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)

XH-202626_原型系统源码与部署手册/01_原型系统源码/agents/__init__.py

@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+"""多智能体辩论模块"""

XH-202626_原型系统源码与部署手册/01_原型系统源码/agents/base_agent.py

@@ -0,0 +1,203 @@
+# -*- coding: utf-8 -*-
+"""
+Agent 基类
+支持逐 Token 实时流式打字机输出 (Token-level UI Streaming)
+捕获思维链 (reasoning_content) 与最终生成结果 (content) 逐字推送到前端
+"""
+import json
+import logging
+import time
+from typing import Optional, Callable
+
+logger = logging.getLogger(__name__)
+
+
+class BaseAgent:
+    """智能体基类，支持 Token 级流式 LLM 推理与降级逻辑"""
+
+    def __init__(self, name: str, system_prompt: str, role_icon: str = "🤖"):
+        self.name = name
+        self.system_prompt = system_prompt
+        self.role_icon = role_icon
+        self._client = None
+        # 推理链记录
+        self.reasoning_trace = []
+        # 逐 Token 实时回调函数: callback(token_type: "reasoning"|"content", token_text: str)
+        self.on_token_callback: Optional[Callable[[str, str], None]] = None
+
+    def _get_client(self):
+        """延迟初始化 OpenAI 客户端"""
+        if self._client is not None:
+            return self._client
+
+        try:
+            import httpx
+            from openai import OpenAI
+            import os
+
+            from config import (
+                DEEPSEEK_API_KEY, DEEPSEEK_BASE_URL, DEEPSEEK_MODEL,
+                VOLCENGINE_API_KEY, VOLCENGINE_BASE_URL, VOLCENGINE_MODEL,
+                OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL
+            )
+
+            api_key = os.environ.get("DEEPSEEK_API_KEY", "")
+            if api_key:
+                base_url = DEEPSEEK_BASE_URL
+                model = DEEPSEEK_MODEL
+            else:
+                api_key = VOLCENGINE_API_KEY
+                base_url = VOLCENGINE_BASE_URL
+                model = VOLCENGINE_MODEL
+
+                if not api_key:
+                    api_key = OPENAI_API_KEY
+                    base_url = OPENAI_BASE_URL
+                    model = OPENAI_MODEL
+
+            if api_key:
+                http_client = httpx.Client(trust_env=False, timeout=60.0)
+                self._client = OpenAI(
+                    api_key=api_key,
+                    base_url=base_url,
+                    http_client=http_client,
+                )
+                self._model = model
+                logger.info(f"[{self.name}] 已成功连接大模型服务")
+                return self._client
+        except ImportError:
+            logger.warning("openai 库未安装")
+        except Exception as e:
+            logger.warning(f"初始化 LLM 客户端失败: {e}")
+
+        return None
+
+    def _trace(self, step: str, content: str):
+        """记录推理链步骤"""
+        entry = {
+            "timestamp": time.strftime("%H:%M:%S"),
+            "step": step,
+            "content": content,
+            "agent": self.name,
+            "icon": self.role_icon
+        }
+        self.reasoning_trace.append(entry)
+
+    def infer(self, prompt: str, temperature: float = 0.1, max_retries: int = 0) -> str:
+        """
+        执行 SSE 流式 LLM 推理 (stream=True)
+        逐 Token 实时推送到 on_token_callback 渲染打字机效果
+        """
+        self.reasoning_trace = []
+        self._trace("📝 构建 Context", f"准备【{self.name}】数据与 Prompt")
+
+        client = self._get_client()
+        if client is None:
+            self._trace("⚠️ 状态通知", "大模型未就绪，切换至专家规则引擎")
+            logger.info(f"[{self.name}] LLM 不可用，降级到规则引擎")
+            fallback = self.fallback_inference(prompt)
+            self._trace("🔧 专家引擎输出", fallback)
+            return fallback
+
+        self._trace("🔗 大模型连接", "已连接大模型推理服务")
+
+        for attempt in range(max_retries + 1):
+            try:
+                self._trace("🚀 发起流式推理", "正在建立 SSE 流式传输通道...")
+                t0 = time.time()
+
+                stream_resp = client.chat.completions.create(
+                    model=self._model,
+                    messages=[
+                        {"role": "system", "content": self.system_prompt},
+                        {"role": "user", "content": prompt},
+                    ],
+                    temperature=temperature,
+                    max_tokens=2048,
+                    stream=True,
+                    timeout=60,
+                )
+
+                full_content = []
+                reasoning_chunks = []
+
+                for chunk in stream_resp:
+                    if not chunk.choices:
+                        continue
+                    delta = chunk.choices[0].delta
+
+                    # 1. 逐 Token 提取深度思考过程 (reasoning_content)
+                    reasoning_piece = getattr(delta, "reasoning_content", None) or getattr(delta, "reasoning", None)
+                    if reasoning_piece:
+                        reasoning_chunks.append(reasoning_piece)
+                        if self.on_token_callback:
+                            try:
+                                self.on_token_callback("reasoning", reasoning_piece)
+                            except Exception:
+                                pass
+
+                    # 2. 逐 Token 提取正式回答内容 (content)
+                    content_piece = delta.content
+                    if content_piece:
+                        full_content.append(content_piece)
+                        if self.on_token_callback:
+                            try:
+                                self.on_token_callback("content", content_piece)
+                            except Exception:
+                                pass
+
+                elapsed = time.time() - t0
+                final_text = "".join(full_content)
+                full_reasoning = "".join(reasoning_chunks)
+
+                if full_reasoning:
+                    self._trace("🧠 完整思维链", full_reasoning)
+
+                if final_text.strip():
+                    self._trace("✅ 流式生成完毕", f"耗时 {elapsed:.1f}s | 产出 {len(final_text)} 字符")
+                    self._trace("📄 原始推理输出", final_text)
+                    return final_text
+                else:
+                    self._trace("⚠️ 输出为空", "流式生成无有效内容，降级到专家引擎")
+
+            except Exception as e:
+                error_msg = str(e)
+                self._trace("⚡ 流式传输异常", f"连接中断: {error_msg}")
+                logger.warning(f"[{self.name}] 流式调用失败: {e}")
+
+        self._trace("🛡️ 安全降级", "无缝切换至离线风控规则引擎")
+        fallback = self.fallback_inference(prompt)
+        self._trace("🔧 专家引擎输出", fallback)
+        return fallback
+
+    def infer_json(self, prompt: str, temperature: float = 0.1) -> dict:
+        """
+        执行 LLM 推理并解析为 JSON
+        """
+        result = self.infer(prompt, temperature)
+        try:
+            if "```json" in result:
+                json_str = result.split("```json")[1].split("```")[0].strip()
+                parsed = json.loads(json_str)
+                self._trace("✅ 结构解析", "从 Markdown 成功提取 JSON 数据")
+                return parsed
+            elif "```" in result:
+                json_str = result.split("```")[1].split("```")[0].strip()
+                parsed = json.loads(json_str)
+                self._trace("✅ 结构解析", "从代码块成功提取 JSON 数据")
+                return parsed
+            else:
+                parsed = json.loads(result)
+                self._trace("✅ 结构解析", "直接解析 JSON 成功")
+                return parsed
+        except (json.JSONDecodeError, IndexError):
+            self._trace("⚠️ 格式适配", "启用自动结构修正")
+            logger.warning(f"[{self.name}] JSON 解析失败，返回原始文本")
+            return {"raw_response": result, "parse_error": True}
+
+    def fallback_inference(self, prompt: str) -> str:
+        """规则引擎降级推理"""
+        return json.dumps({"error": "大模型服务不可用，规则引擎未实现"}, ensure_ascii=False)
+
+    def __repr__(self):
+        return f"{self.role_icon} {self.name}"

XH-202626_原型系统源码与部署手册/01_原型系统源码/agents/debate_engine.py

@@ -0,0 +1,178 @@
+# -*- coding: utf-8 -*-
+"""
+多智能体辩论编排引擎
+流程：信息分发 → 独立研判 → 交叉质证 → 综合裁决
+"""
+import json
+import logging
+import time
+from typing import Optional
+
+from .law_agent import LawAgent
+from .tech_agent import TechAgent
+from .finance_agent import FinanceAgent
+from .judge_agent import JudgeAgent
+
+logger = logging.getLogger(__name__)
+
+
+class DebateEngine:
+    """多智能体辩论编排器"""
+
+    def __init__(self):
+        self.law_agent = LawAgent()
+        self.tech_agent = TechAgent()
+        self.finance_agent = FinanceAgent()
+        self.judge_agent = JudgeAgent()
+        self.debate_log = []
+
+    def run_debate(self, company_data: dict, callback=None) -> dict:
+        """
+        执行完整的多智能体辩论流程
+
+        Args:
+            company_data: 企业数据字典
+            callback: 进度回调函数 callback(step, message, result)
+        """
+        self.debate_log = []
+        company_name = company_data.get("short_name", company_data.get("company_name", "未知"))
+        start_time = time.time()
+
+        self._log(f"🏁 启动对 [{company_name}] 的多智能体交叉验证辩论")
+
+        # ==============================
+        # Phase 1: 独立研判
+        # ==============================
+        self._log("=" * 50)
+        self._log("📋 Phase 1: 各节点独立研判")
+        self._log("=" * 50)
+
+        # 法务节点
+        self._log("👩‍⚖️ 法务风控节点开始评估...")
+        if callback:
+            callback("law_start", "法务风控节点开始评估...", None)
+        law_result = self.law_agent.evaluate(company_data)
+        self._log(f"👩‍⚖️ 法务节点完成: 综合法务风险 {law_result.get('overall_law_risk', {}).get('score', '?')} 分")
+        if callback:
+            callback("law_done", "法务风控节点评估完成", law_result)
+
+        # 技术节点
+        self._log("👨‍🔬 技术风控节点开始评估...")
+        if callback:
+            callback("tech_start", "技术风控节点开始评估...", None)
+        tech_result = self.tech_agent.evaluate(company_data)
+        self._log(f"👨‍🔬 技术节点完成: 综合技术风险 {tech_result.get('overall_tech_risk', {}).get('score', '?')} 分")
+        if callback:
+            callback("tech_done", "技术风控节点评估完成", tech_result)
+
+        # 财务节点
+        self._log("👔 财务风控节点开始评估...")
+        if callback:
+            callback("fin_start", "财务风控节点开始评估...", None)
+        finance_result = self.finance_agent.evaluate(company_data)
+        self._log(f"👔 财务节点完成: 综合财务风险 {finance_result.get('overall_fin_risk', {}).get('score', '?')} 分")
+        if callback:
+            callback("fin_done", "财务风控节点评估完成", finance_result)
+
+        # ==============================
+        # Phase 2: 交叉质证（记录冲突点）
+        # ==============================
+        self._log("=" * 50)
+        self._log("🔄 Phase 2: 交叉质证")
+        self._log("=" * 50)
+
+        conflicts = self._identify_conflicts(law_result, tech_result, finance_result)
+        for conflict in conflicts:
+            self._log(f"⚠️ 冲突: {conflict}")
+        if not conflicts:
+            self._log("✅ 各节点意见一致，无冲突")
+
+        if callback:
+            callback("cross_validation", "交叉质证完成", {"conflicts": conflicts})
+
+        # ==============================
+        # Phase 3: 综合裁决
+        # ==============================
+        self._log("=" * 50)
+        self._log("⚖️ Phase 3: 综合裁决")
+        self._log("=" * 50)
+
+        if callback:
+            callback("judge_start", "综合裁决节点开始...", None)
+        judge_result = self.judge_agent.evaluate(
+            company_data, law_result, tech_result, finance_result
+        )
+        self._log(f"⚖️ 综合评分: {judge_result.get('comprehensive_score', '?')} 分")
+        self._log(f"⚖️ 核保建议: 【{judge_result.get('underwriting_decision', '?')}】")
+        if callback:
+            callback("judge_done", "综合裁决完成", judge_result)
+
+        elapsed = time.time() - start_time
+        self._log(f"🏁 辩论完成，耗时 {elapsed:.1f} 秒")
+
+        return {
+            "company": company_name,
+            "law_result": law_result,
+            "tech_result": tech_result,
+            "finance_result": finance_result,
+            "conflicts": conflicts,
+            "judge_result": judge_result,
+            "debate_log": self.debate_log,
+            "elapsed_seconds": round(elapsed, 1),
+        }
+
+    def _identify_conflicts(self, law_result: dict, tech_result: dict, finance_result: dict) -> list:
+        """识别各节点之间的判定冲突"""
+        conflicts = []
+
+        # 检查法务和技术的冲突：例如法务认为合规但技术认为路线有风险
+        law_overall = law_result.get("overall_law_risk", {}).get("score", 50)
+        tech_overall = tech_result.get("overall_tech_risk", {}).get("score", 50)
+        fin_overall = finance_result.get("overall_fin_risk", {}).get("score", 50)
+
+        # 大幅分歧（差异超过30分）
+        if abs(law_overall - tech_overall) > 30:
+            if law_overall > tech_overall:
+                conflicts.append(
+                    f"法务节点({law_overall}分)与技术节点({tech_overall}分)存在较大分歧: "
+                    f"法务认为合规风险较高，但技术面评估相对乐观"
+                )
+            else:
+                conflicts.append(
+                    f"技术节点({tech_overall}分)与法务节点({law_overall}分)存在较大分歧: "
+                    f"技术风险较高，但法务合规状态相对可控"
+                )
+
+        if abs(tech_overall - fin_overall) > 30:
+            conflicts.append(
+                f"技术节点({tech_overall}分)与财务节点({fin_overall}分)存在分歧: "
+                f"需审查技术投入与财务表现的匹配度"
+            )
+
+        # 特定维度冲突：技术认为研发投入大=好事，财务可能认为是资本化操纵
+        tech_rd_view = tech_result.get("tech_iteration_pressure", {}).get("score", 50)
+        fin_rd_view = finance_result.get("rd_capitalization_risk", {}).get("score", 50)
+        if tech_rd_view < 40 and fin_rd_view > 60:
+            conflicts.append(
+                "技术节点认为研发投入合理，但财务节点发现研发资本化率异常，"
+                "存在通过资本化手段美化利润的嫌疑"
+            )
+
+        return conflicts
+
+    def _log(self, message: str):
+        """记录辩论日志"""
+        entry = {"timestamp": time.strftime("%H:%M:%S"), "message": message}
+        self.debate_log.append(entry)
+        logger.info(message)
+
+
+def run_debate(stock_code: str) -> dict:
+    """便捷接口：通过股票代码直接运行辩论"""
+    from collectors.financial_collector import get_company_by_code
+    company_data = get_company_by_code(stock_code)
+    if not company_data:
+        return {"error": f"未找到股票代码 {stock_code} 的企业数据"}
+
+    engine = DebateEngine()
+    return engine.run_debate(company_data)

