feat(backend): 6-axis dynamic evaluation system with weight engine and template management

2026-07-19 20:59:31 +08:00
parent f4ddcab2ca
commit de53a252e4
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+"""评价权重计算引擎。
+
+6 轴动态权重合并 + 归一化：
+1. 基金类型 × 存续期 → 基础权重模板（32 个预设）
+2. 企业阶段 → 阶段系数调整（5 个预设）
+3. 产业赛道 → 维度裁剪 + 专属指标注入（6 个预设）
+4. 投资策略 → ±5% 微调（4 个预设）
+5. 归一化 — 裁剪后剩余维度权重自动归一化到 100%
+6. 修饰因子叠加 — LP 附加指标 + 地域基准校准
+"""
+
+import logging
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+# --- 基础 14 维度权重（来自 health_calculator.py） ---
+BASE_WEIGHTS: dict[str, float] = {
+    "financial": 0.15,
+    "operational": 0.10,
+    "ai_commercial": 0.10,
+    "ai_cost": 0.05,
+    "org_talent": 0.10,
+    "product_tech": 0.10,
+    "market_compete": 0.10,
+    "governance": 0.05,
+    "financing": 0.05,
+    "synergy": 0.05,
+    "ai_model_product": 0.05,
+    "data_compliance": 0.05,
+    "team_tech": 0.03,
+    "customer_success": 0.07,
+}
+
+# 维度 key 映射：权重 key → 评分字段 key
+DIMENSION_KEY_MAP: dict[str, str] = {
+    "financial": "financial_score",
+    "operational": "operational_score",
+    "ai_commercial": "ai_commercial_score",
+    "ai_cost": "ai_cost_score",
+    "org_talent": "org_talent_score",
+    "product_tech": "product_tech_score",
+    "market_compete": "market_compete_score",
+    "governance": "governance_score",
+    "financing": "financing_score",
+    "synergy": "synergy_score",
+    "ai_model_product": "ai_model_product_score",
+    "data_compliance": "data_compliance_score",
+    "team_tech": "team_tech_score",
+    "customer_success": "customer_success_score",
+}
+
+# --- 轴 5：基金类型 × 存续期基础权重系数（32 个预设） ---
+FUND_LIFECYCLE_MULTIPLIERS: dict[tuple[str, str], dict[str, float]] = {
+    ("angel", "investment"):       {"financial": 0.5, "product_tech": 1.8, "org_talent": 1.5, "market": 1.2},
+    ("angel", "growth"):           {"financial": 0.7, "product_tech": 1.5, "org_talent": 1.3, "market": 1.2},
+    ("angel", "exit_preparation"): {"financial": 1.0, "product_tech": 1.2, "org_talent": 1.0, "financing": 1.5},
+    ("angel", "liquidation"):      {"financial": 1.5, "financing": 2.0, "product_tech": 0.8},
+
+    ("early_vc", "investment"):       {"financial": 0.7, "product_tech": 1.5, "market": 1.2, "customer_success": 0.8},
+    ("early_vc", "growth"):           {"financial": 0.9, "product_tech": 1.3, "market": 1.2, "customer_success": 1.0},
+    ("early_vc", "exit_preparation"): {"financial": 1.3, "market": 1.0, "customer_success": 1.2, "financing": 1.5},
+    ("early_vc", "liquidation"):      {"financial": 1.8, "financing": 2.0, "product_tech": 0.6},
+
+    ("growth_vc", "investment"):       {"financial": 1.0, "market": 1.3, "customer_success": 1.2},
+    ("growth_vc", "growth"):           {"financial": 1.2, "market": 1.2, "customer_success": 1.3},
+    ("growth_vc", "exit_preparation"): {"financial": 1.5, "market": 1.0, "customer_success": 1.2, "financing": 1.5},
+    ("growth_vc", "liquidation"):      {"financial": 2.0, "financing": 2.0, "market": 0.8},
+
+    ("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},
+    ("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},
+    ("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},
+    ("pe", "liquidation"):      {"financial": 3.0, "financing": 2.0, "governance": 1.5, "ai_commercial": 0.3, "ai_cost": 0.3, "ai_model_product": 0.3},
+
+    ("cvc", "investment"):       {"synergy": 2.0, "market": 1.3, "product_tech": 1.2, "financial": 0.7},
+    ("cvc", "growth"):           {"synergy": 1.8, "market": 1.2, "product_tech": 1.2, "financial": 0.8},
+    ("cvc", "exit_preparation"): {"synergy": 1.5, "market": 1.0, "financial": 1.2, "financing": 1.3},
+    ("cvc", "liquidation"):      {"financial": 1.5, "financing": 1.5, "synergy": 1.0},
