpa)', 'Relative humidity (%)',
-       'Power(MW)']
+        check_list = ['date', 'WS_10',
+       'WD_10',
+       'WS_30',
+       'WD_30',
+       'WS_50',
+       'WD_50',
+       'WS_hub',
+       'WD_hub',
+       'Air temperature', 'Atmosphere', 'Relative humidity',
+       '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):
-        raw_data = self.load_pred_data(file_path)
-        input_tensor = self.preprocess_data(raw_data)
-        predictions = self.predict(input_tensor)
-        predictions = self.scaler.inverse_transform(predictions)
-        predictions = predictions.squeeze(0)
-        predictions = predictions.cpu().numpy()
-        return predictions[:,-1:]
+    def run_inference(self, data):
+        raw_data = self.load_pred_data(data)
+        if raw_data['status'] == False:
+            return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'}
+        else:
+            input_tensor = self.preprocess_data(raw_data['reason'])
+            predictions = self.predict(input_tensor)
+            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
-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
-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
-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
-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
-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
-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
-2019-05-23 05:00:00,0.0,0.0,20.96,15.98,6.7,270.74,947.5,0,0,23.6,946.700012,236,4.1,0.0
-2019-05-23 05:15:00,0.0,0.0,20.7,16.15,6.67,268.99,947.54,0,0,23.5,946.900024,236,5.3,0.0
-2019-05-23 05:30:00,16.12,8.55,20.47,16.77,6.64,267.36,947.63,6,4,23.4,947.0,239,5.7,0.10132
-2019-05-23 05:45:00,59.11,42.07,20.4,18.11,6.61,265.27,947.74,16,11,23.200001,947.200012,255,7.7,0.286004
-2019-05-23 06:00:00,92.58,71.04,20.71,19.16,6.59,262.93,947.71,24,17,23.200001,947.299988,222,3.4,0.499151
-2019-05-23 06:15:00,146.51,119.79,21.59,19.47,6.59,261.02,947.73,31,23,23.299999,947.299988,278,5.2,0.779324
-2019-05-23 06:30:00,184.0,154.52,22.75,19.15,6.59,260.08,947.81,38,28,23.5,947.5,210,6.5,1.2091379999999998
-2019-05-23 06:45:00,241.53,208.58,24.17,18.26,6.65,258.62,947.96,74,33,23.9,947.599976,261,8.1,1.76781
-2019-05-23 07:00:00,280.33,245.41,25.48,17.07,6.83,256.79,948.16,124,38,24.1,947.700012,289,5.6,2.484841
-2019-05-23 07:15:00,338.68,301.16,26.6,15.88,7.11,255.64,948.42,177,42,24.299999,947.700012,272,4.1,3.308445
-2019-05-23 07:30:00,377.39,338.34,27.36,15.43,7.35,253.68,948.61,236,46,24.700001,948.0,239,5.2,4.247745
-2019-05-23 07:45:00,434.78,393.69,28.1,15.18,7.49,252.33,948.66,298,49,25.1,948.0,264,4.9,5.251472
-2019-05-23 08:00:00,472.37,430.05,28.88,14.56,7.51,253.68,948.69,355,51,26.0,948.0,196,5.1,6.242268
-2019-05-23 08:15:00,527.39,483.39,29.66,13.81,7.44,254.82,948.71,411,55,26.4,948.0,323,7.2,7.204602
-2019-05-23 08:30:00,563.0,517.99,30.31,13.31,7.27,255.13,948.74,467,59,27.200001,948.0,255,5.1,8.168957
-2019-05-23 08:45:00,614.48,568.07,31.02,12.92,6.93,255.75,948.82,520,63,27.799999,948.099976,230,6.7,9.058953
-2019-05-23 09:00:00,647.35,600.08,31.75,12.24,6.47,257.45,948.9,575,65,28.4,948.200012,196,5.7,9.863159
-2019-05-23 09:15:00,694.23,645.81,32.39,11.4,5.94,259.75,948.9,624,68,29.299999,948.200012,295,3.0,10.61015
-2019-05-23 09:30:00,723.74,674.6,32.94,10.92,5.29,258.12,948.81,671,70,30.200001,948.099976,230,4.1,11.31974
-2019-05-23 09:45:00,765.15,715.07,33.46,10.14,4.48,255.65,948.74,719,67,31.1,948.099976,261,5.0,11.93025
