feat(backend): update dashboard router, seed demo data, and investor agents page
This commit is contained in:
selfrelease
@@ -80,17 +80,28 @@ async def get_dashboard_summary(
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pending_result = await db.execute(pending_query)
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pending_reports = pending_result.scalar_one()
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# 最近评分（最多 10 条）
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# 最近评分 — 每家企业只取最新一条（避免历史数据导致企业重复）
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subq = (
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select(
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HealthScore.company_id,
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func.max(HealthScore.calculated_at).label("max_at"),
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)
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.join(Company, HealthScore.company_id == Company.id)
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.where(Company.tenant_id == tenant_id)
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.group_by(HealthScore.company_id)
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)
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if company_id:
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subq = subq.where(HealthScore.company_id == company_id)
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subq = subq.subquery()
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recent_query = (
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select(HealthScore, Company.name.label("company_name"))
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.join(Company, HealthScore.company_id == Company.id)
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.where(Company.tenant_id == tenant_id)
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)
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if company_id:
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recent_query = recent_query.where(HealthScore.company_id == company_id)
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recent_result = await db.execute(
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recent_query.order_by(HealthScore.calculated_at.desc()).limit(10)
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.join(subq, (HealthScore.company_id == subq.c.company_id) & (HealthScore.calculated_at == subq.c.max_at))
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.order_by(HealthScore.calculated_at.desc())
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.limit(10)
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)
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recent_result = await db.execute(recent_query)
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recent_rows = recent_result.all()
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recent_scores = []
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for row in recent_rows:
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@@ -118,16 +129,27 @@ async def list_health_scores(
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db: AsyncSession = Depends(get_db),
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user: User = Depends(get_current_user),
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):
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"""获取健康度评分列表。"""
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"""获取健康度评分列表 — 每家企业只返回最新一条。"""
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subq = (
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select(
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HealthScore.company_id,
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func.max(HealthScore.calculated_at).label("max_at"),
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)
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.join(Company, HealthScore.company_id == Company.id)
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.where(Company.tenant_id == user.tenant_id)
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.group_by(HealthScore.company_id)
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)
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if company_id:
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subq = subq.where(HealthScore.company_id == company_id)
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subq = subq.subquery()
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query = (
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select(HealthScore, Company.name.label("company_name"))
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.join(Company, HealthScore.company_id == Company.id)
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.where(Company.tenant_id == user.tenant_id)
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.join(subq, (HealthScore.company_id == subq.c.company_id) & (HealthScore.calculated_at == subq.c.max_at))
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.order_by(HealthScore.calculated_at.desc())
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.limit(limit)
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)
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if company_id:
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query = query.where(HealthScore.company_id == company_id)
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query = query.order_by(HealthScore.calculated_at.desc()).limit(limit)
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result = await db.execute(query)
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scores = []
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for row in result.all():
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@@ -11,6 +11,7 @@ Hypothesis / AARRecord / TeamMember / TalentProfile / AuditLog。
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import asyncio
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import json
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import random
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from datetime import datetime, timedelta, timezone
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from sqlalchemy import text
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@@ -19,10 +20,15 @@ from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.database import async_session_factory, engine, Base
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from app.core.security import hash_password
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from app.models import (
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AARRecord, AuditLog, BoardMeeting, Company, FinancialData, HealthScore,
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Hypothesis, InvestmentAgreement, MajorEvent, MilestoneTree, MonthlyReport,
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NudgeRecord, OKR, RiskEvent, SynergyOpportunity, Task, TalentProfile,
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TeamMember, Tenant, User, WeakSignal, DecisionSentinel,
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AARRecord, AgentExecution, AuditLog, BoardMeeting, Company, CompanyFundLink,
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CustomerAcquisitionPlan, DataSource, DecisionSentinel, DigitalTwinModel,
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EvaluationTemplate, ExitPrediction, FinancialData, FirmProfile, Fund,
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FundProfile, HealthScore, Hypothesis, InquiryList, InterventionEvent,
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InterventionResult, InvestmentAgreement, KnowledgeChunk, KnowledgeNode,
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MajorEvent, ManagerProfile, MilestoneTree, MonteCarloSimulation, MonthlyReport,
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NudgeRecord, OKR, PeerLearningCircle, PortfolioRebalancing, PreMortemRecord,
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ProductDiagnostic, RedTeamRecord, RiskEvent, SynergyOpportunity, Task,
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TalentProfile, TeamMember, Tenant, User, WeakSignal,
