# 欧盟AI法案合规检查工具
class EUAIActCompliance:
    def __init__(self):
        self.risk_levels = {
            "不可接受风险": ["社会评分", "实时远程生物识别", "潜意识操纵"],
            "高风险": ["关键基础设施", "教育", "就业", "执法", "司法"],
            "有限风险": ["聊天机器人", "情感识别", "深度伪造"],
            "最小风险": ["垃圾邮件过滤", "推荐系统", "游戏AI"]
        }
        
        self.requirements = {
            "高风险": [
                "风险评估和缓解系统",
                "高质量数据集",
                "活动日志记录",
                "详细文档",
                "人工监督",
                "高准确性和稳健性"
            ],
            "有限风险": [
                "透明度义务",
                "用户知情权",
                "可选择退出"
            ],
            "最小风险": [
                "自愿行为准则",
                "最佳实践遵循"
            ]
        }
    
    def check_application(self, app_description):
        """检查应用的风险等级"""
        risk_level = "最小风险"
        
        for level, keywords in self.risk_levels.items():
            for keyword in keywords:
                if keyword in app_description:
                    risk_level = level
                    break
            if risk_level != "最小风险":
                break
        
        return {
            "风险等级": risk_level,
            "合规要求": self.requirements.get(risk_level, ["遵循通用原则"]),
            "处罚风险": self._calculate_penalty_risk(risk_level)
        }
    
    def _calculate_penalty_risk(self, risk_level):
        """计算处罚风险"""
        penalties = {
            "不可接受风险": "最高全球营业额6%或€3000万",
            "高风险": "最高全球营业额4%或€2000万",
            "有限风险": "最高全球营业额2%或€1000万",
            "最小风险": "警告或€500万以下罚款"
        }
        return penalties.get(risk_level, "警告")

# 使用示例
eu_checker = EUAIActCompliance()
print("欧盟AI法案合规检查:")
test_apps = [
    "面部识别考勤系统",
    "教育个性化推荐",
    "游戏AI对手",
    "社交媒体内容审核"
]

for app in test_apps:
    result = eu_checker.check_application(app)
    print(f"\n应用: {app}")
    print(f"风险等级: {result['风险等级']}")
    print(f"处罚风险: {result['处罚风险']}")
    print("合规要求:")
    for req in result['合规要求'][:3]:
        print(f"  • {req}")

# 美国AI监管分析
class USAIRegulation:
    def __init__(self):
        self.framework = {
            "监管原则": [
                "安全优先，创新友好",
                "基于风险，分级管理",
                "行业自律，政府协调",
                "国际合作，标准统一"
            ],
            "重点领域": [
                "关键基础设施保护",
                "国家安全应用",
                "消费者权益保护",
                "算法公平性"
            ],
            "监管机构": {
                "NIST": "技术标准制定",
                "FTC": "消费者保护执法",
                "DOJ": "反垄断和公平性",
                "DHS": "国家安全监管"
            },
            "合规路径": [
                "自愿性安全框架",
                "第三方认证",
                "透明度报告",
                "影响评估"
            ]
        }
    
    def generate_compliance_strategy(self, company_size, ai_usage):
        """生成合规策略"""
        strategies = {
            "startup": {
                "重点": "基础合规，避免高风险应用",
                "预算": "$50K - $200K",
                "时间": "3-6个月",
                "建议": ["使用认证AI服务", "关注NIST框架", "保留算法文档"]
            },
            "medium": {
                "重点": "建立合规体系，准备审计",
                "预算": "$200K - $1M",
                "时间": "6-12个月",
                "建议": ["设立合规官", "定期风险评估", "参与行业标准制定"]
            },
            "enterprise": {
                "重点": "全面合规，影响政策制定",
                "预算": "$1M+",
                "时间": "12-24个月",
                "建议": ["建立伦理委员会", "全球合规团队", "政府关系维护", "开源合规工具"]
            }
        }
        
        strategy = strategies.get(company_size, strategies["medium"])
        
