图片报告发送调试
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workpc
parent
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@ -1,12 +1,7 @@
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# 读取配置
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# 读取配置
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from lib.dataread import *
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from lib.dataread import *
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from config_juxiting_yuedu import *
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from config_juxiting_yuedu import *
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from lib.tools import SendMail, convert_df_to_pydantic_pp, exception_logger, find_best_models, get_modelsname
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from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_bdwd_png, pp_export_pdf
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import datetime
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import datetime
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import torch
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torch.set_float32_matmul_precision("high")
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global_config.update({
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global_config.update({
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# 核心参数
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# 核心参数
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@ -94,474 +89,26 @@ global_config.update({
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def push_png_report():
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def push_png_report():
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logger.info('发送图片到钉钉工作组')
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current_end_time = global_config['end_time']
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current_end_time = global_config['end_time']
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previous_trading_day = (pd.Timestamp(current_end_time) -
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previous_trading_day = (pd.Timestamp(current_end_time) -
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pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d')
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pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d')
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png_report_files = ['pp_zhouducorrelation.png', 'pp_yueducorrelation.png']
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png_report_files = ['pp_zhouducorrelation.png', 'pp_yueducorrelation.png']
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with open(os.path.join(global_config['dataset'], 'pp_zhouducorrelation.png'), 'rb') as f:
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for png_report_file in png_report_files:
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logger.info(f'发送图片{png_report_file}到钉钉工作组')
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try:
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with open(os.path.join(global_config['dataset'], png_report_file), 'rb') as f:
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base64_data = base64.b64encode(f.read()).decode('utf-8')
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base64_data = base64.b64encode(f.read()).decode('utf-8')
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config.upload_data["data"]["fileBase64"] = base64_data
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config.upload_data["data"]["fileBase64"] = base64_data
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data = global_config['push_png_report_data']
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data = global_config['push_png_report_data']
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data['data']['fileBase64'] = base64_data
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data['data']['fileBase64'] = base64_data
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data['data']['billNo'] = str(time.time())
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pngreportdata = push_png_report(data)
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pngreportdata = push_png_report_to_market(data)
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logger.info(f'{png_report_file}推送图片报告到钉钉成功{pngreportdata}')
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print(pngreportdata)
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def push_market_value():
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logger.info('发送预测结果到市场信息平台')
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current_end_time = global_config['end_time']
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previous_trading_day = (pd.Timestamp(current_end_time) -
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pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d')
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# 读取预测数据和模型评估数据
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best_bdwd_price = find_best_models(
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date=previous_trading_day, global_config=global_config)
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# 获取本月最佳模型的预测价格
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four_month_predict_price = pd.read_csv(
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os.path.join(global_config['dataset'], 'predict.csv'))
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four_month_predict_price['ds'] = pd.to_datetime(
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four_month_predict_price['ds'])
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# 设置索引 次月 次二月 次三月 次四月
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index_labels = ["次月", "次二月", "次三月", "次四月"]
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four_month_predict_price.index = index_labels
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global_config['logger'].info(f"best_bdwd_price: {best_bdwd_price}")
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# 准备要推送的数据
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ciyue_mean = four_month_predict_price[best_bdwd_price['next_month_price']
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['model_name']].iloc[0]
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cieryue_mean = four_month_predict_price[best_bdwd_price['next_february_price']
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['model_name']].iloc[1]
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cisanyue_mean = four_month_predict_price[best_bdwd_price['next_march_price']
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['model_name']].iloc[2]
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cisieryue_mean = four_month_predict_price[best_bdwd_price['next_april_price']
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['model_name']].iloc[3]
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# # 保留两位小数
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ciyue_mean = round(ciyue_mean, 2)
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cieryue_mean = round(cieryue_mean, 2)
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cisanyue_mean = round(cisanyue_mean, 2)
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cisieryue_mean = round(cisieryue_mean, 2)
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predictdata = [
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{
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"dataItemNo": global_config['bdwd_items']['ciyue'],
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"dataDate": global_config['end_time'].replace('-', ''),
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"dataStatus": "add",
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"dataValue": ciyue_mean
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},
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{
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"dataItemNo": global_config['bdwd_items']['cieryue'],
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"dataDate": global_config['end_time'].replace('-', ''),
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"dataStatus": "add",
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"dataValue": cieryue_mean
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},
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{
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"dataItemNo": global_config['bdwd_items']['cisanyue'],
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"dataDate": global_config['end_time'].replace('-', ''),
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"dataStatus": "add",
