qiaoqichaoandClaude Code
d56c699ce6
按 docs/v2/05-implementation-plan.md §1 候选迁移表与 claude_use/T00 白名单， 从 55e2cdb 抽取 224 个文件（2.48 MB）到独立目录 F:/mini-OpenClaw-v2。 - 排除：凭据(.env/server-deploy.conf/旧DELIVERY.md)、运行数据(xingbang_data 等 4 个 SQLite)、依赖(venv/node_modules 符号链接)、归档(_backup*/test-evidence/ ori_file)、Office 临时文件 - 目标目录独立 git 仓库，初始恢复点；Gitea 远程与分支待 OQ-06 Q2 定案 - 未部署、未启动服务、未调用模型 Co-Authored-By: Claude Code <noreply@anthropic.com>
263 lines
9.3 KiB
Python
263 lines
9.3 KiB
Python
"""主审员画像库
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功能：
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1. 存储主审员基础信息（专长、效率、质量评分）
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2. 动态更新主审员负荷
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3. 多维度评分（用于智能分配）
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"""
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import json
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from typing import Dict, List, Optional
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from pathlib import Path
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from datetime import datetime
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import logging
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logger = logging.getLogger(__name__)
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class ReviewerProfileStore:
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"""主审员画像存储"""
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def __init__(self, data_dir: str = "./xingbang_data/reviewers"):
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self.data_dir = Path(data_dir)
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self.data_dir.mkdir(parents=True, exist_ok=True)
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self.profiles_file = self.data_dir / "reviewer_profiles.json"
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# 初始化默认数据
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if not self.profiles_file.exists():
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self._init_default_profiles()
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def _init_default_profiles(self):
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"""初始化默认主审员画像"""
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default_profiles = {
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"李明": {
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"name": "李明",
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"specialty": ["制造业", "新能源"],
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"current_projects": 5,
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"avg_completion_days": 8,
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"quality_score": 0.85, # 0-1，基于历史评审质量
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"efficiency_score": 0.78, # 0-1，基于完成速度
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"workload_capacity": 7, # 最大承载项目数
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"industry_experience": {
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"制造业": 45, # 历史评审项目数
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"新能源": 32,
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"光伏": 18,
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},
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"created_at": datetime.now().isoformat(),
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"updated_at": datetime.now().isoformat(),
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},
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"王芳": {
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"name": "王芳",
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"specialty": ["新能源", "光伏", "风电"],
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"current_projects": 3,
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"avg_completion_days": 7,
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"quality_score": 0.92,
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"efficiency_score": 0.88,
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"workload_capacity": 6,
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"industry_experience": {
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"新能源": 56,
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"光伏": 38,
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"风电": 22,
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},
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"created_at": datetime.now().isoformat(),
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"updated_at": datetime.now().isoformat(),
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},
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"张强": {
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"name": "张强",
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"specialty": ["交通运输", "物流", "制造业"],
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"current_projects": 7,
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"avg_completion_days": 9,
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"quality_score": 0.80,
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"efficiency_score": 0.72,
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"workload_capacity": 8,
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"industry_experience": {
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"交通运输": 67,
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"物流": 43,
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"制造业": 28,
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},
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"created_at": datetime.now().isoformat(),
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"updated_at": datetime.now().isoformat(),
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},
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"刘洋": {
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"name": "刘洋",
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"specialty": ["医疗健康", "服务业", "消费"],
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"current_projects": 4,
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"avg_completion_days": 8,
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"quality_score": 0.88,
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"efficiency_score": 0.82,
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"workload_capacity": 7,
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"industry_experience": {
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"医疗健康": 51,
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"服务业": 34,
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"消费": 29,
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},
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"created_at": datetime.now().isoformat(),
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"updated_at": datetime.now().isoformat(),
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},
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}
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with open(self.profiles_file, "w", encoding="utf-8") as f:
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json.dump(default_profiles, f, ensure_ascii=False, indent=2)
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logger.info("主审员画像初始化完成")
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def get_all_profiles(self) -> Dict[str, Dict]:
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"""获取所有主审员画像"""
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with open(self.profiles_file, "r", encoding="utf-8") as f:
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return json.load(f)
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def get_profile(self, reviewer_name: str) -> Optional[Dict]:
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"""获取指定主审员画像"""
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profiles = self.get_all_profiles()
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return profiles.get(reviewer_name)
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def update_workload(self, reviewer_name: str, current_projects: int):
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"""更新主审员工作负荷"""
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profiles = self.get_all_profiles()
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if reviewer_name in profiles:
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profiles[reviewer_name]["current_projects"] = current_projects
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profiles[reviewer_name]["updated_at"] = datetime.now().isoformat()
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with open(self.profiles_file, "w", encoding="utf-8") as f:
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json.dump(profiles, f, ensure_ascii=False, indent=2)
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logger.info(f"已更新 {reviewer_name} 的负荷: {current_projects} 个项目")
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def update_quality_score(self, reviewer_name: str, quality_score: float):
