From 497cd9f4cea0a515ad8e6f6db0a520180f18d148 Mon Sep 17 00:00:00 2001
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Date: Wed, 23 Jul 2025 14:03:07 +0800
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---
 aisenzhecode/沥青/定性模型数据项12-11.xlsx | Bin 93699 -> 122762 bytes
 aisenzhecode/沥青/沥青定性每日执行.py      |   4 +-
 config_jingbo.py                           | 346 ++++++++++-----------
 config_jingbo_yuedu.py                     | 288 ++++++++---------
 config_jingbo_zhoudu.py                    | 270 ++++++++--------
 config_juxiting.py                         |   4 +-
 config_juxiting_yuedu.py                   |  41 ++-
 config_juxiting_zhoudu.py                  |  10 +-
 lib/dataread.py                            |  12 +-
 main_juxiting.py                           |  68 ++--
 main_juxiting_yuedu.py                     | 122 +++++++-
 main_juxiting_zhoudu.py                    |  61 ++--
 main_yuanyou.py                            |   4 +-
 main_yuanyou_yuedu.py                      |   2 +-
 main_yuanyou_zhoudu.py                     |   2 +-
 models/nerulforcastmodels.py               |  80 ++---
 test/demo.py                               | 140 +++++++++
 17 files changed, 860 insertions(+), 594 deletions(-)
 create mode 100644 test/demo.py

diff --git a/aisenzhecode/沥青/定性模型数据项12-11.xlsx b/aisenzhecode/沥青/定性模型数据项12-11.xlsx
index 7fd829c2f10b93d47fd1dd3444c9b86545742b30..2a13d563ae4a3c8c4c89d60e4ea3a22b86712aa2 100644
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diff --git a/aisenzhecode/沥青/沥青定性每日执行.py b/aisenzhecode/沥青/沥青定性每日执行.py
index 43ef7f3..cc888bc 100644
--- a/aisenzhecode/沥青/沥青定性每日执行.py
+++ b/aisenzhecode/沥青/沥青定性每日执行.py
@@ -950,14 +950,14 @@ def main(start_date=None, token=None, token_push=None):
         date = start_date.strftime('%Y%m%d')
         print(date)
         logging.info("当前日期: %s", date)
-        updateExcelData(date, token)
+        # updateExcelData(date, token)
         queryDataListItemNos(token=token)
         update_e_value('定性模型数据项12-11.xlsx', 8, 1000)
         x = qualitativeModel()
         if x is not None:
             print('**************************************************预测结果：', x)
             logging.info("预测结果: %s", x)
-            cur_time, cur_time2 = getNow(date)
+            # cur_time, cur_time2 = getNow(date)
             pushData(cur_time, x, token_push)
         logging.info("主函数执行完成")
     except Exception as e:
diff --git a/config_jingbo.py b/config_jingbo.py
index 5f9c4c9..884a971 100644
--- a/config_jingbo.py
+++ b/config_jingbo.py
@@ -93,163 +93,24 @@ data = {
 ClassifyId = 1214
 
 
-# 变量定义--线上环境
-server_host = '10.200.32.39'
-login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
-upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
-upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
-query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
-# 上传数据项值
-push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
-# 上传停更数据到市场信息平台
-push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
-# 获取预警数据中取消订阅指标ID
-get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
-
-
-login_data = {
-    "data": {
-        "account": "api_dev",
-        "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
-        "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
-        "terminal": "API"
-    },
-    "funcModule": "API",
-    "funcOperation": "获取token"
-}
-
-
-upload_data = {
-    "funcModule": '研究报告信息',
-    "funcOperation": '上传原油价格预测报告',
-    "data": {
-        "groupNo": '',  # 用户组id
-        "ownerAccount": '27663',  # 报告所属用户账号  27663 - 刘小朋
-        "reportType": 'OIL_PRICE_FORECAST',  # 报告类型，固定为OIL_PRICE_FORECAST
-        "fileName": '',  # 文件名称
-        "fileBase64": '',  # 文件内容base64
-        "categoryNo": 'yyjgycbg',  # 研究报告分类编码
-        "smartBusinessClassCode": 'YCJGYCBG',  # 分析报告分类编码
-        "reportEmployeeCode": "E40482",  # 报告人  E40482  - 管理员  0000027663 - 刘小朋
-        "reportDeptCode": "002000621000",  # 报告部门 - 002000621000  SH期货研究部
-        "productGroupCode": "RAW_MATERIAL"  # 商品分类
-    }
-}
-
-warning_data = {
-    "groupNo": '',  # 用户组id
-    "funcModule": '原油特征停更预警',
-    "funcOperation": '原油特征停更预警',
-    "data": {
-        'WARNING_TYPE_NAME': '特征数据停更预警',
-        'WARNING_CONTENT': '',
-        'WARNING_DATE': ''
-    }
-}
-
-query_data_list_item_nos_data = {
-    "funcModule": "数据项",
-    "funcOperation": "查询",
-    "data": {
-        "dateStart": "20200101",
-        "dateEnd": "20241231",
-        "dataItemNoList": ["Brentzdj", "Brentzgj"]  # 数据项编码,代表 brent最低价和最高价
-    }
-}
-
-
-push_data_value_list_data = {
-    "funcModule": "数据表信息列表",
-    "funcOperation": "新增",
-    "data": [
-        {"dataItemNo": "91230600716676129",
-         "dataDate": "20230113",
-         "dataStatus": "add",
-         "dataValue": 100.11
-         },
-        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
-         "dataDate": "20230113",
-         "dataStatus": "add",
-         "dataValue": 100.55
-         },
-        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
-         "dataDate": "20230113",
-         "dataStatus": "add",
-         "dataValue": 100.55
-         }
-    ]
-}
-
-
-push_waring_data_value_list_data = {
-    "data": {
-        "crudeOilWarningDtoList": [
-            {
-                "lastUpdateDate": "20240501",
-                "updateSuspensionCycle": 1,
-                "dataSource": "8",
-                "frequency": "1",
-                "indicatorName": "美元指数",
-                "indicatorId": "myzs001",
-                "warningDate": "2024-05-13"
-            }
-        ],
-        "dataSource": "8"
-    },
-    "funcModule": "商品数据同步",
-    "funcOperation": "同步"
-}
-
-
-get_waring_data_value_list_data = {
-    "data": "8", "funcModule": "商品数据同步", "funcOperation": "同步"}
-
-
-# 八大维度数据项编码
-bdwd_items = {
-    'ciri': '原油大数据预测|FORECAST|PRICE|T',
-    'benzhou': '原油大数据预测|FORECAST|PRICE|W',
-    'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
-    'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
-    'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
-    'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
-    'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
-    'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
-}
-
-
-# 生产环境数据库
-host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
-port = 3306
-dbusername = 'jingbo'
-password = 'shihua@123'
-dbname = 'jingbo'
-table_name = 'v_tbl_crude_oil_warning'
-
-
-# # # 变量定义--测试环境
-# server_host = '192.168.100.53'  # 内网
-# # server_host = '183.242.74.28'  # 外网
-# login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
-# # 上传报告
-# upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
-# # 停更预警
-# upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
-# # 查询数据项编码
-# query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
+# # 变量定义--线上环境
+# server_host = '10.200.32.39'
+# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
+# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
+# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
+# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
 # # 上传数据项值
-# push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
+# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
 # # 上传停更数据到市场信息平台
-# push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
+# push_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
 # # 获取预警数据中取消订阅指标ID
-# get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
+# get_waring_data_value_list_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/dataList"
 
 
 # login_data = {
 #     "data": {
-#         "account": "api_test",
-#         # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
-#         "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",  # 123456
+#         "account": "api_dev",
+#         "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
 #         "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
 #         "terminal": "API"
 #     },
@@ -257,25 +118,24 @@ table_name = 'v_tbl_crude_oil_warning'
 #     "funcOperation": "获取token"
 # }
 
