From 34770b83a3e07b87cbb9457c52aa047a55a94a44 Mon Sep 17 00:00:00 2001
From: workpc <liurui_test@163.com>
Date: Wed, 9 Jul 2025 17:52:29 +0800
Subject: [PATCH] =?UTF-8?q?=E7=BA=BF=E4=B8=8A=E6=95=B0=E6=8D=AE=E7=BB=B4?=
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MIME-Version: 1.0
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---
 aisenzhecode/沥青/定性模型数据项12-11.xlsx    |  Bin 93024 -> 93699 bytes
 aisenzhecode/沥青/沥青定性每日执行.py         | 1888 ++++++++---------
 aisenzhecode/石油苯/日度价格预测_最佳模型.pkl |  Bin 140607 -> 141833 bytes
 aisenzhecode/石油苯/纯苯数据项.xls            |  Bin 170496 -> 174592 bytes
 aisenzhecode/石油苯/纯苯每日价格预测.py       |  343 ++-
 5 files changed, 1037 insertions(+), 1194 deletions(-)

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 cf7ddade1818252379863fb06dc892348bde7788..7fd829c2f10b93d47fd1dd3444c9b86545742b30 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 0656f30..43ef7f3 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
@@ -1,18 +1,29 @@
+import numpy as np
+import logging
 import requests
 import json
 import xlrd
 import xlwt
-from datetime import datetime, timedelta 
+from datetime import datetime, timedelta
 import time
 import pandas as pd
 pd.set_option('display.max_columns', None)
 
-import numpy as np
+
+# 配置日志功能
+logging.basicConfig(
+    filename='沥青定性每日执行.log',  # 日志文件名
+    level=logging.INFO,  # 日志级别，INFO 表示记录所有信息
+    format='%(asctime)s - %(levelname)s - %(message)s',  # 日志格式
+    datefmt='%Y-%m-%d %H:%M:%S'  # 日期格式
+)
+
 # 变量定义
 login_url = "http://10.200.32.39/jingbo-api/api/server/login"
 login_push_url = "http://10.200.32.39/jingbo-api/api/server/login"
 # query_data_list_item_nos_url
-search_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos"  #jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos
+# jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos
+search_url = "http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos"
 upload_url = "http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList"
 queryDataListItemNos_url = "http://10.200.32.39/jingbo-api//api/warehouse/dwDataItem/queryDataListItemNos"
 
@@ -55,25 +66,40 @@ read_file_path_name = "定性模型数据项12-11.xlsx"
 one_cols = []
 two_cols = []
 
+
 def get_head_auth():
-    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"]
-        return token
-    else:
-        print("获取认证失败")
+    try:
+        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"]
+            logging.info("成功获取认证 token")
+            return token
+        else:
+            logging.error("获取认证失败，响应信息: %s", login_res.text)
+            print("获取认证失败")
+            return None
+    except Exception as e:
+        logging.error("获取认证时发生异常: %s", str(e))
         return None
 
 
 def get_head_push_auth():
-    login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))
-    text = json.loads(login_res.text)
-    if text["status"]:
-        token = text["data"]["accessToken"]
-        return token
-    else:
-        print("获取认证失败")
+    try:
+        login_res = requests.post(
+            url=login_push_url, json=login_push_data, timeout=(3, 5))
+        text = json.loads(login_res.text)
+        if text["status"]:
+            token = text["data"]["accessToken"]
+            logging.info("成功获取推送认证 token")
+            return token
+        else:
+            logging.error("获取推送认证失败，响应信息: %s", login_res.text)
+            print("获取认证失败")
+            return None
+    except Exception as e:
+        logging.error("获取推送认证时发生异常: %s", str(e))
         return None
 
 
@@ -88,1030 +114,858 @@ def update_e_value(file_path, column_index, threshold):
     :param column_index: 需要更新的列索引
     :param threshold: 变化阈值
     """
-    # 读取Excel文件
-    # try:
-    #     df = pd.read_excel(file_path, engine='openpyxl')
-    # except:
-    #     df = pd.read_excel(file_path, engine='xlrd')
-        
-    df = pd.read_excel(file_path)
-    # 所有列列统一数据格式为float
-    df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)
-    
-    # print(df.tail())
-    # 填充缺失值
-    df = df.fillna(method='ffill')
+    try:
+        logging.info("开始更新 Excel 文件中指定列的值，文件路径: %s", file_path)
+        df = pd.read_excel(file_path)
+        df = df.applymap(lambda x: float(
+            x) if isinstance(x, (int, float)) else x)
+        df = df.fillna(method='ffill')
+        df1 = df[-3:-1]
+        previous_value = df1.iloc[0, column_index]
+        current_value = df1.iloc[1, column_index]
+        if abs(current_value - previous_value) > threshold:
+            df.iloc[-2, column_index] = previous_value
+            logging.info("指定列值变化大于阈值，已将当前值修改为前一天的值")
+        df.to_excel(file_path, index=False, engine='openpyxl')
+        logging.info("Excel 文件更新完成")
+    except Exception as e:
+        logging.error("更新 Excel 文件时发生异常: %s", str(e))
 
-    # 获取昨天，前天数据
-    df1 = df[-3:-1]
-    print(df1)
-    # 获取前一天的指定列值
-    previous_value = df1.iloc[0, column_index]
-    print('前一天的',previous_value,type(previous_value))
-    # 获取当前的指定列值
-    current_value = df1.iloc[1, column_index]
-    print('现在的',current_value,type(current_value))
-    # 判断指定列值的变化是否大于阈值
-    if abs(current_value - previous_value) > threshold:
-        # 如果变化大于阈值，将当前的指定列值改为前一天的值
-        df.iloc[-2, column_index] = previous_value
-        print('修改了')
-    # print(df.tail())
-    # 将修改后的数据写回Excel文件
-    df.to_excel(file_path, index=False,engine='openpyxl')
 