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)
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UTC = timezone.utc
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@@ -56,9 +62,14 @@ async def seed() -> None:
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async with async_session_factory() as db:
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await _seed_tenant_users(db)
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await _seed_companies(db)
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await _seed_funds(db)
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await _seed_profiles(db)
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await _seed_evaluation_templates(db)
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await _seed_financial(db)
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await _seed_reports(db)
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await _seed_inquiries(db)
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await _seed_health(db)
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await _seed_health_history(db)
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await _seed_risks(db)
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await _seed_weak_signals(db)
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await _seed_tasks(db)
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@@ -74,6 +85,17 @@ async def seed() -> None:
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await _seed_aars(db)
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await _seed_team_members(db)
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await _seed_talents(db)
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await _seed_agent_executions(db)
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await _seed_customer_plans(db)
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await _seed_exit_predictions(db)
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await _seed_pre_mortems(db)
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await _seed_product_diagnostics(db)
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await _seed_peer_circles(db)
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await _seed_digital_twins(db)
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await _seed_interventions(db)
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await _seed_portfolio_simulations(db)
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await _seed_knowledge_graph(db)
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await _seed_data_sources(db)
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await _seed_audit_logs(db)
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await db.commit()
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@@ -136,30 +158,32 @@ async def _seed_companies(db: AsyncSession) -> None:
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# ─── 财务数据 ──────────────────────────────────────────────────────────
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async def _seed_financial(db: AsyncSession) -> None:
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"""每家企业生成近 3 个月利润表 + 现金流。"""
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"""每家企业生成近 6 个月利润表 + 现金流。"""
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for comp_id, revenue, burn, cash in [
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(COMP_A, 800, 350, 4200), # 月营收350万, 消耗120万, 现金4200万
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(COMP_A, 800, 350, 4200), # 月营收800万, 消耗350万, 现金4200万
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(COMP_B, 150, 120, 1800),
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(COMP_C, 1200, 480, 6500),
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(COMP_D, 0, 80, 600),
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(COMP_E, 80, 200, 2400),
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]:
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for i in range(3):
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for i in range(6):
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y, m = (NOW.year, NOW.month - i) if NOW.month - i > 0 else (NOW.year - 1, 12 + NOW.month - i)
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# 营收逐月递减（历史数据），展示增长趋势
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hist_revenue = int(revenue * (1 - 0.03 * i)) if revenue > 0 else 0
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db.add(FinancialData(
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company_id=comp_id, period_year=y, period_month=m,
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statement_type="income",
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data_json={"revenue": revenue, "cogs": int(revenue * 0.4), "gross_profit": int(revenue * 0.6), "opex": burn, "net_income": revenue - burn - int(revenue * 0.4)},
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data_json={"revenue": hist_revenue, "cogs": int(hist_revenue * 0.4), "gross_profit": int(hist_revenue * 0.6), "opex": burn, "net_income": hist_revenue - burn - int(hist_revenue * 0.4)},
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source="monthly_report", credibility_score=85.0 - i * 5,
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))
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db.add(FinancialData(
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company_id=comp_id, period_year=y, period_month=m,
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statement_type="cash_flow",
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data_json={"operating_cf": revenue - burn, "investing_cf": -50, "financing_cf": 0, "net_cf": revenue - burn - 50, "cash_balance": cash - i * burn},
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data_json={"operating_cf": hist_revenue - burn, "investing_cf": -50, "financing_cf": 0, "net_cf": hist_revenue - burn - 50, "cash_balance": cash - i * burn},
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source="monthly_report", credibility_score=85.0 - i * 5,
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))
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await db.flush()
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print(" ✓ 财务数据 (5 企业 x 3 月)")
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print(" ✓ 财务数据 (5 企业 x 6 月)")
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# ─── 月报 ──────────────────────────────────────────────────────────────
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@@ -201,78 +225,176 @@ async def _seed_reports(db: AsyncSession) -> None:
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for r in reports:
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db.add(r)
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await db.flush()
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print(" ✓ 月报 (5 份)")
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# 历史月报 — 每家企业再补 5 个月，展示趋势
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historical_data = [
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# (company_id, founder_id, months_ago, revenue, growth, new_cust, churned, headcount, summary, concerns)
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(COMP_A, FOUNDER_A, 2, 696, 0.08, 2, 0, 40, "营收稳步增长，新签2家客户。", []),
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(COMP_A, FOUNDER_A, 3, 644, 0.05, 1, 1, 38, "营收增长5%，客户流失1家。", [{"level": "low", "item": "客户流失", "detail": "流失1家小客户"}]),
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(COMP_A, FOUNDER_A, 4, 613, 0.03, 1, 0, 37, "营收小幅增长，团队稳定。", []),
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(COMP_A, FOUNDER_A, 5, 595, 0.02, 0, 0, 35, "营收微增，无新签客户。", [{"level": "medium", "item": "增长放缓", "detail": "环比仅2%"}]),
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(COMP_A, FOUNDER_A, 6, 583, 0.01, 1, 1, 34, "基本持平，团队新增1人。", []),
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(COMP_B, FOUNDER_B, 2, 150, 0.0, 0, 0, 22, "营收持平，产品迭代中。", []),
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(COMP_B, FOUNDER_B, 3, 150, 0.02, 1, 0, 21, "营收微增，新签1家客户。", []),
