chore: 批量新增各类工具脚本与配置文件

1. 新增音频录制、下载、上传相关脚本
2. 新增数据库操作、API调用工具
3. 新增Excel数据处理脚本
4. 新增弱密码检测脚本

智慧校园/本部/消费数据/24-26消费信息/demo.py

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+from requests import get
+import time
+import os
+import pandas as pd
+import tqdm
+from datetime import datetime, timedelta
+
+teamId = 19
+ip = "http://band.hxzhxy.cn"
+param = {
+    "username": "xtl",
+    "password": "xia123456",
+    "client_id": "client",
+    "grant_type": "password",
+    "client_secret": "123456",
+}
+
+data = get(url=f"{ip}/oauth/token", params=param).json()
+token = data["access_token"]
+header = {
+    "content-type": "application/json",
+    "authorization": f"Bearer {token}",
+}
+
+url = f"{ip}/pay/trads/findPayTtadsNews"
+output_dir = r"e:\project\python\工具\智慧校园\本部\消费数据\24-26消费信息"
+
+FIELD_MAP = {
+    "gradeName": "年级名称",
+    "termName": "交易地点",
+    "teamId": "团队ID",
+    "stuId": "学生ID",
+    "areaName": "设备区域名称",
+    "bagName": "交易账户",
+    "userNumb": "学号/工号",
+    "dealCount": "交易流水号/笔次",
+    "className": "班级名称",
+    "remark": "备注/充值类型",
+    "cardCode": "物理卡号",
+    "dealType": "消费方式",
+    "cardValue": "余额",
+    "userXm": "姓名",
+    "cardNo": "卡号",
+    "createDate": "入库时间",
+    "dealValue": "交易金额",
+    "recordType": "记录类型",
+    "id": "记录ID",
+    "recordId": "终端原始记录ID",
+    "userId": "用户唯一ID",
+    "dealTime": "交易时间"
+}
+
+OUTPUT_COLUMNS = list(FIELD_MAP.values())
+
+
+def day_range(start_date, end_date):
+    current = start_date
+    while current <= end_date:
+        yield current
+        current += timedelta(days=1)
+
+
+def month_range(start_date, end_date):
+    current = datetime(start_date.year, start_date.month, 1)
+    end_month = datetime(end_date.year, end_date.month, 1)
+    while current <= end_month:
+        yield current
+        if current.month == 12:
+            current = datetime(current.year + 1, 1, 1)
+        else:
+            current = datetime(current.year, current.month + 1, 1)
+
+
+def to_timestamp_ms(dt):
+    return int(dt.timestamp() * 1000)
+
+
+def fetch_day_records(day_dt):
+    day_start = datetime(day_dt.year, day_dt.month, day_dt.day, 0, 0, 0)
+    day_end = datetime(day_dt.year, day_dt.month, day_dt.day, 23, 59, 59)
+
+    begin_time = to_timestamp_ms(day_start)
+    end_time = to_timestamp_ms(day_end)
+
+    page_number = 0
+    page_size = 100
+    day_records = []
+
+    while True:
+        params = {
+            "teamId": teamId,
+            "pageNumber": page_number,
+            "pageSize": page_size,
+            "screen": 1,
+            "beginTime": begin_time,
+            "endTime": end_time,
+            "name": "",
+            "recordType": 1,
+            "termName": "",
+            "type": 1,
+            "classId": "",
+            "gradeId": ""
+        }
+
+        resp = get(url=url, params=params, headers=header, timeout=60)
+        resp.raise_for_status()
+        result = resp.json()
+
+        if not result.get("success"):
+            raise Exception(f"接口返回失败: {result}")
+
+        data = result.get("data") or {}
+        content = data.get("content") or []
+        total_pages = data.get("totalPages", 0)
+        total_elements = data.get("totalElements", 0)