 def getLogToken():
-    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"]
-    else:
-        print("获取认证失败")
-        token =  None
-    return token
-
-def updateExcelDatabak(date='',token=None):
-    workbook = xlrd.open_workbook(read_file_path_name)
-
-    # 选择第一个表格
-    sheet = workbook.sheet_by_index(0)
-
-    row_data = sheet.row_values(1)
-    one_cols = row_data
-
-    cur_time,cur_time2 = getNow(date)
-    search_data = {
-        "data": {
-            "date": cur_time,
-            "dataItemNoList": one_cols[1:]
-        },
-        "funcModule": "数据项",
-        "funcOperation": "查询"
-    }
-    headers = {"Authorization": token}
-    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
-    search_value = json.loads(search_res.text)["data"]
-#     datas = search_value
-    if search_value:
-        datas = search_value
-    else :
-        datas = None
-   
-
-    append_rows = [cur_time2]
-    dataItemNo_dataValue = {}
-#     for data_value in datas:
-#         dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for data_value in datas:
-        if "dataValue" not in data_value:
-            print(data_value)
-            dataItemNo_dataValue[data_value["dataItemNo"]] = ""
+    try:
+        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"]
+            logging.info("成功获取日志 token")
+            return token
         else:
-            dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for value in one_cols[1:]:
-        if value in dataItemNo_dataValue:
-            append_rows.append(dataItemNo_dataValue[value])
-        else:
-            append_rows.append("")
-
-    workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')
-
-    # 获取所有sheet的个数
-    sheet_count = len(workbook.sheet_names())
-
-    # 获取所有sheet的名称
-    sheet_names = workbook.sheet_names()
-
-    new_workbook = xlwt.Workbook()
-    for i in range(sheet_count):
-        # 获取当前sheet
-        sheet = workbook.sheet_by_index(i)
-
-        # 获取sheet的行数和列数
-        row_count = sheet.nrows
-        col_count = sheet.ncols
-        # 获取原有数据
-        data = []
-        for row in range(row_count):
-            row_data = []
-            for col in range(col_count):
-                row_data.append(sheet.cell_value(row, col))
-            data.append(row_data)
-        # 创建xlwt的Workbook对象
-        # 创建sheet
-        new_sheet = new_workbook.add_sheet(sheet_names[i])
-
-        # 将原有的数据写入新的sheet
-        for row in range(row_count):
-            for col in range(col_count):
-                new_sheet.write(row, col, data[row][col])
-
-        if i == 0:
-            
-            # 在新的sheet中添加数据
-            for col in range(col_count):
-                new_sheet.write(row_count, col, append_rows[col])
-
-    # 保存新的xls文件
-    new_workbook.save("定性模型数据项12-11.xlsx")
-
-def updateYesterdayExcelData(date='', token=None):
-    # 使用pandas读取Excel文件
-    df = pd.read_excel(read_file_path_name, engine='openpyxl')
-
-    # 获取第二行的数据作为列名
-    one_cols = df.iloc[0,:].tolist()
-
-    # 获取当前日期的前一天
-    if date == '':
-        previous_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')
-    else:
-        # 字符串转日期
-        previous_date = (datetime.strptime(date, "%Y-%m-%d")-timedelta(days=1)).strftime('%Y-%m-%d')
-    
-
-    cur_time, cur_time2 = getNow(previous_date)
-    search_data = {
-        "data": {
-            "date": cur_time,
-            "dataItemNoList": one_cols[1:]
-        },
-        "funcModule": "数据项",
-        "funcOperation": "查询"
-    }
-    headers = {"Authorization": token}
-    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
-    search_value = json.loads(search_res.text)["data"]
-    if search_value:
-        datas = search_value
-    else:
-        datas = None
-
-    append_rows = [cur_time2]
-    dataItemNo_dataValue = {}
-    for data_value in datas:
-        if "dataValue" not in data_value:
-            print(data_value)
-            dataItemNo_dataValue[data_value["dataItemNo"]] = ""
-        else:
-            dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for value in one_cols[1:]:
-        if value in dataItemNo_dataValue:
-            append_rows.append(dataItemNo_dataValue[value])
-        else:
-            append_rows.append("")
-
-    print('更新数据前')
-    print(df.tail(1))
-    # 检查日期是否已存在于数据中
-    if previous_date not in df['日期'].values:
-        # 将新的数据添加到DataFrame中
-        new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())
-        df = pd.concat([df, new_row], ignore_index=True)
-    else:
-        # 更新现有数据
-        print('日期存在，即将更新')
-        print('新数据',append_rows[1:])
-        df.loc[df['日期'] == previous_date, df.columns.tolist()[1:]] = append_rows[1:]
-
-    print('更新数据后')
-    print(df.tail(1))
-    # 使用pandas保存Excel文件
-    df.to_excel("定性模型数据项12-11.xlsx", index=False, engine='openpyxl')
-
-
-def updateExcelData(date='', token=None):
-    # 使用pandas读取Excel文件
-    df = pd.read_excel(read_file_path_name, engine='openpyxl')
-
-    # 获取第一行的数据作为列名
-    # one_cols = df.columns.tolist()
-    
-    # 获取第二行的数据作为列名
-    one_cols = df.iloc[0,:].tolist()
-
-    cur_time, cur_time2 = getNow(date)
-    search_data = {
-        "data": {
-            "date": cur_time,
-            "dataItemNoList": one_cols[1:]
-        },
-        "funcModule": "数据项",
-        "funcOperation": "查询"
-    }
-    headers = {"Authorization": token}
-    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
-    search_value = json.loads(search_res.text)["data"]
-    if search_value:
-        datas = search_value
-    else:
-        datas = None
-
-    append_rows = [cur_time2]
-    dataItemNo_dataValue = {}
-    for data_value in datas:
-        if "dataValue" not in data_value:
-            print(data_value)
-            dataItemNo_dataValue[data_value["dataItemNo"]] = ""
-        else:
-            dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for value in one_cols[1:]:
-        if value in dataItemNo_dataValue:
-            append_rows.append(dataItemNo_dataValue[value])
-        else:
-            append_rows.append("")
-
-    # 将新的数据添加到DataFrame中
-    new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())
-    df = pd.concat([df, new_row], ignore_index=True)
-    # df = df.append(pd.Series(append_rows), ignore_index=True)
-
-    # 使用pandas保存Excel文件
-    df.to_excel("定性模型数据项12-11.xlsx", index=False, engine='openpyxl')
-
-
-def qualitativeModel():
-    df = pd.read_excel('定性模型数据项12-11.xlsx')
-
-    df=df.fillna(df.ffill())
-    df1 = df[-3:-1].reset_index()
-    print(df1)
-    '''
-    # if df1.loc[1,'70号沥青开工率'] > 0.3:   
-    2025年1月8日 修改：
-        复盘分析后发现2024-7月开始，开工率数据从0.28 变为了28 ，改为下面的判断规则
-    '''
-    if df1.loc[1,'70号沥青开工率'] / 100  > 0.3:
-        a = -(df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1
-    else :
-        a = 0
-    b = df1.loc[1,'资金因素']
-    
-    print('昨日计划提货偏差改之前',df1.loc[1,'昨日计划提货偏差'])
-    # 昨日计划提货偏差 = 京博产量 - 计划产量
-    df1.loc[1,'昨日计划提货偏差'] = df1.loc[1,'京博产量'] - df1.loc[1,'计划产量']
-    
-    print('昨日计划提货偏差改之后',df1.loc[1,'昨日计划提货偏差'])
-    if df1.loc[1,'昨日计划提货偏差']>0:
-        c = df1.loc[1,'昨日计划提货偏差']*10/2000
-    else :
-        c = df1.loc[1,'昨日计划提货偏差']*10/3000
-        
-    # 生产情况 = (京博产量 - 计划产量)/500*5
-    d = (df1.loc[1,'京博产量'] - df1.loc[1,'计划产量']) / 500 * 5
-    
-    if df1.loc[1,'基质沥青库存']/265007 >0.8:
-        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000
-    else : 
-        e = 0
-#     f = df1.loc[1,'下游客户价格预期']
-    f = 1 # 2025年1月23日修改：价格预期都按1计算
-    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:
-        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100
-    else :
-        g = 0
-    h = df1.loc[1,'订单结构']
-    x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)
-    return x
-
-
-def getNow(date='',offset=0):
-    if date == '':
-        now = datetime.now() - timedelta(days=offset)
-    else:
-        try:
-            date = datetime.strptime(date, "%Y-%m-%d")
-        except:
-            date = datetime.strptime(date, "%Y%m%d")
-        now = date
-
-    year = now.year
-    month = now.month
-    day = now.day
-
-    if month < 10:
-        month = "0" + str(month)
-    if day < 10:
-        day = "0" + str(day)
-    cur_time = str(year) + str(month) + str(day)
-    cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
-    return cur_time,cur_time2
-
-def pushData(cur_time,x,token_push):
-    data1 = {
-        "funcModule": "数据表信息列表",
-        "funcOperation": "新增",
-        "data": [
-            {"dataItemNo": "C01100036|Forecast_Price|DX|ACN",
-             "dataDate": cur_time,
-             "dataStatus": "add",
-             "dataValue": x
-             }
-        ]
-    }
-    headers1 = {"Authorization": token_push}
-    res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))
-    
-def start_2(date='',token=None):
-    workbook = xlrd.open_workbook(read_file_path_name)
-
-    # 选择第一个表格
-    sheet = workbook.sheet_by_index(0)
-
-    # 获取行数和列数
-    num_rows = sheet.nrows
-    
-    row_data = sheet.row_values(1)
-    one_cols = row_data
-
-    cur_time,cur_time2 = getNow(date)
-    
-    
-    
-    search_data = {
-        "data": {
-            "date": cur_time,
-            "dataItemNoList": one_cols[1:]
-        },
-        "funcModule": "数据项",
-        "funcOperation": "查询"
-    }
-    headers = {"Authorization": token}
-    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
-    search_value = json.loads(search_res.text)["data"]
-#     datas = search_value
-    if search_value:
-        datas = search_value
-    else :
-        datas = None
-   
-
-    append_rows = [cur_time2]
-    dataItemNo_dataValue = {}
-#     for data_value in datas:
-#         dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for data_value in datas:
-        if "dataValue" not in data_value:
-            print(data_value)
-            dataItemNo_dataValue[data_value["dataItemNo"]] = ""
-        else:
-            dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for value in one_cols[1:]:
-        if value in dataItemNo_dataValue:
-            append_rows.append(dataItemNo_dataValue[value])
-        else:
-            append_rows.append("")
-
-    workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')
-
-    # 获取所有sheet的个数
-    sheet_count = len(workbook.sheet_names())
-
-    # 获取所有sheet的名称
-    sheet_names = workbook.sheet_names()
-
-    new_workbook = xlwt.Workbook()
-    for i in range(sheet_count):
-        # 获取当前sheet
-        sheet = workbook.sheet_by_index(i)
-
-        # 获取sheet的行数和列数
-        row_count = sheet.nrows
-        col_count = sheet.ncols
-        # 获取原有数据
-        data = []
-        for row in range(row_count):
-            row_data = []
-            for col in range(col_count):
-                row_data.append(sheet.cell_value(row, col))
-            data.append(row_data)
-        # 创建xlwt的Workbook对象
-        # 创建sheet
-        new_sheet = new_workbook.add_sheet(sheet_names[i])
-
-        # 将原有的数据写入新的sheet
-        for row in range(row_count):
-            for col in range(col_count):
-                new_sheet.write(row, col, data[row][col])
-
-        if i == 0:
-            
-            # 在新的sheet中添加数据
-            for col in range(col_count):
-                new_sheet.write(row_count, col, append_rows[col])
-
-    # 保存新的xls文件
-    new_workbook.save("定性模型数据项12-11.xlsx")
-
-    update_e_value('定性模型数据项12-11.xlsx', 8, 1000)
-
-    df = pd.read_excel('定性模型数据项12-11.xlsx')
-
-    df=df.fillna(df.ffill())
-    df1 = df[-2:].reset_index()
-    '''
-    # if df1.loc[1,'70号沥青开工率'] > 0.3:   
-    2025年1月8日 修改：
-        复盘分析后发现2024-7月开始，开工率数据从0.28 变为了28 ，改为下面的判断规则
-    '''
-    if df1.loc[1,'70号沥青开工率'] > 30:
-        a = (df1.loc[1,'70号沥青开工率']-0.2)*5/0.1
-    else :
-        a = 0
-    b = df1.loc[1,'资金因素']
-    if df1.loc[1,'昨日计划提货偏差']>0:
-        c = df1.loc[1,'昨日计划提货偏差']*10/2000
-    else :
-        c = df1.loc[1,'昨日计划提货偏差']*10/3000
-    d = df1.loc[1,'生产情况']
-    if df1.loc[1,'基质沥青库存']/265007 >0.8:
-        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000
-    else : 
-        e = 0
-#     f = df1.loc[1,'下游客户价格预期']
-    f = 1 # 2025年1月23日修改：价格预期都按1计算
-    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:
-        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100
-    else :
-        g = 0
-    h = df1.loc[1,'订单结构']
-    x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)
-
-    login_res1 = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))
-    text1 = json.loads(login_res1.text)
-    token_push = text1["data"]["accessToken"]
-
-
-    data1 = {
-        "funcModule": "数据表信息列表",
-        "funcOperation": "新增",
-        "data": [
-            {"dataItemNo": "C01100036|Forecast_Price|DX|ACN",
-             "dataDate": cur_time,
-             "dataStatus": "add",
-             "dataValue": x
-             }
-
-        ]
-    }
-    headers1 = {"Authorization": token_push}
-    # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))
-    
-
-def start(now=None):
-    workbook = xlrd.open_workbook(read_file_path_name)
-
-
-
-    # 选择第一个表格
-    sheet = workbook.sheet_by_index(0)
-
-    # 获取行数和列数
-    num_rows = sheet.nrows
-
-
-
-    row_data = sheet.row_values(1)
-    one_cols = row_data
-
-
-    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"]
-    else:
-        print("获取认证失败")
-        token =  None
-
-    if now is None:
-        now = datetime.now()
-    year = now.year
-    month = now.month
-    day = now.day
-
-    if month < 10:
-        month = "0" + str(month)
-    if day < 10:
-        day = "0" + str(day)
-    cur_time = str(year) + str(month) + str(day)
-    cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
-    search_data = {
-        "data": {
-            "date": cur_time,
-            "dataItemNoList": one_cols[1:]
-        },
-        "funcModule": "数据项",
-        "funcOperation": "查询"
-    }
-    headers = {"Authorization": token}
-    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
-    search_value = json.loads(search_res.text)["data"]
-#     datas = search_value
-    if search_value:
-        datas = search_value
-    else :
-        datas = None
-   
-
-    append_rows = [cur_time2]
-    dataItemNo_dataValue = {}
-#     for data_value in datas:
-#         dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for data_value in datas:
-        if "dataValue" not in data_value:
-            print(data_value)
-            dataItemNo_dataValue[data_value["dataItemNo"]] = ""
-        else:
-            dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for value in one_cols[1:]:
-        if value in dataItemNo_dataValue:
-            append_rows.append(dataItemNo_dataValue[value])
-        else:
-            append_rows.append("")
-
-    workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')
-
-    # 获取所有sheet的个数
-    sheet_count = len(workbook.sheet_names())
-
-    # 获取所有sheet的名称
-    sheet_names = workbook.sheet_names()
-
-    new_workbook = xlwt.Workbook()
-    for i in range(sheet_count):
-        # 获取当前sheet
-        sheet = workbook.sheet_by_index(i)
-
-        # 获取sheet的行数和列数
-        row_count = sheet.nrows
-        col_count = sheet.ncols
-        # 获取原有数据
-        data = []
-        for row in range(row_count):
-            row_data = []
-            for col in range(col_count):
-                row_data.append(sheet.cell_value(row, col))
-            data.append(row_data)
-        # 创建xlwt的Workbook对象
-        # 创建sheet
-        new_sheet = new_workbook.add_sheet(sheet_names[i])
-
-        # 将原有的数据写入新的sheet
-        for row in range(row_count):
-            for col in range(col_count):
-                new_sheet.write(row, col, data[row][col])
-
-        if i == 0:
-            # 在新的sheet中添加数据
-            for col in range(col_count):
-                new_sheet.write(row_count, col, append_rows[col])
-
-    # 保存新的xls文件
-    new_workbook.save("定性模型数据项12-11.xlsx")
-    
-    
-    update_e_value('定性模型数据项12-11.xlsx', 8, 1000)
-
-    df = pd.read_excel('定性模型数据项12-11.xlsx')
-    df=df.fillna(df.ffill())
-    df1 = df[-2:].reset_index()
-    print(df1)
-    # if df1.loc[1,'70号沥青开工率'] > 0.3:   -- 2025年1月9日 发版更改
-    if df1.loc[1,'70号沥青开工率'] / 100  > 0.3:
-        a = (df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1
-    else :
-        a = 0
-    b = df1.loc[1,'资金因素']
-    if df1.loc[1,'昨日计划提货偏差']>0:
-        c = df1.loc[1,'昨日计划提货偏差']*10/2000
-    else :
-        c = df1.loc[1,'昨日计划提货偏差']*10/3000
-    d = df1.loc[1,'生产情况']
-    if df1.loc[1,'基质沥青库存']/265007 >0.8:
-        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000
-    else : 
-        e = 0
-#     f = df1.loc[1,'下游客户价格预期']
-    f = 1 # 2025年1月23日修改：价格预期都按1计算
-    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:
-        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100
-    else :
-        g = 0
-    h = df1.loc[1,'订单结构']
-    x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)
-    
-
-    # login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30))
-    # text1 = json.loads(login_res1.text)
-    # token_push = text1["data"]["accessToken"]
-
-
-    # data1 = {
-    #     "funcModule": "数据表信息列表",
-    #     "funcOperation": "新增",
-    #     "data": [
-    #         {"dataItemNo": "C01100036|Forecast_Price|DX|ACN",
-    #          "dataDate": cur_time,
-    #          "dataStatus": "add",
-    #          "dataValue": x
-    #          }
-
-    #     ]
-    # }
-    # headers1 = {"Authorization": token_push}
-    # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))
-    
-
-
-def start_test():
-    workbook = xlrd.open_workbook(read_file_path_name)
-
-
-
-    # 选择第一个表格
-    sheet = workbook.sheet_by_index(0)
-
-    # 获取行数和列数
-    num_rows = sheet.nrows
-
-
-
-    row_data = sheet.row_values(1)
-    one_cols = row_data
-
-
-    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"]
-    else:
-        print("获取认证失败")
-        token =  None
-
-
-    now = datetime.now()
-    year = now.year
-    month = now.month
-    day = now.day
-
-    if month < 10:
-        month = "0" + str(month)
-    if day < 10:
-        day = "0" + str(day)
-    cur_time = str(year) + str(month) + str(day)
-    cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
-    search_data = {
-        "data": {
-            "date": cur_time,
-            "dataItemNoList": one_cols[1:]
-        },
-        "funcModule": "数据项",
-        "funcOperation": "查询"
-    }
-    headers = {"Authorization": token}
-    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
-    search_value = json.loads(search_res.text)["data"]
-#     datas = search_value
-    if search_value:
-        datas = search_value
-    else :
-        datas = None
-   
-
-    append_rows = [cur_time2]
-    dataItemNo_dataValue = {}
-#     for data_value in datas:
-#         dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for data_value in datas:
-        if "dataValue" not in data_value:
-            print(data_value)
-            dataItemNo_dataValue[data_value["dataItemNo"]] = ""
-        else:
-            dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for value in one_cols[1:]:
-        if value in dataItemNo_dataValue:
-            append_rows.append(dataItemNo_dataValue[value])
-        else:
-            append_rows.append("")
-
-    workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')
-
-    # 获取所有sheet的个数
-    sheet_count = len(workbook.sheet_names())
-
-    # 获取所有sheet的名称
-    sheet_names = workbook.sheet_names()
-
-    new_workbook = xlwt.Workbook()
-    for i in range(sheet_count):
-        # 获取当前sheet
-        sheet = workbook.sheet_by_index(i)
-
-        # 获取sheet的行数和列数
-        row_count = sheet.nrows
-        col_count = sheet.ncols
-        # 获取原有数据
-        data = []
-        for row in range(row_count):
-            row_data = []
-            for col in range(col_count):
-                row_data.append(sheet.cell_value(row, col))
-            data.append(row_data)
-        # 创建xlwt的Workbook对象
-        # 创建sheet
-        new_sheet = new_workbook.add_sheet(sheet_names[i])
-
-        # 将原有的数据写入新的sheet
-        for row in range(row_count):
-            for col in range(col_count):
-                new_sheet.write(row, col, data[row][col])
-
-        if i == 0:
-            # 在新的sheet中添加数据
-            for col in range(col_count):
-                new_sheet.write(row_count, col, append_rows[col])
-
-    # 保存新的xls文件
-    new_workbook.save("定性模型数据项12-11.xlsx")
-    
-    update_e_value('定性模型数据项12-11.xlsx', 8, 1000)
-
-    df = pd.read_excel('定性模型数据项12-11.xlsx')
-    df=df.fillna(df.ffill())
-    df1 = df[-2:].reset_index()
-    # if df1.loc[1,'70号沥青开工率'] > 0.3:   -- 2025年1月9日 发版更改
-    if df1.loc[1,'70号沥青开工率'] / 100  > 0.3:
-        a = (df1.loc[1,'70号沥青开工率'] / 100 -0.2)*5/0.1
-    else :
-        a = 0
-    b = df1.loc[1,'资金因素']
-    if df1.loc[1,'昨日计划提货偏差']>0:
-        c = df1.loc[1,'昨日计划提货偏差']*10/2000
-    else :
-        c = df1.loc[1,'昨日计划提货偏差']*10/3000
-    d = df1.loc[1,'生产情况']
-    if df1.loc[1,'基质沥青库存']/265007 >0.8:
-        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000
-    else : 
-        e = 0
-#     f = df1.loc[1,'下游客户价格预期']
-    f = 1 # 2025年1月23日修改：价格预期都按1计算
-    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:
-        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100
-    else :
-        g = 0
-    h = df1.loc[1,'订单结构']
-    x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)
-    
-
-    # login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30))
-    # text1 = json.loads(login_res1.text)
-    # token_push = text1["data"]["accessToken"]
-
-
-    # data1 = {
-    #     "funcModule": "数据表信息列表",
-    #     "funcOperation": "新增",
-    #     "data": [
-    #         {"dataItemNo": "C01100036|Forecast_Price|DX|ACN",
-    #          "dataDate": cur_time,
-    #          "dataStatus": "add",
-    #          "dataValue": x
-    #          }
-
-    #     ]
-    # }
-    # headers1 = {"Authorization": token_push}
-    # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))
-    
-
-
-
-
-def start_1():
-    workbook = xlrd.open_workbook(read_file_path_name)
-
-
-
-    # 选择第一个表格
-    sheet = workbook.sheet_by_index(0)
-
-    # 获取行数和列数
-    num_rows = sheet.nrows
-
-
-
-    row_data = sheet.row_values(1)
-    one_cols = row_data
-
-
-    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"]
-    else:
-        print("获取认证失败")
-        token =  None
-
-
-    now = datetime.now()  - timedelta(days=1)  
-    year = now.year
-    month = now.month
-    day = now.day
-
-    if month < 10:
-        month = "0" + str(month)
-    if day < 10:
-        day = "0" + str(day)
-    cur_time = str(year) + str(month) + str(day)
-    cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
-    search_data = {
-        "data": {
-            "date": cur_time,
-            "dataItemNoList": one_cols[1:]
-        },
-        "funcModule": "数据项",
-        "funcOperation": "查询"
-    }
-    headers = {"Authorization": token}
-    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
-    search_value = json.loads(search_res.text)["data"]
-#     datas = search_value
-    if search_value:
-        datas = search_value
-    else :
-        datas = None
-    
-   
-
-    append_rows = [cur_time2]
-    dataItemNo_dataValue = {}
-#     for data_value in datas:
-#         dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for data_value in datas:
-        if "dataValue" not in data_value:
-            print(data_value)
-            dataItemNo_dataValue[data_value["dataItemNo"]] = ""
-        else:
-            dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-    for value in one_cols[1:]:
-        if value in dataItemNo_dataValue:
-            append_rows.append(dataItemNo_dataValue[value])
-        else:
-            append_rows.append("")
-
-    workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')
-
-    # 获取所有sheet的个数
-    sheet_count = len(workbook.sheet_names())
-
-    # 获取所有sheet的名称
-    sheet_names = workbook.sheet_names()
-
-    new_workbook = xlwt.Workbook()
-    for i in range(sheet_count):
-        # 获取当前sheet
-        sheet = workbook.sheet_by_index(i)
-
-        # 获取sheet的行数和列数
-        row_count = sheet.nrows - 1
-        col_count = sheet.ncols
-        # 获取原有数据
-        data = []
-        for row in range(row_count):
-            row_data = []
-            for col in range(col_count):
-                row_data.append(sheet.cell_value(row, col))
-            data.append(row_data)
-        # 创建xlwt的Workbook对象
-        # 创建sheet
-        new_sheet = new_workbook.add_sheet(sheet_names[i])
-
-        # 将原有的数据写入新的sheet
-        for row in range(row_count):
-            for col in range(col_count):
-                new_sheet.write(row, col, data[row][col])
-
-        if i == 0:
-            # 在新的sheet中添加数据
-            for col in range(col_count):
-                new_sheet.write(row_count, col, append_rows[col])
-
-    # 保存新的xls文件
-    new_workbook.save("定性模型数据项12-11.xlsx")
-
-
-
-
-
-
-
-def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):
-
-    search_data = {
-        "funcModule": "数据项",
-        "funcOperation": "查询",
-        "data": {
-            "dateStart": dateStart,
-            "dateEnd": dateEnd,
-            "dataItemNoList": dataItemNoList  # 数据项编码,代表 brent最低价和最高价
-        }
-    }
-
-    headers = {"Authorization": token}
-    search_res = requests.post(url=url, headers=headers, json=search_data, timeout=(3, 5))
-    search_value = json.loads(search_res.text)["data"]
-    if search_value:
-        return search_value
-    else:
+            logging.error("获取日志 token 失败，响应信息: %s", login_res.text)
+            print("获取认证失败")
+            token = None
+        return token
+    except Exception as e:
+        logging.error("获取日志 token 时发生异常: %s", str(e))
         return None
 
