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)

2026-08-14 08:21:26 +08:00
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+# -*- 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"