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Initial commit: 教务系统在线平台

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- 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>
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2025-12-12 18:16:55 +08:00
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frontend_大健康/extract_all_interview_questions.py Normal file
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import json
import re
def parse_all_interview_questions(content):
"""解析所有面试题内容，包括所有问题"""
questions = []
question_id = 1
# 删除"判断题："等前缀
content = re.sub(r'判断题：\s*', '', content)
# 分割成不同的问题类别（一、二、三等）
sections = re.split(r'\n# ([一二三四五六七八九十]+、[^#\n]+)', content)
# 如果没有找到类别标记，尝试直接查找所有问题
all_questions = []
if len(sections) > 1:
# 有类别的情况
for i in range(1, len(sections), 2):
if i >= len(sections):
break
section_title = sections[i].strip()
section_content = sections[i + 1] if i + 1 < len(sections) else ""
# 提取该类别下的所有问题
category_questions = extract_questions_from_section(section_content, question_id)
question_id += len(category_questions)
if category_questions:
all_questions.append({
"category": section_title,
"questions": category_questions
})
else:
# 没有类别的情况，直接提取所有问题
category_questions = extract_questions_from_section(content, question_id)
if category_questions:
all_questions.append({
"category": "综合面试题",
"questions": category_questions
})
return all_questions
def extract_questions_from_section(content, start_id):
"""从内容中提取所有问题和答案"""
questions = []
question_id = start_id
# 使用更宽松的模式匹配问题
# 模式1: 数字. 问题
pattern1 = r'\n(\d+)\.\s*([^\n]+?)[\n\s]+((?:示例)?答案[：:]\s*[^\n]+(?:\n(?!\d+\.).*)*)'
# 模式2: 问题后跟答案段落
pattern2 = r'\n(\d+)\.\s*([^\n]+)\n\s*\n\s*((?:示例)?答案[：:])?\s*\n\s*([^\n]+(?:\n(?!\d+\.|示例答案).*)*)'
# 先尝试模式1
matches = re.findall(pattern1, content, re.MULTILINE)
if not matches:
# 尝试模式2
matches = re.findall(pattern2, content, re.MULTILINE)
matches = [(m[0], m[1], m[3]) for m in matches] # 调整格式
# 如果还是没有匹配，使用更简单的模式
if not matches:
lines = content.split('\n')
current_question = None
current_answer = []
in_answer = False
for line in lines:
line = line.strip()
# 检查是否是新问题
question_match = re.match(r'^(\d+)\.\s*(.+)$', line)
if question_match:
# 保存上一个问题
if current_question and current_answer:
answer_text = ' '.join(current_answer).strip()
if answer_text:
questions.append({
"id": f"q{question_id}",
"question": current_question,
"answer": answer_text
})
question_id += 1
# 开始新问题
current_question = question_match.group(2).strip()
current_answer = []
in_answer = False
# 检查是否是答案开始
elif '答案' in line or '示例答案' in line:
in_answer = True
# 可能答案就在同一行
answer_part = re.sub(r'^(示例)?答案[：:]?\s*', '', line).strip()
if answer_part:
current_answer.append(answer_part)
# 收集答案内容
elif in_answer and line:
# 检查是否是下一个问题的开始
if not re.match(r'^\d+\.', line):
current_answer.append(line)
else:
in_answer = False
# 如果没有明确的答案标记，但有内容，也收集
elif current_question and not in_answer and line and not re.match(r'^\d+\.', line):
current_answer.append(line)
# 保存最后一个问题
if current_question and current_answer:
answer_text = ' '.join(current_answer).strip()
if answer_text:
questions.append({
"id": f"q{question_id}",
"question": current_question,
"answer": answer_text
})
else:
# 处理正则匹配的结果
for match in matches:
question_text = match[1].strip()
answer_text = match[2].strip()
# 清理答案文本
answer_text = re.sub(r'^(示例)?答案[：:]?\s*', '', answer_text).strip()
answer_text = re.sub(r'\s+', ' ', answer_text) # 合并多余空格
if question_text and answer_text:
questions.append({
"id": f"q{question_id}",
"question": question_text,
"answer": answer_text
})
question_id += 1
return questions
def main():
# 读取大健康岗位简历数据
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/大健康岗位简历.json', 'r', encoding='utf-8') as f:
health_data = json.load(f)
# 读取Mock文件
with open('/Users/apple/Documents/cursor/教务系统/frontend_大健康/src/mocks/resumeInterviewMock.js', 'r', encoding='utf-8') as f:
content = f.read()
# 创建岗位群到面试题的映射
industry_questions_map = {}
for item in health_data:
industry = item.get('简历岗位群', '')
interview_content = item.get('面试题内容', '')
if industry and interview_content and industry not in industry_questions_map:
all_categories = parse_all_interview_questions(interview_content)
# 转换为前端期望的格式
questions_array = []
cat_id = 1
for category_data in all_categories:
if category_data['questions']:
questions_array.append({
"id": f"group_q{cat_id}",
"question": category_data['category'],
"subQuestions": category_data['questions']
})
cat_id += 1
if questions_array:
industry_questions_map[industry] = questions_array
total_questions = sum(len(q['subQuestions']) for q in questions_array)
print(f"✓ {industry}: 提取了 {len(questions_array)} 个分类，共 {total_questions} 个面试题")
# 映射岗位群名称到ID
industry_mapping = {
'健康管理': 'health_1',
'健康检查': 'health_2',
'康复治疗': 'health_3',
'慢性病管理': 'health_4',
'轻医美': 'health_5',
'心理健康': 'health_6',
'社群运营': 'health_7',
'药品供应链管理': 'health_8',
'药品生产': 'health_9',
'药品质量检测': 'health_10',
'药物研发': 'health_11'
}
# 更新Mock文件
updates = 0
for orig_name, industry_id in industry_mapping.items():
if orig_name in industry_questions_map:
questions = industry_questions_map[orig_name]
# 生成questions的JSON字符串
questions_json = json.dumps(questions, ensure_ascii=False, indent=2)
# 查找并替换questions字段
# 先删除旧的questions字段
pattern1 = rf'("id":\s*"{industry_id}"[^{{]*?"positions":\s*\[[^\]]*?\]),\s*"questions":\s*\[[^\]]*?\](\s*\}})'
replacement1 = rf'\1\2'
content = re.sub(pattern1, replacement1, content, flags=re.DOTALL)
# 再添加新的questions字段
pattern2 = rf'("id":\s*"{industry_id}"[^{{]*?"positions":\s*\[[^\]]*?\])(\s*\}})'
replacement2 = rf'\1,\n "questions": {questions_json}\2'
new_content, count = re.subn(pattern2, replacement2, content, flags=re.DOTALL)
if count > 0:
content = new_content
updates += 1
# 写回文件
with open('/Users/apple/Documents/cursor/教务系统/frontend_大健康/src/mocks/resumeInterviewMock.js', 'w', encoding='utf-8') as f:
f.write(content)
print(f"\n✅ 完成！更新了 {updates} 个岗位群的完整面试题数据")
if __name__ == "__main__":
main()