 
-
-def save_queryDataListItemNos_xls(data_df,dataItemNoList):
-
-    current_year_month = datetime.now().strftime('%Y-%m')
-    grouped = data_df.groupby("dataDate")
+def updateExcelDatabak(date='', token=None):
+    try:
+        logging.info("开始备份更新 Excel 数据，日期: %s", date)
+        workbook = xlrd.open_workbook(read_file_path_name)
+        sheet = workbook.sheet_by_index(0)
+        row_data = sheet.row_values(1)
+        one_cols = row_data
+        cur_time, cur_time2 = getNow(date)
+        search_data = {
+            "data": {
+                "date": cur_time,
+                "dataItemNoList": one_cols[1:]
+            },
+            "funcModule": "数据项",
+            "funcOperation": "查询"
+        }
+        headers = {"Authorization": token}
+        search_res = requests.post(
+            url=search_url, headers=headers, json=search_data, timeout=(3, 5))
+        search_value = json.loads(search_res.text)["data"]
+        if search_value:
+            datas = search_value
+        else:
+            datas = None
+        append_rows = [cur_time2]
+        dataItemNo_dataValue = {}
+        for data_value in datas:
+            if "dataValue" not in data_value:
+                print(data_value)
+                dataItemNo_dataValue[data_value["dataItemNo"]] = ""
+            else:
+                dataItemNo_dataValue[data_value["dataItemNo"]
+                                     ] = data_value["dataValue"]
+        for value in one_cols[1:]:
+            if value in dataItemNo_dataValue:
+                append_rows.append(dataItemNo_dataValue[value])
+            else:
+                append_rows.append("")
+        workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')
+        sheet_count = len(workbook.sheet_names())
+        sheet_names = workbook.sheet_names()
+        new_workbook = xlwt.Workbook()
+        for i in range(sheet_count):
+            sheet = workbook.sheet_by_index(i)
+            row_count = sheet.nrows
+            col_count = sheet.ncols
+            data = []
+            for row in range(row_count):
+                row_data = []
+                for col in range(col_count):
+                    row_data.append(sheet.cell_value(row, col))
+                data.append(row_data)
+            new_sheet = new_workbook.add_sheet(sheet_names[i])
+            for row in range(row_count):
+                for col in range(col_count):
+                    new_sheet.write(row, col, data[row][col])
+            if i == 0:
+                for col in range(col_count):
+                    new_sheet.write(row_count, col, append_rows[col])
+        new_workbook.save("定性模型数据项12-11.xlsx")
+        logging.info("备份更新 Excel 数据完成")
+    except Exception as e:
+        logging.error("备份更新 Excel 数据时发生异常: %s", str(e))
 
