# AI市场规模预测模型
import numpy as np
import pandas as pd
from datetime import datetime

class AIMarketForecast:
    def __init__(self):
        self.base_year = 2026
        self.current_market = 500  # 2026年市场规模（十亿美元）
        self.growth_factors = {
            "基础模型": 0.25,
            "行业应用": 0.35,
            "硬件设备": 0.20,
            "服务与咨询": 0.15,
            "新兴领域": 0.05
        }
    
    def forecast_market_size(self, target_year=2030):
        """预测市场规模"""
        years = target_year - self.base_year
        
        # 复合年增长率预测
        cagr_scenarios = {
            "乐观": 0.25,    # 25% CAGR
            "基准": 0.18,    # 18% CAGR
            "保守": 0.12     # 12% CAGR
        }
        
        forecasts = {}
        for scenario, cagr in cagr_scenarios.items():
            market_size = self.current_market * ((1 + cagr) ** years)
            forecasts[scenario] = market_size
        
        return forecasts
    
    def segment_forecast(self, target_year=2030):
        """分领域市场预测"""
        years = target_year - self.base_year
        segment_growth = {
            "基础模型": 0.30,  # 30% CAGR
            "行业应用": 0.22,  # 22% CAGR
            "硬件设备": 0.15,  # 15% CAGR
            "服务与咨询": 0.12, # 12% CAGR
            "新兴领域": 0.40   # 40% CAGR
        }
        
        segment_sizes = {}
        total_market = 0
        
        for segment, share in self.growth_factors.items():
            base_size = self.current_market * share
            growth_rate = segment_growth.get(segment, 0.18)
            future_size = base_size * ((1 + growth_rate) ** years)
            segment_sizes[segment] = future_size
            total_market += future_size
        
        return {
            "segment_sizes": segment_sizes,
            "total_market": total_market,
            "segment_shares": {k: v/total_market for k, v in segment_sizes.items()}
        }
    
    def generate_report(self, target_year=2030):
        """生成市场预测报告"""
        market_forecast = self.forecast_market_size(target_year)
        segment_analysis = self.segment_forecast(target_year)
        
        report = f"人工智能市场预测报告 (2026-{target_year})\n"
        report += "=" * 60 + "\n\n"
        
        report += "📊 整体市场规模预测（十亿美元）:\n"
        for scenario, size in market_forecast.items():
            report += f"  {scenario}情景: ${size:,.0f}B (CAGR: {scenario}率)\n"
        
        report += f"\n🎯 {target_year}年预测总规模: ${segment_analysis['total_market']:,.0f}B\n\n"
        
        report += "📈 分领域市场规模预测:\n"
        for segment, size in segment_analysis["segment_sizes"].items():
            share = segment_analysis["segment_shares"][segment] * 100
            report += f"  • {segment}: ${size:,.0f}B ({share:.1f}%)\n"
        
        report += "\n🔍 关键洞察:\n"
        report += "  1. 基础模型和新兴领域增长最快\n"
        report += "  2. 行业应用占据最大市场份额\n"
        report += "  3. 硬件设备市场稳步增长\n"
        report += "  4. 服务咨询需求持续增加\n"
        
        return report

# 使用示例
forecaster = AIMarketForecast()
print(forecaster.generate_report(2030))

class LLMTrendAnalyzer:
    def __init__(self):
        self.trend_data = {
            "年份": [2022, 2023, 2024, 2025, 2026],
            "最大参数量(B)": [0.5, 1.0, 10.0, 100.0, 500.0],
            "训练成本(百万$)": [5, 10, 50, 200, 500],
            "推理速度(tokens/s)": [100, 200, 500, 1000, 2000],
            "能效比(性能/能耗)": [1.0, 1.5, 2.5, 4.0, 6.0]
        }
    
    def analyze_trends(self):
        """分析技术趋势"""
        df = pd.DataFrame(self.trend_data)
        
        # 计算增长率
        df["参数量增长率"] = df["最大参数量(B)"].pct_change() * 100
        df["成本增长率"] = df["训练成本(百万$)"].pct_change() * 100
        df["速度增长率"] = df["推理速度(tokens/s)"].pct_change() * 100
        df["能效增长率"] = df["能效比(性能/能耗)"].pct_change() * 100
        
        insights = []
        
        # 参数量趋势
        param_growth = df["参数量增长率"].mean()
        if param_growth > 100:
            insights.append("参数量呈指数增长")
        elif param_growth > 50:
            insights.append("参数量快速增长")
        else:
            insights.append("参数量增长放缓")
        
        # 成本趋势
        cost_growth = df["成本增长率"].mean()
        if cost_growth > param_growth:
            insights.append("成本增长快于性能增长")
        else:
            insights.append("性能提升效率优化")
        