+
+    ("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},
+    ("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},
+    ("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},
+    ("distress", "liquidation"):      {"financial": 3.0, "financing": 2.0, "governance": 1.5, "ai_commercial": 0.3, "ai_cost": 0.3, "ai_model_product": 0.3},
+
+    ("esg", "investment"):       {"governance": 1.8, "data_compliance": 1.5, "product_tech": 1.2, "financial": 0.8},
+    ("esg", "growth"):           {"governance": 1.5, "data_compliance": 1.3, "customer_success": 1.2},
+    ("esg", "exit_preparation"): {"governance": 1.8, "financial": 1.3, "financing": 1.3},
+    ("esg", "liquidation"):      {"financial": 1.5, "governance": 1.5, "financing": 1.5},
+
+    # FOF 不直接评价单企业，使用默认权重
+    ("fof", "investment"):       {},
+    ("fof", "growth"):           {},
+    ("fof", "exit_preparation"): {},
+    ("fof", "liquidation"):      {},
+}
+
+# --- 轴 2：企业阶段系数（5 个预设） ---
+STAGE_MULTIPLIERS: dict[str, dict[str, float]] = {
+    "seed":    {"financial": 0.6, "product_tech": 1.8, "org_talent": 1.5, "market_compete": 0.5, "governance": 0.6, "customer_success": 0.5},
+    "a":       {"financial": 0.8, "product_tech": 1.5, "org_talent": 1.2, "market_compete": 1.0, "governance": 0.7, "customer_success": 0.8},
+    "b":       {"financial": 1.2, "market_compete": 1.3, "customer_success": 1.3, "product_tech": 1.0, "governance": 1.0},
+    "c":       {"financial": 1.5, "market_compete": 1.3, "governance": 1.3, "customer_success": 1.3, "product_tech": 0.8},
+    "pre_ipo": {"financial": 1.5, "governance": 1.8, "customer_success": 1.3, "market_compete": 1.0, "product_tech": 0.8},
+}
+
+# --- 轴 3：产业赛道配置（6 个预设） ---
+INDUSTRY_CONFIGS: dict[str, dict[str, Any]] = {
+    "ai": {
+        "enabled": list(DIMENSION_KEY_MAP.keys()),
+        "disabled": [],
+        "custom_metrics": [
+            {"key": "model_accuracy", "label": "模型精度", "description": "AI 模型准确率/召回率"},
+            {"key": "inference_cost", "label": "推理成本", "description": "单次推理成本趋势"},
+            {"key": "api_call_volume", "label": "API 调用量", "description": "月度 API 调用次数"},
+            {"key": "poc_conversion_rate", "label": "PoC 转化率", "description": "PoC 到付费转化率"},
+        ],
+    },
+    "saas": {
+        "enabled": ["financial", "operational", "ai_commercial", "org_talent", "product_tech",
+                     "market_compete", "governance", "financing", "synergy", "data_compliance",
+                     "team_tech", "customer_success"],
+        "disabled": ["ai_cost", "ai_model_product"],
+        "custom_metrics": [
+            {"key": "arr_growth", "label": "ARR 增长率", "description": "年度经常性收入增长率"},
+            {"key": "net_revenue_retention", "label": "NRR", "description": "净收入留存率"},
+            {"key": "cac_payback", "label": "CAC 回收期", "description": "获客成本回收月数"},
+            {"key": "rule_of_40", "label": "Rule of 40", "description": "增长率 + 利润率"},
+        ],
+    },
+    "hardware": {
+        "enabled": ["financial", "operational", "org_talent", "product_tech", "market_compete",
+                     "governance", "financing", "synergy", "data_compliance",
+                     "team_tech", "customer_success"],
+        "disabled": ["ai_commercial", "ai_cost", "ai_model_product"],
+        "custom_metrics": [
+            {"key": "patent_count", "label": "专利数", "description": "累计授权专利数量"},
+            {"key": "tape_out_progress", "label": "流片进度", "description": "芯片流片里程碑进展"},
+            {"key": "yield_rate", "label": "良率", "description": "产品良率"},
+            {"key": "rd_investment_ratio", "label": "研发投入比", "description": "研发投入占营收比例"},
+        ],
+    },
+    "biotech": {
+        "enabled": ["financial", "operational", "org_talent", "product_tech", "governance",
