selfrelease
314 lines
16 KiB
Python
314 lines
16 KiB
Python
"""评价权重计算引擎。
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6 轴动态权重合并 + 归一化：
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1. 基金类型 × 存续期 → 基础权重模板（32 个预设）
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2. 企业阶段 → 阶段系数调整（5 个预设）
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3. 产业赛道 → 维度裁剪 + 专属指标注入（6 个预设）
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4. 投资策略 → ±5% 微调（4 个预设）
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5. 归一化 — 裁剪后剩余维度权重自动归一化到 100%
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6. 修饰因子叠加 — LP 附加指标 + 地域基准校准
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"""
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import logging
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from typing import Any
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logger = logging.getLogger(__name__)
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# --- 基础 14 维度权重（来自 health_calculator.py） ---
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BASE_WEIGHTS: dict[str, float] = {
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"financial": 0.15,
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"operational": 0.10,
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"ai_commercial": 0.10,
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"ai_cost": 0.05,
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"org_talent": 0.10,
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"product_tech": 0.10,
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"market_compete": 0.10,
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"governance": 0.05,
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"financing": 0.05,
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"synergy": 0.05,
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"ai_model_product": 0.05,
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"data_compliance": 0.05,
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"team_tech": 0.03,
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"customer_success": 0.07,
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}
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# 维度 key 映射：权重 key → 评分字段 key
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DIMENSION_KEY_MAP: dict[str, str] = {
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"financial": "financial_score",
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"operational": "operational_score",
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"ai_commercial": "ai_commercial_score",
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"ai_cost": "ai_cost_score",
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"org_talent": "org_talent_score",
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"product_tech": "product_tech_score",
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"market_compete": "market_compete_score",
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"governance": "governance_score",
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"financing": "financing_score",
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"synergy": "synergy_score",
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"ai_model_product": "ai_model_product_score",
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"data_compliance": "data_compliance_score",
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"team_tech": "team_tech_score",
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"customer_success": "customer_success_score",
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}
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# --- 轴 5：基金类型 × 存续期基础权重系数（32 个预设） ---
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FUND_LIFECYCLE_MULTIPLIERS: dict[tuple[str, str], dict[str, float]] = {
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("angel", "investment"): {"financial": 0.5, "product_tech": 1.8, "org_talent": 1.5, "market": 1.2},
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("angel", "growth"): {"financial": 0.7, "product_tech": 1.5, "org_talent": 1.3, "market": 1.2},
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("angel", "exit_preparation"): {"financial": 1.0, "product_tech": 1.2, "org_talent": 1.0, "financing": 1.5},
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("angel", "liquidation"): {"financial": 1.5, "financing": 2.0, "product_tech": 0.8},
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("early_vc", "investment"): {"financial": 0.7, "product_tech": 1.5, "market": 1.2, "customer_success": 0.8},
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("early_vc", "growth"): {"financial": 0.9, "product_tech": 1.3, "market": 1.2, "customer_success": 1.0},
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("early_vc", "exit_preparation"): {"financial": 1.3, "market": 1.0, "customer_success": 1.2, "financing": 1.5},
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("early_vc", "liquidation"): {"financial": 1.8, "financing": 2.0, "product_tech": 0.6},
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("growth_vc", "investment"): {"financial": 1.0, "market": 1.3, "customer_success": 1.2},
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("growth_vc", "growth"): {"financial": 1.2, "market": 1.2, "customer_success": 1.3},
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("growth_vc", "exit_preparation"): {"financial": 1.5, "market": 1.0, "customer_success": 1.2, "financing": 1.5},
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("growth_vc", "liquidation"): {"financial": 2.0, "financing": 2.0, "market": 0.8},
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("pe", "investment"): {"financial": 1.8, "governance": 1.5, "customer_success": 1.3, "product_tech": 0.5, "ai_commercial": 0.3, "ai_cost": 0.3, "ai_model_product": 0.3},
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("pe", "growth"): {"financial": 2.0, "governance": 1.5, "customer_success": 1.3, "product_tech": 0.5, "ai_commercial": 0.3, "ai_cost": 0.3, "ai_model_product": 0.3},
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("pe", "exit_preparation"): {"financial": 2.5, "governance": 1.8, "financing": 1.5, "ai_commercial": 0.3, "ai_cost": 0.3, "ai_model_product": 0.3},
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("pe", "liquidation"): {"financial": 3.0, "financing": 2.0, "governance": 1.5, "ai_commercial": 0.3, "ai_cost": 0.3, "ai_model_product": 0.3},
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("cvc", "investment"): {"synergy": 2.0, "market": 1.3, "product_tech": 1.2, "financial": 0.7},
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("cvc", "growth"): {"synergy": 1.8, "market": 1.2, "product_tech": 1.2, "financial": 0.8},
