修改cp/cpWind/temperatureEnvironment/tsr/tsrCpPower/tsrWindSpeed输出json保存到库格式，添加temperatureLargeComponents输出json

dataAnalysisBusiness/algorithm/cpAnalyst.py

@@ -185,7 +185,8 @@ class CpAnalyst(AnalystWithGoodPoint):
             Field_Return_TypeAnalyst: self.typeAnalyst(),
             Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
             Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
-            Field_CodeOfTurbine: turbineModelInfo[Field_MillTypeCode],
+            Field_CodeOfTurbine: 'total',
+            Field_MillTypeCode: turbineModelInfo[Field_MillTypeCode],
             Field_Return_FilePath: output_json_path,
             Field_Return_IsSaveDatabase: True
         })

dataAnalysisBusiness/algorithm/cpWindSpeedAnalyst.py

@@ -152,7 +152,8 @@ class CpWindSpeedAnalyst(AnalystWithGoodPoint):
             Field_Return_TypeAnalyst: self.typeAnalyst(),
             Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
             Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
-            Field_CodeOfTurbine: turbineModelInfo[Field_MillTypeCode],
+            Field_CodeOfTurbine: 'total',
+            Field_MillTypeCode: turbineModelInfo[Field_MillTypeCode],
             Field_Return_FilePath: output_json_path,
             Field_Return_IsSaveDatabase: True
         })

dataAnalysisBusiness/algorithm/temperatureEnvironmentAnalyst.py

@@ -183,7 +183,8 @@ class TemperatureEnvironmentAnalyst(AnalystWithGoodBadLimitPoint):
             Field_Return_TypeAnalyst: self.typeAnalyst(),
             Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
             Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
-            Field_CodeOfTurbine: Const_Output_Total,
+            Field_CodeOfTurbine: 'total',
+            Field_MillTypeCode: 'total',
             Field_Return_FilePath: output_json_path,
             Field_Return_IsSaveDatabase: True
         })
@@ -270,7 +271,8 @@ class TemperatureEnvironmentAnalyst(AnalystWithGoodBadLimitPoint):
             Field_Return_TypeAnalyst: self.typeAnalyst(),
             Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
             Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
-            Field_CodeOfTurbine: Const_Output_Total,
+            Field_CodeOfTurbine: 'total',
+            Field_MillTypeCode: 'total',
             Field_Return_FilePath: output_json_path,
             Field_Return_IsSaveDatabase: True
         })

dataAnalysisBusiness/algorithm/temperatureLargeComponentsAnalyst.py

@@ -132,7 +132,7 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
         outputDir = os.path.join(outputAnalysisDir, "GeneratorTemperature")
         dir.create_directory(outputDir)
         # 按设备名分组数据
-        grouped = dataFrameMerge.groupby(Field_NameOfTurbine)
+        grouped = dataFrameMerge.groupby(Field_CodeOfTurbine)
 
         result_rows = []
         # Create output directories if they don't exist
@@ -148,16 +148,30 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
             # 获取 Plotly Express 中的颜色序列
             colors = px.colors.sequential.Turbo
 
+            # 创建一个列表来存储各个风电机组的数据
+            turbine_data_list = []
+
             # Add traces for each turbine
             for idx, (name, group) in enumerate(grouped):
+                currTurbineInfo_group = self.common.getTurbineInfo(
+                    conf.dataContract.dataFilter.powerFarmID, name, self.turbineInfo)
                 fig.add_trace(go.Scatter(
                     x=group[Field_PowerFloor],
                     y=group[column],
                     mode='lines',
-                    name=name,
+                    name=currTurbineInfo_group[Field_NameOfTurbine],
                     # 从 'Rainbow' 色组中循环选择颜色
                     line=dict(color=colors[idx % len(colors)])
                 ))
+                # 提取数据
+                turbine_data_total = {
+                    "engineName": currTurbineInfo_group[Field_NameOfTurbine],
+                    "engineCode": name,
+                    "xData": group[Field_PowerFloor].tolist(),
+                    "yData": group[column].tolist(),
+                    "color": colors[idx % len(colors)]
+                }
+                turbine_data_list.append(turbine_data_total)
 