+
+        day_records.extend(content)
+
+        print(
+            f"{day_dt.strftime('%Y-%m-%d')} 第 {page_number + 1}/{max(total_pages, 1)} 页，"
+            f"当前页 {len(content)} 条，当天累计 {len(day_records)} 条，总计 {total_elements} 条"
+        )
+
+        page_number += 1
+        if page_number >= total_pages or total_pages == 0:
+            break
+
+        time.sleep(0.1)
+
+    return day_records
+
+
+def parse_date(date_str):
+    return datetime.strptime(date_str, "%Y-%m-%d")
+
+
+def convert_records_to_df(records):
+    rows = []
+    for record in records:
+        row = {}
+        for en_key, cn_key in FIELD_MAP.items():
+            row[cn_key] = record.get(en_key)
+        rows.append(row)
+    if not rows:
+        return pd.DataFrame(columns=OUTPUT_COLUMNS)
+    return pd.DataFrame(rows, columns=OUTPUT_COLUMNS)
+
+
+def get_month_start_end(month_dt, start_date, end_date):
+    month_start = datetime(month_dt.year, month_dt.month, 1)
+    if month_dt.month == 12:
+        next_month = datetime(month_dt.year + 1, 1, 1)
+    else:
+        next_month = datetime(month_dt.year, month_dt.month + 1, 1)
+    month_end = next_month - timedelta(days=1)
+
+    if month_start < start_date:
+        month_start = start_date
+    if month_end > end_date:
+        month_end = end_date
+
+    return month_start, month_end
+
+
+def save_month_excel(month_dt, records):
+    df = convert_records_to_df(records)
+    file_name = f"{month_dt.strftime('%Y%m')}_学生消费记录.xlsx"
+    file_path = os.path.join(output_dir, file_name)
+    df.to_excel(file_path, index=False)
+    return file_path, len(df)
+
+
+def main():
+    print("开始拉取数据...")
+    start_date_str = os.getenv("START_DATE", "2024-01-01")
+    end_date_str = os.getenv("END_DATE", "2026-05-19")
+
+    start_date = parse_date(start_date_str)
+    end_date = parse_date(end_date_str)
+
+    all_month_list = list(month_range(start_date, end_date))
+    total_records = 0
+    success_months = 0
+
+    with tqdm.tqdm(all_month_list, desc="按月拉取消费记录") as month_pbar:
+        for month_dt in month_pbar:
+            try:
+                month_start, month_end = get_month_start_end(month_dt, start_date, end_date)
+                month_records = []
+                month_days = list(day_range(month_start, month_end))
+
+                for day_dt in month_days:
+                    day_records = fetch_day_records(day_dt)
+                    month_records.extend(day_records)
+                    time.sleep(0.2)
+
+                file_path, count = save_month_excel(month_dt, month_records)
+                total_records += count
+                success_months += 1
+                month_pbar.set_postfix({
+                    "month": month_dt.strftime("%Y-%m"),
+                    "count": count,
+                    "total": total_records
+                })
+                print(f"已输出: {file_path}")
+            except Exception as e:
+                print(f"{month_dt.strftime('%Y-%m')} 拉取失败: {e}")
+
+    print("\n完成")
+    print(f"成功月份: {success_months}")
+    print(f"总记录数: {total_records}")
+    print(f"输出目录: {output_dir}")
+
+
+if __name__ == "__main__":
+    main()

