添加诺木洪风场的预训练模型 以及数据库特殊表名处理

Temp_Diag.py

@@ -30,16 +30,23 @@ class MSET_Temp:
         self.feature_weight: np.ndarray | None = None
         self.alpha: float = 0.1
         self.beta:  float = 0.1
-
+    
     def _get_data_by_filter(self) -> pd.DataFrame:
         """
         在线推理专用：根据 self.windTurbineNumberList & 时间拉数据；
         如果列表为空，则拉全场数据。
         """
-        table = f"{self.windCode}_minute"
+        # 特殊风场表名映射
+        special_wind_farms = {
+            "WOF093400005": f"`{self.windCode}-WOB000001_minute`"  # 加上反引号
+        }
+
+        # 根据风场编号获取表名，特殊风场用反引号，其他风场不加反引号
+        table = special_wind_farms.get(self.windCode, f"{self.windCode}_minute")
+
         engine = create_engine(
             #"mysql+pymysql://root:admin123456@106.120.102.238:10336/energy_data_prod"
-             "mysql+pymysql://root:admin123456@192.168.50.235:30306/energy_data_prod"
+            "mysql+pymysql://root:admin123456@192.168.50.235:30306/energy_data_prod"
         )
         if self.windTurbineNumberList:
             turbines = ",".join(f"'{t}'" for t in self.windTurbineNumberList)
@@ -54,7 +61,7 @@ class MSET_Temp:
             ORDER BY time_stamp ASC
         """)
         return pd.read_sql(sql, engine, params={"start": self.startTime, "end": self.endTime})
-
+    
     def calcSimilarity(self, x: np.ndarray, y: np.ndarray, m: str = 'euc') -> float:
         if len(x) != len(y):
             return 0.0