cd2e307402
主要内容： - 包含12个产业的完整教务系统前端代码 - 智能启动脚本 (start-industry.sh) - 可视化产业导航页面 (index.html) - 项目文档 (README.md) 优化内容： - 删除所有node_modules和.yoyo文件夹，从7.5GB减少到2.7GB - 添加.gitignore文件避免上传不必要的文件 - 自动依赖管理和智能启动系统 产业列表： 1. 文旅产业 (5150) 2. 智能制造 (5151) 3. 智能开发 (5152) 4. 财经商贸 (5153) 5. 视觉设计 (5154) 6. 交通物流 (5155) 7. 大健康 (5156) 8. 土木水利 (5157) 9. 食品产业 (5158) 10. 化工产业 (5159) 11. 能源产业 (5160) 12. 环保产业 (5161) 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
116 lines
4.4 KiB
Python
116 lines
4.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import json
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import re
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def fix_quotes_in_content():
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"""修复专家支持中心数据中的引号问题"""
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# 读取原始的土木水利问答内容
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with open('网页未导入数据/土木水利产业/土木水利问答内容.json', 'r', encoding='utf-8') as f:
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qa_data = json.load(f)
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# 转换为专家支持中心格式
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conversations = []
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# 类型映射
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type_mapping = {
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"智能客服": "专业知识",
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"导师问答": "专业知识",
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"就业指导": "就业指导",
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"常规问题": "常规问题"
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}
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# 导师头像映射
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mentor_avatars = {
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"杨清永": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5R3bN.png",
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"罗俊杰": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5BFnj.png",
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"多多畅职机器人": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_icon/recuWmDuekBTlr.png"
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}
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for idx, item in enumerate(qa_data, 1):
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# 提取时间部分（从第一个时间中提取日期）
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time_str = item.get("流程1_时间", "")
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date_parts = time_str.split("/") if time_str else ["2024", "1"]
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date = f"{date_parts[0]}年{int(date_parts[1]) if len(date_parts) > 1 else 1}月"
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# 确定类型
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qa_type = item.get("问答类型", "常规问题")
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conv_type = type_mapping.get(qa_type, "常规问题")
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# 确定导师
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mentor = item.get("查询导师名称", "")
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if not mentor:
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mentor = "多多畅职机器人"
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else:
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mentor = mentor + "老师"
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conversation = {
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"id": idx,
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"title": item.get("问题标题（AI）", "问题"),
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"status": "finish",
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"date": date,
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"type": conv_type,
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"messages": []
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}
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# 添加第一轮问答
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if item.get("问题_流程1"):
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conversation["messages"].append({
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"type": "user",
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"content": item["问题_流程1"],
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"time": item.get("流程1_时间", "")
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})
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if item.get("回答_流程2"):
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# 处理回答内容，将其中的问答格式转换
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answer_content = item["回答_流程2"]
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# 如果内容中包含额外的问答，只取第一个答案
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if "\n问：" in answer_content:
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answer_content = answer_content.split("\n问：")[0]
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conversation["messages"].append({
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"type": "assistant",
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"content": answer_content,
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"mentor": mentor,
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"time": item.get("流程2_时间", ""),
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"mentorAvatar": mentor_avatars.get(mentor.replace("老师", ""), mentor_avatars["多多畅职机器人"])
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})
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# 添加第二轮问答（如果有）
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if item.get("问题_流程3"):
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conversation["messages"].append({
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"type": "user",
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"content": item["问题_流程3"],
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"time": item.get("流程3_时间", "")
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})
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if item.get("回答_流程4"):
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conversation["messages"].append({
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"type": "assistant",
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"content": item["回答_流程4"],
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"mentor": mentor,
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"time": item.get("流程4_时间", ""),
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"mentorAvatar": mentor_avatars.get(mentor.replace("老师", ""), mentor_avatars["多多畅职机器人"])
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})
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conversations.append(conversation)
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# 生成JavaScript文件内容，使用JSON序列化来自动处理引号转义
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js_content = """// 从土木水利问答内容.json转换的专家支持中心数据
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const civilEngineeringExpertSupportData = {
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"conversations": %s
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};
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export default civilEngineeringExpertSupportData;""" % json.dumps(conversations, ensure_ascii=False, indent=2)
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# 写入文件
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with open('src/data/civilEngineeringExpertSupportData.js', 'w', encoding='utf-8') as f:
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f.write(js_content)
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print("✅ 专家支持中心数据已修复")
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print(f"转换了 {len(conversations)} 个对话")
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if __name__ == "__main__":
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fix_quotes_in_content() |