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feat: 初始提交 - 科创企业特有风险的识别与管理 (数智风控系统)

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Chen Xiao
2026-08-14 08:21:26 +08:00
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pages/1_📊_企业风险概览.py
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# -*- coding: utf-8 -*-
"""
📊 企业风险概览页面
选择企业 → 六维风险雷达图 + 关键指标卡片 + 详细财务数据
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import streamlit as st
import plotly.graph_objects as go
import plotly.express as px
from collectors.financial_collector import get_all_companies, get_company_by_code
from risk_engine.risk_scorer import calculate_six_dimension_scores, get_risk_level
from utils.session_helper import render_company_selector, render_sidebar_global_company_selector
st.set_page_config(page_title="企业风险概览", page_icon="📊", layout="wide")
with st.sidebar:
render_sidebar_global_company_selector()
st.markdown("---")
st.markdown("# 📊 企业风险概览")
st.markdown("选择一家科创企业，查看其六维风险画像和关键指标。")
# 企业选择（全局同步）
company = render_company_selector("🏢 选择目标企业", key_suffix="overview_page")
stock_code = company["stock_code"] if company else "688256"
if company:
# 计算六维评分
risk_result = calculate_six_dimension_scores(company)
scores = risk_result["scores"]
comprehensive = risk_result["comprehensive_score"]
level_info = risk_result["risk_level"]
# ============================================================
# 企业信息 + 综合评分
# ============================================================
col1, col2 = st.columns([2, 1])
with col1:
st.markdown(f"### {company['short_name']}")
st.markdown(f"**行业**: {company['industry']} | **领域**: {company['sector']} | **代码**: {company['stock_code']}")
st.markdown(f"**简介**: {company['description']}")
# 核心人员
st.markdown("#### 👤 核心技术人员")
for p in company.get("core_tech_personnel", []):
status_emoji = "✅" if "在职" in p["status"] else "⚠️"
st.markdown(f"- {status_emoji} **{p['name']}** ({p['title']}) - 重要性: {p['importance']} - 状态: {p['status']}")
with col2:
# 综合风险仪表盘
fig = go.Figure(go.Indicator(
mode="gauge+number",
value=comprehensive,
title={"text": "综合风险评分", "font": {"color": "white"}},
number={"font": {"color": "white", "size": 48}},
gauge={
"axis": {"range": [0, 100], "tickcolor": "white"},
"bar": {"color": level_info["color"]},
"steps": [
{"range": [0, 30], "color": "rgba(76,175,80,0.3)"},
{"range": [30, 50], "color": "rgba(255,193,7,0.3)"},
{"range": [50, 70], "color": "rgba(255,152,0,0.3)"},
{"range": [70, 100], "color": "rgba(244,67,54,0.3)"},
],
"threshold": {
"line": {"color": "red", "width": 4},
"thickness": 0.75,
"value": 70,
},
},
))
fig.update_layout(
height=250,
margin=dict(l=20, r=20, t=40, b=10),
paper_bgcolor="rgba(0,0,0,0)",
font=dict(color="white"),
)
st.plotly_chart(fig, use_container_width=True)
st.markdown(f"<div style='text-align:center; font-size:1.2em;'>"
f"{level_info['emoji']} 风险等级: <b>{level_info['level']}</b></div>",
unsafe_allow_html=True)
st.markdown("---")
# ============================================================
# 六维风险雷达图
# ============================================================
col_radar, col_detail = st.columns([1, 1])
with col_radar:
st.markdown("#### 🎯 六维风险雷达图")
dim_names_cn = ["技术路线颠覆", "核心人员流失", "算法/数据合规",
"地缘政治/出口管制", "研发资本化操纵", "客户/供应商集中"]
dim_keys = ["tech_disruption", "talent_loss", "algo_compliance",
"geopolitical", "rd_capitalization", "concentration"]
values = [scores[k] for k in dim_keys]
fig = go.Figure()
fig.add_trace(go.Scatterpolar(
r=values + [values[0]],
theta=dim_names_cn + [dim_names_cn[0]],
fill="toself",
fillcolor="rgba(233,69,96,0.3)",
line=dict(color="#e94560", width=2),
marker=dict(size=8, color="#e94560"),
name=company["short_name"],
))
# 添加警戒线
fig.add_trace(go.Scatterpolar(
r=[70] * 7,
theta=dim_names_cn + [dim_names_cn[0]],
line=dict(color="rgba(244,67,54,0.5)", dash="dash", width=1),
name="高风险线(70)",
))
fig.update_layout(
polar=dict(
radialaxis=dict(visible=True, range=[0, 100], tickfont=dict(color="white")),
angularaxis=dict(tickfont=dict(color="white", size=11)),
bgcolor="rgba(0,0,0,0)",
),
height=420,
margin=dict(l=60, r=60, t=30, b=30),
paper_bgcolor="rgba(0,0,0,0)",
font=dict(color="white"),
showlegend=True,
legend=dict(x=0, y=-0.15),
)
st.plotly_chart(fig, use_container_width=True)
with col_detail:
st.markdown("#### 📋 各维度风险详情")
for k in dim_keys:
detail = risk_result["dimension_details"][k]