        # 根据AI使用情况调整
        if ai_usage == "high_risk":
            strategy["预算"] = f"{strategy['预算']} (可能增加50%)"
            strategy["时间"] = f"{strategy['时间']} (延长3-6个月)"
            strategy["建议"].append("考虑保险覆盖")
        
        return strategy
    
    def assess_regulatory_trend(self, timeframe="2026-2027"):
        """评估监管趋势"""
        trends = {
            "2026": [
                "NIST AI框架2.0发布",
                "FTC加强算法审计",
                "国会通过基础AI法案",
                "州级监管试点"
            ],
            "2027": [
                "联邦AI监管机构成立",
                "强制性安全认证",
                "算法透明度数据库",
                "国际监管协调机制"
            ]
        }
        
        return trends.get(timeframe, trends["2026"])

# 使用示例
us_reg = USAIRegulation()
print("美国AI监管框架:")
for principle in us_reg.framework["监管原则"]:
    print(f"  • {principle}")

print("\n中型企业合规策略:")
strategy = us_reg.generate_compliance_strategy("medium", "medium_risk")
for key, value in strategy.items():
    if isinstance(value, list):
        print(f"{key}:")
        for item in value[:3]:
            print(f"  • {item}")
    else:
        print(f"{key}: {value}")

print("\n2026年监管趋势:")
for trend in us_reg.assess_regulatory_trend("2026"):
    print(f"  • {trend}")

# 中国AI监管分析
class ChinaAIRegulation:
    def __init__(self):
        self.regulations = {
            "数据安全法": {
                "核心要求": ["数据分类分级", "出境安全评估", "个人信息保护"],
                "适用范围": "所有数据处理活动",
                "处罚": "最高营业额5%或¥1000万"
            },
            "算法推荐管理规定": {
                "核心要求": ["算法备案", "透明度", "用户选择权", "反垄断"],
                "适用范围": "推荐算法、排序算法等",
                "备案平台": "互联网信息服务算法备案系统"
            },
            "生成式AI管理办法": {
                "核心要求": ["安全评估", "内容审核", "标识义务", "训练数据合规"],
                "适用范围": "文本、图像、音频、视频生成",
                "备案要求": "上线前完成备案"
            },
            "科技伦理审查办法": {
                "核心要求": ["伦理委员会", "风险评估", "知情同意", "持续监督"],
                "适用范围": "生命科学、AI等前沿科技",
                "审查机构": "国家科技伦理委员会"
            }
        }
        
        self.compliance_timeline = {
            "立即执行": ["数据分类分级", "算法透明度", "内容审核"],
            "3个月内": ["算法备案", "安全评估", "伦理审查"],
            "6个月内": ["出境评估", "系统改造", "人员培训"],
            "1年内": ["全面合规", "认证获取", "持续改进"]
        }
    
    def generate_compliance_roadmap(self, business_type):
        """生成合规路线图"""
        roadmaps = {
            "互联网平台": {
                "优先级": ["算法备案", "内容审核", "数据安全"],
                "难点": ["算法解释", "实时审核", "跨境合规"],
                "解决方案": ["第三方审核", "AI辅助审核", "数据本地化"]
            },
            "AI研发企业": {
                "优先级": ["算法备案", "安全评估", "伦理审查"],
                "难点": ["技术保密", "评估标准", "国际合规"],
                "解决方案": ["分级披露", "标准参与", "双重合规"]
            },
            "传统企业+AI": {
                "优先级": ["数据安全", "系统安全", "人员培训"],
                "难点": ["遗留系统", "技能缺口", "成本控制"],
                "解决方案": ["渐进改造", "外包服务", "政府补贴"]
            }
        }
        
        roadmap = roadmaps.get(business_type, roadmaps["互联网平台"])
        
        report = f"{business_type}AI合规路线图\n"
        report += "=" * 40 + "\n\n"
        
        report += "🎯 优先级任务:\n"
        for task in roadmap["优先级"]:
            timeline = self._get_timeline(task)
            report += f"  • {task} ({timeline})\n"
        
        report += "\n⚠️ 主要难点:\n"
        for challenge in roadmap["难点"]:
            report += f"  • {challenge}\n"
        
        report += "\n💡 解决方案:\n"
        for solution in roadmap["解决方案"]:
            report += f"  • {solution}\n"
        