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(COMP_B, FOUNDER_B, 4, 147, -0.01, 0, 1, 21, "营收微降，流失1家小客户。", [{"level": "medium", "item": "客户流失", "detail": "流失1家"}]),
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(COMP_B, FOUNDER_B, 5, 149, 0.03, 1, 0, 20, "营收回升，新签1家。", []),
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(COMP_B, FOUNDER_B, 6, 145, 0.0, 0, 0, 20, "持平，团队稳定。", []),
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(COMP_C, FOUNDER_C, 2, 984, 0.18, 3, 0, 62, "强劲增长，半导体客户+3。", []),
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(COMP_C, FOUNDER_C, 3, 834, 0.15, 2, 0, 58, "持续增长，新增专利1项。", []),
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(COMP_C, FOUNDER_C, 4, 725, 0.12, 1, 0, 55, "稳定增长，团队扩张。", []),
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(COMP_C, FOUNDER_C, 5, 647, 0.10, 1, 0, 52, "增速放缓，但趋势向上。", []),
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(COMP_C, FOUNDER_C, 6, 588, 0.08, 0, 0, 50, "基线月份，稳步起步。", []),
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(COMP_E, FOUNDER_A, 2, 75, 0.0, 0, 0, 17, "里程碑收入稳定。", []),
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(COMP_E, FOUNDER_A, 3, 80, 0.07, 0, 0, 18, "小幅增长，靶点推进。", []),
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(COMP_E, FOUNDER_A, 4, 75, 0.0, 0, 0, 17, "持平，研发正常。", []),
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(COMP_E, FOUNDER_A, 5, 70, -0.07, 0, 0, 16, "收入微降，无里程碑。", [{"level": "low", "item": "收入波动", "detail": "依赖里程碑付款"}]),
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(COMP_E, FOUNDER_A, 6, 75, 0.04, 0, 0, 16, "基线月份，稳定。", []),
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]
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for comp_id, founder_id, months_ago, revenue, growth, new_c, churned, hc, summary, concerns in historical_data:
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m = NOW.month - months_ago
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y = NOW.year
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if m <= 0:
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m += 12
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y -= 1
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db.add(MonthlyReport(
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company_id=comp_id, period_year=y, period_month=m,
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status="reviewed", submitted_by=founder_id, submitted_at=D(months_ago * 30 + 10),
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reviewed_by=MGR_ID, reviewed_at=D(months_ago * 30 + 5),
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raw_content=f"本月营收{revenue}万，环比{'增长' if growth > 0 else '持平' if growth == 0 else '下降'}{abs(growth)*100:.0f}%。",
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structured_data={"revenue": revenue, "mom_growth": growth, "new_customers": new_c, "churned": churned, "headcount": hc},
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ai_summary=summary,
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ai_concerns=concerns if concerns else None,
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))
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await db.flush()
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print(" ✓ 月报 (5 当月 + 20 历史 = 25 份)")
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# ─── 健康度评分 ────────────────────────────────────────────────────────
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async def _seed_health(db: AsyncSession) -> None:
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"""最新一期健康度评分 — 14 维度完整评分。"""
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scores = [
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# 智链科技 — 良好
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HealthScore(company_id=COMP_A, total_score=78.5, financial_score=82, operational_score=75, ai_commercial_score=80, ai_cost_score=85,
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HealthScore(company_id=COMP_A, total_score=78.5,
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financial_score=82, operational_score=75, ai_commercial_score=80, ai_cost_score=85,
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org_talent_score=72, product_tech_score=80, market_compete_score=78, governance_score=75, financing_score=80,
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trend="up", evidence_json={"financial": "营收增长15%，现金流健康", "ai_cost": "推理成本降低12%"},
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recommendations_json={"action": "关注客户流失率", "owner": MGR_ID, "review_at": D(-30).isoformat()}),
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synergy_score=76, ai_model_product_score=82, data_compliance_score=88, team_tech_score=74, customer_success_score=68,
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trend="up", evidence_json={"financial": "营收增长15%，现金流健康", "ai_cost": "推理成本降低12%", "data_compliance": "通过等保三级", "ai_poc_count": 3, "compliance_issues": 0},
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recommendations_json={"action": "关注客户流失率", "owner": MGR_ID, "review_at": D(-30).isoformat()},
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fund_type="early_vc", fund_lifecycle="investment", company_stage="b", industry="ai", strategy="growth"),
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# 云栈数据 — 中等
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HealthScore(company_id=COMP_B, total_score=62.0, financial_score=55, operational_score=68, ai_commercial_score=60, ai_cost_score=65,
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HealthScore(company_id=COMP_B, total_score=62.0,
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financial_score=55, operational_score=68, ai_commercial_score=60, ai_cost_score=65,
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org_talent_score=70, product_tech_score=72, market_compete_score=58, governance_score=65, financing_score=50,
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trend="stable", evidence_json={"financial": "营收持平，需突破", "financing": "Runway 15月，需启动融资"},
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recommendations_json={"action": "加速融资节奏", "owner": LEAD_ID, "review_at": D(-14).isoformat()}),
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synergy_score=64, ai_model_product_score=68, data_compliance_score=85, team_tech_score=72, customer_success_score=60,
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trend="stable", evidence_json={"financial": "营收持平，需突破", "financing": "Runway 15月，需启动融资", "ai_poc_count": 1, "compliance_issues": 1},
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recommendations_json={"action": "加速融资节奏", "owner": LEAD_ID, "review_at": D(-14).isoformat()},
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fund_type="early_vc", fund_lifecycle="investment", company_stage="a", industry="ai", strategy="growth"),
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# 深瞳智能 — 优秀
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HealthScore(company_id=COMP_C, total_score=85.5, financial_score=88, operational_score=85, ai_commercial_score=90, ai_cost_score=82,
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HealthScore(company_id=COMP_C, total_score=85.5,
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financial_score=88, operational_score=85, ai_commercial_score=90, ai_cost_score=82,
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org_talent_score=83, product_tech_score=88, market_compete_score=86, governance_score=82, financing_score=85,
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trend="up", evidence_json={"financial": "营收增长22%", "market": "半导体渗透加深"},
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recommendations_json={"action": "关注客户集中度", "owner": MGR_ID, "review_at": D(-30).isoformat()}),
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synergy_score=80, ai_model_product_score=86, data_compliance_score=90, team_tech_score=85, customer_success_score=82,
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trend="up", evidence_json={"financial": "营收增长22%", "market": "半导体渗透加深", "ai_poc_count": 5, "compliance_issues": 0},
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recommendations_json={"action": "关注客户集中度", "owner": MGR_ID, "review_at": D(-30).isoformat()},