score = detail["score"]
level = detail["level"]
emoji = level["emoji"]
color = level["color"]
st.markdown(
f"<div style='background:{color}22; padding:10px; border-radius:8px; "
f"margin:5px 0; border-left:4px solid {color};'>"
f"<b>{emoji} {detail['name']}</b>: {score}分 ({level['level']})</div>",
unsafe_allow_html=True,
)
st.markdown("---")
# ============================================================
# 关键财务指标
# ============================================================
st.markdown("#### 💰 关键财务指标")
fin = company.get("financials", {})
col_f1, col_f2, col_f3, col_f4 = st.columns(4)
with col_f1:
revenue = fin.get("revenue_2024", 0)
st.metric("营业收入", f"¥{revenue/1e8:.1f}亿")
with col_f2:
profit = fin.get("net_profit_2024", 0)
st.metric("净利润", f"¥{profit/1e8:.1f}亿",
delta="盈利" if profit > 0 else "亏损",
delta_color="normal" if profit > 0 else "inverse")
with col_f3:
rd = fin.get("rd_expense_2024", 0)
st.metric("研发费用", f"¥{rd/1e8:.1f}亿")
with col_f4:
cap_rate = fin.get("rd_capitalization_rate", 0)
st.metric("研发资本化率", f"{cap_rate:.0%}",
delta="⚠️ 偏高" if cap_rate > 0.3 else "正常",
delta_color="inverse" if cap_rate > 0.3 else "normal")
col_f5, col_f6, col_f7, col_f8 = st.columns(4)
with col_f5:
st.metric("研发/营收比", f"{fin.get('rd_revenue_ratio', 0):.1%}")
with col_f6:
st.metric("前5大客户占比", f"{fin.get('top5_customer_ratio', 0):.0%}")
with col_f7:
st.metric("应收周转率", f"{fin.get('receivable_turnover', 0):.1f}次/年")
with col_f8:
st.metric("现金流比率", f"{fin.get('cash_flow_ratio', 0):.2f}")
# ============================================================
# 技术路线 & 合规信息
# ============================================================
st.markdown("---")
col_tech, col_comp = st.columns(2)
with col_tech:
st.markdown("#### 🔬 技术路线")
tech = company.get("tech_route", {})
st.markdown(f"**当前技术**: {tech.get('current_tech', '未知')}")
st.markdown(f"**技术壁垒**: {tech.get('tech_moat', '未知')}")
st.markdown(f"**专利数量**: {tech.get('patent_count', 0)} 件")
st.markdown("**竞争技术路线**:")
for ct in tech.get("competing_techs", []):
st.markdown(f" - ⚔️ {ct}")
with col_comp:
st.markdown("#### 📋 合规状态")
comp_info = company.get("compliance", {})
st.markdown(f"**算法备案**: {comp_info.get('algo_filing_status', '未知')}")
st.markdown(f"**数据出境风险**: {comp_info.get('data_export_risk', '未知')}")
entity_status = comp_info.get("entity_list_status", "未知")
if "被列入" in entity_status:
st.error(f"⛔ 实体清单: {entity_status}")
if comp_info.get("sanctions_detail"):
st.warning(f"制裁详情: {comp_info['sanctions_detail']}")
else:
st.success(f"✅ 实体清单: {entity_status}")
pages/2_🕸️_供应链知识图谱.py
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# -*- coding: utf-8 -*-
"""
🕸️ 供应链知识图谱页面
交互式图谱展示 + 供应链风险传染路径 + 实体清单命中标记
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import streamlit as st
import streamlit.components.v1 as components
from collectors.financial_collector import get_all_companies
from knowledge_graph.graph_builder import build_graph, get_graph_stats
from knowledge_graph.contagion_analyzer import analyze_contagion
from knowledge_graph.graph_visualizer import (
generate_interactive_graph,
get_subgraph_for_company,
)
st.set_page_config(page_title="供应链知识图谱", page_icon="🕸️", layout="wide")
st.markdown("# 🕸️ 供应链风险传染知识图谱")
st.markdown("可视化科创企业供应链网络，识别风险传染路径和关键断裂节点。")
# 构建图谱
@st.cache_resource
def get_graph():
return build_graph()
G = get_graph()
stats = get_graph_stats(G)
# ============================================================
# 图谱统计
# ============================================================
col1, col2, col3, col4 = st.columns(4)
with col1:
st.metric("📌 总节点数", stats["total_nodes"])
with col2:
st.metric("🔗 总边数", stats["total_edges"])
with col3:
st.metric("⛔ 受制裁节点", stats["sanctioned_nodes"])
with col4:
st.metric("🔀 图密度", f"{stats['density']:.4f}")
# 节点类型分布
with st.expander("📊 节点类型分布"):
for ntype, count in stats["node_types"].items():
st.markdown(f"- **{ntype}**: {count} 个")
st.markdown("---")
# ============================================================
# 图谱视图选择
# ============================================================
view_mode = st.radio(
"🔍 视图模式",
["全局图谱", "企业中心视图（推荐）"],
horizontal=True,
)
from utils.session_helper import render_company_selector, render_sidebar_global_company_selector
with st.sidebar:
render_sidebar_global_company_selector()
st.markdown("---")
if view_mode == "企业中心视图（推荐）":
target_company = render_company_selector("🏢 选择中心企业", key_suffix="graph_page")