 
-    df_old = pd.read_excel('定性模型数据项12-11.xlsx')
-    df_old0 = df_old[:1]
-    result_dict = {df_old0.iloc[0][col] : col  for col in df_old0.columns}
-    df_old1 = df_old[1:].copy()
-
-    df_old1["日期"] = pd.to_datetime(df_old1["日期"])
-    # 删除日期列为本月的数据
-    df_old1 = df_old1[~df_old1["日期"].dt.strftime('%Y-%m').eq(current_year_month)]
-    df_old1["日期"] = df_old1["日期"].dt.strftime('%Y-%m-%d')
+def updateYesterdayExcelData(date='', token=None):
+    try:
+        logging.info("开始更新昨天的 Excel 数据，日期: %s", date)
+        df = pd.read_excel(read_file_path_name, engine='openpyxl')
+        one_cols = df.iloc[0, :].tolist()
+        if date == '':
+            previous_date = (datetime.now() - timedelta(days=1)
+                             ).strftime('%Y-%m-%d')
+        else:
+            previous_date = (datetime.strptime(date, "%Y-%m-%d") -
+                             timedelta(days=1)).strftime('%Y-%m-%d')
+        cur_time, cur_time2 = getNow(previous_date)
+        search_data = {
+            "data": {
+                "date": cur_time,
+                "dataItemNoList": one_cols[1:]
+            },
+            "funcModule": "数据项",
+            "funcOperation": "查询"
+        }
+        headers = {"Authorization": token}
+        search_res = requests.post(
+            url=search_url, headers=headers, json=search_data, timeout=(3, 5))
+        search_value = json.loads(search_res.text)["data"]
+        if search_value:
+            datas = search_value
+        else:
+            datas = None
+        append_rows = [cur_time2]
+        dataItemNo_dataValue = {}
+        for data_value in datas:
+            if "dataValue" not in data_value:
+                print(data_value)
+                dataItemNo_dataValue[data_value["dataItemNo"]] = ""
+            else:
+                dataItemNo_dataValue[data_value["dataItemNo"]
+                                     ] = data_value["dataValue"]
+        for value in one_cols[1:]:
+            if value in dataItemNo_dataValue:
+                append_rows.append(dataItemNo_dataValue[value])
+            else:
+                append_rows.append("")
+        print('更新数据前')
+        print(df.tail(1))
+        if previous_date not in df['日期'].values:
+            new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())
+            df = pd.concat([df, new_row], ignore_index=True)
+        else:
+            print('日期存在，即将更新')
+            print('新数据', append_rows[1:])
+            df.loc[df['日期'] == previous_date,
+                   df.columns.tolist()[1:]] = append_rows[1:]
+        print('更新数据后')
+        print(df.tail(1))
+        df.to_excel("定性模型数据项12-11.xlsx", index=False, engine='openpyxl')
+        logging.info("更新昨天的 Excel 数据完成")
+    except Exception as e:
+        logging.error("更新昨天的 Excel 数据时发生异常: %s", str(e))
 
 
-    list_data = []
-    for date, group in grouped:
-        dict_data = {"日期": date}
-        for index, row in group.iterrows():
-            dict_data[result_dict[row['dataItemNo']]] = row['dataValue']
-        list_data.append(dict_data)
-
-    df_current_year_month = pd.DataFrame(list_data)
-    df_current_year_month
-
-    df_merged = pd.concat([df_old0, df_old1, df_current_year_month], ignore_index=True)
-
-    df_merged.to_excel('定性模型数据项12-11.xlsx', index=False)
+def updateExcelData(date='', token=None):
+    try:
+        logging.info("开始更新 Excel 数据，日期: %s", date)
+        df = pd.read_excel(read_file_path_name, engine='openpyxl')
+        one_cols = df.iloc[0, :].tolist()
+        cur_time, cur_time2 = getNow(date)
+        search_data = {
+            "data": {
+                "date": cur_time,
+                "dataItemNoList": one_cols[1:]
+            },
+            "funcModule": "数据项",
+            "funcOperation": "查询"
+        }
+        headers = {"Authorization": token}
+        search_res = requests.post(
+            url=search_url, headers=headers, json=search_data, timeout=(3, 5))
+        search_value = json.loads(search_res.text)["data"]
+        if search_value:
+            datas = search_value
+        else:
+            datas = None
+        append_rows = [cur_time2]
+        dataItemNo_dataValue = {}
+        for data_value in datas:
+            if "dataValue" not in data_value:
+                print(data_value)
+                dataItemNo_dataValue[data_value["dataItemNo"]] = ""
+            else:
+                dataItemNo_dataValue[data_value["dataItemNo"]
+                                     ] = data_value["dataValue"]
+        for value in one_cols[1:]:
+            if value in dataItemNo_dataValue:
+                append_rows.append(dataItemNo_dataValue[value])
+            else:
+                append_rows.append("")
+        new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())
+        df = pd.concat([df, new_row], ignore_index=True)
+        df.to_excel("定性模型数据项12-11.xlsx", index=False, engine='openpyxl')
+        logging.info("更新 Excel 数据完成")
+    except Exception as e:
+        logging.error("更新 Excel 数据时发生异常: %s", str(e))
 
 
+def qualitativeModel():
+    try:
+        logging.info("开始执行定性模型计算")
+        df = pd.read_excel('定性模型数据项12-11.xlsx')
+        df = df.fillna(df.ffill())
+        df1 = df[-3:-1].reset_index()
+        if df1.loc[1, '70号沥青开工率'] / 100 > 0.3:
+            a = -(df1.loc[1, '70号沥青开工率'] / 100 - 0.2)*5/0.1
+        else:
+            a = 0
+        b = df1.loc[1, '资金因素']
+        df1.loc[1, '昨日计划提货偏差'] = df1.loc[1, '京博产量'] - df1.loc[1, '计划产量']
+        if df1.loc[1, '昨日计划提货偏差'] > 0:
+            c = df1.loc[1, '昨日计划提货偏差']*10/2000
+        else:
+            c = df1.loc[1, '昨日计划提货偏差']*10/3000
+        d = (df1.loc[1, '京博产量'] - df1.loc[1, '计划产量']) / 500 * 5
+        if df1.loc[1, '基质沥青库存']/265007 > 0.8:
+            e = (df1.loc[1, '基质沥青库存'] - df1.loc[0, '基质沥青库存'])*10/-5000
+        else:
+            e = 0
+        f = 1
+        if abs(df1.loc[1, '即期成本'] - df1.loc[0, '即期成本']) >= 100:
+            g = (df1.loc[1, '即期成本'] - df1.loc[0, '即期成本'])*50/100
+        else:
+            g = 0
+        h = df1.loc[1, '订单结构']
+        x = round(0.08*a+0*b+0.15*c+0.08*d + 0.03*e + 0.08 *
+                  f + 0.4*g+0.18*h+df1.loc[0, '京博指导价'], 2)
+        logging.info("定性模型计算完成，预测结果: %s", x)
+        return x
+    except Exception as e:
+        logging.error("定性模型计算时发生异常: %s", str(e))
+        return None
 
-def queryDataListItemNos(date=None,token=None):
-    df = pd.read_excel('定性模型数据项12-11.xlsx')
-    dataItemNoList = df.iloc[0].tolist()[1:]
 