+
 # upload_data = {
-#     "groupNo": '',  # 用户组id
 #     "funcModule": '研究报告信息',
 #     "funcOperation": '上传原油价格预测报告',
 #     "data": {
-#         "ownerAccount": 'arui',  # 报告所属用户账号
+#         "groupNo": '',  # 用户组id
+#         "ownerAccount": '27663',  # 报告所属用户账号  27663 - 刘小朋
 #         "reportType": 'OIL_PRICE_FORECAST',  # 报告类型，固定为OIL_PRICE_FORECAST
-#         "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf',  # 文件名称
+#         "fileName": '',  # 文件名称
 #         "fileBase64": '',  # 文件内容base64
 #         "categoryNo": 'yyjgycbg',  # 研究报告分类编码
 #         "smartBusinessClassCode": 'YCJGYCBG',  # 分析报告分类编码
-#         "reportEmployeeCode": "E40116",  # 报告人
-#         "reportDeptCode": "D0044",  # 报告部门
+#         "reportEmployeeCode": "E40482",  # 报告人  E40482  - 管理员  0000027663 - 刘小朋
+#         "reportDeptCode": "002000621000",  # 报告部门 - 002000621000  SH期货研究部
 #         "productGroupCode": "RAW_MATERIAL"  # 商品分类
 #     }
 # }
 
-
-# # 已弃用
 # warning_data = {
 #     "groupNo": '',  # 用户组id
 #     "funcModule": '原油特征停更预警',
@@ -297,6 +157,7 @@ table_name = 'v_tbl_crude_oil_warning'
 #     }
 # }
 
+
 # push_data_value_list_data = {
 #     "funcModule": "数据表信息列表",
 #     "funcOperation": "新增",
@@ -319,6 +180,7 @@ table_name = 'v_tbl_crude_oil_warning'
 #     ]
 # }
 
+
 # push_waring_data_value_list_data = {
 #     "data": {
 #         "crudeOilWarningDtoList": [
@@ -342,27 +204,165 @@ table_name = 'v_tbl_crude_oil_warning'
 # get_waring_data_value_list_data = {
 #     "data": "8", "funcModule": "商品数据同步", "funcOperation": "同步"}
 
+
 # # 八大维度数据项编码
 # bdwd_items = {
-#     'ciri': 'yyycbdwdcr',
-#     'benzhou': 'yyycbdwdbz',
-#     'cizhou': 'yyycbdwdcz',
-#     'gezhou': 'yyycbdwdgz',
-#     'ciyue': 'yyycbdwdcy',
-#     'cieryue': 'yyycbdwdcey',
-#     'cisanyue': 'yyycbdwdcsy',
-#     'cisiyue': 'yyycbdwdcsiy',
+#     'ciri': '原油大数据预测|FORECAST|PRICE|T',
+#     'benzhou': '原油大数据预测|FORECAST|PRICE|W',
+#     'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
+#     'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
+#     'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
+#     'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
+#     'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
+#     'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
 # }
 
 
-# # 北京环境数据库
-# host = '192.168.101.27'
+# # 生产环境数据库
+# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
 # port = 3306
-# dbusername = 'root'
-# password = '123456'
-# dbname = 'jingbo_test'
+# dbusername = 'jingbo'
+# password = 'shihua@123'
+# dbname = 'jingbo'
 # table_name = 'v_tbl_crude_oil_warning'
 
+
+# # 变量定义--测试环境
+server_host = '192.168.100.53'  # 内网
+# server_host = '183.242.74.28'  # 外网
+login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
+# 上传报告
+upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
+# 停更预警
+upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
+# 查询数据项编码
+query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
+# 上传数据项值
+push_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/dw/dataValue/pushDataValueList"
+# 上传停更数据到市场信息平台
+push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
+# 获取预警数据中取消订阅指标ID
+get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
+
+
+login_data = {
+    "data": {
+        "account": "api_test",
+        # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
+        "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",  # 123456
+        "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
+        "terminal": "API"
+    },
+    "funcModule": "API",
+    "funcOperation": "获取token"
+}
+
+upload_data = {
+    "groupNo": '',  # 用户组id
+    "funcModule": '研究报告信息',
+    "funcOperation": '上传原油价格预测报告',
+    "data": {
+        "ownerAccount": 'arui',  # 报告所属用户账号
+        "reportType": 'OIL_PRICE_FORECAST',  # 报告类型，固定为OIL_PRICE_FORECAST
+        "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf',  # 文件名称
+        "fileBase64": '',  # 文件内容base64
+        "categoryNo": 'yyjgycbg',  # 研究报告分类编码
+        "smartBusinessClassCode": 'YCJGYCBG',  # 分析报告分类编码
+        "reportEmployeeCode": "E40116",  # 报告人
+        "reportDeptCode": "D0044",  # 报告部门
+        "productGroupCode": "RAW_MATERIAL"  # 商品分类
+    }
+}
+
+
+# 已弃用
+warning_data = {
+    "groupNo": '',  # 用户组id
+    "funcModule": '原油特征停更预警',
+    "funcOperation": '原油特征停更预警',
+    "data": {
+        'WARNING_TYPE_NAME': '特征数据停更预警',
+        'WARNING_CONTENT': '',
+        'WARNING_DATE': ''
+    }
+}
+
+query_data_list_item_nos_data = {
+    "funcModule": "数据项",
+    "funcOperation": "查询",
+    "data": {
+        "dateStart": "20200101",
+        "dateEnd": "20241231",
+        "dataItemNoList": ["Brentzdj", "Brentzgj"]  # 数据项编码,代表 brent最低价和最高价
+    }
+}
+
+push_data_value_list_data = {
+    "funcModule": "数据表信息列表",
+    "funcOperation": "新增",
+    "data": [
+        {"dataItemNo": "91230600716676129",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.11
+         },
+        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.55
+         },
+        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.55
+         }
+    ]
+}
+
+push_waring_data_value_list_data = {
+    "data": {
+        "crudeOilWarningDtoList": [
+            {
+                "lastUpdateDate": "20240501",
+                "updateSuspensionCycle": 1,
+                "dataSource": "8",
+                "frequency": "1",
+                "indicatorName": "美元指数",
+                "indicatorId": "myzs001",
+                "warningDate": "2024-05-13"
+            }
+        ],
+        "dataSource": "8"
+    },
+    "funcModule": "商品数据同步",
+    "funcOperation": "同步"
+}
+
+
+get_waring_data_value_list_data = {
+    "data": "8", "funcModule": "商品数据同步", "funcOperation": "同步"}
+
+# 八大维度数据项编码
+bdwd_items = {
+    'ciri': 'yyycbdwdcr',
+    'benzhou': 'yyycbdwdbz',
+    'cizhou': 'yyycbdwdcz',
+    'gezhou': 'yyycbdwdgz',
+    'ciyue': 'yyycbdwdcy',
+    'cieryue': 'yyycbdwdcey',
+    'cisanyue': 'yyycbdwdcsy',
+    'cisiyue': 'yyycbdwdcsiy',
+}
+
+
+# 北京环境数据库
+host = '192.168.101.27'
+port = 3306
+dbusername = 'root'
+password = '123456'
+dbname = 'jingbo_test'
+table_name = 'v_tbl_crude_oil_warning'
+
 DEFAULT_CONFIG = {
     'feature_factor_frequency': 'D',
     'strategy_id': 1,
@@ -380,14 +380,14 @@ DEFAULT_CONFIG = {
 # 开关
 is_train = True  # 是否训练
 is_debug = False  # 是否调试
-is_eta = True  # 是否使用eta接口
+is_eta = False  # 是否使用eta接口
 is_market = True  # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
 is_timefurture = True  # 是否使用时间特征
 is_fivemodels = False  # 是否使用之前保存的最佳的5个模型
 is_edbcode = False  # 特征使用edbcoding列表中的
 is_edbnamelist = False  # 自定义特征，对应上面的edbnamelist
-is_update_eta = True  # 预测结果上传到eta
-is_update_report = True  # 是否上传报告
+is_update_eta = False  # 预测结果上传到eta
+is_update_report = False  # 是否上传报告
 is_update_warning_data = True  # 是否上传预警数据
 is_update_predict_value = True  # 是否上传预测值到市场信息平台
 is_del_corr = 0.6  # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
diff --git a/config_jingbo_yuedu.py b/config_jingbo_yuedu.py
index d4fb83f..96fb729 100644
--- a/config_jingbo_yuedu.py
+++ b/config_jingbo_yuedu.py
@@ -172,131 +172,19 @@ data = {
 ClassifyId = 1214
 