selected_company = target_company["short_name"] if target_company else "寒武纪"
depth = st.slider("穿透深度", 1, 3, 2)
# 提取子图
subgraph = get_subgraph_for_company(G, selected_company, depth=depth)
if subgraph.number_of_nodes() > 0:
html_content = generate_interactive_graph(
subgraph,
highlight_company=selected_company,
height="550px",
)
components.html(html_content, height=600, scrolling=True)
# ============================================================
# 供应链风险传染分析
# ============================================================
st.markdown("---")
st.markdown(f"### ⚠️ {selected_company} 供应链风险传染分析")
contagion = analyze_contagion(G, selected_company)
# 风险评分
supply_risk_score = contagion.get("risk_score", 0)
if supply_risk_score >= 60:
st.error(f"🔴 供应链风险评分: **{supply_risk_score}/100** — 供应链断裂风险极高")
elif supply_risk_score >= 30:
st.warning(f"🟡 供应链风险评分: **{supply_risk_score}/100** — 存在一定供应链风险")
else:
st.success(f"🟢 供应链风险评分: **{supply_risk_score}/100** — 供应链风险可控")
# 直接风险
if contagion.get("direct_risks"):
st.markdown("#### 🔴 直接风险（一度关联）")
for risk in contagion["direct_risks"]:
icon = "⛔" if risk.get("status") == "受限" else "⚠️"
st.markdown(
f"- {icon} **{risk['entity']}** ({risk['node_type']}) — "
f"{risk['relation']} — 状态: {risk.get('status', '未知')} "
f"{'🔑 关键供应' if risk.get('is_critical') else ''}"
)
# 间接风险
if contagion.get("indirect_risks"):
st.markdown("#### 🟡 间接风险（二度及以上关联）")
for risk in contagion["indirect_risks"]:
st.markdown(
f"- ⚠️ **{risk['entity']}** (距离: {risk['distance']}层) — {risk['relation']}"
)
# 传染路径
if contagion.get("contagion_paths"):
st.markdown("#### 🔗 风险传染路径")
for path in contagion["contagion_paths"]:
severity_icon = "🔴" if path["severity"] == "高" else "🟡"
st.markdown(f"- {severity_icon} `{path['path_str']}` (长度: {path['length']})")
# 关键断裂节点
if contagion.get("critical_nodes"):
st.markdown("#### 🔑 关键断裂节点")
for node in contagion["critical_nodes"]:
alt_info = f"✅ 有{node['alternative_count']}个替代" if node["has_alternative"] else "❌ 无替代方案"
st.markdown(
f"- **{node['node']}** — {node['relation']} — "
f"状态: {node['status']} — {alt_info}"
)
else:
st.warning(f"未找到 {selected_company} 的相关图谱数据")
else:
# 全局视图
st.info("💡 全局图谱节点较多，加载可能需要几秒钟。推荐使用“企业中心视图”获得更好的体验。")
html_content = generate_interactive_graph(G, height="650px")
components.html(html_content, height=700, scrolling=True)
# ============================================================
# 图例
# ============================================================
st.markdown("---")
st.markdown("#### 🎨 图例说明")
col_l1, col_l2, col_l3 = st.columns(3)
with col_l1:
st.markdown("""
**节点颜色**
- 🟢 绿色: 科创企业 (正常)
- 🟡 金色: 选中的中心企业
- 🟠 橙色: 供应商
- 🟣 紫色: 客户
- 🔵 蓝色: 核心人员
- 🔴 红色: 受制裁实体
""")
with col_l2:
st.markdown("""
**边类型**
- 实线: 正常关系
- 红色虚线: 受限关系
- 黄色粗线: 关键供应关系
""")
with col_l3:
st.markdown("""
**交互操作**
- 鼠标悬浮: 查看节点详情
- 拖拽: 移动节点
- 滚轮: 缩放图谱
- 双击: 聚焦节点
""")
pages/3_⚖️_多智能体辩论诊断.py
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# -*- coding: utf-8 -*-
"""
⚖️ 多智能体辩论诊断页面 (Premium Financial-Grade Design)
选择企业 → 真实 Token 级打字机流式推演 → 三方交叉质证 → 综合裁决与核保看板
使用 Glassmorphism 玻璃拟态 + 暗黑金融科技 CSS 调色盘
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import streamlit as st
import plotly.graph_objects as go
import time
from collectors.financial_collector import get_all_companies, get_company_by_code
from agents.debate_engine import DebateEngine
st.set_page_config(page_title="多智能体辩论诊断", page_icon="⚖️", layout="wide")
# ============================================================
# 自定义 UI 视觉增强样式 (CSS 注入)
# ============================================================
st.markdown("""
<style>
/* 引入 Google 科技字体 */
@import url('https://fonts.googleapis.com/css2?family=Fira+Code:wght@400;600&family=Inter:wght@400;600;700&display=swap');
/* 顶栏 Hero 区域 */
.hero-banner {
background: linear-gradient(135deg, rgba(26, 26, 46, 0.95), rgba(22, 33, 62, 0.9), rgba(15, 52, 96, 0.95));
border: 1px solid rgba(233, 69, 96, 0.25);
border-radius: 16px;
padding: 24px 30px;
margin-bottom: 24px;
box-shadow: 0 8px 32px rgba(0, 0, 0, 0.37);
backdrop-filter: blur(12px);
}
.hero-title {
color: #FFFFFF;
font-size: 2.0rem;
font-weight: 700;
margin: 0 0 8px 0;
display: flex;
align-items: center;
gap: 12px;
}
.hero-subtitle {
color: #94A3B8;
font-size: 1.0rem;
margin: 0;
}
.step-badge-container {
display: flex;
gap: 12px;
margin-top: 16px;
flex-wrap: wrap;
}
.step-badge {