-    if token is None:
-        token = getLogToken()
+def getNow(date='', offset=0):
+    try:
+        if date == '':
+            now = datetime.now() - timedelta(days=offset)
+        else:
+            try:
+                date = datetime.strptime(date, "%Y-%m-%d")
+            except:
+                date = datetime.strptime(date, "%Y%m%d")
+            now = date
+        year = now.year
+        month = now.month
+        day = now.day
+        if month < 10:
+            month = "0" + str(month)
+        if day < 10:
+            day = "0" + str(day)
+        cur_time = str(year) + str(month) + str(day)
+        cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
+        return cur_time, cur_time2
+    except Exception as e:
+        logging.error("获取当前日期时发生异常: %s", str(e))
+        return None, None
+
+
+def pushData(cur_time, x, token_push):
+    try:
+        logging.info("开始推送数据，日期: %s，预测值: %s", cur_time, x)
+        data1 = {
+            "funcModule": "数据表信息列表",
+            "funcOperation": "新增",
+            "data": [
+                {"dataItemNo": "C01100036|Forecast_Price|DX|ACN",
+                 "dataDate": cur_time,
+                 "dataStatus": "add",
+                 "dataValue": x
+                 }
+            ]
+        }
+        headers1 = {"Authorization": token_push}
+        res = requests.post(url=upload_url, headers=headers1,
+                            json=data1, timeout=(3, 5))
+        logging.info("数据推送完成，响应信息: %s", res.text)
+    except Exception as e:
+        logging.error("数据推送时发生异常: %s", str(e))
+
+
+def start_2(date='', token=None):
+    try:
+        logging.info("开始执行 start_2 函数，日期: %s", date)
+        workbook = xlrd.open_workbook(read_file_path_name)
+        sheet = workbook.sheet_by_index(0)
+        num_rows = sheet.nrows
+        row_data = sheet.row_values(1)
+        one_cols = row_data
+        cur_time, cur_time2 = getNow(date)
+        search_data = {
+            "data": {
+                "date": cur_time,
+                "dataItemNoList": one_cols[1:]
+            },
+            "funcModule": "数据项",
+            "funcOperation": "查询"
+        }
+        headers = {"Authorization": token}
+        search_res = requests.post(
+            url=search_url, headers=headers, json=search_data, timeout=(3, 5))
+        search_value = json.loads(search_res.text)["data"]
+        if search_value:
+            datas = search_value
+        else:
+            datas = None
+        append_rows = [cur_time2]
+        dataItemNo_dataValue = {}
+        for data_value in datas:
+            if "dataValue" not in data_value:
+                print(data_value)
+                dataItemNo_dataValue[data_value["dataItemNo"]] = ""
+            else:
+                dataItemNo_dataValue[data_value["dataItemNo"]
+                                     ] = data_value["dataValue"]
+        for value in one_cols[1:]:
+            if value in dataItemNo_dataValue:
+                append_rows.append(dataItemNo_dataValue[value])
+            else:
+                append_rows.append("")
+        workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')
+        sheet_count = len(workbook.sheet_names())
+        sheet_names = workbook.sheet_names()
+        new_workbook = xlwt.Workbook()
+        for i in range(sheet_count):
+            sheet = workbook.sheet_by_index(i)
+            row_count = sheet.nrows
+            col_count = sheet.ncols
+            data = []
+            for row in range(row_count):
+                row_data = []
+                for col in range(col_count):
+                    row_data.append(sheet.cell_value(row, col))
+                data.append(row_data)
+            new_sheet = new_workbook.add_sheet(sheet_names[i])
+            for row in range(row_count):
+                for col in range(col_count):
+                    new_sheet.write(row, col, data[row][col])
+            if i == 0:
+                for col in range(col_count):
+                    new_sheet.write(row_count, col, append_rows[col])
+        new_workbook.save("定性模型数据项12-11.xlsx")
+        update_e_value('定性模型数据项12-11.xlsx', 8, 1000)
+        df = pd.read_excel('定性模型数据项12-11.xlsx')
+        df = df.fillna(df.ffill())
+        df1 = df[-2:].reset_index()
+        if df1.loc[1, '70号沥青开工率'] > 30:
+            a = (df1.loc[1, '70号沥青开工率']-0.2)*5/0.1
+        else:
+            a = 0
+        b = df1.loc[1, '资金因素']
+        if df1.loc[1, '昨日计划提货偏差'] > 0:
+            c = df1.loc[1, '昨日计划提货偏差']*10/2000
+        else:
+            c = df1.loc[1, '昨日计划提货偏差']*10/3000
+        d = df1.loc[1, '生产情况']
+        if df1.loc[1, '基质沥青库存']/265007 > 0.8:
+            e = (df1.loc[1, '基质沥青库存'] - df1.loc[0, '基质沥青库存'])*10/-5000
+        else:
+            e = 0
+        f = 1
+        if abs(df1.loc[1, '即期成本'] - df1.loc[0, '即期成本']) >= 100:
+            g = (df1.loc[1, '即期成本'] - df1.loc[0, '即期成本'])*50/100
+        else:
+            g = 0
+        h = df1.loc[1, '订单结构']
+        x = round(0.08*a+0*b+0.15*c+0.08*d + 0.03*e + 0.08 *
+                  f + 0.4*g+0.18*h+df1.loc[0, '京博指导价'], 2)
+        login_res1 = requests.post(
+            url=login_push_url, json=login_push_data, timeout=(3, 5))
+        text1 = json.loads(login_res1.text)
+        token_push = text1["data"]["accessToken"]
+        data1 = {
+            "funcModule": "数据表信息列表",
+            "funcOperation": "新增",
+            "data": [
+                {"dataItemNo": "C01100036|Forecast_Price|DX|ACN",
+                 "dataDate": cur_time,
+                 "dataStatus": "add",
+                 "dataValue": x
+                 }
+            ]
+        }
+        headers1 = {"Authorization": token_push}
+        # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))
+        logging.info("start_2 函数执行完成")
+    except Exception as e:
+        logging.error("start_2 函数执行时发生异常: %s", str(e))
+
+
+def start(now=None):
+    try:
+        logging.info("开始执行 start 函数")
+        workbook = xlrd.open_workbook(read_file_path_name)
+        sheet = workbook.sheet_by_index(0)
+        num_rows = sheet.nrows
+        row_data = sheet.row_values(1)
+        one_cols = row_data
+        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"]
+        else:
+            logging.error("获取认证失败，响应信息: %s", login_res.text)
+            print("获取认证失败")
+            token = None
+        if now is None:
+            now = datetime.now()
+        year = now.year
+        month = now.month
+        day = now.day
+        if month < 10:
+            month = "0" + str(month)
+        if day < 10:
+            day = "0" + str(day)
+        cur_time = str(year) + str(month) + str(day)
+        cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
+        search_data = {
+            "data": {
+                "date": cur_time,
+                "dataItemNoList": one_cols[1:]
+            },
+            "funcModule": "数据项",
+            "funcOperation": "查询"
+        }
+        headers = {"Authorization": token}
+        search_res = requests.post(
+            url=search_url, headers=headers, json=search_data, timeout=(3, 5))
+        search_value = json.loads(search_res.text)["data"]
+        if search_value:
+            datas = search_value
+        else:
+            datas = None
+        append_rows = [cur_time2]
+        dataItemNo_dataValue = {}
+        for data_value in datas:
+            if "dataValue" not in data_value:
+                print(data_value)
+                dataItemNo_dataValue[data_value["dataItemNo"]] = ""
+            else:
+                dataItemNo_dataValue[data_value["dataItemNo"]
+                                     ] = data_value["dataValue"]
+        for value in one_cols[1:]:
+            if value in dataItemNo_dataValue:
+                append_rows.append(dataItemNo_dataValue[value])
+            else:
+                append_rows.append("")
+        workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')
+        sheet_count = len(workbook.sheet_names())
+        sheet_names = workbook.sheet_names()
+        new_workbook = xlwt.Workbook()
+        for i in range(sheet_count):
+            sheet = workbook.sheet_by_index(i)
+            row_count = sheet.nrows
+            col_count = sheet.ncols
+            data = []
+            for row in range(row_count):
+                row_data = []
+                for col in range(col_count):
+                    row_data.append(sheet.cell_value(row, col))
+                data.append(row_data)
+            new_sheet = new_workbook.add_sheet(sheet_names[i])
+            for row in range(row_count):
+                for col in range(col_count):
+                    new_sheet.write(row, col, data[row][col])
+            if i == 0:
+                for col in range(col_count):
+                    new_sheet.write(row_count, col, append_rows[col])
+        new_workbook.save("定性模型数据项12-11.xlsx")
+        update_e_value('定性模型数据项12-11.xlsx', 8, 1000)
+        df = pd.read_excel('定性模型数据项12-11.xlsx')
+        df = df.fillna(df.ffill())
+        df1 = df[-2:].reset_index()
+        if df1.loc[1, '70号沥青开工率'] / 100 > 0.3:
+            a = (df1.loc[1, '70号沥青开工率'] / 100 - 0.2)*5/0.1
+        else:
+            a = 0
+        b = df1.loc[1, '资金因素']
+        if df1.loc[1, '昨日计划提货偏差'] > 0:
+            c = df1.loc[1, '昨日计划提货偏差']*10/2000
+        else:
+            c = df1.loc[1, '昨日计划提货偏差']*10/3000
+        d = df1.loc[1, '生产情况']
+        if df1.loc[1, '基质沥青库存']/265007 > 0.8:
+            e = (df1.loc[1, '基质沥青库存'] - df1.loc[0, '基质沥青库存'])*10/-5000
+        else:
+            e = 0
+        f = 1
+        if abs(df1.loc[1, '即期成本'] - df1.loc[0, '即期成本']) >= 100:
+            g = (df1.loc[1, '即期成本'] - df1.loc[0, '即期成本'])*50/100
+        else:
+            g = 0
+        h = df1.loc[1, '订单结构']
+        x = round(0.08*a+0*b+0.15*c+0.08*d + 0.03*e + 0.08 *
+                  f + 0.4*g+0.18*h+df1.loc[0, '京博指导价'], 2)
+        # login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30))
+        # text1 = json.loads(login_res1.text)
+        # token_push = text1["data"]["accessToken"]
+        # data1 = {
+        #     "funcModule": "数据表信息列表",
+        #     "funcOperation": "新增",
+        #     "data": [
+        #         {"dataItemNo": "C01100036|Forecast_Price|DX|ACN",
+        #          "dataDate": cur_time,
+        #          "dataStatus": "add",
+        #          "dataValue": x
+        #          }
+        #     ]
+        # }
+        # headers1 = {"Authorization": token_push}
+        # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))
+        logging.info("start 函数执行完成")
+    except Exception as e:
+        logging.error("start 函数执行时发生异常: %s", str(e))
+
+
+def start_test():
+    try:
+        logging.info("开始执行 start_test 函数")
+        workbook = xlrd.open_workbook(read_file_path_name)
+        sheet = workbook.sheet_by_index(0)
+        num_rows = sheet.nrows
+        row_data = sheet.row_values(1)
+        one_cols = row_data
+        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"]
+        else:
+            logging.error("获取认证失败，响应信息: %s", login_res.text)
+            print("获取认证失败")
+            token = None
+        now = datetime.now()
+        year = now.year
+        month = now.month
+        day = now.day
+        if month < 10:
+            month = "0" + str(month)
+        if day < 10:
+            day = "0" + str(day)
+        cur_time = str(year) + str(month) + str(day)
+        cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
+        search_data = {
+            "data": {
+                "date": cur_time,
+                "dataItemNoList": one_cols[1:]
+            },
+            "funcModule": "数据项",
+            "funcOperation": "查询"
+        }
+        headers = {"Authorization": token}
+        search_res = requests.post(
+            url=search_url, headers=headers, json=search_data, timeout=(3, 5))
+        search_value = json.loads(search_res.text)["data"]
+        if search_value:
+            datas = search_value
+        else:
+            datas = None
+        append_rows = [cur_time2]
+        dataItemNo_dataValue = {}
+        for data_value in datas:
+            if "dataValue" not in data_value:
+                print(data_value)
+                dataItemNo_dataValue[data_value["dataItemNo"]] = ""
+            else:
+                dataItemNo_dataValue[data_value["dataItemNo"]
+                                     ] = data_value["dataValue"]
+        for value in one_cols[1:]:
+            if value in dataItemNo_dataValue:
+                append_rows.append(dataItemNo_dataValue[value])
+            else:
+                append_rows.append("")
+        workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')
+        sheet_count = len(workbook.sheet_names())
+        sheet_names = workbook.sheet_names()
+        new_workbook = xlwt.Workbook()
+        for i in range(sheet_count):
+            sheet = workbook.sheet_by_index(i)
+            row_count = sheet.nrows
+            col_count = sheet.ncols
+            data = []
+            for row in range(row_count):
+                row_data = []
+                for col in range(col_count):
+                    row_data.append(sheet.cell_value(row, col))
+                data.append(row_data)
+            new_sheet = new_workbook.add_sheet(sheet_names[i])
+            for row in range(row_count):
+                for col in range(col_count):
+                    new_sheet.write(row, col, data[row][col])
+            if i == 0:
+                for col in range(col_count):
+                    new_sheet.write(row_count, col, append_rows[col])
+        new_workbook.save("定性模型数据项12-11.xlsx")
+        update_e_value('定性模型数据项12-11.xlsx', 8, 1000)
+        df = pd.read_excel('定性模型数据项12-11.xlsx')
+        df = df.fillna(df.ffill())
+        df1 = df[-2:].reset_index()
+        if df1.loc[1, '70号沥青开工率'] / 100 > 0.3:
+            a = (df1.loc[1, '70号沥青开工率'] / 100 - 0.2)*5/0.1
+        else:
+            a = 0
+        b = df1.loc[1, '资金因素']
+        if df1.loc[1, '昨日计划提货偏差'] > 0:
+            c = df1.loc[1, '昨日计划提货偏差']*10/2000
+        else:
+            c = df1.loc[1, '昨日计划提货偏差']*10/3000
+        d = df1.loc[1, '生产情况']
+        if df1.loc[1, '基质沥青库存']/265007 > 0.8:
+            e = (df1.loc[1, '基质沥青库存'] - df1.loc[0, '基质沥青库存'])*10/-5000
+        else:
+            e = 0
+        f = 1
+        if abs(df1.loc[1, '即期成本'] - df1.loc[0, '即期成本']) >= 100:
+            g = (df1.loc[1, '即期成本'] - df1.loc[0, '即期成本'])*50/100
+        else:
+            g = 0
+        h = df1.loc[1, '订单结构']
+        x = round(0.08*a+0*b+0.15*c+0.08*d + 0.03*e + 0.08 *
+                  f + 0.4*g+0.18*h+df1.loc[0, '京博指导价'], 2)
+        # login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30))
+        # text1 = json.loads(login_res1.text)
+        # token_push = text1["data"]["accessToken"]
+        # data1 = {
+        #     "funcModule": "数据表信息列表",
+        #     "funcOperation": "新增",
+        #     "data": [
+        #         {"dataItemNo": "C01100036|Forecast_Price|DX|ACN",
+        #          "dataDate": cur_time,
+        #          "dataStatus": "add",
+        #          "dataValue": x
+        #          }
+        #     ]
+        # }
+        # headers1 = {"Authorization": token_push}
+        # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))
+        logging.info("start_test 函数执行完成")
+    except Exception as e:
+        logging.error("start_test 函数执行时发生异常: %s", str(e))
+
+
+def start_1():
+    try:
+        logging.info("开始执行 start_1 函数")
+        workbook = xlrd.open_workbook(read_file_path_name)
+        sheet = workbook.sheet_by_index(0)
+        num_rows = sheet.nrows
+        row_data = sheet.row_values(1)
+        one_cols = row_data
+        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"]
+        else:
+            logging.error("获取认证失败，响应信息: %s", login_res.text)
+            print("获取认证失败")
+            token = None
+        now = datetime.now() - timedelta(days=1)
+        year = now.year
+        month = now.month
+        day = now.day
+        if month < 10:
+            month = "0" + str(month)
+        if day < 10:
+            day = "0" + str(day)
+        cur_time = str(year) + str(month) + str(day)
+        cur_time2 = str(year) + "-" + str(month) + "-" + str(day)
+        search_data = {
+            "data": {
+                "date": cur_time,
+                "dataItemNoList": one_cols[1:]
+            },
+            "funcModule": "数据项",
+            "funcOperation": "查询"
+        }
+        headers = {"Authorization": token}
+        search_res = requests.post(
+            url=search_url, headers=headers, json=search_data, timeout=(3, 5))
+        search_value = json.loads(search_res.text)["data"]
+        if search_value:
+            datas = search_value
+        else:
+            datas = None
+        append_rows = [cur_time2]
+        dataItemNo_dataValue = {}
+        for data_value in datas:
+            if "dataValue" not in data_value:
+                print(data_value)
+                dataItemNo_dataValue[data_value["dataItemNo"]] = ""
+            else:
+                dataItemNo_dataValue[data_value["dataItemNo"]
+                                     ] = data_value["dataValue"]
+        for value in one_cols[1:]:
+            if value in dataItemNo_dataValue:
+                append_rows.append(dataItemNo_dataValue[value])
+            else:
+                append_rows.append("")
+        workbook = xlrd.open_workbook('定性模型数据项12-11.xlsx')
+        sheet_count = len(workbook.sheet_names())
+        sheet_names = workbook.sheet_names()
+        new_workbook = xlwt.Workbook()
+        for i in range(sheet_count):
+            sheet = workbook.sheet_by_index(i)
+            row_count = sheet.nrows - 1
+            col_count = sheet.ncols
+            data = []
+            for row in range(row_count):
+                row_data = []
+                for col in range(col_count):
+                    row_data.append(sheet.cell_value(row, col))
+                data.append(row_data)
+            new_sheet = new_workbook.add_sheet(sheet_names[i])
+            for row in range(row_count):
+                for col in range(col_count):
+                    new_sheet.write(row, col, data[row][col])
+            if i == 0:
+                for col in range(col_count):
+                    new_sheet.write(row_count, col, append_rows[col])
+        new_workbook.save("定性模型数据项12-11.xlsx")
+        logging.info("start_1 函数执行完成")
+    except Exception as e:
+        logging.error("start_1 函数执行时发生异常: %s", str(e))
+
+
+def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):
+    try:
+        logging.info("开始获取查询数据列表项值，日期范围: %s 至 %s", dateStart, dateEnd)
+        search_data = {
+            "funcModule": "数据项",
+            "funcOperation": "查询",
+            "data": {
+                "dateStart": dateStart,
+                "dateEnd": dateEnd,
+                "dataItemNoList": dataItemNoList  # 数据项编码,代表 brent最低价和最高价
+            }
+        }
+        headers = {"Authorization": token}
+        search_res = requests.post(
+            url=url, headers=headers, json=search_data, timeout=(3, 5))
+        search_value = json.loads(search_res.text)["data"]
+        if search_value:
+            logging.info("成功获取查询数据列表项值")
+            return search_value
+        else:
+            logging.warning("未获取到查询数据列表项值")
+            return None
+    except Exception as e:
+        logging.error("获取查询数据列表项值时发生异常: %s", str(e))
+        return None
+
+
+def save_queryDataListItemNos_xls(data_df, dataItemNoList):
+    try:
+        logging.info("开始保存查询数据列表项到 Excel 文件")
+        current_year_month = datetime.now().strftime('%Y-%m')
+        grouped = data_df.groupby("dataDate")
+        df_old = pd.read_excel('定性模型数据项12-11.xlsx')
+        df_old0 = df_old[:1]
+        result_dict = {df_old0.iloc[0][col]: col for col in df_old0.columns}
+        df_old1 = df_old[1:].copy()
+        df_old1["日期"] = pd.to_datetime(df_old1["日期"])
+        df_old1 = df_old1[~df_old1["日期"].dt.strftime(
+            '%Y-%m').eq(current_year_month)]
+        df_old1["日期"] = df_old1["日期"].dt.strftime('%Y-%m-%d')
+        list_data = []
+        for date, group in grouped:
+            dict_data = {"日期": date}
+            for index, row in group.iterrows():
+                dict_data[result_dict[row['dataItemNo']]] = row['dataValue']
+            list_data.append(dict_data)
+        df_current_year_month = pd.DataFrame(list_data)
+        df_merged = pd.concat(
+            [df_old0, df_old1, df_current_year_month], ignore_index=True)
+        df_merged.to_excel('定性模型数据项12-11.xlsx', index=False)
+        logging.info("保存查询数据列表项到 Excel 文件完成")
+    except Exception as e:
+        logging.error("保存查询数据列表项到 Excel 文件时发生异常: %s", str(e))
+
+
+def queryDataListItemNos(date=None, token=None):
+    try:
+        logging.info("开始查询数据列表项，日期: %s", date)
+        df = pd.read_excel('定性模型数据项12-11.xlsx')
+        dataItemNoList = df.iloc[0].tolist()[1:]
         if token is None:
-            print("获取token失败")
-            return
-
-    # 获取当前日期
-    if date is None:
-        date = datetime.now()
-    current_date = date
-
-    # 获取当月1日
-    first_day_of_month = current_date.replace(day=1)
-
-    # 格式化为 YYYYMMDD 格式
-    dateEnd = current_date.strftime('%Y%m%d')
-    dateStart = first_day_of_month.strftime('%Y%m%d')
-
-    search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)
-    # print("search_value",search_value)
-
+            token = getLogToken()
+            if token is None:
+                logging.error("获取token失败")
+                print("获取token失败")
+                return
+        if date is None:
+            date = datetime.now()
+        current_date = date
+        first_day_of_month = current_date.replace(day=1)
+        dateEnd = current_date.strftime('%Y%m%d')
+        dateStart = first_day_of_month.strftime('%Y%m%d')
+        search_value = get_queryDataListItemNos_value(
+            token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)
+        data_df = pd.DataFrame(search_value)
+        data_df["dataDate"] = pd.to_datetime(data_df["dataDate"])
+        data_df["dataDate"] = data_df["dataDate"].dt.strftime('%Y-%m-%d')
+        save_queryDataListItemNos_xls(data_df, dataItemNoList)
+        logging.info("查询数据列表项完成")
+    except Exception as e:
+        logging.error("查询数据列表项时发生异常: %s", str(e))
 