-2019-05-23 10:00:00,790.73,740.07,33.85,9.36,3.57,250.97,948.66,771,68,32.099998,948.099976,213,5.8,12.4637
-2019-05-23 10:15:00,825.7,774.27,34.15,8.75,2.72,236.32,948.57,821,67,32.400002,948.200012,64,3.8,12.90708
-2019-05-23 10:30:00,846.62,794.75,34.37,8.29,2.16,212.24,948.58,866,65,33.599998,948.099976,236,4.7,13.3642
-2019-05-23 10:45:00,874.29,821.81,34.59,8.01,2.01,187.37,948.64,899,66,34.0,948.0,247,6.2,13.65609
-2019-05-23 11:00:00,890.2,837.39,34.8,7.9,2.09,177.01,948.63,937,66,35.0,947.900024,253,3.1,13.88812
-2019-05-23 11:15:00,910.24,857.01,35.02,7.72,2.2,175.58,948.59,964,65,35.400002,947.799988,224,3.5,14.202179999999998
-2019-05-23 11:30:00,920.93,867.48,35.22,7.59,2.32,179.46,948.55,977,64,36.0,947.599976,196,1.6,14.27024
-2019-05-23 11:45:00,932.97,879.28,35.44,7.39,2.54,187.69,948.45,1014,64,36.700001,947.299988,227,3.6,14.58771
-2019-05-23 12:00:00,938.36,884.59,35.63,7.17,2.94,198.01,948.33,1016,67,36.900002,947.200012,241,3.1,14.63442
-2019-05-23 12:15:00,942.58,888.69,35.81,7.0,3.44,205.26,948.27,1003,70,35.900002,947.0,191,0.8,14.49719
-2019-05-23 12:30:00,942.71,888.84,35.98,6.86,3.94,209.65,948.25,1000,82,36.599998,947.0,233,2.3,14.55544
-2019-05-23 12:45:00,938.94,885.14,36.09,6.74,4.3,212.75,948.12,998,85,36.099998,946.700012,247,2.7,14.22331
-2019-05-23 13:00:00,933.6,879.93,36.18,6.66,4.48,215.34,947.9,1006,103,36.700001,946.5,272,3.0,14.41867
-2019-05-23 13:15:00,921.25,867.84,36.27,6.64,4.54,217.21,947.71,972,101,36.799999,946.299988,250,2.8,14.14035
-2019-05-23 13:30:00,910.03,856.86,36.35,6.66,4.51,217.93,947.52,977,89,37.400002,946.099976,284,5.7,14.022570000000002
-2019-05-23 13:45:00,888.83,836.09,36.43,6.73,4.47,217.26,947.36,926,89,37.200001,945.900024,84,1.2,13.35658
-2019-05-23 14:00:00,871.96,819.58,36.51,6.84,4.43,214.63,947.23,942,87,37.599998,945.700012,264,3.5,13.52902
-2019-05-23 14:15:00,842.84,791.07,36.57,6.97,4.42,211.04,947.08,918,103,37.599998,945.599976,222,1.7,13.01729
-2019-05-23 14:30:00,821.0,769.7,36.61,7.12,4.41,208.37,946.92,848,120,37.0,945.299988,194,1.5,12.18924
-2019-05-23 14:45:00,784.91,734.39,36.63,7.28,4.35,208.3,946.78,834,140,37.700001,945.200012,284,4.0,11.89244
-2019-05-23 15:00:00,758.65,708.72,36.65,7.45,4.26,210.43,946.65,797,146,37.099998,945.0,261,4.2,11.66711
-2019-05-23 15:15:00,715.76,666.8,36.66,7.72,4.32,211.2,946.53,730,107,37.200001,944.900024,56,0.6,10.51391
-2019-05-23 15:30:00,684.97,636.72,36.65,8.19,4.76,205.78,946.44,338,125,37.299999,944.900024,270,3.0,5.716215
-2019-05-23 15:45:00,636.31,589.26,36.52,8.83,5.64,197.05,946.43,597,160,37.299999,944.599976,129,0.9,9.436924
-2019-05-23 16:00:00,602.98,556.77,36.31,9.38,6.56,191.87,946.49,452,160,37.700001,944.299988,253,4.1,7.061485
-2019-05-23 16:15:00,552.1,507.28,36.06,9.63,7.06,189.97,946.43,372,144,37.5,944.400024,28,0.0,5.140048999999999
-2019-05-23 16:30:00,517.4,473.6,35.9,9.55,7.11,187.68,946.35,290,147,37.599998,944.5,255,2.9,4.654119000000001
-2019-05-23 16:45:00,463.61,421.48,35.76,9.4,6.97,183.17,946.27,340,161,37.299999,944.299988,258,0.5,5.033188
-2019-05-23 17:00:00,426.69,385.79,35.66,9.33,6.75,177.54,946.19,220,147,37.099998,944.200012,227,5.8,3.616041000000001
-2019-05-23 17:15:00,370.08,331.22,35.55,9.38,6.24,173.95,946.09,163,112,36.0,944.099976,286,2.8,2.6656
-2019-05-23 17:30:00,331.83,294.5,35.53,9.46,5.34,177.17,945.97,153,110,35.700001,944.099976,278,4.1,2.467118
-2019-05-23 17:45:00,274.34,239.58,35.42,9.6,4.59,197.16,945.83,111,80,35.099998,944.400024,267,6.7,1.811398
