服务器运维工资多少

#!/usr/bin/env python3
"""Offer 折算：把不同结构的 offer 统一折算为年总包与时薪，便于横向比较。"""

def package(monthly: float, months: float, bonus: float = 0.0,
            allowance: float = 0.0, fund_base: float = 0.0,
            fund_rate: float = 0.12, hours_per_week: float = 40.0,
            oncall_nights: int = 0) -> dict:
    """monthly 月薪；months 发薪月数；bonus 年终奖；allowance 月度补贴合计；
    fund_base 公积金基数；fund_rate 单位缴存比例；oncall_nights 每月值班夜数。"""
    salary_year = monthly * months
    fund_year = fund_base * fund_rate * 12 * 2          # 个人 + 单位双边
    total = salary_year + bonus + allowance * 12 + fund_year
    work_hours = hours_per_week * 4.33 * 12 + oncall_nights * 12 * 8
    return {"年总包": round(total), "年总包(万元)": round(total / 10000, 1),
            "时薪": round(total / work_hours, 1)}


if __name__ == "__main__":
    a = package(monthly=22000, months=13, bonus=20000, allowance=1000,
                fund_base=22000, hours_per_week=55, oncall_nights=6)
    b = package(monthly=18000, months=14, bonus=60000, allowance=1500,
                fund_base=18000, hours_per_week=40, oncall_nights=0)
    print("A（高月薪/996/值班）:", a)
    print("B（低月薪/965/无值班）:", b)

# —— 统计招聘 JD 中的技能关键词频次，判断方向溢价 ——
# 前提：把若干条 JD 正文合并保存为 jd.txt
for kw in Kubernetes Docker Terraform Prometheus Ansible Python Go MySQL Redis CISP PMP K8s; do
  n=$(grep -o -i "$kw" jd.txt | wc -l)
  printf "%-12s %s\n" "$kw" "$n"
done | sort -k2 -rn

# 顺带看下薪资区间的分布（形如 15-25K 的表述）
grep -oE '[0-9]{1,2}-[0-9]{1,2}[Kk]' jd.txt | sort -t- -k1 -n

#!/usr/bin/env python3
"""运维薪资区间粗算：城市基准 × 年限系数 × 方向系数 + 加分项（仅作参考）。"""

CITY_BASE = {"一线": 16000, "新一线": 13500, "二线": 10500}      # 中级基准月薪（元）
YEAR_FACTOR = {(0, 1): 0.6, (1, 3): 0.8, (3, 5): 1.0,
               (5, 8): 1.5, (8, 99): 2.1}                        # 年限系数
DIR_FACTOR = {"传统运维": 0.85, "云原生SRE": 1.35, "DBA": 1.2,
              "安全运维": 1.15, "网络运维": 1.0, "外企": 1.45}     # 方向系数
BONUS = {"承担P0值班": 1500, "英语工作语言": 2500, "持有有效证书": 800}


def estimate(city: str, years: float, direction: str, extras: list) -> tuple:
    base = CITY_BASE[city]
    yf = next(v for (lo, hi), v in YEAR_FACTOR.items() if lo <= years < hi)
    df = DIR_FACTOR[direction]
    mid = base * yf * df + sum(BONUS[e] for e in extras)
    return int(mid * 0.85), int(mid * 1.15)     # 给出 ±15% 的区间


if __name__ == "__main__":
    low, high = estimate("一线", 4, "云原生SRE", ["承担P0值班", "英语工作语言"])
    print(f"估算区间：{low} ~ {high} 元/月（不含年终奖与股票）")