 
-# 变量定义--线上环境
-server_host = '10.200.32.39'
-login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
-upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
-upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
-query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
-# 上传数据项值
-push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
-
-login_data = {
-    "data": {
-        "account": "api_dev",
-        "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
-        "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
-        "terminal": "API"
-    },
-    "funcModule": "API",
-    "funcOperation": "获取token"
-}
-
-
-upload_data = {
-    "funcModule": '研究报告信息',
-    "funcOperation": '上传原油价格预测报告',
-    "data": {
-        "groupNo": '',  # 用户组id
-        "ownerAccount": '27663',  # 报告所属用户账号  27663 - 刘小朋
-        "reportType": 'OIL_PRICE_FORECAST',  # 报告类型，固定为OIL_PRICE_FORECAST
-        "fileName": '',  # 文件名称
-        "fileBase64": '',  # 文件内容base64
-        "categoryNo": 'yyjgycbg',  # 研究报告分类编码
-        "smartBusinessClassCode": 'YCJGYCBG',  # 分析报告分类编码
-        "reportEmployeeCode": "E40482",  # 报告人  E40482  - 管理员  0000027663 - 刘小朋
-        "reportDeptCode": "002000621000",  # 报告部门 - 002000621000  SH期货研究部
-        "productGroupCode": "RAW_MATERIAL"  # 商品分类
-    }
-}
-
-warning_data = {
-    "groupNo": '',  # 用户组id
-    "funcModule": '原油特征停更预警',
-    "funcOperation": '原油特征停更预警',
-    "data": {
-        'WARNING_TYPE_NAME': '特征数据停更预警',
-        'WARNING_CONTENT': '',
-        'WARNING_DATE': ''
-    }
-}
-
-query_data_list_item_nos_data = {
-    "funcModule": "数据项",
-    "funcOperation": "查询",
-    "data": {
-        "dateStart": "20200101",
-        "dateEnd": "20241231",
-        "dataItemNoList": ["Brentzdj", "Brentzgj"]  # 数据项编码,代表 brent最低价和最高价
-    }
-}
-
-
-push_data_value_list_data = {
-    "funcModule": "数据表信息列表",
-    "funcOperation": "新增",
-    "data": [
-        {"dataItemNo": "91230600716676129",
-         "dataDate": "20230113",
-         "dataStatus": "add",
-         "dataValue": 100.11
-         },
-        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
-         "dataDate": "20230113",
-         "dataStatus": "add",
-         "dataValue": 100.55
-         },
-        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
-         "dataDate": "20230113",
-         "dataStatus": "add",
-         "dataValue": 100.55
-         }
-    ]
-}
-# 八大维度数据项编码
-bdwd_items = {
-    'ciri': '原油大数据预测|FORECAST|PRICE|T',
-    'benzhou': '原油大数据预测|FORECAST|PRICE|W',
-    'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
-    'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
-    'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
-    'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
-    'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
-    'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
-}
-
-# 报告中八大维度数据项重命名
-columnsrename = {
-    '原油大数据预测|FORECAST|PRICE|T': '次日', '原油大数据预测|FORECAST|PRICE|W': '本周',
-    '原油大数据预测|FORECAST|PRICE|W_1': '次周', '原油大数据预测|FORECAST|PRICE|W_2': '隔周',
-    '原油大数据预测|FORECAST|PRICE|M_1': '次月', '原油大数据预测|FORECAST|PRICE|M_2': '次二月',
-    '原油大数据预测|FORECAST|PRICE|M_3': '次三月', '原油大数据预测|FORECAST|PRICE|M_4': '次四月'
-}
-
-# 生产环境数据库
-host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
-port = 3306
-dbusername = 'jingbo'
-password = 'shihua@123'
-dbname = 'jingbo'
-table_name = 'v_tbl_crude_oil_warning'
-
-
-# # 变量定义--测试环境
-# server_host = '192.168.100.53:8080'  # 内网
-# # server_host = '183.242.74.28'  # 外网
-# login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
-# upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
-# upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
-# query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
+# # 变量定义--线上环境
+# server_host = '10.200.32.39'
+# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
+# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
+# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
+# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/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"
+# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
 
 # login_data = {
 #     "data": {
-#         "account": "api_test",
-#         # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
-#         "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",  # 123456
+#         "account": "api_dev",
+#         "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
 #         "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
 #         "terminal": "API"
 #     },
@@ -304,24 +192,24 @@ table_name = 'v_tbl_crude_oil_warning'
 #     "funcOperation": "获取token"
 # }
 
+
 # upload_data = {
-#     "groupNo": '',  # 用户组id
 #     "funcModule": '研究报告信息',
 #     "funcOperation": '上传原油价格预测报告',
 #     "data": {
-#         "ownerAccount": 'arui',  # 报告所属用户账号
+#         "groupNo": '',  # 用户组id
+#         "ownerAccount": '27663',  # 报告所属用户账号  27663 - 刘小朋
 #         "reportType": 'OIL_PRICE_FORECAST',  # 报告类型，固定为OIL_PRICE_FORECAST
-#         "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf',  # 文件名称
+#         "fileName": '',  # 文件名称
 #         "fileBase64": '',  # 文件内容base64
 #         "categoryNo": 'yyjgycbg',  # 研究报告分类编码
 #         "smartBusinessClassCode": 'YCJGYCBG',  # 分析报告分类编码
-#         "reportEmployeeCode": "E40116",  # 报告人
-#         "reportDeptCode": "D0044",  # 报告部门
+#         "reportEmployeeCode": "E40482",  # 报告人  E40482  - 管理员  0000027663 - 刘小朋
+#         "reportDeptCode": "002000621000",  # 报告部门 - 002000621000  SH期货研究部
 #         "productGroupCode": "RAW_MATERIAL"  # 商品分类
 #     }
 # }
 
-
 # warning_data = {
 #     "groupNo": '',  # 用户组id
 #     "funcModule": '原油特征停更预警',
@@ -343,6 +231,7 @@ table_name = 'v_tbl_crude_oil_warning'
 #     }
 # }
 
+
 # push_data_value_list_data = {
 #     "funcModule": "数据表信息列表",
 #     "funcOperation": "新增",
@@ -366,27 +255,138 @@ table_name = 'v_tbl_crude_oil_warning'
 # }
 # # 八大维度数据项编码
 # bdwd_items = {
-#     'ciri': 'yyycbdwdcr',
-#     'benzhou': 'yyycbdwdbz',
-#     'cizhou': 'yyycbdwdcz',
-#     'gezhou': 'yyycbdwdgz',
-#     'ciyue': 'yyycbdwdcy',
-#     'cieryue': 'yyycbdwdcey',
-#     'cisanyue': 'yyycbdwdcsy',
-#     'cisiyue': 'yyycbdwdcsiy',
+#     'ciri': '原油大数据预测|FORECAST|PRICE|T',
+#     'benzhou': '原油大数据预测|FORECAST|PRICE|W',
+#     'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
+#     'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
+#     'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
+#     'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
+#     'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
+#     'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
 # }
 