             # Update layout and axes
             fig.update_layout(
@@ -190,8 +204,33 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
             # Save the plot as a PNG/HTML file
             filePathOfImage = os.path.join(outputPath, f"{columnZH}-{turbineModelInfo[Field_MillTypeCode]}.png")
             fig.write_image(filePathOfImage, scale=3)
-            filePathOfHtml = os.path.join(outputPath, f"{columnZH}-{turbineModelInfo[Field_MillTypeCode]}.html")
-            fig.write_html(filePathOfHtml)
+            # filePathOfHtml = os.path.join(outputPath, f"{columnZH}-{turbineModelInfo[Field_MillTypeCode]}.html")
+            #fig.write_html(filePathOfHtml)
+
+            engineTypeCode = turbineModelInfo.get(Field_MillTypeCode, "")
+            if isinstance(engineTypeCode, pd.Series):
+                engineTypeCode = engineTypeCode.iloc[0]
+
+            engineTypeName = turbineModelInfo.get(Field_MachineTypeCode, "")
+            if isinstance(engineTypeName, pd.Series):
+                engineTypeName = engineTypeName.iloc[0]
+            # 构建最终的JSON对象
+            json_output = {
+                "analysisTypeCode": "大部件温度传感器分析",
+                "typecode": turbineModelInfo[Field_MillTypeCode],
+                "engineCode": engineTypeCode,
+                "engineTypeName": engineTypeName,
+                "title": f'{columnZH}分布-{turbineModelInfo[Field_MachineTypeCode]}',
+                "xaixs": "功率(kW)",
+                "yaixs": y_name,
+                "data": turbine_data_list
+            }
+
+            # 将JSON对象保存到文件
+            output_json_path = os.path.join(outputPath,f"{columnZH}-{turbineModelInfo[Field_MillTypeCode]}.json")
+            with open(output_json_path, 'w', encoding='utf-8') as f:
+                import json
+                json.dump(json_output, f, ensure_ascii=False, indent=4)
 
             result_rows.append({
                 Field_Return_TypeAnalyst: self.typeAnalyst(),
@@ -202,12 +241,23 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
                 Field_Return_IsSaveDatabase: False
             })
 
+            # result_rows.append({
+            #    Field_Return_TypeAnalyst: self.typeAnalyst(),
+            #    Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
+            #    Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
+            #    Field_CodeOfTurbine: Const_Output_Total,
+            #    Field_Return_FilePath: filePathOfHtml,
+            #    Field_Return_IsSaveDatabase: True
+            # })
+
+            # 如果需要返回DataFrame，可以包含文件路径
             result_rows.append({
                 Field_Return_TypeAnalyst: self.typeAnalyst(),
                 Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
                 Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
-                Field_CodeOfTurbine: Const_Output_Total,
-                Field_Return_FilePath: filePathOfHtml,
+                Field_CodeOfTurbine: 'total',
+                Field_MillTypeCode: turbineModelInfo[Field_MillTypeCode],
+                Field_Return_FilePath: output_json_path,
                 Field_Return_IsSaveDatabase: True
             })
 
@@ -215,19 +265,34 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
             # for name, group in grouped:
             for idx, (name, group) in enumerate(grouped):
                 single_fig = go.Figure()
+                currTurbineInfo_each = self.common.getTurbineInfo(
+                    conf.dataContract.dataFilter.powerFarmID, name, self.turbineInfo)
+
+                # 创建一个列表来存储各个风电机组的数据
+                turbine_data_list_each = []
 
                 # Add all other turbines in grey first
                 # for other_name, other_group in grouped:
                 for idx, (other_name, other_group) in enumerate(grouped):
                     if other_name != name:
+                        tempTurbineInfo = self.common.getTurbineInfo(
+                            conf.dataContract.dataFilter.powerFarmID, other_name, self.turbineInfo)
                         single_fig.add_trace(go.Scatter(
                             x=other_group[Field_PowerFloor],
                             y=other_group[column],
                             mode='lines',
-                            name=other_name,
+                            name=tempTurbineInfo[Field_NameOfTurbine],
                             line=dict(color='lightgrey', width=1),
                             showlegend=False
                         ))
+                        # 提取数据
+                        turbine_data_other_each = {
+                            "engineName": tempTurbineInfo[Field_NameOfTurbine],
+                            "engineCode": other_name,
+                            "xData": other_group[Field_PowerFloor].tolist(),
+                            "yData": other_group[column].tolist(),
+                        }
+                        turbine_data_list_each.append(turbine_data_other_each)
 
