重构系统界面与页面命名，全面清除emoji元素并规范专业展示

2026-10-12 16:56:53 +08:00
parent 33ed60acc0
commit 29b26452be
73 changed files with 3211 additions and 932 deletions
@@ -0,0 +1,177 @@
+# -*- 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", 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"]:
+                status_tag = "[受限]" if risk.get("status") == "受限" else "[关注]"
+                critical_tag = " [关键供应]" if risk.get("is_critical") else ""
+                st.markdown(
+                    f"- {status_tag} **{risk['entity']}** ({risk['node_type']}) — "
+                    f"{risk['relation']} — 状态: {risk.get('status', '未知')}{critical_tag}"
+                )
+
+        # 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"- [间接传导] **{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_tag = "[高风险]" if path["severity"] == "高" else "[中风险]"
+                st.markdown(f"- {severity_tag} `{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']} 个替代方案" 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
+    """)