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