diff --git a/new_zentao.py b/new_zentao.py
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index 0000000..301b45b
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+++ b/new_zentao.py
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+import pandas as pd
+import pymysql
+from datetime import date
+import os
+import openpyxl
+from openpyxl.styles import PatternFill
+from openpyxl.styles import Font
+# 建立与MySQL数据库的连接
+conn = pymysql.connect(
+    host='192.168.10.28',
+    user='root',
+    port=3307,
+    password='123456',
+    database='zentao'
+)
+today = date.today()
+#打开游标
+cursor = conn.cursor()
+query = "SELECT account FROM zt_user"
+cursor.execute(query)
+account_result = cursor.fetchall()
+account_of_list = []
+for row in account_result:
+    account_of_list.append(row)
+account_list = [item[0] for item in account_of_list]
+account_list.pop(0)
+#print(account_list)
+for accounts in account_list:
+    query_effort = "SELECT e.account,u.realname,e.consumed,e.work,t.name,t.desc from zt_effort e , zt_user u ,zt_task t where    e.DATE = current_date AND e.account = u.account AND e.objectID = t.id;"
+    cursor.execute(query_effort)
+    df = pd.read_sql(query_effort, con=conn)
+
+# 关闭数据库连接
+conn.close()
+excel_file = '{}_zentao.xlsx'.format(today) # 指定要输出的Excel文件名
+df.to_excel(excel_file, index=False)  # index=False 表示不包含行号
+excel_file_path = excel_file
+df = pd.read_excel(excel_file_path)
+#将相同的name排在相邻行
+df_sorted = df.sort_values(by='account')
+df_sorted = df_sorted.groupby('account').apply(lambda x: x.reset_index(drop=True))
+df_sorted.reset_index(drop=True, inplace=True)
+output_excel_path = '{}工时.xlsx'.format(today)  # 替换为你想保存的Excel文件路径
+df_sorted.to_excel(output_excel_path, index=False)
+os.remove(excel_file)
+print(f'Processed data saved to: {output_excel_path}')
+excel_file_new = output_excel_path  # 替换为你的Excel文件路径
+df = pd.read_excel(excel_file_new)
+# 将新数据添加到DataFrame
+df.rename(columns={'realname': '员工姓名','name': '任务名称','desc':'任务描述','work':'工时对应的工作内容','account':'员工账号','consumed':'消耗工时'}, inplace=True)
+pd.set_option("display.max_colwidth",1000)
+# 保存更新后的数据到同一Excel文件
+df.to_excel(excel_file_new, sheet_name='员工工时详情',index=False)
+#sheet2工时操作
+df_grouped = df.groupby('员工姓名')['消耗工时'].sum().reset_index()
+df = pd.read_excel(excel_file_new,header=None)
+# 对原始数据进行处理，并将结果保存到result变量中
+# 创建一个ExcelWriter对象
+writer = pd.ExcelWriter(excel_file_new)
+# 将原始数据写入到Excel文件中
+df.to_excel(writer, sheet_name='员工工时详情', index=False,header=False)
+# 将处理后的结果写入到新的sheet页中
+df_grouped.to_excel(writer, sheet_name='员工今日总工时', index=False,header=False)
+# 关闭ExcelWriter对象
+writer._save()
+
+# 打开Excel文件
+workbook = openpyxl.load_workbook(excel_file_new)
+
+# 总工时新开一个sheet页面
+sheet = workbook['员工今日总工时']
+sheet.insert_rows(1)
+data_to_insert = ["员工姓名", "员工今日总工时"]  # 替换为你要插入的实际数据
+for col_num, value in enumerate(data_to_insert, start=1):
+    cell = sheet.cell(row=1, column=col_num, value=value)
+    # 加粗字体
+    cell.font = Font(bold=True)
+sheet_1 = workbook['员工工时详情']
+first_row = sheet_1[1]
+for cell in first_row:
+    cell.font = Font(bold=True)
+# 自动调整列宽以适应内容
+for column in sheet_1.columns:
+    max_length = 0
+    column = [cell for cell in column]
+    for cell in column:
+        try:
+            if len(str(cell.value)) > max_length:
+                max_length = len(cell.value)
+        except:
+            pass
+    adjusted_width = (max_length + 2)
+    sheet_1.column_dimensions[column[0].column_letter].width = adjusted_width
+# 创建一个红色填充样式
+red_fill = PatternFill(start_color='FFFF0000',
+                       end_color='FFFF0000',
+                       fill_type='solid')
+# 创建一个黄色填充样式
+yellow_fill = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid")
+# 遍历第二列的每一行
+for row in sheet.iter_rows(min_row=2, min_col=2, max_col=2):
+    cell = row[0]
+    if cell.value is not None and cell.value < 8:
+        cell.fill = red_fill
+    if cell.value is not None and cell.value > 8:
+        cell.fill = yellow_fill
+
+# 保存修改后的Excel文件
+workbook.save(excel_file_new)
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