 # # 报告中八大维度数据项重命名
-# columnsrename = {'yyycbdwdbz': '本周', 'yyycbdwdcey': '次二月', 'yyycbdwdcr': '次日', 'yyycbdwdcsiy': '次四月',
-#                  'yyycbdwdcsy': '次三月', 'yyycbdwdcy': '次月', 'yyycbdwdcz': '次周', 'yyycbdwdgz': '隔周', }
-# # 北京环境数据库
-# host = '192.168.101.27'
+# columnsrename = {
+#     '原油大数据预测|FORECAST|PRICE|T': '次日', '原油大数据预测|FORECAST|PRICE|W': '本周',
+#     '原油大数据预测|FORECAST|PRICE|W_1': '次周', '原油大数据预测|FORECAST|PRICE|W_2': '隔周',
+#     '原油大数据预测|FORECAST|PRICE|M_1': '次月', '原油大数据预测|FORECAST|PRICE|M_2': '次二月',
+#     '原油大数据预测|FORECAST|PRICE|M_3': '次三月', '原油大数据预测|FORECAST|PRICE|M_4': '次四月'
+# }
+
+# # 生产环境数据库
+# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
 # port = 3306
-# dbusername = 'root'
-# password = '123456'
-# dbname = 'jingbo_test'
+# dbusername = 'jingbo'
+# password = 'shihua@123'
+# dbname = 'jingbo'
 # table_name = 'v_tbl_crude_oil_warning'
 
+
+# 变量定义--测试环境
+server_host = '192.168.100.53:8080'  # 内网
+# server_host = '183.242.74.28'  # 外网
+login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
+upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
+upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
+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"
+
+login_data = {
+    "data": {
+        "account": "api_test",
+        # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
+        "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",  # 123456
+        "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
+        "terminal": "API"
+    },
+    "funcModule": "API",
+    "funcOperation": "获取token"
+}
+
+upload_data = {
+    "groupNo": '',  # 用户组id
+    "funcModule": '研究报告信息',
+    "funcOperation": '上传原油价格预测报告',
+    "data": {
+        "ownerAccount": 'arui',  # 报告所属用户账号
+        "reportType": 'OIL_PRICE_FORECAST',  # 报告类型，固定为OIL_PRICE_FORECAST
+        "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf',  # 文件名称
+        "fileBase64": '',  # 文件内容base64
+        "categoryNo": 'yyjgycbg',  # 研究报告分类编码
+        "smartBusinessClassCode": 'YCJGYCBG',  # 分析报告分类编码
+        "reportEmployeeCode": "E40116",  # 报告人
+        "reportDeptCode": "D0044",  # 报告部门
+        "productGroupCode": "RAW_MATERIAL"  # 商品分类
+    }
+}
+
+
+warning_data = {
+    "groupNo": '',  # 用户组id
+    "funcModule": '原油特征停更预警',
+    "funcOperation": '原油特征停更预警',
+    "data": {
+        'WARNING_TYPE_NAME': '特征数据停更预警',
+        'WARNING_CONTENT': '',
+        'WARNING_DATE': ''
+    }
+}
+
+query_data_list_item_nos_data = {
+    "funcModule": "数据项",
+    "funcOperation": "查询",
+    "data": {
+        "dateStart": "20200101",
+        "dateEnd": "20241231",
+        "dataItemNoList": ["Brentzdj", "Brentzgj"]  # 数据项编码,代表 brent最低价和最高价
+    }
+}
+
+push_data_value_list_data = {
+    "funcModule": "数据表信息列表",
+    "funcOperation": "新增",
+    "data": [
+        {"dataItemNo": "91230600716676129",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.11
+         },
+        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.55
+         },
+        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.55
+         }
+    ]
+}
+# 八大维度数据项编码
+bdwd_items = {
+    'ciri': 'yyycbdwdcr',
+    'benzhou': 'yyycbdwdbz',
+    'cizhou': 'yyycbdwdcz',
+    'gezhou': 'yyycbdwdgz',
+    'ciyue': 'yyycbdwdcy',
+    'cieryue': 'yyycbdwdcey',
+    'cisanyue': 'yyycbdwdcsy',
+    'cisiyue': 'yyycbdwdcsiy',
+}
+
+# 报告中八大维度数据项重命名
+columnsrename = {'yyycbdwdbz': '本周', 'yyycbdwdcey': '次二月', 'yyycbdwdcr': '次日', 'yyycbdwdcsiy': '次四月',
+                 'yyycbdwdcsy': '次三月', 'yyycbdwdcy': '次月', 'yyycbdwdcz': '次周', 'yyycbdwdgz': '隔周', }
+# 北京环境数据库
+host = '192.168.101.27'
+port = 3306
+dbusername = 'root'
+password = '123456'
+dbname = 'jingbo_test'
+table_name = 'v_tbl_crude_oil_warning'
+
 DEFAULT_CONFIG = {
     'feature_factor_frequency': 'D',
     'strategy_id': 1,
@@ -404,14 +404,14 @@ DEFAULT_CONFIG = {
 # 开关
 is_train = True  # 是否训练
 is_debug = False  # 是否调试
-is_eta = True  # 是否使用eta接口
+is_eta = False  # 是否使用eta接口
 is_market = False  # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
 is_timefurture = True  # 是否使用时间特征
 is_fivemodels = False  # 是否使用之前保存的最佳的5个模型
 is_edbcode = False  # 特征使用edbcoding列表中的
 is_edbnamelist = False  # 自定义特征，对应上面的edbnamelist
 is_update_eta = True  # 预测结果上传到eta
-is_update_report = True  # 是否上传报告
+is_update_report = False  # 是否上传报告
 is_update_warning_data = False  # 是否上传预警数据
 is_update_predict_value = True  # 是否上传预测值到市场信息平台
 is_del_corr = 0.6  # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
diff --git a/config_jingbo_zhoudu.py b/config_jingbo_zhoudu.py
index 784d8d9..fd3ecbc 100644
--- a/config_jingbo_zhoudu.py
+++ b/config_jingbo_zhoudu.py
@@ -119,125 +119,19 @@ data = {
 ClassifyId = 1214
 
 
-# 变量定义--线上环境
-server_host = '10.200.32.39'
-login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
-upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
-upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
-query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
-# 上传数据项值
-push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
-
-login_data = {
-    "data": {
-        "account": "api_dev",
-        "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
-        "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
-        "terminal": "API"
-    },
-    "funcModule": "API",
-    "funcOperation": "获取token"
-}
-
-
-upload_data = {
-    "funcModule": '研究报告信息',
-    "funcOperation": '上传原油价格预测报告',
-    "data": {
-        "groupNo": '',  # 用户组id
-        "ownerAccount": '27663',  # 报告所属用户账号  27663 - 刘小朋
-        "reportType": 'OIL_PRICE_FORECAST',  # 报告类型，固定为OIL_PRICE_FORECAST
-        "fileName": '',  # 文件名称
-        "fileBase64": '',  # 文件内容base64
-        "categoryNo": 'yyjgycbg',  # 研究报告分类编码
-        "smartBusinessClassCode": 'YCJGYCBG',  # 分析报告分类编码
-        "reportEmployeeCode": "E40482",  # 报告人  E40482  - 管理员  0000027663 - 刘小朋
-        "reportDeptCode": "002000621000",  # 报告部门 - 002000621000  SH期货研究部
-        "productGroupCode": "RAW_MATERIAL"  # 商品分类
-    }
-}
-
-warning_data = {
-    "groupNo": '',  # 用户组id
-    "funcModule": '原油特征停更预警',
-    "funcOperation": '原油特征停更预警',
-    "data": {
-        'WARNING_TYPE_NAME': '特征数据停更预警',
-        'WARNING_CONTENT': '',
-        'WARNING_DATE': ''
-    }
-}
-
-query_data_list_item_nos_data = {
-    "funcModule": "数据项",
-    "funcOperation": "查询",
-    "data": {
-        "dateStart": "20200101",
-        "dateEnd": "20241231",
-        "dataItemNoList": ["Brentzdj", "Brentzgj"]  # 数据项编码,代表 brent最低价和最高价
-    }
-}
-
-
-push_data_value_list_data = {
-    "funcModule": "数据表信息列表",
-    "funcOperation": "新增",
-    "data": [
-        {"dataItemNo": "91230600716676129",
-         "dataDate": "20230113",
-         "dataStatus": "add",
-         "dataValue": 100.11
-         },
-        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
-         "dataDate": "20230113",
-         "dataStatus": "add",
-         "dataValue": 100.55
-         },
-        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
-         "dataDate": "20230113",
-         "dataStatus": "add",
-         "dataValue": 100.55
-         }
-    ]
-}
-# 八大维度数据项编码
-bdwd_items = {
-    'ciri': '原油大数据预测|FORECAST|PRICE|T',
-    'benzhou': '原油大数据预测|FORECAST|PRICE|W',
-    'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
-    'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
-    'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
-    'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
-    'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
-    'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
-}
-
-
-# 生产环境数据库
-host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
-port = 3306
-dbusername = 'jingbo'
-password = 'shihua@123'
-dbname = 'jingbo'
-table_name = 'v_tbl_crude_oil_warning'
-
-
-# # # 变量定义--测试环境
-# server_host = '192.168.100.53:8080'  # 内网
-# # server_host = '183.242.74.28'  # 外网
-
-# login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
-# upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
-# upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
-# query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
+# # 变量定义--线上环境
+# server_host = '10.200.32.39'
+# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
+# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
+# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
+# query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/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"
+# push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
 
