38350dca36
主要更新： - 更新所有12个产业的教务系统数据和功能 - 删除所有 node_modules 文件夹（节省3.7GB） - 删除所有 .yoyo 缓存文件夹（节省1.2GB） - 删除所有 dist 构建文件（节省55MB） 项目优化： - 项目大小从 8.1GB 减少到 3.2GB（节省60%空间） - 保留完整的源代码和配置文件 - .gitignore 已配置，防止再次提交大文件 启动脚本： - start-industry.sh/bat/ps1 脚本会自动检测并安装依赖 - 首次启动时自动运行 npm install - 支持单个或批量启动产业系统 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
192 lines
6.8 KiB
Python
192 lines
6.8 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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from datetime import datetime
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# 读取智能制造岗位简历数据
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print("正在读取智能制造岗位简历.json...")
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with open('网页未导入数据/智能制造产业/智能制造岗位简历.json', 'r', encoding='utf-8') as f:
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smart_mfg_data = json.load(f)
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# 读取当前mock文件
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print("正在读取当前mock文件...")
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with open('src/mocks/resumeInterviewMock.js', 'r', encoding='utf-8') as f:
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mock_content = f.read()
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# 备份原文件
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backup_time = datetime.now().strftime('%Y%m%d_%H%M%S')
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backup_file = f'src/mocks/resumeInterviewMock.js.backup_complete_{backup_time}'
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with open(backup_file, 'w', encoding='utf-8') as f:
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f.write(mock_content)
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print(f"已创建备份文件: {backup_file}")
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# 解析智能制造岗位简历数据，构建完整的面试题映射
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interview_data_map = {}
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for item in smart_mfg_data:
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job_group = item.get('简历岗位群', '')
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interview_content = item.get('面试题内容', '')
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if job_group and interview_content:
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# 解析面试题内容
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questions_data = []
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lines = interview_content.split('\n')
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current_section = None
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current_section_questions = []
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section_id = 1
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i = 0
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while i < len(lines):
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line = lines[i].strip()
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if not line:
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i += 1
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continue
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# 大标题（如 # 一、xxx）
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if line.startswith('# '):
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# 保存上一个section
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if current_section and current_section_questions:
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questions_data.append({
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'id': f'group_q{section_id}',
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'title': current_section,
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'questions': current_section_questions
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})
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section_id += 1
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current_section = line[2:].strip()
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current_section_questions = []
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# 问题编号（如 1. xxx 或 1、xxx）
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elif re.match(r'^(\d+)[\.、]\s+', line):
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# 提取问题
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question_match = re.match(r'^(\d+)[\.、]\s+(.+)', line)
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if question_match:
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question_text = question_match.group(2).strip()
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# 查找答案
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answer_lines = []
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i += 1 # 移动到下一行
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# 继续读取直到找到答案或下一个问题
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while i < len(lines):
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next_line = lines[i].strip()
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# 如果遇到下一个问题或章节，停止
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if next_line.startswith('#') or re.match(r'^(\d+)[\.、]\s+', next_line):
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i -= 1 # 回退一行，让外层循环处理
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break
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# 如果是答案标记
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if re.match(r'^(示例答案|答案)[：:]?\s*', next_line):
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# 提取答案内容
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answer_text = re.sub(r'^(示例答案|答案)[：:]?\s*', '', next_line)
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if answer_text:
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answer_lines.append(answer_text)
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elif next_line:
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# 其他非空行也作为答案的一部分
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answer_lines.append(next_line)
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i += 1
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answer = ' '.join(answer_lines).strip()
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if not answer:
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answer = "请根据实际情况回答"
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current_section_questions.append({
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'question': question_text,
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'answer': answer
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})
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continue
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i += 1
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# 保存最后一个section
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if current_section and current_section_questions:
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questions_data.append({
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'id': f'group_q{section_id}',
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'title': current_section,
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'questions': current_section_questions
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})
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interview_data_map[job_group] = questions_data
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print(f"\n共解析 {len(interview_data_map)} 个岗位群的面试题数据")
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# 统计解析结果
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for group, data in interview_data_map.items():
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total_questions = sum(len(section['questions']) for section in data)
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print(f" - {group}: {len(data)} 个章节，{total_questions} 道题")
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# 更新mock文件中的面试题
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def update_industry_questions(match):
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full_match = match.group(0)
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industry_name = match.group(1)
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# 查找对应的面试题数据
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if industry_name not in interview_data_map:
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print(f" - {industry_name}: 保持原样（未找到对应数据）")
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return full_match
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questions_data = interview_data_map[industry_name]
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# 构建新的questions数组
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new_questions = []
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for section_data in questions_data:
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sub_questions = []
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for idx, q in enumerate(section_data['questions']):
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# 转义特殊字符
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question_text = q['question'].replace('\\', '\\\\').replace('"', '\\"')
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answer_text = q['answer'].replace('\\', '\\\\').replace('"', '\\"').replace('\n', '\\n')
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sub_questions.append(f'''{{
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"id": "q{section_data['id'][7:]}_{idx+1}",
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"question": "{question_text}",
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"answer": "{answer_text}"
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}}''')
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section_title = section_data['title'].replace('\\', '\\\\').replace('"', '\\"')
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new_questions.append(f'''{{
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"id": "{section_data['id']}",
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"question": "# {section_title}",
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"subQuestions": [
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{','.join(sub_questions)}
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]
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}}''')
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# 替换questions部分
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questions_str = ',\n '.join(new_questions)
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# 构建完整的替换内容
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before_questions = match.group(0).split('"questions": [')[0]
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result = before_questions + '"questions": [\n ' + questions_str + '\n ]'
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# 统计题目数量
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total_questions = sum(len(section['questions']) for section in questions_data)
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print(f" - {industry_name}: 更新为 {len(questions_data)} 个章节，{total_questions} 道题")
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return result
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# 执行更新
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print("\n正在更新面试题数据...")
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pattern = r'"name":\s*"([^"]+)"[^}]*?"questions":\s*\[.*?\]\s*(?=\})'
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new_content = re.sub(pattern, update_industry_questions, mock_content, flags=re.DOTALL)
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# 保存更新后的文件
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with open('src/mocks/resumeInterviewMock.js', 'w', encoding='utf-8') as f:
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f.write(new_content)
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print("\n✅ 面试题数据更新完成！")
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# 验证语法
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import subprocess
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result = subprocess.run(['node', '-c', 'src/mocks/resumeInterviewMock.js'],
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capture_output=True, text=True)
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if result.returncode == 0:
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print("✅ 语法检查通过！")
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else:
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print("❌ 语法错误：")
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print(result.stderr) |