From cc0295822a2e72c5a7af1bfff708a975d0e09ded Mon Sep 17 00:00:00 2001 From: workpc Date: Tue, 5 Aug 2025 15:49:29 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E5=9B=BE=E7=89=87=E6=8A=A5?= =?UTF-8?q?=E5=91=8A=E8=B0=83=E8=AF=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- config_juxiting_yuedu.py | 17 ++ juxiting_push_png_report.py | 567 ++++++++++++++++++++++++++++++++++++ lib/dataread.py | 26 ++ main_juxiting_yuedu.py | 2 +- 4 files changed, 611 insertions(+), 1 deletion(-) create mode 100644 juxiting_push_png_report.py diff --git a/config_juxiting_yuedu.py b/config_juxiting_yuedu.py index 1be7581..4f1d92f 100644 --- a/config_juxiting_yuedu.py +++ b/config_juxiting_yuedu.py @@ -329,6 +329,8 @@ upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOil query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos" # 上传数据项值 push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList" +# 上传图片报告 +push_png_report_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/priceForecastImg" login_data = { "data": { @@ -401,6 +403,21 @@ push_data_value_list_data = { } ] } + + +push_png_report_data = { + "funcModule": '研究报告信息', + "funcOperation": '上传聚烯烃PP价格预测报告', + "data": { + "groupNo": "000161", + "updateTime": "2024-09-06 15:01:29", + "fileBase64": '', # 文件内容base64 + "title": '2025年8月5日日度周度预测结果', + "billNo": '', + } +} + + # 八大维度数据项编码 bdwd_items = { 'ciri': 'jxtppbdwdcr', diff --git a/juxiting_push_png_report.py b/juxiting_push_png_report.py new file mode 100644 index 0000000..601a30d --- /dev/null +++ b/juxiting_push_png_report.py @@ -0,0 +1,567 @@ +# 读取配置 + +from lib.dataread import * +from config_juxiting_yuedu import * +from lib.tools import SendMail, convert_df_to_pydantic_pp, exception_logger, find_best_models, get_modelsname +from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_bdwd_png, pp_export_pdf +import datetime +import torch +torch.set_float32_matmul_precision("high") + +global_config.update({ + # 核心参数 + 'logger': logger, + 'dataset': dataset, + 'y': y, + # 'offsite_col': offsite_col, + # 'avg_cols': avg_cols, + # 'offsite': offsite, + 'edbcodenamedict': edbcodenamedict, + 'is_debug': is_debug, + 'is_train': is_train, + 'is_fivemodels': is_fivemodels, + 'is_update_report': is_update_report, + 'settings': settings, + 'bdwdname': bdwdname, + 'columnsrename': columnsrename, + 'price_columns': price_columns, + + + # 模型参数 + 'data_set': data_set, + 'input_size': input_size, + 'horizon': horizon, + 'train_steps': train_steps, + 'val_check_steps': val_check_steps, + 'val_size': val_size, + 'test_size': test_size, + 'modelsindex': modelsindex, + 'rote': rote, + 'bdwd_items': bdwd_items, + + # 特征工程开关 + 'is_del_corr': is_del_corr, + 'is_del_tow_month': is_del_tow_month, + 'is_eta': is_eta, + 'is_update_eta': is_update_eta, + 'is_fivemodels': is_fivemodels, + 'is_update_predict_value': is_update_predict_value, + 'early_stop_patience_steps': early_stop_patience_steps, + + # 时间参数 + 'start_year': start_year, + 'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"), + 'freq': freq, # 保持列表结构 + + # 接口配置 + 'login_pushreport_url': login_pushreport_url, + 'login_data': login_data, + 'upload_url': upload_url, + 'upload_data': upload_data, + 'upload_warning_url': upload_warning_url, + 'warning_data': warning_data, + + # 查询接口 + 'query_data_list_item_nos_url': query_data_list_item_nos_url, + 'query_data_list_item_nos_data': query_data_list_item_nos_data, + + # 上传数据项 + 