background: rgba(255, 255, 255, 0.06);
border: 1px solid rgba(255, 255, 255, 0.12);
color: #CBD5E1;
padding: 4px 12px;
border-radius: 20px;
font-size: 0.82rem;
font-weight: 500;
}
.step-badge-active {
background: rgba(233, 69, 96, 0.15);
border-color: #e94560;
color: #ff758c;
}
/* 智能体研判卡片 (Glassmorphism) */
.agent-card {
background: rgba(22, 33, 62, 0.7);
border: 1px solid rgba(255, 255, 255, 0.08);
border-radius: 14px;
padding: 20px;
box-shadow: 0 4px 20px rgba(0, 0, 0, 0.25);
transition: all 0.3s ease;
height: 100%;
}
.agent-card:hover {
border-color: rgba(233, 69, 96, 0.4);
transform: translateY(-2px);
}
.agent-header {
display: flex;
justify-content: space-between;
align-items: center;
border-bottom: 1px solid rgba(255, 255, 255, 0.08);
padding-bottom: 12px;
margin-bottom: 16px;
}
.agent-name {
font-size: 1.15rem;
font-weight: 700;
color: #F8FAFC;
}
/* 风险指标 Badge */
.risk-pill {
display: inline-block;
padding: 3px 8px;
border-radius: 4px;
font-size: 0.78rem;
font-weight: 600;
margin-left: 6px;
}
.pill-high { background: rgba(244, 67, 54, 0.2); color: #ff6b6b; border: 1px solid rgba(244, 67, 54, 0.4); }
.pill-med { background: rgba(255, 152, 0, 0.2); color: #ffb74d; border: 1px solid rgba(255, 152, 0, 0.4); }
.pill-low { background: rgba(76, 175, 80, 0.2); color: #81c784; border: 1px solid rgba(76, 175, 80, 0.4); }
/* 模拟黑客流式终端 (Terminal Box) */
.terminal-box {
background: #0B0E14;
border: 1px solid #1E293B;
border-radius: 10px;
padding: 14px;
font-family: 'Fira Code', monospace;
font-size: 0.84rem;
color: #38BDF8;
line-height: 1.5;
overflow-x: auto;
box-shadow: inset 0 2px 6px rgba(0, 0, 0, 0.8);
}
.terminal-thinking {
color: #94A3B8;
font-style: italic;
border-left: 2px solid #6366F1;
padding-left: 10px;
margin-bottom: 10px;
}
.terminal-output {
color: #4ADE80;
border-left: 2px solid #22C55E;
padding-left: 10px;
}
/* 裁决结果看板 */
.verdict-banner {
background: linear-gradient(135deg, #1E1B4B, #31103F);
border: 1px solid #6366F1;
border-radius: 14px;
padding: 24px;
text-align: center;
margin-top: 16px;
}
.verdict-title {
font-size: 1.8rem;
font-weight: 800;
letter-spacing: 1px;
}
</style>
""", unsafe_allow_html=True)
# ============================================================
# 页面顶部 Banner
# ============================================================
st.markdown("""
<div class="hero-banner">
<div class="hero-title">
<span>⚖️</span>
<span>多智能体交叉验证辩论诊断</span>
</div>
<div class="hero-subtitle">
基于 Multi-Agent 辩论图谱 · 法务 / 技术 / 财务专家分布式研判 · 全过程打字机流式追溯
</div>
<div class="step-badge-container">
<span class="step-badge step-badge-active">Phase 1: 独立穿透研判</span>
<span class="step-badge">Phase 2: 三方交叉质证</span>
<span class="step-badge">Phase 3: 委员会综合裁决</span>
<span class="step-badge">🛡️ 保险精算核保建议</span>
</div>
</div>
""", unsafe_allow_html=True)
from utils.session_helper import render_company_selector, render_sidebar_global_company_selector
with st.sidebar:
render_sidebar_global_company_selector()
st.markdown("---")
col_ctrl1, col_ctrl2 = st.columns([3, 1])
with col_ctrl1:
company = render_company_selector("🏢 选择待诊断科创企业", key_suffix="debate_page")
stock_code = company["stock_code"] if company else "688256"
# 初始化 session_state
if "debate_results" not in st.session_state:
st.session_state["debate_results"] = None
with col_ctrl2:
st.markdown("<div style='height: 28px;'></div>", unsafe_allow_html=True)
start_btn = st.button("🚀 启动多智能体辩论", type="primary", use_container_width=True)
# ============================================================
# 辩论触发与打字机流式渲染
# ============================================================
if start_btn:
company = get_company_by_code(stock_code)
if not company:
st.error("未找到企业数据")
st.stop()
engine = DebateEngine()
company_name = company.get("short_name", "未知")
st.markdown("---")
st.markdown("### 📡 智能体实时思考与流式推演控制台 (Live Streaming Terminal)")
progress_bar = st.progress(0, text="▶️ 正在初始化分布式智能体网络...")
# 动态渲染控制台
live_box = st.container(border=True)
def run_agent_with_typewriter(agent, agent_title, start_progress, end_progress):
progress_bar.progress(start_progress, text=f"{agent.role_icon} {agent_title} 正在穿透企业数据并与大模型通信...")
with live_box:
st.markdown(f"##### {agent.role_icon} {agent_title}")
status_placeholder = st.empty()
thinking_placeholder = st.empty()
content_placeholder = st.empty()
thinking_buf = []
content_buf = []
status_placeholder.info("🔗 建立 SSE 流式通信中...")
