上传图片报告调试

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',

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')

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

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)