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fund_type="early_vc", fund_lifecycle="growth", company_stage="b", industry="ai", strategy="growth"),
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# 量子芯微 — 早期风险
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HealthScore(company_id=COMP_D, total_score=48.0, financial_score=35, operational_score=50, ai_commercial_score=45, ai_cost_score=55,
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HealthScore(company_id=COMP_D, total_score=48.0,
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financial_score=35, operational_score=50, ai_commercial_score=45, ai_cost_score=55,
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org_talent_score=52, product_tech_score=60, market_compete_score=42, governance_score=55, financing_score=40,
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trend="down", evidence_json={"financial": "无营收，现金紧张", "financing": "Runway 7月"},
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recommendations_json={"action": "紧急启动天使+轮融资", "owner": GP_ID, "review_at": D(-7).isoformat()}),
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synergy_score=45, ai_model_product_score=58, data_compliance_score=65, team_tech_score=62, customer_success_score=38,
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trend="down", evidence_json={"financial": "无营收，现金紧张", "financing": "Runway 7月", "ai_poc_count": 0, "compliance_issues": 2},
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recommendations_json={"action": "紧急启动天使+轮融资", "owner": GP_ID, "review_at": D(-7).isoformat()},
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fund_type="angel", fund_lifecycle="investment", company_stage="seed", industry="hardware", strategy="growth"),
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# 光合生物 — 中等偏上
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HealthScore(company_id=COMP_E, total_score=68.5, financial_score=60, operational_score=72, ai_commercial_score=70, ai_cost_score=65,
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HealthScore(company_id=COMP_E, total_score=68.5,
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financial_score=60, operational_score=72, ai_commercial_score=70, ai_cost_score=65,
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org_talent_score=68, product_tech_score=75, market_compete_score=65, governance_score=70, financing_score=62,
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trend="stable", evidence_json={"financial": "里程碑收入稳定", "product": "3靶点推进中"},
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recommendations_json={"action": "月报提交及时性改善", "owner": MGR_ID, "review_at": D(-30).isoformat()}),
|
||||
synergy_score=66, ai_model_product_score=72, data_compliance_score=80, team_tech_score=70, customer_success_score=65,
|
||||
trend="stable", evidence_json={"financial": "里程碑收入稳定", "product": "3靶点推进中", "ai_poc_count": 2, "compliance_issues": 0},
|
||||
recommendations_json={"action": "月报提交及时性改善", "owner": MGR_ID, "review_at": D(-30).isoformat()},
|
||||
fund_type="early_vc", fund_lifecycle="investment", company_stage="a", industry="biotech", strategy="growth"),
|
||||
]
|
||||
for s in scores:
|
||||
s.calculated_at = D(5)
|
||||
db.add(s)
|
||||
await db.flush()
|
||||
print(" ✓ 健康度评分 (5 企业)")
|
||||
print(" ✓ 健康度评分 (5 企业 x 14 维度)")
|
||||
|
||||
|
||||
# ─── 风险事件 ──────────────────────────────────────────────────────────
|
||||
async def _seed_risks(db: AsyncSession) -> None:
|
||||
risks = [
|
||||
# COMP_A — 智链科技
|
||||
RiskEvent(company_id=COMP_A, type="operational", severity="medium", status="in_progress",
|
||||
title="客户流失率环比上升", description="本月流失1家年合同客户，流失率从2%升至4%",
|
||||
evidence_json={"metric": "churn_rate", "current": 0.04, "previous": 0.02, "threshold": 0.03},
|
||||
suggested_action="联系流失客户了解原因，加强客户成功团队", assigned_to=MGR_ID, due_at=D(-7)),
|
||||
RiskEvent(company_id=COMP_B, type="financial", severity="high", status="assigned",
|
||||
title="营收增长停滞", description="连续2个月环比0%增长，未达预期",
|
||||
evidence_json={"metric": "revenue_growth", "current": 0.0, "expected": 0.15, "months": 2},
|
||||
suggested_action="与创始人讨论增长策略，评估产品定价和市场拓展", assigned_to=LEAD_ID, due_at=D(-3)),
|
||||
RiskEvent(company_id=COMP_D, type="financial", severity="critical", status="open",
|
||||
title="现金Runway不足7个月", description="按当前消耗速度，现金仅可维持7个月",
|
||||
evidence_json={"metric": "runway_months", "current": 7, "threshold": 9},
|
||||
suggested_action="紧急启动天使+轮融资，准备BP和财务预测", assigned_to=GP_ID, due_at=D(-14)),
|
||||
RiskEvent(company_id=COMP_E, type="operational", severity="medium", status="resolved",
|
||||
title="月报延迟提交", description="上月月报延迟20天提交",
|
||||
evidence_json={"metric": "report_delay_days", "current": 20, "threshold": 5},
|
||||
suggested_action="与创始人沟通提交规范，设置自动提醒", assigned_to=MGR_ID, due_at=D(-10), closed_at=D(-3)),
|
||||
RiskEvent(company_id=COMP_C, type="org", severity="low", status="open",
|
||||
title="核心技术人员稳定性下降", description="CTO提及外部机会，稳定性评分从0.85降至0.65",
|
||||
evidence_json={"metric": "cto_stability", "current": 0.65, "previous": 0.85},
|
||||
suggested_action="了解CTO诉求，评估股权激励调整", assigned_to=LEAD_ID),
|
||||
RiskEvent(company_id=COMP_A, type="ai_specific", severity="low", status="closed",
|
||||
title="AI模型推理成本超标", description="上月推理成本占营收比12%，超10%阈值",
|
||||
evidence_json={"metric": "ai_cost_ratio", "current": 0.12, "threshold": 0.10},
|
||||
suggested_action="优化模型量化策略", assigned_to=MGR_ID, closed_at=D(-5)),
|
||||
RiskEvent(company_id=COMP_A, type="market", severity="medium", status="open",
|
||||
title="竞品低价策略冲击", description="杉数科技以低于我方30%的价格争夺制造业客户",
|
||||
evidence_json={"competitor": "杉数科技", "price_diff": -0.30, "affected_customers": 3},
|
||||
suggested_action="强化差异化话术，加速多模态产品迭代", assigned_to=LEAD_ID),
|
||||
RiskEvent(company_id=COMP_A, type="financial", severity="low", status="resolved",
|
||||
title="应收账款周期延长", description="2家客户付款周期从30天延至45天",
|
||||
evidence_json={"metric": "ar_days", "current": 45, "previous": 30, "threshold": 60},
|
||||
suggested_action="与客户协商缩短账期", assigned_to=MGR_ID, closed_at=D(-15)),
|
||||
# COMP_B — 云栈数据
|
||||
RiskEvent(company_id=COMP_B, type="financial", severity="high", status="assigned",
|
||||
title="营收增长停滞", description="连续2个月环比0%增长，未达预期",
|
||||
evidence_json={"metric": "revenue_growth", "current": 0.0, "expected": 0.15, "months": 2},
|
||||
suggested_action="与创始人讨论增长策略，评估产品定价和市场拓展", assigned_to=LEAD_ID, due_at=D(-3)),
|
||||
RiskEvent(company_id=COMP_B, type="financial", severity="high", status="open",
|
||||
title="现金Runway缩短至15个月", description="按当前消耗速度，现金仅可维持15个月",
|
||||
evidence_json={"metric": "runway_months", "current": 15, "threshold": 18},
|
||||
suggested_action="启动A+轮融资准备，控制招聘节奏", assigned_to=GP_ID),
|
||||
RiskEvent(company_id=COMP_B, type="market", severity="medium", status="open",
|
||||
title="金融行业准入门槛高", description="银行客户POC周期长达6-12月，转化率低",
|
||||
evidence_json={"metric": "poc_conversion", "current": 0.15, "expected": 0.30},
|
||||
suggested_action="调整目标客户画像，探索保险/证券赛道", assigned_to=MGR_ID),
|
||||
# COMP_C — 深瞳智能
|
||||
RiskEvent(company_id=COMP_C, type="org", severity="medium", status="open",
|
||||
title="核心技术人员稳定性下降", description="CTO提及外部机会，稳定性评分从0.85降至0.65",
|
||||
evidence_json={"metric": "cto_stability", "current": 0.65, "previous": 0.85},
|
||||
suggested_action="了解CTO诉求，评估股权激励调整", assigned_to=LEAD_ID),
|
||||
RiskEvent(company_id=COMP_C, type="market", severity="medium", status="in_progress",
|
||||
title="客户集中度过高", description="半导体行业占比60%，周期下行风险大",
|
||||
evidence_json={"metric": "customer_concentration", "current": 0.60, "threshold": 0.50, "industry": "semiconductor"},
|
||||
suggested_action="拓展新能源、消费电子等新行业客户", assigned_to=MGR_ID, due_at=D(-30)),
|
||||
RiskEvent(company_id=COMP_C, type="operational", severity="low", status="closed",
|
||||
title="产品交付延迟", description="1条产线视觉系统部署延迟2周",
|
||||
evidence_json={"metric": "delivery_delay_days", "current": 14, "threshold": 7},
|
||||
suggested_action="增加现场实施人员", assigned_to=MGR_ID, closed_at=D(-20)),
|
||||
# COMP_D — 量子芯微
|
||||
RiskEvent(company_id=COMP_D, type="financial", severity="critical", status="open",
|
||||
title="现金Runway不足7个月", description="按当前消耗速度，现金仅可维持7个月",
|
||||
