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>
318 lines
13 KiB
Python
318 lines
13 KiB
Python
"""兴邦金租历史评审数据源（虚拟示例数据，2024-2026年）
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用于智能分配算法的数据驱动决策：
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- 主审员产能画像（平均评审天数、行业专长、质量分）
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- 历史项目追溯（按主审员/行业查询）
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- 多维度分配推荐依据
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"""
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import random
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from datetime import datetime, timedelta
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from typing import List, Dict, Optional
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# ============ 历史评审项目数据结构 ============
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class HistoricalProject:
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"""历史评审项目记录"""
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def __init__(
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self,
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project_id: str,
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project_name: str,
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industry: str,
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credit_amount: float, # 万元
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complexity: int, # 1-5，难度
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planned_days: int, # 计划评审天数
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actual_days: int, # 实际评审天数
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reviewer: str, # 主审员
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outcome: str, # 通过/否决/撤并
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quality_score: int, # 0-100，历史质量分
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review_date: str, # YYYY-MM-DD
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):
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self.project_id = project_id
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self.project_name = project_name
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self.industry = industry
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self.credit_amount = credit_amount
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self.complexity = complexity
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self.planned_days = planned_days
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self.actual_days = actual_days
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self.reviewer = reviewer
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self.outcome = outcome
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self.quality_score = quality_score
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self.review_date = review_date
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def to_dict(self) -> dict:
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return {
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"project_id": self.project_id,
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"project_name": self.project_name,
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"industry": self.industry,
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"credit_amount": self.credit_amount,
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"complexity": self.complexity,
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"planned_days": self.planned_days,
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"actual_days": self.actual_days,
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"reviewer": self.reviewer,
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"outcome": self.outcome,
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"quality_score": self.quality_score,
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"review_date": self.review_date,
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}
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# ============ 虚拟数据生成（体现真实规律）============
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def _generate_historical_data() -> List[HistoricalProject]:
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"""生成2024-2026年虚拟历史评审数据
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数据规律设计：
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- 李明：制造业专长，效率高（实际天数<计划），质量分85-95
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- 王芳：新能源专长，质量高（90-98），但项目复杂度高时需时较长
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- 张强：交通运输专长，负荷较重（项目多），质量稳定80-90
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- 刘洋：医疗健康专长，细致但速度慢（实际天数略超计划），质量92-98
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- 陈晨：设备租赁专长，全行业覆盖，效率中等，质量78-88
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- 赵磊：环保行业专长，项目少但质量高，88-96
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"""
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projects = []
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project_counter = 1
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# 主审员配置：(姓名, 主行业, 次要行业, 项目数, 效率倍数, 质量基数, 质量波动)
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reviewers_config = [
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("李明", "制造业", ["装备制造", "机械设备", "汽车零部件"], 22, 0.85, 88, 7),
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("王芳", "新能源", ["光伏设备", "风电设备", "新能源汽车"], 20, 1.05, 92, 6),
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("张强", "交通运输", ["物流运输", "港口码头", "公路客运"], 25, 0.95, 84, 8),
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("刘洋", "医疗健康", ["医疗器械", "医药制造", "医院设备"], 18, 1.15, 94, 4),
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("陈晨", "设备租赁", ["工程机械", "办公设备", "生产设备"], 20, 1.0, 82, 8),
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("赵磊", "环保", ["环保设备", "污水处理", "固废处理"], 16, 1.08, 90, 6),
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]
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# 行业项目名称模板
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industry_templates = {
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"制造业": ["精密机械加工设备", "自动化生产线", "数控机床", "模具制造设备"],
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"装备制造": ["重型装备", "专用设备", "成套装备"],
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"机械设备": ["工业机器人", "包装机械", "印刷设备"],
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"汽车零部件": ["汽车配件生产线", "发动机零部件", "车身冲压设备"],
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"新能源": ["分布式光伏电站", "储能设备", "充电桩建设"],
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"光伏设备": ["光伏组件生产线", "硅片切割设备", "光伏发电项目"],
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"风电设备": ["风力发电机组", "风电场建设", "海上风电项目"],
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"新能源汽车": ["新能源客车", "电动物流车", "充电设施"],
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"交通运输": ["货运车队", "冷链物流车辆", "运输设备更新"],
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"物流运输": ["智能仓储设备", "配送车辆", "物流信息系统"],
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"港口码头": ["港口装卸设备", "集装箱堆场", "码头机械"],
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"公路客运": ["客运班车", "旅游大巴", "公交车辆"],
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"医疗健康": ["医学影像设备", "手术器械", "检验检测设备"],
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"医疗器械": ["CT设备", "超声设备", "监护仪"],
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"医药制造": ["制药生产线", "GMP车间改造", "原料药设备"],
