Merge remote-tracking branch 'origin/develop_v1.2.202407292348' into develop_v1.2.202407292348

dataAnalysisBusiness/algorithm/generatorSpeedTorqueAnalyst.py

@@ -49,11 +49,11 @@ class GeneratorSpeedTorqueAnalyst(AnalystWithGoodPoint):
             currDataFrameOfTurbines[Field_NameOfTurbine] = currDataFrameOfTurbines[Field_CodeOfTurbine].map(currTurbineInfos_dict).fillna("")
 
             result2D = self.drawScatter2DMonthly(
-                currDataFrameOfTurbines, outputAnalysisDir, conf)
+                currDataFrameOfTurbines, outputAnalysisDir, conf,currTurbineModeInfo)
             returnDatas.extend(result2D)
 
             result3D = self.drawScatterGraph(
-                currDataFrameOfTurbines, outputAnalysisDir, conf)
+                currDataFrameOfTurbines, outputAnalysisDir, conf,currTurbineModeInfo)
             returnDatas.extend(result3D)
 
             resultTotal = self.drawScatterGraphForTurbines(
@@ -64,7 +64,7 @@ class GeneratorSpeedTorqueAnalyst(AnalystWithGoodPoint):
 
         return returnDataFrame
 
-    def drawScatter2DMonthlyOfTurbine(self, dataFrame: pd.DataFrame, outputAnalysisDir: str, conf: Contract, turbineName: str):
+    def drawScatter2DMonthlyOfTurbine(self, dataFrame: pd.DataFrame, turbineModelInfo: pd.Series, outputAnalysisDir: str, conf: Contract, name: str):
         # 设置颜色条参数
         dataFrame = dataFrame.sort_values(by=Field_YearMonth)
 
@@ -103,7 +103,7 @@ class GeneratorSpeedTorqueAnalyst(AnalystWithGoodPoint):
         # 以下是以比例方式进行色彩的可视化处理
         fig.update_layout(
             title={
-                "text": f'月度发电机转速扭矩散点图: {turbineName}',
+                "text": f'月度发电机转速扭矩散点图: {name[0]}',
                 # "x": 0.5
             },
             xaxis=dict(
@@ -134,37 +134,78 @@ class GeneratorSpeedTorqueAnalyst(AnalystWithGoodPoint):
             #     )
             # )
         )
+        # 确保从 Series 中提取的是具体的值
+        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": "发电机转速和转矩分析",
+            "engineCode":  engineTypeCode,    
+            "engineTypeName": engineTypeName, 
+            "xaixs": "发电机转速（r/min）",
+            "yaixs": "扭矩（N·m）",
+            "data": [{
+                "engineName": name[0],
+                "engineCode": name[1],
+                "title":f' 发电机转速和转矩分析{name[0]}',
+                "xData": dataFrame[Field_GeneratorSpeed].tolist(),
+                "yData":dataFrame[Field_GeneratorTorque].tolist(),
+                "color": dataFrame['monthIntTime'].tolist(),
+                "colorbartitle": "时间",
+                "mode":'markers'
+            
+        }]
+        }
 
         # 保存图片
         outputFilePathPNG = os.path.join(
-            outputAnalysisDir, f"{turbineName}.png")
+            outputAnalysisDir, f"{name[0]}.png")
         fig.write_image(outputFilePathPNG, width=800, height=600, scale=3)
 
-        # 保存html
-        outputFileHtml = os.path.join(outputAnalysisDir, f"{turbineName}.html")
-        fig.write_html(outputFileHtml)
-
+        # # 保存html
+        # outputFileHtml = os.path.join(outputAnalysisDir, f"{name[0]}.html")
+        # fig.write_html(outputFileHtml)
+        
+        # 将JSON对象保存到文件
+        output_json_path = os.path.join(outputAnalysisDir, f"speed_torque{name[0]}.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 = []
+        # 如果需要返回DataFrame，可以包含文件路径
         result.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: outputFilePathPNG,
-            Field_Return_IsSaveDatabase: False
+            Field_CodeOfTurbine: name[1],
+            Field_Return_FilePath: output_json_path,
+            Field_Return_IsSaveDatabase: True
         })
         result.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: outputFileHtml,
-            Field_Return_IsSaveDatabase: True
+            Field_Return_FilePath: outputFilePathPNG,
+            Field_Return_IsSaveDatabase: False
         })
+        # result.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: outputFileHtml,
+        #     Field_Return_IsSaveDatabase: True
+        # })
 
         return result
 
-    def drawScatterGraphOfTurbine(self, dataFrame: pd.DataFrame,  outputAnalysisDir: str, conf: Contract, turbineName: str):
+    def drawScatterGraphOfTurbine(self, dataFrame: pd.DataFrame,turbineModelInfo: pd.Series,outputAnalysisDir: str, conf: Contract, name: str):
         # 创建3D散点图
         fig = px.scatter_3d(dataFrame,
                             x=Field_GeneratorSpeed,
@@ -181,7 +222,7 @@ class GeneratorSpeedTorqueAnalyst(AnalystWithGoodPoint):
         # 更新图形的布局
         fig.update_layout(
             title={
-                "text": f'月度发电机转速扭矩3D散点图: {turbineName}',
+                "text": f'月度发电机转速扭矩3D散点图: {name[0]}',
                 "x": 0.5
             },
             scene=dict(
@@ -219,37 +260,81 @@ class GeneratorSpeedTorqueAnalyst(AnalystWithGoodPoint):
                 itemwidth=80  # Set the width of legend items to 50 pixels
             )
         )
-
+                # 构建最终的JSON对象
+                # 确保从 Series 中提取的是具体的值
+        # 确保从 Series 中提取的是具体的值
+        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": "发电机转速和转矩分析",
+            "engineCode":  engineTypeCode,    
+            "engineTypeName": engineTypeName, 
+            "xaixs": "发电机转速（r/min）",
+            "yaixs": "时间",
+            "zaixs": "扭矩（N·m）",
+            "data": [{
+                "engineName": name[0],
+                "engineCode": name[1],
+                "title":f' 发电机转速和转矩分析{name[0]}',
+                "xData": dataFrame[Field_GeneratorSpeed].tolist(),
+                "yData":dataFrame[Field_YearMonth].tolist(),
+                "zData":dataFrame[Field_GeneratorTorque].tolist(),
+                "color": dataFrame[Field_YearMonth].tolist(),
+                "mode":'markers'
+            
+        }]
+        }
         # 保存图像
-        outputFileHtml = os.path.join(
-            outputAnalysisDir, "{}_3D.html".format(turbineName))
+        # outputFileHtml = os.path.join(
+        #     outputAnalysisDir, "{}_3D.html".format(name[0]))
 