智慧校园/本部/消费数据/学生消费信息/Untitled-1.py

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+import pandas as pd
+import json
+from datetime import datetime
+
+# 读取Excel文件
+file_path = r'e:\project\python\工具\智慧校园\本部\消费数据\2024学生.xlsx'
+df = pd.read_excel(file_path)
+
+print("=" * 80)
+print("学生消费数据提取")
+print("=" * 80)
+
+# 1. 提取年级ID和班级ID（供用户查表）
+grade_ids = sorted(df['gradeId'].unique().tolist())
+class_ids = sorted(df['classId'].unique().tolist())
+
+print(f"\n【年级ID列表】(共{len(grade_ids)}个):")
+print(grade_ids)
+
+print(f"\n【班级ID列表】(共{len(class_ids)}个):")
+print(class_ids)
+
+# 2. 提取学生消费信息
+student_records = []
+
+for _, row in df.iterrows():
+    record = {
+        '学号': str(row['stuNo']),
+        '姓名': row['stuName'],
+        '年级ID': row['gradeId'],
+        '班级ID': row['classId'],
+        '支付金额(元)': row['totalFee'] / 100,  # 转换为元
+        '支付状态': '支付成功' if row['payState'] == 1 else '其他',
+        '创建时间': row['createTime'].strftime('%Y-%m-%d %H:%M:%S') if pd.notna(row['createTime']) else '',
+        '支付时间': row['payTime'].strftime('%Y-%m-%d %H:%M:%S') if pd.notna(row['payTime']) else '',
+        '物理卡号': str(row['serialNo']) if pd.notna(row['serialNo']) else '',
+        'teamId': row['teamId']
+    }
+    student_records.append(record)
+
+# 3. 统计信息
+print(f"\n【数据统计】")
+print(f"总记录数: {len(student_records)}")
+print(f"唯一学生数: {df['stuNo'].nunique()}")
+print(f"总金额: {df['totalFee'].sum() / 100:.2f} 元")
+
+# 4. 按年级统计
+print(f"\n【按年级统计】")
+grade_stats = df.groupby('gradeId').agg({
+    'stuNo': 'nunique',
+    'totalFee': 'sum'
+}).reset_index()
+grade_stats.columns = ['年级ID', '学生人数', '总金额(分)']
+grade_stats['总金额(元)'] = grade_stats['总金额(分)'] / 100
+print(grade_stats.to_string(index=False))
+
+# 5. 按班级统计
+print(f"\n【按班级统计】")
+class_stats = df.groupby('classId').agg({
+    'stuNo': 'nunique',
+    'totalFee': 'sum'
+}).reset_index()
+class_stats.columns = ['班级ID', '学生人数', '总金额(分)']
+class_stats['总金额(元)'] = class_stats['总金额(分)'] / 100
+print(class_stats.to_string(index=False))
+
+# 6. 保存详细记录到JSON
+output_file = r'e:\project\python\工具\智慧校园\本部\消费数据\student_records.json'
+with open(output_file, 'w', encoding='utf-8') as f:
+    json.dump(student_records, f, ensure_ascii=False, indent=2)
+
+print(f"\n【输出文件】")
+print(f"详细记录已保存到: {output_file}")
+
+# 7. 保存ID映射表（等待用户补充年级和班级名称）
+id_mapping = {
+    'grade_ids': grade_ids,
+    'class_ids': class_ids,
+    'note': '请查询数据库补充年级名称和班级名称'
+}
+
+id_mapping_file = r'e:\project\python\工具\智慧校园\本部\消费数据\id_mapping.json'
+with open(id_mapping_file, 'w', encoding='utf-8') as f:
+    json.dump(id_mapping, f, ensure_ascii=False, indent=2)
+
+print(f"ID映射表已保存到: {id_mapping_file}")
+print("\n请查询数据库，告诉我以下ID对应的名称：")
+print(f"年级ID: {grade_ids}")
+print(f"班级ID: {class_ids}")

智慧校园/本部/消费数据/学生消费信息/demo.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72fd36ef",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

智慧校园/本部/消费数据/学生消费信息/read_excel.py

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+import pandas as pd
+import json
+
+# 读取Excel文件
+df = pd.read_excel('e:/project/python/工具/智慧校园/本部/消费数据/2024学生.xlsx')
+
+print("=== Excel文件基本信息 ===")
+print(f"数据形状: {df.shape}")
+print(f"\n列名: {df.columns.tolist()}")
+
+print("\n=== 前10行数据 ===")
+print(df.head(10))
+
+print("\n=== 数据类型 ===")
+print(df.dtypes)
+
+# 提取唯一值
+print("\n=== 唯一值统计 ===")
+for col in df.columns:
+    unique_count = df[col].nunique()
+    print(f"{col}: {unique_count} 个唯一值")
+    if unique_count <= 20:
+        print(f"  值: {df[col].unique().tolist()}")
+
+# 检查是否有年级和班级相关字段
+grade_class_cols = [col for col in df.columns if any(keyword in col.lower() for keyword in ['年级', '班级', 'grade', 'class'])]
+print(f"\n=== 年级/班级相关字段: {grade_class_cols} ===")
+
+# 保存列名信息供后续使用
+with open('e:/project/python/工具/智慧校园/本部/消费数据/excel_info.json', 'w', encoding='utf-8') as f:
+    info = {
+        'columns': df.columns.tolist(),
+        'shape': df.shape,
+        'sample_data': df.head(5).to_dict('records')
+    }
+    json.dump(info, f, ensure_ascii=False, indent=2)
+
+print("\n=== 信息已保存到 excel_info.json ===")