                 # Add the turbine of interest in dark blue
                 single_fig.add_trace(go.Scatter(
@@ -239,10 +304,18 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
                     showlegend=False  # Disable legend for cleaner look
                 ))
 
+                turbine_data_curr = {
+                    "engineName": currTurbineInfo_each[Field_NameOfTurbine],
+                    "engineCode": currTurbineInfo_each[Field_CodeOfTurbine],
+                    "xData": group[Field_PowerFloor].tolist(),
+                    "yData": group[column].tolist(),
+                }
+                turbine_data_list_each.append(turbine_data_curr)
+
                 # Update layout and axes for the individual plot
                 single_fig.update_layout(
                     # title={'text': f'Turbine: {name}', 'x': 0.5},
-                    title={'text': f'{columnZH}分布: {name}', 'x': 0.5},
+                    title={'text': f'{columnZH}分布: {currTurbineInfo_each[Field_NameOfTurbine]}', 'x': 0.5},
                     xaxis_title='功率',
                     yaxis_title=y_name,
                     xaxis=dict(
@@ -255,12 +328,37 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
                     )
                 )
 
+                engineTypeCode = turbineModelInfo.get(Field_MillTypeCode, "")
+                if isinstance(engineTypeCode, pd.Series):
+                    engineTypeCode = engineTypeCode.iloc[0]
+
+                engineTypeName = turbineModelInfo.get(Field_MachineTypeCode, "")
+                if isinstance(engineTypeName, pd.Series):
+                    engineTypeName = engineTypeName.iloc[0]
+                # 构建最终的JSON对象
+                json_output = {
+                    "analysisTypeCode": "大部件温度传感器分析",
+                    "typecode": turbineModelInfo[Field_MillTypeCode],
+                    "engineCode": engineTypeCode,
+                    "engineTypeName": engineTypeName,
+                    "title": f'{columnZH}分布: {currTurbineInfo_each[Field_NameOfTurbine]}',
+                    "xaixs": "功率(kW)",
+                    "yaixs": y_name,
+                    "data": turbine_data_list_each
+                }
+
+                # 将JSON对象保存到文件
+                output_json_path_each = os.path.join(outputPath, f"{currTurbineInfo_each[Field_NameOfTurbine]}.json")
+                with open(output_json_path_each, 'w', encoding='utf-8') as f:
+                    import json
+                    json.dump(json_output, f, ensure_ascii=False, indent=4)
+
                 filePathOfImage = os.path.join(
-                    outputPath, f"{name}.png")
+                    outputPath, f"{currTurbineInfo_each[Field_NameOfTurbine]}.png")
                 single_fig.write_image(filePathOfImage, scale=3)
-                filePathOfHtml = os.path.join(
-                    outputPath, f"{name}.html")
-                single_fig.write_html(filePathOfHtml)
+                # filePathOfHtml = os.path.join(
+                #     outputPath, f"{name}.html")
+                # single_fig.write_html(filePathOfHtml)
 
                 result_rows.append({
                     Field_Return_TypeAnalyst: self.typeAnalyst(),
@@ -271,15 +369,25 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
                     Field_Return_IsSaveDatabase: False
                 })
 
+                # 如果需要返回DataFrame，可以包含文件路径
                 result_rows.append({
                     Field_Return_TypeAnalyst: self.typeAnalyst(),
                     Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
                     Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
                     Field_CodeOfTurbine: group[Field_CodeOfTurbine].iloc[0],
-                    Field_Return_FilePath: filePathOfHtml,
+                    Field_Return_FilePath: output_json_path_each,
                     Field_Return_IsSaveDatabase: True
                 })
 
+                # result_rows.append({
+                #    Field_Return_TypeAnalyst: self.typeAnalyst(),
+                #    Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
+                #    Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
+                #    Field_CodeOfTurbine: group[Field_CodeOfTurbine].iloc[0],
+                #    Field_Return_FilePath: filePathOfHtml,
+                #    Field_Return_IsSaveDatabase: True
+                # })
+
         for idx, (name, group) in enumerate(grouped):
             # 绘制每台机组发电机的，驱动轴承温度、非驱动轴承温度、发电机轴承温度BIAS、发电机轴承温度和机舱温度BIAS 均与有功功率的折线图
             if not Field_GeneratorDE in group.columns or not Field_GeneratorNDE in group.columns or group[Field_GeneratorDE].isna().all()  or group[Field_GeneratorNDE].isna().all():
@@ -295,7 +403,10 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
 
