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>
79 lines
2.8 KiB
Python
79 lines
2.8 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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print("=== 分析智能制造岗位简历.json 中的面试题内容 ===\n")
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# 读取智能制造岗位简历数据
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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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# 分析每个岗位群的面试题
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interview_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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position = item.get('岗位名称', '')
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interview_type = item.get('面试题', '')
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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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if job_group not in interview_map:
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interview_map[job_group] = {
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'type': interview_type,
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'positions': [],
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'sections': [],
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'total_questions': 0
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}
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interview_map[job_group]['positions'].append(position)
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# 解析面试题内容
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lines = interview_content.split('\n')
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current_section = None
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question_count = 0
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for line in lines:
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line = line.strip()
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if not line:
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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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section_title = line[2:].strip()
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if section_title and section_title not in interview_map[job_group]['sections']:
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interview_map[job_group]['sections'].append(section_title)
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current_section = section_title
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# 问题编号（如 1. xxx）
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elif re.match(r'^\d+\.\s+', line):
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question_count += 1
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interview_map[job_group]['total_questions'] = max(
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interview_map[job_group]['total_questions'],
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question_count
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)
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# 打印分析结果
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for group, data in sorted(interview_map.items()):
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print(f"{group} ({data['type']}):")
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print(f" 包含岗位: {', '.join(set(data['positions']))}")
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print(f" 面试题章节: {len(data['sections'])} 个")
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for section in data['sections']:
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print(f" - {section}")
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print(f" 总题目数: {data['total_questions']} 道\n")
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# 检查一个具体例子
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print("\n=== 示例：PLC类岗位面试题详细内容 ===\n")
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for item in smart_mfg_data:
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if item.get('简历岗位群') == 'PLC' and item.get('岗位名称') == 'PLC编程工程师':
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content = item.get('面试题内容', '')
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# 提取所有问题
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questions = re.findall(r'(\d+\.\s+[^\n]+)', content)
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print(f"找到 {len(questions)} 个问题：")
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for i, q in enumerate(questions[:10], 1): # 只显示前10个
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print(f" {q[:80]}..." if len(q) > 80 else f" {q}")
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if len(questions) > 10:
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print(f" ... 还有 {len(questions) - 10} 个问题")
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break |