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"医院设备": ["病床护理设备", "消毒灭菌设备", "医用气体系统"],
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"设备租赁": ["工程机械租赁", "办公设备租赁", "生产设备租赁"],
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"工程机械": ["挖掘机", "起重机", "推土机"],
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"办公设备": ["复印打印设备", "办公家具", "会议系统"],
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"生产设备": ["注塑机", "冲压设备", "焊接设备"],
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"环保": ["环保治理设备", "节能改造项目", "清洁能源设备"],
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"环保设备": ["废气处理设备", "除尘设备", "脱硫脱硝装置"],
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"污水处理": ["污水处理站", "中水回用系统", "污泥处理设备"],
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"固废处理": ["垃圾焚烧设备", "危废处理设施", "资源回收系统"],
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}
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# 生成每位主审员的历史项目
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start_date = datetime(2024, 1, 1)
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end_date = datetime(2026, 8, 31)
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for reviewer_name, main_industry, sub_industries, num_projects, efficiency, quality_base, quality_range in reviewers_config:
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# 该主审员的行业池（主行业占70%，次要行业占30%）
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industries_pool = [main_industry] * 7 + sub_industries * 3
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for i in range(num_projects):
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# 随机日期（制造业、新能源集中在2-6月、9-11月）
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if main_industry in ["制造业", "新能源", "装备制造"]:
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# 集中在业务旺季
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month_choices = [2, 3, 4, 5, 6, 9, 10, 11]
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month = random.choice(month_choices)
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else:
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month = random.randint(1, 12)
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year = random.choice([2024, 2025, 2026])
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if year == 2026 and month > 8:
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month = random.randint(1, 8)
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day = random.randint(1, 28)
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review_date = datetime(year, month, day)
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# 行业选择（主行业为主）
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industry = random.choice(industries_pool)
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# 项目名称
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company_prefix = random.choice([
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"阳光", "华润", "中联", "鑫盛", "宏泰", "瑞丰", "恒通", "盛达",
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"金辉", "博瑞", "创新", "天成", "嘉诚", "永安", "兴旺", "腾飞"
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])
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equipment_name = random.choice(industry_templates.get(industry, ["设备租赁项目"]))
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project_name = f"{company_prefix}{equipment_name}融资租赁项目"
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# 授信金额（万元）：复杂度越高金额越大
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complexity = random.choices([1, 2, 3, 4, 5], weights=[5, 15, 40, 30, 10])[0]
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if complexity == 1:
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credit_amount = random.randint(500, 1500)
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elif complexity == 2:
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credit_amount = random.randint(1200, 3000)
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elif complexity == 3:
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credit_amount = random.randint(2500, 6000)
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elif complexity == 4:
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credit_amount = random.randint(5000, 10000)
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else: # 5
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credit_amount = random.randint(8000, 20000)
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# 计划评审天数（复杂度决定）
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planned_days = {1: 5, 2: 7, 3: 10, 4: 12, 5: 15}[complexity]
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# 实际评审天数（受主审员效率影响）
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actual_days = int(planned_days * efficiency * random.uniform(0.9, 1.1))
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actual_days = max(3, actual_days) # 最少3天
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# 质量分（受主审员能力+复杂度影响）
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# 复杂度高的项目，质量分略有波动
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quality_penalty = (complexity - 3) * 2 if complexity >= 4 else 0
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quality_score = quality_base + random.randint(-quality_range, quality_range) - quality_penalty
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quality_score = max(60, min(100, quality_score))
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# 评审结果（质量分高=通过率高）
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if quality_score >= 85:
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outcome = random.choices(["通过", "否决", "撤并"], weights=[90, 5, 5])[0]
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elif quality_score >= 75:
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outcome = random.choices(["通过", "否决", "撤并"], weights=[80, 12, 8])[0]
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else:
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outcome = random.choices(["通过", "否决", "撤并"], weights=[60, 25, 15])[0]
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# 如果返工（实际天数超出计划较多），质量分降低
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if actual_days > planned_days * 1.3:
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quality_score = max(quality_score - 10, 60)
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project = HistoricalProject(
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project_id=f"PROJ-{year}-{project_counter:04d}",
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project_name=project_name,
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industry=industry,
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credit_amount=credit_amount,
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complexity=complexity,
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planned_days=planned_days,
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actual_days=actual_days,
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reviewer=reviewer_name,
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outcome=outcome,
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quality_score=quality_score,
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review_date=review_date.strftime("%Y-%m-%d"),
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)
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projects.append(project)