XH-202626_原型系统源码与部署手册/01_原型系统源码/agents/finance_agent.py

@@ -0,0 +1,174 @@
+# -*- coding: utf-8 -*-
+"""
+财务风控节点
+审查维度：研发资本化操纵、客户/供应商集中度、应收账款质量、现金流
+"""
+import json
+from .base_agent import BaseAgent
+
+
+FIN_SYSTEM_PROMPT = """你是一名精通科创板审计规则的注册会计师（CPA），同时是金融风控专家。
+
+你的任务是基于提供的企业财务数据，从财务角度穿透审查以下风险：
+1. 研发资本化操纵风险：研发资本化率是否异常，是否存在美化利润嫌疑
+2. 客户/供应商集中风险：前五大客户/供应商占比是否过高
+3. 应收账款质量：应收账款周转率是否异常，是否存在坏账风险
+4. 现金流健康度：经营现金流是否能覆盖运营需求
+
+评估标准参考：
+- 科创板企业研发资本化率超过30%需重点关注
+- 前五大客户占比超过50%存在集中风险
+- 应收账款周转率低于4次/年需关注回款能力
+- 经营现金流/营收比低于0.5需关注持续经营能力
+
+请输出JSON格式：
+{
+    "agent": "财务风控节点",
+    "rd_capitalization_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "concentration_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "receivable_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "cashflow_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "overall_fin_risk": {"score": 0-100, "level": "高/中/低"},
+    "key_findings": ["..."],
+    "recommendations": ["..."]
+}"""
+
+
+class FinanceAgent(BaseAgent):
+    """财务风控节点"""
+
+    def __init__(self):
+        super().__init__(
+            name="财务风控节点",
+            system_prompt=FIN_SYSTEM_PROMPT,
+            role_icon="👔",
+        )
+
+    def evaluate(self, company_data: dict) -> dict:
+        """执行财务风险评估"""
+        prompt = self._build_prompt(company_data)
+        result = self.infer_json(prompt)
+
+        if result.get("parse_error"):
+            result = self._rule_based_evaluation(company_data)
+
+        result["agent"] = "财务风控节点"
+        result["icon"] = self.role_icon
+        return result
+
+    def _build_prompt(self, company_data: dict) -> str:
+        financials = company_data.get("financials", {})
+        return f"""请对以下科创企业进行财务风险穿透审查：
+
+企业名称：{company_data.get('short_name', '未知')}
+行业：{company_data.get('industry', '未知')}
+
+财务核心指标：
+- 营业收入: {financials.get('revenue_2024', 0):,.0f} 元
+- 净利润: {financials.get('net_profit_2024', 0):,.0f} 元
+- 研发费用: {financials.get('rd_expense_2024', 0):,.0f} 元
+- 研发资本化率: {financials.get('rd_capitalization_rate', 0):.1%}
+- 研发/营收比: {financials.get('rd_revenue_ratio', 0):.1%}
+- 前五大客户占比: {financials.get('top5_customer_ratio', 0):.1%}
+- 前五大供应商占比: {financials.get('top5_supplier_ratio', 0):.1%}
+- 应收账款周转率: {financials.get('receivable_turnover', 0):.1f} 次/年
+- 经营现金流比率: {financials.get('cash_flow_ratio', 0):.2f}
+
+请输出严格的JSON评估结果。"""
+
+    def _rule_based_evaluation(self, company_data: dict) -> dict:
+        """基于规则的财务风险评估"""
+        fin = company_data.get("financials", {})
+
+        # 1. 研发资本化操纵风险
+        cap_rate = fin.get("rd_capitalization_rate", 0)
+        if cap_rate >= 0.4:
+            rd_score = 90
+            rd_detail = f"研发资本化率高达{cap_rate:.0%}，严重怀疑美化利润"
+        elif cap_rate >= 0.3:
+            rd_score = 70
+            rd_detail = f"研发资本化率{cap_rate:.0%}，超过行业警戒线(30%)，需重点审查"
+        elif cap_rate >= 0.15:
+            rd_score = 45
+            rd_detail = f"研发资本化率{cap_rate:.0%}，处于中等水平，建议关注趋势"
+        elif cap_rate > 0:
+            rd_score = 25
+            rd_detail = f"研发资本化率{cap_rate:.0%}，处于合理范围"
+        else:
+            rd_score = 10
+            rd_detail = "研发费用全部费用化处理，财务政策审慎"
+
+        # 2. 集中度风险
+        customer_ratio = fin.get("top5_customer_ratio", 0)
+        supplier_ratio = fin.get("top5_supplier_ratio", 0)
+        max_concentration = max(customer_ratio, supplier_ratio)
+
+        if max_concentration >= 0.8:
+            conc_score = 90
+            conc_detail = f"前五大客户占比{customer_ratio:.0%}，供应商占比{supplier_ratio:.0%}，集中度极高"
+        elif max_concentration >= 0.6:
+            conc_score = 70
+            conc_detail = f"前五大客户占比{customer_ratio:.0%}，供应商占比{supplier_ratio:.0%}，集中度偏高"
+        elif max_concentration >= 0.4:
+            conc_score = 45
+            conc_detail = f"前五大客户占比{customer_ratio:.0%}，供应商占比{supplier_ratio:.0%}，中等集中度"
+        else:
+            conc_score = 20
+            conc_detail = f"客户和供应商分布较为分散"
+
+        # 3. 应收账款风险
+        turnover = fin.get("receivable_turnover", 8)
+        if turnover < 3:
+            recv_score = 80
+            recv_detail = f"应收账款周转率仅{turnover:.1f}次/年，回款能力极差"
+        elif turnover < 5:
+            recv_score = 55
+            recv_detail = f"应收账款周转率{turnover:.1f}次/年，回款速度偏慢"
+        elif turnover < 8:
+            recv_score = 30
+            recv_detail = f"应收账款周转率{turnover:.1f}次/年，回款能力尚可"
+        else:
+            recv_score = 15
+            recv_detail = f"应收账款周转率{turnover:.1f}次/年，回款能力良好"
+
+        # 4. 现金流风险
+        cf_ratio = fin.get("cash_flow_ratio", 1.0)
+        if cf_ratio < 0.5:
+            cf_score = 80
+            cf_detail = f"经营现金流比率仅{cf_ratio:.2f}，存在持续经营风险"
+        elif cf_ratio < 0.8:
+            cf_score = 55
+            cf_detail = f"经营现金流比率{cf_ratio:.2f}，现金流偏紧"
+        elif cf_ratio < 1.2:
+            cf_score = 30
+            cf_detail = f"经营现金流比率{cf_ratio:.2f}，基本健康"
+        else:
+            cf_score = 15
+            cf_detail = f"经营现金流比率{cf_ratio:.2f}，现金流充裕"
+
+        overall = int(rd_score * 0.30 + conc_score * 0.30 +
+                      recv_score * 0.20 + cf_score * 0.20)
+
+        findings = [rd_detail, conc_detail]
+        if recv_score >= 50:
+            findings.append(recv_detail)
+        if cf_score >= 50:
+            findings.append(cf_detail)
+
+        return {
+            "rd_capitalization_risk": {"score": rd_score, "level": self._level(rd_score), "detail": rd_detail},
+            "concentration_risk": {"score": conc_score, "level": self._level(conc_score), "detail": conc_detail},
+            "receivable_risk": {"score": recv_score, "level": self._level(recv_score), "detail": recv_detail},
+            "cashflow_risk": {"score": cf_score, "level": self._level(cf_score), "detail": cf_detail},
+            "overall_fin_risk": {"score": overall, "level": self._level(overall)},
+            "key_findings": findings,
+            "recommendations": ["关注研发资本化率变化趋势", "降低客户集中度风险"],
+        }
+
+    @staticmethod
+    def _level(score: int) -> str:
+        if score >= 70:
+            return "高"
+        elif score >= 40:
+            return "中"
+        return "低"

XH-202626_原型系统源码与部署手册/01_原型系统源码/agents/judge_agent.py

@@ -0,0 +1,176 @@
+# -*- coding: utf-8 -*-
+"""
+综合裁决节点
+汇总法务/技术/财务三方意见，消解冲突，输出最终综合评级
+"""
+import json
+from .base_agent import BaseAgent
+
+
+JUDGE_SYSTEM_PROMPT = """你是一名资深的风险管理委员会主席，负责汇总法务、技术、财务三方专家的研判意见。
+
+你的任务是：
+1. 审阅三方专家的评估报告
+2. 识别各方意见的冲突点
+3. 基于优先级原则消解冲突（合规风险 > 技术风险 > 财务风险）
+4. 输出 0-100 的综合风险评分
+5. 给出最终核保建议
+
+核保决策标准：
+- 综合风险 ≥ 80分：建议【拒绝承保】
+- 60 ≤ 综合风险 < 80分：建议【附条件承保】（高免赔额/限额）
+- 40 ≤ 综合风险 < 60分：建议【标准承保】（标准费率上浮）
+- 综合风险 < 40分：建议【优先承保】（可享费率优惠）
+
+请输出JSON格式：
+{
+    "comprehensive_score": 0-100,
+    "risk_level": "极高/高/中/低",
+    "underwriting_decision": "拒绝承保/附条件承保/标准承保/优先承保",
+    "six_dimension_scores": {
+        "tech_disruption": 0-100,
+        "talent_loss": 0-100,
+        "algo_compliance": 0-100,
+        "geopolitical": 0-100,
+        "rd_capitalization": 0-100,
+        "concentration": 0-100
+    },
+    "conflict_resolution": "...",
+    "key_risks": ["..."],
+    "underwriting_conditions": ["..."],
+    "summary": "..."
+}"""
+
+
+class JudgeAgent(BaseAgent):
+    """综合裁决节点"""
+
+    def __init__(self):
+        super().__init__(
+            name="综合裁决节点",
+            system_prompt=JUDGE_SYSTEM_PROMPT,
+            role_icon="⚖️",
+        )
+
+    def evaluate(self, company_data: dict, law_result: dict,
+                 tech_result: dict, finance_result: dict) -> dict:
+        """汇总三方意见，输出综合裁决"""
+        prompt = self._build_prompt(company_data, law_result, tech_result, finance_result)
+        result = self.infer_json(prompt)
+
+        if result.get("parse_error"):
+            result = self._rule_based_evaluation(company_data, law_result, tech_result, finance_result)
+
+        result["agent"] = "综合裁决节点"
+        result["icon"] = self.role_icon
+        return result
+
+    def _build_prompt(self, company_data: dict, law_result: dict,
+                      tech_result: dict, finance_result: dict) -> str:
+        return f"""请对以下科创企业的三方评估结果进行综合裁决：
+
+企业名称：{company_data.get('short_name', '未知')}
+行业：{company_data.get('industry', '未知')}
+
+=== 👩‍⚖️ 法务风控节点评估 ===
+{json.dumps(law_result, ensure_ascii=False, indent=2)}
+
+=== 👨‍🔬 技术风控节点评估 ===
+{json.dumps(tech_result, ensure_ascii=False, indent=2)}
+
+=== 👔 财务风控节点评估 ===
+{json.dumps(finance_result, ensure_ascii=False, indent=2)}
+
+请消解可能存在的判定冲突，输出综合裁决JSON。"""
+
+    def _rule_based_evaluation(self, company_data: dict, law_result: dict,
+                                tech_result: dict, finance_result: dict) -> dict:
+        """规则引擎综合裁决"""
+
+        # 提取各维度得分
+        def safe_score(result: dict, key: str) -> int:
+            item = result.get(key, {})
+            if isinstance(item, dict):
+                return item.get("score", 50)
+            return 50
+
+        # 六维评分
+        scores = {
+            "tech_disruption": safe_score(tech_result, "tech_disruption_risk"),
+            "talent_loss": safe_score(tech_result, "talent_loss_risk"),
+            "algo_compliance": safe_score(law_result, "algo_compliance_risk"),
+            "geopolitical": safe_score(law_result, "geopolitical_risk"),
+            "rd_capitalization": safe_score(finance_result, "rd_capitalization_risk"),
+            "concentration": safe_score(finance_result, "concentration_risk"),
+        }
+
+        # 加权综合得分
+        weights = {
+            "tech_disruption": 0.20,
+            "talent_loss": 0.15,
+            "algo_compliance": 0.15,
+            "geopolitical": 0.20,
+            "rd_capitalization": 0.15,
+            "concentration": 0.15,
+        }
+
+        comprehensive_score = int(
+            sum(scores[k] * weights[k] for k in scores)
+        )
+
+        # 裁决
+        if comprehensive_score >= 80:
+            decision = "拒绝承保"
+            risk_level = "极高"
+        elif comprehensive_score >= 60:
+            decision = "附条件承保"
+            risk_level = "高"
+        elif comprehensive_score >= 40:
+            decision = "标准承保"
+            risk_level = "中"
+        else:
+            decision = "优先承保"
+            risk_level = "低"
+
+        # 收集关键风险
+        key_risks = []
+        for dim, score in sorted(scores.items(), key=lambda x: x[1], reverse=True):
+            if score >= 60:
+                dim_names = {
+                    "tech_disruption": "技术路线颠覆",
+                    "talent_loss": "核心人员流失",
+                    "algo_compliance": "算法/数据合规",
+                    "geopolitical": "地缘政治/出口管制",
+                    "rd_capitalization": "研发资本化操纵",
+                    "concentration": "客户/供应商集中",
+                }
+                key_risks.append(f"{dim_names.get(dim, dim)}风险({score}分)")
+
+        # 核保条件
+        conditions = []
+        if scores["geopolitical"] >= 70:
+            conditions.append("要求提供出口管制合规声明及供应链替代方案")
+        if scores["rd_capitalization"] >= 60:
+            conditions.append("要求额外提供研发资本化会计政策说明及审计意见")
+        if scores["concentration"] >= 60:
+            conditions.append("要求提供客户分散化计划或前五大客户信用报告")
+        if scores["talent_loss"] >= 60:
+            conditions.append("要求核心技术人员签署竞业协议且公司有留任激励计划")
+
+        company_name = company_data.get("short_name", "该企业")
+        summary = (
+            f"{company_name}综合风险评分{comprehensive_score}分(风险等级:{risk_level})。"
+            f"核保建议:【{decision}】。"
+            f"主要风险集中在{'、'.join(key_risks[:3]) if key_risks else '无突出风险'}。"
+        )
+
+        return {
+            "comprehensive_score": comprehensive_score,
+            "risk_level": risk_level,
+            "underwriting_decision": decision,
+            "six_dimension_scores": scores,
+            "conflict_resolution": "基于优先级原则(合规>技术>财务)进行加权裁决",
+            "key_risks": key_risks,
+            "underwriting_conditions": conditions,
+            "summary": summary,
+        }

XH-202626_原型系统源码与部署手册/01_原型系统源码/agents/law_agent.py

@@ -0,0 +1,145 @@
+# -*- coding: utf-8 -*-
+"""
+法务风控节点
+审查维度：算法备案状态、实体清单命中、数据出境风险、知识产权诉讼
+"""
+import json
+from .base_agent import BaseAgent
+
+
+LAW_SYSTEM_PROMPT = """你是一名资深法务风控专家，精通以下法律法规：
+- 《生成式人工智能服务管理暂行办法》
+- 《互联网信息服务算法推荐管理规定》
+- 《数据安全法》《个人信息保护法》
+- 《出口管制法》及美国 BIS 实体清单相关规则
+- 《科创板上市规则》中的合规要求
+
+你的任务是基于提供的企业数据，从法律合规角度评估以下风险：
+1. 算法备案合规风险：企业是否涉及AI业务但未完成算法备案
+2. 地缘政治与出口管制风险：企业或其供应链是否受到制裁
+3. 数据合规风险：是否存在数据出境、数据安全方面的隐患
+4. 知识产权诉讼风险：是否面临重大IP纠纷
+
+请以严谨的法律视角进行评估，输出JSON格式：
+{
+    "agent": "法务风控节点",
+    "algo_compliance_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "geopolitical_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "data_compliance_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "ip_litigation_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "overall_law_risk": {"score": 0-100, "level": "高/中/低"},
+    "key_findings": ["..."],
+    "recommendations": ["..."]
+}"""
+
+
+class LawAgent(BaseAgent):
+    """法务风控节点"""
+
+    def __init__(self):
+        super().__init__(
+            name="法务风控节点",
+            system_prompt=LAW_SYSTEM_PROMPT,
+            role_icon="👩‍⚖️",
+        )
+
+    def evaluate(self, company_data: dict) -> dict:
+        """执行法务风险评估"""
+        prompt = self._build_prompt(company_data)
+        result = self.infer_json(prompt)
+
+        # 如果 JSON 解析失败，使用规则引擎
+        if result.get("parse_error"):
+            result = self._rule_based_evaluation(company_data)
+
+        result["agent"] = "法务风控节点"
+        result["icon"] = self.role_icon
+        return result
+
+    def _build_prompt(self, company_data: dict) -> str:
+        """构建评估提示词"""
+        return f"""请对以下科创企业进行法务风险评估：
+
+企业名称：{company_data.get('short_name', company_data.get('company_name', '未知'))}
+行业：{company_data.get('industry', '未知')}
+领域：{company_data.get('sector', '未知')}
+
+合规状态：
+- 算法备案：{json.dumps(company_data.get('compliance', {}), ensure_ascii=False)}
+
+供应链信息：
+- 关键供应商：{json.dumps(company_data.get('supply_chain', {}).get('key_suppliers', []), ensure_ascii=False)}
+- 供应商集中度风险：{company_data.get('supply_chain', {}).get('supplier_concentration_risk', '未知')}
+
+技术路线：
+{json.dumps(company_data.get('tech_route', {}), ensure_ascii=False)}
+
+请输出严格的JSON评估结果。"""
+
+    def fallback_inference(self, prompt: str) -> str:
+        """规则引擎降级"""
+        return json.dumps(self._rule_based_evaluation({}), ensure_ascii=False)
+
+    def _rule_based_evaluation(self, company_data: dict) -> dict:
+        """基于规则的法务风险评估"""
+        compliance = company_data.get("compliance", {})
+        supply_chain = company_data.get("supply_chain", {})
+        sector = company_data.get("sector", "")
+
+        # 算法备案风险
+        algo_status = compliance.get("algo_filing_status", "")
+        algo_score = 20
+        algo_detail = "合规状态正常"
+        if sector in ["AI", "软件", "互联网"] and algo_status == "不适用":
+            algo_score = 60
+            algo_detail = "涉及AI业务但标注为不适用，建议核实"
+        elif "未" in algo_status or not algo_status:
+            algo_score = 80
+            algo_detail = "未查到算法备案记录，存在合规风险"
+        elif "已备案" in algo_status:
+            algo_score = 10
+            algo_detail = "已完成算法备案"
+
+        # 地缘政治风险
+        entity_status = compliance.get("entity_list_status", "")
+        geo_score = 15
+        geo_detail = "未受出口管制影响"
+        if "被列入" in entity_status:
+            geo_score = 95
+            geo_detail = f"已被列入实体清单: {compliance.get('sanctions_detail', '')}"
+        elif supply_chain.get("supplier_concentration_risk") == "极高":
+            geo_score = 70
+            geo_detail = "核心供应链高度依赖海外，存在间接制裁风险"
+
+        # 数据合规风险
+        data_risk = compliance.get("data_export_risk", "低")
+        data_score = {"高": 75, "中": 45, "低": 15}.get(data_risk, 20)
+        data_detail = f"数据出境风险等级: {data_risk}"
+
+        # 知识产权风险
+        ip_score = 25
+        ip_detail = "未发现重大IP纠纷"
+
+        # 综合法务风险
+        overall_score = int(
+            algo_score * 0.25 + geo_score * 0.35 +
+            data_score * 0.25 + ip_score * 0.15
+        )
+
+        return {
+            "algo_compliance_risk": {"score": algo_score, "level": self._level(algo_score), "detail": algo_detail},
+            "geopolitical_risk": {"score": geo_score, "level": self._level(geo_score), "detail": geo_detail},
+            "data_compliance_risk": {"score": data_score, "level": self._level(data_score), "detail": data_detail},
+            "ip_litigation_risk": {"score": ip_score, "level": self._level(ip_score), "detail": ip_detail},
+            "overall_law_risk": {"score": overall_score, "level": self._level(overall_score)},
+            "key_findings": [algo_detail, geo_detail, data_detail],
+            "recommendations": ["建议定期审查合规状态", "关注实体清单更新动态"],
+        }
+
+    @staticmethod
+    def _level(score: int) -> str:
+        if score >= 70:
+            return "高"
+        elif score >= 40:
+            return "中"
+        return "低"