 # login_data = {
 #     "data": {
-#         "account": "api_test",
-#         # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
-#         "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",  # 123456
+#         "account": "api_dev",
+#         "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
 #         "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
 #         "terminal": "API"
 #     },
@@ -245,24 +139,26 @@ table_name = 'v_tbl_crude_oil_warning'
 #     "funcOperation": "获取token"
 # }
 
+
 # upload_data = {
 #     "funcModule": '研究报告信息',
 #     "funcOperation": '上传原油价格预测报告',
 #     "data": {
-#         "ownerAccount": 'arui',  # 报告所属用户账号
+#         "groupNo": '',  # 用户组id
+#         "ownerAccount": '27663',  # 报告所属用户账号  27663 - 刘小朋
 #         "reportType": 'OIL_PRICE_FORECAST',  # 报告类型，固定为OIL_PRICE_FORECAST
-#         "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf',  # 文件名称
+#         "fileName": '',  # 文件名称
 #         "fileBase64": '',  # 文件内容base64
 #         "categoryNo": 'yyjgycbg',  # 研究报告分类编码
 #         "smartBusinessClassCode": 'YCJGYCBG',  # 分析报告分类编码
-#         "reportEmployeeCode": "E40116",  # 报告人
-#         "reportDeptCode": "D0044",  # 报告部门
+#         "reportEmployeeCode": "E40482",  # 报告人  E40482  - 管理员  0000027663 - 刘小朋
+#         "reportDeptCode": "002000621000",  # 报告部门 - 002000621000  SH期货研究部
 #         "productGroupCode": "RAW_MATERIAL"  # 商品分类
 #     }
 # }
 
-
 # warning_data = {
+#     "groupNo": '',  # 用户组id
 #     "funcModule": '原油特征停更预警',
 #     "funcOperation": '原油特征停更预警',
 #     "data": {
@@ -282,6 +178,7 @@ table_name = 'v_tbl_crude_oil_warning'
 #     }
 # }
 
+
 # push_data_value_list_data = {
 #     "funcModule": "数据表信息列表",
 #     "funcOperation": "新增",
@@ -305,26 +202,129 @@ table_name = 'v_tbl_crude_oil_warning'
 # }
 # # 八大维度数据项编码
 # bdwd_items = {
-#     'ciri': 'yyycbdwdcr',
-#     'benzhou': 'yyycbdwdbz',
-#     'cizhou': 'yyycbdwdcz',
-#     'gezhou': 'yyycbdwdgz',
-#     'ciyue': 'yyycbdwdcy',
-#     'cieryue': 'yyycbdwdcey',
-#     'cisanyue': 'yyycbdwdcsy',
-#     'cisiyue': 'yyycbdwdcsiy',
+#     'ciri': '原油大数据预测|FORECAST|PRICE|T',
+#     'benzhou': '原油大数据预测|FORECAST|PRICE|W',
+#     'cizhou': '原油大数据预测|FORECAST|PRICE|W_1',
+#     'gezhou': '原油大数据预测|FORECAST|PRICE|W_2',
+#     'ciyue': '原油大数据预测|FORECAST|PRICE|M_1',
+#     'cieryue': '原油大数据预测|FORECAST|PRICE|M_2',
+#     'cisanyue': '原油大数据预测|FORECAST|PRICE|M_3',
+#     'cisiyue': '原油大数据预测|FORECAST|PRICE|M_4',
 # }
 
 
-# # 北京环境数据库
-# host = '192.168.101.27'
+# # 生产环境数据库
+# host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
 # port = 3306
-# dbusername = 'root'
-# password = '123456'
-# dbname = 'jingbo_test'
+# dbusername = 'jingbo'
+# password = 'shihua@123'
+# dbname = 'jingbo'
 # table_name = 'v_tbl_crude_oil_warning'
 
 
+# # 变量定义--测试环境
+server_host = '192.168.100.53:8080'  # 内网
+# server_host = '183.242.74.28'  # 外网
+
+login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
+upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
+upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
+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"
+
+login_data = {
+    "data": {
+        "account": "api_test",
+        # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
+        "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",  # 123456
+        "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
+        "terminal": "API"
+    },
+    "funcModule": "API",
+    "funcOperation": "获取token"
+}
+
+upload_data = {
+    "funcModule": '研究报告信息',
+    "funcOperation": '上传原油价格预测报告',
+    "data": {
+        "ownerAccount": 'arui',  # 报告所属用户账号
+        "reportType": 'OIL_PRICE_FORECAST',  # 报告类型，固定为OIL_PRICE_FORECAST
+        "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf',  # 文件名称
+        "fileBase64": '',  # 文件内容base64
+        "categoryNo": 'yyjgycbg',  # 研究报告分类编码
+        "smartBusinessClassCode": 'YCJGYCBG',  # 分析报告分类编码
+        "reportEmployeeCode": "E40116",  # 报告人
+        "reportDeptCode": "D0044",  # 报告部门
+        "productGroupCode": "RAW_MATERIAL"  # 商品分类
+    }
+}
+
+
+warning_data = {
+    "funcModule": '原油特征停更预警',
+    "funcOperation": '原油特征停更预警',
+    "data": {
+        'WARNING_TYPE_NAME': '特征数据停更预警',
+        'WARNING_CONTENT': '',
+        'WARNING_DATE': ''
+    }
+}
+
+query_data_list_item_nos_data = {
+    "funcModule": "数据项",
+    "funcOperation": "查询",
+    "data": {
+        "dateStart": "20200101",
+        "dateEnd": "20241231",
+        "dataItemNoList": ["Brentzdj", "Brentzgj"]  # 数据项编码,代表 brent最低价和最高价
+    }
+}
+
+push_data_value_list_data = {
+    "funcModule": "数据表信息列表",
+    "funcOperation": "新增",
+    "data": [
+        {"dataItemNo": "91230600716676129",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.11
+         },
+        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.55
+         },
+        {"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
+         "dataDate": "20230113",
+         "dataStatus": "add",
+         "dataValue": 100.55
+         }
+    ]
+}
+# 八大维度数据项编码
+bdwd_items = {
+    'ciri': 'yyycbdwdcr',
+    'benzhou': 'yyycbdwdbz',
+    'cizhou': 'yyycbdwdcz',
+    'gezhou': 'yyycbdwdgz',
+    'ciyue': 'yyycbdwdcy',
+    'cieryue': 'yyycbdwdcey',
+    'cisanyue': 'yyycbdwdcsy',
+    'cisiyue': 'yyycbdwdcsiy',
+}
+
+
+# 北京环境数据库
+host = '192.168.101.27'
+port = 3306
+dbusername = 'root'
+password = '123456'
+dbname = 'jingbo_test'
+table_name = 'v_tbl_crude_oil_warning'
+
+
 DEFAULT_CONFIG = {
     'feature_factor_frequency': 'D',
     'strategy_id': 1,
@@ -342,14 +342,14 @@ DEFAULT_CONFIG = {
 # 开关
 is_train = True  # 是否训练
 is_debug = False  # 是否调试
-is_eta = True  # 是否使用eta接口
+is_eta = False  # 是否使用eta接口
 is_market = False  # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
 is_timefurture = True  # 是否使用时间特征
 is_fivemodels = False  # 是否使用之前保存的最佳的5个模型
 is_edbcode = False  # 特征使用edbcoding列表中的
 is_edbnamelist = False  # 自定义特征，对应上面的edbnamelist
 is_update_eta = True  # 预测结果上传到eta
-is_update_report = True  # 是否上传报告
+is_update_report = False  # 是否上传报告
 is_update_warning_data = False  # 是否上传预警数据
 is_update_predict_value = True  # 是否上传预测值到市场信息平台
 is_del_corr = 0.6  # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
diff --git a/config_juxiting.py b/config_juxiting.py
index 92a8f38..7603e39 100644
--- a/config_juxiting.py
+++ b/config_juxiting.py
@@ -432,8 +432,8 @@ is_timefurture = True  # 是否使用时间特征
 is_fivemodels = False  # 是否使用之前保存的最佳的5个模型
 is_edbcode = False  # 特征使用edbcoding列表中的
 is_edbnamelist = False  # 自定义特征，对应上面的edbnamelist
-is_update_eta = True  # 预测结果上传到eta
-is_update_report = False  # 是否上传报告
+is_update_eta = False  # 预测结果上传到eta
+is_update_report = True  # 是否上传报告
 is_update_warning_data = True  # 是否上传预警数据
 is_update_predict_value = True  # 是否上传预测值到市场信息平台
 is_del_corr = 0.6  # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
diff --git a/config_juxiting_yuedu.py b/config_juxiting_yuedu.py
index 1f51263..ab8b83b 100644
--- a/config_juxiting_yuedu.py
+++ b/config_juxiting_yuedu.py
@@ -1,3 +1,4 @@
+from decimal import Decimal
 import logging
 import os
 import logging.handlers
@@ -416,18 +417,32 @@ password = '123456'
 dbname = 'jingbo_test'
 table_name = 'v_tbl_crude_oil_warning'
 