         return result_df
 
-    def drawGeneratorTemperature(self, dataFrame: pd.DataFrame, conf: Contract, yAxisDE, yAxisNDE, diffTemperature, xAxis, turbineName, outputDir):
+    def drawGeneratorTemperature(self, dataFrame: pd.DataFrame, conf: Contract, yAxisDE, yAxisNDE, diffTemperature, xAxis, turbineCode, outputDir):
+        tempTurbineInfo1 = self.common.getTurbineInfo(
+            conf.dataContract.dataFilter.powerFarmID, turbineCode, self.turbineInfo)
+        turbineName = tempTurbineInfo1[Field_NameOfTurbine]
         # 发电机驱动轴承温度 和 发电机非驱动轴承 温差
         fieldBIAS_DE_NDE = 'BIAS_DE-NDE'
         fieldBIAS_DE = 'BIAS_DE'
@@ -311,25 +422,73 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
         # Create a plot with dual y-axes
         fig = make_subplots(specs=[[{"secondary_y": True}]])
 
+        plot_data_list_each = []
+
         # Plot DE Bearing Temperature
         fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[yAxisDE], name='驱动端轴承温度', line=dict(
             color='blue')), secondary_y=False)
 
+        plot_data_curr = {
+            "Name": '驱动端轴承温度',
+            "xData": dataFrame[xAxis].tolist(),
+            "yData": dataFrame[yAxisDE].tolist(),
+            "color": 'blue',
+        }
+        plot_data_list_each.append(plot_data_curr)
+
         # Plot NDE Bearing Temperature
         fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[yAxisNDE], name='非驱动端轴承温度', line=dict(
             color='green')), secondary_y=False)
 
+        plot_data_curr = {
+            "Name": '非驱动端轴承温度',
+            "xData": dataFrame[xAxis].tolist(),
+            "yData": dataFrame[yAxisNDE].tolist(),
+            "color": 'green',
+        }
+        plot_data_list_each.append(plot_data_curr)
+
         # Plot Temperature Differences
         fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[fieldBIAS_DE], name='驱动端轴承温度与机舱温度偏差', line=dict(
             color='blue', dash='dot')), secondary_y=False)
+        plot_data_curr = {
+            "Name": '驱动端轴承温度与机舱温度偏差',
+            "xData": dataFrame[xAxis].tolist(),
+            "yData": dataFrame[fieldBIAS_DE].tolist(),
+            "color": 'blue',
+        }
+        plot_data_list_each.append(plot_data_curr)
+
         fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[fieldBIAS_NDE], name='非驱动端轴承温度与机舱温度偏差', line=dict(
             color='green', dash='dot')), secondary_y=False)
+        plot_data_curr = {
+            "Name": '非驱动端轴承温度与机舱温度偏差',
+            "xData": dataFrame[xAxis].tolist(),
+            "yData": dataFrame[fieldBIAS_NDE].tolist(),
+            "color": 'green',
+        }
+        plot_data_list_each.append(plot_data_curr)
+
         fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[fieldBIAS_DE_NDE],
                       name='驱动端轴承与非驱动端轴承温度偏差', line=dict(color='black', dash='dash')), secondary_y=False)
+        plot_data_curr = {
+            "Name": '驱动端轴承与非驱动端轴承温度偏差',
+            "xData": dataFrame[xAxis].tolist(),
+            "yData": dataFrame[fieldBIAS_DE_NDE].tolist(),
+            "color": 'black',
+        }
+        plot_data_list_each.append(plot_data_curr)
 
         # Plot Nacelle Temperature
         fig.add_trace(go.Scatter(x=dataFrame[xAxis], y=dataFrame[diffTemperature],
                       name='机舱温度', line=dict(color='orange')), secondary_y=False)
+        plot_data_curr = {
+            "Name": '机舱温度',
+            "xData": dataFrame[xAxis].tolist(),
+            "yData": dataFrame[diffTemperature].tolist(),
+            "color": 'orange',
+        }
+        plot_data_list_each.append(plot_data_curr)
 
         # Add horizontal reference lines
         fig.add_hline(y=5, line_dash="dot", line_color="#FFDB58")
@@ -350,11 +509,28 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
 
         fig.update_yaxes(range=[-20, 100], secondary_y=False)
 