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"""更新主审员质量评分"""
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profiles = self.get_all_profiles()
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if reviewer_name in profiles:
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profiles[reviewer_name]["quality_score"] = quality_score
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profiles[reviewer_name]["updated_at"] = datetime.now().isoformat()
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with open(self.profiles_file, "w", encoding="utf-8") as f:
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json.dump(profiles, f, ensure_ascii=False, indent=2)
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logger.info(f"已更新 {reviewer_name} 的质量评分: {quality_score:.2f}")
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def calculate_match_score(
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self,
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reviewer_name: str,
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project_industry: str,
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project_complexity: int = 3
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) -> Dict:
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"""计算主审员与项目的匹配度（多维度评分）
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评分维度：
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- 35% 行业匹配度（专长+历史经验）
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- 35% 可用性（当前负荷）
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- 20% 质量评分（历史评审质量）
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- 10% 团队平衡（避免某人长期超载）
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"""
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profile = self.get_profile(reviewer_name)
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if not profile:
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return {"total_score": 0, "breakdown": {}}
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# 1. 行业匹配度（35%）
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industry_match = 0
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if any(spec in project_industry or project_industry in spec for spec in profile["specialty"]):
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industry_match = 0.8 # 专长匹配
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# 检查历史经验
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exp_count = 0
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for industry_key in profile["industry_experience"]:
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if industry_key in project_industry or project_industry in industry_key:
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exp_count = profile["industry_experience"][industry_key]
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break
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if exp_count >= 30:
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industry_match = 1.0
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elif exp_count >= 10:
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industry_match = max(industry_match, 0.6)
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elif exp_count > 0:
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industry_match = max(industry_match, 0.3)
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industry_score = industry_match * 35
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# 2. 可用性（35%）
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current_load = profile["current_projects"]
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capacity = profile["workload_capacity"]
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if current_load >= capacity:
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availability = 0.1 # 超载
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elif current_load >= capacity * 0.8:
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availability = 0.5 # 偏高
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else:
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availability = 1.0 - (current_load / capacity) # 线性递减
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availability_score = availability * 35
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# 3. 质量评分（20%）
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quality_score = profile["quality_score"] * 20
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# 4. 团队平衡（10%）- 优先分配给负荷较低的
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all_profiles = self.get_all_profiles()
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avg_load = sum(p["current_projects"] for p in all_profiles.values()) / len(all_profiles)
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if current_load < avg_load:
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balance_score = 10
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elif current_load == avg_load:
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balance_score = 7
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else:
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balance_score = max(0, 10 - (current_load - avg_load) * 2)
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# 总分
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total_score = industry_score + availability_score + quality_score + balance_score
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return {
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"total_score": round(total_score, 2),
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"breakdown": {
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"industry_match": round(industry_score, 2),
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"availability": round(availability_score, 2),
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"quality": round(quality_score, 2),
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"balance": round(balance_score, 2),
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},
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"estimated_days": profile["avg_completion_days"] + current_load // 2, # 考虑负荷影响
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}
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def get_recommended_reviewers(
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self,
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project_industry: str,
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project_complexity: int = 3,
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top_n: int = 3
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) -> List[Dict]:
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"""获取推荐主审员列表（Top N）"""
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all_profiles = self.get_all_profiles()
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recommendations = []
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for reviewer_name in all_profiles:
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match_result = self.calculate_match_score(
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reviewer_name,
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project_industry,
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project_complexity
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)
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profile = all_profiles[reviewer_name]
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recommendations.append({
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"reviewer": reviewer_name,
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"score": match_result["total_score"],
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"breakdown": match_result["breakdown"],
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"estimated_days": match_result["estimated_days"],
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"current_projects": profile["current_projects"],
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"specialty": profile["specialty"],
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})
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# 按总分降序排序
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recommendations.sort(key=lambda x: x["score"], reverse=True)
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return recommendations[:top_n]
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# 全局单例
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_reviewer_store: Optional[ReviewerProfileStore] = None
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def get_reviewer_store() -> ReviewerProfileStore:
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"""获取主审员画像存储单例"""
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global _reviewer_store
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if _reviewer_store is None:
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_reviewer_store = ReviewerProfileStore()
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return _reviewer_store
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