        # 效率趋势
        efficiency_growth = df["能效增长率"].mean()
        if efficiency_growth > 0:
            insights.append(f"能效提升{efficiency_growth:.1f}%每年")
        
        return df, insights
    
    def predict_2030(self):
        """预测2030年技术指标"""
        current = self.trend_data["年份"][-1]
        years_to_2030 = 2030 - current
        
        # 基于历史趋势的预测
        predictions = {
            "最大参数量(B)": 500 * (1.5 ** years_to_2030),  # 50%年增长
            "训练成本(百万$)": 500 * (1.2 ** years_to_2030),  # 20%年增长
            "推理速度(tokens/s)": 2000 * (1.4 ** years_to_2030),  # 40%年增长
            "能效比(性能/能耗)": 6.0 * (1.3 ** years_to_2030)  # 30%年增长
        }
        
        return predictions

# 使用示例
analyzer = LLMTrendAnalyzer()
df, insights = analyzer.analyze_trends()

print("📊 大语言模型技术趋势分析:")
for insight in insights:
    print(f"  • {insight}")

predictions = analyzer.predict_2030()
print(f"\n🎯 2030年技术预测:")
for metric, value in predictions.items():
    if "参数量" in metric:
        print(f"  {metric}: {value:,.0f}B")
    elif "成本" in metric:
        print(f"  {metric}: ${value:,.0f}M")
    elif "速度" in metric:
        print(f"  {metric}: {value:,.0f} tokens/s")
    else:
        print(f"  {metric}: {value:.1f}x")

class MultimodalAIAnalysis:
    def __init__(self):
        self.modalities = ["文本", "图像", "音频", "视频", "3D"]
        self.capabilities = {
            "理解": [0.9, 0.8, 0.7, 0.6, 0.5],  # 能力成熟度 (0-1)
            "生成": [0.8, 0.7, 0.6, 0.5, 0.4],
            "转换": [0.7, 0.6, 0.5, 0.4, 0.3],
            "融合": [0.6, 0.5, 0.4, 0.3, 0.2]
        }
    
    def analyze_capability_gaps(self):
        """分析能力差距"""
        gaps = {}
        
        for modality in self.modalities:
            idx = self.modalities.index(modality)
            modality_capabilities = {
                cap: self.capabilities[cap][idx]
                for cap in self.capabilities
            }
            
            # 计算平均能力
            avg_capability = np.mean(list(modality_capabilities.values()))
            
            # 识别最弱能力
            weakest = min(modality_capabilities, key=modality_capabilities.get)
            weakest_score = modality_capabilities[weakest]
            
            gaps[modality] = {
                "平均能力": avg_capability,
                "最弱环节": weakest,
                "最弱得分": weakest_score,
                "改进空间": 1 - weakest_score
            }
        
        return gaps
    
    def predict_convergence_timeline(self):
        """预测技术收敛时间线"""
        convergence_threshold = 0.8  # 80%成熟度
        
        timeline = {}
        for modality in self.modalities:
            idx = self.modalities.index(modality)
            
            # 计算当前平均成熟度
            current_maturity = np.mean([
                self.capabilities[cap][idx]
                for cap in self.capabilities
            ])
            
            # 基于历史增长率预测
            growth_rate = 0.15  # 15%年增长
            years_to_convergence = np.log(convergence_threshold / current_maturity) / np.log(1 + growth_rate)
            
            timeline[modality] = {
                "当前成熟度": current_maturity,
                "预计收敛年份": 2026 + max(0, years_to_convergence),
                "所需年数": max(0, years_to_convergence)
            }
        
        return timeline

# 使用示例
multimodal = MultimodalAIAnalysis()
gaps = multimodal.analyze_capability_gaps()

print("🔍 多模态AI能力差距分析:")
for modality, data in gaps.items():
    print(f"\n{modality}:")
    print(f"  平均能力: {data['平均能力']:.1%}")
    print(f"  最弱环节: {data['最弱环节']} ({data['最弱得分']:.1%})")
    print(f"  改进空间: {data['改进空间']:.1%}")

timeline = multimodal.predict_convergence_timeline()
print("\n⏰ 多模态技术收敛时间线:")
for modality, data in timeline.items():
    print(f"  {modality}: {data['预计收敛年份']:.0f}年 (还需{data['所需年数']:.1f}年)")

class HealthcareAIAnalysis:
    def __init__(self):
        self.application_areas = {
            "医学影像诊断": {
                "成熟度": 0.85,
                "市场规模": 15,  # 十亿美元
                "增长率": 0.25,
                "主要玩家": ["Google Health", "IBM Watson", "国内AI医疗公司"]
            },
            "药物发现": {
                "成熟度": 0.70,
                "市场规模": 8,
                "增长率": 0.35,
                "主要玩家": ["DeepMind", "Insilico Medicine", "晶泰科技"]
            },
            "个性化治疗": {
                "成熟度": 0.60,
                "市场规模": 12,
                "增长率": 0.30,
                "主要玩家": ["Tempus", "Flatiron Health", "医渡科技"]
            },
            "健康管理": {
                "成熟度": 0.75,
                "市场规模": 20,
                "增长率": 0.20,
                "主要玩家": ["Apple", "Fitbit", "华为健康"]
            }
        }
    
    def analyze_market_potential(self, target_year=2030):
        """分析市场潜力"""
        years = target_year - 2026
        analysis = {}
        
        for area, data in self.application_areas.items():
            current_market = data["市场规模"]
            growth_rate = data["增长率"]
            
