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>
109 lines
5.2 KiB
Python
109 lines
5.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import json
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import random
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from datetime import datetime, timedelta
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def expand_logistics_jobs():
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"""扩展交通物流岗位数据，增加更多岗位"""
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# 读取现有的岗位数据
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with open('src/data/companyJobsNew.json', 'r', encoding='utf-8') as f:
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existing_jobs = json.load(f)
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# 额外的交通物流岗位列表
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additional_positions = [
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"供应链分析师", "仓库主管", "货运代理", "物流规划师", "报关员",
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"调度员", "配送主管", "冷链物流专员", "跨境电商物流专员", "智慧物流工程师",
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"运输经理", "物流成本分析师", "供应链优化专员", "货运司机", "仓储主管",
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"物流信息系统管理员", "国际物流专员", "快递网点经理", "物流客服主管", "供应链培训师"
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]
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# 公司列表
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logistics_companies = [
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"顺丰控股", "京东物流", "中通快递", "圆通速递", "申通快递",
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"德邦物流", "百世物流", "韵达速递", "中外运", "招商局物流",
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"菜鸟网络", "苏宁物流", "邮政速递", "极兔速递", "跨越速运"
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]
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# 工作地点
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locations = ["上海", "北京", "深圳", "广州", "杭州", "南京", "苏州", "成都", "武汉", "西安", "天津", "重庆"]
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# 标签库
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all_tags = ["物流管理", "供应链", "仓储", "运输", "智能设备", "数据分析", "销售", "技术支持", "客户服务", "项目管理", "成本控制", "国际物流"]
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def generate_job_description(position):
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"""生成职位描述"""
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base_desc = "负责"
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if "分析" in position:
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return base_desc + "物流数据分析和优化工作，提供决策支持，提升物流运营效率。"
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elif "主管" in position or "经理" in position:
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return base_desc + "团队管理和业务运营，确保物流服务质量和效率。"
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elif "专员" in position:
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return base_desc + "相关业务的执行和协调工作，保障物流流程顺畅运行。"
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elif "工程师" in position:
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return base_desc + "物流技术系统的开发、维护和优化工作。"
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else:
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return base_desc + "物流相关业务的日常运营和管理工作。"
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def get_salary_range(position):
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"""根据岗位生成薪资范围"""
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if "经理" in position or "主管" in position:
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return random.choice(["15K-25K", "18K-28K", "20K-35K"])
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elif "工程师" in position or "分析师" in position:
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return random.choice(["12K-20K", "15K-25K", "18K-30K"])
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elif "专员" in position:
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return random.choice(["8K-12K", "10K-15K", "12K-18K"])
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else:
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return random.choice(["6K-10K", "8K-12K", "10K-14K"])
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# 生成额外的岗位数据
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new_jobs = []
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# 生成未来3个月内的截止日期
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base_date = datetime(2025, 1, 1)
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for i, position in enumerate(additional_positions):
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# 随机生成截止日期（1-90天后）
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days_offset = random.randint(30, 120)
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deadline = base_date + timedelta(days=days_offset)
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deadline_str = deadline.strftime("%Y/%-m/%-d")
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job_data = {
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"内推岗位名称": position,
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"岗位相关标签": "专业相关岗位",
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"岗位标签": random.choice(["就业", "实习"]),
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"薪资": get_salary_range(position),
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"工作地点": random.choice(locations),
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"学历要求": random.choice(["本科", "大专及以上", "本科及以上", "硕士及以上"]),
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"招聘人数": str(random.randint(1, 10)),
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"截止时间": deadline_str,
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"职位标签": random.sample(all_tags, random.randint(3, 5)),
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"福利标签": ["五险一金", "带薪年假", "节日福利", "员工旅游", "培训机会", "弹性工作"],
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"职位描述": generate_job_description(position),
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"任职要求": f"1. {random.choice(['物流管理', '交通运输', '供应链管理', '工业工程', '信息管理'])}等相关专业；\n2. 具备良好的沟通协调能力和团队合作精神；\n3. 熟悉物流行业运作流程；\n4. 有相关实习或工作经验者优先。",
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"公司介绍": f"{random.choice(logistics_companies)}是国内领先的物流企业，致力于为客户提供高效、智能、可靠的物流解决方案，业务覆盖全国。"
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}
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new_jobs.append(job_data)
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# 合并现有岗位和新岗位
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all_jobs = existing_jobs + new_jobs
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# 写入更新后的岗位数据
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with open('src/data/companyJobsNew.json', 'w', encoding='utf-8') as f:
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json.dump(all_jobs, f, ensure_ascii=False, indent=2)
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print(f"已扩展岗位数据:")
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print(f"- 原有岗位: {len(existing_jobs)} 个")
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print(f"- 新增岗位: {len(new_jobs)} 个")
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print(f"- 总计岗位: {len(all_jobs)} 个")
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# 显示新增的前5个岗位
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print("\n新增岗位示例:")
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for i, job in enumerate(new_jobs[:5], 1):
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print(f"{i}. {job['内推岗位名称']}: {job['薪资']} - {job['工作地点']} ({job['岗位标签']})")
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if __name__ == "__main__":
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expand_logistics_jobs() |