+                     "financing", "synergy", "data_compliance", "team_tech"],
+        "disabled": ["ai_commercial", "ai_cost", "ai_model_product", "market_compete", "customer_success"],
+        "custom_metrics": [
+            {"key": "clinical_stage", "label": "临床阶段", "description": "当前临床试验阶段"},
+            {"key": "pipeline_progress", "label": "管线进度", "description": "在研管线推进情况"},
+            {"key": "regulatory_milestone", "label": "审批节点", "description": "监管审批里程碑"},
+            {"key": "patent_landscape", "label": "专利布局", "description": "核心专利布局覆盖度"},
+        ],
+    },
+    "consumer": {
+        "enabled": ["financial", "operational", "org_talent", "product_tech", "market_compete",
+                     "governance", "financing", "synergy", "team_tech", "customer_success"],
+        "disabled": ["ai_commercial", "ai_cost", "ai_model_product", "data_compliance"],
+        "custom_metrics": [
+            {"key": "gmv", "label": "GMV", "description": "月度交易总额"},
+            {"key": "repurchase_rate", "label": "复购率", "description": "客户复购率"},
+            {"key": "brand_index", "label": "品牌指数", "description": "品牌知名度/美誉度"},
+            {"key": "channel_coverage", "label": "渠道覆盖率", "description": "销售渠道覆盖广度"},
+        ],
+    },
+    "fintech": {
+        "enabled": ["financial", "operational", "ai_commercial", "org_talent", "product_tech",
+                     "market_compete", "governance", "financing", "synergy", "ai_model_product",
+                     "data_compliance", "team_tech", "customer_success"],
+        "disabled": ["ai_cost"],
+        "custom_metrics": [
+            {"key": "license_progress", "label": "牌照进度", "description": "金融牌照获取进展"},
+            {"key": "risk_control_score", "label": "风控指标", "description": "风控模型评分"},
+            {"key": "compliance_events", "label": "合规事件", "description": "合规事件数量"},
+            {"key": "npl_ratio", "label": "坏账率", "description": "不良贷款率"},
+        ],
+    },
+    "manufacturing": {
+        "enabled": ["financial", "operational", "org_talent", "product_tech", "market_compete",
+                     "governance", "financing", "synergy", "data_compliance",
+                     "team_tech", "customer_success"],
+        "disabled": ["ai_commercial", "ai_cost", "ai_model_product"],
+        "custom_metrics": [
+            {"key": "capacity_utilization", "label": "产能利用率", "description": "实际产能/设计产能"},
+            {"key": "delivery_cycle", "label": "交付周期", "description": "订单交付周期天数"},
+            {"key": "supply_chain_stability", "label": "供应链稳定性", "description": "供应链中断风险评分"},
+            {"key": "rd_investment_ratio", "label": "研发投入比", "description": "研发投入占营收比例"},
+        ],
+    },
+}
+
+# --- 轴 4：投资策略微调（4 个预设，±5%） ---
+STRATEGY_ADJUSTMENTS: dict[str, dict[str, float]] = {
+    "growth":      {"market_compete": 5, "product_tech": 5, "customer_success": 5, "financial": -5, "governance": -5},
+    "value":       {"financial": 5, "governance": 5, "customer_success": 5, "market_compete": -5, "product_tech": -5},
+    "empowerment": {"synergy": 5, "org_talent": 5, "product_tech": 5, "financial": -5, "market_compete": -5},
+    "turnaround":  {"financial": 5, "governance": 5, "market_compete": -5, "product_tech": -5},
+}
+
+
+def compute_weights(
+    fund_type: str,
+    fund_lifecycle: str,
+    company_stage: str,
+    industry: str,
+    strategy: str = "growth",
+    investor_type: str = "investor",
+) -> dict[str, Any]:
+    """计算 6 轴动态权重。
+
+    Args:
+        fund_type: 基金类型 — angel/early_vc/growth_vc/pe/cvc/fof/distress/esg
+        fund_lifecycle: 存续期阶段 — investment/growth/exit_preparation/liquidation
+        company_stage: 企业阶段 — seed/a/b/c/pre_ipo
+        industry: 产业赛道 — ai/saas/hardware/biotech/consumer/fintech/manufacturing
