import os import numpy as np import pandas as pd import plotly.graph_objects as go from algorithmContract.confBusiness import * from algorithmContract.contract import Contract from behavior.analystWithGoodBadLimitPoint import AnalystWithGoodBadLimitPoint from geopy.distance import geodesic from plotly.subplots import make_subplots class TemperatureEnvironmentAnalyst(AnalystWithGoodBadLimitPoint): """ 风电机组环境温度传感器分析 """ def typeAnalyst(self): return "temperature_environment" def turbinesAnalysis(self, outputAnalysisDir, conf: Contract, turbineCodes): dictionary = self.processTurbineData(turbineCodes, conf, [ Field_DeviceCode, Field_Time,Field_EnvTemp, Field_WindSpeed, Field_ActiverPower]) dataFrameOfTurbines = self.userDataFrame( dictionary, conf.dataContract.configAnalysis, self) # 检查所需列是否存在 required_columns = {Field_CodeOfTurbine,Field_EnvTemp} if not required_columns.issubset(dataFrameOfTurbines.columns): raise ValueError(f"DataFrame缺少必要的列。需要的列有: {required_columns}") # 环境温度分析 turbineEnvTempData = dataFrameOfTurbines.groupby(Field_CodeOfTurbine).agg( {Field_EnvTemp: 'median'}) turbineEnvTempData = turbineEnvTempData.reset_index() mergeData = self.mergeData(self.turbineInfo, turbineEnvTempData) # 分机型 turbrineInfos = self.common.getTurbineInfos( conf.dataContract.dataFilter.powerFarmID, turbineCodes, self.turbineInfo) returnResult= self.draw(mergeData, outputAnalysisDir, conf,turbrineInfos) return returnResult # return self.draw(mergeData, outputAnalysisDir, conf) def mergeData(self, turbineInfos: pd.DataFrame, turbineEnvTempData): """ 将每台机组的环境温度均值数据与机组信息，按机组合并 参数: turbineInfos (pandas.DataFrame): 机组信息数据 turbineEnvTempData (pandas.DataFrame): 每台机组的环境温度均值数据 返回: pandas.DataFrame: 每台机组的环境温度均值数据与机组信息合并数据 """ """ 合并类型how的选项包括： 'inner': 内连接，只保留两个DataFrame中都有的键的行。 'outer': 外连接，保留两个DataFrame中任一或两者都有的键的行。 'left': 左连接，保留左边DataFrame的所有键，以及右边DataFrame中匹配的键的行。 'right': 右连接，保留右边DataFrame的所有键，以及左边DataFrame中匹配的键的行。 """ # turbineInfos[fieldTurbineName]=turbineInfos[fieldTurbineName].astype(str).apply(confData.add_W_if_starts_with_digit) # turbineEnvTempData[Field_NameOfTurbine] = turbineEnvTempData[Field_NameOfTurbine].astype( # str) tempDataFrame = pd.merge(turbineInfos, turbineEnvTempData, on=[ Field_CodeOfTurbine], how='inner') # 保留指定字段，例如 'Key' 和 'Value1' mergeDataFrame = tempDataFrame[[Field_CodeOfTurbine, Field_NameOfTurbine, Field_Latitude,Field_Longitude, Field_EnvTemp]] return mergeDataFrame # 定义查找给定半径内点的函数 def find_points_within_radius(self, data, center, field_temperature_env, radius): points_within_radius = [] for index, row in data.iterrows(): distance = geodesic( (center[2], center[1]), (row[Field_Latitude], row[Field_Longitude])).meters if distance <= radius: points_within_radius.append( (row[Field_NameOfTurbine], row[field_temperature_env])) return points_within_radius fieldTemperatureDiff = "temperature_diff" # def draw(self, dataFrame: pd.DataFrame, outputAnalysisDir, conf: Contract, charset=charset_unify): def draw(self, dataFrame: pd.DataFrame, outputAnalysisDir, conf: Contract, turbineModelInfo: pd.Series): # 处理数据 dataFrame['new'] = dataFrame.loc[:, [Field_NameOfTurbine, Field_Longitude, Field_Latitude, Field_EnvTemp]].apply(tuple, axis=1) coordinates = dataFrame['new'].tolist() # df = pd.DataFrame(coordinates, columns=[Field_NameOfTurbine, Field_Longitude, Field_Latitude, confData.field_env_temp]) # 查找半径内的点 points_within_radius = {coord: self.find_points_within_radius( dataFrame, coord, Field_EnvTemp, self.turbineModelInfo[Field_RotorDiameter].iloc[0]*10) for coord in coordinates} res = [] for center, nearby_points in points_within_radius.items(): current_temp = dataFrame[dataFrame[Field_NameOfTurbine] == center[0]][Field_EnvTemp].iloc[0] target_tuple = (center[0], current_temp) if target_tuple in nearby_points: nearby_points.remove(target_tuple) median_temp = np.median( [i[1] for i in nearby_points]) if nearby_points else current_temp res.append((center[0], nearby_points, median_temp, current_temp)) res = pd.DataFrame( res, columns=[Field_NameOfTurbine, '周边机组', '周边机组温度', '当前机组温度']) res[self.fieldTemperatureDiff] = res['当前机组温度'] - res['周边机组温度'] # 使用plotly进行数据可视化 fig1 = make_subplots(rows=1, cols=1) # 温度差异条形图 