evidence_json={"metric": "runway_months", "current": 7, "threshold": 9},
|
||||
suggested_action="紧急启动天使+轮融资，准备BP和财务预测", assigned_to=GP_ID, due_at=D(-14)),
|
||||
RiskEvent(company_id=COMP_D, type="technical", severity="high", status="in_progress",
|
||||
title="NPU流片进度风险", description="EDA工具链兼容性问题可能导致流片延迟1-2月",
|
||||
evidence_json={"metric": "tapeout_delay_risk", "risk_level": "high", "eta_impact": "1-2月"},
|
||||
suggested_action="协调备用EDA工具，增加验证人力", assigned_to=GP_ID, due_at=D(-7)),
|
||||
RiskEvent(company_id=COMP_D, type="market", severity="medium", status="open",
|
||||
title="边缘NPU赛道巨头入场", description="英伟达发布边缘端Jetson新品，可能挤压创业公司空间",
|
||||
evidence_json={"competitor": "NVIDIA", "product": "Jetson Orin Nano", "threat_level": "medium"},
|
||||
suggested_action="强化低功耗差异化，加速客户绑定", assigned_to=LEAD_ID),
|
||||
# COMP_E — 光合生物
|
||||
RiskEvent(company_id=COMP_E, type="operational", severity="medium", status="resolved",
|
||||
title="月报延迟提交", description="上月月报延迟20天提交",
|
||||
evidence_json={"metric": "report_delay_days", "current": 20, "threshold": 5},
|
||||
suggested_action="与创始人沟通提交规范，设置自动提醒", assigned_to=MGR_ID, due_at=D(-10), closed_at=D(-3)),
|
||||
RiskEvent(company_id=COMP_E, type="financial", severity="medium", status="open",
|
||||
title="研发投入产出周期长", description="3靶点尚处先导化合物阶段，距临床还需12-18月",
|
||||
evidence_json={"metric": "time_to_clinic", "current": 18, "threshold": 12, "targets": 3},
|
||||
suggested_action="评估是否引入大药企联合开发", assigned_to=LEAD_ID),
|
||||
]
|
||||
for r in risks:
|
||||
r.identified_at = D(15)
|
||||
db.add(r)
|
||||
await db.flush()
|
||||
print(" ✓ 风险事件 (6 条)")
|
||||
print(" ✓ 风险事件 (15 条)")
|
||||
|
||||
|
||||
# ─── 弱信号 ────────────────────────────────────────────────────────────
|
||||
@@ -290,12 +412,23 @@ async def _seed_weak_signals(db: AsyncSession) -> None:
|
||||
confidence=0.60, risk_probability=0.55, status="new"),
|
||||
WeakSignal(company_id=COMP_E, signal_type="market", source="FDA新闻", content="同类靶点药物获FDA快速审批",
|
||||
confidence=0.75, risk_probability=0.30, status="new"),
|
||||
WeakSignal(company_id=COMP_C, signal_type="market", source="半导体行业协会", content="半导体周期指标连续2月下行",
|
||||
confidence=0.70, risk_probability=0.62, status="correlated",
|
||||
correlation_result={"related_signals": 1, "pattern": "行业周期风险"}),
|
||||
WeakSignal(company_id=COMP_A, signal_type="technical", source="GitHub", content="竞品开源多模态决策引擎，star数暴涨",
|
||||
confidence=0.65, risk_probability=0.40, status="new"),
|
||||
WeakSignal(company_id=COMP_B, signal_type="org", source="脉脉", content="云栈数据销售总监更新简历",
|
||||
confidence=0.78, risk_probability=0.60, status="alerted",
|
||||
correlation_result={"related_signals": 1, "pattern": "销售负责人不稳定"}),
|
||||
WeakSignal(company_id=COMP_D, signal_type="market", source="英伟达发布会", content="英伟达发布边缘端Jetson Orin Nano，价格下探",
|
||||
confidence=0.82, risk_probability=0.70, status="alerted",
|
||||
correlation_result={"related_signals": 2, "pattern": "巨头入场边缘AI"}),
|
||||
]
|
||||
for s in signals:
|
||||
s.detected_at = D(7)
|
||||
db.add(s)
|
||||
await db.flush()
|
||||
print(" ✓ 弱信号 (5 条)")
|
||||
print(" ✓ 弱信号 (9 条)")
|
||||
|
||||
|
||||
# ─── 任务 ──────────────────────────────────────────────────────────────
|
||||
@@ -633,5 +766,512 @@ async def _seed_audit_logs(db: AsyncSession) -> None:
|
||||
print(" ✓ 审计日志 (10 条)")
|
||||
|
||||
|
||||
# ─── 健康度历史趋势（近 6 个月） ──────────────────────────────────────
|
||||
async def _seed_health_history(db: AsyncSession) -> None:
|
||||
"""每家企业近 6 个月健康度趋势，用于趋势图展示。"""
|
||||
# 基线分数和月度变化趋势
|
||||
trends = [
|
||||
# (company_id, base_score, monthly_deltas)
|
||||
(COMP_A, 70.0, [+1.5, +2.0, +1.5, +2.0, +1.5, +0.0]), # 上升趋势
|
||||
(COMP_B, 65.0, [-0.5, -0.5, +0.0, -0.5, -1.0, +0.0]), # 下降趋势
|
||||
(COMP_C, 75.0, [+1.5, +2.0, +2.5, +2.0, +1.5, +1.0]), # 强上升
|
||||
(COMP_D, 58.0, [-2.0, -2.0, -2.5, -1.5, -1.0, -1.0]), # 持续下降
|
||||
(COMP_E, 65.0, [+0.5, +1.0, +0.5, +1.0, +0.5, +0.0]), # 缓慢上升
|
||||
]
|
||||
for comp_id, base, deltas in trends:
|
||||
score = base
|
||||
for i, delta in enumerate(deltas):
|
||||
score += delta
|
||||
m = (NOW.month - i - 1) if (NOW.month - i - 1) > 0 else (12 + NOW.month - i - 1)
|
||||
y = NOW.year if (NOW.month - i - 1) > 0 else NOW.year - 1
|
||||
db.add(HealthScore(
|
||||
company_id=comp_id, total_score=round(score, 1),
|
||||
financial_score=round(score * 1.05, 1), operational_score=round(score * 0.95, 1),
|
||||
ai_commercial_score=round(score * 1.02, 1), ai_cost_score=round(score * 1.08, 1),
|
||||
org_talent_score=round(score * 0.92, 1), product_tech_score=round(score * 1.03, 1),
|
||||
market_compete_score=round(score * 0.98, 1), governance_score=round(score * 0.96, 1),
|
||||
financing_score=round(score * 0.94, 1),
|
||||
synergy_score=round(score * 0.97, 1), ai_model_product_score=round(score * 1.04, 1),
|
||||
data_compliance_score=round(score * 1.12, 1), team_tech_score=round(score * 0.95, 1),
|
||||
customer_success_score=round(score * 0.87, 1),
|
||||
trend="up" if delta > 0 else "down" if delta < 0 else "stable",
|
||||
evidence_json={"historical": True, "month": f"{y}-{m:02d}"},
|
||||
calculated_at=datetime(y, m, 1, tzinfo=UTC),
|
||||
fund_type="early_vc", fund_lifecycle="investment", company_stage="b", industry="ai", strategy="growth",
|
||||
))
|
||||
await db.flush()
|
||||
print(" ✓ 健康度历史趋势 (5 企业 x 6 月)")
|
||||
|
||||
|
||||
# ─── 基金 ──────────────────────────────────────────────────────────────
|
||||
async def _seed_funds(db: AsyncSession) -> None:
|
||||
"""基金信息 + 企业-基金关联。"""
|
||||
fund1 = Fund(tenant_id=TENANT_ID, name="远见三期人民币基金", fund_type="early_vc", strategy="growth",
|
||||
established_date=datetime(2023, 1, 1).date(), total_lifespan_months=84, investment_period_months=48,
|
||||
lp_composition_json={"government": 30, "market": 50, "corporate": 20}, primary_market="china_mainland")
|
||||
fund2 = Fund(tenant_id=TENANT_ID, name="远见天使基金", fund_type="angel", strategy="growth",
|
||||
established_date=datetime(2024, 1, 1).date(), total_lifespan_months=72, investment_period_months=36,
|
||||
lp_composition_json={"government": 20, "market": 60, "corporate": 20}, primary_market="china_mainland")
|
||||
for f in [fund1, fund2]:
|
||||
db.add(f)
|
||||
await db.flush()
|
||||
|
||||
links = [
|
||||
CompanyFundLink(company_id=COMP_A, fund_id=fund1.id, investment_date=datetime(2023, 6, 1).date(),
|
||||
investment_stage="b", round="B轮", amount=5000, ownership_pct=12.5, is_current=True),
|
||||
CompanyFundLink(company_id=COMP_B, fund_id=fund1.id, investment_date=datetime(2023, 3, 1).date(),
|
||||
investment_stage="a", round="A轮", amount=2000, ownership_pct=15.0, is_current=True),
|
||||
CompanyFundLink(company_id=COMP_C, fund_id=fund1.id, investment_date=datetime(2022, 12, 1).date(),
|
||||
investment_stage="b", round="B轮", amount=8000, ownership_pct=20.0, is_current=True),
|
||||
CompanyFundLink(company_id=COMP_E, fund_id=fund1.id, investment_date=datetime(2023, 9, 1).date(),
|
||||
investment_stage="a", round="A轮", amount=3000, ownership_pct=10.0, is_current=True),
|
||||
CompanyFundLink(company_id=COMP_D, fund_id=fund2.id, investment_date=datetime(2024, 3, 1).date(),
|
||||
investment_stage="seed", round="天使轮", amount=500, ownership_pct=8.0, is_current=True),
|
||||
]
|
||||
for l in links:
|
||||
db.add(l)
|
||||
await db.flush()
|
||||
print(" ✓ 基金 (2 支) + 关联 (5 条)")
|
||||
|
||||
|
||||
# ─── 多主体画像 ────────────────────────────────────────────────────────
|
||||
async def _seed_profiles(db: AsyncSession) -> None:
|
||||
"""投资机构、基金、投资经理画像。"""
|
||||
firm = FirmProfile(tenant_id=TENANT_ID, name="远见资本",
|
||||
focus_areas={"sectors": ["AI", "硬科技", "生物医药"], "stages": ["seed", "A", "B"]},
|
||||
stage_preference="seed-B", description="专注早期科技投资，管理规模15亿人民币。")
|
||||
db.add(firm)
|
||||
await db.flush()
|
||||
|
||||
db.add(FundProfile(firm_id=firm.id, name="远见三期人民币基金", fund_size="5亿人民币", vintage_year=2023,
|
||||
strategy="早期科技，AI+硬科技为主，赋能式投后管理。"))
|
||||
db.add(FundProfile(firm_id=firm.id, name="远见天使基金", fund_size="1亿人民币", vintage_year=2024,
|
||||
strategy="天使阶段，聚焦AI应用和芯片设计。"))
|
||||
|
||||
db.add(ManagerProfile(firm_id=firm.id, user_id=MGR_ID, name="王经理",
|
||||
focus_areas={"sectors": ["AI/供应链", "AI/数据平台"], "companies": [COMP_A, COMP_B]},
|
||||
portfolio_count=2))