        # 成本估算
        cost_estimate = self._estimate_compliance_cost(business_type)
        report += f"\n💰 成本估算: {cost_estimate}\n"
        
        return report
    
    def _get_timeline(self, task):
        """获取任务时间线"""
        for timeline, tasks in self.compliance_timeline.items():
            if task in tasks:
                return timeline
        return "根据具体情况确定"
    
    def _estimate_compliance_cost(self, business_type):
        """估算合规成本"""
        costs = {
            "互联网平台": "¥500万 - ¥2000万",
            "AI研发企业": "¥300万 - ¥1000万",
            "传统企业+AI": "¥100万 - ¥500万"
        }
        return costs.get(business_type, "¥200万 - ¥800万")
    
    def check_algorithm_filing(self, algorithm_type):
        """检查算法备案要求"""
        filing_requirements = {
            "推荐算法": {
                "必须备案": True,
                "备案内容": ["基本原理", "运行机制", "干预措施"],
                "更新频率": "重大变更时",
                "公示要求": "部分信息公示"
            },
            "生成算法": {
                "必须备案": True,
                "备案内容": ["训练数据", "生成规则", "安全措施"],
                "更新频率": "模型更新时",
                "公示要求": "生成内容标识"
            },
            "排序算法": {
                "必须备案": True,
                "备案内容": ["排序规则", "影响因素", "人工干预"],
                "更新频率": "规则变更时",
                "公示要求": "排序原则公示"
            },
            "辅助算法": {
                "必须备案": False,
                "备案内容": ["自愿备案"],
                "更新频率": "N/A",
                "公示要求": "无强制要求"
            }
        }
        
        return filing_requirements.get(algorithm_type, {"必须备案": "需咨询主管部门"})

# 使用示例
china_reg = ChinaAIRegulation()
print("中国AI监管体系:")
for law, details in china_reg.regulations.items():
    print(f"\n{law}:")
    print(f"  核心要求: {', '.join(details['核心要求'][:2])}...")

print("\n" + china_reg.generate_compliance_roadmap("AI研发企业"))

print("\n生成算法备案要求:")
filing_req = china_reg.check_algorithm_filing("生成算法")
for key, value in filing_req.items():
    print(f"  {key}: {value}")

# 全球合规挑战分析
class GlobalComplianceMatrix:
    def __init__(self):
        self.regions = ["欧盟", "美国", "中国", "英国", "新加坡", "日本"]
        
        self.compliance_factors = {
            "数据本地化": {"欧盟": "中等", "美国": "低", "中国": "高", "英国": "低", "新加坡": "中等", "日本": "低"},
            "算法透明度": {"欧盟": "高", "美国": "中等", "中国": "高", "英国": "中等", "新加坡": "中等", "日本": "低"},
            "安全评估": {"欧盟": "高", "美国": "中等", "中国": "高", "英国": "中等", "新加坡": "高", "日本": "中等"},
            "处罚力度": {"欧盟": "高", "美国": "中等", "中国": "高", "英国": "中等", "新加坡": "中等", "日本": "低"},
            "创新友好度": {"欧盟": "低", "美国": "高", "中国": "中等", "英国": "高", "新加坡": "高", "日本": "中等"}
        }
    
    def analyze_complexity(self, target_regions):
        """分析合规复杂度"""
        complexity_scores = {}
        
        for region in target_regions:
            if region not in self.regions:
                continue
            
            score = 0
            for factor, levels in self.compliance_factors.items():
                level = levels.get(region, "低")
                level_score = {"高": 3, "中等": 2, "低": 1}.get(level, 1)
                score += level_score
            
            complexity_scores[region] = {
                "总分": score,
                "等级": self._get_complexity_level(score),
                "主要挑战": self._identify_main_challenges(region)
            }
        