+DEFAULT_CONFIG = {
+    'feature_factor_frequency': 'D',
+    'strategy_id': 2,
+    'model_evaluation_id': 1,
+    'tenant_code': '',
+    'version_num': Decimal(1),
+    'delete_flag': '0',
+    'create_user': 'admin',
+    'create_date': datetime.datetime.now(),
+    'update_user': 'admin',
+    'update_date': datetime.datetime.now(),
+    'oil_code': 'PP',
+    'oil_name': 'PP期货',
+}
 
 # 开关
 is_train = True  # 是否训练
 is_debug = False  # 是否调试
-is_eta = True  # 是否使用eta接口
+is_eta = False  # 是否使用eta接口
 is_market = False  # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
 is_timefurture = True  # 是否使用时间特征
 is_fivemodels = False  # 是否使用之前保存的最佳的5个模型
 is_edbcode = False  # 特征使用edbcoding列表中的
 is_edbnamelist = False  # 自定义特征，对应上面的edbnamelist
 is_update_eta = True  # 预测结果上传到eta
-is_update_report = True  # 是否上传报告
+is_update_report = False  # 是否上传报告
 is_update_warning_data = False  # 是否上传预警数据
 is_update_predict_value = True  # 是否上传预测值到市场信息平台
 is_del_corr = 0  # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
@@ -452,16 +467,20 @@ add_kdj = False  # 是否添加kdj指标
 if add_kdj and is_edbnamelist:
     edbnamelist = edbnamelist+['K', 'D', 'J']
 
+
 # 模型参数
-y = 'AVG-金能大唐久泰青州'
-avg_cols = [
-    'PP：拉丝：1102K：出厂价：青州：国家能源宁煤（日）',
-    'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
-    'PP：拉丝：L5E89：出厂价：河北、鲁北：大唐内蒙多伦（日）',
-    'PP：拉丝：HP550J：市场价：青岛：金能化学（日）'
-]
-offsite = 80
-offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
+# y = 'AVG-金能大唐久泰青州'
+# avg_cols = [
+#     'PP：拉丝：1102K：出厂价：青州：国家能源宁煤（日）',
+#     'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
+#     'PP：拉丝：L5E89：出厂价：河北、鲁北：大唐内蒙多伦（日）',
+#     'PP：拉丝：HP550J：市场价：青岛：金能化学（日）'
+# ]
+# offsite = 80
+# offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
+
+y = 'MAIN_CONFT_SETTLE_PRICE'
+
 horizon = 4  # 预测的步长
 input_size = 16  # 输入序列长度
 train_steps = 50 if is_debug else 1000  # 训练步数,用来限定epoch次数
diff --git a/config_juxiting_zhoudu.py b/config_juxiting_zhoudu.py
index d298eb2..b7d58cb 100644
--- a/config_juxiting_zhoudu.py
+++ b/config_juxiting_zhoudu.py
@@ -450,15 +450,15 @@ DEFAULT_CONFIG = {
 # 开关
 is_train = True  # 是否训练
 is_debug = False  # 是否调试
-is_eta = False  # 是否使用eta接口
+is_eta = True  # 是否使用eta接口
 is_market = True  # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
 is_timefurture = True  # 是否使用时间特征
 is_fivemodels = False  # 是否使用之前保存的最佳的5个模型
 is_edbcode = False  # 特征使用edbcoding列表中的
 is_edbnamelist = False  # 自定义特征，对应上面的edbnamelist
-is_update_eta = True  # 预测结果上传到eta
-is_update_report = False  # 是否上传报告
-is_update_warning_data = True  # 是否上传预警数据
+is_update_eta = False  # 预测结果上传到eta
+is_update_report = True  # 是否上传报告
+is_update_warning_data = False  # 是否上传预警数据
 is_update_predict_value = True  # 是否上传预测值到市场信息平台
 is_del_corr = 0.6  # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
 is_del_tow_month = True  # 是否删除两个月不更新的特征
@@ -475,7 +475,7 @@ print("数据库连接成功", host, dbname, dbusername)
 # 数据截取日期
 start_year = 2015  # 数据开始年份
 end_time = ''  # 数据截取日期
-freq = 'WW'  # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日 "WW" 自定义周
+freq = 'W'  # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日 "WW" 自定义周
 delweekenday = True if freq == 'B' else False  # 是否删除周末数据
 is_corr = False  # 特征是否参与滞后领先提升相关系数
 add_kdj = False  # 是否添加kdj指标
diff --git a/lib/dataread.py b/lib/dataread.py
index c909179..41b254e 100644
--- a/lib/dataread.py
+++ b/lib/dataread.py
@@ -516,7 +516,8 @@ def featureAnalysis(df, dataset, y):
     import matplotlib.pyplot as plt
     # 选择特征和标签列
     X = df.drop(['ds', 'y'], axis=1)  # 特征集，排除时间戳和标签列
-    yy = df['y']  # 标签集
+    yy = df['y'] # 标签集
+    
 
     # 标签集自相关函数分析
     from statsmodels.graphics.tsaplots import plot_acf
@@ -2439,6 +2440,15 @@ def get_high_low_data(df):
     df = pd.merge(df, df1, how='left', on='date')
     return df
 
+def get_shujuxiang_data(df):
+    # 读取excel 从第五行开始
+    df1 = pd.read_excel(os.path.join(config.dataset, '数据项下载.xls'), header=5, names=[
+                        'numid', 'date', 'MAIN_CONFT_SETTLE_PRICE'])
+    df1['MAIN_CONFT_SETTLE_PRICE'] = df1['MAIN_CONFT_SETTLE_PRICE'].str.replace(',', '').astype(float)
+    # 合并数据
+    df = pd.merge(df, df1, how='left', on='date')
+    return df
+
 
 def get_waring_data():
     '''获取取消订阅的指标数据'''
diff --git a/main_juxiting.py b/main_juxiting.py
index 4469ff6..a202f29 100644
--- a/main_juxiting.py
+++ b/main_juxiting.py
@@ -191,7 +191,7 @@ def sql_inset_predict(global_config):
         ]
 
         insert_query = f"""
-        INSERT INTO v_tbl_predict_prediction_results (
+        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,
@@ -300,18 +300,17 @@ def predict_main():
 
