fix: initialize windRose frequency_data before early return

dataAnalysisBusiness/algorithm/windDirectionFrequencyAnalyst.py

@@ -147,6 +147,8 @@ class WindDirectionFrequencyAnalyst(AnalystNotFilter):
         return result_df
     '''
     def windRoseAnalysis(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract):
+        # 提前初始化，保证即使数据缺失提前返回，后续 outputJsonData 也能安全访问
+        self.frequency_data = {}
         # 检查所需列是否存在
         required_columns = {Field_WindDirection, Field_WindSpeed}
         if not required_columns.issubset(dataFrameMerge.columns) or dataFrameMerge[Field_WindDirection].isna().all() or dataFrameMerge[Field_WindSpeed].isna().all():
@@ -154,9 +156,6 @@ class WindDirectionFrequencyAnalyst(AnalystNotFilter):
             self.logger.warning(msg)
             return pd.DataFrame()
 
-        # 定义类属性 self.frequency_data
-        self.frequency_data = {}
-
         # 风速区间
         bins = [0, 3, 6, 9, np.inf]
         speed_labels = ['[0,3)', '[3,6)', '[6,9)', '>=9']
@@ -275,6 +274,9 @@ class WindDirectionFrequencyAnalyst(AnalystNotFilter):
     #     return returnDatas
 
     def outputJsonData(self, conf: Contract, outputAnalysisDir: str, turbineModelInfo: pd.Series, dataFrameMerge: pd.DataFrame) -> pd.DataFrame:
+        # 兼容 windRoseAnalysis 因数据缺失提前返回的情况
+        if not hasattr(self, "frequency_data"):
+            self.frequency_data = {}
         turbineCodes = dataFrameMerge[Field_CodeOfTurbine].unique()
 
         result_rows = []