chore(v2): 从兴邦预审v2白名单抽取独立可运行基座

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>
2026-09-29 12:58:26 +08:00
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+"""主审员画像库
+
+功能：
+1. 存储主审员基础信息（专长、效率、质量评分）
+2. 动态更新主审员负荷
+3. 多维度评分（用于智能分配）
+"""
+
+import json
+from typing import Dict, List, Optional
+from pathlib import Path
+from datetime import datetime
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class ReviewerProfileStore:
+    """主审员画像存储"""
+
+    def __init__(self, data_dir: str = "./xingbang_data/reviewers"):
+        self.data_dir = Path(data_dir)
+        self.data_dir.mkdir(parents=True, exist_ok=True)
+        self.profiles_file = self.data_dir / "reviewer_profiles.json"
+
+        # 初始化默认数据
+        if not self.profiles_file.exists():
+            self._init_default_profiles()
+
+    def _init_default_profiles(self):
+        """初始化默认主审员画像"""
+        default_profiles = {
+            "李明": {
+                "name": "李明",
+                "specialty": ["制造业", "新能源"],
+                "current_projects": 5,
+                "avg_completion_days": 8,
+                "quality_score": 0.85,  # 0-1，基于历史评审质量
+                "efficiency_score": 0.78,  # 0-1，基于完成速度
+                "workload_capacity": 7,  # 最大承载项目数
+                "industry_experience": {
+                    "制造业": 45,  # 历史评审项目数
+                    "新能源": 32,
+                    "光伏": 18,
+                },
+                "created_at": datetime.now().isoformat(),
+                "updated_at": datetime.now().isoformat(),
+            },
+            "王芳": {
+                "name": "王芳",
+                "specialty": ["新能源", "光伏", "风电"],
+                "current_projects": 3,
+                "avg_completion_days": 7,
+                "quality_score": 0.92,
+                "efficiency_score": 0.88,
+                "workload_capacity": 6,
+                "industry_experience": {
+                    "新能源": 56,
+                    "光伏": 38,
+                    "风电": 22,
+                },
+                "created_at": datetime.now().isoformat(),
+                "updated_at": datetime.now().isoformat(),
+            },
+            "张强": {
+                "name": "张强",
+                "specialty": ["交通运输", "物流", "制造业"],
+                "current_projects": 7,
+                "avg_completion_days": 9,
+                "quality_score": 0.80,
+                "efficiency_score": 0.72,
+                "workload_capacity": 8,
+                "industry_experience": {
+                    "交通运输": 67,
+                    "物流": 43,
+                    "制造业": 28,
+                },
+                "created_at": datetime.now().isoformat(),
+                "updated_at": datetime.now().isoformat(),
+            },
+            "刘洋": {
+                "name": "刘洋",
+                "specialty": ["医疗健康", "服务业", "消费"],
+                "current_projects": 4,
+                "avg_completion_days": 8,
+                "quality_score": 0.88,
+                "efficiency_score": 0.82,
+                "workload_capacity": 7,
+                "industry_experience": {
+                    "医疗健康": 51,
+                    "服务业": 34,
+                    "消费": 29,
+                },
+                "created_at": datetime.now().isoformat(),
+                "updated_at": datetime.now().isoformat(),
+            },
+        }
+
+        with open(self.profiles_file, "w", encoding="utf-8") as f:
+            json.dump(default_profiles, f, ensure_ascii=False, indent=2)
+
+        logger.info("主审员画像初始化完成")
+
+    def get_all_profiles(self) -> Dict[str, Dict]:
+        """获取所有主审员画像"""
+        with open(self.profiles_file, "r", encoding="utf-8") as f:
+            return json.load(f)
+
+    def get_profile(self, reviewer_name: str) -> Optional[Dict]:
+        """获取指定主审员画像"""
+        profiles = self.get_all_profiles()
+        return profiles.get(reviewer_name)
+
+    def update_workload(self, reviewer_name: str, current_projects: int):
+        """更新主审员工作负荷"""
+        profiles = self.get_all_profiles()
+
+        if reviewer_name in profiles:
+            profiles[reviewer_name]["current_projects"] = current_projects
+            profiles[reviewer_name]["updated_at"] = datetime.now().isoformat()
+
+            with open(self.profiles_file, "w", encoding="utf-8") as f:
+                json.dump(profiles, f, ensure_ascii=False, indent=2)
+
+            logger.info(f"已更新 {reviewer_name} 的负荷: {current_projects} 个项目")
+
+    def update_quality_score(self, reviewer_name: str, quality_score: float):