|
||||
db.add(ManagerProfile(firm_id=firm.id, user_id=LEAD_ID, name="李投后",
|
||||
focus_areas={"sectors": ["AI/计算机视觉", "AI/医疗"], "companies": [COMP_C, COMP_E]},
|
||||
portfolio_count=2))
|
||||
await db.flush()
|
||||
print(" ✓ 画像 (机构1 + 基金2 + 经理2)")
|
||||
|
||||
|
||||
# ─── 评价模板 ──────────────────────────────────────────────────────────
|
||||
async def _seed_evaluation_templates(db: AsyncSession) -> None:
|
||||
"""6 轴动态评价模板 — 覆盖常见组合。"""
|
||||
templates = [
|
||||
EvaluationTemplate(tenant_id=TENANT_ID, name="早期VC-AI-B轮-成长策略",
|
||||
fund_type="early_vc", fund_lifecycle="investment", company_stage="b",
|
||||
industry="ai", strategy="growth", investor_type="investor",
|
||||
weights_json={"financial": 0.12, "operational": 0.10, "ai_commercial": 0.12, "ai_cost": 0.08,
|
||||
"org_talent": 0.10, "product_tech": 0.12, "market_compete": 0.10, "governance": 0.06,
|
||||
"financing": 0.08, "synergy": 0.04, "ai_model_product": 0.04, "data_compliance": 0.02,
|
||||
"team_tech": 0.01, "customer_success": 0.01},
|
||||
enabled_dimensions=["financial", "operational", "ai_commercial", "ai_cost", "org_talent",
|
||||
"product_tech", "market_compete", "governance", "financing", "synergy",
|
||||
"ai_model_product", "data_compliance", "team_tech", "customer_success"],
|
||||
disabled_dimensions=[],
|
||||
custom_metrics_json={"ai_poc_count": {"label": "AI PoC 数量", "source": "月报"}, "compliance_issues": {"label": "合规整改项", "source": "审计"}},
|
||||
is_default=True),
|
||||
EvaluationTemplate(tenant_id=TENANT_ID, name="天使-硬件-种子期-成长策略",
|
||||
fund_type="angel", fund_lifecycle="investment", company_stage="seed",
|
||||
industry="hardware", strategy="growth", investor_type="gp",
|
||||
weights_json={"financial": 0.06, "operational": 0.08, "ai_commercial": 0.08, "ai_cost": 0.06,
|
||||
"org_talent": 0.15, "product_tech": 0.20, "market_compete": 0.10, "governance": 0.05,
|
||||
"financing": 0.12, "synergy": 0.03, "ai_model_product": 0.04, "data_compliance": 0.01,
|
||||
"team_tech": 0.02, "customer_success": 0.00},
|
||||
enabled_dimensions=["financial", "operational", "ai_commercial", "ai_cost", "org_talent",
|
||||
"product_tech", "market_compete", "governance", "financing", "synergy",
|
||||
"ai_model_product", "data_compliance", "team_tech"],
|
||||
disabled_dimensions=["customer_success"],
|
||||
is_default=True),
|
||||
EvaluationTemplate(tenant_id=TENANT_ID, name="早期VC-生物医疗-A轮-成长策略",
|
||||
fund_type="early_vc", fund_lifecycle="investment", company_stage="a",
|
||||
industry="biotech", strategy="growth", investor_type="investor",
|
||||
weights_json={"financial": 0.08, "operational": 0.10, "ai_commercial": 0.10, "ai_cost": 0.06,
|
||||
"org_talent": 0.12, "product_tech": 0.18, "market_compete": 0.08, "governance": 0.08,
|
||||
"financing": 0.10, "synergy": 0.03, "ai_model_product": 0.04, "data_compliance": 0.03,
|
||||
"team_tech": 0.00, "customer_success": 0.00},
|
||||
enabled_dimensions=["financial", "operational", "ai_commercial", "ai_cost", "org_talent",
|
||||
"product_tech", "market_compete", "governance", "financing", "synergy",
|
||||
"ai_model_product", "data_compliance"],
|
||||
disabled_dimensions=["team_tech", "customer_success"],
|
||||
is_default=True),
|
||||
]
|
||||
for t in templates:
|
||||
db.add(t)
|
||||
await db.flush()
|
||||
print(" ✓ 评价模板 (3 套)")
|
||||
|
||||
|
||||
# ─── 追问清单 ──────────────────────────────────────────────────────────
|
||||
async def _seed_inquiries(db: AsyncSession) -> None:
|
||||
"""月报追问清单 — AI 生成补充问题。"""
|
||||
inquiries = [
|
||||
InquiryList(company_id=COMP_A,
|
||||
questions=[{"q": "本月流失客户的具体原因是什么？", "a": "客户反馈技术支持响应慢，竞品以低价策略抢客。"},
|
||||
{"q": "AI推理成本降低12%的具体措施？", "a": "采用模型量化+推理优化，单次调用成本从0.05元降至0.044元。"},
|
||||
{"q": "新签3家客户的年合同金额？", "a": "合计约480万/年，平均160万/家。"}],
|
||||
status="answered", sent_at=D(9), answered_at=D(5)),
|
||||
InquiryList(company_id=COMP_B,
|
||||
questions=[{"q": "营收连续2个月持平的原因？", "a": "现有客户续约稳定，但新客户拓展速度放缓。"},
|
||||
{"q": "金融客户拓展进展？", "a": "1家银行已进POC阶段，预计Q3签约。"},
|
||||
{"q": "A+轮融资准备情况？", "a": "BP初稿完成，正在整理财务预测。"}],
|
||||
status="answered", sent_at=D(7), answered_at=D(3)),
|
||||
InquiryList(company_id=COMP_D,
|
||||
questions=[{"q": "NPU原型流片时间表？", "a": "预计下月流片，Q4出工程样片。"},
|
||||
{"q": "天使+轮BP何时可以准备好？", "a": "正在准备中，预计2周内完成。"}],
|
||||
status="sent", sent_at=D(2)),
|
||||
]
|
||||
for i in inquiries:
|
||||
db.add(i)
|
||||
await db.flush()
|
||||
print(" ✓ 追问清单 (3 条)")
|
||||
|
||||
|
||||
# ─── Agent 执行记录 ────────────────────────────────────────────────────
|
||||
async def _seed_agent_executions(db: AsyncSession) -> None:
|
||||
"""AI Agent L1-L4 分级执行记录。"""
|
||||
agents = [
|
||||
AgentExecution(tenant_id=TENANT_ID, agent_name="月报解析Agent", autonomy_level="L3",
|
||||
input_summary="智链科技2025-06月报", output_summary="解析完成：营收800万(+15%)，客户净增2家",
|
||||
output_detail={"revenue": 800, "mom_growth": 0.15, "new_customers": 3, "churned": 1},
|
||||
review_status="auto_approved", model_version="gpt-4o-2024", duration_ms=3200),
|
||||
AgentExecution(tenant_id=TENANT_ID, agent_name="风险预警Agent", autonomy_level="L2",
|
||||
input_summary="量子芯微财务数据扫描", output_summary="发现1项critical风险：Runway不足7月",
|
||||
output_detail={"risks_found": 1, "severity": "critical", "metric": "runway", "value": 7},
|
||||
review_status="approved", reviewer_id=MGR_ID, reviewed_at=D(3), model_version="gpt-4o-2024", duration_ms=1800),
|
||||
AgentExecution(tenant_id=TENANT_ID, agent_name="弱信号监测Agent", autonomy_level="L3",
|
||||
input_summary="深瞳智能外部信号扫描", output_summary="LinkedIn检测到CTO更新简历",
|
||||
output_detail={"signal_type": "org", "source": "LinkedIn", "confidence": 0.85},
|
||||
review_status="auto_approved", model_version="gpt-4o-2024", duration_ms=2400),
|
||||
AgentExecution(tenant_id=TENANT_ID, agent_name="协同匹配Agent", autonomy_level="L2",
|
||||
input_summary="Portfolio协同分析", output_summary="发现3个协同机会",
|
||||
output_detail={"matches": 3, "types": ["customer", "tech", "talent"]},
|
||||
review_status="approved", reviewer_id=LEAD_ID, reviewed_at=D(5), model_version="gpt-4o-2024", duration_ms=5600),
|
||||
AgentExecution(tenant_id=TENANT_ID, agent_name="健康度计算Agent", autonomy_level="L4",
|
||||
input_summary="5家企业健康度季度评估", output_summary="14维度评分完成，2家上升1家下降",
|
||||
output_detail={"companies": 5, "trends": {"up": 2, "stable": 2, "down": 1}},
|
||||
review_status="auto_approved", model_version="gpt-4o-2024", duration_ms=8200),
|
||||
AgentExecution(tenant_id=TENANT_ID, agent_name="月报解析Agent", autonomy_level="L3",
|
||||
input_summary="云栈数据2025-06月报", output_summary="解析完成：营收150万(持平)",
|
||||
output_detail={"revenue": 150, "mom_growth": 0.0},
|
||||
review_status="auto_approved", model_version="gpt-4o-2024", duration_ms=2800),
|
||||
AgentExecution(tenant_id=TENANT_ID, agent_name="决策哨兵Agent", autonomy_level="L2",
|
||||
input_summary="云栈数据融资时机分析", output_summary="建议现在启动A+轮融资",
|
||||
output_detail={"decision": "funding", "recommendation": "now", "confidence": 0.72},
|
||||
review_status="pending", model_version="gpt-4o-2024", duration_ms=4200),
|
||||
AgentExecution(tenant_id=TENANT_ID, agent_name="数据校验Agent", autonomy_level="L4",
|
||||
input_summary="5家企业财务数据交叉校验", output_summary="校验通过，数据一致性98.5%",
|
||||
output_detail={"checked": 30, "passed": 29, "inconsistencies": 1},
|
||||
review_status="auto_approved", model_version="gpt-4o-2024", duration_ms=1500),
|
||||
]
|
||||
for idx, a in enumerate(agents):
|
||||
a.created_at = D(10 - idx)
|
||||
db.add(a)
|
||||
await db.flush()
|
||||
print(" ✓ Agent 执行记录 (8 条)")
|
||||
|
||||
|
||||
# ─── 客户获取计划 ──────────────────────────────────────────────────────
|
||||
async def _seed_customer_plans(db: AsyncSession) -> None:
|
||||
"""AI 客户增长引擎 — LP 资源匹配 + 客户获取方案。"""
|
||||
plans = [
|
||||
CustomerAcquisitionPlan(company_id=COMP_A, target_customer="年营收10-50亿制造业CIO/供应链总监",
|
||||
entry_angle="从供应链预测场景切入，ROI 3个月可见",
|
||||
decision_chain={"roles": ["CIO", "供应链VP", "采购总监"], "influence": {"CIO": 0.4, "供应链VP": 0.4, "采购总监": 0.2}, "cycle": "3-6月"},
|
||||
pricing_strategy="SaaS订阅：年费80-200万，按模块阶梯定价",
|
||||
competitive_analysis={"direct": ["杉数科技", "京东物流AI"], "advantage": "多模态决策引擎，部署快3倍", "weakness": "品牌知名度低"},
|
||||
lp_resources={"lp_company": "某制造业LP", "intro_channel": "GP引荐", "warmth": "high"},
|
||||