-2019-05-23 18:00:00,236.29,203.5,35.15,9.89,4.61,225.21,945.74,89,71,34.700001,944.299988,281,4.0,1.493179
-2019-05-23 18:15:00,179.93,150.66,34.42,10.54,5.02,235.8,945.74,79,55,34.299999,944.400024,244,5.4,1.316404
-2019-05-23 18:30:00,143.43,116.86,33.52,11.47,5.17,236.82,945.81,63,52,33.700001,944.5,258,9.8,1.057303
-2019-05-23 18:45:00,88.81,67.28,32.36,12.77,5.08,236.18,945.95,42,33,33.299999,944.799988,233,3.9,0.671365
-2019-05-23 19:00:00,57.48,40.56,31.19,14.07,5.03,236.28,946.12,17,10,32.799999,945.0,222,3.5,0.272553
-2019-05-23 19:15:00,15.33,8.0,29.93,14.97,5.09,237.12,946.32,7,4,32.200001,945.200012,219,2.1,0.1109609999999999
-2019-05-23 19:30:00,0.0,0.0,28.93,15.25,5.15,239.21,946.5,0,0,31.700001,945.200012,239,1.6,0.0
-2019-05-23 19:45:00,0.0,0.0,28.13,15.55,5.18,242.16,946.66,0,0,31.1,945.400024,213,2.3,0.0
-2019-05-23 20:00:00,0.0,0.0,27.49,15.91,5.17,245.72,946.85,0,0,30.799999,945.599976,286,1.6,0.0
-2019-05-23 20:15:00,0.0,0.0,26.95,16.34,5.14,249.8,947.03,0,0,30.799999,945.799988,216,2.0,0.0
-2019-05-23 20:30:00,0.0,0.0,26.49,16.84,5.11,254.32,947.19,0,0,30.4,945.900024,224,2.0,0.0
-2019-05-23 20:45:00,0.0,0.0,26.09,17.37,5.08,257.82,947.34,0,0,29.799999,946.200012,289,0.8,0.0
-2019-05-23 21:00:00,0.0,0.0,25.76,17.96,5.07,259.95,947.48,0,0,29.1,946.400024,300,0.3,0.0
-2019-05-23 21:15:00,0.0,0.0,25.51,18.6,5.08,261.26,947.63,0,0,28.5,946.5,284,0.3,0.0
-2019-05-23 21:30:00,0.0,0.0,25.27,19.36,5.08,262.37,947.77,0,0,28.5,946.599976,16,0.2,0.0
-2019-05-23 21:45:00,0.0,0.0,25.05,20.21,5.1,264.01,947.92,0,0,28.1,946.599976,28,0.3,0.0
-2019-05-23 22:00:00,0.0,0.0,24.83,21.22,5.21,265.1,948.04,0,0,28.0,946.700012,351,0.0,0.0
-2019-05-23 22:15:00,0.0,0.0,24.68,22.33,5.47,265.38,948.25,0,0,28.0,946.700012,255,0.2,0.0
-2019-05-23 22:30:00,0.0,0.0,24.64,23.42,5.88,265.97,948.43,0,0,28.5,946.799988,216,0.2,0.0
-2019-05-23 22:45:00,0.0,0.0,24.73,24.02,6.36,264.64,948.57,0,0,28.200001,946.700012,329,0.8,0.0
-2019-05-23 23:00:00,0.0,0.0,24.74,24.2,6.74,263.69,948.7,0,0,27.9,946.700012,326,1.0,0.0
-2019-05-23 23:15:00,0.0,0.0,24.71,24.03,6.97,263.0,948.84,0,0,28.1,946.700012,250,2.0,0.0
-2019-05-23 23:30:00,0.0,0.0,24.61,23.8,7.09,262.7,948.98,0,0,27.4,946.900024,239,1.3,0.0
-2019-05-23 23:45:00,0.0,0.0,24.46,23.74,7.17,263.17,949.14,0,0,27.200001,946.799988,253,0.5,0.0
-2019-05-24 00:00:00,0.0,0.0,22.21,28.53,5.36,278.74,947.86,0,0,27.1,946.599976,247,0.0,0.0
-2019-05-24 00:15:00,0.0,0.0,22.13,28.49,5.41,278.24,947.91,0,0,26.5,946.5,19,0.0,0.0
-2019-05-24 00:30:00,0.0,0.0,21.98,28.52,5.52,278.31,947.96,0,0,26.200001,946.599976,267,1.1,0.0
-2019-05-24 00:45:00,0.0,0.0,21.91,28.36,5.65,278.29,948.0,0,0,26.200001,946.700012,298,0.3,0.0
-2019-05-24 01:00:00,0.0,0.0,21.82,28.18,5.7,276.61,947.76,0,0,25.9,946.5,303,0.3,0.0
-2019-05-24 01:15:00,0.0,0.0,21.7,28.08,5.71,274.62,947.61,0,0,25.6,946.5,264,0.0,0.0
-2019-05-24 01:30:00,0.0,0.0,21.6,27.96,5.7,274.52,947.66,0,0,25.200001,946.5,295,0.4,0.0
-2019-05-24 01:45:00,0.0,0.0,21.48,27.95,5.73,275.36,947.94,0,0,25.299999,946.400024,185,0.2,0.0
-2019-05-24 02:00:00,0.0,0.0,21.38,27.98,5.78,275.26,948.04,0,0,25.299999,946.299988,213,0.0,0.0
-2019-05-24 02:15:00,0.0,0.0,21.29,28.1,5.8,275.69,948.04,0,0,25.200001,946.200012,11,0.0,0.0
-2019-05-24 02:30:00,0.0,0.0,21.17,28.32,5.77,276.26,948.03,0,0,24.9,946.200012,30,0.4,0.0
-2019-05-24 02:45:00,0.0,0.0,21.05,28.61,5.75,275.82,947.92,0,0,24.9,946.099976,340,0.5,0.0
-2019-05-24 03:00:00,0.0,0.0,20.93,28.94,5.72,275.49,947.73,0,0,25.0,946.0,16,0.5,0.0
-2019-05-24 03:15:00,0.0,0.0,20.83,29.28,5.7,275.45,947.6,0,0,24.700001,945.900024,0,0.9,0.0