+        strategy: 投资策略 — growth/value/empowerment/turnaround
+        investor_type: 投资人类型 — gp/post_invest_lead/investor（不影响权重）
+
+    Returns:
+        包含 weights、enabled_dimensions、disabled_dimensions、custom_metrics 的字典
+    """
+    # Step 1：复制基础权重
+    weights = dict(BASE_WEIGHTS)
+
+    # Step 2：基金类型 × 存续期系数
+    fund_key = (fund_type, fund_lifecycle)
+    fund_multipliers = FUND_LIFECYCLE_MULTIPLIERS.get(fund_key, {})
+    for dim, multiplier in fund_multipliers.items():
+        if dim in weights:
+            weights[dim] *= multiplier
+
+    # Step 3：企业阶段系数
+    stage_multipliers = STAGE_MULTIPLIERS.get(company_stage, {})
+    for dim, multiplier in stage_multipliers.items():
+        if dim in weights:
+            weights[dim] *= multiplier
+
+    # Step 4：产业赛道 — 维度裁剪
+    industry_config = INDUSTRY_CONFIGS.get(industry, INDUSTRY_CONFIGS["ai"])
+    enabled_dims = industry_config["enabled"]
+    disabled_dims = industry_config["disabled"]
+
+    # 禁用的维度权重置零
+    for dim in disabled_dims:
+        if dim in weights:
+            weights[dim] = 0.0
+
+    # Step 5：投资策略微调（±5%，基于百分比点）
+    strategy_adj = STRATEGY_ADJUSTMENTS.get(strategy, {})
+    for dim, adjustment in strategy_adj.items():
+        if dim in weights and weights[dim] > 0:
+            # 将百分比点转换为权重调整量
+            weights[dim] += adjustment / 100.0
+
+    # 确保非负
+    for dim in weights:
+        weights[dim] = max(0.0, weights[dim])
+
+    # Step 6：归一化 — 只对启用维度归一化到 1.0
+    enabled_weights = {dim: weights[dim] for dim in enabled_dims if dim in weights}
+    total = sum(enabled_weights.values())
+
+    if total > 0:
+        normalized = {dim: w / total for dim, w in enabled_weights.items()}
+    else:
+        # 极端情况：所有权重为零，均分
+        count = len(enabled_dims) if enabled_dims else 1
+        normalized = {dim: 1.0 / count for dim in enabled_dims}
+
+    # 转换为百分比格式（保留 4 位小数）
+    final_weights = {dim: round(w * 100, 2) for dim, w in normalized.items()}
+
+    logger.info(
+        "权重计算完成: fund_type=%s, lifecycle=%s, stage=%s, industry=%s, strategy=%s → %s",
+        fund_type, fund_lifecycle, company_stage, industry, strategy, final_weights,
+    )
+
+    return {
+        "weights": final_weights,
+        "enabled_dimensions": enabled_dims,
+        "disabled_dimensions": disabled_dims,
+        "custom_metrics": industry_config.get("custom_metrics", []),
+    }
+
+
+def get_dimension_score_key(weight_key: str) -> str:
+    """将权重 key 转换为评分字段 key。"""
+    return DIMENSION_KEY_MAP.get(weight_key, f"{weight_key}_score")
+
+
+def calculate_weighted_score(
+    dimension_scores: dict[str, float],
+    weights: dict[str, float],
+) -> float:
+    """根据维度评分和权重计算加权总分。
+
+    Args:
+        dimension_scores: 各维度评分（0-100），key 为权重 key（如 financial）
+        weights: 各维度权重（百分比），key 为权重 key
+
+    Returns:
+        加权总分（0-100）
+    """
+    total = 0.0
+    weight_sum = 0.0
+
+    for dim, weight in weights.items():
+        score = dimension_scores.get(dim)
+        if score is not None and weight > 0:
+            total += score * weight
+            weight_sum += weight
+
+    if weight_sum > 0:
+        return round(total / weight_sum, 1)
+    return 0.0
@@ -0,0 +1,154 @@
+"""评价模板预设配置种子数据。
+
+启动时自动初始化 50 个预设配置单元到数据库。
+"""
+
+import logging
+from typing import Any
+
+from app.services.evaluation_engine import compute_weights
+
+logger = logging.getLogger(__name__)
+
+# 基金类型列表
+FUND_TYPES = ["angel", "early_vc", "growth_vc", "pe", "cvc", "fof", "distress", "esg"]
+
+# 存续期阶段列表
+FUND_LIFECYCLES = ["investment", "growth", "exit_preparation", "liquidation"]
+
+# 企业阶段列表
+COMPANY_STAGES = ["seed", "a", "b", "c", "pre_ipo"]
+
+# 产业赛道列表
+INDUSTRIES = ["ai", "saas", "hardware", "biotech", "consumer", "fintech", "manufacturing"]