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"dataValue": cisanyue_mean
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},
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{
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"dataItemNo": global_config['bdwd_items']['cisiyue'],
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"dataDate": global_config['end_time'].replace('-', ''),
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"dataStatus": "add",
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"dataValue": cisieryue_mean
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}
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]
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print(predictdata)
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# 推送数据到市场信息平台
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try:
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push_market_data(predictdata)
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except Exception as e:
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except Exception as e:
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logger.error(f"推送数据失败: {e}")
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logger.error(f'{png_report_file}推送图片报告到钉钉失败：{e}')
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def sql_inset_predict(global_config):
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df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
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df['created_dt'] = pd.to_datetime(df['created_dt'])
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df['ds'] = pd.to_datetime(df['ds'])
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# 获取次月预测结果
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next_month_price_df = df[df['ds'] == df['ds'].min()]
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# 获取次二月预测结果
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next_february_price_df = df.iloc[[1]]
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# 获取次三月预测结果
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next_march_price_df = df.iloc[[2]]
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# 获取次四月预测结果
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next_april_price_df = df[df['ds'] == df['ds'].max()]
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wd = ['next_month_price', 'next_february_price',
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'next_march_price', 'next_april_price']
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model_name_list, model_id_name_dict = get_modelsname(df, global_config)
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PRICE_COLUMNS = [
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'day_price', 'week_price', 'second_week_price', 'next_week_price',
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'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
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]
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params_list = []
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for df, price_type in zip([next_month_price_df, next_february_price_df, next_march_price_df, next_april_price_df], wd):
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update_columns = [
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"feature_factor_frequency = VALUES(feature_factor_frequency)",
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"oil_code = VALUES(oil_code)",
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"oil_name = VALUES(oil_name)",
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"data_date = VALUES(data_date)",
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"market_price = VALUES(market_price)",
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f"{price_type} = VALUES({price_type})",
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"model_evaluation_id = VALUES(model_evaluation_id)",
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"tenant_code = VALUES(tenant_code)",
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"version_num = VALUES(version_num)",
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"delete_flag = VALUES(delete_flag)",
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"update_user = VALUES(update_user)",
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"update_date = VALUES(update_date)"
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]
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insert_query = f"""
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INSERT INTO v_tbl_predict_pp_prediction_results (
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feature_factor_frequency, strategy_id, oil_code, oil_name, data_date,
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market_price, day_price, week_price, second_week_price, next_week_price,
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next_month_price, next_february_price, next_march_price, next_april_price,
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model_evaluation_id, model_id, tenant_code, version_num, delete_flag,
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create_user, create_date, update_user, update_date
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) VALUES (
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%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
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)
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ON DUPLICATE KEY UPDATE
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{', '.join(update_columns)}
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"""
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next_day_df = df[['ds', 'created_dt'] + model_name_list]
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pydantic_results = convert_df_to_pydantic_pp(
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next_day_df, model_id_name_dict, global_config)
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if pydantic_results:
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for result in pydantic_results:
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price_values = [None] * len(PRICE_COLUMNS)
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price_index = PRICE_COLUMNS.index(price_type)
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price_values[price_index] = next_day_df[model_id_name_dict[result.model_id]].values[0]
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params = (
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result.feature_factor_frequency,
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result.strategy_id,
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result.oil_code,
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result.oil_name,
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next_day_df['created_dt'].values[0],
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result.market_price,
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*price_values,
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result.model_evaluation_id,
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result.model_id,
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result.tenant_code,
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1,
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'0',
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result.create_user,
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result.create_date,
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result.update_user,
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result.update_date
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)
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params_list.append(params)
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affected_rows = config.db_mysql.execute_batch_insert(
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insert_query, params_list)
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config.logger.info(f"成功插入或更新 {affected_rows} 条记录")
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config.db_mysql.close()
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def predict_main():
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"""
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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。
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参数:
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signature (BinanceAPI): Binance API 实例。
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etadata (EtaReader): ETA 数据读取器实例。
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is_eta (bool): 是否从 ETA 获取数据。