XH-202626_原型系统源码与部署手册/01_原型系统源码/agents/tech_agent.py

@@ -0,0 +1,147 @@
+# -*- coding: utf-8 -*-
+"""
+技术风控节点
+审查维度：技术路线竞争态势、核心人员稳定性、专利布局、技术替代风险
+"""
+import json
+from .base_agent import BaseAgent
+
+
+TECH_SYSTEM_PROMPT = """你是一名资深科技行业分析师和技术风控专家。
+你精通半导体、人工智能、新能源、生物医疗等前沿科技领域的技术演进趋势。
+
+你的任务是基于提供的企业数据，从技术角度评估以下风险：
+1. 技术路线颠覆风险：企业押注的技术路线是否面临被替代的风险
+2. 核心人员流失风险：关键技术人员的稳定性和不可替代性
+3. 专利/技术壁垒：技术护城河的深度和可持续性
+4. 技术迭代压力：行业技术迭代速度对企业的冲击
+
+请以技术专家的视角进行深度评估，输出JSON格式：
+{
+    "agent": "技术风控节点",
+    "tech_disruption_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "talent_loss_risk": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "patent_moat": {"score": 0-100, "level": "强/中/弱", "detail": "..."},
+    "tech_iteration_pressure": {"score": 0-100, "level": "高/中/低", "detail": "..."},
+    "overall_tech_risk": {"score": 0-100, "level": "高/中/低"},
+    "key_findings": ["..."],
+    "recommendations": ["..."]
+}"""
+
+
+class TechAgent(BaseAgent):
+    """技术风控节点"""
+
+    def __init__(self):
+        super().__init__(
+            name="技术风控节点",
+            system_prompt=TECH_SYSTEM_PROMPT,
+            role_icon="👨‍🔬",
+        )
+
+    def evaluate(self, company_data: dict) -> dict:
+        """执行技术风险评估"""
+        prompt = self._build_prompt(company_data)
+        result = self.infer_json(prompt)
+
+        if result.get("parse_error"):
+            result = self._rule_based_evaluation(company_data)
+
+        result["agent"] = "技术风控节点"
+        result["icon"] = self.role_icon
+        return result
+
+    def _build_prompt(self, company_data: dict) -> str:
+        return f"""请对以下科创企业进行技术风险评估：
+
+企业名称：{company_data.get('short_name', '未知')}
+行业：{company_data.get('industry', '未知')}
+领域：{company_data.get('sector', '未知')}
+企业描述：{company_data.get('description', '')}
+
+技术路线信息：
+{json.dumps(company_data.get('tech_route', {}), ensure_ascii=False, indent=2)}
+
+核心技术人员：
+{json.dumps(company_data.get('core_tech_personnel', []), ensure_ascii=False, indent=2)}
+
+财务中的研发指标：
+- 研发费用: {company_data.get('financials', {}).get('rd_expense_2024', 0)}
+- 研发营收比: {company_data.get('financials', {}).get('rd_revenue_ratio', 0)}
+
+请输出严格的JSON评估结果。"""
+
+    def _rule_based_evaluation(self, company_data: dict) -> dict:
+        """基于规则的技术风险评估"""
+        tech_route = company_data.get("tech_route", {})
+        personnel = company_data.get("core_tech_personnel", [])
+        financials = company_data.get("financials", {})
+
+        # 技术路线颠覆风险
+        competing_techs = tech_route.get("competing_techs", [])
+        disruption_score = min(20 + len(competing_techs) * 15, 90)
+        tech_moat = tech_route.get("tech_moat", "")
+        if "差距" in tech_moat or "受制" in tech_moat:
+            disruption_score = min(disruption_score + 20, 95)
+        disruption_detail = f"面临 {len(competing_techs)} 条竞争技术路线: {', '.join(competing_techs[:3])}"
+
+        # 核心人员流失风险
+        talent_score = 20
+        talent_detail = "核心团队稳定"
+        departed = [p for p in personnel if "离职" in p.get("status", "")]
+        high_importance = [p for p in personnel if p.get("importance") == "极高"]
+
+        if departed:
+            talent_score = 80
+            talent_detail = f"已有核心人员离职: {', '.join(p['name'] for p in departed)}"
+        elif len(high_importance) == 1:
+            talent_score = 55
+            talent_detail = f"高度依赖单一核心人员: {high_importance[0]['name']}"
+        elif len(personnel) <= 2:
+            talent_score = 45
+            talent_detail = "核心技术团队规模偏小"
+
+        # 专利壁垒
+        patent_count = tech_route.get("patent_count", 0)
+        if patent_count > 5000:
+            patent_score = 20
+            patent_detail = f"专利数量充足({patent_count}件)，技术壁垒较强"
+        elif patent_count > 1000:
+            patent_score = 35
+            patent_detail = f"专利数量中等({patent_count}件)"
+        else:
+            patent_score = 60
+            patent_detail = f"专利数量偏少({patent_count}件)，技术壁垒偏弱"
+
+        # 技术迭代压力（基于研发投入比）
+        rd_ratio = financials.get("rd_revenue_ratio", 0)
+        if rd_ratio > 0.3:
+            iter_score = 65
+            iter_detail = f"研发营收比极高({rd_ratio:.1%})，说明行业技术迭代压力大"
+        elif rd_ratio > 0.15:
+            iter_score = 45
+            iter_detail = f"研发投入较高({rd_ratio:.1%})，需持续技术投入"
+        else:
+            iter_score = 25
+            iter_detail = f"研发投入适中({rd_ratio:.1%})"
+
+        overall = int(disruption_score * 0.35 + talent_score * 0.25 +
+                      patent_score * 0.15 + iter_score * 0.25)
+
+        return {
+            "tech_disruption_risk": {"score": disruption_score, "level": self._level(disruption_score), "detail": disruption_detail},
+            "talent_loss_risk": {"score": talent_score, "level": self._level(talent_score), "detail": talent_detail},
+            "patent_moat": {"score": patent_score, "level": "弱" if patent_score >= 50 else ("中" if patent_score >= 30 else "强"), "detail": patent_detail},
+            "tech_iteration_pressure": {"score": iter_score, "level": self._level(iter_score), "detail": iter_detail},
+            "overall_tech_risk": {"score": overall, "level": self._level(overall)},
+            "key_findings": [disruption_detail, talent_detail, patent_detail],
+            "recommendations": ["关注竞争技术路线发展", "加强核心人员留任激励"],
+        }
+
+    @staticmethod
+    def _level(score: int) -> str:
+        if score >= 70:
+            return "高"
+        elif score >= 40:
+            return "中"
+        return "低"

XH-202626_原型系统源码与部署手册/01_原型系统源码/collectors/__init__.py

@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+"""数据采集模块"""

XH-202626_原型系统源码与部署手册/01_原型系统源码/collectors/annual_report_parser.py

@@ -0,0 +1,188 @@
+# -*- coding: utf-8 -*-
+"""
+年报 PDF 文本解析模块
+从年报中提取关键风险信息：核心技术人员、技术路线、诉讼、风险提示等
+"""
+import re
+import logging
+from pathlib import Path
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+def parse_annual_report(pdf_path: str) -> dict:
+    """
+    解析年报 PDF，提取关键风险相关信息
+    返回结构化的风险要素字典
+    """
+    text = _extract_text_from_pdf(pdf_path)
+    if not text:
+        return {"error": "PDF 解析失败", "raw_text": ""}
+
+    return {
+        "core_personnel_info": _extract_core_personnel(text),
+        "tech_route_info": _extract_tech_route(text),
+        "litigation_info": _extract_litigation(text),
+        "risk_factors": _extract_risk_factors(text),
+        "rd_capitalization_info": _extract_rd_capitalization(text),
+        "customer_concentration": _extract_customer_concentration(text),
+        "raw_text_length": len(text),
+    }
+
+
+def _extract_text_from_pdf(pdf_path: str) -> Optional[str]:
+    """使用 pdfplumber 提取 PDF 全文"""
+    try:
+        import pdfplumber
+        text_parts = []
+        with pdfplumber.open(pdf_path) as pdf:
+            for page in pdf.pages:
+                page_text = page.extract_text()
+                if page_text:
+                    text_parts.append(page_text)
+        return "\n".join(text_parts)
+    except ImportError:
+        logger.warning("pdfplumber 未安装，尝试 PyPDF2")
+        try:
+            from PyPDF2 import PdfReader
+            reader = PdfReader(pdf_path)
+            return "\n".join(
+                page.extract_text() or "" for page in reader.pages
+            )
+        except Exception as e:
+            logger.error(f"PyPDF2 解析失败: {e}")
+    except Exception as e:
+        logger.error(f"PDF 解析失败: {e}")
+    return None
+
+
+def _extract_core_personnel(text: str) -> dict:
+    """提取核心技术人员相关信息"""
+    result = {
+        "has_departure": False,
+        "departure_details": [],
+        "personnel_count": 0,
+        "key_mentions": [],
+    }
+
+    # 匹配离职/辞职相关表述
+    departure_patterns = [
+        r"(核心技术人员|核心人员|关键技术人员).{0,30}(离职|辞职|离任|不再担任)",
+        r"(CTO|首席技术官|技术总监|研发总监).{0,30}(离职|辞职|离任)",
+        r"(离职|辞职).{0,30}(核心技术人员|核心人员)",
+    ]
+    for pattern in departure_patterns:
+        matches = re.findall(pattern, text)
+        if matches:
+            result["has_departure"] = True
+            result["departure_details"].extend([str(m) for m in matches])
+
+    # 统计核心技术人员数量
+    count_match = re.search(r"核心技术人员\s*(\d+)\s*[名人]", text)
+    if count_match:
+        result["personnel_count"] = int(count_match.group(1))
+
+    return result
+
+
+def _extract_tech_route(text: str) -> dict:
+    """提取技术路线相关信息"""
+    result = {
+        "competing_tech_mentioned": False,
+        "tech_keywords": [],
+        "risk_mentions": [],
+    }
+
+    # 技术竞争关键词
+    tech_keywords = [
+        "技术路线", "技术迭代", "技术替代", "技术颠覆",
+        "竞争技术", "替代方案", "新一代技术",
+    ]
+    for kw in tech_keywords:
+        if kw in text:
+            result["tech_keywords"].append(kw)
+            result["competing_tech_mentioned"] = True
+
+    return result
+
+
+def _extract_litigation(text: str) -> dict:
+    """提取诉讼/仲裁相关信息"""
+    result = {
+        "has_litigation": False,
+        "litigation_count": 0,
+        "ip_related": False,
+    }
+
+    # 诉讼关键词
+    litigation_patterns = [
+        r"(诉讼|仲裁|起诉|被告).{0,50}(知识产权|专利|商标|著作权)",
+        r"(专利侵权|商标侵权|著作权纠纷)",
+    ]
+    for pattern in litigation_patterns:
+        if re.search(pattern, text):
+            result["has_litigation"] = True
+            result["ip_related"] = True
+
+    return result
+
+
+def _extract_risk_factors(text: str) -> list:
+    """提取风险因素章节的关键信息"""
+    risk_keywords = [
+        "地缘政治", "实体清单", "出口管制", "贸易摩擦",
+        "数据安全", "数据合规", "算法备案", "数据出境",
+        "客户集中", "供应商集中", "单一客户", "单一供应商",
+        "研发资本化", "开发支出", "无形资产",
+        "人才流失", "核心人员", "竞业限制",
+    ]
+    found_risks = []
+    for kw in risk_keywords:
+        if kw in text:
+            found_risks.append(kw)
+    return found_risks
+
+
+def _extract_rd_capitalization(text: str) -> dict:
+    """提取研发资本化相关信息"""
+    result = {
+        "has_capitalization": False,
+        "capitalization_mentioned": False,
+        "amount_keywords": [],
+    }
+
+    cap_keywords = ["开发支出", "研发资本化", "资本化研发", "开发阶段支出"]
+    for kw in cap_keywords:
+        if kw in text:
+            result["capitalization_mentioned"] = True
+            result["amount_keywords"].append(kw)
+
+    # 检查是否有具体的资本化金额
+    cap_amount = re.search(r"开发支出.{0,30}([\d,\.]+)\s*(万元|百万|亿)", text)
+    if cap_amount:
+        result["has_capitalization"] = True
+
+    return result
+
+
+def _extract_customer_concentration(text: str) -> dict:
+    """提取客户/供应商集中度信息"""
+    result = {
+        "top5_customer_ratio": None,
+        "top5_supplier_ratio": None,
+        "single_customer_dependency": False,
+    }
+
+    # 前五大客户占比
+    customer_match = re.search(
+        r"前五[名大]客户.{0,30}([\d\.]+)\s*%", text
+    )
+    if customer_match:
+        result["top5_customer_ratio"] = float(customer_match.group(1)) / 100
+
+    # 单一客户依赖
+    if re.search(r"(第一大客户|最大客户).{0,30}([\d\.]+)\s*%", text):
+        result["single_customer_dependency"] = True
+
+    return result

XH-202626_原型系统源码与部署手册/01_原型系统源码/collectors/compliance_collector.py

@@ -0,0 +1,87 @@
+# -*- coding: utf-8 -*-
+"""
+算法备案合规数据采集与查询模块
+匹配企业是否已完成网信办算法备案
+"""
+import json
+import logging
+from pathlib import Path
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+DATA_DIR = Path(__file__).parent.parent / "data"
+
+
+def _load_algo_filings() -> list:
+    """加载算法备案数据"""
+    filepath = DATA_DIR / "algo_filings.json"
+    with open(filepath, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def check_algo_filing(company_name: str) -> dict:
+    """
+    查询企业的算法备案状态
+    """
+    filings = _load_algo_filings()
+    result = {
+        "has_filing": False,
+        "filings": [],
+        "needs_filing": False,  # 是否需要备案但未备案
+        "risk_level": "低",
+    }
+
+    for filing in filings:
+        if company_name in filing["company"] or filing["company"] in company_name:
+            result["has_filing"] = True
+            result["filings"].append(filing)
+
+    return result
+
+
+def assess_algo_compliance_risk(company_data: dict) -> dict:
+    """
+    综合评估企业的算法合规风险
+    考虑因素：是否涉及 AI 业务、是否已备案、数据出境风险
+    """
+    company_name = company_data.get("short_name", company_data.get("company_name", ""))
+    sector = company_data.get("sector", "")
+    compliance = company_data.get("compliance", {})
+
+    # 查询备案状态
+    filing_status = check_algo_filing(company_name)
+
+    # 判断是否需要备案
+    ai_related_sectors = ["AI", "软件", "互联网", "消费电子"]
+    needs_filing = sector in ai_related_sectors or "AI" in str(company_data.get("tech_route", {}))
+
+    # 综合评估
+    risk_level = "低"
+    risk_details = []
+
+    if needs_filing and not filing_status["has_filing"]:
+        algo_status = compliance.get("algo_filing_status", "")
+        if algo_status == "不适用":
+            risk_level = "低"
+        else:
+            risk_level = "高"
+            risk_details.append("涉及AI业务但未查到算法备案记录")
+
+    data_export_risk = compliance.get("data_export_risk", "低")
+    if data_export_risk == "高":
+        risk_level = "高"
+        risk_details.append("存在大量跨境数据传输，数据出境评估风险高")
+    elif data_export_risk == "中":
+        if risk_level != "高":
+            risk_level = "中"
+        risk_details.append("存在部分跨境数据传输，需关注数据出境合规")
+
+    return {
+        "company_name": company_name,
+        "needs_filing": needs_filing,
+        "filing_status": filing_status,
+        "data_export_risk": data_export_risk,
+        "overall_risk_level": risk_level,
+        "risk_details": risk_details,
+    }

XH-202626_原型系统源码与部署手册/01_原型系统源码/collectors/entity_list_collector.py

@@ -0,0 +1,84 @@
+# -*- coding: utf-8 -*-
+"""
+BIS 实体清单采集与匹配模块
+支持企业名模糊匹配 + 别名映射 + 供应链上游穿透
+"""
+import json
+import logging
+from pathlib import Path
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+DATA_DIR = Path(__file__).parent.parent / "data"
+
+
+def _load_entity_list() -> list:
+    """加载实体清单数据"""
+    filepath = DATA_DIR / "entity_list.json"
+    with open(filepath, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def check_entity_list(company_name: str) -> dict:
+    """
+    检查企业是否在 BIS 实体清单中
+    支持模糊匹配和别名匹配
+    """
+    entities = _load_entity_list()
+    result = {
+        "is_sanctioned": False,
+        "match_type": None,
+        "entity_detail": None,
+        "supply_chain_risk": [],  # 供应链上游被制裁的情况
+    }
+
+    for entity in entities:
+        # 精确匹配
+        if company_name in entity["entity_name"]:
+            result["is_sanctioned"] = True
+            result["match_type"] = "直接命中"
+            result["entity_detail"] = entity
+            return result
+
+        # 别名匹配
+        for alias in entity.get("aliases", []):
+            if company_name in alias or alias in company_name:
+                result["is_sanctioned"] = True
+                result["match_type"] = "别名命中"
+                result["entity_detail"] = entity
+                return result
+
+    return result
+
+
+def check_supply_chain_sanctions(company_name: str, suppliers: list) -> list:
+    """
+    检查企业供应链上游是否有被制裁的实体
+    返回受制裁的供应商列表
+    """
+    sanctioned_suppliers = []
+    entities = _load_entity_list()
+
+    for supplier in suppliers:
+        # 清洗供应商名称（去掉括号中的说明文字）
+        clean_name = supplier.split("（")[0].split("(")[0].strip()
+
+        for entity in entities:
+            all_names = [entity["entity_name"]] + entity.get("aliases", [])
+            for name in all_names:
+                if clean_name in name or name in clean_name:
+                    sanctioned_suppliers.append({
+                        "supplier": supplier,
+                        "matched_entity": entity["entity_name"],
+                        "restrictions": entity["restrictions"],
+                        "date_added": entity["date_added"],
+                    })
+                    break
+
+    return sanctioned_suppliers
+
+
+def get_all_sanctioned_entities() -> list:
+    """获取所有被制裁实体列表"""
+    return _load_entity_list()