+
+# 投资策略列表
+STRATEGIES = ["growth", "value", "empowerment", "turnaround"]
+
+# 投资人类型列表
+INVESTOR_TYPES = ["gp", "post_invest_lead", "investor"]
+
+# 模板名称中文名
+FUND_TYPE_LABELS = {
+    "angel": "天使/种子基金",
+    "early_vc": "早期VC",
+    "growth_vc": "成长期VC",
+    "pe": "PE/并购基金",
+    "cvc": "产业基金",
+    "fof": "母基金",
+    "distress": "困境/特殊机会基金",
+    "esg": "ESG/影响力基金",
+}
+
+LIFECYCLE_LABELS = {
+    "investment": "投资期",
+    "growth": "成长期",
+    "exit_preparation": "退出准备期",
+    "liquidation": "清算期",
+}
+
+STAGE_LABELS = {
+    "seed": "种子/天使",
+    "a": "A轮",
+    "b": "B轮",
+    "c": "C轮+",
+    "pre_ipo": "Pre-IPO",
+}
+
+INDUSTRY_LABELS = {
+    "ai": "AI/SaaS",
+    "saas": "企业服务",
+    "hardware": "硬科技/芯片",
+    "biotech": "生物医药",
+    "consumer": "消费品牌",
+    "fintech": "金融科技",
+    "manufacturing": "新能源/先进制造",
+}
+
+STRATEGY_LABELS = {
+    "growth": "成长型",
+    "value": "价值型",
+    "empowerment": "投后赋能型",
+    "turnaround": "困境反转型",
+}
+
+
+def generate_preset_templates() -> list[dict[str, Any]]:
+    """生成全部预设模板配置。
+
+    策略：为每个 (fund_type, lifecycle, stage, industry) 组合生成默认模板，
+    strategy 默认使用 growth，investor_type 默认使用 investor。
+    用户可以在前端创建自定义策略/投资人类型的模板。
+
+    Returns:
+        模板配置字典列表
+    """
+    templates: list[dict[str, Any]] = []
+
+    for fund_type in FUND_TYPES:
+        for lifecycle in FUND_LIFECYCLES:
+            for stage in COMPANY_STAGES:
+                for industry in INDUSTRIES:
+                    # FOF 不生成单企业模板
+                    if fund_type == "fof":
+                        continue
+
+                    result = compute_weights(
+                        fund_type=fund_type,
+                        fund_lifecycle=lifecycle,
+                        company_stage=stage,
+                        industry=industry,
+                        strategy="growth",
+                        investor_type="investor",
+                    )
+
+                    name = (
+                        f"{FUND_TYPE_LABELS[fund_type]}-{LIFECYCLE_LABELS[lifecycle]}-"
+                        f"{STAGE_LABELS[stage]}-{INDUSTRY_LABELS[industry]}"
+                    )
+
+                    templates.append({
+                        "name": name,
+                        "fund_type": fund_type,
+                        "fund_lifecycle": lifecycle,
+                        "company_stage": stage,
+                        "industry": industry,
+                        "strategy": "growth",
+                        "investor_type": "investor",
+                        "weights_json": result["weights"],
+                        "enabled_dimensions": result["enabled_dimensions"],
+                        "disabled_dimensions": result["disabled_dimensions"],
+                        "custom_metrics_json": {"metrics": result["custom_metrics"]},
+                        "is_default": True,
+                        "is_active": True,
+                        "version": 1,
+                    })
+
+    logger.info("生成预设模板 %d 个", len(templates))
+    return templates
+
+
+async def seed_evaluation_templates(db_session) -> None:
+    """将预设模板写入数据库（仅当表为空时）。
+
+    Args:
+        db_session: 异步数据库会话
+    """
+    from sqlalchemy import select
+    from app.models.evaluation_template import EvaluationTemplate
+
+    # 检查是否已有数据
+    result = await db_session.execute(select(EvaluationTemplate).limit(1))
+    existing = result.scalar_one_or_none()
+
+    if existing:
+        logger.info("评价模板表已有数据，跳过种子初始化")
+        return
+
+    templates = generate_preset_templates()
+    for tmpl_data in templates:
+        tmpl = EvaluationTemplate(**tmpl_data)
+        db_session.add(tmpl)
+
+    await db_session.flush()
+    logger.info("预设评价模板已写入数据库: %d 个", len(templates))