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("cvc", "exit_preparation"): {"synergy": 1.5, "market": 1.0, "financial": 1.2, "financing": 1.3},
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("cvc", "liquidation"): {"financial": 1.5, "financing": 1.5, "synergy": 1.0},
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("distress", "investment"): {"financial": 2.5, "governance": 1.5, "product_tech": 0.5, "market": 0.5, "ai_commercial": 0.3, "ai_cost": 0.3, "ai_model_product": 0.3},
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("distress", "growth"): {"financial": 2.5, "governance": 1.5, "product_tech": 0.5, "ai_commercial": 0.3, "ai_cost": 0.3, "ai_model_product": 0.3},
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("distress", "exit_preparation"): {"financial": 3.0, "governance": 1.5, "financing": 1.5, "ai_commercial": 0.3, "ai_cost": 0.3, "ai_model_product": 0.3},
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("distress", "liquidation"): {"financial": 3.0, "financing": 2.0, "governance": 1.5, "ai_commercial": 0.3, "ai_cost": 0.3, "ai_model_product": 0.3},
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("esg", "investment"): {"governance": 1.8, "data_compliance": 1.5, "product_tech": 1.2, "financial": 0.8},
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("esg", "growth"): {"governance": 1.5, "data_compliance": 1.3, "customer_success": 1.2},
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("esg", "exit_preparation"): {"governance": 1.8, "financial": 1.3, "financing": 1.3},
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("esg", "liquidation"): {"financial": 1.5, "governance": 1.5, "financing": 1.5},
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# FOF 不直接评价单企业，使用默认权重
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("fof", "investment"): {},
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("fof", "growth"): {},
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("fof", "exit_preparation"): {},
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("fof", "liquidation"): {},
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}
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# --- 轴 2：企业阶段系数（5 个预设） ---
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STAGE_MULTIPLIERS: dict[str, dict[str, float]] = {
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"seed": {"financial": 0.6, "product_tech": 1.8, "org_talent": 1.5, "market_compete": 0.5, "governance": 0.6, "customer_success": 0.5},
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"a": {"financial": 0.8, "product_tech": 1.5, "org_talent": 1.2, "market_compete": 1.0, "governance": 0.7, "customer_success": 0.8},
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"b": {"financial": 1.2, "market_compete": 1.3, "customer_success": 1.3, "product_tech": 1.0, "governance": 1.0},
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"c": {"financial": 1.5, "market_compete": 1.3, "governance": 1.3, "customer_success": 1.3, "product_tech": 0.8},
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"pre_ipo": {"financial": 1.5, "governance": 1.8, "customer_success": 1.3, "market_compete": 1.0, "product_tech": 0.8},
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}
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# --- 轴 3：产业赛道配置（6 个预设） ---
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INDUSTRY_CONFIGS: dict[str, dict[str, Any]] = {
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"ai": {
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"enabled": list(DIMENSION_KEY_MAP.keys()),
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"disabled": [],
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"custom_metrics": [
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{"key": "model_accuracy", "label": "模型精度", "description": "AI 模型准确率/召回率"},
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{"key": "inference_cost", "label": "推理成本", "description": "单次推理成本趋势"},
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{"key": "api_call_volume", "label": "API 调用量", "description": "月度 API 调用次数"},
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{"key": "poc_conversion_rate", "label": "PoC 转化率", "description": "PoC 到付费转化率"},
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],
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},
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"saas": {
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"enabled": ["financial", "operational", "ai_commercial", "org_talent", "product_tech",
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"market_compete", "governance", "financing", "synergy", "data_compliance",
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"team_tech", "customer_success"],
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"disabled": ["ai_cost", "ai_model_product"],
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"custom_metrics": [
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{"key": "arr_growth", "label": "ARR 增长率", "description": "年度经常性收入增长率"},
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{"key": "net_revenue_retention", "label": "NRR", "description": "净收入留存率"},
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{"key": "cac_payback", "label": "CAC 回收期", "description": "获客成本回收月数"},
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{"key": "rule_of_40", "label": "Rule of 40", "description": "增长率 + 利润率"},
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],
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},
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"hardware": {
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"enabled": ["financial", "operational", "org_talent", "product_tech", "market_compete",
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"governance", "financing", "synergy", "data_compliance",
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"team_tech", "customer_success"],
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"disabled": ["ai_commercial", "ai_cost", "ai_model_product"],
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"custom_metrics": [
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{"key": "patent_count", "label": "专利数", "description": "累计授权专利数量"},
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{"key": "tape_out_progress", "label": "流片进度", "description": "芯片流片里程碑进展"},
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{"key": "yield_rate", "label": "良率", "description": "产品良率"},
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{"key": "rd_investment_ratio", "label": "研发投入比", "description": "研发投入占营收比例"},
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],
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},
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"biotech": {