fig1.add_trace( go.Bar(x=res[Field_NameOfTurbine], y=res[self.fieldTemperatureDiff], marker_color='dodgerblue'), row=1, col=1 ) fig1.update_layout( title={'text': f'温度偏差', 'x': 0.5}, xaxis_title='机组名称', yaxis_title='温度偏差', shapes=[ {'type': 'line', 'x0': 0, 'x1': 1, 'xref': 'paper', 'y0': 5, 'y1': 5, 'line': {'color': 'red', 'dash': 'dot'}}, {'type': 'line', 'x0': 0, 'x1': 1, 'xref': 'paper', 'y0': - 5, 'y1': -5, 'line': {'color': 'red', 'dash': 'dot'}} ], xaxis=dict(tickangle=-45) # 设置x轴刻度旋转角度为45度 ) # 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": "风电机组环境温度传感器分析", "engineCode": engineTypeCode, "engineTypeName": engineTypeName, "xaixs": "机组名称", "yaixs": "温度偏差(℃)", "data": [{ "engineName": "", # Field_NameOfTurbine "engineCode": "", # Field_CodeOfTurbine "title": f'温度偏差', "xData": res[Field_NameOfTurbine].tolist(), "yData": res[self.fieldTemperatureDiff].tolist(), }] } result_rows = [] # 保存图像 pngFileName = '{}环境温差Bias.png'.format( self.powerFarmInfo[Field_PowerFarmName].iloc[0]) pngFilePath = os.path.join(outputAnalysisDir, pngFileName) fig1.write_image(pngFilePath, scale=3) # 保存HTML # htmlFileName = '{}环境温差Bias.html'.format( # self.powerFarmInfo[Field_PowerFarmName].iloc[0]) # htmlFilePath = os.path.join(outputAnalysisDir, htmlFileName) # fig1.write_html(htmlFilePath) # 将JSON对象保存到文件 output_json_path = os.path.join(outputAnalysisDir, f"total_Bias.json") with open(output_json_path, 'w', encoding='utf-8') as f: import json json.dump(json_output, f, ensure_ascii=False, indent=4) # 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: 'total', Field_MillTypeCode: 'total_Bias', 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: Const_Output_Total, 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: Const_Output_Total, # Field_Return_FilePath: htmlFilePath, # Field_Return_IsSaveDatabase: True # }) # 环境温度中位数条形图 fig2 = make_subplots(rows=1, cols=1) fig2.add_trace( go.Bar(x=res[Field_NameOfTurbine], y=res['当前机组温度'], marker_color='dodgerblue'), row=1, col=1 ) fig2.update_layout( title={'text': f'平均温度', 'x': 0.5}, xaxis_title='机组名称', yaxis_title=' 温度', xaxis=dict(tickangle=-45) # 为x轴也设置旋转角度 ) # 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": "风电机组环境温度传感器分析", "engineCode": engineTypeCode, "engineTypeName": engineTypeName, "xaixs": "机组名称", "yaixs": "温度(℃)", "data": [{ "engineName": "", # Field_NameOfTurbine "engineCode": "", # Field_CodeOfTurbine "title": f'平均温度', "xData": res[Field_NameOfTurbine].tolist(), "yData": res['当前机组温度'].tolist(), }] } # 保存图像 pngFileName = '{}环境温度中位数.png'.format( self.powerFarmInfo[Field_PowerFarmName].iloc[0]) pngFilePath = os.path.join(outputAnalysisDir, pngFileName) fig2.write_image(pngFilePath, scale=3) # 保存HTML # htmlFileName = '{}环境温度中位数.html'.format( # self.powerFarmInfo[Field_PowerFarmName].iloc[0]) # htmlFilePath = os.path.join(outputAnalysisDir, htmlFileName) # fig2.write_html(htmlFilePath) # 将JSON对象保存到文件 output_json_path = os.path.join(outputAnalysisDir, f"total_Mid.json") with open(output_json_path, 'w', encoding='utf-8') as f: import json json.dump(json_output, f, ensure_ascii=False, indent=4) # 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: 'total', Field_MillTypeCode: 'total_Mid', 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: Const_Output_Total, 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: Const_Output_Total, # Field_Return_FilePath: htmlFilePath, # Field_Return_IsSaveDatabase: True # }) result_df = pd.DataFrame(result_rows) return result_df """ fig, ax = plt.subplots(figsize=(16,8),dpi=96) # 设置x轴刻度值旋转角度为45度 plt.tick_params(axis='x', rotation=45) sns.barplot(x=Field_NameOfTurbine,y=self.fieldTemperatureDiff,data=res,ax=ax,color='dodgerblue') plt.axhline(y=5,ls=":",c="red")#添加水平直线 plt.axhline(y=-5,ls=":",c="red")#添加水平直线 ax.set_ylabel('temperature_difference') ax.set_title('temperature Bias') plt.savefig(outputAnalysisDir +'//'+ "{}环境温差Bias.png".format(confData.farm_name),bbox_inches='tight',dpi=120) fig2, ax2 = plt.subplots(figsize=(16,8),dpi=96) # 设置x轴刻度值旋转角度为45度 plt.tick_params(axis='x', rotation=45) sns.barplot(x=Field_NameOfTurbine ,y='当前机组温度',data=res,ax=ax2,color='dodgerblue') ax2.set_ylabel('temperature') ax2.set_title('temperature median') plt.savefig(outputAnalysisDir +'//'+ "{}环境温度均值.png".format(confData.farm_name),bbox_inches='tight',dpi=120) """