-2019-05-24 03:30:00,0.0,0.0,20.66,29.74,5.72,274.81,947.43,0,0,25.1,945.799988,345,0.9,0.0
-2019-05-24 03:45:00,0.0,0.0,20.68,29.85,5.8,274.23,947.45,0,0,25.1,945.700012,2,1.8,0.0
-2019-05-24 04:00:00,0.0,0.0,20.65,29.99,5.91,273.47,947.56,0,0,24.5,945.700012,345,2.0,0.0
-2019-05-24 04:15:00,0.0,0.0,20.68,30.01,6.03,272.43,947.55,0,0,24.200001,945.599976,2,1.4,0.0
-2019-05-24 04:30:00,0.0,0.0,20.72,29.99,6.16,270.87,947.4,0,0,24.4,945.700012,11,0.7,0.0
-2019-05-24 04:45:00,0.0,0.0,20.66,30.14,6.3,270.39,947.58,0,0,24.200001,945.799988,5,1.3,0.0
-2019-05-24 05:00:00,0.0,0.0,20.44,30.58,6.4,270.51,947.67,0,0,24.700001,945.799988,334,1.2,0.0
-2019-05-24 05:15:00,0.0,0.0,20.17,31.14,6.42,271.43,947.79,0,0,24.6,946.0,233,0.0,0.0
-2019-05-24 05:30:00,14.12,7.27,19.96,31.95,6.39,273.05,947.98,5,3,24.200001,945.700012,216,0.6,0.0734929999999999
-2019-05-24 05:45:00,49.81,33.88,19.96,32.82,6.33,274.56,947.98,22,17,24.0,946.0,149,0.7,0.368792
-2019-05-24 06:00:00,81.21,61.31,20.25,33.14,6.28,275.12,947.84,37,34,24.0,946.0,180,1.2,0.660454
-2019-05-24 06:15:00,130.42,105.85,20.89,32.69,6.3,274.34,947.75,37,29,23.700001,946.0,90,0.5,0.685913
-2019-05-24 06:30:00,166.49,139.24,21.64,31.71,6.36,272.76,947.55,56,43,23.4,946.099976,126,0.3,1.036116
-2019-05-24 06:45:00,219.95,189.95,22.49,30.34,6.39,270.27,947.58,71,60,23.6,946.200012,157,0.1,1.327835
-2019-05-24 07:00:00,254.05,222.32,23.23,29.19,6.4,266.9,947.78,87,69,24.1,946.299988,253,1.7,1.611819
-2019-05-24 07:15:00,303.96,270.24,23.98,27.85,6.44,262.5,947.98,117,90,24.700001,946.299988,233,1.8,2.124651
-2019-05-24 07:30:00,332.99,295.73,24.6,26.7,6.49,258.2,948.07,115,88,25.0,946.400024,222,2.5,2.124017
-2019-05-24 07:45:00,380.55,344.44,25.25,25.52,6.53,254.23,948.07,131,107,25.1,946.299988,216,1.9,2.470755
-2019-05-24 08:00:00,407.73,369.11,25.81,24.37,6.55,251.55,948.04,115,96,25.299999,946.400024,233,1.4,2.208017
-2019-05-24 08:15:00,445.64,401.8,26.37,23.5,6.46,248.55,948.02,92,62,24.9,946.5,250,1.6,1.765559
-2019-05-24 08:30:00,473.64,429.44,26.93,22.7,6.29,245.35,948.06,148,93,24.9,946.5,239,0.9,2.804388
-2019-05-24 08:45:00,520.96,477.52,27.42,21.54,6.08,241.72,948.0,188,124,25.1,946.400024,275,1.0,3.491398
-2019-05-24 09:00:00,551.69,501.43,27.9,21.06,5.76,237.75,947.89,466,230,25.6,946.400024,154,1.3,7.727365
-2019-05-24 09:15:00,591.47,548.47,28.42,20.21,5.37,232.06,947.75,341,255,26.799999,946.5,227,0.8,6.248734
-2019-05-24 09:30:00,603.17,557.18,28.82,19.62,5.04,227.31,947.56,146,104,26.9,946.0,216,2.4,2.809699
-2019-05-24 09:45:00,624.09,575.34,29.26,19.03,4.81,224.27,947.31,282,210,27.5,946.0,210,3.7,5.099175
-2019-05-24 10:00:00,628.72,580.69,29.68,18.37,4.72,221.37,947.0,322,232,27.700001,946.0,236,1.1,5.879827
-2019-05-24 10:15:00,626.35,543.45,30.07,17.73,4.72,219.58,946.86,473,250,28.1,946.099976,292,3.8,7.433452000000001
-2019-05-24 10:30:00,608.57,514.54,30.43,17.08,4.76,216.3,946.81,372,257,29.1,946.0,244,1.4,6.322285
-2019-05-24 10:45:00,622.88,581.48,30.84,16.56,4.74,213.04,946.8,826,349,29.299999,946.099976,196,2.4,12.15367
-2019-05-24 11:00:00,552.53,445.58,31.11,16.3,4.67,208.8,946.76,1045,278,29.299999,945.900024,255,4.7,16.25565
-2019-05-24 11:15:00,421.88,272.1,31.22,16.3,4.69,202.14,946.63,1219,310,30.299999,945.799988,298,1.7,16.52508
-2019-05-24 11:30:00,392.07,320.34,31.53,16.13,4.83,198.63,946.63,887,258,30.299999,945.900024,196,3.5,13.80319
-2019-05-24 11:45:00,206.33,133.68,31.08,16.73,5.05,195.72,946.68,294,233,29.799999,945.599976,154,1.3,5.337551