        # 按复杂度排序
        sorted_regions = sorted(complexity_scores.items(), key=lambda x: -x[1]["总分"])
        
        return sorted_regions
    
    def _get_complexity_level(self, score):
        """获取复杂度等级"""
        if score >= 12:
            return "极高"
        elif score >= 10:
            return "高"
        elif score >= 8:
            return "中等"
        else:
            return "低"
    
    def _identify_main_challenges(self, region):
        """识别主要挑战"""
        challenges = {
            "欧盟": ["GDPR扩展", "高风险分类", "全生命周期监管"],
            "美国": ["多机构监管", "州级差异", "快速变化"],
            "中国": ["算法备案", "数据出境", "内容审核"],
            "英国": ["脱欧影响", "国际协调", "创新平衡"],
            "新加坡": ["东盟协调", "国际标准", "小国挑战"],
            "日本": ["文化差异", "语言障碍", "传统企业转型"]
        }
        return challenges.get(region, ["通用合规挑战"])
    
    def generate_global_strategy(self, company_type="multinational"):
        """生成全球策略"""
        strategies = {
            "multinational": {
                "核心原则": "全球统一框架，本地灵活适配",
                "组织架构": "中央合规团队 + 区域专家",
                "技术方案": "模块化合规系统",
                "预算分配": "欧盟40%，美国25%，中国20%，其他15%"
            },
            "regional": {
                "核心原则": "重点市场深度合规，其他市场基础合规",
                "组织架构": "区域主导，总部支持",
                "技术方案": "重点市场定制方案",
                "预算分配": "根据市场规模和监管强度"
            },
            "startup": {
                "核心原则": "合规最小化，专注核心市场",
                "组织架构": "外包为主，内部兼职",
                "技术方案": "使用合规SaaS服务",
                "预算分配": "集中资源攻克1-2个市场"
            }
        }
        
        strategy = strategies.get(company_type, strategies["regional"])
        
        report = f"{company_type}企业全球合规策略\n"
        report += "=" * 50 + "\n\n"
        
        for key, value in strategy.items():
            report += f"{key}: {value}\n"
        
        # 实施建议
        report += "\n🚀 实施建议:\n"
        if company_type == "multinational":
            report += "  1. 建立全球合规数据库\n"
            report += "  2. 开发自动化合规工具\n"
            report += "  3. 定期全球合规审计\n"
            report += "  4. 参与国际标准制定\n"
        elif company_type == "regional":
            report += "  1. 深度理解重点市场\n"
            report += "  2. 建立本地合作伙伴\n"
            report += "  3. 关注监管变化\n"
            report +=            report += "  4. 准备扩展预案\n"
        else:
            report += "  1. 选择监管友好市场\n"
            report += "  2. 利用合规云服务\n"
            report += "  3. 关注监管沙盒\n"
            report += "  4. 保持灵活性\n"
        
        return report

# 使用示例
global_matrix = GlobalComplianceMatrix()
print("全球合规复杂度分析:")
regions_to_analyze = ["欧盟", "美国", "中国", "新加坡"]
complexities = global_matrix.analyze_complexity(regions_to_analyze)

for region, data in complexities:
    print(f"\n{region}:")
    print(f"  复杂度: {data['等级']} (总分: {data['总分']})")
    print(f"  主要挑战: {', '.join(data['主要挑战'][:2])}")

print("\n" + global_matrix.generate_global_strategy("multinational"))