智慧校园/本部/消费数据/学生消费信息/read_excel_simple.py

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+import pandas as pd
+import sys
+
+# 读取Excel文件
+file_path = r'e:\project\python\工具\智慧校园\本部\消费数据\2024学生.xlsx'
+df = pd.read_excel(file_path)
+
+print("数据形状:", df.shape)
+print("\n列名:")
+for i, col in enumerate(df.columns):
+    print(f"{i+1}. {col}")
+
+print("\n前5行数据:")
+print(df.head().to_string())
+
+# 检查年级和班级相关字段
+print("\n查找年级/班级相关字段...")
+for col in df.columns:
+    if any(keyword in str(col) for keyword in ['年级', '班级', 'grade', 'class', 'Grade', 'Class']):
+        print(f"找到字段: {col}")
+        print(f"唯一值: {df[col].unique()[:10]}")  # 只显示前10个唯一值
+
+print("\n数据类型:")
+print(df.dtypes)

智慧校园/本部/消费数据/学生消费信息/read_tables.py

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+import pandas as pd
+
+# 读取年级表和班级表
+grade_df = pd.read_excel(r'e:\project\python\工具\智慧校园\本部\消费数据\年级表.xlsx')
+class_df = pd.read_excel(r'e:\project\python\工具\智慧校园\本部\消费数据\班级表.xlsx')
+
+print("=== 年级表 ===")
+print(f"形状: {grade_df.shape}")
+print(f"列名: {grade_df.columns.tolist()}")
+print("\n前10行:")
+print(grade_df.head(10))
+
+print("\n=== 班级表 ===")
+print(f"形状: {class_df.shape}")
+print(f"列名: {class_df.columns.tolist()}")
+print("\n前10行:")
+print(class_df.head(10))