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"enabled": ["financial", "operational", "org_talent", "product_tech", "governance",
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"financing", "synergy", "data_compliance", "team_tech"],
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"disabled": ["ai_commercial", "ai_cost", "ai_model_product", "market_compete", "customer_success"],
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"custom_metrics": [
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{"key": "clinical_stage", "label": "临床阶段", "description": "当前临床试验阶段"},
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{"key": "pipeline_progress", "label": "管线进度", "description": "在研管线推进情况"},
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{"key": "regulatory_milestone", "label": "审批节点", "description": "监管审批里程碑"},
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{"key": "patent_landscape", "label": "专利布局", "description": "核心专利布局覆盖度"},
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],
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},
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"consumer": {
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"enabled": ["financial", "operational", "org_talent", "product_tech", "market_compete",
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"governance", "financing", "synergy", "team_tech", "customer_success"],
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"disabled": ["ai_commercial", "ai_cost", "ai_model_product", "data_compliance"],
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"custom_metrics": [
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{"key": "gmv", "label": "GMV", "description": "月度交易总额"},
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{"key": "repurchase_rate", "label": "复购率", "description": "客户复购率"},
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{"key": "brand_index", "label": "品牌指数", "description": "品牌知名度/美誉度"},
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{"key": "channel_coverage", "label": "渠道覆盖率", "description": "销售渠道覆盖广度"},
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],
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},
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"fintech": {
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"enabled": ["financial", "operational", "ai_commercial", "org_talent", "product_tech",
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"market_compete", "governance", "financing", "synergy", "ai_model_product",
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"data_compliance", "team_tech", "customer_success"],
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"disabled": ["ai_cost"],
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"custom_metrics": [
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{"key": "license_progress", "label": "牌照进度", "description": "金融牌照获取进展"},
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{"key": "risk_control_score", "label": "风控指标", "description": "风控模型评分"},
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{"key": "compliance_events", "label": "合规事件", "description": "合规事件数量"},
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{"key": "npl_ratio", "label": "坏账率", "description": "不良贷款率"},
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],
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},
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"manufacturing": {
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"enabled": ["financial", "operational", "org_talent", "product_tech", "market_compete",
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"governance", "financing", "synergy", "data_compliance",
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"team_tech", "customer_success"],
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"disabled": ["ai_commercial", "ai_cost", "ai_model_product"],
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"custom_metrics": [
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{"key": "capacity_utilization", "label": "产能利用率", "description": "实际产能/设计产能"},
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{"key": "delivery_cycle", "label": "交付周期", "description": "订单交付周期天数"},
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{"key": "supply_chain_stability", "label": "供应链稳定性", "description": "供应链中断风险评分"},
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{"key": "rd_investment_ratio", "label": "研发投入比", "description": "研发投入占营收比例"},
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],
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},
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}
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# --- 轴 4：投资策略微调（4 个预设，±5%） ---
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STRATEGY_ADJUSTMENTS: dict[str, dict[str, float]] = {
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"growth": {"market_compete": 5, "product_tech": 5, "customer_success": 5, "financial": -5, "governance": -5},
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"value": {"financial": 5, "governance": 5, "customer_success": 5, "market_compete": -5, "product_tech": -5},
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"empowerment": {"synergy": 5, "org_talent": 5, "product_tech": 5, "financial": -5, "market_compete": -5},
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"turnaround": {"financial": 5, "governance": 5, "market_compete": -5, "product_tech": -5},
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}
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def compute_weights(
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fund_type: str,
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fund_lifecycle: str,
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company_stage: str,
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industry: str,
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strategy: str = "growth",
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investor_type: str = "investor",
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) -> dict[str, Any]:
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"""计算 6 轴动态权重。
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Args:
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fund_type: 基金类型 — angel/early_vc/growth_vc/pe/cvc/fof/distress/esg
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fund_lifecycle: 存续期阶段 — investment/growth/exit_preparation/liquidation
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company_stage: 企业阶段 — seed/a/b/c/pre_ipo
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industry: 产业赛道 — ai/saas/hardware/biotech/consumer/fintech/manufacturing
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strategy: 投资策略 — growth/value/empowerment/turnaround