         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文档获取市场信息平台指标')
-                    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('市场信息平台数据项-eta数据项 拼接失败')
+            # 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文档获取市场信息平台指标')
+                df_zhibiaoshuju = get_shujuxiang_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('市场信息平台数据项-eta数据项 拼接失败')
 
         # 保存到xlsx文件的sheet表
         with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
@@ -503,21 +502,20 @@ def predict_main():
 
     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')
+    # 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')
 
-    # # 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大模型日度预测--{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),
+    # # # 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大模型日度预测--{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')
-    logger.info('模型训练完成')
+    # logger.info('制作报告end')
 
     push_market_value()
     sql_inset_predict(global_config)
@@ -547,15 +545,17 @@ def predict_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('2025-4-14', '2025-4-15', freq='B'):
-    #     try:
-    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-    #         predict_main()
-    #     except Exception as e:
-    #         logger.info(f'预测失败：{e}')
-    #         continue
+    for i_time in pd.date_range('2025-6-2', '2025-7-23', 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()
 
-    predict_main()
 
     # push_market_value()
     # sql_inset_predict(global_config)
diff --git a/main_juxiting_yuedu.py b/main_juxiting_yuedu.py
index 99087d9..6063256 100644
--- a/main_juxiting_yuedu.py
+++ b/main_juxiting_yuedu.py
@@ -2,8 +2,8 @@
 
 from lib.dataread import *
 from config_juxiting_yuedu import *
-from lib.tools import SendMail, exception_logger
-from models.nerulforcastmodels import ex_Model, model_losss_juxiting, tansuanli_export_pdf, pp_export_pdf
+from lib.tools import SendMail, convert_df_to_pydantic, exception_logger, get_modelsname
+from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_export_pdf
 import datetime
 import torch
 torch.set_float32_matmul_precision("high")
@@ -13,9 +13,9 @@ global_config.update({
     'logger': logger,
     'dataset': dataset,
     'y': y,
-    'offsite_col': offsite_col,
-    'avg_cols': avg_cols,
-    'offsite': offsite,
+    # 'offsite_col': offsite_col,
+    # 'avg_cols': avg_cols,
+    # 'offsite': offsite,
     'edbcodenamedict': edbcodenamedict,
     'is_debug': is_debug,
     'is_train': is_train,
@@ -23,6 +23,7 @@ global_config.update({
     'is_update_report': is_update_report,
     'settings': settings,
     'bdwdname': bdwdname,
+    'columnsrename': columnsrename,
 
 
     # 模型参数
@@ -84,7 +85,8 @@ global_config.update({
     # 数据库配置
     'sqlitedb': sqlitedb,
     'is_bdwd': is_bdwd,
-    'columnsrename':columnsrename,
+    'db_mysql': db_mysql,
+    'DEFAULT_CONFIG': DEFAULT_CONFIG,
 })
 
 
@@ -160,6 +162,97 @@ def push_market_value():
         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(
+            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。
@@ -392,15 +485,16 @@ def predict_main():
 
     # push_market_value()
     # # 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')
-    logger.info('模型训练完成')
+    # logger.info('制作报告end')
+    # logger.info('模型训练完成')
+    sql_inset_predict(global_config)
 
     # # LSTM 单变量模型
     # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
diff --git a/main_juxiting_zhoudu.py b/main_juxiting_zhoudu.py
index 3ff723d..30438a0 100644
--- a/main_juxiting_zhoudu.py
+++ b/main_juxiting_zhoudu.py
@@ -158,12 +158,12 @@ 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'])
-    # 获取次日预测结果
-    next_day_df = df[df['ds'] == df['ds'].min()]
-    # 获取本周预测结果
-    this_week_df = df[df['ds'] == df['ds'].max()]
+    # 获取次周预测结果
+    second_week_price_df = df[df['ds'] == df['ds'].min()]
+    # 获取隔周周预测结果
+    next_week_price_df = df[df['ds'] == df['ds'].max()]
 
-    wd = ['day_price', 'week_price']
+    wd = ['second_week_price', 'next_week_price']
     model_name_list, model_id_name_dict = get_modelsname(df, global_config)
 
     PRICE_COLUMNS = [
@@ -172,7 +172,7 @@ def sql_inset_predict(global_config):
     ]
 
     params_list = []
-    for df, price_type in zip([next_day_df, this_week_df], wd):
+    for df, price_type in zip([second_week_price_df, next_week_price_df], wd):
 
         update_columns = [
             "feature_factor_frequency = VALUES(feature_factor_frequency)",
@@ -190,7 +190,7 @@ def sql_inset_predict(global_config):
         ]
 
         insert_query = f"""
-        INSERT INTO v_tbl_predict_prediction_results (
+        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,
@@ -238,7 +238,6 @@ def sql_inset_predict(global_config):
         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。
@@ -303,7 +302,7 @@ def predict_main():
                 # 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文档获取市场信息平台指标')
-                    df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
+                    df_zhibiaoshuju = get_shujuxiang_data(df_zhibiaoshuju)
                 else:
                     logger.info('从市场信息平台获取数据')
                     df_zhibiaoshuju = get_market_data(
@@ -318,7 +317,7 @@ def predict_main():
             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,
+        df = zhoududatachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
                                 end_time=end_time)
 
     else:
@@ -465,21 +464,20 @@ def predict_main():
 
     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')
+    # 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')
 
-    # # 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大模型周度预测--{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),
+    # # # 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大模型周度预测--{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')
-    logger.info('模型训练完成')
+    # logger.info('制作报告end')
 
     push_market_value()
     sql_inset_predict(global_config)
@@ -509,15 +507,16 @@ def predict_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('2025-4-14', '2025-4-15', freq='B'):
-    #     try:
-    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-    #         predict_main()
-    #     except Exception as e:
-    #         logger.info(f'预测失败：{e}')
-    #         continue
+    for i_time in pd.date_range('2025-7-18', '2025-7-23', 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()
-    sql_inset_predict(global_config)
+    # sql_inset_predict(global_config)
diff --git a/main_yuanyou.py b/main_yuanyou.py
index 3aaeed2..ee2f185 100644
--- a/main_yuanyou.py
+++ b/main_yuanyou.py
@@ -580,8 +580,8 @@ def predict_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('2025-6-19', '2025-6-28', freq='B'):
+    # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
+    # for i_time in pd.date_range('2024-9-12', '2024-10-7', freq='B'):
     #     global_config['end_time'] = i_time.strftime('%Y-%m-%d')
     #     global_config['db_mysql'].connect()
     #     predict_main()
diff --git a/main_yuanyou_yuedu.py b/main_yuanyou_yuedu.py
index 5ff3673..9e6ba2e 100644
--- a/main_yuanyou_yuedu.py
+++ b/main_yuanyou_yuedu.py
@@ -590,7 +590,7 @@ def predict_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('2025-6-4', '2025-6-30', freq='B'):
+    for i_time in pd.date_range('2025-3-17', '2025-3-31', freq='B'):
         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
         global_config['db_mysql'].connect()
         predict_main()
diff --git a/main_yuanyou_zhoudu.py b/main_yuanyou_zhoudu.py
index ebb4d29..9824dc9 100644
--- a/main_yuanyou_zhoudu.py
+++ b/main_yuanyou_zhoudu.py
@@ -492,7 +492,7 @@ def predict_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('2025-6-23', '2025-6-30', freq='B'):
+    for i_time in pd.date_range('2025-3-5', '2025-3-18', freq='B'):
         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
         global_config['db_mysql'].connect()
         predict_main()
diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 7f6082c..739379a 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -165,8 +165,8 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
 
         # VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),  //报错了
         # Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
-        # NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
-        # NBEATSx (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ),   //报错
+        NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
+        # NBEATSx(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ),   //报错
         # HINT(h=horizon),
 