-2019-05-24 12:00:00,261.79,142.03,31.43,16.76,5.51,193.66,946.64,342,266,30.1,945.599976,202,3.4,6.440347
-2019-05-24 12:15:00,180.82,112.54,31.01,17.57,6.22,194.07,946.66,1025,275,30.6,945.5,244,1.9,15.52713
-2019-05-24 12:30:00,201.7,100.64,30.97,18.01,6.9,194.98,946.68,318,234,30.799999,945.099976,219,3.5,5.495908999999999
-2019-05-24 12:45:00,149.92,88.58,30.51,18.77,7.26,194.31,946.53,378,219,30.299999,944.900024,168,5.2,6.033394
-2019-05-24 13:00:00,124.31,72.79,30.15,19.49,7.45,191.68,946.37,812,273,29.700001,944.5,168,4.3,14.23589
-2019-05-24 13:15:00,156.61,123.75,30.29,19.53,7.63,188.71,946.18,340,217,30.700001,944.299988,165,1.7,5.763612
-2019-05-24 13:30:00,272.17,247.16,30.88,19.0,7.76,185.41,945.99,1086,200,31.299999,944.099976,253,1.8,15.61292
-2019-05-24 13:45:00,306.6,283.91,31.15,18.96,7.86,181.69,945.79,1003,327,30.6,944.200012,202,3.6,16.333579999999998
-2019-05-24 14:00:00,338.29,317.27,31.47,19.0,8.06,179.12,945.57,872,211,30.799999,944.099976,202,5.9,11.52492
-2019-05-24 14:15:00,332.57,311.45,31.61,19.29,8.37,177.69,945.37,713,155,31.5,943.700012,134,1.4,10.58435
-2019-05-24 14:30:00,330.07,308.75,31.69,19.65,8.75,176.86,945.18,888,133,32.299999,943.400024,199,2.2,12.95199
-2019-05-24 14:45:00,319.47,298.23,31.7,19.9,9.1,176.27,945.01,503,261,31.4,943.299988,118,4.7,7.650524000000001
-2019-05-24 15:00:00,309.39,288.37,31.69,19.99,9.36,176.12,944.9,699,193,31.799999,943.299988,208,4.6,9.340569
-2019-05-24 15:15:00,291.88,271.3,31.64,20.07,9.51,176.79,944.8,201,90,31.200001,943.299988,140,3.9,8.49445
-2019-05-24 15:30:00,283.07,262.57,31.65,19.95,9.57,177.73,944.71,671,87,32.400002,943.0,171,3.8,10.11193
-2019-05-24 15:45:00,275.49,254.6,31.6,19.76,9.6,177.87,944.64,635,93,31.799999,942.799988,182,4.7,9.481666
-2019-05-24 16:00:00,283.88,261.59,31.52,20.18,9.59,177.12,944.64,594,105,31.299999,942.799988,163,2.7,8.852041
-2019-05-24 16:15:00,290.91,266.7,31.36,20.76,9.52,176.28,944.53,526,92,31.6,942.5,163,6.0,7.797799
-2019-05-24 16:30:00,308.53,281.72,31.19,21.13,9.41,176.14,944.42,177,93,31.6,942.900024,146,3.3,6.29873
-2019-05-24 16:45:00,311.15,282.07,30.97,21.26,9.32,176.13,944.32,451,105,31.299999,942.5,149,4.0,5.853386
-2019-05-24 17:00:00,318.26,286.86,30.82,21.06,9.27,176.34,944.26,370,96,31.299999,942.5,191,3.2,4.284115
-2019-05-24 17:15:00,301.64,268.99,30.58,21.33,9.24,176.34,944.16,329,101,30.700001,942.5,16,1.7,4.775473000000001
-2019-05-24 17:30:00,290.04,256.37,30.31,22.14,9.17,176.44,944.06,247,76,30.700001,942.400024,174,3.2,3.427027
-2019-05-24 17:45:00,251.31,218.37,29.94,23.21,9.04,177.1,943.91,201,80,30.1,942.5,174,1.9,2.801674
-2019-05-24 18:00:00,222.29,190.28,29.56,24.31,8.87,178.48,943.81,135,56,29.9,942.599976,154,3.9,1.88593
-2019-05-24 18:15:00,170.92,141.88,29.1,25.59,8.66,179.96,943.72,92,49,29.6,942.799988,92,2.5,1.406235
-2019-05-24 18:30:00,136.35,109.9,28.65,26.89,8.43,181.66,943.75,60,40,29.1,942.900024,154,3.1,0.969608
-2019-05-24 18:45:00,85.9,64.5,28.13,28.3,8.22,183.25,943.83,39,29,28.799999,943.0,115,1.8,0.610111
-2019-05-24 19:00:00,54.78,37.8,27.61,29.8,7.99,184.87,943.92,24,18,28.4,943.099976,174,1.7,0.396965
-2019-05-24 19:15:00,15.36,7.6,27.06,31.14,7.72,186.57,943.97,6,3,27.9,943.200012,199,1.3,0.1029359999999999
-2019-05-24 19:30:00,0.0,0.0,26.62,32.13,7.41,188.59,944.06,0,0,27.1,943.5,171,0.7,0.0
-2019-05-24 19:45:00,0.0,0.0,26.26,33.09,7.04,191.71,944.24,0,0,26.299999,943.400024,182,1.2,0.0
-2019-05-24 20:00:00,0.0,0.0,25.92,34.1,6.61,195.03,944.39,0,0,26.200001,943.799988,171,1.6,0.0