def on_token(token_type: str, token_text: str):
if token_type == "reasoning":
thinking_buf.append(token_text)
t_str = "".join(thinking_buf)
status_placeholder.markdown("🧠 **[大模型深度思考中...]**")
display_text = t_str[-350:] if len(t_str) > 350 else t_str
thinking_placeholder.markdown(
f"""<div class="terminal-box terminal-thinking">
<b>[THINKING]</b> {display_text}▌
</div>""",
unsafe_allow_html=True
)
elif token_type == "content":
content_buf.append(token_text)
c_str = "".join(content_buf)
status_placeholder.markdown("📝 **[正在实时生成审查报告...]**")
display_c = c_str[-280:] if len(c_str) > 280 else c_str
content_placeholder.markdown(
f"""<div class="terminal-box terminal-output">
<b>[REPORT OUTPUT]</b> {display_c}▌
</div>""",
unsafe_allow_html=True
)
agent.on_token_callback = on_token
result = agent.evaluate(company if agent != engine.judge_agent else None)
progress_bar.progress(end_progress, text=f"✅ {agent_title} 研判完毕")
status_placeholder.success(f"✅ {agent_title} 完成风险评级")
return result, list(agent.reasoning_trace)
# 1. 法务 Agent
law_result, law_trace = run_agent_with_typewriter(engine.law_agent, "法务风控节点", 10, 35)
# 2. 技术 Agent
tech_result, tech_trace = run_agent_with_typewriter(engine.tech_agent, "技术风控节点", 35, 60)
# 3. 财务 Agent
finance_result, finance_trace = run_agent_with_typewriter(engine.finance_agent, "财务风控节点", 60, 85)
# 4. 交叉质证
progress_bar.progress(88, text="🔄 正在比对三方判定结论，进行交叉质证分析...")
conflicts = engine._identify_conflicts(law_result, tech_result, finance_result)
# 5. 综合裁决 Agent
progress_bar.progress(92, text="⚖️ 综合裁决委员会进行权重复核...")
with live_box:
st.markdown("##### ⚖️ 综合裁决节点")
j_status = st.empty()
j_thinking = st.empty()
j_content = st.empty()
j_think_buf = []
j_cont_buf = []
j_status.info("🚀 综合裁决委员会正在消解分歧...")
def judge_on_token(token_type: str, token_text: str):
if token_type == "reasoning":
j_think_buf.append(token_text)
t_str = "".join(j_think_buf)
j_status.markdown("🧠 **[裁决委员会讨论中...]**")
display_t = t_str[-350:] if len(t_str) > 350 else t_str
j_thinking.markdown(
f"""<div class="terminal-box terminal-thinking">
<b>[COMMITTEE THOUGHTS]</b> {display_t}▌
</div>""",
unsafe_allow_html=True
)
elif token_type == "content":
j_cont_buf.append(token_text)
c_str = "".join(j_cont_buf)
j_status.markdown("📝 **[生成核保决议中...]**")
display_c = c_str[-280:] if len(c_str) > 280 else c_str
j_content.markdown(
f"""<div class="terminal-box terminal-output">
<b>[FINAL VERDICT]</b> {display_c}▌
</div>""",
unsafe_allow_html=True
)
engine.judge_agent.on_token_callback = judge_on_token
judge_result = engine.judge_agent.evaluate(company, law_result, tech_result, finance_result)
judge_trace = list(engine.judge_agent.reasoning_trace)
j_status.success("✅ 综合裁决完成")
progress_bar.progress(100, text="✅ 辩论诊断流程全量完成！")
# 持久化结果
st.session_state["debate_results"] = {
"company": company,
"law_result": law_result,
"tech_result": tech_result,
"finance_result": finance_result,
"law_trace": law_trace,
"tech_trace": tech_trace,
"finance_trace": finance_trace,
"judge_trace": judge_trace,
"conflicts": conflicts,
"judge_result": judge_result,
"debate_log": engine.debate_log,
}
# ============================================================
# 结果持久化渲染与面板展示
# ============================================================
def render_trace_expander(trace: list, title: str):
"""渲染研判结果对应的详细辩论链"""
with st.expander(f"🔗 查看 【{title}】 详细辩论链与凭据 (共 {len(trace)} 步)", expanded=False):
for step in trace:
ts = step.get("timestamp", "")
step_name = step.get("step", "")
content = step.get("content", "")
if "思维链" in step_name or "原始" in step_name or "Context" in step_name or len(content) > 150:
st.markdown(f"**`[{ts}]` {step_name}**")
st.code(content, language="text")
else:
st.markdown(f"- **`[{ts}]` {step_name}**: {content}")
def render_risk_card(result: dict, keys: list, trace: list, agent_title: str, overall_key: str):
"""渲染三方风控精美卡片"""
overall = result.get(overall_key, {}).get("score", 50)
level = result.get(overall_key, {}).get("level", "中")
pill_class = "pill-high" if level == "高" else ("pill-med" if level == "中" else "pill-low")
st.markdown(f"""
<div class="agent-card">
<div class="agent-header">
<span class="agent-name">{agent_title}</span>
<span><span class="risk-pill {pill_class}">{level}风险</span> <b>{overall}分</b></span>
</div>
""", unsafe_allow_html=True)
# 细分指标渲染
for key in keys:
item = result.get(key, {})
if isinstance(item, dict) and "detail" in item:
score = item.get("score", 0)
color = "#EF4444" if score >= 70 else ("#F59E0B" if score >= 40 else "#10B981")
st.markdown(
f"<div style='padding:8px 12px; border-radius:6px; margin:6px 0; "
f"border-left:4px solid {color}; background:rgba(255,255,255,0.03); font-size:0.88rem;'>"
