109 lines
5.2 KiB
Python
109 lines
5.2 KiB
Python
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#!/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()
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