dataAnalysisBusiness/algorithm/tsrCpPowerScatterAnalyst.py

@@ -24,6 +24,8 @@ class TSRCpPowerScatterAnalyst(AnalystWithGoodPoint):
 
     def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes):
         dictionary = self.processTurbineData(turbineCodes,conf,[Field_DeviceCode,Field_Time,Field_WindSpeed,Field_ActiverPower,Field_RotorSpeed,Field_GeneratorSpeed])
+        turbineInfos = self.common.getTurbineInfos(conf.dataContract.dataFilter.powerFarmID, turbineCodes,
+                                                   self.turbineInfo)
         dataFrameMerge = self.userDataFrame(dictionary,conf.dataContract.configAnalysis,self)
         # return self.plot_tsr_distribution(self.tsr(dataFrameMerge), outputAnalysisDir, conf)
         dataFrameMerge[Field_PowerFarmName] = self.currPowerFarmInfo.loc[Field_PowerFarmName]
@@ -42,9 +44,9 @@ class TSRCpPowerScatterAnalyst(AnalystWithGoodPoint):
         # Group by 'power_floor' and calculate median, max, and min of TSR
         dataFrameMerge[Field_TSRModified] = dataFrameMerge[Field_TSR] / (dataFrameMerge[Field_Cp] ** (1/3))
 
-        return self.plot_tsrcp_distribution(dataFrameMerge, outputAnalysisDir, conf)
+        return self.plot_tsrcp_distribution(dataFrameMerge, turbineInfos, outputAnalysisDir, conf)
 
-    def plot_tsrcp_distribution(self, dataFrameMerge: pd.DataFrame, outputAnalysisDir, conf: Contract, encoding=charset_unify):
+    def plot_tsrcp_distribution(self, dataFrameMerge: pd.DataFrame, turbineModelInfo: pd.Series, outputAnalysisDir, conf: Contract, encoding=charset_unify):
         """
         Generates tsr distribution plots for turbines in a wind farm.
 
@@ -80,33 +82,75 @@ class TSRCpPowerScatterAnalyst(AnalystWithGoodPoint):
             fig.update_traces(marker=dict(size=3))
             fig.update_xaxes(tickangle=-45)
 
+            # 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": "机组叶尖速比-Cp-功率散点分析",
+                "engineCode": engineTypeCode,
+                "engineTypeName": engineTypeName,
+                "xaixs": "功率(kW)",
+                "yaixs": "叶尖速比/风能利用系数分析^(1/3)",
+                "data": [{
+                    "engineName": name[0],  # Field_NameOfTurbine
+                    "engineCode": name[1],  # Field_CodeOfTurbine
+                    "title": f' 机组: {format(name[0])}',
+                    "xData": group[x_name].tolist(),
+                    "yData": group[y_name].tolist(),
+
+                }]
+            }
+
             # 保存图像
             pngFileName = f"{name[0]}.png"
             pngFilePath = os.path.join(outputAnalysisDir, pngFileName)
             fig.write_image(pngFilePath, scale=3)
 
+            # 将JSON对象保存到文件
+            output_json_path = os.path.join(outputAnalysisDir, f"tsr_cp_power_scatter{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)
+
             # 保存HTML
-            htmlFileName = f"{name[0]}.html"
-            htmlFilePath = os.path.join(outputAnalysisDir, htmlFileName)
-            fig.write_html(htmlFilePath)
+            # htmlFileName = f"{name[0]}.html"
+            # htmlFilePath = os.path.join(outputAnalysisDir, htmlFileName)
+            # fig.write_html(htmlFilePath)
 
+            # 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: name[1],
-                Field_Return_FilePath: pngFilePath,
-                Field_Return_IsSaveDatabase: False
+                Field_Return_FilePath: output_json_path,
+                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: name[1],
-                Field_Return_FilePath: htmlFilePath,
-                Field_Return_IsSaveDatabase: True
+                Field_Return_FilePath: pngFilePath,
+                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: name[1],
+            #     Field_Return_FilePath: htmlFilePath,
+            #     Field_Return_IsSaveDatabase: True
+            # })
+
         result_df = pd.DataFrame(result_rows)
         return result_df