03101630

common/database_dml_koi.py

@@ -491,12 +491,12 @@ def get_scaler_model_from_mongo(args: Dict[str, Any], only_feature_scaler: bool
     except PyMongoError as e:
         raise RuntimeError(f"🔌 数据库操作失败: {str(e)}") from e
     except RuntimeError as e:
-        raise  # 直接传递已封装的异常
+        raise  RuntimeError(f"🔌 mongo操作失败: {str(e)}") from e# 直接传递已封装的异常
     except Exception as e:
         raise RuntimeError(f"❌ 未知系统异常: {str(e)}") from e
     finally:
         # ------------------------- 资源清理 -------------------------
-        if client:
-            client.close()
-
+        # if client:
+        #     client.close()
+        pass
 

models_processing/model_koi/tf_bp_pre.py

@@ -23,7 +23,7 @@ np.random.seed(42)  # NumPy随机种子
 app = Flask('tf_bp_pre——service')
 
 with app.app_context():
-    with open('./models_processing/model_koi/bp.yaml', 'r', encoding='utf-8') as f:
+    with open('../model_koi/bp.yaml', 'r', encoding='utf-8') as f:
         args = yaml.safe_load(f)
     dh = DataHandler(logger, args)
     bp = BPHandler(logger, args)
@@ -62,8 +62,9 @@ def model_prediction_bp():
         pre_data['cdq'] = args.get('cdq', 1)
         pre_data['dq'] = args.get('dq', 1)
         pre_data['zq'] = args.get('zq', 1)
+        pre_data['model'] = 'bp'
         res_cols = ['date_time', 'power_forecast', 'farm_id', 'cdq', 'dq', 'zq']
-        res_cols += [args['target']] if args['algorithm_test'] else res_cols
+        res_cols += [args['target'], 'model'] if args['algorithm_test'] else res_cols
         pre_data.rename(columns={args['col_time']: 'date_time'}, inplace=True)
         pre_data = pre_data[res_cols]
 

models_processing/model_koi/tf_cnn_pre.py

@@ -63,8 +63,9 @@ def model_prediction_bp():
         pre_data['cdq'] = args.get('cdq', 1)
         pre_data['dq'] = args.get('dq', 1)
         pre_data['zq'] = args.get('zq', 1)
+        pre_data['model'] = 'cnn'
         res_cols = ['date_time', 'power_forecast', 'farm_id', 'cdq', 'dq', 'zq']
-        res_cols += [args['target']] if args['algorithm_test'] else res_cols
+        res_cols += [args['target'], 'model'] if args['algorithm_test'] else res_cols
         pre_data.rename(columns={args['col_time']: 'date_time'}, inplace=True)
         pre_data = pre_data[res_cols]
 

models_processing/model_koi/tf_lstm_pre.py

@@ -62,8 +62,9 @@ def model_prediction_bp():
         pre_data['cdq'] = args.get('cdq', 1)
         pre_data['dq'] = args.get('dq', 1)
         pre_data['zq'] = args.get('zq', 1)
+        pre_data['model'] = 'lstm'
         res_cols = ['date_time', 'power_forecast', 'farm_id', 'cdq', 'dq', 'zq']
-        res_cols += [args['target']] if args['algorithm_test'] else res_cols
+        res_cols += [args['target'], 'model'] if args['algorithm_test'] else res_cols
         pre_data.rename(columns={args['col_time']: 'date_time'}, inplace=True)
         pre_data = pre_data[res_cols]