# 合规技术工具包
class ComplianceTechStack:
    def __init__(self):
        self.tools = {
            "数据治理": {
                "分类分级": ["数据发现工具", "敏感数据识别", "分类引擎"],
                "访问控制": ["RBAC系统", "数据脱敏", "审计日志"],
                "生命周期": ["数据保留", "安全删除", "归档管理"]
            },
            "算法管理": {
                "透明度": ["算法文档生成", "可解释AI工具", "影响评估"],
                "监控": ["性能监控", "偏见检测", "异常预警"],
                "版本控制": ["模型注册表", "实验跟踪", "部署管理"]
            },
            "安全合规": {
                "评估": ["安全扫描", "漏洞检测", "渗透测试"],
                "认证": ["合规认证", "第三方审计", "持续验证"],
                "响应": ["事件管理", "应急响应", "恢复计划"]
            },
            "文档管理": {
                "生成": ["自动文档", "模板库", "合规检查"],
                "存储": ["版本管理", "访问控制", "审计跟踪"],
                "提交": ["自动提交", "状态跟踪", "提醒通知"]
            }
        }
        
        self.vendor_solutions = {
            "综合平台": ["OneTrust", "TrustArc", "BigID"],
            "数据安全": ["Varonis", "Imperva", "Forcepoint"],
            "AI治理": ["Fiddler AI", "Arthur AI", "WhyLabs"],
            "合规自动化": ["Drata", "Vanta", "Secureframe"]
        }
    
    def recommend_stack(self, company_size, budget):
        """推荐技术栈"""
        recommendations = {
            "startup": {
                "重点": "低成本、易用性、云服务",
                "数据治理": ["开源分类工具", "云原生访问控制"],
                "算法管理": ["Hugging Face模型卡", "MLflow基础版"],
                "安全合规": ["云安全服务", "基础扫描工具"],
                "文档管理": ["Notion模板", "GitHub Wiki"],
                "年度成本": "$10K - $50K"
            },
            "medium": {
                "重点": "集成性、可扩展性、自动化",
                "数据治理": ["商业分类工具", "统一访问管理"],
                "算法管理": ["MLOps平台", "可解释AI工具"],
                "安全合规": ["综合安全平台", "定期审计服务"],
                "文档管理": ["合规文档系统", "自动生成工具"],
                "年度成本": "$50K - $200K"
            },
            "enterprise": {
                "重点": "全面性、定制化、智能化",
                "数据治理": ["企业级数据平台", "智能分类引擎"],
                "算法管理": ["全生命周期平台", "实时监控系统"],
                "安全合规": ["专用安全团队", "持续合规监控"],
                "文档管理": ["企业内容管理", "智能文档生成"],
                "年度成本": "$200K - $1M+"
            }
        }
        
        rec = recommendations.get(company_size, recommendations["medium"])
        
        # 根据预算调整
        budget_range = rec["年度成本"].replace("$", "").replace("K", "000").replace("M", "000000").split(" - ")
        try:
            min_budget = float(budget_range[0])
            max_budget = float(budget_range[1]) if len(budget_range) > 1 else min_budget * 2
            
            if budget < min_budget:
                rec["建议"] = "考虑开源方案或简化需求"
            elif budget > max_budget:
                rec["建议"] = "可考虑更全面的解决方案"
            else:
                rec["建议"] = "预算匹配推荐方案"
        except:
            rec["建议"] = "需详细预算分析"
        
        return rec
    
    def calculate_roi(self, investment, risk_reduction, efficiency_gain):
        """计算投资回报率"""
        # 假设风险减少带来的价值
        risk_value = investment * risk_reduction * 3  # 3倍乘数
        
        # 效率提升带来的价值
        efficiency_value = investment * efficiency_gain * 2  # 2年回收
        
        total_value = risk_value + efficiency_value
        roi = (total_value - investment) / investment * 100
        
        return {
            "投资额": f"${investment:,.0f}",
            "风险减少价值": f"${risk_value:,.0f}",
            "效率提升价值": f"${efficiency_value:,.0f}",
            "总价值": f"${total_value:,.0f}",
            "投资回报率": f"{roi:.1f}%",
            "回收期": f"{investment/efficiency_value*12:.1f}个月" if efficiency_value > 0 else "N/A"
        }