智慧校园/本部/消费数据/学生消费信息/整理消费数据.py

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+import pandas as pd
+from datetime import datetime
+
+# 读取所有数据
+print("正在读取数据...")
+consumption_df = pd.read_excel(r'e:\project\python\工具\智慧校园\本部\消费数据\2024学生.xlsx')
+grade_df = pd.read_excel(r'e:\project\python\工具\智慧校园\本部\消费数据\年级表.xlsx')
+class_df = pd.read_excel(r'e:\project\python\工具\智慧校园\本部\消费数据\班级表.xlsx')
+
+print(f"消费数据: {consumption_df.shape[0]} 条记录")
+print(f"年级表: {grade_df.shape[0]} 个年级")
+print(f"班级表: {class_df.shape[0]} 个班级")
+
+# 创建年级ID到名称的映射
+grade_map = dict(zip(grade_df['id'], grade_df['grade_name']))
+
+# 创建班级ID到名称的映射
+class_map = dict(zip(class_df['id'], class_df['class_name']))
+
+# 创建班级ID到年级ID的映射（用于验证）
+class_to_grade_map = dict(zip(class_df['id'], class_df['grade_id']))
+
+print("\n正在处理消费数据...")
+
+# 处理消费数据
+processed_data = []
+
+for _, row in consumption_df.iterrows():
+    grade_id = row['gradeId']
+    class_id = row['classId']
+    
+    # 获取年级和班级名称
+    grade_name = grade_map.get(grade_id, f'未知年级({grade_id})')
+    class_name = class_map.get(class_id, f'未知班级({class_id})')
+    
+    # 处理时间格式
+    create_time = row['createTime']
+    pay_time = row['payTime']
+    
+    if pd.notna(create_time):
+        if isinstance(create_time, str):
+            create_time_str = create_time
+        else:
+            create_time_str = create_time.strftime('%Y-%m-%d %H:%M:%S')
+    else:
+        create_time_str = ''
+    
+    if pd.notna(pay_time):
+        if isinstance(pay_time, str):
+            pay_time_str = pay_time
+        else:
+            pay_time_str = pay_time.strftime('%Y-%m-%d %H:%M:%S')
+    else:
+        pay_time_str = ''
+    
+    # 处理支付状态
+    pay_state_map = {0: '确认中', 1: '支付成功', 2: '已取消', 3: '退款'}
+    pay_state = pay_state_map.get(row['payState'], f'未知状态({row["payState"]})')
+    
+    # 处理充值状态
+    change_state_map = {0: '充值中', 1: '已取消', 2: '充值成功', 3: '充值失败'}
+    change_state = change_state_map.get(row['changeState'], f'未知状态({row["changeState"]})')
+    
+    record = {
+        '学号': str(row['stuNo']),
+        '姓名': row['stuName'],
+        '年级ID': grade_id,
+        '年级名称': grade_name,
+        '班级ID': class_id,
+        '班级名称': class_name,
+        '支付金额(元)': row['totalFee'] / 100,
+        '支付状态': pay_state,
+        '充值状态': change_state,
+        '创建时间': create_time_str,
+        '支付时间': pay_time_str,
+        '物理卡号': str(int(row['serialNo'])) if pd.notna(row['serialNo']) else '',
+        '订单编号': row['outTradeNo'],
+        '微信订单号': row['transactionId'] if pd.notna(row['transactionId']) else '',
+        '支付手机号': str(row['wxPhone']),
+        'teamId': row['teamId']
+    }
+    processed_data.append(record)
+
+# 创建DataFrame
+result_df = pd.DataFrame(processed_data)
+
+# 生成输出文件名
+output_file = r'e:\project\python\工具\智慧校园\本部\消费数据\学生消费记录整理.xlsx'
+
+# 写入Excel，使用多个sheet
+with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
+    # Sheet 1: 详细记录
+    result_df.to_excel(writer, sheet_name='详细记录', index=False)
+    
+    # Sheet 2: 按年级汇总
+    grade_summary = result_df.groupby(['年级ID', '年级名称']).agg({
+        '学号': 'nunique',
+        '支付金额(元)': 'sum',
+        '姓名': 'count'
+    }).reset_index()
+    grade_summary.columns = ['年级ID', '年级名称', '学生人数', '总金额(元)', '消费笔数']
+    grade_summary = grade_summary.sort_values('年级ID')
+    grade_summary.to_excel(writer, sheet_name='按年级汇总', index=False)
+    
+    # Sheet 3: 按班级汇总
+    class_summary = result_df.groupby(['班级ID', '班级名称', '年级名称']).agg({
+        '学号': 'nunique',
+        '支付金额(元)': 'sum',
+        '姓名': 'count'
+    }).reset_index()
+    class_summary.columns = ['班级ID', '班级名称', '年级名称', '学生人数', '总金额(元)', '消费笔数']
+    class_summary = class_summary.sort_values(['年级名称', '班级ID'])
+    class_summary.to_excel(writer, sheet_name='按班级汇总', index=False)
+    
+    # Sheet 4: 按学生汇总
+    student_summary = result_df.groupby(['学号', '姓名', '年级名称', '班级名称']).agg({
+        '支付金额(元)': 'sum',
+        '创建时间': 'count'
+    }).reset_index()
+    student_summary.columns = ['学号', '姓名', '年级名称', '班级名称', '总金额(元)', '消费笔数']
+    student_summary = student_summary.sort_values(['年级名称', '班级名称', '学号'])
+    student_summary.to_excel(writer, sheet_name='按学生汇总', index=False)
+
+print(f"\n✅ 数据整理完成！")
+print(f"📁 输出文件: {output_file}")
+print(f"\n📊 统计信息:")
+print(f"   - 总记录数: {len(result_df)}")
+print(f"   - 唯一学生数: {result_df['学号'].nunique()}")
+print(f"   - 总金额: {result_df['支付金额(元)'].sum():.2f} 元")
+print(f"\n📋 Excel包含以下工作表:")
+print(f"   1. 详细记录 - 所有消费明细")
+print(f"   2. 按年级汇总 - 各年级消费统计")
+print(f"   3. 按班级汇总 - 各班级消费统计")
+print(f"   4. 按学生汇总 - 各学生消费统计")
+
+# 显示年级分布
+print(f"\n🏫 年级分布:")
+grade_dist = result_df.groupby('年级名称')['学号'].nunique().sort_values(ascending=False)
+for grade, count in grade_dist.items():
+    print(f"   {grade}: {count} 人")