f"<b style='color:{color};'>{score}分</b> — {item.get('detail', '')}</div>",
unsafe_allow_html=True,
)
# 关键发现
findings = result.get("key_findings", [])
if findings:
st.markdown("<p style='font-size:0.85rem; font-weight:700; color:#94A3B8; margin-top:12px; margin-bottom:4px;'>📌 关键风险发现:</p>", unsafe_allow_html=True)
for f in findings:
st.markdown(f"<span style='font-size:0.83rem; color:#E2E8F0;'>• {f}</span>", unsafe_allow_html=True)
# 建议
recs = result.get("recommendations", [])
if recs:
st.markdown("<p style='font-size:0.85rem; font-weight:700; color:#94A3B8; margin-top:10px; margin-bottom:4px;'>💡 专家处置建议:</p>", unsafe_allow_html=True)
for r in recs:
st.markdown(f"<span style='font-size:0.83rem; color:#CBD5E1;'>• {r}</span>", unsafe_allow_html=True)
st.markdown("</div>", unsafe_allow_html=True)
st.markdown("<br>", unsafe_allow_html=True)
render_trace_expander(trace, agent_title)
res = st.session_state.get("debate_results")
if res is not None:
company = res["company"]
law_result = res["law_result"]
tech_result = res["tech_result"]
finance_result = res["finance_result"]
conflicts = res["conflicts"]
judge_result = res["judge_result"]
st.markdown("---")
st.markdown(f"### 📊 多智能体交叉辩论诊断报告 — {company['short_name']} ({company['stock_code']})")
# ---- Phase 1: 三方独立研判 ----
st.markdown("#### 📋 Phase 1: 三方专家节点穿透研判全景")
col_law, col_tech, col_fin = st.columns(3)
with col_law:
render_risk_card(
law_result,
["algo_compliance_risk", "geopolitical_risk", "data_compliance_risk", "ip_litigation_risk"],
res.get("law_trace", []),
"👩‍⚖️ 法务风控节点",
"overall_law_risk",
)
with col_tech:
render_risk_card(
tech_result,
["tech_disruption_risk", "talent_loss_risk", "patent_moat", "tech_iteration_pressure"],
res.get("tech_trace", []),
"👨‍🔬 技术风控节点",
"overall_tech_risk",
)
with col_fin:
render_risk_card(
finance_result,
["rd_capitalization_risk", "concentration_risk", "receivable_risk", "cashflow_risk"],
res.get("finance_trace", []),
"👔 财务风控节点",
"overall_fin_risk",
)
# ---- Phase 2: 交叉质证分析 ----
st.markdown("---")
st.markdown("#### 🔄 Phase 2: 三方交叉质证与冲突判定")
if conflicts:
for conflict in conflicts:
st.markdown(f"""
<div style='background:rgba(239, 68, 68, 0.1); border:1px solid rgba(239, 68, 68, 0.3); border-radius:8px; padding:12px 16px; margin:6px 0; color:#FCA5A5;'>
⚠️ <b>判定冲突警示</b>：{conflict}
</div>
""", unsafe_allow_html=True)
else:
st.markdown("""
<div style='background:rgba(16, 185, 129, 0.1); border:1px solid rgba(16, 185, 129, 0.3); border-radius:8px; padding:12px 16px; color:#6EE7B7;'>
✅ <b>一致性确认</b>：法务、技术、财务三方判定逻辑高度契合，无重大矛盾分歧。
</div>
""", unsafe_allow_html=True)
# ---- Phase 3: 综合裁决与保险核保 ----
st.markdown("---")
st.markdown("#### ⚖️ Phase 3: 风险委员会综合裁决与核保决策")
comp_score = judge_result.get("comprehensive_score", 50)
decision = judge_result.get("underwriting_decision", "标准承保")
risk_level = judge_result.get("risk_level", "中")
col_j1, col_j2 = st.columns([1, 1])
with col_j1:
# Plotly 仪表盘 (暗黑高精质感)
fig = go.Figure(go.Indicator(
mode="gauge+number",
value=comp_score,
title={"text": "综合风险指数 (0-100)", "font": {"color": "#F8FAFC", "size": 16}},
number={"font": {"color": "#FFFFFF", "size": 48}},
gauge={
"axis": {"range": [0, 100], "tickcolor": "#94A3B8"},
"bar": {"color": "#e94560", "width": 0.3},
"bgcolor": "rgba(0,0,0,0)",
"bordercolor": "rgba(255,255,255,0.1)",
"steps": [
{"range": [0, 40], "color": "rgba(16, 185, 129, 0.25)"},
{"range": [40, 60], "color": "rgba(245, 158, 11, 0.25)"},
{"range": [60, 80], "color": "rgba(249, 115, 22, 0.25)"},
{"range": [80, 100], "color": "rgba(239, 68, 68, 0.25)"},
],
},
))
fig.update_layout(
height=260,
margin=dict(l=20, r=20, t=40, b=10),
paper_bgcolor="rgba(0,0,0,0)",
font=dict(color="white"),
)
st.plotly_chart(fig, use_container_width=True)
with col_j2:
if "拒绝" in decision:
v_color = "#EF4444"
v_bg = "rgba(239, 68, 68, 0.15)"
elif "附条件" in decision:
v_color = "#F59E0B"
v_bg = "rgba(245, 158, 11, 0.15)"
elif "优先" in decision:
v_color = "#10B981"
v_bg = "rgba(16, 185, 129, 0.15)"
else:
v_color = "#3B82F6"
v_bg = "rgba(59, 130, 246, 0.15)"
st.markdown(f"""
<div class="verdict-banner" style="border-color:{v_color}; background:{v_bg};">
<div class="verdict-title" style="color:{v_color};">核保决策: 【{decision}】</div>
<p style="color:#CBD5E1; margin-top:8px;">综合风险等级: <b>{risk_level}</b> | 加权裁决得分: <b>{comp_score}分</b></p>
</div>
""", unsafe_allow_html=True)
st.markdown(f"**📝 裁决终审意见**: {judge_result.get('summary', '')}")
conditions = judge_result.get("underwriting_conditions", [])
if conditions:
st.markdown("**📌 保险核保附加条件**:")
for c in conditions:
st.markdown(f"- <span style='color:#F59E0B;'>{c}</span>", unsafe_allow_html=True)