-2019-05-24 20:15:00,0.0,0.0,25.58,35.13,6.12,199.26,944.57,0,0,26.200001,943.900024,149,2.1,0.0
-2019-05-24 20:30:00,0.0,0.0,25.2,36.21,5.53,204.28,944.74,0,0,25.6,944.0,160,0.9,0.0
-2019-05-24 20:45:00,0.0,0.0,24.77,37.35,4.84,210.93,944.86,0,0,25.6,944.200012,137,1.3,0.0
-2019-05-24 21:00:00,0.0,0.0,24.3,38.47,4.17,222.5,944.98,0,0,24.9,944.200012,250,0.3,0.0
-2019-05-24 21:15:00,0.0,0.0,23.89,39.35,3.66,238.81,945.13,0,0,24.4,944.299988,16,0.2,0.0
-2019-05-24 21:30:00,0.0,0.0,23.5,40.14,3.38,255.47,945.18,0,0,24.200001,944.400024,160,0.5,0.0
-2019-05-24 21:45:00,0.0,0.0,23.22,40.66,3.34,271.08,945.3,0,0,23.200001,944.400024,337,0.6,0.0
-2019-05-24 22:00:00,0.0,0.0,22.99,41.12,3.43,283.36,945.4,0,0,23.200001,944.400024,5,1.2,0.0
-2019-05-24 22:15:00,0.0,0.0,22.78,41.68,3.57,291.06,945.55,0,0,23.5,944.5,22,1.5,0.0
-2019-05-24 22:30:00,0.0,0.0,22.58,42.37,3.69,294.68,945.65,0,0,22.299999,944.400024,16,0.8,0.0
-2019-05-24 22:45:00,0.0,0.0,22.4,43.22,3.79,296.78,945.72,0,0,22.4,944.5,354,2.0,0.0
-2019-05-24 23:00:00,0.0,0.0,22.24,44.23,3.88,297.76,945.78,0,0,22.4,944.200012,289,1.2,0.0
-2019-05-24 23:15:00,0.0,0.0,22.09,45.45,3.92,297.58,945.83,0,0,21.700001,944.200012,343,1.1,0.0
-2019-05-24 23:30:00,0.0,0.0,21.96,46.88,3.94,295.31,945.86,0,0,21.6,944.299988,326,1.8,0.0
-2019-05-24 23:45:00,0.0,0.0,21.82,48.45,3.97,293.76,945.95,0,0,21.799999,944.099976,8,2.1,0.0
-2019-05-25 00:00:00,0.0,0.0,20.71,47.15,4.23,285.61,945.14,0,0,22.5,944.099976,59,0.2,0.0
-2019-05-25 00:15:00,0.0,0.0,20.71,47.84,4.4,283.5,945.13,0,0,22.5,944.0,343,0.5,0.0
-2019-05-25 00:30:00,0.0,0.0,20.77,48.59,4.66,282.49,945.27,0,0,22.0,944.0,0,1.4,0.0
-2019-05-25 00:45:00,0.0,0.0,20.87,49.5,4.95,281.02,945.09,0,0,21.0,943.900024,314,0.4,0.0
-2019-05-25 01:00:00,0.0,0.0,20.95,50.59,5.22,280.1,945.0,0,0,22.0,943.799988,326,0.8,0.0
-2019-05-25 01:15:00,0.0,0.0,21.02,51.72,5.43,280.52,944.73,0,0,21.9,943.700012,222,1.0,0.0
-2019-05-25 01:30:00,0.0,0.0,21.08,52.81,5.55,281.29,944.82,0,0,22.0,943.599976,216,1.1,0.0
-2019-05-25 01:45:00,0.0,0.0,21.1,53.85,5.6,282.45,945.04,0,0,22.0,943.599976,250,1.8,0.0
-2019-05-25 02:00:00,0.0,0.0,21.06,54.85,5.61,283.17,945.12,0,0,22.0,943.599976,329,0.4,0.0
-2019-05-25 02:15:00,0.0,0.0,20.99,55.78,5.64,283.79,945.0,0,0,21.9,943.5,351,0.0,0.0
-2019-05-25 02:30:00,0.0,0.0,20.92,56.57,5.7,284.32,944.86,0,0,21.9,943.299988,244,1.1,0.0
-2019-05-25 02:45:00,0.0,0.0,20.87,57.17,5.78,284.82,944.93,0,0,21.799999,943.299988,292,0.4,0.0
-2019-05-25 03:00:00,0.0,0.0,20.78,57.68,5.87,285.87,944.77,0,0,22.6,943.200012,241,2.1,0.0
-2019-05-25 03:15:00,0.0,0.0,20.69,58.05,5.96,286.88,944.63,0,0,22.6,943.200012,216,1.1,0.0
-2019-05-25 03:30:00,0.0,0.0,20.6,58.3,6.03,287.64,944.58,0,0,22.299999,943.0,244,0.8,0.0
-2019-05-25 03:45:00,0.0,0.0,20.51,58.41,6.02,288.36,944.64,0,0,22.1,943.0,281,0.6,0.0
-2019-05-25 04:00:00,0.0,0.0,20.38,58.59,5.94,288.89,944.56,0,0,22.200001,943.099976,267,0.4,0.0
-2019-05-25 04:15:00,0.0,0.0,20.24,58.78,5.84,289.5,944.31,0,0,22.200001,943.099976,295,0.0,0.0
-2019-05-25 04:30:00,0.0,0.0,20.12,58.83,5.73,289.56,944.09,0,0,21.4,943.099976,345,0.6,0.0
-2019-05-25 04:45:00,0.0,0.0,20.07,58.63,5.63,289.2,944.27,0,0,21.1,943.299988,14,0.0,0.0
-2019-05-25 05:00:00,0.0,0.0,20.0,58.5,5.49,289.45,944.27,0,0,21.5,943.400024,345,1.4,0.0
-2019-05-25 05:15:00,0.05,0.0,19.9,58.5,5.33,289.69,944.27,0,0,20.299999,943.5,306,0.5,0.0
-2019-05-25 05:30:00,16.46,8.32,19.92,58.26,5.11,289.84,944.31,7,5,20.6,943.599976,8,1.2,0.118986