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data_set (str): 数据集名称。
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dataset (str): 数据集路径。
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add_kdj (bool): 是否添加 KDJ 指标。
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is_timefurture (bool): 是否添加时间衍生特征。
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end_time (str): 结束时间。
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is_edbnamelist (bool): 是否使用 EDB 名称列表。
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edbnamelist (list): EDB 名称列表。
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y (str): 预测目标列名。
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sqlitedb (SQLiteDB): SQLite 数据库实例。
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is_corr (bool): 是否进行相关性分析。
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horizon (int): 预测时域。
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input_size (int): 输入数据大小。
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train_steps (int): 训练步数。
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val_check_steps (int): 验证检查步数。
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early_stop_patience_steps (int): 早停耐心步数。
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is_debug (bool): 是否调试模式。
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dataset (str): 数据集名称。
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is_train (bool): 是否训练模型。
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is_fivemodels (bool): 是否使用五个模型。
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val_size (float): 验证集大小。
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test_size (float): 测试集大小。
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settings (dict): 模型设置。
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now (str): 当前时间。
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etadata (EtaReader): ETA 数据读取器实例。
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modelsindex (list): 模型索引列表。
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data (str): 数据类型。
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is_eta (bool): 是否从 ETA 获取数据。
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返回:
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None
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"""
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# end_time = global_config['end_time']
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# signature = BinanceAPI(APPID, SECRET)
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# etadata = EtaReader(signature=signature,
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# classifylisturl=global_config['classifylisturl'],
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# classifyidlisturl=global_config['classifyidlisturl'],
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# edbcodedataurl=global_config['edbcodedataurl'],
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# edbcodelist=global_config['edbcodelist'],
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# edbdatapushurl=global_config['edbdatapushurl'],
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# edbdeleteurl=global_config['edbdeleteurl'],
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# edbbusinessurl=global_config['edbbusinessurl'],
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# classifyId=global_config['ClassifyId'],
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# )
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# # 获取数据
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# if is_eta:
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# logger.info('从eta获取数据...')
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# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
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# data_set=data_set, dataset=dataset) # 原始数据，未处理
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# if is_market:
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# logger.info('从市场信息平台获取数据...')
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# try:
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# # 如果是测试环境，最高价最低价取excel文档
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# if server_host == '192.168.100.53':
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# logger.info('从excel文档获取最高价最低价')
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# df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
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# else:
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# logger.info('从市场信息平台获取数据')
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# df_zhibiaoshuju = get_market_data(
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# end_time, df_zhibiaoshuju)
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# except:
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# logger.info('最高最低价拼接失败')
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# # 保存到xlsx文件的sheet表
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# with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
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# df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
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# df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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# # 数据处理
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# df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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# end_time=end_time)
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# else:
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# # 读取数据
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# logger.info('读取本地数据：' + os.path.join(dataset, data_set))
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# df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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# is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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# # 更改预测列名称
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# df.rename(columns={y: 'y'}, inplace=True)
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# if is_edbnamelist:
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# df = df[edbnamelist]
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# df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
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# # 保存最新日期的y值到数据库
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# # 取第一行数据存储到数据库中
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# first_row = df[['ds', 'y']].tail(1)
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# # 判断y的类型是否为float
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# if not isinstance(first_row['y'].values[0], float):
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# logger.info(f'{end_time}预测目标数据为空，跳过')
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# return None
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# # 将最新真实值保存到数据库
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# if not sqlitedb.check_table_exists('trueandpredict'):
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# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
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# else:
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# for row in first_row.itertuples(index=False):
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||||||
# 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__':
|
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()
|
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')
|
|
||||||
|
|||||||
@ -2392,7 +2392,7 @@ def get_market_data(end_time, df):
|
|||||||
return df
|
return df
|
||||||
|
|
||||||
|
|
||||||
def push_png_report(data):
|
def push_png_report_to_market(data):
|
||||||
'''
|
'''
|
||||||
上传预测价格到市场信息平台
|
上传预测价格到市场信息平台
|
||||||
data: 预测价格数据,示例：
|
data: 预测价格数据,示例：
|
||||||
@ -2403,6 +2403,8 @@ def push_png_report(data):
|
|||||||
# 发送请求
|
# 发送请求
|
||||||
headers = {"Authorization": token}
|
headers = {"Authorization": token}
|
||||||
config.logger.info('推送图片报告中...')
|
config.logger.info('推送图片报告中...')
|
||||||
|
config.logger.info(f'推送图片报告URL：{config.push_png_report_url}')
|
||||||
|
# config.logger.info(f'推送图片报告数据：{data}')
|
||||||
items_res = requests.post(url=config.push_png_report_url, headers=headers,
|
items_res = requests.post(url=config.push_png_report_url, headers=headers,
|
||||||
json=data, timeout=(3, 35))
|
json=data, timeout=(3, 35))
|
||||||
json_data = json.loads(items_res.text)
|
json_data = json.loads(items_res.text)
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user