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investor_type: 投资人类型 — gp/post_invest_lead/investor（不影响权重）
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Returns:
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包含 weights、enabled_dimensions、disabled_dimensions、custom_metrics 的字典
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"""
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# Step 1：复制基础权重
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weights = dict(BASE_WEIGHTS)
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# Step 2：基金类型 × 存续期系数
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fund_key = (fund_type, fund_lifecycle)
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fund_multipliers = FUND_LIFECYCLE_MULTIPLIERS.get(fund_key, {})
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for dim, multiplier in fund_multipliers.items():
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if dim in weights:
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weights[dim] *= multiplier
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# Step 3：企业阶段系数
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stage_multipliers = STAGE_MULTIPLIERS.get(company_stage, {})
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for dim, multiplier in stage_multipliers.items():
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if dim in weights:
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weights[dim] *= multiplier
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# Step 4：产业赛道 — 维度裁剪
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industry_config = INDUSTRY_CONFIGS.get(industry, INDUSTRY_CONFIGS["ai"])
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enabled_dims = industry_config["enabled"]
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disabled_dims = industry_config["disabled"]
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# 禁用的维度权重置零
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for dim in disabled_dims:
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if dim in weights:
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weights[dim] = 0.0
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# Step 5：投资策略微调（±5%，基于百分比点）
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strategy_adj = STRATEGY_ADJUSTMENTS.get(strategy, {})
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for dim, adjustment in strategy_adj.items():
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if dim in weights and weights[dim] > 0:
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# 将百分比点转换为权重调整量
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weights[dim] += adjustment / 100.0
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# 确保非负
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for dim in weights:
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weights[dim] = max(0.0, weights[dim])
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# Step 6：归一化 — 只对启用维度归一化到 1.0
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enabled_weights = {dim: weights[dim] for dim in enabled_dims if dim in weights}
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total = sum(enabled_weights.values())
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if total > 0:
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normalized = {dim: w / total for dim, w in enabled_weights.items()}
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else:
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# 极端情况：所有权重为零，均分
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count = len(enabled_dims) if enabled_dims else 1
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normalized = {dim: 1.0 / count for dim in enabled_dims}
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# 转换为百分比格式（保留 4 位小数）
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final_weights = {dim: round(w * 100, 2) for dim, w in normalized.items()}
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logger.info(
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"权重计算完成: fund_type=%s, lifecycle=%s, stage=%s, industry=%s, strategy=%s → %s",
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fund_type, fund_lifecycle, company_stage, industry, strategy, final_weights,
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)
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return {
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"weights": final_weights,
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"enabled_dimensions": enabled_dims,
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"disabled_dimensions": disabled_dims,
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"custom_metrics": industry_config.get("custom_metrics", []),
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}
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def get_dimension_score_key(weight_key: str) -> str:
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"""将权重 key 转换为评分字段 key。"""
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return DIMENSION_KEY_MAP.get(weight_key, f"{weight_key}_score")
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def calculate_weighted_score(
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dimension_scores: dict[str, float],
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weights: dict[str, float],
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) -> float:
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"""根据维度评分和权重计算加权总分。
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Args:
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dimension_scores: 各维度评分（0-100），key 为权重 key（如 financial）
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weights: 各维度权重（百分比），key 为权重 key
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Returns:
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加权总分（0-100）
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"""
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total = 0.0
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weight_sum = 0.0
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for dim, weight in weights.items():
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score = dimension_scores.get(dim)
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if score is not None and weight > 0:
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total += score * weight
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weight_sum += weight
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if weight_sum > 0:
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return round(total / weight_sum, 1)
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return 0.0
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