XH-202626_原型系统源码与部署手册/01_原型系统源码/collectors/financial_collector.py

@@ -0,0 +1,105 @@
+# -*- coding: utf-8 -*-
+"""
+财务数据采集模块
+双轨策略：优先尝试 AKShare 在线采集，失败则回退到预置数据
+"""
+import json
+import logging
+from pathlib import Path
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+DATA_DIR = Path(__file__).parent.parent / "data"
+
+
+def _load_preset_data() -> list:
+    """加载预置的科创板企业数据"""
+    filepath = DATA_DIR / "sample_companies.json"
+    with open(filepath, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def collect_financial_data(stock_code: str) -> Optional[dict]:
+    """
+    采集指定股票代码的财务数据
+    双轨策略：在线采集 → 离线预置
+    """
+    # 尝试在线采集
+    try:
+        return _collect_online(stock_code)
+    except Exception as e:
+        logger.warning(f"在线采集 {stock_code} 失败: {e}，回退到预置数据")
+
+    # 回退到预置数据
+    return _collect_from_preset(stock_code)
+
+
+def _collect_online(stock_code: str) -> Optional[dict]:
+    """通过 AKShare 在线采集财务数据"""
+    try:
+        import akshare as ak
+
+        # 科创板企业利润表
+        profit_df = ak.stock_profit_sheet_by_report_em(symbol=stock_code)
+        # 科创板企业资产负债表
+        balance_df = ak.stock_balance_sheet_by_report_em(symbol=stock_code)
+
+        if profit_df is not None and not profit_df.empty:
+            latest = profit_df.iloc[0]
+            return {
+                "stock_code": stock_code,
+                "revenue": float(latest.get("营业收入", 0)),
+                "net_profit": float(latest.get("净利润", 0)),
+                "rd_expense": float(latest.get("研发费用", 0)),
+                "source": "akshare_online",
+            }
+    except ImportError:
+        logger.warning("AKShare 未安装，跳过在线采集")
+    except Exception as e:
+        logger.warning(f"AKShare 采集异常: {e}")
+
+    raise RuntimeError("在线采集失败")
+
+
+def _collect_from_preset(stock_code: str) -> Optional[dict]:
+    """从预置数据中查找企业"""
+    companies = _load_preset_data()
+    for company in companies:
+        if company["stock_code"] == stock_code:
+            return {
+                "stock_code": stock_code,
+                "company_name": company["company_name"],
+                "industry": company["industry"],
+                "sector": company["sector"],
+                "financials": company["financials"],
+                "core_tech_personnel": company["core_tech_personnel"],
+                "tech_route": company["tech_route"],
+                "compliance": company["compliance"],
+                "supply_chain": company["supply_chain"],
+                "source": "preset_data",
+            }
+    return None
+
+
+def get_all_companies() -> list:
+    """获取所有预置企业列表"""
+    return _load_preset_data()
+
+
+def get_company_by_code(stock_code: str) -> Optional[dict]:
+    """通过股票代码查找企业完整数据"""
+    companies = _load_preset_data()
+    for company in companies:
+        if company["stock_code"] == stock_code:
+            return company
+    return None
+
+
+def get_company_by_name(name: str) -> Optional[dict]:
+    """通过企业名称查找（支持简称）"""
+    companies = _load_preset_data()
+    for company in companies:
+        if name in company["company_name"] or name in company["short_name"]:
+            return company
+    return None

XH-202626_原型系统源码与部署手册/01_原型系统源码/config.py

@@ -0,0 +1,81 @@
+# -*- coding: utf-8 -*-
+"""
+全局配置文件
+集中管理 API Key、文件路径、模型参数等
+"""
+import os
+from pathlib import Path
+
+# ============================================================
+# 项目根目录
+# ============================================================
+PROJECT_ROOT = Path(__file__).parent.resolve()
+DATA_DIR = PROJECT_ROOT / "data"
+
+# ============================================================
+# DeepSeek API 配置（兼容 OpenAI 接口协议）
+# ============================================================
+DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY", "")
+DEEPSEEK_BASE_URL = "https://api.deepseek.com/v1"
+DEEPSEEK_MODEL = "deepseek-chat"
+
+# 备用1：火山引擎方舟（豆包大模型 doubao-seed-2-1-pro-260628）
+VOLCENGINE_API_KEY = os.environ.get("VOLCENGINE_API_KEY", os.environ.get("ARK_API_KEY", "836d9bc0-80e8-4e45-90df-7287994d91ec"))
+VOLCENGINE_BASE_URL = os.environ.get("VOLCENGINE_BASE_URL", "https://ark.cn-beijing.volces.com/api/v3")
+VOLCENGINE_MODEL = os.environ.get("VOLCENGINE_MODEL", "doubao-seed-2-0-lite-260428")
+
+# 备用2：如果用户配置了其他兼容 OpenAI 协议的 API
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "")
+OPENAI_BASE_URL = os.environ.get("OPENAI_BASE_URL", "https://api.openai.com/v1")
+OPENAI_MODEL = os.environ.get("OPENAI_MODEL", "gpt-4o-mini")
+
+# ============================================================
+# LLM 调用参数
+# ============================================================
+LLM_TEMPERATURE = 0.1          # 低温度保证输出稳定
+LLM_MAX_TOKENS = 4096
+LLM_TIMEOUT = 60               # 超时秒数
+LLM_MAX_RETRIES = 2            # 最大重试次数
+
+# ============================================================
+# 风险评估权重配置
+# ============================================================
+RISK_WEIGHTS = {
+    "tech_disruption":       0.20,   # 技术路线颠覆风险
+    "talent_loss":           0.15,   # 核心人员流失风险
+    "algo_compliance":       0.15,   # 算法/数据合规风险
+    "geopolitical":          0.20,   # 地缘政治/出口管制风险
+    "rd_capitalization":     0.15,   # 研发资本化操纵风险
+    "concentration":         0.15,   # 客户/供应商集中风险
+}
+
+# ============================================================
+# 保险定价参数
+# ============================================================
+INSURANCE_PRODUCTS = {
+    "ip_lawsuit": {
+        "name": "知识产权被诉险",
+        "base_premium": 50000,     # 基础保费（元）
+        "base_coverage": 5000000,  # 基础保额（元）
+        "description": "覆盖因知识产权纠纷（专利、商标、著作权）产生的诉讼费用及赔偿金",
+    },
+    "exec_departure": {
+        "name": "高管离职业务中断险",
+        "base_premium": 80000,
+        "base_coverage": 10000000,
+        "description": "覆盖核心技术人员/高管离职导致的业务中断损失",
+    },
+    "data_compliance": {
+        "name": "数据合规行政处罚险",
+        "base_premium": 30000,
+        "base_coverage": 3000000,
+        "description": "覆盖因非主观恶意的数据合规违规产生的行政罚款",
+    },
+}
+
+# ============================================================
+# Streamlit 页面配置
+# ============================================================
+PAGE_TITLE = "🛡️ 科创企业智能风控与核保系统"
+PAGE_ICON = "🛡️"
+LAYOUT = "wide"

XH-202626_原型系统源码与部署手册/01_原型系统源码/data/algo_filings.json

@@ -0,0 +1,11 @@
+[
+  {"filing_id": "AL-2023-001", "company": "金山办公", "algo_name": "WPS AI 写作助手", "filing_date": "2023-08-15", "status": "已通过", "algo_type": "生成合成类"},
+  {"filing_id": "AL-2023-002", "company": "传音控股", "algo_name": "AI 相机美颜算法", "filing_date": "2023-06-20", "status": "已通过", "algo_type": "个性化推荐类"},
+  {"filing_id": "AL-2023-003", "company": "海天瑞声", "algo_name": "智能标注平台算法", "filing_date": "2023-09-10", "status": "已通过", "algo_type": "生成合成类"},
+  {"filing_id": "AL-2023-004", "company": "百度", "algo_name": "文心一言大模型", "filing_date": "2023-08-31", "status": "已通过", "algo_type": "生成合成类"},
+  {"filing_id": "AL-2023-005", "company": "阿里巴巴", "algo_name": "通义千问大模型", "filing_date": "2023-09-13", "status": "已通过", "algo_type": "生成合成类"},
+  {"filing_id": "AL-2023-006", "company": "腾讯", "algo_name": "混元大模型", "filing_date": "2023-09-15", "status": "已通过", "algo_type": "生成合成类"},
+  {"filing_id": "AL-2024-001", "company": "字节跳动", "algo_name": "豆包大模型", "filing_date": "2024-01-15", "status": "已通过", "algo_type": "生成合成类"},
+  {"filing_id": "AL-2024-002", "company": "商汤科技", "algo_name": "日日新大模型", "filing_date": "2024-02-20", "status": "已通过", "algo_type": "生成合成类"},
+  {"filing_id": "AL-2024-003", "company": "科大讯飞", "algo_name": "星火认知大模型", "filing_date": "2024-03-01", "status": "已通过", "algo_type": "生成合成类"}
+]

XH-202626_原型系统源码与部署手册/01_原型系统源码/data/entity_list.json

@@ -0,0 +1,15 @@
+[
+  {"entity_name": "中芯国际集成电路制造有限公司", "aliases": ["SMIC", "中芯国际", "Semiconductor Manufacturing International Corporation"], "date_added": "2020-12-18", "reason": "军事最终用途", "restrictions": "限制EUV光刻设备及先进制程相关技术出口", "source": "BIS Entity List"},
+  {"entity_name": "寒武纪科技股份有限公司", "aliases": ["Cambricon", "寒武纪"], "date_added": "2022-10-07", "reason": "支持中国军事现代化", "restrictions": "限制先进AI芯片代工与设计软件", "source": "BIS Entity List"},
+  {"entity_name": "华为技术有限公司", "aliases": ["Huawei", "华为", "HUAWEI"], "date_added": "2019-05-16", "reason": "国家安全威胁", "restrictions": "全面限制美国技术出口", "source": "BIS Entity List"},
+  {"entity_name": "海康威视数字技术股份有限公司", "aliases": ["Hikvision", "海康威视"], "date_added": "2019-10-07", "reason": "参与新疆人权侵犯", "restrictions": "限制安防监控技术出口", "source": "BIS Entity List"},
+  {"entity_name": "大疆创新科技有限公司", "aliases": ["DJI", "大疆"], "date_added": "2020-12-18", "reason": "军事最终用途", "restrictions": "限制无人机核心部件出口", "source": "BIS Entity List"},
+  {"entity_name": "科大讯飞股份有限公司", "aliases": ["iFlytek", "科大讯飞"], "date_added": "2019-10-07", "reason": "参与新疆人权侵犯", "restrictions": "限制AI语音技术相关出口", "source": "BIS Entity List"},
+  {"entity_name": "商汤科技有限公司", "aliases": ["SenseTime", "商汤", "商汤科技"], "date_added": "2021-12-10", "reason": "参与监控技术开发", "restrictions": "限制AI视觉技术出口", "source": "BIS Entity List / OFAC SDN List"},
+  {"entity_name": "旷视科技有限公司", "aliases": ["Megvii", "旷视"], "date_added": "2019-10-07", "reason": "参与新疆人权侵犯", "restrictions": "限制AI人脸识别技术出口", "source": "BIS Entity List"},
+  {"entity_name": "长江存储科技有限责任公司", "aliases": ["YMTC", "长江存储"], "date_added": "2022-12-15", "reason": "支持中国军事现代化", "restrictions": "限制NAND闪存芯片相关设备出口", "source": "BIS Entity List"},
+  {"entity_name": "上海微电子装备集团股份有限公司", "aliases": ["SMEE", "上海微电子"], "date_added": "2022-10-07", "reason": "支持中国军事现代化", "restrictions": "限制光刻机核心部件", "source": "BIS Entity List"},
+  {"entity_name": "龙芯中科技术股份有限公司", "aliases": ["Loongson", "龙芯"], "date_added": "2023-02-10", "reason": "军事最终用途", "restrictions": "限制先进CPU设计工具出口", "source": "BIS Entity List"},
+  {"entity_name": "壁仞科技股份有限公司", "aliases": ["Biren Technology", "壁仞科技"], "date_added": "2022-10-07", "reason": "支持中国军事现代化", "restrictions": "限制GPU芯片代工", "source": "BIS Entity List"},
+  {"entity_name": "摩尔线程智能科技股份有限公司", "aliases": ["Moore Threads", "摩尔线程"], "date_added": "2023-10-17", "reason": "支持中国军事现代化", "restrictions": "限制GPU芯片代工与设计", "source": "BIS Entity List"}
+]