     data_df = pd.DataFrame(search_value)
 
     data_df["dataDate"] = pd.to_datetime(data_df["dataDate"])
     data_df["dataDate"] = data_df["dataDate"].dt.strftime('%Y-%m-%d')
-    save_queryDataListItemNos_xls(data_df,dataItemNoList)
+    save_queryDataListItemNos_xls(data_df, dataItemNoList)
 
-def main(start_date=None,token=None,token_push=None):
-    if start_date is None:
-        start_date = datetime.now()
-    if token is None:
-        token = get_head_auth()
-    if token_push is None:
-        token_push = get_head_push_auth()
-    date = start_date.strftime('%Y%m%d')
-    print(date)
-    updateExcelData(date,token)
-    queryDataListItemNos(token=token)
-    update_e_value('定性模型数据项12-11.xlsx', 8, 1000)
-    x = qualitativeModel()
-    print('**************************************************预测结果：',x)
-    cur_time,cur_time2 = getNow(date)
-    pushData(cur_time,x,token)
+
+def main(start_date=None, token=None, token_push=None):
+    try:
+        logging.info("开始执行主函数")
+        if start_date is None:
+            start_date = datetime.now()
+        if token is None:
+            token = get_head_auth()
+        if token_push is None:
+            token_push = get_head_push_auth()
+        date = start_date.strftime('%Y%m%d')
+        print(date)
+        logging.info("当前日期: %s", date)
+        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)
+            pushData(cur_time, x, token_push)
+        logging.info("主函数执行完成")
+    except Exception as e:
+        logging.error("主函数执行时发生异常: %s", str(e))
 
 
 if __name__ == "__main__":
     print("运行中...")
+    logging.info("程序启动")
     main()
-
+    logging.info("程序结束")
diff --git a/aisenzhecode/石油苯/日度价格预测_最佳模型.pkl b/aisenzhecode/石油苯/日度价格预测_最佳模型.pkl
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diff --git a/aisenzhecode/石油苯/纯苯数据项.xls b/aisenzhecode/石油苯/纯苯数据项.xls
index b7039c959a84c6b71f4d0c829fd48b87df2cc1ab..2e563910f5cd261d3e9a3da455ba4131b4637151 100644
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diff --git a/aisenzhecode/石油苯/纯苯每日价格预测.py b/aisenzhecode/石油苯/纯苯每日价格预测.py
index 5ce59a6..3d6d517 100644
--- a/aisenzhecode/石油苯/纯苯每日价格预测.py
+++ b/aisenzhecode/石油苯/纯苯每日价格预测.py
@@ -1,8 +1,34 @@
+from statsmodels.tools.eval_measures import mse, rmse
+from pandas import Series, DataFrame
+import cufflinks as cf
+from sklearn.metrics import r2_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+import pickle
+import warnings
+from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import mean_absolute_error
+from xgboost import plot_importance, plot_tree
+import xgboost as xgb
+import plotly.graph_objects as go
+import plotly.express as px
+import statsmodels.api as sm
+from xgboost import XGBRegressor
+from sklearn.linear_model import Lasso
+import sklearn.datasets as datasets
+from sklearn import preprocessing
+from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
+from plotly import __version__
+import random
+import seaborn as sn
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
 import requests
 import json
 import xlrd
 import xlwt
-from datetime import datetime,timedelta
+from datetime import datetime, timedelta
 import time
 # 变量定义
 login_url = "http://10.200.32.39/jingbo-api/api/server/login"
@@ -38,69 +64,29 @@ read_file_path_name = "纯苯数据项.xls"
 one_cols = []
 two_cols = []
 
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sn
-import random
-import time
 
+# 导入机器学习算法模型
 
-
-
-from plotly import __version__
-from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
-
-from sklearn import preprocessing
-
-from pandas import Series,DataFrame
-
-import matplotlib.pyplot as plt
-
-import sklearn.datasets as datasets
-
-#导入机器学习算法模型
-from sklearn.linear_model import Lasso
-from xgboost import XGBRegressor
-
-import statsmodels.api as sm
 try:
     from keras.preprocessing.sequence import TimeseriesGenerator
 except:
     from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
 