execution_status="executing",
|
||||
result={"customers_contacted": 5, "poc_started": 2, "signed": 1, "revenue": 160}),
|
||||
CustomerAcquisitionPlan(company_id=COMP_B, target_customer="金融机构数据治理负责人",
|
||||
entry_angle="合规驱动+AI训练数据管理，从数据治理切入",
|
||||
decision_chain={"roles": ["数据治理总监", "合规负责人", "CTO"], "influence": {"数据治理总监": 0.5, "合规负责人": 0.3, "CTO": 0.2}, "cycle": "6-12月"},
|
||||
pricing_strategy="平台授权+实施服务：首年120万+实施80万",
|
||||
competitive_analysis={"direct": ["亿信华辰", "数美科技"], "advantage": "AI原生，支持大模型训练数据管理", "weakness": "金融行业案例少"},
|
||||
lp_resources={"lp_company": "某银行LP", "intro_channel": "GP引荐", "warmth": "medium"},
|
||||
execution_status="planned",
|
||||
result=None),
|
||||
CustomerAcquisitionPlan(company_id=COMP_C, target_customer="半导体晶圆厂质检负责人",
|
||||
entry_angle="替代人工目检，从良率提升切入",
|
||||
decision_chain={"roles": ["质量总监", "生产VP", "CIO"], "influence": {"质量总监": 0.5, "生产VP": 0.3, "CIO": 0.2}, "cycle": "3-6月"},
|
||||
pricing_strategy="设备+软件：单条产线200-400万",
|
||||
competitive_analysis={"direct": ["康代智能", "奥普特"], "advantage": "AI算法精度99.2%，行业领先", "weakness": "硬件成本偏高"},
|
||||
lp_resources={"lp_company": "某半导体LP", "intro_channel": "LP直接引荐", "warmth": "high"},
|
||||
execution_status="completed",
|
||||
result={"customers_contacted": 8, "poc_started": 5, "signed": 3, "revenue": 800}),
|
||||
]
|
||||
for p in plans:
|
||||
db.add(p)
|
||||
await db.flush()
|
||||
print(" ✓ 客户获取计划 (3 条)")
|
||||
|
||||
|
||||
# ─── 退出预测 ──────────────────────────────────────────────────────────
|
||||
async def _seed_exit_predictions(db: AsyncSession) -> None:
|
||||
"""退出时机预测 — IPO/并购/二手份额。"""
|
||||
predictions = [
|
||||
ExitPrediction(company_id=COMP_C, exit_path="ipo",
|
||||
timing_window={"earliest": "2027-Q1", "latest": "2028-Q3"},
|
||||
expected_return=4.2, hold_return=2.8, confidence=0.75,
|
||||
signals={"positive": ["营收年增50%+", "半导体赛道热度高", "专利壁垒"], "negative": ["客户集中度60%", "CTO稳定性"]},
|
||||
recommendation="建议2027年Q1启动IPO准备，当前应解决客户集中度和CTO retention问题。"),
|
||||
ExitPrediction(company_id=COMP_A, exit_path="acquisition",
|
||||
timing_window={"earliest": "2026-Q3", "latest": "2027-Q4"},
|
||||
expected_return=3.5, hold_return=3.0, confidence=0.65,
|
||||
signals={"positive": ["多模态技术差异化", "制造业客户基础"], "negative": ["客户流失率上升", "竞争加剧"]},
|
||||
recommendation="建议关注产业并购机会，优先接触京东物流、菜鸟等供应链平台。"),
|
||||
ExitPrediction(company_id=COMP_E, exit_path="acquisition",
|
||||
timing_window={"earliest": "2028-Q1", "latest": "2030-Q4"},
|
||||
expected_return=5.0, hold_return=3.5, confidence=0.55,
|
||||
signals={"positive": ["AI药物发现赛道升温", "3靶点进入先导"], "negative": ["研发周期长", "现金流压力"]},
|
||||
recommendation="建议继续持有，等待靶点临床验证结果后评估并购机会。"),
|
||||
]
|
||||
for p in predictions:
|
||||
db.add(p)
|
||||
await db.flush()
|
||||
print(" ✓ 退出预测 (3 条)")
|
||||
|
||||
|
||||
# ─── Pre-mortem + Red Team ─────────────────────────────────────────────
|
||||
async def _seed_pre_mortems(db: AsyncSession) -> None:
|
||||
"""失败推演 + 对抗分析。"""
|
||||
pre_mortems = [
|
||||
PreMortemRecord(company_id=COMP_D, decision_context="天使+轮融资1500万，估值8000万",
|
||||
failure_paths=[{"path": "流片失败导致估值缩水", "probability": 0.25, "impact": "high"},
|
||||
{"path": "融资周期超3月，现金耗尽", "probability": 0.15, "impact": "critical"},
|
||||
{"path": "竞品发布同类NPU，差异化丧失", "probability": 0.20, "impact": "medium"}],
|
||||
risk_checklist={"items": ["流片进度", "现金Runway", "竞品动态", "核心团队稳定性"], "checked": 3, "total": 4},
|
||||
mitigations={"actions": ["准备过桥贷款方案", "与2家竞品做技术对标", "CTO股权激励绑定"]}),
|
||||
PreMortemRecord(company_id=COMP_B, decision_context="A+轮融资3000万 vs 等待Q2数据验证",
|
||||
failure_paths=[{"path": "现在融资估值偏低，稀释过多", "probability": 0.40, "impact": "medium"},
|
||||
{"path": "等待期间现金耗尽", "probability": 0.20, "impact": "critical"},
|
||||
{"path": "Q2数据不及预期，融资更难", "probability": 0.30, "impact": "high"}],
|
||||
risk_checklist={"items": ["现金Runway", "Q2营收预期", "竞品定价压力", "金融客户签约"], "checked": 2, "total": 4},
|
||||
mitigations={"actions": ["准备两套BP", "与现有股东沟通过桥", "加速金融客户POC"]}),
|
||||
]
|
||||
for p in pre_mortems:
|
||||
db.add(p)
|
||||
|
||||
red_teams = [
|
||||
RedTeamRecord(company_id=COMP_A, perspective="competitor",
|
||||
analysis="如果我是杉数科技，会以低价+行业深耕策略抢智链科技的制造业客户。智链的多模态优势在具体场景中并未形成壁垒。",
|
||||
vulnerabilities={"areas": ["客户成功团队薄弱", "品牌知名度低", "定价偏高"], "severity": "medium"},
|
||||
counterarguments={"defense": ["多模态技术差异化", "客户切换成本高"]}),
|
||||
RedTeamRecord(company_id=COMP_C, perspective="pessimistic_investor",
|
||||
analysis="深瞳智能半导体占比60%是定时炸弹。半导体周期下行时营收将大幅缩水。CTO稳定性问题如果恶化，技术优势可能丧失。",
|
||||
vulnerabilities={"areas": ["客户集中度60%", "CTO稳定性0.65", "半导体周期风险"], "severity": "high"},
|
||||
counterarguments={"defense": ["正在拓展新能源客户", "CTO激励方案设计中"]}),
|
||||
RedTeamRecord(company_id=COMP_D, perspective="devils_advocate",
|
||||
analysis="量子芯微的NPU在边缘端确实有需求，但巨头(英伟达/高通)一旦下沉，创业公司很难竞争。7月Runway意味着没有试错空间。",
|
||||
vulnerabilities={"areas": ["巨头竞争风险", "Runway 7月", "无营收"], "severity": "critical"},
|
||||
counterarguments={"defense": ["边缘端低功耗是差异化", "已有3家潜在客户"]}),
|
||||
]
|
||||
for r in red_teams:
|
||||
db.add(r)
|
||||
await db.flush()
|
||||
print(" ✓ Pre-mortem (2) + Red Team (3)")
|
||||
|
||||
|
||||
# ─── 产品竞争力诊断 ────────────────────────────────────────────────────
|
||||
async def _seed_product_diagnostics(db: AsyncSession) -> None:
|
||||
"""产品竞争力诊断 — 热力图 + 竞品对比。"""
|
||||
diagnostics = [
|
||||
ProductDiagnostic(company_id=COMP_A, product_name="多模态供应链决策引擎",
|
||||
dimensions={"技术壁垒": 82, "产品成熟度": 75, "用户体验": 70, "生态集成": 65, "定价竞争力": 60, "客户支持": 55},
|
||||
heatmap_data={"strengths": ["技术壁垒", "产品成熟度"], "weaknesses": ["客户支持", "定价竞争力"], "neutral": ["用户体验", "生态集成"]},
|
||||
competitors=[{"name": "杉数科技", "scores": {"技术壁垒": 78, "产品成熟度": 85, "用户体验": 80, "生态集成": 75, "定价竞争力": 70, "客户支持": 82}},
|
||||
{"name": "京东物流AI", "scores": {"技术壁垒": 70, "产品成熟度": 88, "用户体验": 85, "生态集成": 90, "定价竞争力": 65, "客户支持": 78}}],
|
||||
roadmap_suggestions="建议优先提升客户支持能力（增加2名CSM）和优化定价策略（引入阶梯定价），同时加强生态集成（对接ERP系统）。"),
|
||||
ProductDiagnostic(company_id=COMP_C, product_name="工业质检视觉AI平台",
|
||||
dimensions={"技术壁垒": 88, "产品成熟度": 85, "用户体验": 78, "生态集成": 72, "定价竞争力": 65, "客户支持": 80},
|
||||
heatmap_data={"strengths": ["技术壁垒", "产品成熟度", "客户支持"], "weaknesses": ["定价竞争力", "生态集成"], "neutral": ["用户体验"]},
|
||||
competitors=[{"name": "康代智能", "scores": {"技术壁垒": 82, "产品成熟度": 90, "用户体验": 82, "生态集成": 85, "定价竞争力": 75, "客户支持": 85}},
|
||||
{"name": "奥普特", "scores": {"技术壁垒": 75, "产品成熟度": 82, "用户体验": 78, "生态集成": 80, "定价竞争力": 82, "客户支持": 80}}],
|
||||
roadmap_suggestions="技术优势明显，建议降低硬件成本（考虑国产替代方案）并加强生态集成（对接MES系统）。"),
|
||||
]
|
||||
for d in diagnostics:
|
||||
db.add(d)
|
||||
await db.flush()
|
||||
print(" ✓ 产品诊断 (2 条)")
|
||||
|
||||
|
||||
# ─── 同行学习圈 ────────────────────────────────────────────────────────
|
||||
async def _seed_peer_circles(db: AsyncSession) -> None:
|
||||
"""同行学习圈 — AI 匹配面临类似挑战的创始人。"""
|
||||
circles = [
|
||||
PeerLearningCircle(tenant_id=TENANT_ID, topic="融资时机选择：现在 vs 等待验证",
|
||||
description="云栈数据和量子芯微创始人均面临融资时机决策，AI匹配讨论。",
|
||||
members=[{"founder_id": FOUNDER_B, "company_id": COMP_B, "name": "刘云栈"},
|
||||
{"founder_id": FOUNDER_C, "company_id": COMP_D, "name": "赵深瞳(代)"}],
|
||||
discussion_framework={"steps": ["各自分享融资困境", "分析共同风险", "讨论应对策略", "制定行动承诺"]},
|
||||
conclusions="两位创始人一致认为：在营收未达预期时，应优先保证现金Runway > 12月，融资宁可早不要晚。",
|
||||
action_commitments=[{"founder": "刘云栈", "action": "2周内完成BP", "deadline": "14天"},
|
||||
{"founder": "赵深瞳(代)", "action": "启动天使+轮", "deadline": "7天"}],
|
||||
status="completed"),
|
||||
PeerLearningCircle(tenant_id=TENANT_ID, topic="客户集中度风险管理",
|
||||
description="深瞳智能和智链科技都面临客户集中度/流失问题。",
|
||||
members=[{"founder_id": FOUNDER_A, "company_id": COMP_A, "name": "陈智链"},
|
||||
{"founder_id": FOUNDER_C, "company_id": COMP_C, "name": "赵深瞳"}],
|
||||
discussion_framework={"steps": ["分享客户结构", "分析流失原因", "讨论多元化策略", "制定行动承诺"]},
|
||||
conclusions="深瞳：半导体占比60%需降低；智链：流失率4%需降至3%以下。共同策略：建立客户健康度预警机制。",
|
||||
action_commitments=[{"founder": "陈智链", "action": "建立客户健康度周报", "deadline": "30天"},
|
||||
{"founder": "赵深瞳", "action": "启动新能源客户拓展", "deadline": "60天"}],
|
||||
status="active"),
|
||||
]
|
||||
for c in circles:
|
||||
db.add(c)
|
||||
await db.flush()
|
||||
print(" ✓ 同行学习圈 (2 个)")
|
||||
|
||||
|
||||
# ─── 数字孪生 ──────────────────────────────────────────────────────────
|
