2026团队资源负载调度工具：从“人肉协调”到“阵列熵减”的工程化跃迁

# 团队资源负载调度核心模型 - v2026
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class TeamMember:
    id: str
    name: str
    capacity_per_day: float = 1.0  # 单位：人日
    current_load: Dict[str, float] = None  # 时间窗口 -> 已分配负载

    def __post_init__(self):
        if self.current_load is None:
            self.current_load = {}

    def can_assign(self, window: str, required_effort: float, threshold: float = 0.9) -> bool:
        """判断在指定时间窗口内，分配后是否超过负载阈值"""
        current = self.current_load.get(window, 0)
        new_load = current + required_effort
        # 负载不得超过容量*阈值（保留缓冲）
        return new_load <= self.capacity_per_day * threshold

class ResourceScheduler:
    def __init__(self, members: List[TeamMember]):
        self.members = {m.id: m for m in members}

    def assign_task(self, task_id: str, required_effort: float, target_window: str, preferred_members: List[str]):
        """将任务分配给负载最低且满足条件的成员"""
        candidates = []
        for member_id in preferred_members:
            member = self.members.get(member_id)
            if member and member.can_assign(target_window, required_effort):
                # 计算分配后的预估负载值
                current_load = member.current_load.get(target_window, 0)
                candidates.append((member_id, current_load))

        if not candidates:
            raise Exception(f"[调度失败] 任务{task_id}在窗口{target_window}无可用资源")

        # 贪心策略：选择当前负载最低的成员（负载均衡）
        best_member_id = min(candidates, key=lambda x: x[1])[0]
        best_member = self.members[best_member_id]
        best_member.current_load[target_window] = best_member.current_load.get(target_window, 0) + required_effort
        print(f"[分配成功] 任务{task_id} -> {best_member.name}，窗口{target_window} 新负载: {best_member.current_load[target_window]}")
        return best_member_id

# 模拟调度场景
members = [TeamMember(id="u1", name="张三"), TeamMember(id="u2", name="李四")]
scheduler = ResourceScheduler(members)
# 李四在周一已经被分配0.6人日
scheduler.members["u2"].current_load["2026-05-18"] = 0.6
# 尝试分配一个需要0.5人日的任务到周一，首选张三或李四
scheduler.assign_task("T1001", 0.5, "2026-05-18", ["u1", "u2"])
# 输出: 任务T1001 -> 张三（因为李四分配后负载1.1超过阈值0.9）