184 lines
6.3 KiB
Python
184 lines
6.3 KiB
Python
from __future__ import annotations
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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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from pathlib import Path
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ROOT = Path(__file__).resolve().parent
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random.seed(42)
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skills = [
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"签到",
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"引导",
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"登记",
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"促销",
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"地推",
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"导购",
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"会展接待",
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"分拣",
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"打包",
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"客服",
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"电话邀约",
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"安装",
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"配送",
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"仓储",
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"陈列",
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"数据录入",
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"物料搬运",
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"收银",
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"盘点",
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"直播协助",
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"短视频拍摄",
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"面销",
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"海报派发",
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"问卷访问",
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"现场执行",
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"活动控场",
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"礼仪接待",
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"样品派发",
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"售后支持",
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"装卸",
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"拣货",
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"骑手配送",
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"司机协助",
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"设备调试",
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"展台搭建",
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"线上客服",
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"社群运营",
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"线索收集",
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"POS操作",
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"报表整理",
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]
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extra_skills = [f"扩展技能{i:02d}" for i in range(1, 61)]
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skills = (skills + extra_skills)[:100]
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categories = [
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"促销",
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"地推",
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"导购",
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"会展",
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"分拣",
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"客服",
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"安装",
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"配送",
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"仓储",
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"活动执行",
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"礼仪",
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"数据录入",
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"盘点",
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"装卸",
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"直播协助",
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"社群运营",
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"收银",
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"拣货",
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"线下推广",
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"电话销售",
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"问卷调研",
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"样品派发",
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"售后服务",
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"展台搭建",
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"酒店服务",
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"商超导购",
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"会场服务",
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"物流协助",
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"活动控场",
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"物料执行",
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]
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regions = [
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{"city": "深圳", "region": "南山"},
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{"city": "深圳", "region": "福田"},
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{"city": "深圳", "region": "宝安"},
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{"city": "深圳", "region": "龙岗"},
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{"city": "深圳", "region": "罗湖"},
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{"city": "广州", "region": "天河"},
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{"city": "广州", "region": "海珠"},
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{"city": "广州", "region": "番禺"},
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{"city": "广州", "region": "白云"},
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{"city": "广州", "region": "越秀"},
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{"city": "上海", "region": "浦东"},
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{"city": "上海", "region": "徐汇"},
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{"city": "上海", "region": "静安"},
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{"city": "上海", "region": "闵行"},
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{"city": "上海", "region": "杨浦"},
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{"city": "杭州", "region": "西湖"},
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{"city": "杭州", "region": "滨江"},
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{"city": "杭州", "region": "余杭"},
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{"city": "成都", "region": "高新"},
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{"city": "成都", "region": "武侯"},
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]
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time_tags = ["weekend", "weekday_am", "weekday_pm", "anytime"]
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experience_tags = ["商场", "会展", "活动执行", "物流", "零售", "校园推广", "客服中心", "展台", "仓库", "快消"]
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work_modes = ["排班制", "兼职", "临时工", "项目制"]
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def region_label(item: dict) -> str:
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return f"{item['city']}{item['region']}"
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jobs = []
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for index in range(1, 101):
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region = random.choice(regions)
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category = random.choice(categories)
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job_skills = random.sample(skills[:40], k=random.randint(3, 5))
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start_at = datetime(2026, 4, 1, 9, 0, 0) + timedelta(days=random.randint(0, 20), hours=random.choice([0, 4, 8]))
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tags = list(set(random.sample(experience_tags, k=2) + [random.choice(["女生优先", "有经验优先", "可连做优先", "沟通好"]) ]))
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salary_amount = random.choice([120, 150, 180, 200, 220, 260, 300])
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jobs.append(
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{
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"job_id": f"job_{index:03d}",
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"title": f"{category}兼职{index:03d}",
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"category": category,
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"description": f"{start_at.month}月{start_at.day}日{region['city']}{region['region']}需要{random.randint(1,4)}名{category}兼职，负责{'、'.join(job_skills[:3])}，{random.randint(4,8)}小时，{salary_amount}元/天。",
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"skills": job_skills,
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"city": region["city"],
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"region": region["region"],
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"location_detail": f"{region_label(region)}核心商圈点位{random.randint(1, 20)}号",
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"start_time": start_at.isoformat() + "+08:00",
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"duration_hours": random.randint(4, 8),
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"headcount": random.randint(1, 4),
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"salary": {"type": "daily", "amount": salary_amount, "currency": "CNY"},
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"work_mode": random.choice(work_modes),
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"tags": tags,
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"confidence": round(random.uniform(0.82, 0.96), 2),
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}
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)
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workers = []
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family_names = "赵钱孙李周吴郑王冯陈褚卫蒋沈韩杨朱秦尤许何吕施张孔曹严华金魏陶姜"
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given_names = ["伟", "芳", "娜", "敏", "静", "磊", "洋", "勇", "艳", "杰", "娟", "涛", "明", "超", "秀英", "霞"]
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for index in range(1, 301):
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primary_region = random.choice(regions)
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extra_region = random.choice([r for r in regions if r["city"] == primary_region["city"] and r["region"] != primary_region["region"]])
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worker_skills = random.sample(skills[:40], k=random.randint(3, 6))
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name = random.choice(list(family_names)) + random.choice(given_names)
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availability = random.sample(time_tags, k=random.randint(1, 2))
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exp_tags = list(set(random.sample(experience_tags, k=2) + [random.choice(categories[:10])]))
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workers.append(
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{
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"worker_id": f"worker_{index:03d}",
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"name": name,
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"description": f"我做过{'、'.join(exp_tags[:2])}相关兼职，擅长{'、'.join(worker_skills[:3])}，平时{ '和'.join(availability) }都能接单，{primary_region['region']}和{extra_region['region']}都方便。",
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"skills": [{"name": item, "score": round(random.uniform(0.62, 0.94), 2)} for item in worker_skills],
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"cities": [primary_region["city"]],
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"regions": [primary_region["region"], extra_region["region"]],
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"availability": availability,
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"experience_tags": exp_tags,
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"reliability_score": round(random.uniform(0.65, 0.95), 2),
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"profile_completion": round(random.uniform(0.55, 0.98), 2),
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"confidence": round(random.uniform(0.8, 0.96), 2),
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}
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)
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(ROOT / "skills.json").write_text(json.dumps(skills, ensure_ascii=False, indent=2), encoding="utf-8")
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(ROOT / "categories.json").write_text(json.dumps(categories, ensure_ascii=False, indent=2), encoding="utf-8")
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(ROOT / "regions.json").write_text(json.dumps(regions, ensure_ascii=False, indent=2), encoding="utf-8")
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(ROOT / "jobs.json").write_text(json.dumps(jobs, ensure_ascii=False, indent=2), encoding="utf-8")
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(ROOT / "workers.json").write_text(json.dumps(workers, ensure_ascii=False, indent=2), encoding="utf-8")
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