# 使用示例
tech_stack = ComplianceTechStack()
print("合规技术工具分类:")
for category, tools in tech_stack.tools.items():
    print(f"\n{category}:")
    for subcat, items in tools.items():
        print(f"  {subcat}: {', '.join(items[:2])}")

print("\n中型企业技术栈推荐:")
rec = tech_stack.recommend_stack("medium", 100000)
for key, value in rec.items():
    if isinstance(value, list):
        print(f"{key}:")
        for item in value[:2]:
            print(f"  • {item}")
    else:
        print(f"{key}: {value}")

print("\n合规投资ROI分析:")
roi = tech_stack.calculate_roi(100000, 0.3, 0.2)
for key, value in roi.items():
    print(f"{key}: {value}")

# 合规实施框架
class ComplianceImplementation:
    def __init__(self):
        self.phases = {
            "评估阶段 (1-3个月)": {
                "目标": "理解现状，识别差距",
                "活动": [
                    "监管要求分析",
                    "现状评估",
                    "差距分析",
                    "风险评估",
                    "优先级确定"
                ],
                "交付物": ["合规评估报告", "差距分析矩阵", "实施路线图"],
                "关键成功因素": ["高层支持", "跨部门协作", "准确评估"]
            },
            "设计阶段 (3-6个月)": {
                "目标": "设计解决方案，制定计划",
                "活动": [
                    "架构设计",
                    "流程设计",
                    "技术选型",
                    "组织设计",
                    "培训计划"
                ],
                "交付物": ["解决方案设计", "实施计划", "预算计划", "组织架构"],
                "关键成功因素": ["用户参与", "可行性验证", "资源保障"]
            },
            "实施阶段 (6-12个月)": {
                "目标": "部署系统，建立能力",
                "活动": [
                    "系统开发",
                    "流程实施",
                    "人员培训",
                    "测试验证",
                    "文档编制"
                ],
                "交付物": ["运行系统", "操作手册", "培训材料", "测试报告"],
                "关键成功因素": ["项目管理", "变更管理", "质量保证"]
            },
            "运营阶段 (持续)": {
                "目标": "持续改进，确保合规",
                "活动": [
                    "监控审计",
                    "持续改进",
                    "法规跟踪",
                    "报告沟通",
                    "更新维护"
                ],
                "交付物": ["合规报告", "审计报告", "改进计划", "培训记录"],
                "关键成功因素": ["持续监控", "快速响应", "文化融入"]
            }
        }
    
    def generate_implementation_plan(self, company_profile):
        """生成实施计划"""
        plan = f"{company_profile['name']} AI合规实施计划\n"
        plan += "=" * 60 + "\n\n"
        
        plan += "🏢 公司概况:\n"
        plan += f"  规模: {company_profile.get('size', '中型')}\n"
        plan += f"  行业: {company_profile.get('industry', '科技')}\n"
        plan += f"  AI应用: {company_profile.get('ai_usage', '中等')}\n"
        plan += f"  目标市场: {', '.join(company_profile.get('markets', ['中国', '美国']))}\n\n"
        
        total_months = 0
        total_budget = 0
        
        for phase, details in self.phases.items():
            duration = int(phase.split('(')[1].split('-')[0])
            total_months += duration
            
            # 估算预算
            budget_multiplier = {
                "评估阶段": 0.1,
                "设计阶段": 0.2,
                "实施阶段": 0.6,
                "运营阶段": 0.1
            }
            
            phase_budget = company_profile.get('total_budget', 1000000) * budget_multiplier.get(phase.split(' ')[0], 0.1)
            total_budget += phase_budget
            
            plan += f"📋 {phase}:\n"
            plan += f"  预算: ${phase_budget:,.0f}\n"
            plan += f"  主要活动:\n"
            for activity in details['活动'][:3]:
                plan += f"    • {activity}\n"
            plan += f"  关键交付物: {', '.join(details['交付物'][:2])}\n"
            plan += f"  成功因素: {details['关键成功因素'][0]}\n\n"
        