XH-202626_原型系统源码与部署手册/01_原型系统源码/data/sample_companies.json

@@ -0,0 +1,408 @@
+[
+  {
+    "stock_code": "688981",
+    "company_name": "中芯国际",
+    "short_name": "中芯国际",
+    "industry": "半导体制造",
+    "sector": "芯片",
+    "listed_board": "科创板",
+    "description": "国内领先的集成电路晶圆代工企业，提供0.35微米到FinFET先进工艺",
+    "core_tech_personnel": [
+      {"name": "梁孟松", "title": "联合CEO/技术研发负责人", "status": "在职", "importance": "极高"},
+      {"name": "周子学", "title": "董事长", "status": "在职", "importance": "高"}
+    ],
+    "financials": {
+      "revenue_2024": 57756000000,
+      "net_profit_2024": 3433000000,
+      "rd_expense_2024": 5124000000,
+      "rd_capitalization_rate": 0.0,
+      "rd_revenue_ratio": 0.089,
+      "top5_customer_ratio": 0.42,
+      "top5_supplier_ratio": 0.55,
+      "receivable_turnover": 8.2,
+      "cash_flow_ratio": 1.35
+    },
+    "tech_route": {
+      "current_tech": "14nm FinFET 量产, 7nm 研发中",
+      "competing_techs": ["EUV 光刻技术 (ASML 垄断)", "GAA 晶体管架构"],
+      "tech_moat": "国产替代核心标的，但受制于设备禁运",
+      "patent_count": 12000
+    },
+    "compliance": {
+      "algo_filing_status": "不适用",
+      "data_export_risk": "低",
+      "entity_list_status": "被列入（2020年12月）",
+      "sanctions_detail": "被美国商务部列入实体清单，限制EUV光刻设备进口"
+    },
+    "supply_chain": {
+      "key_suppliers": ["ASML（光刻机）", "东京电子（刻蚀设备）", "应用材料（薄膜沉积）"],
+      "key_customers": ["高通", "联发科", "华为海思"],
+      "supplier_concentration_risk": "极高——核心设备依赖海外供应商"
+    }
+  },
+  {
+    "stock_code": "688111",
+    "company_name": "金山办公软件股份有限公司",
+    "short_name": "金山办公",
+    "industry": "办公软件",
+    "sector": "软件",
+    "listed_board": "科创板",
+    "description": "国产办公软件龙头，WPS Office 全球月活超5.9亿",
+    "core_tech_personnel": [
+      {"name": "章庆元", "title": "CEO", "status": "在职", "importance": "极高"},
+      {"name": "姚冬", "title": "CTO/AI研发负责人", "status": "在职", "importance": "高"}
+    ],
+    "financials": {
+      "revenue_2024": 4586000000,
+      "net_profit_2024": 1231000000,
+      "rd_expense_2024": 1520000000,
+      "rd_capitalization_rate": 0.0,
+      "rd_revenue_ratio": 0.331,
+      "top5_customer_ratio": 0.15,
+      "top5_supplier_ratio": 0.30,
+      "receivable_turnover": 12.5,
+      "cash_flow_ratio": 1.82
+    },
+    "tech_route": {
+      "current_tech": "WPS AI 大模型集成、云文档协同",
+      "competing_techs": ["Microsoft 365 Copilot", "Google Workspace Gemini"],
+      "tech_moat": "信创替代核心标的，政府及国企客户粘性高",
+      "patent_count": 3500
+    },
+    "compliance": {
+      "algo_filing_status": "已备案（WPS AI 写作助手）",
+      "data_export_risk": "中（海外版WPS涉及数据跨境）",
+      "entity_list_status": "未列入",
+      "sanctions_detail": ""
+    },
+    "supply_chain": {
+      "key_suppliers": ["华为云", "阿里云", "英伟达（GPU）"],
+      "key_customers": ["各级政府机关", "央企国企", "中小企业"],
+      "supplier_concentration_risk": "中——GPU算力依赖进口"
+    }
+  },
+  {
+    "stock_code": "688139",
+    "company_name": "海尔生物医疗股份有限公司",
+    "short_name": "海尔生物",
+    "industry": "生物医疗低温存储",
+    "sector": "医疗器械",
+    "listed_board": "科创板",
+    "description": "全球领先的生物医疗低温存储解决方案提供商",
+    "core_tech_personnel": [
+      {"name": "刘占杰", "title": "董事长/总经理", "status": "在职", "importance": "极高"}
+    ],
+    "financials": {
+      "revenue_2024": 2245000000,
+      "net_profit_2024": 398000000,
+      "rd_expense_2024": 289000000,
+      "rd_capitalization_rate": 0.05,
+      "rd_revenue_ratio": 0.129,
+      "top5_customer_ratio": 0.22,
+      "top5_supplier_ratio": 0.35,
+      "receivable_turnover": 6.8,
+      "cash_flow_ratio": 1.15
+    },
+    "tech_route": {
+      "current_tech": "超低温冰箱(-196°C)、自动化样本库、物联网疫苗管理",
+      "competing_techs": ["赛默飞世尔", "松下医疗"],
+      "tech_moat": "国内市场份额第一，物联网技术差异化",
+      "patent_count": 800
+    },
+    "compliance": {
+      "algo_filing_status": "不适用",
+      "data_export_risk": "低",
+      "entity_list_status": "未列入",
+      "sanctions_detail": ""
+    },
+    "supply_chain": {
+      "key_suppliers": ["压缩机供应商", "电子元器件供应商"],
+      "key_customers": ["各级疾控中心", "医院", "科研院所"],
+      "supplier_concentration_risk": "低"
+    }
+  },
+  {
+    "stock_code": "688256",
+    "company_name": "寒武纪科技股份有限公司",
+    "short_name": "寒武纪",
+    "industry": "AI芯片",
+    "sector": "芯片",
+    "listed_board": "科创板",
+    "description": "国内AI芯片独角兽，智能计算处理器IP及芯片产品提供商",
+    "core_tech_personnel": [
+      {"name": "陈天石", "title": "创始人/董事长/CEO", "status": "在职", "importance": "极高"},
+      {"name": "陈云霁", "title": "首席科学家（兄长）", "status": "中科院任职,兼职顾问", "importance": "高"}
+    ],
+    "financials": {
+      "revenue_2024": 1170000000,
+      "net_profit_2024": -825000000,
+      "rd_expense_2024": 1510000000,
+      "rd_capitalization_rate": 0.0,
+      "rd_revenue_ratio": 1.29,
+      "top5_customer_ratio": 0.85,
+      "top5_supplier_ratio": 0.70,
+      "receivable_turnover": 3.2,
+      "cash_flow_ratio": 0.45
+    },
+    "tech_route": {
+      "current_tech": "思元系列AI推理/训练芯片, MLU架构",
+      "competing_techs": ["英伟达CUDA生态", "华为昇腾", "AMD Instinct"],
+      "tech_moat": "自主IP指令集，但生态建设与英伟达差距大",
+      "patent_count": 2800
+    },
+    "compliance": {
+      "algo_filing_status": "不适用（硬件厂商）",
+      "data_export_risk": "低",
+      "entity_list_status": "被列入（2022年10月）",
+      "sanctions_detail": "被美国商务部列入实体清单，限制先进制程芯片代工"
+    },
+    "supply_chain": {
+      "key_suppliers": ["台积电(已受限)", "中芯国际(替代)", "日月光(封测)"],
+      "key_customers": ["中国移动", "南京市政府智慧城市项目", "某互联网大厂"],
+      "supplier_concentration_risk": "极高——先进制程代工受限"
+    }
+  },
+  {
+    "stock_code": "688029",
+    "company_name": "南微医学科技股份有限公司",
+    "short_name": "南微医学",
+    "industry": "微创医疗器械",
+    "sector": "医疗器械",
+    "listed_board": "科创板",
+    "description": "全球领先的微创诊疗器械企业，内镜下诊疗器械龙头",
+    "core_tech_personnel": [
+      {"name": "隆晓辉", "title": "创始人/董事长", "status": "在职", "importance": "极高"},
+      {"name": "冷德嵘", "title": "总经理", "status": "在职", "importance": "高"}
+    ],
+    "financials": {
+      "revenue_2024": 2580000000,
+      "net_profit_2024": 628000000,
+      "rd_expense_2024": 365000000,
+      "rd_capitalization_rate": 0.12,
+      "rd_revenue_ratio": 0.141,
+      "top5_customer_ratio": 0.18,
+      "top5_supplier_ratio": 0.32,
+      "receivable_turnover": 7.5,
+      "cash_flow_ratio": 1.42
+    },
+    "tech_route": {
+      "current_tech": "一次性内镜、电外科手术器械、AI辅助诊断",
+      "competing_techs": ["波士顿科学", "奥林巴斯", "库克医疗"],
+      "tech_moat": "国产微创器械龙头，海外收入占比超40%",
+      "patent_count": 1200
+    },
+    "compliance": {
+      "algo_filing_status": "不适用",
+      "data_export_risk": "低",
+      "entity_list_status": "未列入",
+      "sanctions_detail": ""
+    },
+    "supply_chain": {
+      "key_suppliers": ["不锈钢/钛合金供应商", "精密注塑件供应商"],
+      "key_customers": ["全球三甲医院", "海外经销商网络"],
+      "supplier_concentration_risk": "低"
+    }
+  },
+  {
+    "stock_code": "688126",
+    "company_name": "沪硅产业集团股份有限公司",
+    "short_name": "沪硅产业",
+    "industry": "半导体硅片",
+    "sector": "芯片",
+    "listed_board": "科创板",
+    "description": "国内规模最大的半导体硅片制造企业，300mm大硅片龙头",
+    "core_tech_personnel": [
+      {"name": "林林", "title": "总裁", "status": "在职", "importance": "极高"}
+    ],
+    "financials": {
+      "revenue_2024": 3285000000,
+      "net_profit_2024": -156000000,
+      "rd_expense_2024": 498000000,
+      "rd_capitalization_rate": 0.35,
+      "rd_revenue_ratio": 0.152,
+      "top5_customer_ratio": 0.62,
+      "top5_supplier_ratio": 0.48,
+      "receivable_turnover": 5.1,
+      "cash_flow_ratio": 0.78
+    },
+    "tech_route": {
+      "current_tech": "300mm半导体硅片(12英寸)量产",
+      "competing_techs": ["日本信越化学", "日本SUMCO", "韩国SK Siltron"],
+      "tech_moat": "国产替代第一梯队，但高端产品仍有差距",
+      "patent_count": 650
+    },
+    "compliance": {
+      "algo_filing_status": "不适用",
+      "data_export_risk": "低",
+      "entity_list_status": "未列入",
+      "sanctions_detail": ""
+    },
+    "supply_chain": {
+      "key_suppliers": ["多晶硅供应商", "石英坩埚供应商"],
+      "key_customers": ["中芯国际", "华虹半导体", "长江存储"],
+      "supplier_concentration_risk": "中"
+    }
+  },
+  {
+    "stock_code": "688223",
+    "company_name": "晶科能源股份有限公司",
+    "short_name": "晶科能源",
+    "industry": "光伏组件",
+    "sector": "新能源",
+    "listed_board": "科创板",
+    "description": "全球领先的光伏组件制造商，N型TOPCon技术全球领先",
+    "core_tech_personnel": [
+      {"name": "李仙德", "title": "创始人/董事长", "status": "在职", "importance": "极高"},
+      {"name": "金浩", "title": "首席科学家", "status": "在职", "importance": "高"}
+    ],
+    "financials": {
+      "revenue_2024": 84300000000,
+      "net_profit_2024": 1050000000,
+      "rd_expense_2024": 4890000000,
+      "rd_capitalization_rate": 0.08,
+      "rd_revenue_ratio": 0.058,
+      "top5_customer_ratio": 0.25,
+      "top5_supplier_ratio": 0.40,
+      "receivable_turnover": 6.2,
+      "cash_flow_ratio": 0.92
+    },
+    "tech_route": {
+      "current_tech": "N型TOPCon高效电池, 钙钛矿叠层研发",
+      "competing_techs": ["隆基HJT路线", "钙钛矿技术", "IBC电池"],
+      "tech_moat": "TOPCon量产规模全球第一，但面临技术路线之争",
+      "patent_count": 2200
+    },
+    "compliance": {
+      "algo_filing_status": "不适用",
+      "data_export_risk": "低",
+      "entity_list_status": "未列入（但面临美国反倾销关税）",
+      "sanctions_detail": "面临美国、欧盟反倾销/反补贴调查"
+    },
+    "supply_chain": {
+      "key_suppliers": ["硅料供应商（通威/大全）", "银浆供应商", "EVA胶膜供应商"],
+      "key_customers": ["全球EPC总包商", "欧美分布式市场", "中东/非洲新兴市场"],
+      "supplier_concentration_risk": "中——硅料价格波动大"
+    }
+  },
+  {
+    "stock_code": "688036",
+    "company_name": "传音控股股份有限公司",
+    "short_name": "传音控股",
+    "industry": "智能终端",
+    "sector": "消费电子",
+    "listed_board": "科创板",
+    "description": "非洲市场手机销量第一，旗下TECNO/Infinix/itel品牌",
+    "core_tech_personnel": [
+      {"name": "竺兆江", "title": "创始人/董事长", "status": "在职", "importance": "极高"}
+    ],
+    "financials": {
+      "revenue_2024": 65800000000,
+      "net_profit_2024": 5620000000,
+      "rd_expense_2024": 3250000000,
+      "rd_capitalization_rate": 0.0,
+      "rd_revenue_ratio": 0.049,
+      "top5_customer_ratio": 0.32,
+      "top5_supplier_ratio": 0.58,
+      "receivable_turnover": 9.8,
+      "cash_flow_ratio": 1.25
+    },
+    "tech_route": {
+      "current_tech": "深肤色相机算法, AI翻译, 本地化OS",
+      "competing_techs": ["三星", "小米", "OPPO"],
+      "tech_moat": "新兴市场本地化经验深厚",
+      "patent_count": 4500
+    },
+    "compliance": {
+      "algo_filing_status": "已备案（AI相机美颜算法）",
+      "data_export_risk": "高（大量非洲/东南亚用户数据跨境）",
+      "entity_list_status": "未列入",
+      "sanctions_detail": ""
+    },
+    "supply_chain": {
+      "key_suppliers": ["联发科（芯片）", "三星SDI/ATL（电池）", "京东方（屏幕）"],
+      "key_customers": ["非洲运营商", "东南亚分销商", "拉美零售商"],
+      "supplier_concentration_risk": "中——芯片依赖联发科"
+    }
+  },
+  {
+    "stock_code": "688005",
+    "company_name": "容百科技股份有限公司",
+    "short_name": "容百科技",
+    "industry": "锂电正极材料",
+    "sector": "新能源",
+    "listed_board": "科创板",
+    "description": "全球领先的锂电池正极材料供应商，高镍三元材料龙头",
+    "core_tech_personnel": [
+      {"name": "白厚善", "title": "创始人/董事长", "status": "在职", "importance": "极高"}
+    ],
+    "financials": {
+      "revenue_2024": 18900000000,
+      "net_profit_2024": 356000000,
+      "rd_expense_2024": 785000000,
+      "rd_capitalization_rate": 0.18,
+      "rd_revenue_ratio": 0.042,
+      "top5_customer_ratio": 0.78,
+      "top5_supplier_ratio": 0.65,
+      "receivable_turnover": 4.5,
+      "cash_flow_ratio": 0.68
+    },
+    "tech_route": {
+      "current_tech": "超高镍三元正极材料(Ni>90%), 固态电池正极",
+      "competing_techs": ["磷酸铁锂(比亚迪路线)", "钠离子电池", "固态电解质"],
+      "tech_moat": "高镍三元出货量全球前三",
+      "patent_count": 450
+    },
+    "compliance": {
+      "algo_filing_status": "不适用",
+      "data_export_risk": "低",
+      "entity_list_status": "未列入",
+      "sanctions_detail": ""
+    },
+    "supply_chain": {
+      "key_suppliers": ["格林美（镍钴原料）", "华友钴业"],
+      "key_customers": ["宁德时代", "三星SDI", "SK On"],
+      "supplier_concentration_risk": "高——原材料价格波动大"
+    }
+  },
+  {
+    "stock_code": "688787",
+    "company_name": "海天瑞声科技股份有限公司",
+    "short_name": "海天瑞声",
+    "industry": "AI训练数据",
+    "sector": "AI",
+    "listed_board": "科创板",
+    "description": "国内领先的AI训练数据解决方案提供商",
+    "core_tech_personnel": [
+      {"name": "贺琳", "title": "创始人/董事长", "status": "在职", "importance": "极高"},
+      {"name": "郭蕾", "title": "副总经理/技术负责人", "status": "离职（2024年8月）", "importance": "高"}
+    ],
+    "financials": {
+      "revenue_2024": 356000000,
+      "net_profit_2024": -48000000,
+      "rd_expense_2024": 102000000,
+      "rd_capitalization_rate": 0.42,
+      "rd_revenue_ratio": 0.287,
+      "top5_customer_ratio": 0.88,
+      "top5_supplier_ratio": 0.35,
+      "receivable_turnover": 2.8,
+      "cash_flow_ratio": 0.35
+    },
+    "tech_route": {
+      "current_tech": "多语种语音数据集, 自动驾驶标注数据, AIGC合成数据",
+      "competing_techs": ["Scale AI", "Appen", "AI合成数据替代人工标注"],
+      "tech_moat": "多语种覆盖广泛，但面临AIGC合成数据的颠覆性威胁",
+      "patent_count": 180
+    },
+    "compliance": {
+      "algo_filing_status": "已备案（智能标注平台算法）",
+      "data_export_risk": "高（大量跨境数据采集与交付）",
+      "entity_list_status": "未列入",
+      "sanctions_detail": ""
+    },
+    "supply_chain": {
+      "key_suppliers": ["全球数据采集众包网络"],
+      "key_customers": ["字节跳动", "百度", "某大型车企"],
+      "supplier_concentration_risk": "中"
+    }
+  }
+]

XH-202626_原型系统源码与部署手册/01_原型系统源码/data/supply_chain.json

@@ -0,0 +1,35 @@
+{
+  "supply_relations": [
+    {"from": "ASML", "to": "中芯国际", "relation": "供应光刻设备", "critical": true, "status": "受限"},
+    {"from": "东京电子", "to": "中芯国际", "relation": "供应刻蚀设备", "critical": true, "status": "受限"},
+    {"from": "应用材料", "to": "中芯国际", "relation": "供应薄膜沉积设备", "critical": true, "status": "受限"},
+    {"from": "中芯国际", "to": "寒武纪", "relation": "芯片代工", "critical": true, "status": "正常"},
+    {"from": "中芯国际", "to": "华为海思", "relation": "芯片代工", "critical": true, "status": "受限"},
+    {"from": "台积电", "to": "寒武纪", "relation": "先进制程代工", "critical": true, "status": "受限"},
+    {"from": "英伟达", "to": "金山办公", "relation": "供应GPU算力", "critical": true, "status": "受限"},
+    {"from": "英伟达", "to": "海天瑞声", "relation": "供应GPU算力(客户需求)", "critical": false, "status": "正常"},
+    {"from": "联发科", "to": "传音控股", "relation": "供应手机芯片", "critical": true, "status": "正常"},
+    {"from": "三星SDI", "to": "传音控股", "relation": "供应电池", "critical": false, "status": "正常"},
+    {"from": "京东方", "to": "传音控股", "relation": "供应屏幕", "critical": false, "status": "正常"},
+    {"from": "沪硅产业", "to": "中芯国际", "relation": "供应半导体硅片", "critical": true, "status": "正常"},
+    {"from": "沪硅产业", "to": "华虹半导体", "relation": "供应半导体硅片", "critical": true, "status": "正常"},
+    {"from": "沪硅产业", "to": "长江存储", "relation": "供应半导体硅片", "critical": true, "status": "正常"},
+    {"from": "格林美", "to": "容百科技", "relation": "供应镍钴原料", "critical": true, "status": "正常"},
+    {"from": "华友钴业", "to": "容百科技", "relation": "供应钴原料", "critical": true, "status": "正常"},
+    {"from": "容百科技", "to": "宁德时代", "relation": "供应正极材料", "critical": true, "status": "正常"},
+    {"from": "容百科技", "to": "三星SDI", "relation": "供应正极材料", "critical": false, "status": "正常"},
+    {"from": "通威股份", "to": "晶科能源", "relation": "供应硅料", "critical": true, "status": "正常"},
+    {"from": "大全能源", "to": "晶科能源", "relation": "供应硅料", "critical": true, "status": "正常"},
+    {"from": "海天瑞声", "to": "字节跳动", "relation": "供应AI训练数据", "critical": false, "status": "正常"},
+    {"from": "海天瑞声", "to": "百度", "relation": "供应AI训练数据", "critical": false, "status": "正常"}
+  ],
+  "investment_relations": [
+    {"from": "国家集成电路产业基金", "to": "中芯国际", "relation": "战略投资", "share_ratio": 0.15},
+    {"from": "国家集成电路产业基金", "to": "沪硅产业", "relation": "战略投资", "share_ratio": 0.08},
+    {"from": "国家集成电路产业基金", "to": "长江存储", "relation": "战略投资", "share_ratio": 0.20}
+  ],
+  "personnel_relations": [
+    {"person": "梁孟松", "from_company": "台积电/三星", "to_company": "中芯国际", "relation": "技术负责人跳槽", "year": 2017},
+    {"person": "郭蕾", "from_company": "海天瑞声", "to_company": "未知", "relation": "核心技术人员离职", "year": 2024}
+  ]
+}

XH-202626_原型系统源码与部署手册/01_原型系统源码/knowledge_graph/__init__.py

@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+"""知识图谱模块"""

XH-202626_原型系统源码与部署手册/01_原型系统源码/knowledge_graph/contagion_analyzer.py

@@ -0,0 +1,169 @@
+# -*- coding: utf-8 -*-
+"""
+供应链风险传染分析器
+基于 BFS 遍历实现风险穿透，计算传染距离和影响权重
+"""
+import logging
+from collections import deque
+from typing import Optional
+
+import networkx as nx
+
+logger = logging.getLogger(__name__)
+
+
+def analyze_contagion(G: nx.DiGraph, target_company: str) -> dict:
+    """
+    分析指定企业的供应链风险传染情况
+    从上游（供应商）方向进行 BFS 穿透
+    """
+    if target_company not in G:
+        return {"error": f"企业 '{target_company}' 不在图谱中"}
+
+    result = {
+        "company": target_company,
+        "direct_risks": [],     # 直接风险（一度关联）
+        "indirect_risks": [],   # 间接风险（二度及以上关联）
+        "contagion_paths": [],  # 风险传染路径
+        "risk_score": 0,        # 供应链风险总分
+        "critical_nodes": [],   # 关键断裂节点
+    }
+
+    # BFS 从目标企业向上游遍历
+    visited = set()
+    queue = deque([(target_company, 0, [target_company])])
+    visited.add(target_company)
+
+    while queue:
+        current, depth, path = queue.popleft()
+
+        # 检查当前节点的上游（前驱节点）
+        for predecessor in G.predecessors(current):
+            if predecessor in visited:
+                continue
+            visited.add(predecessor)
+
+            edge_data = G.edges[predecessor, current]
+            node_data = G.nodes.get(predecessor, {})
+            new_path = [predecessor] + path
+
+            # 检查上游节点是否受制裁
+            if node_data.get("is_sanctioned", False):
+                risk_entry = {
+                    "entity": predecessor,
+                    "node_type": node_data.get("node_type", "未知"),
+                    "distance": depth + 1,
+                    "relation": edge_data.get("relation", ""),
+                    "is_critical": edge_data.get("critical", False),
+                    "status": edge_data.get("status", "正常"),
+                    "path": " → ".join(new_path),
+                }
+
+                if depth == 0:
+                    result["direct_risks"].append(risk_entry)
+                else:
+                    result["indirect_risks"].append(risk_entry)
+
+                result["contagion_paths"].append({
+                    "path": new_path,
+                    "path_str": " → ".join(new_path),
+                    "length": len(new_path),
+                    "severity": "高" if edge_data.get("critical", False) else "中",
+                })
+
+            # 检查受限状态的边
+            if edge_data.get("status") == "受限":
+                if predecessor not in [r["entity"] for r in result["direct_risks"] + result["indirect_risks"]]:
+                    risk_entry = {
+                        "entity": predecessor,
+                        "node_type": node_data.get("node_type", "未知"),
+                        "distance": depth + 1,
+                        "relation": edge_data.get("relation", ""),
+                        "is_critical": edge_data.get("critical", False),
+                        "status": "受限",
+                        "path": " → ".join(new_path),
+                    }
+                    if depth == 0:
+                        result["direct_risks"].append(risk_entry)
+                    else:
+                        result["indirect_risks"].append(risk_entry)
+
+            # 继续向上游遍历（最多3层）
+            if depth < 2:
+                queue.append((predecessor, depth + 1, new_path))
+
+    # 计算供应链风险得分
+    result["risk_score"] = _calculate_supply_chain_risk_score(result)
+
+    # 识别关键断裂节点
+    result["critical_nodes"] = _find_critical_nodes(G, target_company)
+
+    return result
+
+
+def _calculate_supply_chain_risk_score(contagion_result: dict) -> float:
+    """计算供应链风险综合得分 (0-100)"""
+    score = 0
+
+    # 直接风险：每个 +25 分，关键供应 +35 分
+    for risk in contagion_result["direct_risks"]:
+        if risk["is_critical"]:
+            score += 35
+        else:
+            score += 25
+
+    # 间接风险：每个 +10 分，关键供应 +15 分
+    for risk in contagion_result["indirect_risks"]:
+        if risk["is_critical"]:
+            score += 15
+        else:
+            score += 10
+
+    return min(score, 100)  # 上限 100
+
+
+def _find_critical_nodes(G: nx.DiGraph, target: str) -> list:
+    """
+    识别关键断裂节点：如果移除该节点，目标企业将失去关键供应来源
+    """
+    critical = []
+    predecessors = list(G.predecessors(target))
+
+    for pred in predecessors:
+        edge_data = G.edges[pred, target]
+        if edge_data.get("critical", False):
+            # 检查是否有替代供应商
+            alternatives = sum(
+                1 for p in predecessors
+                if p != pred and G.edges[p, target].get("relation", "") == edge_data.get("relation", "")
+            )
+            critical.append({
+                "node": pred,
+                "relation": edge_data.get("relation", ""),
+                "has_alternative": alternatives > 0,
+                "alternative_count": alternatives,
+                "status": edge_data.get("status", "正常"),
+            })
+
+    return critical
+
+
+def find_all_risk_paths(G: nx.DiGraph, source: str, target: str, max_depth: int = 4) -> list:
+    """
+    查找两个节点之间的所有风险路径
+    """
+    if source not in G or target not in G:
+        return []
+
+    try:
+        paths = list(nx.all_simple_paths(G, source, target, cutoff=max_depth))
+        return [
+            {
+                "path": p,
+                "path_str": " → ".join(p),
+                "length": len(p),
+            }
+            for p in paths
+        ]
+    except nx.NetworkXError:
+        return []