     ]
@@ -439,12 +439,12 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
             config.dataset, '*.joblib')), key=os.path.getctime)
         config.logger.info('读取模型：' + filename)
         nf = load(filename)
-    # 测试集预测
-    nf_test_preds = nf.cross_validation(
-        df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
-    # 测试集预测结果保存
-    nf_test_preds.to_csv(os.path.join(
-        config.dataset, "cross_validation.csv"), index=False)
+    # # 测试集预测
+    # nf_test_preds = nf.cross_validation(
+    #     df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
+    # # 测试集预测结果保存
+    # nf_test_preds.to_csv(os.path.join(
+    #     config.dataset, "cross_validation.csv"), index=False)
 
     df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
 
@@ -2344,38 +2344,42 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
     # 去掉created_dt 列
     df4 = df4.drop(columns=['created_dt'])
     # 计算模型偏差率
-    # 计算各列对于y列的差值百分比
-    df3 = pd.DataFrame()  # 存储偏差率
+    try:
+        # 计算各列对于y列的差值百分比
+        df3 = pd.DataFrame()  # 存储偏差率
 
-    # 删除有null的行
-    df4 = df4.dropna()
-    df3['ds'] = df4['ds']
-    for col in fivemodels_list:
-        df3[col] = round(abs(df4[col] - df4['y']) / df4['y'] * 100, 2)
-    # 找出决定系数前五的偏差率
-    df3 = df3[['ds']+fivemodels_list.tolist()][-inputsize:]
-    # 找出上一预测区间的时间
-    stime = df3['ds'].iloc[0]
-    etime = df3['ds'].iloc[-1]
-    # 添加偏差率表格
-    fivemodels = '、'.join(eval_df['模型(Model)'].values[:5])  # 字符串形式，后面写入字符串使用
-    content.append(Graphs.draw_text(
-        f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
-    # # 添加偏差率表格
-    df3 = df3.T
-    df3 = df3.reset_index()
-    data = df3.values.tolist()
-    col_width = 500/len(df3.columns)
-    content.append(Graphs.draw_table(col_width, *data))
+        # 删除有null的行
+        df4 = df4.dropna()
+        df3['ds'] = df4['ds']
+        for col in fivemodels_list:
+            df3[col] = round(abs(df4[col] - df4['y']) / df4['y'] * 100, 2)
+        # 找出决定系数前五的偏差率
+        df3 = df3[['ds']+fivemodels_list.tolist()][-inputsize:]
+        # 找出上一预测区间的时间
+        stime = df3['ds'].iloc[0]
+        etime = df3['ds'].iloc[-1]
+        # 添加偏差率表格
+        fivemodels = '、'.join(eval_df['模型(Model)'].values[:5])  # 字符串形式，后面写入字符串使用
+        content.append(Graphs.draw_text(
+            f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
+        # # 添加偏差率表格
+        df3 = df3.T
+        df3 = df3.reset_index()
+        data = df3.values.tolist()
+        col_width = 500/len(df3.columns)
+        content.append(Graphs.draw_table(col_width, *data))
 
-    content.append(Graphs.draw_little_title('上一周预测准确率：'))
-    df4 = sqlitedb.select_data('accuracy_rote', order_by='结束日期 desc', limit=1)
-    df4 = df4.T
-    df4 = df4.reset_index()
-    df4 = df4.T
-    data = df4.values.tolist()
-    col_width = 500/len(df4.columns)
-    content.append(Graphs.draw_table(col_width, *data))
+        content.append(Graphs.draw_little_title('上一周预测准确率：'))
+        df4 = sqlitedb.select_data('accuracy_rote', order_by='结束日期 desc', limit=1)
+        df4 = df4.T
+        df4 = df4.reset_index()
+        df4 = df4.T
+        data = df4.values.tolist()
+        col_width = 500/len(df4.columns)
+        content.append(Graphs.draw_table(col_width, *data))
+    except:
+        content.append(Graphs.draw_text('暂无'))
+        config.logger.info('偏差率计算错误，跳过')
 
     content.append(Graphs.draw_little_title('三、预测过程解析：'))
     # 特征、模型、参数配置
diff --git a/test/demo.py b/test/demo.py
new file mode 100644
index 0000000..7af5090
--- /dev/null
+++ b/test/demo.py
@@ -0,0 +1,140 @@
+import requests
+import json
+
+
+def get_cptcha():
+    url = 'https://marketinfo.jbshihua.com/jingbo-api/captcha'
+    res = requests.get(url)
+    res = json.loads(res.text)
+    res = res["data"]["img"]
+    imgurl = 'data:image/jpeg;base64,/' + res
+    res = requests.get(imgurl)
+    with open('cptcha.png', 'wb') as f:
+        f.write(res.content)
+
+
+def main() -> dict:
+    login_url = "http://marketinfo.jbshihua.com/jingbo-api/api/server/login"
+
+    login_data = {
+        "data": {
+            "account": "api_dev",
+            "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
+            "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
+            "terminal": "API"
+        },
+        "funcModule": "API",
+        "funcOperation": "获取token"
+    }
+
+    query_data_list_item_nos_url = f"http://marketinfo.jbshihua.com/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
+
+    query_data_list_item_nos_data = {
+        "funcModule": "数据项",
+        "funcOperation": "查询",
+        "data": {
+            "dateStart": "20200101",
+            "dateEnd": "20241231",
+            "dataItemNoList": ["Brentjsj"]  # 数据项编码,代表 brent结算价
+        }
+    }
+    login_res = requests.post(
+        url=login_url, json=login_data, timeout=(3, 5))
+    text = json.loads(login_res.text)
+    if text["status"]:
+        token = text["data"]["accessToken"]
+        print('获取到的token', token)
+        headers = {"Authorization": token}
+        print('获取数据中...')
+        items_res = requests.post(url=query_data_list_item_nos_url, headers=headers,
+                                  json=query_data_list_item_nos_data, timeout=(3, 35))
+        json_data = json.loads(items_res.text)
+        print(json_data)
+        return text
+
+    return {
+        "result": token,
+    }
+
+
+class AuthHandler:
+    def __init__(self):
+        # 初始化登录表单数据
+        self.loginForm = {
+            'captchaToken': '',
+            'src': ''
+        }
+        self.loading = False
+
+    def loadCaptcha(self):
+        """加载验证码图片"""
+        try:
+            self.loading = True
+
+            # 发送 POST 请求获取验证码
+            response = requests.post(
+                url="https://marketinfo.jbshihua.com/jingbo-api/captcha",
+                json={},  # 发送空 JSON 数据体
+                timeout=10  # 设置超时时间为 10 秒
+            )
+
+            # 检查响应状态码
+            response.raise_for_status()
+
+            # 处理响应数据
+            data = response.json()
+            if data:
+                self.loginForm['captchaToken'] = data.get('token', '')
+                self.loginForm['src'] = "data:image/jpeg;base64," + \
+                    data.get('img', '')
+
+                # 保存图片
+                with open('cptcha.png', 'wb') as f:
+                    f.write(res.content)
+
+        except requests.exceptions.RequestException as e:
+            print(f"请求出错: {e}")
+        finally:
+            self.loading = False
+
+
+def main2() -> dict:
+    import requests
+    import json
+    login_url = "http://marketinfo.jbshihua.com/jingbo-api/api/server/login"
+
+    login_data = {
+        "data": {
+            "account": "admin",
+            "password": "OWZlYjcyNDAwZDRkYjEwZjE1ZjA0MTIwNDAwOGI5NjI=",
+            "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
+            "terminal": "PC"
+        },
+        "funcModule": "登录页面",
+        "funcOperation": "登录"
+    }
+    login_res = requests.post(
+        url=login_url, json=login_data, timeout=(3, 5))
+    text = json.loads(login_res.text)
+    print(text)
+    if text["status"]:
+        token = text["data"]["accessToken"]
+
+        return {
+            "accessToken": token,
+        }
+    return {
+        "accessToken": "",
+    }
+
+
+# 使用示例
+if __name__ == "__main__":
+    # auth = AuthHandler()
+    # auth.loadCaptcha()
+
+    # # 打印获取到的验证码信息
+    # print(f"验证码 Token: {auth.loginForm['captchaToken']}")
+    # print(f"验证码图片: {auth.loginForm['src']}...")  # 只显示前50个字符
+    # main2()
+    main()