        plan += f"📊 总体计划:\n"
        plan += f"  总时长: {total_months}个月\n"
        plan += f"  总预算: ${total_budget:,.0f}\n"
        plan += f"  月均投入: ${total_budget/total_months:,.0f}\n\n"
        
        plan += "🎯 成功指标:\n"
        plan += "  1. 合规覆盖率 > 95%\n"
        plan += "  2. 审计通过率 100%\n"
        plan += "  3. 员工培训完成率 > 90%\n"
        plan += "  4. 违规事件数 = 0\n"
        plan += "  5. 监管变化响应时间 < 30天\n"
        
        return plan
    
    def identify_common_pitfalls(self):
        """识别常见陷阱"""
        pitfalls = [
            {
                "陷阱": "低估复杂度",
                "表现": "预算和时间严重不足",
                "后果": "项目失败，合规风险",
                "预防": "详细评估，预留缓冲"
            },
            {
                "陷阱": "技术导向",
                "表现": "忽视流程和组织",
                "后果": "系统闲置，合规无效",
                "预防": "业务驱动，全员参与"
            },
            {
                "陷阱": "静态思维",
                "表现": "一次性项目心态",
                "后果": "快速过时，持续违规",
                "预防": "持续改进，动态调整"
            },
            {
                "陷阱": "孤岛实施",
                "表现": "部门各自为政",
                "后果": "重复投入，标准不一",
                "预防": "统一规划，协同实施"
            },
            {
                "陷阱": "忽视文化",
                "表现": "强制推行，缺乏认同",
                "后果": "员工抵触，执行困难",
                "预防": "文化建设，培训沟通"
            }
        ]
        
        return pitfalls

# 使用示例
implementation = ComplianceImplementation()
company_profile = {
    "name": "智科科技",
    "size": "中型",
    "industry": "AI解决方案",
    "ai_usage": "高",
    "markets": ["中国", "美国", "欧盟"],
    "total_budget": 1500000
}

print(implementation.generate_implementation_plan(company_profile))

print("⚠️ 常见实施陷阱:")
pitfalls = implementation.identify_common_pitfalls()
for i, pitfall in enumerate(pitfalls[:3], 1):
    print(f"\n{i}. {pitfall['陷阱']}:")
    print(f"   表现: {pitfall['表现']}")
    print(f"   后果: {pitfall['后果']}")
    print(f"   预防: {pitfall['预防']}")

# 未来监管预测
class FutureRegulationForecast:
    def __init__(self):
        self.trends = {
            "技术监管": [
                "AI系统分级认证制度",
                "算法可解释性标准统一",
                "AI安全测试基准建立",
                "量子AI监管框架初现"
            ],
            "数据治理": [
                "数据主权成为国际规则",
                "隐私计算技术标准化",
                "数据要素市场规范化",
                "跨境数据流动新机制"
            ],
            "伦理治理": [
                "AI伦理国际共识形成",
                "算法公平性量化评估",
                "AI权利与责任界定",
                "人机协作伦理规范"
            ],
            "国际协调": [
                "全球AI治理机构成立",
                "监管互认机制建立",
                "跨国执法合作加强",
                "标准统一进程加速"
            ]
        }
        
        self.scenarios = {
            "乐观": {
                "描述": "创新友好，国际协调",
                "特征": ["监管沙盒普及", "标准统一", "国际合作", "创新加速"],
                "增长率": "AI投资年增25-35%",
                "风险": "监管滞后，安全挑战"
            },
            "基准": {
                "描述": "平衡发展，渐进改革",
                "特征": ["风险分级", "行业自律", "区域差异", "逐步统一"],
                "增长率": "AI投资年增15-25%",
                "风险": "合规成本，市场分割"
            },
            "悲观": {
                "描述": "监管碎片，创新受限",
                "特征": ["保护主义", "过度监管", "标准冲突", "创新放缓"],
                "增长率": "AI投资年增5-15%",
                "风险": "技术垄断，发展失衡"
            }
        }
    