+        """更新主审员质量评分"""
+        profiles = self.get_all_profiles()
+
+        if reviewer_name in profiles:
+            profiles[reviewer_name]["quality_score"] = quality_score
+            profiles[reviewer_name]["updated_at"] = datetime.now().isoformat()
+
+            with open(self.profiles_file, "w", encoding="utf-8") as f:
+                json.dump(profiles, f, ensure_ascii=False, indent=2)
+
+            logger.info(f"已更新 {reviewer_name} 的质量评分: {quality_score:.2f}")
+
+    def calculate_match_score(
+        self,
+        reviewer_name: str,
+        project_industry: str,
+        project_complexity: int = 3
+    ) -> Dict:
+        """计算主审员与项目的匹配度（多维度评分）
+
+        评分维度：
+        - 35% 行业匹配度（专长+历史经验）
+        - 35% 可用性（当前负荷）
+        - 20% 质量评分（历史评审质量）
+        - 10% 团队平衡（避免某人长期超载）
+        """
+        profile = self.get_profile(reviewer_name)
+        if not profile:
+            return {"total_score": 0, "breakdown": {}}
+
+        # 1. 行业匹配度（35%）
+        industry_match = 0
+        if any(spec in project_industry or project_industry in spec for spec in profile["specialty"]):
+            industry_match = 0.8  # 专长匹配
+
+        # 检查历史经验
+        exp_count = 0
+        for industry_key in profile["industry_experience"]:
+            if industry_key in project_industry or project_industry in industry_key:
+                exp_count = profile["industry_experience"][industry_key]
+                break
+
+        if exp_count >= 30:
+            industry_match = 1.0
+        elif exp_count >= 10:
+            industry_match = max(industry_match, 0.6)
+        elif exp_count > 0:
+            industry_match = max(industry_match, 0.3)
+
+        industry_score = industry_match * 35
+
+        # 2. 可用性（35%）
+        current_load = profile["current_projects"]
+        capacity = profile["workload_capacity"]
+
+        if current_load >= capacity:
+            availability = 0.1  # 超载
+        elif current_load >= capacity * 0.8:
+            availability = 0.5  # 偏高
+        else:
+            availability = 1.0 - (current_load / capacity)  # 线性递减
+
+        availability_score = availability * 35
+
+        # 3. 质量评分（20%）
+        quality_score = profile["quality_score"] * 20
+
+        # 4. 团队平衡（10%）- 优先分配给负荷较低的
+        all_profiles = self.get_all_profiles()
+        avg_load = sum(p["current_projects"] for p in all_profiles.values()) / len(all_profiles)
+
+        if current_load < avg_load:
+            balance_score = 10
+        elif current_load == avg_load:
+            balance_score = 7
+        else:
+            balance_score = max(0, 10 - (current_load - avg_load) * 2)
+
+        # 总分
+        total_score = industry_score + availability_score + quality_score + balance_score
+
+        return {
+            "total_score": round(total_score, 2),
+            "breakdown": {
+                "industry_match": round(industry_score, 2),
+                "availability": round(availability_score, 2),
+                "quality": round(quality_score, 2),
+                "balance": round(balance_score, 2),
+            },
+            "estimated_days": profile["avg_completion_days"] + current_load // 2,  # 考虑负荷影响
+        }
+
+    def get_recommended_reviewers(
+        self,
+        project_industry: str,
+        project_complexity: int = 3,
+        top_n: int = 3
+    ) -> List[Dict]:
+        """获取推荐主审员列表（Top N）"""
+        all_profiles = self.get_all_profiles()
+
+        recommendations = []
+        for reviewer_name in all_profiles:
+            match_result = self.calculate_match_score(
+                reviewer_name,
+                project_industry,
+                project_complexity
+            )
+
+            profile = all_profiles[reviewer_name]
+            recommendations.append({
+                "reviewer": reviewer_name,
+                "score": match_result["total_score"],
+                "breakdown": match_result["breakdown"],
+                "estimated_days": match_result["estimated_days"],
+                "current_projects": profile["current_projects"],
+                "specialty": profile["specialty"],
+            })
+
+        # 按总分降序排序
+        recommendations.sort(key=lambda x: x["score"], reverse=True)
+
+        return recommendations[:top_n]
+
+
+# 全局单例
+_reviewer_store: Optional[ReviewerProfileStore] = None
+
+
+def get_reviewer_store() -> ReviewerProfileStore:
+    """获取主审员画像存储单例"""
+    global _reviewer_store
+    if _reviewer_store is None:
+        _reviewer_store = ReviewerProfileStore()
+    return _reviewer_store