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>
107 lines
4.0 KiB
Python
107 lines
4.0 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 extract_questions_from_content(content):
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"""从面试题内容中提取问题和答案"""
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questions = []
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# 按问题编号分割内容
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pattern = r'\d+\.\s*问题[：:]?\s*(.*?)(?=\d+\.\s*问题|$)'
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matches = re.findall(pattern, content, re.DOTALL)
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for i, match in enumerate(matches, 1):
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# 提取问题和答案
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q_a_pattern = r'^(.*?)\n\s*(?:参考回答[：:]?|回答[：:]?)\s*(.*?)$'
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q_a_match = re.search(q_a_pattern, match.strip(), re.DOTALL)
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if q_a_match:
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question_text = q_a_match.group(1).strip()
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answer_text = q_a_match.group(2).strip()
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# 保留原始格式，包括换行和列表符号
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questions.append({
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"id": f"q{i}",
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"question": question_text,
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"answer": answer_text
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})
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return questions
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def process_visual_design_data():
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"""处理视觉设计岗位简历数据"""
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# 读取视觉设计岗位简历.json
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with open('网页未导入数据/视觉设计产业/视觉设计岗位简历.json', 'r', encoding='utf-8') as f:
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data = json.load(f)
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# 为每个岗位提取面试题
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position_questions = {}
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for item in data:
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position_name = item.get('岗位名称', '')
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interview_content = item.get('面试题内容', '')
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if position_name and interview_content:
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# 提取该岗位的面试题
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questions = extract_questions_from_content(interview_content)
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# 按类别分组（专业能力类、行为经历类、情景应变类）
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grouped_questions = {
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'专业能力类': [],
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'行为经历类': [],
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'情景应变类': []
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}
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# 根据内容判断类别
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current_category = '专业能力类'
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for q in questions:
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question_text = q['question'].lower()
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if '经历' in question_text or '分享' in question_text or '描述' in question_text or '项目' in question_text:
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current_category = '行为经历类'
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elif '如果' in question_text or '假设' in question_text or '应对' in question_text or '处理' in question_text:
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current_category = '情景应变类'
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grouped_questions[current_category].append(q)
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position_questions[position_name] = {
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'all': questions,
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'grouped': grouped_questions
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}
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return position_questions
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def update_resume_interview_mock(position_questions):
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"""更新resumeInterviewMock.js文件"""
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# 读取现有的mock文件
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with open('src/mocks/resumeInterviewMock.js', 'r', encoding='utf-8') as f:
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content = f.read()
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# 为每个岗位更新questions字段
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for position_name, questions_data in position_questions.items():
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# 查找该岗位在mock文件中的位置
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pattern = rf'name:\s*["\']({re.escape(position_name)})["\'].*?questions:\s*\[(.*?)\]'
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# 构建新的questions数组
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new_questions = json.dumps(questions_data['all'], ensure_ascii=False, indent=6)
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# 替换questions内容
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# 这里需要更精确的处理，因为JS文件格式与JSON略有不同
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# 为了保证数据完整性，生成一个单独的JSON文件供手动整合
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output = {}
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for position_name, questions_data in position_questions.items():
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output[position_name] = questions_data['all']
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with open('visual_design_interview_questions.json', 'w', encoding='utf-8') as f:
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json.dump(output, f, ensure_ascii=False, indent=2)
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print(f"已生成visual_design_interview_questions.json，包含{len(output)}个岗位的面试题数据")
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print("请手动将这些数据整合到resumeInterviewMock.js中对应岗位的questions字段")
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if __name__ == '__main__':
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position_questions = process_visual_design_data()
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update_resume_interview_mock(position_questions) |