    def generate_forecast_report(self, preferred_scenario="基准"):
        """生成预测报告"""
        scenario = self.scenarios.get(preferred_scenario, self.scenarios["基准"])
        
        report = f"AI监管未来趋势预测 ({preferred_scenario}情景)\n"
        report += "=" * 60 + "\n\n"
        
        report += f"📝 情景描述: {scenario['描述']}\n\n"
        
        report += "🔮 主要趋势:\n"
        for category, trends in self.trends.items():
            report += f"\n{category}:\n"
            for trend in trends[:2]:
                report += f"  • {trend}\n"
        
        report += f"\n📊 情景特征:\n"
        for feature in scenario["特征"]:
            report += f"  • {feature}\n"
        
        report += f"\n💰 经济影响: {scenario['增长率']}\n"
        report += f"⚠️ 主要风险: {scenario['风险']}\n\n"
        
        report += "🎯 战略建议:\n"
        if preferred_scenario == "乐观":
            report += "  1. 加大研发投入，抢占先机\n"
            report += "  2. 参与标准制定，影响规则\n"
            report += "  3. 布局全球市场，利用协调\n"
            report += "  4. 投资安全技术，建立信任\n"
        elif preferred_scenario == "基准":
            report += "  1. 建立灵活合规体系\n"
            report += "  2. 关注区域监管变化\n"
            report += "  3. 平衡创新与合规\n"
            report += "  4. 准备多种情景预案\n"
        else:
            report += "  1. 聚焦核心市场\n"
            report += "  2. 控制合规成本\n"
            report += "  3. 寻求监管确定性\n"
            report += "  4. 建立风险缓冲\n"
        
        return report
    
    def calculate_preparedness_score(self, current_capabilities):
        """计算准备度评分"""
        capabilities = current_capabilities
        
        # 能力权重
        weights = {
            "监管跟踪": 0.15,
            "风险评估": 0.20,
            "合规体系": 0.25,
            "技术工具": 0.20,
            "人员能力": 0.20
        }
        
        # 计算总分
        total_score = 0
        for capability, weight in weights.items():
            score = capabilities.get(capability, 0)
            total_score += score * weight
        
        # 评估等级
        if total_score >= 80:
            level = "领先"
            readiness = "完全准备好应对未来监管"
        elif total_score >= 60:
            level = "良好"
            readiness = "基本准备好，需持续改进"
        elif total_score >= 40:
            level = "基础"
            readiness = "有基础，需重点加强"
        else:
            level = "薄弱"
            readiness = "需立即行动，建立能力"
        
        return {
            "总分": f"{total_score:.1f}/100",
            "等级": level,
            "准备度": readiness,
            "改进重点": self.__identify_improvement_areas(capabilities)
        }
    
    def _identify_improvement_areas(self, capabilities):
        """识别改进领域"""
        improvement_areas = []
        
        if capabilities.get("监管跟踪", 0) < 70:
            improvement_areas.append("建立系统化监管跟踪机制")
        
        if capabilities.get("风险评估", 0) < 70:
            improvement_areas.append("完善风险评估框架")
        
        if capabilities.get("合规体系", 0) < 70:
            improvement_areas.append("建立全面合规管理体系")
        
        if capabilities.get("技术工具", 0) < 70:
            improvement_areas.append("投资合规技术工具")
        
        if capabilities.get("人员能力", 0) < 70:
            improvement_areas.append("加强人员培训和能力建设")
        
        return improvement_areas[:3]  # 返回前3个重点

# 使用示例
forecast = FutureRegulationForecast()
print(forecast.generate_forecast_report("基准"))

print("\n📋 企业准备度评估:")
sample_capabilities = {
    "监管跟踪": 65,
    "风险评估": 70,
    "合规体系": 60,
    "技术工具": 55,
    "人员能力": 75
}

preparedness = forecast.calculate_preparedness_score(sample_capabilities)
for key, value in preparedness.items():
    if isinstance(value, list):
        print(f"{key}:")
        for item in value:
            print(f"  • {item}")
    else:
        print(f"{key}: {value}")