'push_data_value_list_url': push_data_value_list_url, + 'push_data_value_list_data': push_data_value_list_data, + 'push_png_report_url': push_png_report_url, + 'push_png_report_data': push_png_report_data, + + # eta 配置 + 'APPID': APPID, + 'SECRET': SECRET, + 'etadata': data, + 'edbcodelist': edbcodelist, + 'ClassifyId': ClassifyId, + 'edbcodedataurl': edbcodedataurl, + 'classifyidlisturl': classifyidlisturl, + 'edbdatapushurl': edbdatapushurl, + 'edbdeleteurl': edbdeleteurl, + 'edbbusinessurl': edbbusinessurl, + 'ClassifyId': ClassifyId, + 'classifylisturl': classifylisturl, + + # 数据库配置 + 'sqlitedb': sqlitedb, + 'is_bdwd': is_bdwd, + 'db_mysql': db_mysql, + 'DEFAULT_CONFIG': DEFAULT_CONFIG, +}) + + +def push_png_report(): + logger.info('发送图片到钉钉工作组') + current_end_time = global_config['end_time'] + previous_trading_day = (pd.Timestamp(current_end_time) - + pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d') + + png_report_files = ['pp_zhouducorrelation.png', 'pp_yueducorrelation.png'] + with open(os.path.join(global_config['dataset'], 'pp_zhouducorrelation.png'), 'rb') as f: + base64_data = base64.b64encode(f.read()).decode('utf-8') + config.upload_data["data"]["fileBase64"] = base64_data + data = global_config['push_png_report_data'] + data['data']['fileBase64'] = base64_data + + pngreportdata = push_png_report(data) + + print(pngreportdata) + + +def push_market_value(): + logger.info('发送预测结果到市场信息平台') + current_end_time = global_config['end_time'] + previous_trading_day = (pd.Timestamp(current_end_time) - + pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d') + + # 读取预测数据和模型评估数据 + best_bdwd_price = find_best_models( + date=previous_trading_day, global_config=global_config) + + # 获取本月最佳模型的预测价格 + four_month_predict_price = pd.read_csv( + os.path.join(global_config['dataset'], 'predict.csv')) + four_month_predict_price['ds'] = pd.to_datetime( + four_month_predict_price['ds']) + # 设置索引 次月 次二月 次三月 次四月 + index_labels = ["次月", "次二月", "次三月", "次四月"] + four_month_predict_price.index = index_labels + global_config['logger'].info(f"best_bdwd_price: {best_bdwd_price}") + + # 准备要推送的数据 + ciyue_mean = four_month_predict_price[best_bdwd_price['next_month_price'] + ['model_name']].iloc[0] + cieryue_mean = four_month_predict_price[best_bdwd_price['next_february_price'] + ['model_name']].iloc[1] + cisanyue_mean = four_month_predict_price[best_bdwd_price['next_march_price'] + ['model_name']].iloc[2] + cisieryue_mean = four_month_predict_price[best_bdwd_price['next_april_price'] + ['model_name']].iloc[3] + # # 保留两位小数 + ciyue_mean = round(ciyue_mean, 2) + cieryue_mean = round(cieryue_mean, 2) + cisanyue_mean = round(cisanyue_mean, 2) + cisieryue_mean = round(cisieryue_mean, 2) + + predictdata = [ + { + "dataItemNo": global_config['bdwd_items']['ciyue'], + "dataDate": global_config['end_time'].replace('-', ''), + "dataStatus": "add", + "dataValue": ciyue_mean + }, + { + "dataItemNo": global_config['bdwd_items']['cieryue'], + "dataDate": global_config['end_time'].replace('-', ''), + "dataStatus": "add", + "dataValue": cieryue_mean + }, + { + "dataItemNo": global_config['bdwd_items']['cisanyue'], + "dataDate": global_config['end_time'].replace('-', ''), + "dataStatus": "add", + "dataValue": cisanyue_mean + }, + { + "dataItemNo": global_config['bdwd_items']['cisiyue'], + "dataDate": global_config['end_time'].replace('-', ''), + "dataStatus": "add", + "dataValue": cisieryue_mean + } + ] + + print(predictdata) + + # 推送数据到市场信息平台 + try: + push_market_data(predictdata) + except Exception as e: + logger.error(f"推送数据失败: {e}") + + +def sql_inset_predict(global_config): + df = pd.read_csv(os.path.join(config.dataset, 'predict.csv')) + df['created_dt'] = pd.to_datetime(df['created_dt']) + df['ds'] = pd.to_datetime(df['ds']) + # 获取次月预测结果 + next_month_price_df = df[df['ds'] == df['ds'].min()] + # 获取次二月预测结果 + next_february_price_df = df.iloc[[1]] + # 获取次三月预测结果 + next_march_price_df = df.iloc[[2]] + # 获取次四月预测结果 + next_april_price_df = df[df['ds'] == df['ds'].max()] + + wd = ['next_month_price', 'next_february_price', + 'next_march_price', 'next_april_price'] + model_name_list, model_id_name_dict = get_modelsname(df, global_config) + + PRICE_COLUMNS = [ + 'day_price', 'week_price', 'second_week_price', 'next_week_price', + 'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price' + ] + + params_list = [] + for df, price_type in zip([next_month_price_df, next_february_price_df, next_march_price_df, next_april_price_df], wd): + + update_columns = [ + "feature_factor_frequency = VALUES(feature_factor_frequency)", + "oil_code = VALUES(oil_code)", + "oil_name = VALUES(oil_name)", + "data_date = VALUES(data_date)", + "market_price = VALUES(market_price)", + f"{price_type} = VALUES({price_type})", + "model_evaluation_id = VALUES(model_evaluation_id)", + "tenant_code = VALUES(tenant_code)", + "version_num = VALUES(version_num)", + "delete_flag = VALUES(delete_flag)", + "update_user = VALUES(update_user)", + "update_date = VALUES(update_date)" + ] + + insert_query = f""" + INSERT INTO v_tbl_predict_pp_prediction_results ( + feature_factor_frequency, strategy_id, oil_code, oil_name, data_date, + market_price, day_price, week_price, second_week_price, next_week_price, + next_month_price, next_february_price, next_march_price, next_april_price, + model_evaluation_id, model_id, tenant_code, version_num, delete_flag, + create_user, create_date, update_user, update_date + ) VALUES ( + %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s + ) + ON DUPLICATE KEY UPDATE + {', '.join(update_columns)} + """ + + next_day_df = df[['ds', 'created_dt'] + model_name_list] + + pydantic_results = convert_df_to_pydantic_pp( + next_day_df, model_id_name_dict, global_config) + if pydantic_results: + + for result in pydantic_results: + price_values = [None] * len(PRICE_COLUMNS) + price_index = PRICE_COLUMNS.index(price_type) + price_values[price_index] = next_day_df[model_id_name_dict[result.model_id]].values[0] + + params = ( + result.feature_factor_frequency, + result.strategy_id, + result.oil_code, + result.oil_name, + next_day_df['created_dt'].values[0], + result.market_price, + *price_values, + result.model_evaluation_id, + result.model_id, + result.tenant_code, + 1, + '0', + result.create_user, + result.create_date, + result.update_user, + result.update_date + ) + params_list.append(params) + affected_rows = config.db_mysql.execute_batch_insert( + insert_query, params_list) + config.logger.info(f"成功插入或更新 {affected_rows} 条记录") + config.db_mysql.close() + + +def predict_main(): + """ + 主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。 + + 参数: + signature (BinanceAPI): Binance API 实例。 + etadata (EtaReader): ETA 数据读取器实例。 + is_eta (bool): 是否从 ETA 获取数据。 + data_set (str): 数据集名称。 + dataset (str): 数据集路径。 + add_kdj (bool): 是否添加 KDJ 指标。 + is_timefurture (bool): 是否添加时间衍生特征。 + end_time (str): 结束时间。 + is_edbnamelist (bool): 是否使用 EDB 名称列表。 + edbnamelist (list): EDB 名称列表。 + y (str): 预测目标列名。 + sqlitedb (SQLiteDB): SQLite 数据库实例。 + is_corr (bool): 是否进行相关性分析。 + horizon (int): 预测时域。 + input_size (int): 输入数据大小。 + train_steps (int): 训练步数。 + val_check_steps (int): 验证检查步数。 + early_stop_patience_steps (int): 早停耐心步数。 + is_debug (bool): 是否调试模式。 + dataset (str): 数据集名称。 + is_train (bool): 是否训练模型。 + is_fivemodels (bool): 是否使用五个模型。 + val_size (float): 验证集大小。 + test_size (float): 测试集大小。 + settings (dict): 模型设置。 + now (str): 当前时间。 + etadata (EtaReader): ETA 数据读取器实例。 + modelsindex (list): 模型索引列表。 + data (str): 数据类型。 + is_eta (bool): 是否从 ETA 获取数据。 + + 返回: + None + """ + # end_time = global_config['end_time'] + # signature = BinanceAPI(APPID, SECRET) + # etadata = EtaReader(signature=signature, + # classifylisturl=global_config['classifylisturl'], + # classifyidlisturl=global_config['classifyidlisturl'], + # edbcodedataurl=global_config['edbcodedataurl'], + # edbcodelist=global_config['edbcodelist'], + # edbdatapushurl=global_config['edbdatapushurl'], + # edbdeleteurl=global_config['edbdeleteurl'], + # edbbusinessurl=global_config['edbbusinessurl'], + # classifyId=global_config['ClassifyId'], + # ) + # # 获取数据 + # if is_eta: + # logger.info('从eta获取数据...') + + # df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data( + # data_set=data_set, dataset=dataset) # 原始数据，未处理 + + # if is_market: + # logger.info('从市场信息平台获取数据...') + # try: + # # 如果是测试环境，最高价最低价取excel文档 + # if server_host == '192.168.100.53': + # logger.info('从excel文档获取最高价最低价') + # df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju) + # else: + # logger.info('从市场信息平台获取数据') + # df_zhibiaoshuju = get_market_data( + # end_time, df_zhibiaoshuju) + + # except: + # logger.info('最高最低价拼接失败') + + # # 保存到xlsx文件的sheet表 + # with pd.ExcelWriter(os.path.join(dataset, data_set)) as file: + # df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False) + # df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False) + + # # 数据处理 + # df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture, + # end_time=end_time) + + # else: + # # 读取数据 + # logger.info('读取本地数据：' + os.path.join(dataset, data_set)) + # df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj, + # is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理 + + # # 更改预测列名称 + # df.rename(columns={y: 'y'}, inplace=True) + + # if is_edbnamelist: + # df = df[edbnamelist] + # df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False) + # # 保存最新日期的y值到数据库 + # # 取第一行数据存储到数据库中 + # first_row = df[['ds', 'y']].tail(1) + # # 判断y的类型是否为float + # if not isinstance(first_row['y'].values[0], float): + # logger.info(f'{end_time}预测目标数据为空，跳过') + # return None + + # # 将最新真实值保存到数据库 + # if not sqlitedb.check_table_exists('trueandpredict'): + # first_row.to_sql('trueandpredict', sqlitedb.connection, index=False) + # else: + # for row in first_row.itertuples(index=False): + # row_dict = row._asdict() + # config.logger.info(f'要保存的真实值：{row_dict}') + # # 判断ds是否为字符串类型,如果不是则转换为字符串类型 + # if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)): + # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d') + # elif not isinstance(row_dict['ds'], str): + # try: + # row_dict['ds'] = pd.to_datetime( + # row_dict['ds']).strftime('%Y-%m-%d') + # except: + # logger.warning(f"无法解析的时间格式: {row_dict['ds']}") + # # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d') + # # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S') + # check_query = sqlitedb.select_data( + # 'trueandpredict', where_condition=f"ds = '{row.ds}'") + # if len(check_query) > 0: + # set_clause = ", ".join( + # [f"{key} = '{value}'" for key, value in row_dict.items()]) + # sqlitedb.update_data( + # 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'") + # continue + # sqlitedb.insert_data('trueandpredict', tuple( + # row_dict.values()), columns=row_dict.keys()) + + # # 更新accuracy表的y值 + # if not sqlitedb.check_table_exists('accuracy'): + # pass + # else: + # update_y = sqlitedb.select_data( + # 'accuracy', where_condition="y is null") + # if len(update_y) > 0: + # logger.info('更新accuracy表的y值') + # # 找到update_y 中ds且df中的y的行 + # update_y = update_y[update_y['ds'] <= end_time] + # logger.info(f'要更新y的信息：{update_y}') + # # try: + # for row in update_y.itertuples(index=False): + # try: + # row_dict = row._asdict() + # yy = df[df['ds'] == row_dict['ds']]['y'].values[0] + # LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0] + # HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0] + # sqlitedb.update_data( + # 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'") + # except: + # logger.info(f'更新accuracy表的y值失败：{row_dict}') + # # except Exception as e: + # # logger.info(f'更新accuracy表的y值失败：{e}') + + # # 判断当前日期是不是周一 + # is_weekday = datetime.datetime.now().weekday() == 0 + # if is_weekday: + # logger.info('今天是周一，更新预测模型') + # # 计算最近60天预测残差最低的模型名称 + # model_results = sqlitedb.select_data( + # 'trueandpredict', order_by="ds DESC", limit="60") + # # 删除空值率为90%以上的列 + # if len(model_results) > 10: + # model_results = model_results.dropna( + # thresh=len(model_results)*0.1, axis=1) + # # 删除空行 + # model_results = model_results.dropna() + # modelnames = model_results.columns.to_list()[2:-1] + # for col in model_results[modelnames].select_dtypes(include=['object']).columns: + # model_results[col] = model_results[col].astype(np.float32) + # # 计算每个预测值与真实值之间的偏差率 + # for model in modelnames: + # model_results[f'{model}_abs_error_rate'] = abs( + # model_results['y'] - model_results[model]) / model_results['y'] + # # 获取每行对应的最小偏差率值 + # min_abs_error_rate_values = model_results.apply( + # lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1) + # # 获取每行对应的最小偏差率值对应的列名 + # min_abs_error_rate_column_name = model_results.apply( + # lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1) + # # 将列名索引转换为列名 + # min_abs_error_rate_column_name = min_abs_error_rate_column_name.map( + # lambda x: x.split('_')[0]) + # # 取出现次数最多的模型名称 + # most_common_model = min_abs_error_rate_column_name.value_counts().idxmax() + # logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}") + # # 保存结果到数据库 + # if not sqlitedb.check_table_exists('most_model'): + # sqlitedb.create_table( + # 'most_model', columns="ds datetime, most_common_model TEXT") + # sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime( + # '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',)) + + # if is_corr: + # df = corr_feature(df=df) + + # df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用 + # logger.info(f"开始训练模型...") + # row, col = df.shape + + # now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') + # ex_Model(df, + # horizon=global_config['horizon'], + # input_size=global_config['input_size'], + # train_steps=global_config['train_steps'], + # val_check_steps=global_config['val_check_steps'], + # early_stop_patience_steps=global_config['early_stop_patience_steps'], + # is_debug=global_config['is_debug'], + # dataset=global_config['dataset'], + # is_train=global_config['is_train'], + # is_fivemodels=global_config['is_fivemodels'], + # val_size=global_config['val_size'], + # test_size=global_config['test_size'], + # settings=global_config['settings'], + # now=now, + # etadata=etadata, + # modelsindex=global_config['modelsindex'], + # data=data, + # is_eta=global_config['is_eta'], + # end_time=global_config['end_time'], + # ) + + # logger.info('模型训练完成') + + # logger.info('训练数据绘图ing') + # model_results3 = model_losss_juxiting( + # sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels']) + # logger.info('训练数据绘图end') + + push_market_value() + + # sql_inset_predict(global_config) + + # # 模型报告 + # logger.info('制作报告ing') + # title = f'{settings}--{end_time}-预测报告' # 报告标题 + # reportname = f'聚烯烃PP大模型月度预测--{end_time}.pdf' # 报告文件名 + # reportname = reportname.replace(':', '-') # 替换冒号 + # pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time, + # reportname=reportname, sqlitedb=sqlitedb), + + # logger.info('制作报告end') + + # 图片报告 + logger.info('图片报告ing') + pp_bdwd_png(global_config=global_config) + logger.info('图片报告end') + + # # LSTM 单变量模型 + # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset) + + # # lstm 多变量模型 + # ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset) + + # # GRU 模型 + # # ex_GRU(df) + + # 发送邮件 + # m = SendMail( + # username=username, + # passwd=passwd, + # recv=recv, + # title=title, + # content=content, + # file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime), + # ssl=ssl, + # ) + # m.send_mail() + + +if __name__ == '__main__': + # global end_time + # 遍历2024-11-25 到 2024-12-3 之间的工作日日期 + # for i_time in pd.date_range('2025-7-28', '2025-7-29', freq='B'): + # try: + # global_config['end_time'] = i_time.strftime('%Y-%m-%d') + # global_config['db_mysql'].connect() + # predict_main() + # except Exception as e: + # logger.info(f'预测失败：{e}') + # continue + + # predict_main() + # push_market_value() + push_png_report() + + # 图片报告 + # global_config['end_time'] = '2025-07-31' + # logger.info('图片报告ing') + # pp_bdwd_png(global_config=global_config) + # logger.info('图片报告end') diff --git a/lib/dataread.py b/lib/dataread.py index 2a549c1..74b70d2 100644 --- a/lib/dataread.py +++ b/lib/dataread.py @@ -1381,6 +1381,14 @@ class Config: def get_waring_data_value_list_data( self): return global_config['get_waring_data_value_list_data'] + @property + def push_png_report_url( + self): return global_config['push_png_report_url'] + + @property + def push_png_report_data( + self): return global_config['push_png_report_data'] + @property def bdwd_items(self): return global_config['bdwd_items'] @@ -2384,6 +2392,24 @@ def get_market_data(end_time, df): return df +def push_png_report(data): + ''' + 上传预测价格到市场信息平台 + data: 预测价格数据,示例： + + ''' + # 获取token + token = get_head_auth_report() + # 发送请求 + headers = {"Authorization": token} + config.logger.info('推送图片报告中...') + items_res = requests.post(url=config.push_png_report_url, headers=headers, + json=data, timeout=(3, 35)) + json_data = json.loads(items_res.text) + config.logger.info(f"推送图片报告结果:{json_data}") + return json_data + + def push_market_data(data): ''' 上传预测价格到市场信息平台 diff --git a/main_juxiting_yuedu.py b/main_juxiting_yuedu.py index 8d3f1b7..f4075fa 100644 --- a/main_juxiting_yuedu.py +++ b/main_juxiting_yuedu.py @@ -477,7 +477,7 @@ def predict_main(): # sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels']) # logger.info('训练数据绘图end') - # push_market_value() + push_market_value() # sql_inset_predict(global_config)