||||
async def _seed_digital_twins(db: AsyncSession) -> None:
|
||||
"""数字孪生模型。"""
|
||||
twins = [
|
||||
DigitalTwinModel(company_id=COMP_A, model_params={"revenue_model": "saaS_subscription", "churn_rate": 0.03, "growth_rate": 0.15, "cac": 50, "ltv": 400},
|
||||
scenarios=[{"name": "乐观", "revenue_y0": 800, "revenue_y1": 1200, "runway_months": 18},
|
||||
{"name": "基准", "revenue_y0": 800, "revenue_y1": 1000, "runway_months": 14},
|
||||
{"name": "悲观", "revenue_y0": 800, "revenue_y1": 700, "runway_months": 10}],
|
||||
accuracy_score=0.82, last_calibrated_at=D(7)),
|
||||
DigitalTwinModel(company_id=COMP_C, model_params={"revenue_model": "hardware+software", "semi_ratio": 0.60, "growth_rate": 0.22, "gross_margin": 0.55},
|
||||
scenarios=[{"name": "乐观", "revenue_y0": 1200, "revenue_y1": 1800, "valuation": "15亿"},
|
||||
{"name": "基准", "revenue_y0": 1200, "revenue_y1": 1500, "valuation": "12亿"},
|
||||
{"name": "悲观", "revenue_y0": 1200, "revenue_y1": 1000, "valuation": "8亿"}],
|
||||
accuracy_score=0.78, last_calibrated_at=D(14)),
|
||||
]
|
||||
for t in twins:
|
||||
db.add(t)
|
||||
await db.flush()
|
||||
print(" ✓ 数字孪生 (2 个)")
|
||||
|
||||
|
||||
# ─── 干预事件 + 结果（Alpha 归因） ─────────────────────────────────────
|
||||
async def _seed_interventions(db: AsyncSession) -> None:
|
||||
"""投后管理 Alpha 归因 — 干预 → 指标变化 → 估值影响。"""
|
||||
interventions = [
|
||||
InterventionEvent(company_id=COMP_A, intervention_type="customer_intro",
|
||||
title="引荐LP制造业客户给智链科技", description="通过LP关系引荐3家制造业客户，加速客户拓展。",
|
||||
executed_by=GP_ID, executed_at=D(30)),
|
||||
InterventionEvent(company_id=COMP_C, intervention_type="strategy",
|
||||
title="建议深瞳智能拓展新能源客户", description="降低半导体客户集中度，拓展新能源赛道。",
|
||||
executed_by=LEAD_ID, executed_at=D(45)),
|
||||
InterventionEvent(company_id=COMP_A, intervention_type="governance",
|
||||
title="推动智链科技建立客户成功团队", description="增加2名CSM，建立客户健康度周报机制。",
|
||||
executed_by=MGR_ID, executed_at=D(60)),
|
||||
InterventionEvent(company_id=COMP_D, intervention_type="funding",
|
||||
title="协助量子芯微准备天使+轮融资", description="GP直接参与BP打磨和投资人对接。",
|
||||
executed_by=GP_ID, executed_at=D(10)),
|
||||
]
|
||||
for i in interventions:
|
||||
db.add(i)
|
||||
await db.flush()
|
||||
|
||||
results = [
|
||||
InterventionResult(intervention_id=interventions[0].id,
|
||||
metric_changes={"customers_new": 2, "revenue_increase": 320, "sales_cycle_reduction": 30},
|
||||
valuation_impact=0.5, return_contribution=0.15, alpha_score=0.82,
|
||||
evidence={"before": {"customers": 48, "revenue": 800}, "after": {"customers": 50, "revenue": 960}}),
|
||||
InterventionResult(intervention_id=interventions[1].id,
|
||||
metric_changes={"semi_ratio": -0.05, "new_customers": 1, "revenue_diversification": 0.10},
|
||||
valuation_impact=0.3, return_contribution=0.08, alpha_score=0.68,
|
||||
evidence={"before": {"semi_ratio": 0.65, "customers": 20}, "after": {"semi_ratio": 0.60, "customers": 21}}),
|
||||
InterventionResult(intervention_id=interventions[2].id,
|
||||
metric_changes={"churn_rate": -0.01, "csat": 8, "response_time": -40},
|
||||
valuation_impact=0.2, return_contribution=0.06, alpha_score=0.72,
|
||||
evidence={"before": {"churn_rate": 0.04, "csat": 7.2}, "after": {"churn_rate": 0.03, "csat": 8.0}}),
|
||||
]
|
||||
for r in results:
|
||||
db.add(r)
|
||||
await db.flush()
|
||||
print(" ✓ 干预事件 (4) + 结果 (3) — Alpha 归因")
|
||||
|
||||
|
||||
# ─── 组合再平衡 + Monte Carlo ──────────────────────────────────────────
|
||||
async def _seed_portfolio_simulations(db: AsyncSession) -> None:
|
||||
"""组合再平衡建议 + Monte Carlo 模拟结果。"""
|
||||
db.add(PortfolioRebalancing(
|
||||
tenant_id=TENANT_ID,
|
||||
marginal_returns={"COMP_A": 0.25, "COMP_B": 0.08, "COMP_C": 0.35, "COMP_D": -0.05, "COMP_E": 0.15},
|
||||
reallocation_plan={"reduce": [{"company": "COMP_B", "amount": 500, "reason": "边际回报低"}],
|
||||
"increase": [{"company": "COMP_C", "amount": 300, "reason": "高增长高回报"}, {"company": "COMP_A", "amount": 200, "reason": "上升趋势明确"}]},
|
||||
irr_impact=0.03, dpi_impact=0.02, status="proposed",
|
||||
))
|
||||
|
||||
# Monte Carlo — 生成 IRR/DPI 概率分布
|
||||
random.seed(42)
|
||||
irr_samples = [random.gauss(0.22, 0.08) for _ in range(10000)]
|
||||
irr_buckets = {}
|
||||
for s in irr_samples:
|
||||
bucket = round(s * 2) / 2 # 0.5% 区间
|
||||
irr_buckets[str(bucket)] = irr_buckets.get(str(bucket), 0) + 1
|
||||
|
||||
dpi_samples = [max(0, random.gauss(1.8, 0.4)) for _ in range(10000)]
|
||||
dpi_buckets = {}
|
||||
for s in dpi_samples:
|
||||
bucket = round(s * 2) / 2
|
||||
dpi_buckets[str(bucket)] = dpi_buckets.get(str(bucket), 0) + 1
|
||||
|
||||
db.add(MonteCarloSimulation(
|
||||
tenant_id=TENANT_ID, iterations=10000,
|
||||
irr_distribution=irr_buckets, dpi_distribution=dpi_buckets,
|
||||
percentile_p5=0.08, percentile_p50=0.22, percentile_p95=0.36,
|
||||
))
|
||||
await db.flush()
|
||||
print(" ✓ 组合再平衡 (1) + Monte Carlo (1)")
|
||||
|
||||
|
||||
# ─── 知识图谱 ──────────────────────────────────────────────────────────
|
||||
async def _seed_knowledge_graph(db: AsyncSession) -> None:
|
||||
"""知识图谱节点 — 企业特征 + 管理动作 + 环境上下文 → 结果 → 回报影响。"""
|
||||
nodes = [
|
||||
KnowledgeNode(tenant_id=TENANT_ID, entity_type="company", entity_id=COMP_A,
|
||||
attributes={"name": "智链科技", "industry": "AI/供应链", "stage": "B"},
|
||||
relations=[{"target": "action_1", "type": "received"}, {"target": "context_1", "type": "in"}]),
|
||||
KnowledgeNode(tenant_id=TENANT_ID, entity_type="action", entity_id="action_1",
|
||||
attributes={"type": "customer_intro", "title": "引荐LP制造业客户"},
|
||||
relations=[{"target": "result_1", "type": "led_to"}]),
|
||||
KnowledgeNode(tenant_id=TENANT_ID, entity_type="context", entity_id="context_1",
|
||||
attributes={"type": "market", "description": "制造业数字化转型加速"},
|
||||
relations=[{"target": "result_1", "type": "influenced"}]),
|
||||
KnowledgeNode(tenant_id=TENANT_ID, entity_type="result", entity_id="result_1",
|
||||
attributes={"metric": "revenue", "change": "+20%", "duration": "3月"},
|
||||
relations=[{"target": "return_1", "type": "contributed_to"}]),
|
||||
KnowledgeNode(tenant_id=TENANT_ID, entity_type="return", entity_id="return_1",
|
||||
attributes={"irr_impact": 0.15, "alpha_score": 0.82},
|
||||
relations=[]),
|
||||
]
|
||||
for n in nodes:
|
||||
db.add(n)
|
||||
await db.flush()
|
||||
print(" ✓ 知识图谱 (5 节点)")
|
||||
|
||||
|
||||
# ─── 数据源 ────────────────────────────────────────────────────────────
|
||||
async def _seed_data_sources(db: AsyncSession) -> None:
|
||||
"""外部数据源配置。"""
|
||||
sources = [
|
||||
DataSource(tenant_id=TENANT_ID, source_type="crunchbase", name="Crunchbase API",
|
||||
api_endpoint="https://api.crunchbase.com/v3.1", status="active", last_synced_at=D(1),
|
||||
config={"sync_frequency": "daily"}),
|
||||
DataSource(tenant_id=TENANT_ID, source_type="github", name="GitHub 活跃度监测",
|
||||
api_endpoint="https://api.github.com", status="active", last_synced_at=D(1),
|
||||
config={"repos": ["zhilian/ai-engine", "yunzhan/data-platform"]}),
|
||||
DataSource(tenant_id=TENANT_ID, source_type="business_registry", name="工商信息同步",
|
||||
api_endpoint="https://api.qcc.com/v2", status="active", last_synced_at=D(3),
|
||||
config={"sync_frequency": "weekly"}),
|
||||
DataSource(tenant_id=TENANT_ID, company_id=COMP_C, source_type="custom", name="半导体行业数据库",
|
||||
api_endpoint="https://semi-data.example.com/api", status="active", last_synced_at=D(2),
|
||||
config={"metrics": ["industry_growth", "competitor_funding"]}),
|
||||
]
|
||||
for s in sources:
|
||||
db.add(s)
|
||||
await db.flush()
|
||||
print(" ✓ 数据源 (4 个)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(seed())
|
||||
|
||||
@@ -26,8 +26,14 @@ export default function AgentsPage() {
|
||||
|
||||
const load = () => {
|
||||
listAgentExecutions()
|
||||
.then((resp) => setItems((resp.data as AgentExecution[]) ?? []))
|
||||
.catch(() => setItems([]))
|
||||
.then((resp) => {
|
||||
console.log("[agents] API response:", resp);
|
||||
setItems((resp.data as AgentExecution[]) ?? []);
|
||||
})
|
||||
.catch((err) => {
|
||||
console.error("[agents] API error:", err);
|
||||
setItems([]);
|
||||
})
|
||||
.finally(() => setIsLoading(false));
|
||||
};
|
||||
|
||||
|
||||
Reference in New Issue
Block a user