JSON传输时间戳格式修改

dataAnalysisBusiness/algorithm/generatorSpeedPowerAnalyst.py

@@ -140,6 +140,10 @@ class GeneratorSpeedPowerAnalyst(AnalystWithGoodPoint):
 
         print(dataFrame[Field_UnixYearMonth].head())
         print(dataFrame[Field_UnixYearMonth].dtype)
+
+        n_dataFrame = pd.DataFrame({
+            'DateTime': pd.to_datetime(dataFrame[Field_UnixYearMonth], unit='s').dt.strftime('%Y-%m-%d %H:%M:%S')
+        })
         # 使用 apply() 对每个元素调用 datetime.fromtimestamp
         dataFrame[Field_UnixYearMonth]= dataFrame[Field_UnixYearMonth].apply(lambda x: datetime.fromtimestamp(x).strftime('%Y-%m'))
         # 构建最终的JSON对象
@@ -155,7 +159,7 @@ class GeneratorSpeedPowerAnalyst(AnalystWithGoodPoint):
                 "title":f' 月度发电机转速功率散点图:{name[0]}',
                 "xData": dataFrame[Field_GeneratorSpeed].tolist(),
                 "yData":dataFrame[Field_ActiverPower].tolist(),
-                "timeData": dataFrame[Field_UnixYearMonth].tolist(),
+                "timeData": n_dataFrame['DateTime'].tolist(),
                 "color": dataFrame[Field_UnixYearMonth].tolist(),
                 "colorbartitle": "时间",
                 "mode":'markers'

dataAnalysisBusiness/algorithm/generatorSpeedTorqueAnalyst.py

@@ -145,6 +145,8 @@ class GeneratorSpeedTorqueAnalyst(AnalystWithGoodPoint):
 
         # 使用 apply() 对每个元素调用 datetime.fromtimestamp
         dataFrame['monthIntTime']=dataFrame['monthIntTime'].apply(lambda x: datetime.fromtimestamp(x).strftime('%Y-%m'))
+        dataFrame[Field_UnixYearMonth] = pd.to_datetime(dataFrame[Field_UnixYearMonth], unit='s').dt.strftime(
+            '%Y-%m-%d %H:%M:%S')
 
         # 构建最终的JSON对象
         json_output = {

dataAnalysisBusiness/algorithm/pitchGeneratorSpeedAnalyst.py

@@ -112,6 +112,7 @@ class PitchGeneratorSpeedAnalyst(AnalystWithGoodBadPoint):
                 engineTypeName = engineTypeName.iloc[0]
 
             # 使用 apply() 对每个元素调用 datetime.fromtimestamp
+            groupNew[Field_UnixYearMonth] = pd.to_datetime(group[Field_UnixYearMonth], unit='s').dt.strftime('%Y-%m-%d %H:%M:%S')
             group[Field_UnixYearMonth] = group[Field_UnixYearMonth].apply(lambda x: datetime.fromtimestamp(x).strftime('%Y-%m'))
 
             # 构建最终的JSON对象
@@ -127,7 +128,7 @@ class PitchGeneratorSpeedAnalyst(AnalystWithGoodBadPoint):
                     "title": f' 机组: {name[0]}',
                     "xData": groupNew[Field_GeneratorSpeed].tolist(),
                     "yData": groupNew[Field_PitchAngel1].tolist(),
-                    "timeData": group[Field_UnixYearMonth].tolist(),
+                    "timeData": groupNew[Field_UnixYearMonth].tolist(),
                     "colorbar":group[Field_UnixYearMonth] .tolist(),
 
                 }]

dataAnalysisBusiness/algorithm/pitchPowerAnalyst.py

@@ -137,6 +137,9 @@ class PitchPowerAnalyst(AnalystWithGoodBadPoint):
             engineTypeName = turbineModelInfo.get(Field_MachineTypeCode, "")
             if isinstance(engineTypeName, pd.Series):
                 engineTypeName = engineTypeName.iloc[0]
+
+            group[Field_UnixYearMonth] = pd.to_datetime(group[Field_UnixYearMonth], unit='s').dt.strftime(
+                '%Y-%m-%d %H:%M:%S')
             # 构建最终的JSON对象
             json_output = {
                 "analysisTypeCode": "变桨和有功功率协调性分析",

dataAnalysisBusiness/algorithm/powerScatter2DAnalyst.py

@@ -65,6 +65,9 @@ class PowerScatter2DAnalyst(AnalystWithGoodBadPoint):
             engineTypeName = self.common.getTurbineModelByCode(millTypeCode, self.turbineModelInfo)[Field_MachineTypeCode]
             # 使用 apply() 对每个元素调用 datetime.fromtimestamp
             group['monthIntTime'] = group['monthIntTime'].apply(lambda x: datetime.fromtimestamp(x).strftime('%Y-%m'))
+
+            group[Field_UnixYearMonth] = pd.to_datetime(group[Field_UnixYearMonth], unit='s').dt.strftime(
+                '%Y-%m-%d %H:%M:%S')
             # 定义要替换的空值类型
             na_values = {pd.NA, float('nan')}
             # 构建最终的JSON对象