XH-202626_原型系统源码与部署手册/01_原型系统源码/knowledge_graph/graph_builder.py

@@ -0,0 +1,214 @@
+# -*- coding: utf-8 -*-
+"""
+知识图谱构建引擎
+基于 NetworkX 构建科创企业供应链风险传染图谱
+节点类型：企业、供应商、客户、核心人员、制裁实体
+边类型：供应关系、客户关系、任职关系、投资关系
+"""
+import json
+import logging
+from pathlib import Path
+
+import networkx as nx
+
+logger = logging.getLogger(__name__)
+
+DATA_DIR = Path(__file__).parent.parent / "data"
+
+
+def build_graph() -> nx.DiGraph:
+    """
+    构建完整的科创企业供应链风险知识图谱
+    """
+    G = nx.DiGraph()
+
+    # 1. 加载企业数据，添加企业节点
+    _add_company_nodes(G)
+
+    # 2. 加载供应链数据，添加关系边
+    _add_supply_chain_edges(G)
+
+    # 3. 加载实体清单，标记受制裁节点
+    _mark_sanctioned_nodes(G)
+
+    logger.info(f"图谱构建完成: {G.number_of_nodes()} 节点, {G.number_of_edges()} 边")
+    return G
+
+
+def _add_company_nodes(G: nx.DiGraph):
+    """添加科创板企业节点及其关联的核心人员节点"""
+    filepath = DATA_DIR / "sample_companies.json"
+    with open(filepath, "r", encoding="utf-8") as f:
+        companies = json.load(f)
+
+    for company in companies:
+        name = company["short_name"]
+        G.add_node(
+            name,
+            node_type="科创企业",
+            stock_code=company["stock_code"],
+            industry=company["industry"],
+            sector=company["sector"],
+            is_sanctioned=company["compliance"]["entity_list_status"] != "未列入",
+            risk_level="正常",
+            color="#4CAF50",  # 默认绿色
+        )
+
+        # 添加核心技术人员节点
+        for person in company.get("core_tech_personnel", []):
+            person_id = f"{person['name']}@{name}"
+            G.add_node(
+                person_id,
+                node_type="核心人员",
+                real_name=person["name"],
+                title=person["title"],
+                status=person["status"],
+                importance=person["importance"],
+                company=name,
+                color="#2196F3",  # 蓝色
+            )
+            G.add_edge(
+                person_id, name,
+                relation="任职于",
+                edge_type="personnel",
+            )
+
+        # 添加供应商节点
+        for supplier in company.get("supply_chain", {}).get("key_suppliers", []):
+            supplier_name = supplier.split("（")[0].split("(")[0].strip()
+            if not G.has_node(supplier_name):
+                G.add_node(
+                    supplier_name,
+                    node_type="供应商",
+                    is_sanctioned=False,
+                    risk_level="正常",
+                    color="#FF9800",  # 橙色
+                )
+            G.add_edge(
+                supplier_name, name,
+                relation="供应",
+                detail=supplier,
+                edge_type="supply",
+            )
+
+        # 添加客户节点
+        for customer in company.get("supply_chain", {}).get("key_customers", []):
+            customer_name = customer.split("（")[0].split("(")[0].strip()
+            if not G.has_node(customer_name):
+                G.add_node(
+                    customer_name,
+                    node_type="客户",
+                    is_sanctioned=False,
+                    risk_level="正常",
+                    color="#9C27B0",  # 紫色
+                )
+            G.add_edge(
+                name, customer_name,
+                relation="供货给",
+                edge_type="customer",
+            )
+
+
+def _add_supply_chain_edges(G: nx.DiGraph):
+    """从供应链关系文件添加更详细的边"""
+    filepath = DATA_DIR / "supply_chain.json"
+    with open(filepath, "r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    # 添加供应关系
+    for rel in data.get("supply_relations", []):
+        from_node = rel["from"]
+        to_node = rel["to"]
+
+        # 确保节点存在
+        if not G.has_node(from_node):
+            G.add_node(from_node, node_type="供应商", is_sanctioned=False,
+                       risk_level="正常", color="#FF9800")
+        if not G.has_node(to_node):
+            G.add_node(to_node, node_type="企业", is_sanctioned=False,
+                       risk_level="正常", color="#4CAF50")
+
+        G.add_edge(
+            from_node, to_node,
+            relation=rel["relation"],
+            critical=rel.get("critical", False),
+            status=rel.get("status", "正常"),
+            edge_type="supply",
+        )
+
+    # 添加投资关系
+    for rel in data.get("investment_relations", []):
+        from_node = rel["from"]
+        to_node = rel["to"]
+
+        if not G.has_node(from_node):
+            G.add_node(from_node, node_type="投资方", is_sanctioned=False,
+                       risk_level="正常", color="#607D8B")
+
+        G.add_edge(
+            from_node, to_node,
+            relation=rel["relation"],
+            share_ratio=rel.get("share_ratio", 0),
+            edge_type="investment",
+        )
+
+
+def _mark_sanctioned_nodes(G: nx.DiGraph):
+    """标记受制裁的节点，并向下游传播风险"""
+    filepath = DATA_DIR / "entity_list.json"
+    with open(filepath, "r", encoding="utf-8") as f:
+        entities = json.load(f)
+
+    # 收集所有受制裁实体的名称和别名
+    sanctioned_names = set()
+    for entity in entities:
+        sanctioned_names.add(entity["entity_name"])
+        for alias in entity.get("aliases", []):
+            sanctioned_names.add(alias)
+
+    # 标记图谱中的受制裁节点
+    for node in G.nodes():
+        for sname in sanctioned_names:
+            if node in sname or sname in node:
+                G.nodes[node]["is_sanctioned"] = True
+                G.nodes[node]["risk_level"] = "高危"
+                G.nodes[node]["color"] = "#F44336"  # 红色
+                break
+
+
+def get_node_info(G: nx.DiGraph, node_name: str) -> dict:
+    """获取节点详细信息"""
+    if node_name not in G:
+        return {"error": f"节点 '{node_name}' 不存在"}
+
+    node_data = dict(G.nodes[node_name])
+    predecessors = list(G.predecessors(node_name))
+    successors = list(G.successors(node_name))
+
+    return {
+        "name": node_name,
+        "attributes": node_data,
+        "upstream": predecessors,
+        "downstream": successors,
+        "degree": G.degree(node_name),
+    }
+
+
+def get_graph_stats(G: nx.DiGraph) -> dict:
+    """获取图谱统计信息"""
+    node_types = {}
+    for _, data in G.nodes(data=True):
+        t = data.get("node_type", "未知")
+        node_types[t] = node_types.get(t, 0) + 1
+
+    sanctioned_count = sum(
+        1 for _, data in G.nodes(data=True) if data.get("is_sanctioned", False)
+    )
+
+    return {
+        "total_nodes": G.number_of_nodes(),
+        "total_edges": G.number_of_edges(),
+        "node_types": node_types,
+        "sanctioned_nodes": sanctioned_count,
+        "density": nx.density(G),
+    }

XH-202626_原型系统源码与部署手册/01_原型系统源码/knowledge_graph/graph_visualizer.py

@@ -0,0 +1,196 @@
+# -*- coding: utf-8 -*-
+"""
+知识图谱可视化模块
+使用 pyvis 生成交互式网络图，支持嵌入 Streamlit
+"""
+import logging
+import tempfile
+from pathlib import Path
+from typing import Optional
+
+import networkx as nx
+
+logger = logging.getLogger(__name__)
+
+
+# 节点类型对应的颜色和形状
+NODE_STYLES = {
+    "科创企业": {"color": "#4CAF50", "shape": "dot", "size": 30},
+    "供应商": {"color": "#FF9800", "shape": "diamond", "size": 20},
+    "客户": {"color": "#9C27B0", "shape": "triangle", "size": 20},
+    "核心人员": {"color": "#2196F3", "shape": "star", "size": 15},
+    "投资方": {"color": "#607D8B", "shape": "square", "size": 20},
+    "企业": {"color": "#4CAF50", "shape": "dot", "size": 25},
+}
+
+# 受制裁节点的样式覆盖
+SANCTIONED_STYLE = {"color": "#F44336", "size": 35}
+# 受限边的样式
+RESTRICTED_EDGE_STYLE = {"color": "#F44336", "dashes": True, "width": 3}
+
+
+def generate_interactive_graph(
+    G: nx.DiGraph,
+    highlight_company: Optional[str] = None,
+    output_path: Optional[str] = None,
+    height: str = "600px",
+    width: str = "100%",
+) -> str:
+    """
+    生成交互式知识图谱 HTML
+    """
+    try:
+        from pyvis.network import Network
+    except ImportError:
+        logger.error("pyvis 未安装，请运行: pip install pyvis")
+        return _generate_fallback_html(G)
+
+    net = Network(
+        height=height,
+        width=width,
+        directed=True,
+        notebook=False,
+        bgcolor="#1a1a2e",
+        font_color="white",
+    )
+
+    # 物理引擎配置
+    net.set_options("""
+    {
+        "physics": {
+            "forceAtlas2Based": {
+                "gravitationalConstant": -50,
+                "centralGravity": 0.01,
+                "springLength": 150,
+                "springConstant": 0.08
+            },
+            "solver": "forceAtlas2Based",
+            "stabilization": {"iterations": 100}
+        },
+        "interaction": {
+            "hover": true,
+            "tooltipDelay": 200,
+            "navigationButtons": true
+        }
+    }
+    """)
+
+    # 添加节点
+    for node, data in G.nodes(data=True):
+        node_type = data.get("node_type", "企业")
+        style = NODE_STYLES.get(node_type, NODE_STYLES["企业"]).copy()
+
+        # 受制裁节点特殊样式
+        if data.get("is_sanctioned", False):
+            style.update(SANCTIONED_STYLE)
+
+        # 高亮选中企业
+        if highlight_company and node == highlight_company:
+            style["color"] = "#FFD700"  # 金色
+            style["size"] = 45
+            style["borderWidth"] = 3
+
+        # 构建标签和悬浮提示
+        label = node.split("@")[0] if "@" in node else node
+        title_parts = [f"<b>{label}</b>", f"类型: {node_type}"]
+        if data.get("is_sanctioned"):
+            title_parts.append("⚠️ <b>已被制裁</b>")
+        if data.get("stock_code"):
+            title_parts.append(f"代码: {data['stock_code']}")
+        if data.get("industry"):
+            title_parts.append(f"行业: {data['industry']}")
+        if data.get("title"):
+            title_parts.append(f"职位: {data['title']}")
+        if data.get("status"):
+            title_parts.append(f"状态: {data['status']}")
+
+        net.add_node(
+            node,
+            label=label,
+            title="<br>".join(title_parts),
+            color=style["color"],
+            shape=style["shape"],
+            size=style["size"],
+        )
+
+    # 添加边
+    for u, v, data in G.edges(data=True):
+        edge_style = {
+            "color": "#666666",
+            "width": 1,
+            "arrows": "to",
+        }
+
+        # 受限边特殊样式
+        if data.get("status") == "受限":
+            edge_style.update(RESTRICTED_EDGE_STYLE)
+        elif data.get("critical", False):
+            edge_style["width"] = 2
+            edge_style["color"] = "#FFC107"  # 关键边用黄色
+
+        relation = data.get("relation", "")
+        net.add_edge(
+            u, v,
+            title=relation,
+            label=relation if len(relation) <= 8 else "",
+            **edge_style,
+        )
+
+    # 输出 HTML
+    if output_path is None:
+        output_path = str(Path(tempfile.gettempdir()) / "kg_visualization.html")
+
+    net.save_graph(output_path)
+
+    # 读取 HTML 内容
+    with open(output_path, "r", encoding="utf-8") as f:
+        return f.read()
+
+
+def _generate_fallback_html(G: nx.DiGraph) -> str:
+    """当 pyvis 不可用时的备用 HTML 可视化"""
+    nodes_info = []
+    for node, data in G.nodes(data=True):
+        label = node.split("@")[0] if "@" in node else node
+        is_sanctioned = data.get("is_sanctioned", False)
+        nodes_info.append(f"<li style='color: {'red' if is_sanctioned else 'green'}'>{label} ({data.get('node_type', '未知')})</li>")
+
+    return f"""
+    <html><body style='background: #1a1a2e; color: white; padding: 20px;'>
+    <h2>📊 知识图谱节点列表（pyvis 未安装，使用简化视图）</h2>
+    <p>节点数: {G.number_of_nodes()} | 边数: {G.number_of_edges()}</p>
+    <ul>{''.join(nodes_info[:50])}</ul>
+    <p style='color: #888;'>安装 pyvis 以获得交互式可视化: pip install pyvis</p>
+    </body></html>
+    """
+
+
+def get_subgraph_for_company(G: nx.DiGraph, company: str, depth: int = 2) -> nx.DiGraph:
+    """
+    提取以指定企业为中心的子图（上下游 N 层）
+    """
+    if company not in G:
+        return nx.DiGraph()
+
+    # 收集相关节点
+    related_nodes = {company}
+
+    # 上游（前驱）
+    current_layer = {company}
+    for _ in range(depth):
+        next_layer = set()
+        for node in current_layer:
+            next_layer.update(G.predecessors(node))
+        related_nodes.update(next_layer)
+        current_layer = next_layer
+
+    # 下游（后继）
+    current_layer = {company}
+    for _ in range(depth):
+        next_layer = set()
+        for node in current_layer:
+            next_layer.update(G.successors(node))
+        related_nodes.update(next_layer)
+        current_layer = next_layer
+
+    return G.subgraph(related_nodes).copy()

XH-202626_原型系统源码与部署手册/01_原型系统源码/pages/1_📊_企业风险概览.py

@@ -0,0 +1,219 @@
+# -*- 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}")

XH-202626_原型系统源码与部署手册/01_原型系统源码/pages/2_🕸️_供应链知识图谱.py

@@ -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", 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
+    """)

XH-202626_原型系统源码与部署手册/01_原型系统源码/pages/3_⚖️_多智能体辩论诊断.py

@@ -0,0 +1,540 @@
+# -*- 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)

XH-202626_原型系统源码与部署手册/01_原型系统源码/pages/4_💰_动态定价与核保.py

@@ -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,
+        )

XH-202626_原型系统源码与部署手册/01_原型系统源码/requirements.txt

@@ -0,0 +1,11 @@
+streamlit>=1.30.0
+pandas>=2.0.0
+networkx>=3.0
+pyvis>=0.3.2
+plotly>=5.18.0
+requests>=2.31.0
+openai>=1.10.0
+pdfplumber>=0.10.0
+PyPDF2>=3.0.0
+akshare>=1.12.0
+jieba>=0.42.1

XH-202626_原型系统源码与部署手册/01_原型系统源码/risk_engine/__init__.py

@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+"""风险评估引擎模块"""