-2019-05-25 05:45:00,56.28,39.03,20.21,57.5,4.68,289.6,944.26,17,14,20.6,943.700012,14,0.3,0.280346
-2019-05-25 06:00:00,87.5,65.89,20.63,56.59,3.92,288.35,944.18,28,23,20.9,943.900024,2,1.4,0.575588
-2019-05-25 06:15:00,138.06,111.42,21.39,54.69,2.88,289.66,944.11,37,30,20.700001,943.799988,281,0.3,0.86979
-2019-05-25 06:30:00,173.35,143.99,22.28,52.57,1.67,293.29,944.25,48,39,21.200001,944.0,59,0.6,1.247819
-2019-05-25 06:45:00,227.67,194.92,23.31,50.09,0.73,298.19,944.55,77,47,22.299999,944.099976,73,0.0,1.7254349999999998
-2019-05-25 07:00:00,264.48,229.79,24.09,47.17,0.58,91.95,944.9,116,53,23.200001,944.200012,146,0.0,2.335373
-2019-05-25 07:15:00,320.09,282.83,24.72,45.62,1.1,111.5,945.06,159,60,24.9,944.5,208,0.1,3.024289
-2019-05-25 07:30:00,357.13,318.38,25.28,43.28,1.73,112.11,945.22,206,66,25.200001,944.5,205,0.0,3.844083
-2019-05-25 07:45:00,412.17,371.4,25.73,41.89,2.21,115.47,945.25,257,72,25.200001,944.799988,165,0.8,4.681369
-2019-05-25 08:00:00,448.26,406.27,26.24,39.93,2.56,115.14,945.25,313,77,25.700001,944.5,163,1.7,5.576908
-2019-05-25 08:15:00,501.13,457.48,26.73,37.85,2.84,109.73,945.27,365,84,26.1,944.799988,146,1.3,6.45426
-2019-05-25 08:30:00,535.37,490.71,27.17,36.78,3.06,111.37,945.34,409,89,26.299999,944.900024,272,1.2,7.267761
-2019-05-25 08:45:00,584.94,538.9,27.67,34.96,3.21,111.21,945.34,460,96,26.5,945.0,326,0.3,8.027109
-2019-05-25 09:00:00,616.63,569.76,28.01,33.55,3.35,108.86,945.25,504,104,26.799999,945.0,213,1.7,8.775027
-2019-05-25 09:15:00,661.88,613.86,28.37,32.59,3.43,109.04,945.14,555,107,27.299999,945.099976,236,0.6,9.491481
-2019-05-25 09:30:00,690.37,641.65,28.7,31.49,3.4,106.4,945.12,600,109,27.6,944.900024,326,0.4,10.20661
-2019-05-25 09:45:00,730.35,680.68,29.03,30.59,3.28,102.96,944.97,644,114,28.200001,944.799988,199,2.5,10.7916
-2019-05-25 10:00:00,755.08,704.85,29.3,29.98,3.16,100.72,944.75,695,117,27.799999,944.799988,129,0.9,11.34098
-2019-05-25 10:15:00,788.99,737.99,29.62,29.4,3.13,98.82,944.69,748,118,28.299999,944.599976,146,1.4,11.94266
-2019-05-25 10:30:00,809.32,757.88,29.96,28.84,3.18,97.86,944.69,793,118,28.700001,944.5,196,2.2,12.38189
-2019-05-25 10:45:00,836.3,784.28,30.32,28.29,3.26,97.06,944.7,820,124,28.700001,944.5,143,2.4,12.73844
-2019-05-25 11:00:00,851.88,799.52,30.67,27.75,3.34,97.57,944.75,857,126,29.299999,944.5,213,1.3,13.08097
-2019-05-25 11:15:00,871.49,818.72,31.02,27.25,3.34,100.05,944.71,896,126,29.299999,944.299988,177,1.8,13.37407
-2019-05-25 11:30:00,882.03,829.05,31.27,26.91,3.32,106.2,944.62,893,129,29.5,944.099976,196,3.4,13.59137
-2019-05-25 11:45:00,893.74,840.5,31.57,26.66,3.5,111.2,944.55,894,147,30.1,944.200012,171,0.8,13.53405
-2019-05-25 12:00:00,898.67,845.34,31.86,26.51,3.86,115.74,944.5,907,157,30.200001,944.099976,163,3.8,13.57146
-2019-05-25 12:15:00,901.73,848.31,32.14,26.51,4.22,120.66,944.42,920,149,30.1,944.0,137,4.3,13.83477
-2019-05-25 12:30:00,900.91,847.5,32.36,26.65,4.5,125.98,944.34,914,151,30.6,943.799988,73,1.2,13.6162
-2019-05-25 12:45:00,895.57,842.27,32.55,26.97,4.74,129.4,944.2,922,153,30.799999,943.5,171,3.7,13.4889
-2019-05-25 13:00:00,889.48,836.3,32.73,27.11,4.94,131.82,944.04,909,145,31.299999,943.400024,90,2.4,13.72075
-2019-05-25 13:15:00,876.65,823.74,32.87,27.19,5.09,134.67,943.86,943,142,31.200001,943.200012,174,3.1,13.69333
-2019-05-25 13:30:00,865.59,812.92,32.97,27.36,5.22,136.75,943.71,863,163,31.6,943.200012,14,2.7,13.06515
-2019-05-25 13:45:00,845.41,793.15,33.06,27.37,5.34,138.51,943.64,826,170,31.9,943.099976,95,1.5,12.70744