# 挂载裁决节点的辩论链
st.markdown("<br>", unsafe_allow_html=True)
render_trace_expander(res.get("judge_trace", []), "⚖️ 综合裁决委员会")
else:
# 默认引导说明
st.markdown("""
<div style='background:rgba(255,255,255,0.02); border:1px solid rgba(255,255,255,0.08); border-radius:12px; padding:24px; margin-top:20px;'>
<h4 style='color:#F8FAFC; margin-top:0;'>💡 多智能体辩论与风险识别流程说明</h4>
<ol style='color:#94A3B8; line-height:1.8;'>
<li><b>Phase 1 - 三方穿透研判</b>: 法务风控节点审查算法与出口管制、技术风控节点审查路线与人员、财务风控节点穿透资本化与集中度。</li>
<li><b>Phase 2 - 交叉质证分析</b>: 识别法务、技术、财务意见间的潜在分歧（如：研发投入大 vs 资本化美化利润）。</li>
<li><b>Phase 3 - 委员会综合裁决</b>: 基于【合规 > 技术 > 财务】优先级规则进行加权综合评分并输出精算核保决议。</li>
</ol>
</div>
""", unsafe_allow_html=True)
pages/4_💰_动态定价与核保.py
+283
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@@ -0,0 +1,283 @@
# -*- coding: utf-8 -*-
"""
💰 动态定价与核保页面
保险产品选择 → 费率计算器 → 核保报告生成
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import streamlit as st
import plotly.graph_objects as go
from collectors.financial_collector import get_all_companies, get_company_by_code
from risk_engine.risk_scorer import calculate_six_dimension_scores
from risk_engine.dynamic_pricing import (
calculate_premium, calculate_all_products,
get_enterprise_scale, INDUSTRY_RISK_FACTORS,
)
from risk_engine.report_generator import generate_report, format_report_markdown
from config import INSURANCE_PRODUCTS
st.set_page_config(page_title="动态定价与核保", page_icon="💰", layout="wide")
st.markdown("# 💰 科创特有风险综合险 · 动态定价引擎")
st.markdown("基于六维风险评分的“千企千面”精准核保与定价。")
from utils.session_helper import render_company_selector, render_sidebar_global_company_selector
with st.sidebar:
render_sidebar_global_company_selector()
st.markdown("---")
# 企业选择（全局同步）
company = render_company_selector("🏢 选择目标企业", key_suffix="pricing_page")
stock_code = company["stock_code"] if company else "688256"
if company:
# 计算风险评分
risk_result = calculate_six_dimension_scores(company)
scores = risk_result["scores"]
comprehensive = risk_result["comprehensive_score"]
level_info = risk_result["risk_level"]
revenue = company.get("financials", {}).get("revenue_2024", 0)
sector = company.get("sector", "")
scale = get_enterprise_scale(revenue)
st.markdown("---")
# ============================================================
# 风险概要
# ============================================================
col_info, col_score = st.columns([2, 1])
with col_info:
st.markdown(f"### {company['short_name']}")
st.markdown(f"**行业**: {company['industry']} | **领域**: {sector} | **规模**: {scale}")
st.markdown(f"**营收**: ¥{revenue/1e8:.1f}亿 | **行业风险系数**: {INDUSTRY_RISK_FACTORS.get(sector, 1.0):.2f}")
with col_score:
color = level_info["color"]
st.markdown(
f"<div style='background:{color}22; padding:20px; border-radius:12px; text-align:center; "
f"border:2px solid {color};'>"
f"<div style='font-size:2.5em; font-weight:bold; color:{color};'>{comprehensive}</div>"
f"<div style='color:{color};'>综合风险评分 {level_info['emoji']} {level_info['level']}</div>"
f"</div>",
unsafe_allow_html=True,
)
st.markdown("---")
# ============================================================
# 保险产品费率计算
# ============================================================
st.markdown("### 📊 保险产品费率方案")
pricing_results = calculate_all_products(
comprehensive, scores, sector, revenue
)
# 三列展示三个险种
cols = st.columns(3)
for i, pricing in enumerate(pricing_results):
with cols[i]:
product = INSURANCE_PRODUCTS[pricing["product_key"]]
is_insurable = pricing["is_insurable"]
if is_insurable:
border_color = "#4CAF50" if comprehensive < 40 else ("#FF9800" if comprehensive < 70 else "#F44336")
else:
border_color = "#B71C1C"
st.markdown(
f"<div style='background:#16213e; padding:20px; border-radius:12px; "
f"border:2px solid {border_color}; min-height: 300px;'>"
f"<h4 style='text-align:center; color:white;'>{product['name']}</h4>"
f"<p style='color:#a8a8b3; font-size:0.85em; text-align:center;'>{product['description']}</p>"
f"</div>",
unsafe_allow_html=True,
)
if is_insurable:
st.metric("基础保费", f"¥{pricing['base_premium']:,.0f}")
st.metric("最终保费", f"¥{pricing['final_premium']:,.0f}",
delta=f"×{pricing['risk_multiplier']:.2f}",
delta_color="inverse" if pricing['risk_multiplier'] > 1.2 else "normal")
st.metric("保额", f"¥{pricing['adjusted_coverage']:,.0f}")
st.metric("免赔率", f"{pricing['deductible_rate']:.0%}")
with st.expander("📐 定价明细"):
st.markdown(f"- 风险系数: {pricing['risk_multiplier']:.3f}")
st.markdown(f"- 行业调整: {pricing['industry_factor']:.3f}")
st.markdown(f"- 规模折扣: {pricing['scale_factor']:.3f}")
st.markdown(f"- 维度调整: {pricing['dimension_adjustment']:.3f}")
st.markdown(f"- **计算公式**: {pricing['pricing_breakdown']}")
else:
st.error("⛔ 风险过高，建议拒保")