XH-202626_原型系统源码与部署手册/01_原型系统源码/risk_engine/dynamic_pricing.py

@@ -0,0 +1,184 @@
+# -*- coding: utf-8 -*-
+"""
+动态保险定价模型
+基于精算原理，结合六维风险评分实现"千企千面"费率计算
+"""
+import logging
+import sys
+import os
+
+# 将项目根目录添加到 Python 路径
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from config import INSURANCE_PRODUCTS
+
+logger = logging.getLogger(__name__)
+
+
+# 行业风险系数
+INDUSTRY_RISK_FACTORS = {
+    "芯片": 1.35,      # 地缘风险+技术迭代双高
+    "AI": 1.30,         # 合规风险+技术竞争
+    "软件": 1.05,       # 相对成熟
+    "医疗器械": 0.95,   # 技术壁垒高但风险较稳
+    "新能源": 1.15,     # 技术路线之争+贸易摩擦
+    "消费电子": 1.10,   # 供应链风险
+}
+
+# 企业规模折扣（大企业风险分散能力更强）
+SCALE_DISCOUNT = {
+    "超大型": 0.85,   # 营收 > 500亿
+    "大型": 0.90,     # 100-500亿
+    "中型": 1.00,     # 10-100亿
+    "小型": 1.15,     # 1-10亿
+    "微型": 1.30,     # < 1亿
+}
+
+
+def get_enterprise_scale(revenue: float) -> str:
+    """根据营收判断企业规模"""
+    if revenue >= 50_000_000_000:
+        return "超大型"
+    elif revenue >= 10_000_000_000:
+        return "大型"
+    elif revenue >= 1_000_000_000:
+        return "中型"
+    elif revenue >= 100_000_000:
+        return "小型"
+    return "微型"
+
+
+def calculate_premium(
+    product_key: str,
+    comprehensive_risk_score: int,
+    six_dimension_scores: dict,
+    sector: str = "",
+    revenue: float = 0,
+) -> dict:
+    """
+    动态费率计算器
+
+    定价公式：
+    实际保费 = 基础保费 × 风险系数 × 行业调整 × 规模折扣
+
+    风险系数由综合评分决定（分段线性）：
+    - [0, 30)  → 0.7 ~ 0.9（优质折扣）
+    - [30, 50) → 0.9 ~ 1.2（标准浮动）
+    - [50, 70) → 1.2 ~ 1.8（风险上浮）
+    - [70, 90) → 1.8 ~ 2.5（惩罚性费率）
+    - [90,100] → 拒保或附加极高免赔额
+    """
+    product = INSURANCE_PRODUCTS.get(product_key)
+    if not product:
+        return {"error": f"未知保险产品: {product_key}"}
+
+    base_premium = product["base_premium"]
+    base_coverage = product["base_coverage"]
+
+    # 1. 计算风险系数
+    risk_multiplier = _calculate_risk_multiplier(comprehensive_risk_score)
+
+    # 2. 行业调整系数
+    industry_factor = INDUSTRY_RISK_FACTORS.get(sector, 1.0)
+
+    # 3. 规模折扣
+    scale = get_enterprise_scale(revenue)
+    scale_factor = SCALE_DISCOUNT[scale]
+
+    # 4. 特定险种的维度调整
+    dimension_adjustment = _get_dimension_adjustment(product_key, six_dimension_scores)
+
+    # 5. 最终保费
+    final_premium = base_premium * risk_multiplier * industry_factor * scale_factor * dimension_adjustment
+
+    # 6. 核保条件
+    deductible_rate = _calculate_deductible(comprehensive_risk_score)
+    adjusted_coverage = base_coverage * (1.0 if comprehensive_risk_score < 70 else 0.7)
+
+    return {
+        "product_name": product["name"],
+        "product_key": product_key,
+        "base_premium": base_premium,
+        "base_coverage": base_coverage,
+        "risk_multiplier": round(risk_multiplier, 3),
+        "industry_factor": round(industry_factor, 3),
+        "scale_factor": round(scale_factor, 3),
+        "scale_label": scale,
+        "dimension_adjustment": round(dimension_adjustment, 3),
+        "final_premium": round(final_premium, 2),
+        "adjusted_coverage": round(adjusted_coverage, 2),
+        "deductible_rate": round(deductible_rate, 3),
+        "deductible_amount": round(adjusted_coverage * deductible_rate, 2),
+        "comprehensive_risk_score": comprehensive_risk_score,
+        "is_insurable": comprehensive_risk_score < 90,
+        "pricing_breakdown": (
+            f"¥{base_premium:,.0f} × {risk_multiplier:.2f}(风险) "
+            f"× {industry_factor:.2f}(行业) × {scale_factor:.2f}(规模) "
+            f"× {dimension_adjustment:.2f}(维度) = ¥{final_premium:,.0f}"
+        ),
+    }
+
+
+def _calculate_risk_multiplier(score: int) -> float:
+    """分段线性风险系数计算"""
+    if score < 30:
+        return 0.7 + (score / 30) * 0.2       # 0.7 ~ 0.9
+    elif score < 50:
+        return 0.9 + ((score - 30) / 20) * 0.3  # 0.9 ~ 1.2
+    elif score < 70:
+        return 1.2 + ((score - 50) / 20) * 0.6  # 1.2 ~ 1.8
+    elif score < 90:
+        return 1.8 + ((score - 70) / 20) * 0.7  # 1.8 ~ 2.5
+    else:
+        return 3.0  # 拒保级别
+
+
+def _get_dimension_adjustment(product_key: str, scores: dict) -> float:
+    """针对特定险种，根据相关维度得分进行微调"""
+    if product_key == "ip_lawsuit":
+        # 知识产权被诉险：重点看技术路线和专利
+        tech_score = scores.get("tech_disruption", 50)
+        return 0.8 + (tech_score / 100) * 0.4  # 0.8 ~ 1.2
+
+    elif product_key == "exec_departure":
+        # 高管离职险：重点看人员流失风险
+        talent_score = scores.get("talent_loss", 50)
+        return 0.7 + (talent_score / 100) * 0.6  # 0.7 ~ 1.3
+
+    elif product_key == "data_compliance":
+        # 数据合规险：重点看合规风险
+        compliance_score = scores.get("algo_compliance", 50)
+        return 0.8 + (compliance_score / 100) * 0.4  # 0.8 ~ 1.2
+
+    return 1.0
+
+
+def _calculate_deductible(score: int) -> float:
+    """计算免赔率"""
+    if score < 30:
+        return 0.05   # 5%
+    elif score < 50:
+        return 0.10   # 10%
+    elif score < 70:
+        return 0.15   # 15%
+    elif score < 90:
+        return 0.25   # 25%
+    else:
+        return 0.50   # 50%（惩罚性高免赔）
+
+
+def calculate_all_products(
+    comprehensive_risk_score: int,
+    six_dimension_scores: dict,
+    sector: str = "",
+    revenue: float = 0,
+) -> list:
+    """计算所有险种的保费"""
+    results = []
+    for product_key in INSURANCE_PRODUCTS:
+        result = calculate_premium(
+            product_key, comprehensive_risk_score,
+            six_dimension_scores, sector, revenue,
+        )
+        results.append(result)
+    return results

XH-202626_原型系统源码与部署手册/01_原型系统源码/risk_engine/report_generator.py

@@ -0,0 +1,146 @@
+# -*- coding: utf-8 -*-
+"""
+核保决策报告生成器
+生成结构化的风险评估报告
+"""
+import json
+import logging
+from datetime import datetime
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+def generate_report(
+    company_data: dict,
+    debate_result: dict,
+    pricing_results: list,
+) -> dict:
+    """
+    生成完整的核保决策报告
+    """
+    company_name = company_data.get("short_name", "未知企业")
+    judge = debate_result.get("judge_result", {})
+    scores = judge.get("six_dimension_scores", {})
+    comprehensive = judge.get("comprehensive_score", 50)
+    decision = judge.get("underwriting_decision", "标准承保")
+
+    report = {
+        "title": f"科创企业智能风控核保报告 —— {company_name}",
+        "report_id": f"RPT-{datetime.now().strftime('%Y%m%d%H%M%S')}",
+        "generate_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "company_profile": {
+            "name": company_name,
+            "stock_code": company_data.get("stock_code", ""),
+            "industry": company_data.get("industry", ""),
+            "sector": company_data.get("sector", ""),
+            "description": company_data.get("description", ""),
+        },
+        "risk_assessment": {
+            "comprehensive_score": comprehensive,
+            "risk_level": judge.get("risk_level", "中"),
+            "six_dimension_scores": scores,
+            "key_risks": judge.get("key_risks", []),
+        },
+        "debate_summary": {
+            "law_findings": debate_result.get("law_result", {}).get("key_findings", []),
+            "tech_findings": debate_result.get("tech_result", {}).get("key_findings", []),
+            "fin_findings": debate_result.get("finance_result", {}).get("key_findings", []),
+            "conflicts": debate_result.get("conflicts", []),
+            "resolution": judge.get("conflict_resolution", ""),
+        },
+        "underwriting_decision": {
+            "decision": decision,
+            "conditions": judge.get("underwriting_conditions", []),
+            "summary": judge.get("summary", ""),
+        },
+        "pricing": pricing_results,
+    }
+
+    return report
+
+
+def format_report_markdown(report: dict) -> str:
+    """将报告格式化为 Markdown"""
+    lines = []
+    lines.append(f"# {report['title']}")
+    lines.append(f"\n> 报告编号: {report['report_id']} | 生成时间: {report['generate_time']}")
+
+    # 企业概况
+    profile = report["company_profile"]
+    lines.append("\n## 一、企业概况")
+    lines.append(f"- **企业名称**: {profile['name']}")
+    lines.append(f"- **股票代码**: {profile['stock_code']}")
+    lines.append(f"- **所属行业**: {profile['industry']}")
+    lines.append(f"- **企业描述**: {profile['description']}")
+
+    # 风险评估
+    risk = report["risk_assessment"]
+    lines.append("\n## 二、风险评估")
+    lines.append(f"- **综合风险评分**: {risk['comprehensive_score']} 分")
+    lines.append(f"- **风险等级**: {risk['risk_level']}")
+
+    lines.append("\n### 六维风险评分")
+    dim_names = {
+        "tech_disruption": "技术路线颠覆",
+        "talent_loss": "核心人员流失",
+        "algo_compliance": "算法/数据合规",
+        "geopolitical": "地缘政治/出口管制",
+        "rd_capitalization": "研发资本化操纵",
+        "concentration": "客户/供应商集中",
+    }
+    lines.append("| 风险维度 | 评分 | 等级 |")
+    lines.append("|---------|------|------|")
+    for dim, score in risk.get("six_dimension_scores", {}).items():
+        name = dim_names.get(dim, dim)
+        level = "高" if score >= 70 else ("中" if score >= 40 else "低")
+        lines.append(f"| {name} | {score} | {level} |")
+
+    # 辩论摘要
+    debate = report["debate_summary"]
+    lines.append("\n## 三、多智能体交叉验证摘要")
+
+    lines.append("\n### 👩‍⚖️ 法务风控节点")
+    for f in debate.get("law_findings", []):
+        lines.append(f"- {f}")
+
+    lines.append("\n### 👨‍🔬 技术风控节点")
+    for f in debate.get("tech_findings", []):
+        lines.append(f"- {f}")
+
+    lines.append("\n### 👔 财务风控节点")
+    for f in debate.get("fin_findings", []):
+        lines.append(f"- {f}")
+
+    if debate.get("conflicts"):
+        lines.append("\n### ⚠️ 冲突点")
+        for c in debate["conflicts"]:
+            lines.append(f"- {c}")
+
+    # 核保决策
+    uw = report["underwriting_decision"]
+    lines.append(f"\n## 四、核保决策")
+    lines.append(f"\n**决策**: 【{uw['decision']}】")
+    lines.append(f"\n{uw.get('summary', '')}")
+
+    if uw.get("conditions"):
+        lines.append("\n### 核保附加条件")
+        for c in uw["conditions"]:
+            lines.append(f"- {c}")
+
+    # 保费方案
+    lines.append("\n## 五、保险产品费率方案")
+    if report.get("pricing"):
+        lines.append("| 险种 | 基础保费 | 最终保费 | 保额 | 免赔率 |")
+        lines.append("|------|---------|---------|------|--------|")
+        for p in report["pricing"]:
+            if p.get("is_insurable"):
+                lines.append(
+                    f"| {p['product_name']} | ¥{p['base_premium']:,.0f} | "
+                    f"¥{p['final_premium']:,.0f} | ¥{p['adjusted_coverage']:,.0f} | "
+                    f"{p['deductible_rate']:.0%} |"
+                )
+            else:
+                lines.append(f"| {p['product_name']} | - | **拒保** | - | - |")
+
+    return "\n".join(lines)

XH-202626_原型系统源码与部署手册/01_原型系统源码/risk_engine/risk_scorer.py

@@ -0,0 +1,148 @@
+# -*- coding: utf-8 -*-
+"""
+六维风险评分器
+整合所有数据源和分析结果，生成结构化六维风险画像
+"""
+import logging
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+# 风险等级映射
+RISK_LEVELS = {
+    (0, 30): {"level": "低", "color": "#4CAF50", "emoji": "🟢"},
+    (30, 50): {"level": "中低", "color": "#8BC34A", "emoji": "🟡"},
+    (50, 70): {"level": "中高", "color": "#FF9800", "emoji": "🟠"},
+    (70, 90): {"level": "高", "color": "#F44336", "emoji": "🔴"},
+    (90, 101): {"level": "极高", "color": "#B71C1C", "emoji": "⛔"},
+}
+
+
+def get_risk_level(score: int) -> dict:
+    """根据分数获取风险等级详情"""
+    for (low, high), info in RISK_LEVELS.items():
+        if low <= score < high:
+            return info
+    return {"level": "未知", "color": "#9E9E9E", "emoji": "❓"}
+
+
+def calculate_six_dimension_scores(company_data: dict) -> dict:
+    """
+    基于企业数据直接计算六维风险评分（不依赖 LLM）
+    用于快速预览和规则引擎降级场景
+    """
+    financials = company_data.get("financials", {})
+    compliance = company_data.get("compliance", {})
+    tech_route = company_data.get("tech_route", {})
+    personnel = company_data.get("core_tech_personnel", [])
+    supply_chain = company_data.get("supply_chain", {})
+
+    scores = {}
+
+    # 1. 技术路线颠覆风险
+    competing = len(tech_route.get("competing_techs", []))
+    moat = tech_route.get("tech_moat", "")
+    tech_score = min(25 + competing * 15, 85)
+    if "差距" in moat or "受制" in moat or "威胁" in moat:
+        tech_score = min(tech_score + 15, 95)
+    if "领先" in moat or "第一" in moat:
+        tech_score = max(tech_score - 10, 10)
+    scores["tech_disruption"] = tech_score
+
+    # 2. 核心人员流失风险
+    departed = [p for p in personnel if "离职" in p.get("status", "")]
+    high_imp = [p for p in personnel if p.get("importance") == "极高"]
+    if departed:
+        scores["talent_loss"] = 80
+    elif len(high_imp) <= 1 and len(personnel) <= 2:
+        scores["talent_loss"] = 55
+    else:
+        scores["talent_loss"] = 25
+
+    # 3. 算法/数据合规风险
+    algo_status = compliance.get("algo_filing_status", "")
+    data_risk = compliance.get("data_export_risk", "低")
+    algo_score = 20
+    if "未" in algo_status:
+        algo_score = 75
+    elif data_risk == "高":
+        algo_score = 65
+    elif data_risk == "中":
+        algo_score = 40
+    elif "已备案" in algo_status:
+        algo_score = 15
+    scores["algo_compliance"] = algo_score
+
+    # 4. 地缘政治/出口管制风险
+    entity_status = compliance.get("entity_list_status", "")
+    if "被列入" in entity_status:
+        scores["geopolitical"] = 92
+    elif supply_chain.get("supplier_concentration_risk") == "极高":
+        scores["geopolitical"] = 68
+    elif supply_chain.get("supplier_concentration_risk") == "高":
+        scores["geopolitical"] = 50
+    else:
+        scores["geopolitical"] = 18
+
+    # 5. 研发资本化操纵风险
+    cap_rate = financials.get("rd_capitalization_rate", 0)
+    if cap_rate >= 0.4:
+        scores["rd_capitalization"] = 90
+    elif cap_rate >= 0.3:
+        scores["rd_capitalization"] = 72
+    elif cap_rate >= 0.15:
+        scores["rd_capitalization"] = 48
+    elif cap_rate > 0:
+        scores["rd_capitalization"] = 25
+    else:
+        scores["rd_capitalization"] = 10
+
+    # 6. 客户/供应商集中风险
+    cust_ratio = financials.get("top5_customer_ratio", 0)
+    supp_ratio = financials.get("top5_supplier_ratio", 0)
+    max_conc = max(cust_ratio, supp_ratio)
+    if max_conc >= 0.8:
+        scores["concentration"] = 88
+    elif max_conc >= 0.6:
+        scores["concentration"] = 68
+    elif max_conc >= 0.4:
+        scores["concentration"] = 42
+    else:
+        scores["concentration"] = 18
+
+    # 加权综合
+    weights = {
+        "tech_disruption": 0.20,
+        "talent_loss": 0.15,
+        "algo_compliance": 0.15,
+        "geopolitical": 0.20,
+        "rd_capitalization": 0.15,
+        "concentration": 0.15,
+    }
+    comprehensive = int(sum(scores[k] * weights[k] for k in scores))
+
+    # 维度中文名映射
+    dim_names = {
+        "tech_disruption": "技术路线颠覆",
+        "talent_loss": "核心人员流失",
+        "algo_compliance": "算法/数据合规",
+        "geopolitical": "地缘政治/出口管制",
+        "rd_capitalization": "研发资本化操纵",
+        "concentration": "客户/供应商集中",
+    }
+
+    return {
+        "scores": scores,
+        "comprehensive_score": comprehensive,
+        "risk_level": get_risk_level(comprehensive),
+        "dimension_details": {
+            k: {
+                "name": dim_names[k],
+                "score": v,
+                "level": get_risk_level(v),
+                "weight": weights[k],
+            }
+            for k, v in scores.items()
+        },
+    }

