Initial commit: 教务系统在线平台

- 包含4个产业方向的前端项目：智能开发、智能制造、大健康、财经商贸
- 已清理node_modules、.yoyo等大文件，项目大小从2.6GB优化至631MB
- 配置完善的.gitignore文件

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

frontend_大健康/convert_health_qa_data.py

@@ -0,0 +1,171 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import json
+from datetime import datetime
+
+# 读取大健康问答内容数据
+with open('/Users/apple/Documents/cursor/教务系统/frontend_大健康/网页未导入数据/大健康产业/大健康问答内容.json', 'r', encoding='utf-8') as f:
+    health_qa_data = json.load(f)
+
+# 导师头像映射
+mentor_avatars = {
+    "李奇": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dDIg60Tg.png",
+    "宋积极(配方师gigi)": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5E4Al.png",
+    "吴兰": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5t1Ii.jpeg",
+    # 其他导师可以添加更多映射
+}
+
+# 默认机器人头像
+robot_avatar = "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_icon/recuWmDuekBTlr.png"
+
+# 转换为专家支持中心格式
+conversations = []
+
+for idx, item in enumerate(health_qa_data, 1):
+    messages = []
+
+    # 构建对话消息
+    # 流程1 - 用户问题
+    if item.get("问题_流程1"):
+        messages.append({
+            "type": "user",
+            "content": item["问题_流程1"],
+            "time": item["流程1_时间"]
+        })
+
+    # 流程2 - 助手回答
+    if item.get("回答_流程2"):
+        mentor_name = item.get("查询导师名称", "多多畅职机器人")
+        if item.get("问答类型") == "AI答疑":
+            mentor_name = "多多畅职机器人"
+            mentor_avatar = robot_avatar
+        else:
+            mentor_avatar = mentor_avatars.get(mentor_name, robot_avatar)
+            if mentor_name != "多多畅职机器人":
+                mentor_name += "老师"
+
+        messages.append({
+            "type": "assistant",
+            "content": item["回答_流程2"],
+            "mentor": mentor_name,
+            "time": item["流程2_时间"],
+            "mentorAvatar": mentor_avatar
+        })
+
+    # 流程3 - 用户问题
+    if item.get("问题_流程3"):
+        messages.append({
+            "type": "user",
+            "content": item["问题_流程3"],
+            "time": item["流程3_时间"]
+        })
+
+    # 流程4 - 助手回答
+    if item.get("回答_流程4"):
+        mentor_name = item.get("查询导师名称", "多多畅职机器人")
+        if item.get("问答类型") == "AI答疑":
+            mentor_name = "多多畅职机器人"
+            mentor_avatar = robot_avatar
+        else:
+            mentor_avatar = mentor_avatars.get(mentor_name, robot_avatar)
+            if mentor_name != "多多畅职机器人":
+                mentor_name += "老师"
+
+        messages.append({
+            "type": "assistant",
+            "content": item["回答_流程4"],
+            "mentor": mentor_name,
+            "time": item["流程4_时间"],
+            "mentorAvatar": mentor_avatar
+        })
+
+    # 流程5 - 用户问题
+    if item.get("问题_流程5"):
+        messages.append({
+            "type": "user",
+            "content": item["问题_流程5"],
+            "time": item["流程5_时间"]
+        })
+
+    # 流程6 - 助手回答
+    if item.get("回答_流程6"):
+        mentor_name = item.get("查询导师名称", "多多畅职机器人")
+        if item.get("问答类型") == "AI答疑":
+            mentor_name = "多多畅职机器人"
+            mentor_avatar = robot_avatar
+        else:
+            mentor_avatar = mentor_avatars.get(mentor_name, robot_avatar)
+            if mentor_name != "多多畅职机器人":
+                mentor_name += "老师"
+
+        messages.append({
+            "type": "assistant",
+            "content": item["回答_流程6"],
+            "mentor": mentor_name,
+            "time": item["流程6_时间"],
+            "mentorAvatar": mentor_avatar
+        })
+
+    # 获取对话日期（从第一条消息的时间提取年月）
+    if messages:
+        first_time = messages[0]["time"]
+        try:
+            # 解析时间格式 "2023/9/12 9:23"
+            date_obj = datetime.strptime(first_time, "%Y/%m/%d %H:%M")
+            conversation_date = f"{date_obj.year}年{date_obj.month}月"
+        except:
+            conversation_date = "2024年"
+
+    # 确定问答类型
+    qa_type = item.get("问答类型", "常规问题")
+    if qa_type == "导师问答":
+        qa_type = "专业知识"
+    elif qa_type == "AI答疑":
+        qa_type = "AI答疑"
+    else:
+        qa_type = "常规问题"
+
+    # 构建对话对象
+    if messages:
+        conversation = {
+            "id": idx,
+            "title": item.get("问题标题（AI）", f"问题{idx}"),
+            "status": "finish",
+            "date": conversation_date,
+            "type": qa_type,
+            "messages": messages
+        }
+        conversations.append(conversation)
+
+# 按时间排序（从最早的第一条消息时间开始）
+def get_first_message_time(conv):
+    if conv["messages"]:
+        time_str = conv["messages"][0]["time"]
+        try:
+            return datetime.strptime(time_str, "%Y/%m/%d %H:%M")
+        except:
+            return datetime.now()
+    return datetime.now()
+
+# 按时间升序排序（早的在前）
+conversations.sort(key=get_first_message_time)
+
+# 构建最终的数据结构
+expert_support_data = {
+    "conversations": conversations
+}
+
+# 生成JS文件内容
+js_content = f"""// 从大健康问答内容.json转换的专家支持中心数据
+const expertSupportData = {json.dumps(expert_support_data, ensure_ascii=False, indent=2)};
+
+export default expertSupportData;"""
+
+# 保存到文件
+output_path = 'src/data/expertSupportData.js'
+with open(output_path, 'w', encoding='utf-8') as f:
+    f.write(js_content)
+
+print(f"✅ 成功转换 {len(conversations)} 条对话数据")
+print(f"✅ 已保存到 {output_path}")