-import plotly.express as px
-import plotly.graph_objects as go
 
-import xgboost as xgb
-from xgboost import plot_importance, plot_tree
-from sklearn.metrics import mean_absolute_error
-from statsmodels.tools.eval_measures import mse,rmse
-from sklearn.model_selection import GridSearchCV
-from xgboost import XGBRegressor
-import warnings
-import pickle
+# 切割训练数据和样本数据
 
-from sklearn.metrics import mean_squared_error
-
-#切割训练数据和样本数据
-from sklearn.model_selection import train_test_split
-
-#用于模型评分
-from sklearn.metrics import r2_score
+# 用于模型评分
 
 le = preprocessing.LabelEncoder()
 
 # print(__version__) # requires version >= 1.9.0
 
 
-import cufflinks as cf
 cf.go_offline()
 
 random.seed(100)
 
 
-
 # 数据获取
 
 def get_head_auth():
@@ -114,7 +100,7 @@ def get_head_auth():
         return None
 
 
-def get_data_value(token, dataItemNoList,date):
+def get_data_value(token, dataItemNoList, date):
     search_data = {
         "data": {
             "date": getNow(date)[0],
@@ -124,7 +110,8 @@ def get_data_value(token, dataItemNoList,date):
         "funcOperation": "查询"
     }
     headers = {"Authorization": token}
-    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))
+    search_res = requests.post(
+        url=search_url, headers=headers, json=search_data, timeout=(3, 5))
     search_value = json.loads(search_res.text)["data"]
     if search_value:
         return search_value
@@ -136,10 +123,7 @@ def get_data_value(token, dataItemNoList,date):
 # xls文件处理
 
 
-
-
-
-def write_xls(data,date):
+def write_xls(data, date):
     # 创建一个Workbook对象
     workbook = xlwt.Workbook()
 
@@ -155,7 +139,6 @@ def write_xls(data,date):
     workbook.save(get_cur_time(date)[0] + '.xls')
 
 
-
 def getNow(date='', offset=0):
     """生成指定日期的两种格式字符串
     Args:
@@ -168,7 +151,7 @@ def getNow(date='', offset=0):
         tuple: (紧凑日期字符串, 标准日期字符串)
     """
     # 日期解析逻辑
-    from datetime import datetime,timedelta
+    from datetime import datetime, timedelta
     if isinstance(date, datetime):
         now = date
     else:
@@ -186,7 +169,7 @@ def getNow(date='', offset=0):
 
     # 应用日期偏移
     now = now - timedelta(days=offset)
-    
+
     # 统一格式化输出
     date_str = now.strftime("%Y-%m-%d")
     compact_date = date_str.replace("-", "")
@@ -215,7 +198,8 @@ def get_cur_time(date=''):
 
 
 def get_head_push_auth():
-    login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))
+    login_res = requests.post(
+        url=login_push_url, json=login_push_data, timeout=(3, 5))
     text = json.loads(login_res.text)
     if text["status"]:
         token = text["data"]["accessToken"]
@@ -225,8 +209,7 @@ def get_head_push_auth():
         return None
 
 
-
-def upload_data_to_system(token_push,date):
+def upload_data_to_system(token_push, date):
     datavalue = forecast_price()
     data = {
         "funcModule": "数据表信息列表",
@@ -242,85 +225,85 @@ def upload_data_to_system(token_push,date):
     }
     print(data)
     headers = {"Authorization": token_push}
-    res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))
+    res = requests.post(url=upload_url, headers=headers,
+                        json=data, timeout=(3, 5))
     print(res.text)
 
-   
-    
+
 def forecast_price():
     # df_test = pd.read_csv('定价模型数据收集0212.csv')
-    df_test = pd.read_excel('纯苯数据项.xls',sheet_name='Sheet1')
-    df_test.drop([0],inplace=True)
+    df_test = pd.read_excel('纯苯数据项.xls', sheet_name='Sheet1')
+    df_test.drop([0], inplace=True)
     # df_test['Date']=pd.to_datetime(df_test['Date'],  format='%m/%d/%Y',infer_datetime_format=True)
-    df_test['Date']=pd.to_datetime(df_test['Date'],  format=r'%Y-%m-%d',infer_datetime_format=True)
-
+    df_test['Date'] = pd.to_datetime(
+        df_test['Date'],  format=r'%Y-%m-%d', infer_datetime_format=True)
 
     df_test_1 = df_test
-    df_test_1=df_test_1.fillna(df_test.ffill())
-    df_test_1=df_test_1.fillna(df_test_1.bfill())
+    df_test_1 = df_test_1.fillna(df_test.ffill())
+    df_test_1 = df_test_1.fillna(df_test_1.bfill())
 
     # 选择用于模型训练的列名称
     col_for_training = df_test_1.columns
 
-
     import joblib
     Best_model_DalyLGPrice = joblib.load("日度价格预测_最佳模型.pkl")
     # 最新的一天为最后一行的数据
-   
+
     df_test_1_Day = df_test_1.tail(1)
     # 移除不需要的列
     df_test_1_Day.index = df_test_1_Day["Date"]
-    df_test_1_Day = df_test_1_Day.drop(["Date"], axis= 1)
-    df_test_1_Day=df_test_1_Day.drop('Price',axis=1)
-    df_test_1_Day=df_test_1_Day.dropna()
+    df_test_1_Day = df_test_1_Day.drop(["Date"], axis=1)
+    df_test_1_Day = df_test_1_Day.drop('Price', axis=1)
+    df_test_1_Day = df_test_1_Day.dropna()
 
     for col in df_test_1_Day.columns:
-        df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col],errors='coerce')
-    #预测今日价格，显示至小数点后两位
-    Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day)
+        df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col], errors='coerce')
+    # 预测今日价格，显示至小数点后两位
+    Ypredict_Today = Best_model_DalyLGPrice.predict(df_test_1_Day)
 
-    df_test_1_Day['日度预测价格']=Ypredict_Today
+    df_test_1_Day['日度预测价格'] = Ypredict_Today
     print(df_test_1_Day['日度预测价格'])
     a = df_test_1_Day['日度预测价格']
     a = a[0]
     a = float(a)
-    a = round(a,2)
+    a = round(a, 2)
     return a
+
+
 def optimize_Model():
     from sklearn.model_selection import train_test_split
-    from sklearn.impute  import SimpleImputer
+    from sklearn.impute import SimpleImputer
     from sklearn.preprocessing import OrdinalEncoder
     from sklearn.feature_selection import SelectFromModel
     from sklearn.metrics import mean_squared_error, r2_score
     import pandas as pd
 
-    pd.set_option('display.max_rows',40)  
-    pd.set_option('display.max_columns',40)  
+    pd.set_option('display.max_rows', 40)
+    pd.set_option('display.max_columns', 40)
     df_test = pd.read_excel('纯苯数据项.xls')
-    df_test.drop([0],inplace=True)
+    df_test.drop([0], inplace=True)
     # df_test['Date']=pd.to_datetime(df_test['Date'],  format='%m/%d/%Y',infer_datetime_format=True)
-    df_test['Date']=pd.to_datetime(df_test['Date'],  format='%Y-%m-%d',infer_datetime_format=True)
+    df_test['Date'] = pd.to_datetime(
+        df_test['Date'],  format='%Y-%m-%d', infer_datetime_format=True)
 
-    
-    #将缺失值补为前一个或者后一个数值
+    # 将缺失值补为前一个或者后一个数值
     df_test_1 = df_test
-    df_test_1=df_test_1.fillna(df_test.ffill())
-    df_test_1=df_test_1.fillna(df_test_1.bfill())
+    df_test_1 = df_test_1.fillna(df_test.ffill())
+    df_test_1 = df_test_1.fillna(df_test_1.bfill())
     df_test_1["Date"] = pd.to_datetime(df_test_1["Date"])
     df_test_1.index = df_test_1["Date"]
-    df_test_1 = df_test_1.drop(["Date"], axis= 1)
+    df_test_1 = df_test_1.drop(["Date"], axis=1)
     df_test_1 = df_test_1.astype('float')
-    
-    
+
     import numpy as np
     import pandas as pd
-    from pandas import Series,DataFrame
+    from pandas import Series, DataFrame
 
     import matplotlib.pyplot as plt
 
     import sklearn.datasets as datasets
 
-    #导入机器学习算法模型
+    # 导入机器学习算法模型
     from sklearn.linear_model import Lasso
     from xgboost import XGBRegressor
 
@@ -336,7 +319,7 @@ def optimize_Model():
     import xgboost as xgb
     from xgboost import plot_importance, plot_tree
     from sklearn.metrics import mean_absolute_error
-    from statsmodels.tools.eval_measures import mse,rmse
+    from statsmodels.tools.eval_measures import mse, rmse
     from sklearn.model_selection import GridSearchCV
     from xgboost import XGBRegressor
     import warnings
@@ -344,63 +327,67 @@ def optimize_Model():
 
     from sklearn.metrics import mean_squared_error
 
-    #切割训练数据和样本数据
+    # 切割训练数据和样本数据
     from sklearn.model_selection import train_test_split
 
-    #用于模型评分
+    # 用于模型评分
     from sklearn.metrics import r2_score
 
-    dataset1=df_test_1.drop('Price',axis=1)#.astype(float)
+    dataset1 = df_test_1.drop('Price', axis=1)  # .astype(float)
 
-    y=df_test_1['Price']
+    y = df_test_1['Price']
 
-    x=dataset1 
+    x = dataset1
 
     train = x
     target = y
 
-    #切割数据样本集合测试集
-    X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)
+    # 切割数据样本集合测试集
+    X_train, x_test, y_train, y_true = train_test_split(
+        train, target, test_size=0.2, random_state=0)
 
-    #模型缩写
-    Lasso =  Lasso(random_state=0)
+    # 模型缩写
+    Lasso = Lasso(random_state=0)
     XGBR = XGBRegressor(random_state=0)
-    #训练模型
-    Lasso.fit(X_train,y_train)
-    XGBR.fit(X_train,y_train)
-    #模型拟合
+    # 训练模型
+    Lasso.fit(X_train, y_train)
+    XGBR.fit(X_train, y_train)
+    # 模型拟合
     y_pre_Lasso = Lasso.predict(x_test)
     y_pre_XGBR = XGBR.predict(x_test)
 
-    #计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²
-    Lasso_score = r2_score(y_true,y_pre_Lasso)
-    XGBR_score=r2_score(y_true,y_pre_XGBR)
+    # 计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²
+    Lasso_score = r2_score(y_true, y_pre_Lasso)
+    XGBR_score = r2_score(y_true, y_pre_XGBR)
 
-    #计算Lasso、XGBR的MSE和RMSE
-    Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso)
-    XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR)
+    # 计算Lasso、XGBR的MSE和RMSE
+    Lasso_MSE = mean_squared_error(y_true, y_pre_Lasso)
+    XGBR_MSE = mean_squared_error(y_true, y_pre_XGBR)
 