-2019-05-25 14:00:00,829.69,777.78,33.16,27.07,5.38,140.34,943.52,845,178,31.799999,943.099976,157,3.8,12.34182
-2019-05-25 14:15:00,803.04,751.72,33.26,26.47,5.27,141.58,943.37,758,179,32.799999,942.900024,241,1.6,11.83927
-2019-05-25 14:30:00,783.23,732.36,33.38,25.68,5.09,141.9,943.22,716,177,32.400002,942.700012,120,4.8,10.84125
-2019-05-25 14:45:00,750.23,700.12,33.5,24.91,4.93,142.24,943.1,665,188,32.200001,942.5,143,5.7,9.825459
-2019-05-25 15:00:00,725.96,676.43,33.62,24.2,4.85,143.17,943.09,718,205,32.200001,942.5,115,4.8,10.49763
-2019-05-25 15:15:00,686.44,637.86,33.71,23.6,4.83,144.86,943.03,594,180,32.299999,942.400024,168,2.5,9.114202
-2019-05-25 15:30:00,658.2,610.3,33.8,23.06,4.84,146.66,942.99,563,172,31.700001,942.400024,123,3.6,8.315826
-2019-05-25 15:45:00,613.23,566.48,33.83,22.73,4.88,148.77,942.91,485,158,31.799999,942.200012,151,2.8,8.039521
-2019-05-25 16:00:00,581.71,535.8,33.87,22.53,4.94,150.5,942.88,510,147,31.4,942.0,180,2.4,7.585171000000001
-2019-05-25 16:15:00,532.46,487.93,33.84,22.53,5.01,150.75,942.72,428,140,31.5,942.099976,146,4.1,6.605402000000001
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+5/28/2019 1:00,0,0,14.98,33.63,4.22,295.1,958.67,0,0,18.200001,957.5,354,0.9,0
+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):
-
-        data = pd.read_csv(data_path)
+    def load_pred_data(self,data):
         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):
-        raw_data = self.load_pred_data(file_path)
-        input_tensor = self.preprocess_data(raw_data)
-        predictions = self.predict(input_tensor)
-        predictions = self.scaler.inverse_transform(predictions)
-        predictions = predictions.squeeze(0)
-        predictions = predictions.cpu().numpy()
-        return predictions[:,-1:]
+    def run_inference(self, data):
+        raw_data = self.load_pred_data(data)
+        if raw_data['status'] == False:
+            return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'}
+        else:
+            input_tensor = self.preprocess_data(raw_data['reason'])
+            predictions = self.predict(input_tensor)
+            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
-    ('xgb', xgb.XGBRegressor(objective='reg:squarederror', 
-                            random_state=42,
-                            n_jobs=-1))
-])
-# 定义超参数网格
+     ('scaler', StandardScaler()),  # 可替换为MinMaxScaler()
+     ('gbdt', GradientBoostingRegressor(random_state=42))
+ ])
 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  
+    'gbdt__n_estimators': [20, 30, 40, 50, 60, 70, 80,100],       # 树的数量
+    'gbdt__learning_rate': [0.01, 0.1, 0.2],    # 学习率
+    'gbdt__max_depth': [3, 5, 7],               # 树的最大深度
+    'gbdt__min_samples_split': [2, 5],          # 分裂所需最小样本数
+    'gbdt__min_samples_leaf': [1, 2,3,4,5],           # 叶节点最小样本数
+    'gbdt__max_features': ['sqrt', 'log2'],     # 特征选择方式
 }
-
-# 设置5折交叉验证
+ # 设置K折交叉验证和网格搜索
 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
-)
+grid_search = GridSearchCV(pipeline, param_grid, cv=kf, scoring='neg_mean_squared_error', 
+                           n_jobs=-1, verbose=2)
 
 # 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_XGB.joblib')
+dump(best_model, './model/SSA_GDBT.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','XGB')
-
-# 加载模型
-loaded_pipeline = load('./model/SSA_XGB.joblib')
-
-# 使用模型预测
+draw_picture(valid_y,pred_y,acc,'./pic/SSA','GDBT')
+loaded_pipeline = load('./model/SSA_GDBT.joblib')
 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 = {