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project_counter += 1
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# 按日期排序
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projects.sort(key=lambda p: p.review_date)
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return projects
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# ============ 全局数据缓存 ============
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_HISTORICAL_PROJECTS: List[HistoricalProject] = _generate_historical_data()
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# ============ 查询接口 ============
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def get_all_projects() -> List[Dict]:
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"""获取所有历史项目"""
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return [p.to_dict() for p in _HISTORICAL_PROJECTS]
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def get_projects_by_reviewer(reviewer: str) -> List[Dict]:
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"""按主审员查询历史项目"""
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filtered = [p for p in _HISTORICAL_PROJECTS if p.reviewer == reviewer]
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return [p.to_dict() for p in filtered]
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def get_projects_by_industry(industry: str) -> List[Dict]:
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"""按行业查询历史项目（模糊匹配）"""
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filtered = [p for p in _HISTORICAL_PROJECTS if industry in p.industry]
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return [p.to_dict() for p in filtered]
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def get_projects_filtered(reviewer: Optional[str] = None, industry: Optional[str] = None) -> List[Dict]:
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"""组合过滤查询"""
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filtered = _HISTORICAL_PROJECTS
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if reviewer:
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filtered = [p for p in filtered if p.reviewer == reviewer]
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if industry:
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filtered = [p for p in filtered if industry in p.industry]
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return [p.to_dict() for p in filtered]
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def get_reviewer_stats() -> Dict[str, Dict]:
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"""获取所有主审员的统计画像
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返回格式：
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{
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"李明": {
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"total_projects": 22,
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"avg_actual_days": 8.5,
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"avg_quality_score": 88.2,
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"industry_expertise": {
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"制造业": {"count": 15, "avg_quality": 90.1},
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"装备制造": {"count": 7, "avg_quality": 86.3}
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},
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"pass_rate": 0.91
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},
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...
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}
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"""
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stats = {}
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reviewers = list(set(p.reviewer for p in _HISTORICAL_PROJECTS))
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for reviewer in reviewers:
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projects = [p for p in _HISTORICAL_PROJECTS if p.reviewer == reviewer]
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# 基础统计
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total_projects = len(projects)
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avg_actual_days = sum(p.actual_days for p in projects) / total_projects if total_projects > 0 else 0
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avg_quality = sum(p.quality_score for p in projects) / total_projects if total_projects > 0 else 0
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pass_count = len([p for p in projects if p.outcome == "通过"])
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pass_rate = pass_count / total_projects if total_projects > 0 else 0
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# 行业专长统计
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industry_expertise = {}
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for project in projects:
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industry = project.industry
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if industry not in industry_expertise:
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industry_expertise[industry] = {"count": 0, "quality_sum": 0}
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industry_expertise[industry]["count"] += 1
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industry_expertise[industry]["quality_sum"] += project.quality_score
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# 计算每个行业的平均质量分
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for industry in industry_expertise:
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count = industry_expertise[industry]["count"]
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quality_sum = industry_expertise[industry]["quality_sum"]
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industry_expertise[industry] = {
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"count": count,
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"avg_quality": quality_sum / count if count > 0 else 0
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}
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stats[reviewer] = {
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"total_projects": total_projects,
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"avg_actual_days": round(avg_actual_days, 1),
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"avg_quality_score": round(avg_quality, 1),
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"industry_expertise": industry_expertise,
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"pass_rate": round(pass_rate, 2)
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}
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return stats
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# ============ 初始化时输出统计信息 ============
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if __name__ == "__main__":
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print(f"✅ 已生成 {len(_HISTORICAL_PROJECTS)} 条历史评审记录")
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print("\n主审员统计：")
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stats = get_reviewer_stats()
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for reviewer, data in stats.items():
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print(f" {reviewer}: {data['total_projects']}个项目, 平均{data['avg_actual_days']}天, 质量{data['avg_quality_score']}分")
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