-    Lasso_RMSE=np.sqrt(Lasso_MSE)
-    XGBR_RMSE=np.sqrt(XGBR_MSE)
+    Lasso_RMSE = np.sqrt(Lasso_MSE)
+    XGBR_RMSE = np.sqrt(XGBR_MSE)
     # 将不同模型的不同误差值整合成一个表格
     model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],
                                  ['XgBoost', XGBR_RMSE, XGBR_score]],
-                   columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score'])
-    #将模型名称(Model)列设置为索引
-    model_results1=model_results.set_index('模型(Model)')
+                                 columns=['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
+    # 将模型名称(Model)列设置为索引
+    model_results1 = model_results.set_index('模型(Model)')
 
     model_results1
-    #定义plot_feature_importance函数，该函数用于计算特征重要性。此部分代码无需调整
-    def plot_feature_importance(importance,names,model_type):
+    # 定义plot_feature_importance函数，该函数用于计算特征重要性。此部分代码无需调整
+
+    def plot_feature_importance(importance, names, model_type):
         feature_importance = np.array(importance)
         feature_names = np.array(names)
 
-        data={'feature_names':feature_names,'feature_importance':feature_importance}
+        data = {'feature_names': feature_names,
+                'feature_importance': feature_importance}
         fi_df = pd.DataFrame(data)
 
-        fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
+        fi_df.sort_values(by=['feature_importance'],
+                          ascending=False, inplace=True)
 
-        plt.figure(figsize=(10,8))
+        plt.figure(figsize=(10, 8))
         sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])
 
         plt.title(model_type + " "+'FEATURE IMPORTANCE')
@@ -408,60 +395,56 @@ def optimize_Model():
         plt.ylabel('FEATURE NAMES')
     from pylab import mpl
     mpl.rcParams['font.sans-serif'] = ['SimHei']
-    ## Xgboost 模型参数优化-初步
-    #参考: https://juejin.im/post/6844903661013827598 
-    #每次调参时，备选参数数值以同数量级的1、3、10设置即可（比如设置1、3、10，或0.1、0.3、1.0，或0.01,0.03,0.10即可）
+    # Xgboost 模型参数优化-初步
+    # 参考: https://juejin.im/post/6844903661013827598
+    # 每次调参时，备选参数数值以同数量级的1、3、10设置即可（比如设置1、3、10，或0.1、0.3、1.0，或0.01,0.03,0.10即可）
 
     from xgboost import XGBRegressor
     from sklearn.model_selection import GridSearchCV
 
     estimator = XGBRegressor(random_state=0,
-        nthread=4,
-        seed=0
-    )
+                             nthread=4,
+                             seed=0
+                             )
     parameters = {
-        'max_depth': range (2, 11, 2),     # 树的最大深度
-        'n_estimators': range (50, 101, 10),  # 迭代次数
+        'max_depth': range(2, 11, 2),     # 树的最大深度
+        'n_estimators': range(50, 101, 10),  # 迭代次数
         'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
     }
 
     grid_search_XGB = GridSearchCV(
         estimator=estimator,
         param_grid=parameters,
-    #     n_jobs = 10,
-        cv = 3,
+        #     n_jobs = 10,
+        cv=3,
         verbose=True
     )
 
     grid_search_XGB.fit(X_train, y_train)
-    #如果电脑在此步骤报错，可能是因为计算量太大，超过硬件可支持程度，可注释掉“n_jobs=10”一行
+    # 如果电脑在此步骤报错，可能是因为计算量太大，超过硬件可支持程度，可注释掉“n_jobs=10”一行
 
     best_parameters = grid_search_XGB.best_estimator_.get_params()
     y_pred = grid_search_XGB.predict(x_test)
 
-    op_XGBR_score = r2_score(y_true,y_pred)
-    op_XGBR_MSE= mean_squared_error(y_true, y_pred)
-    op_XGBR_RMSE= np.sqrt(op_XGBR_MSE)
+    op_XGBR_score = r2_score(y_true, y_pred)
+    op_XGBR_MSE = mean_squared_error(y_true, y_pred)
+    op_XGBR_RMSE = np.sqrt(op_XGBR_MSE)
 
     model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],
-                   columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
-    model_results2=model_results2.set_index('模型(Model)')
+                                  columns=['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])
+    model_results2 = model_results2.set_index('模型(Model)')
 
     # results = model_results1.append(model_results2, ignore_index = False)
-    results = pd.concat([model_results1,model_results2],ignore_index=True)
-
-
+    results = pd.concat([model_results1, model_results2], ignore_index=True)
 
     import pickle
 
-    Pkl_Filename = "日度价格预测_最佳模型.pkl"  
+    Pkl_Filename = "日度价格预测_最佳模型.pkl"
 
-    with open(Pkl_Filename, 'wb') as file:  
-       pickle.dump(grid_search_XGB, file)
+    with open(Pkl_Filename, 'wb') as file:
+        pickle.dump(grid_search_XGB, file)
 
 
-   
-
 def read_xls_data():
     global one_cols, two_cols
     # 打开 XLS 文件
@@ -492,9 +475,7 @@ def read_xls_data():
     # workbook.close()
 
 
-
-
-def start(date=None,token=None,token_push=None):
+def start(date=None, token=None, token_push=None):
     read_xls_data()
     if date == None:
         date = getNow()[0]
@@ -502,7 +483,7 @@ def start(date=None,token=None,token_push=None):
         token = get_head_auth()
         token_push = get_head_push_auth()
 
-    datas = get_data_value(token, one_cols,date)
+    datas = get_data_value(token, one_cols, date)
     if not datas:
         print("今天没有新数据")
         return
@@ -516,21 +497,22 @@ def start(date=None,token=None,token_push=None):
             print(data_value)
             dataItemNo_dataValue[data_value["dataItemNo"]] = ""
         else:
-            dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-       
+            dataItemNo_dataValue[data_value["dataItemNo"]
+                                 ] = data_value["dataValue"]
+
     for value in one_cols:
         if value in dataItemNo_dataValue:
             append_rows.append(dataItemNo_dataValue[value])
         else:
             append_rows.append("")
     save_xls(append_rows)
-    
+
     # 获取当月的数据写入到指定文件，如果是补充数据，不需要执行
-    queryDataListItemNos()
+    queryDataListItemNos(date=date)
     # 模型训练
     optimize_Model()
     # 上传预测数据
-    upload_data_to_system(token_push,date)
+    upload_data_to_system(token_push, date)
     # data_list.append(three_cols)
     # write_xls(data_list)
 
@@ -542,9 +524,8 @@ def start_1(date=None):
     token = get_head_auth()
     if not token:
         return
-    
 
-    datas = get_data_value(token, one_cols,date=date)
+    datas = get_data_value(token, one_cols, date=date)
 #     if not datas:
 #         return
 
@@ -556,8 +537,9 @@ def start_1(date=None):
             print(data_value)
             dataItemNo_dataValue[data_value["dataItemNo"]] = ""
         else:
-            dataItemNo_dataValue[data_value["dataItemNo"]] = data_value["dataValue"]
-       
+            dataItemNo_dataValue[data_value["dataItemNo"]
+                                 ] = data_value["dataValue"]
+
     for value in one_cols:
         if value in dataItemNo_dataValue:
             append_rows.append(dataItemNo_dataValue[value])
@@ -565,10 +547,10 @@ def start_1(date=None):
             append_rows.append("")
     save_xls_1(append_rows)
 
-    
     # data_list.append(three_cols)
     # write_xls(data_list)
-    
+
+
 def save_xls_1(append_rows):
 
     # 打开xls文件
@@ -610,11 +592,9 @@ def save_xls_1(append_rows):
                 new_sheet.write(row_count, col, append_rows[col])
 
     # 保存新的xls文件
-    new_workbook.save("纯苯数据项.xls")    
+    new_workbook.save("纯苯数据项.xls")
+
 
-    
-    
-    
 def check_data(dataItemNo):
     token = get_head_auth()
     if not token:
@@ -669,8 +649,6 @@ def save_xls(append_rows):
     new_workbook.save("纯苯数据项.xls")
 
 
-
-
 def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):
 
     search_data = {
@@ -684,7 +662,8 @@ def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEn
     }
 
     headers = {"Authorization": token}
-    search_res = requests.post(url=url, headers=headers, json=search_data, timeout=(3, 5))
+    search_res = requests.post(
+        url=url, headers=headers, json=search_data, timeout=(3, 5))
     search_value = json.loads(search_res.text)["data"]
     if search_value:
         return search_value
@@ -692,9 +671,8 @@ def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEn
         return None
 
 
-
-def save_queryDataListItemNos_xls(data_df,dataItemNoList):
-    from datetime import datetime,timedelta
+def save_queryDataListItemNos_xls(data_df, dataItemNoList):
+    from datetime import datetime, timedelta
     current_year_month = datetime.now().strftime('%Y-%m')
     grouped = data_df.groupby("dataDate")
 
@@ -727,7 +705,6 @@ def save_queryDataListItemNos_xls(data_df,dataItemNoList):
         # 创建sheet
         new_sheet = new_workbook.add_sheet(sheet_names[i])
 
-
         current_year_month_row = 0
         # 将原有的数据写入新的sheet
         for row in range(row_count):
@@ -739,29 +716,28 @@ def save_queryDataListItemNos_xls(data_df,dataItemNoList):
                     break
                 new_sheet.write(row, col, data[row][col])
 
-
         # print("current_year_month_row",current_year_month_row)
         if i == 0:
             rowFlag = 0
             # 查看每组数据
             for date, group in grouped:
-                new_sheet.write(row_count + rowFlag - current_year_month_row, 0, date)
+                new_sheet.write(row_count + rowFlag -
+                                current_year_month_row, 0, date)
                 for j in range(len(dataItemNoList)):
                     dataItemNo = dataItemNoList[j]
 
                     if group[group["dataItemNo"] == dataItemNo]["dataValue"].values and (not str(group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0]) == 'nan'):
 
-                        new_sheet.write(row_count + rowFlag - current_year_month_row, j + 1, group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0])
+                        new_sheet.write(row_count + rowFlag - current_year_month_row, j + 1,
+                                        group[group["dataItemNo"] == dataItemNo]["dataValue"].values[0])
 
                 rowFlag += 1
 
-
     # 保存新的xls文件
     new_workbook.save("纯苯数据项.xls")
 
 
-
-def queryDataListItemNos(date=None,token=None):
+def queryDataListItemNos(date=None, token=None):
     from datetime import datetime, timedelta
     df = pd.read_excel('纯苯数据项.xls')
     dataItemNoList = df.iloc[0].tolist()[1:]
@@ -779,16 +755,29 @@ def queryDataListItemNos(date=None,token=None):
     first_day_of_month = current_date.replace(day=1)
     # 格式化为 YYYYMMDD 格式
     dateEnd = current_date.strftime('%Y%m%d')
+    # dateEnd = date.strftime('%Y%m%d')
     dateStart = first_day_of_month.strftime('%Y%m%d')
-    search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)
+    search_value = get_queryDataListItemNos_value(
+        token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)
     data_df = pd.DataFrame(search_value)
     data_df["dataDate"] = pd.to_datetime(data_df["dataDate"])
     data_df["dataDate"] = data_df["dataDate"].dt.strftime('%Y-%m-%d')
-    save_queryDataListItemNos_xls(data_df,dataItemNoList)
+    save_queryDataListItemNos_xls(data_df, dataItemNoList)
     print('当月数据更新完成')
 
 
 if __name__ == "__main__":
     print('运行中ing')
     start()
+    # 自定义日期执行预测
+    # start_date = datetime(2025, 7, 6)
+    # end_date = datetime(2025, 7, 7)
 
+    # token = get_head_auth()
+    # token_push = get_head_push_auth()
+    # while start_date < end_date:
+    #     print(start_date.strftime('%Y%m%d'))
+    #     start(start_date, token, token_push)
+    #     time.sleep(2)
+    #     # start_1(start_date)
+    #     start_date += timedelta(days=1)