+
+        # 构建最终的JSON对象
+        json_output = {
+            "analysisTypeCode": "发电机温度传感器分析",
+            "turbineName": tempTurbineInfo1[Field_NameOfTurbine],
+            "turbineCode": tempTurbineInfo1[Field_CodeOfTurbine],
+            "title": f'发电机温度偏差: {turbineName}',
+            "xaixs": "功率(kW)",
+            "yaixs": '轴承温度 & 偏差',
+            "data": plot_data_list_each
+        }
+
+        # 将JSON对象保存到文件
+        output_json_path_each = os.path.join(outputDir, f"{turbineName}.json")
+        with open(output_json_path_each, 'w', encoding='utf-8') as f:
+            import json
+            json.dump(json_output, f, ensure_ascii=False, indent=4)
         # Save the plot as a PNG/HTML file
         filePathOfImage = os.path.join(outputDir, f"{turbineName}.png")
         fig.write_image(filePathOfImage,  width=800, height=600, scale=3)
-        filePathOfHtml = os.path.join(outputDir, f"{turbineName}.html")
-        fig.write_html(filePathOfHtml)
+        # filePathOfHtml = os.path.join(outputDir, f"{turbineName}.html")
+        # fig.write_html(filePathOfHtml)
 
         result_rows1 = []
         result_rows1.append({
@@ -366,13 +542,23 @@ class TemperatureLargeComponentsAnalyst(AnalystWithGoodBadLimitPoint):
             Field_Return_IsSaveDatabase: False
         })
 
+        # 如果需要返回DataFrame，可以包含文件路径
         result_rows1.append({
             Field_Return_TypeAnalyst: self.typeAnalyst(),
             Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
             Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
             Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
-            Field_Return_FilePath: filePathOfHtml,
+            Field_Return_FilePath: output_json_path_each,
             Field_Return_IsSaveDatabase: True
         })
 
+        # result_rows1.append({
+        #    Field_Return_TypeAnalyst: self.typeAnalyst(),
+        #    Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
+        #    Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
+        #    Field_CodeOfTurbine: dataFrame[Field_CodeOfTurbine].iloc[0],
+        #    Field_Return_FilePath: filePathOfHtml,
+        #    Field_Return_IsSaveDatabase: True
+        # })
+
         return result_rows1

dataAnalysisBusiness/algorithm/tsrAnalyst.py

@@ -234,7 +234,8 @@ class TSRAnalyst(AnalystWithGoodPoint):
             Field_Return_TypeAnalyst: self.typeAnalyst(),
             Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
             Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
-            Field_CodeOfTurbine: turbineModelInfo[Field_MillTypeCode],
+            Field_CodeOfTurbine: 'total',
+            Field_MillTypeCode: turbineModelInfo[Field_MillTypeCode],
             Field_Return_FilePath: output_json_path,
             Field_Return_IsSaveDatabase: True
         })

dataAnalysisBusiness/algorithm/tsrCpPowerAnalyst.py

@@ -247,7 +247,8 @@ class TSRCpPowerAnalyst(AnalystWithGoodPoint):
             Field_Return_TypeAnalyst: self.typeAnalyst(),
             Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
             Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
-            Field_CodeOfTurbine: turbineModelInfo[Field_MillTypeCode],
+            Field_CodeOfTurbine: 'total',
+            Field_MillTypeCode: turbineModelInfo[Field_MillTypeCode],
             Field_Return_FilePath: output_json_path,
             Field_Return_IsSaveDatabase: True
         })

dataAnalysisBusiness/algorithm/tsrWindSpeedAnalyst.py

@@ -251,7 +251,8 @@ class TSRWindSpeedAnalyst(AnalystWithGoodPoint):
             Field_Return_TypeAnalyst: self.typeAnalyst(),
             Field_PowerFarmCode: conf.dataContract.dataFilter.powerFarmID,
             Field_Return_BatchCode: conf.dataContract.dataFilter.dataBatchNum,
-            Field_CodeOfTurbine: turbineModelInfo[Field_MillTypeCode],
+            Field_CodeOfTurbine: 'total',
+            Field_MillTypeCode: turbineModelInfo[Field_MillTypeCode],
             Field_Return_FilePath: output_json_path,
             Field_Return_IsSaveDatabase: True
         })