# ============================================================
# 费率对比图
# ============================================================
st.markdown("---")
st.markdown("### 📈 费率构成分析")
col_chart1, col_chart2 = st.columns(2)
with col_chart1:
# 基础保费 vs 最终保费对比
product_names = [p["product_name"] for p in pricing_results if p["is_insurable"]]
base_premiums = [p["base_premium"] for p in pricing_results if p["is_insurable"]]
final_premiums = [p["final_premium"] for p in pricing_results if p["is_insurable"]]
max_val = max(max(base_premiums, default=100000), max(final_premiums, default=100000))
fig = go.Figure(data=[
go.Bar(
name="基础保费",
x=product_names,
y=base_premiums,
marker_color="#3B82F6",
text=[f"¥{v:,.0f}" for v in base_premiums],
textposition="outside",
textfont=dict(size=11, color="#93C5FD")
),
go.Bar(
name="调整后保费",
x=product_names,
y=final_premiums,
marker_color="#EF4444",
text=[f"¥{v:,.0f}" for v in final_premiums],
textposition="outside",
textfont=dict(size=11, color="#FCA5A5")
),
])
fig.update_layout(
title=dict(
text="<b>📊 基础保费 vs 调整后保费对比</b>",
font=dict(size=15, color="#F8FAFC"),
x=0.02,
y=0.96
),
barmode="group",
height=380,
margin=dict(l=20, r=20, t=75, b=30),
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font=dict(color="white"),
legend=dict(
x=0.02,
y=0.88,
orientation="h",
bgcolor="rgba(0,0,0,0)",
font=dict(color="#CBD5E1", size=12)
),
xaxis=dict(gridcolor="rgba(255,255,255,0.05)"),
yaxis=dict(
gridcolor="rgba(255,255,255,0.08)",
range=[0, max_val * 1.3]
),
)
st.plotly_chart(fig, use_container_width=True)
with col_chart2:
# 定价因子贡献瀑布图（选第一个可投保的产品）
insurable = [p for p in pricing_results if p["is_insurable"]]
if insurable:
p = insurable[0]
factors = ["基础保费", "风险系数", "行业调整", "规模折扣", "维度调整", "最终保费"]
values = [
p["base_premium"],
p["base_premium"] * (p["risk_multiplier"] - 1),
p["base_premium"] * p["risk_multiplier"] * (p["industry_factor"] - 1),
p["base_premium"] * p["risk_multiplier"] * p["industry_factor"] * (p["scale_factor"] - 1),
p["base_premium"] * p["risk_multiplier"] * p["industry_factor"] * p["scale_factor"] * (p["dimension_adjustment"] - 1),
p["final_premium"],
]
measures = ["absolute", "relative", "relative", "relative", "relative", "total"]
fig2 = go.Figure(go.Waterfall(
name=p["product_name"],
orientation="v",
measure=measures,
x=factors,
y=values,
text=[f"¥{v:,.0f}" for v in values],
textposition="outside",
connector={"line": {"color": "rgba(255,255,255,0.3)"}},
increasing={"marker": {"color": "#EF4444"}},
decreasing={"marker": {"color": "#10B981"}},
totals={"marker": {"color": "#3B82F6"}},
))
fig2.update_layout(
title=dict(
text=f"<b>📉 {p['product_name']} — 定价因子分解</b>",
font=dict(size=15, color="#F8FAFC"),
x=0.02,
y=0.96
),
height=380,
margin=dict(l=20, r=20, t=75, b=30),
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font=dict(color="white"),
xaxis=dict(gridcolor="rgba(255,255,255,0.05)"),
yaxis=dict(gridcolor="rgba(255,255,255,0.08)"),
)
st.plotly_chart(fig2, use_container_width=True)
# ============================================================
# 核保报告生成
# ============================================================
st.markdown("---")
st.markdown("### 📋 核保决策报告")
# 检查 session_state 中是否有该企业的真实大模型辩论结果
has_real_debate = False
debate_res_in_session = st.session_state.get("debate_results")
if debate_res_in_session and debate_res_in_session.get("company", {}).get("stock_code") == stock_code:
has_real_debate = True
st.success("🤖 **已成功链接页面 3 的【大模型多智能体辩论】真实研判与穿透凭据！**")
else:
st.info("💡 **提示**：建议先至 **【⚖️ 多智能体辩论诊断】** 页面为该企业发起大模型辩论，本报告将自动整合最深度的 AI 审查凭据。")
if st.button("📄 生成完整核保报告", type="primary", use_container_width=True):
if has_real_debate:
debate_result = debate_res_in_session
else:
# 若尚未发起辩论，则回退到离线规则引擎评估
from agents.law_agent import LawAgent
from agents.tech_agent import TechAgent
from agents.finance_agent import FinanceAgent
from agents.judge_agent import JudgeAgent
law_result = LawAgent()._rule_based_evaluation(company)
tech_result = TechAgent()._rule_based_evaluation(company)
fin_result = FinanceAgent()._rule_based_evaluation(company)
judge_result = JudgeAgent()._rule_based_evaluation(
company, law_result, tech_result, fin_result
)
debate_result = {
"law_result": law_result,
"tech_result": tech_result,
"finance_result": fin_result,
"judge_result": judge_result,
"conflicts": [],
}
report = generate_report(company, debate_result, pricing_results)
markdown_report = format_report_markdown(report)
st.markdown(markdown_report)
# 下载按钮
st.download_button(
label="📥 下载核保报告 (Markdown 格式)",
data=markdown_report,
file_name=f"核保报告_{company['short_name']}_{report['report_id']}.md",
mime="text/markdown",
use_container_width=True,
)