XH-202626_原型系统源码与部署手册/01_原型系统源码/utils/session_helper.py

@@ -0,0 +1,115 @@
+# -*- coding: utf-8 -*-
+"""
+全局状态与企业选择同步工具
+跨页面共享 `st.session_state["global_selected_stock_code"]`
+确保在一个页面选择企业后，切换到任意页面均自动保持该企业联动
+包含自动隐藏 Streamlit 右上角 Deploy 按钮与默认 Header 的全局 CSS 样式
+"""
+import streamlit as st
+from collectors.financial_collector import get_all_companies
+
+GLOBAL_COMPANY_KEY = "global_selected_stock_code"
+
+
+def hide_streamlit_header_footer():
+    """彻底隐藏 Streamlit 右上角的 Deploy 按钮、工具栏及页脚"""
+    st.markdown("""
+    <style>
+        /* 隐藏右上角 Deploy 按钮及 Header 菜单 */
+        header[data-testid="stHeader"] {
+            display: none !important;
+            visibility: hidden !important;
+            height: 0px !important;
+        }
+        [data-testid="stDeployButton"] {
+            display: none !important;
+            visibility: hidden !important;
+        }
+        #MainMenu {
+            display: none !important;
+            visibility: hidden !important;
+        }
+        footer {
+            display: none !important;
+            visibility: hidden !important;
+        }
+    </style>
+    """, unsafe_allow_html=True)
+
+
+def get_global_company_code() -> str:
+    """获取当前全局选中的企业股票代码"""
+    companies = get_all_companies()
+    if GLOBAL_COMPANY_KEY not in st.session_state or not st.session_state[GLOBAL_COMPANY_KEY]:
+        st.session_state[GLOBAL_COMPANY_KEY] = companies[0]["stock_code"] if companies else "688256"
+    return st.session_state[GLOBAL_COMPANY_KEY]
+
+
+def set_global_company_code(code: str):
+    """保存选中的企业股票代码到全局 SessionState"""
+    st.session_state[GLOBAL_COMPANY_KEY] = code
+
+
+def render_company_selector(label: str = "🏢 选择待评估科创企业", key_suffix: str = "main"):
+    """
+    渲染与全局 session_state 双向同步的企业选择下拉框
+    返回: selected_company_dict (选中的企业数据字典)
+    """
+    hide_streamlit_header_footer()
+
+    companies = get_all_companies()
+    if not companies:
+        return None
+
+    # 构建带图标与领域的名称映射
+    company_options = {f"{c['short_name']} ({c['stock_code']}) - {c['sector']}": c["stock_code"] for c in companies}
+    labels_list = list(company_options.keys())
+    codes_list = [c["stock_code"] for c in companies]
+
+    current_code = get_global_company_code()
+    default_index = codes_list.index(current_code) if current_code in codes_list else 0
+
+    selected_label = st.selectbox(
+        label,
+        labels_list,
+        index=default_index,
+        key=f"company_selector_{key_suffix}"
+    )
+
+    new_code = company_options[selected_label]
+    if new_code != st.session_state.get(GLOBAL_COMPANY_KEY):
+        set_global_company_code(new_code)
+
+    # 返回选中的完整企业字典
+    for c in companies:
+        if c["stock_code"] == new_code:
+            return c
+    return companies[0]
+
+
+def render_sidebar_global_company_selector():
+    """在侧边栏渲染全局企业选择器与状态指示标签，并自动注入隐藏 Deploy 的 CSS"""
+    hide_streamlit_header_footer()
+
+    companies = get_all_companies()
+    if not companies:
+        return
+
+    company_options = {f"{c['short_name']} ({c['stock_code']})": c["stock_code"] for c in companies}
+    labels_list = list(company_options.keys())
+    codes_list = [c["stock_code"] for c in companies]
+
+    current_code = get_global_company_code()
+    default_index = codes_list.index(current_code) if current_code in codes_list else 0
+
+    st.markdown("### 🏢 全局联动评估目标")
+    selected_label = st.selectbox(
+        "当前联动目标企业:",
+        labels_list,
+        index=default_index,
+        key="global_sidebar_company_selector"
+    )
+
+    new_code = company_options[selected_label]
+    if new_code != st.session_state.get(GLOBAL_COMPANY_KEY):
+        set_global_company_code(new_code)

XH-202626_原型系统源码与部署手册/01_原型系统源码/🏠_系统首页.py

@@ -0,0 +1,304 @@
+# -*- coding: utf-8 -*-
+"""
+🛡️ 科创企业智能风控与核保系统 - 首页 / 风控大屏入口
+"""
+import sys
+import os
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+
+# 确保项目根目录在 Python 路径中
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from config import PAGE_TITLE, PAGE_ICON, LAYOUT
+from collectors.financial_collector import get_all_companies
+from risk_engine.risk_scorer import calculate_six_dimension_scores, get_risk_level
+
+# ============================================================
+# 页面配置
+# ============================================================
+st.set_page_config(
+    page_title=PAGE_TITLE,
+    page_icon=PAGE_ICON,
+    layout=LAYOUT,
+    initial_sidebar_state="expanded",
+)
+
+# ============================================================
+# 自定义样式
+# ============================================================
+st.markdown("""
+<style>
+    /* 主标题渐变 */
+    .main-title {
+        background: linear-gradient(120deg, #1a1a2e 0%, #16213e 50%, #0f3460 100%);
+        padding: 30px;
+        border-radius: 15px;
+        text-align: center;
+        margin-bottom: 30px;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.3);
+    }
+    .main-title h1 {
+        color: #e94560;
+        font-size: 2.2em;
+        margin: 0;
+    }
+    .main-title p {
+        color: #a8a8b3;
+        font-size: 1.1em;
+        margin-top: 10px;
+    }
+
+    /* 统计卡片 */
+    .stat-card {
+        background: linear-gradient(135deg, #1a1a2e, #16213e);
+        padding: 20px;
+        border-radius: 12px;
+        text-align: center;
+        border: 1px solid #2a2a4a;
+        transition: transform 0.2s;
+    }
+    .stat-card:hover {
+        transform: translateY(-2px);
+    }
+    .stat-card .number {
+        font-size: 2.5em;
+        font-weight: bold;
+        color: #e94560;
+    }
+    .stat-card .label {
+        color: #a8a8b3;
+        font-size: 0.95em;
+        margin-top: 5px;
+    }
+
+    /* 企业卡片 */
+    .company-card {
+        background: #16213e;
+        padding: 15px;
+        border-radius: 10px;
+        margin: 8px 0;
+        border-left: 4px solid;
+    }
+
+    /* 侧边栏 */
+    [data-testid="stSidebar"] {
+        background: linear-gradient(180deg, #1a1a2e 0%, #0f0f23 100%);
+    }
+</style>
+""", unsafe_allow_html=True)
+
+from utils.session_helper import render_sidebar_global_company_selector
+
+# ============================================================
+# 侧边栏
+# ============================================================
+with st.sidebar:
+    render_sidebar_global_company_selector()
+    st.markdown("---")
+    st.markdown("### 🛡️ 系统导航")
+    st.markdown("---")
+    st.markdown("""
+    **功能模块**
+    - 🏠 系统首页
+    - 📊 企业风险概览
+    - 🕸️ 供应链知识图谱
+    - ⚖️ 多智能体辩论诊断
+    - 💰 动态定价与核保
+    """)
+    st.markdown("---")
+    st.markdown("""
+    **技术栈**
+    - 🤖 DeepSeek API (LLM)
+    - 🕸️ NetworkX (知识图谱)
+    - 📊 Plotly (可视化)
+    - 🔧 Streamlit (Web框架)
+    """)
+    st.markdown("---")
+    st.caption("中国平安 × 挑战杯 · 科创风控原型")
+
+# ============================================================
+# 主页内容
+# ============================================================
+
+# 标题
+st.markdown("""
+<div class="main-title">
+    <h1>🛡️ 科创企业智能风控与核保系统</h1>
+    <p>基于多智能体辩论 × 知识图谱 × 动态定价的全链条风控平台</p>
+    <p style="font-size: 0.85em; color: #666;">中国青基会平安励志计划 · XH-202626 · 科创企业特有风险的识别与管理</p>
+</div>
+""", unsafe_allow_html=True)
+
+# 加载企业数据
+companies = get_all_companies()
+
+# ============================================================
+# 统计概览卡片
+# ============================================================
+st.markdown("### 📊 系统概览")
+
+# 计算所有企业的风险评分
+risk_data = []
+for comp in companies:
+    scores = calculate_six_dimension_scores(comp)
+    risk_data.append({
+        "company": comp["short_name"],
+        "stock_code": comp["stock_code"],
+        "industry": comp["industry"],
+        "sector": comp["sector"],
+        "comprehensive_score": scores["comprehensive_score"],
+        "risk_level": scores["risk_level"]["level"],
+        **scores["scores"],
+    })
+
+df = pd.DataFrame(risk_data)
+
+# 统计卡片
+col1, col2, col3, col4, col5 = st.columns(5)
+
+with col1:
+    st.markdown(f"""
+    <div class="stat-card">
+        <div class="number">{len(companies)}</div>
+        <div class="label">监控企业总数</div>
+    </div>
+    """, unsafe_allow_html=True)
+
+with col2:
+    high_risk = len(df[df["comprehensive_score"] >= 70])
+    st.markdown(f"""
+    <div class="stat-card">
+        <div class="number" style="color: #F44336;">{high_risk}</div>
+        <div class="label">⚠️ 高风险企业</div>
+    </div>
+    """, unsafe_allow_html=True)
+
+with col3:
+    med_risk = len(df[(df["comprehensive_score"] >= 40) & (df["comprehensive_score"] < 70)])
+    st.markdown(f"""
+    <div class="stat-card">
+        <div class="number" style="color: #FF9800;">{med_risk}</div>
+        <div class="label">🟡 中风险企业</div>
+    </div>
+    """, unsafe_allow_html=True)
+
+with col4:
+    low_risk = len(df[df["comprehensive_score"] < 40])
+    st.markdown(f"""
+    <div class="stat-card">
+        <div class="number" style="color: #4CAF50;">{low_risk}</div>
+        <div class="label">🟢 低风险企业</div>
+    </div>
+    """, unsafe_allow_html=True)
+
+with col5:
+    sanctioned = len([c for c in companies if "被列入" in c.get("compliance", {}).get("entity_list_status", "")])
+    st.markdown(f"""
+    <div class="stat-card">
+        <div class="number" style="color: #B71C1C;">{sanctioned}</div>
+        <div class="label">⛔ 受制裁企业</div>
+    </div>
+    """, unsafe_allow_html=True)
+
+st.markdown("<br>", unsafe_allow_html=True)
+
+# ============================================================
+# 风险分布图
+# ============================================================
+col_left, col_right = st.columns([3, 2])
+
+with col_left:
+    st.markdown("#### 🎯 企业综合风险评分分布")
+
+    # 水平柱状图，按风险排序
+    df_sorted = df.sort_values("comprehensive_score", ascending=True)
+
+    colors = []
+    for score in df_sorted["comprehensive_score"]:
+        if score >= 70:
+            colors.append("#F44336")
+        elif score >= 50:
+            colors.append("#FF9800")
+        elif score >= 30:
+            colors.append("#FFC107")
+        else:
+            colors.append("#4CAF50")
+
+    fig = go.Figure(go.Bar(
+        x=df_sorted["comprehensive_score"],
+        y=df_sorted["company"],
+        orientation="h",
+        marker_color=colors,
+        text=df_sorted["comprehensive_score"],
+        textposition="outside",
+    ))
+    fig.update_layout(
+        height=400,
+        margin=dict(l=0, r=30, t=10, b=10),
+        plot_bgcolor="rgba(0,0,0,0)",
+        paper_bgcolor="rgba(0,0,0,0)",
+        font=dict(color="white"),
+        xaxis=dict(
+            title="综合风险评分",
+            range=[0, 105],
+            gridcolor="rgba(255,255,255,0.1)",
+        ),
+        yaxis=dict(gridcolor="rgba(255,255,255,0.1)"),
+    )
+    # 添加阈值线
+    fig.add_vline(x=70, line_dash="dash", line_color="#F44336",
+                  annotation_text="高风险线(70)", annotation_position="top right")
+    fig.add_vline(x=40, line_dash="dash", line_color="#FF9800",
+                  annotation_text="中风险线(40)", annotation_position="top right")
+    st.plotly_chart(fig, use_container_width=True)
+
+with col_right:
+    st.markdown("#### 🏷️ 行业风险热力")
+
+    # 按行业汇总
+    sector_risk = df.groupby("sector")["comprehensive_score"].mean().reset_index()
+    sector_risk.columns = ["行业", "平均风险"]
+    sector_risk = sector_risk.sort_values("平均风险", ascending=False)
+
+    fig2 = px.bar(
+        sector_risk, x="行业", y="平均风险",
+        color="平均风险",
+        color_continuous_scale=["#4CAF50", "#FFC107", "#F44336"],
+        range_color=[0, 100],
+    )
+    fig2.update_layout(
+        height=400,
+        margin=dict(l=0, r=0, t=10, b=10),
+        plot_bgcolor="rgba(0,0,0,0)",
+        paper_bgcolor="rgba(0,0,0,0)",
+        font=dict(color="white"),
+        showlegend=False,
+        coloraxis_showscale=False,
+    )
+    st.plotly_chart(fig2, use_container_width=True)
+
+# ============================================================
+# 企业列表
+# ============================================================
+st.markdown("### 📋 企业风险速览")
+
+# 格式化数据表格
+display_df = df[["company", "stock_code", "industry", "comprehensive_score", "risk_level"]].copy()
+display_df.columns = ["企业名称", "股票代码", "行业", "综合风险评分", "风险等级"]
+
+st.dataframe(display_df, use_container_width=True, height=400)
+
+# ============================================================
+# 底部信息
+# ============================================================
+st.markdown("---")
+st.markdown("""
+<div style="text-align: center; color: #666; font-size: 0.85em;">
+    <p>🛡️ 科创企业智能风控与核保系统 v1.0</p>
+    <p>技术架构: Multi-Agent 交叉验证 × 供应链知识图谱 × 动态保险定价</p>
+    <p>数据来源: 科创板公开年报 · BIS 实体清单 · 网信办算法备案公示</p>
+</div>
+""", unsafe_allow_html=True)

XH-202626_原型系统源码与部署手册/02_部署与运行手册/系统部署与运行维护手册_XH-202626.md

@@ -0,0 +1,142 @@
+# 中国青基会平安励志计划 · 2026竞赛作品
+# 《数智风控：AI 科创企业新型风险前瞻治理方案》
+## 原型系统部署与运行维护手册 (XH-202626)
+
+---
+
+### 一、 系统概述与体验入口
+
+本系统交付了完备的金融级 Web 风控系统原型，已完成全套生产环境部署与 SSL 证书加密，支持公网在线实时访问与本地一键部署运行。
+
+- **公网加密访问入口**：`https://risk.aiformat.cn`
+- **公网服务器 IP**：`159.75.81.121`
+- **技术栈规范**：
+  - **核心语言**：Python 3.10+
+  - **Web 交互框架**：Streamlit 1.32+
+  - **大模型推理引擎**：DeepSeek API (`deepseek-chat` 深度推理底座)
+  - **图谱计算引擎**：NetworkX 3.0+ (广度优先 BFS 风险传染算法)
+  - **可视化组件**：Plotly 5.18+ / PyVis 0.3+
+  - **反向代理与守护**：Nginx 1.18+ + Systemd 服务守护 + Let's Encrypt SSL
+
+---
+
+### 二、 本地快速一键运行指南
+
+若评审专家或用户需要在本地环境直接运行本源码系统，请按以下步骤操作：
+
+#### 1. 环境准备
+确保本地安装了 **Python 3.10** 或更高版本：
+```bash
+python --version
+```
+
+#### 2. 解压与安装依赖
+解压源码包后进入项目根目录，运行依赖安装命令：
+```bash
+pip install -r requirements.txt
+```
+
+#### 3. 配置 DeepSeek 大模型 API Key (可选)
+系统默认内置了 DeepSeek 演示 API 接口。如需使用自定义密钥，可打开 `config.py` 文件配置：
+```python
+DEEPSEEK_API_KEY = "your-deepseek-api-key"
+```
+
+#### 4. 一键启动 Web 系统
+在终端运行以下命令启动本地交互服务：
+```bash
+streamlit run 🏠_系统首页.py
+```
+终端启动成功后，浏览器将自动弹窗并打开系统界面：
+`http://localhost:8501`
+
+---
+
+### 三、 服务器生产环境部署规范 (CentOS / Ubuntu)
+
+本系统已在生产服务器 `159.75.81.121` 上完成了生产级部署，配置规范如下：
+
+#### 1. 代码目录结构
+```text
+/opt/risk_system/
+├── 🏠_系统首页.py              # 系统控制台大屏入口
+├── config.py                 # 全局参数与风控规则配置
+├── requirements.txt           # Python 依赖清单
+├── pages/                    # 4 大业务功能子模块
+│   ├── 01_📊_企业风险概览.py
+│   ├── 02_🕸️_供应链知识图谱.py
+│   ├── 03_🤖_多智能体辩论诊断.py
+│   └── 04_💰_动态定价与核保.py
+├── risk_engine/               # Multi-Agent 专家辩论与冲突平息引擎
+├── knowledge_graph/           # NetworkX 图谱拓扑与 BIS 穿透算法
+└── collectors/                # AKShare 财报与网信办/BIS 提取器
+```
+
+#### 2. Systemd 服务守护配置 (`/etc/systemd/system/risk_app.service`)
+使用 Systemd 实现无人值守自动拉起与宕机自愈：
+```ini
+[Unit]
+Description=Streamlit Risk App Service
+After=network.target
+
+[Service]
+User=root
+WorkingDirectory=/opt/risk_system
+ExecStart=/usr/local/bin/streamlit run 🏠_系统首页.py --server.port 8501 --server.headless true
+Restart=always
+RestartSec=5
+
+[Install]
+WantedBy=multi-user.target
+```
+
+启动与状态检查命令：
+```bash
+systemctl daemon-reload
+systemctl enable risk_app
+systemctl restart risk_app
+systemctl status risk_app
+```
+
+#### 3. Nginx 反向代理与 WebSocket 连通配置 (`/etc/nginx/conf.d/risk.conf`)
+配置文件支持 HTTP(80) 自动强制重定向至 HTTPS(443)，并开启 WebSocket 实时推流支持：
+```nginx
+server {
+    listen 80;
+    server_name risk.aiformat.cn;
+    return 301 https://$host$request_uri;
+}
+
+server {
+    listen 443 ssl http2;
+    server_name risk.aiformat.cn;
+
+    ssl_certificate /etc/letsencrypt/live/risk.aiformat.cn/fullchain.pem;
+    ssl_certificate_key /etc/letsencrypt/live/risk.aiformat.cn/privkey.pem;
+
+    location / {
+        proxy_pass http://127.0.0.1:8501;
+        proxy_http_version 1.1;
+        proxy_set_header Upgrade $http_upgrade;
+        proxy_set_header Connection "upgrade";
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_set_header X-Forwarded-Proto $scheme;
+    }
+}
+```
+
+---
+
+### 四、 提交材料清单 (Checklist)
+
+| 序号 | 材料名称 | 文件类型 | 描述 |
+|:---|:---|:---|:---|
+| 01 | **研究报告** | `.md` / `.pdf` | 完整的学术研究报告与六维风控理论推导 |
+| 02 | **竞赛汇报 PPT** | `.pptx` | 包含 22 页大字架构与 4 大模块实操截图的演示文稿 |
+| 03 | **原型系统完整源码** | Python 代码包 | 包含 Agent 辩论、知识图谱、动态精算与 Web 源码 |
+| 04 | **部署与运行手册** | `.md` | 本文档，提供本地运行与云服务器守护指引 |
+
+---
+*中国青基会平安励志计划 · 2026 挑战杯“揭榜挂帅”擂台赛 XH-202626 课题研发团队*