-        fig.write_html(outputFileHtml)
+        # fig.write_html(outputFileHtml)
 
         result = []
-
+        # 将JSON对象保存到文件
+        output_json_path = os.path.join(outputAnalysisDir, f"3D_{name[0]}.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 = []
+        # 如果需要返回DataFrame，可以包含文件路径
         result.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: outputFileHtml,
+            Field_CodeOfTurbine: name[1],
+            Field_Return_FilePath: output_json_path,
             Field_Return_IsSaveDatabase: True
         })
+        # result.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: outputFileHtml,
+        #     Field_Return_IsSaveDatabase: True
+        # })
 
         return result
 
-    def drawScatter2DMonthly(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract):
+    def drawScatter2DMonthly(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract,turbineModelInfo: pd.Series):
         results = []
-        grouped = dataFrameMerge.groupby(Field_NameOfTurbine)
+        grouped = dataFrameMerge.groupby(
+            [Field_NameOfTurbine, Field_CodeOfTurbine])
         for name, group in grouped:
             result = self.drawScatter2DMonthlyOfTurbine(
-                group, outputAnalysisDir, conf, name)
+                group,turbineModelInfo, outputAnalysisDir, conf, name)
             results.extend(result)
 
         return results
 
-    def drawScatterGraph(self, dataFrame: pd.DataFrame,  outputAnalysisDir, conf: Contract):
+    def drawScatterGraph(self, dataFrame: pd.DataFrame,  outputAnalysisDir, conf: Contract,turbineModelInfo: pd.Series):
         """
         绘制风速-功率分布图并保存为文件。
 
@@ -262,13 +347,13 @@ class GeneratorSpeedTorqueAnalyst(AnalystWithGoodPoint):
         dataFrame = dataFrame[(dataFrame[Field_GeneratorTorque] > 0)].sort_values(
             by=Field_YearMonth)
 
-        grouped = dataFrame.groupby(Field_NameOfTurbine)
-
+        grouped = dataFrame.groupby(
+            [Field_NameOfTurbine, Field_CodeOfTurbine])
         # 遍历每个设备的数据
         for name, group in grouped:
             if len(group[Field_YearMonth].unique()) > 1:
                 result = self.drawScatterGraphOfTurbine(
-                    group, outputAnalysisDir, conf, name)
+                    group,turbineModelInfo, outputAnalysisDir, conf, name)
                 results.extend(result)
 
         return results
@@ -337,22 +422,61 @@ class GeneratorSpeedTorqueAnalyst(AnalystWithGoodPoint):
                 itemwidth=80  # Set the width of legend items to 50 pixels
             )
         )
-
-        # 保存图像
-        outputFileHtml = os.path.join(
-            outputAnalysisDir, "{}-{}.html".format(self.typeAnalyst(),turbineModelInfo[Field_MillTypeCode]))
-
-        fig.write_html(outputFileHtml)
+                # 确保从 Series 中提取的是具体的值
+        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": "发电机转速和转矩分析",
+            "engineCode":  engineTypeCode,    
+            "engineTypeName": engineTypeName, 
+            "xaixs": "发电机转速（r/min）",
+            "yaixs": "机组",
+            "zaixs": "实际扭矩（N·m）",
+            "data": [{
+                "title":f' 发电机转速扭矩3D散点图-{turbineModelInfo[Field_MachineTypeCode]}',
+                "xData": dataFrame[Field_GeneratorSpeed].tolist(),
+                "yData":dataFrame[Field_NameOfTurbine].tolist(),
+                "zData":dataFrame[Field_GeneratorTorque].tolist(),
+                "color": dataFrame[Field_NameOfTurbine].tolist(),
+                "mode":'markers'
+            
+        }]
+        }
+        # # 保存图像
+        # outputFileHtml = os.path.join(
+        #     outputAnalysisDir, "{}-{}.html".format(self.typeAnalyst(),turbineModelInfo[Field_MillTypeCode]))
+
+        # fig.write_html(outputFileHtml)
 
         result = []
-
+        # 将JSON对象保存到文件
+        output_json_path = os.path.join(outputAnalysisDir, f"total_3D_{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 = []
+        # 如果需要返回DataFrame，可以包含文件路径
         result.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: outputFileHtml,
+            Field_CodeOfTurbine:Const_Output_Total,
+            Field_Return_FilePath: output_json_path,
             Field_Return_IsSaveDatabase: True
         })
+        # result.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: outputFileHtml,
+        #     Field_Return_IsSaveDatabase: True
+        # })
 
         return result