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