            # 市场规模预测
            future_market = current_market * ((1 + growth_rate) ** years)
            
            # 技术成熟度影响
            maturity_factor = data["成熟度"]
            adjusted_market = future_market * maturity_factor
            
            analysis[area] = {
                "当前市场": current_market,
                "预测市场": future_market,
                "调整后市场": adjusted_market,
                "年复合增长率": growth_rate,
                "成熟度": maturity_factor,
                "增长潜力": (future_market - current_market) / current_market
            }
        
        return analysis
    
    def identify_opportunities(self, threshold=0.3):
        """识别投资机会"""
        analysis = self.analyze_market_potential()
        opportunities = []
        
        for area, data in analysis.items():
            if data["增长潜力"] > threshold and data["成熟度"] > 0.6:
                opportunity_score = data["增长潜力"] * data["成熟度"] * 100
                opportunities.append({
                    "领域": area,
                    "机会分数": opportunity_score,
                    "理由": f"高增长潜力({data['增长潜力']:.0%}) + 技术成熟({data['成熟度']:.0%})"
                })
        
        # 按机会分数排序
        opportunities.sort(key=lambda x: -x["机会分数"])
        return opportunities

# 使用示例
healthcare = HealthcareAIAnalysis()
market_analysis = healthcare.analyze_market_potential()

print("🏥 医疗健康AI市场分析 (2030年预测):")
for area, data in market_analysis.items():
    print(f"\n{area}:")
    print(f"  当前市场: ${data['当前市场']}B")
    print(f"  预测市场: ${data['预测市场']:.1f}B")
    print(f"  CAGR: {data['年复合增长率']:.0%}")
    print(f"  增长潜力: {data['增长潜力']:.0%}")

opportunities = healthcare.identify_opportunities()
print("\n💡 推荐投资机会:")
for opp in opportunities[:3]:
    print(f"  • {opp['领域']}: {opp['机会分数']:.1f}分 - {opp['理由']}")

class FintechAIAnalysis:
    def __init__(self):
        self.applications = {
            "风险管理": {
                "渗透率": 0.65,  # 当前渗透率
                "效率提升": 0.40,  # 效率提升比例
                "准确率提升": 0.25,
                "主要技术": ["异常检测", "图神经网络", "时序预测"]
            },
            "智能投顾": {
                "渗透率": 0.45,
                "效率提升": 0.50,
                "准确率提升": 0.15,
                "主要技术": ["推荐系统", "个性化模型", "投资组合优化"]
            },
            "算法交易": {
                "渗透率": 0.70,
                "效率提升": 0.60,
                "准确率提升": 0.20,
                "主要技术": ["强化学习", "高频交易", "市场预测"]
            },
            "合规监控": {
                "渗透率": 0.55,
                "效率提升": 0.35,
                "准确率提升": 0.30,
                "主要技术": ["NLP", "知识图谱", "规则引擎"]
            }
        }
    
    def calculate_roi(self, investment, time_horizon=3):
        """计算投资回报率"""
        roi_analysis = {}
        
        for app, data in self.applications.items():
            # 基础假设
            base_efficiency = 1.0
            new_efficiency = base_efficiency * (1 + data["效率提升"])
            
            # 准确率提升带来的价值
            accuracy_value = data["准确率提升"] * 100  # 每1%准确率提升的价值
            
            # 渗透率增长带来的市场机会
            market_growth = (1 - data["渗透率"]) * 0.5  # 剩余市场的一半
            
            # 年化收益计算
            annual_benefit = (new_efficiency - base_efficiency) * 100 + accuracy_value
            total_benefit = annual_benefit * time_horizon * (1 + market_growth)
            
            # ROI计算
            roi = (total_benefit - investment) / investment * 100
            
            roi_analysis[app] = {
                "年化收益": annual_benefit,
                "总收益": total_benefit,
                "投资回报率": roi,
                "回收期": investment / annual_benefit if annual_benefit > 0 else float('inf'),
                "推荐投资": roi > 50  # ROI超过50%推荐投资
            }
        
        return roi_analysis
    
    def generate_investment_strategy(self, total_budget=100):
        """生成投资策略"""
        roi_analysis = self.calculate_roi(total_budget / 4)  # 假设每个领域投资1/4
        
        # 按ROI排序
        sorted_apps = sorted(
            roi_analysis.items(),
            key=lambda x: -x[1]["投资回报率"]
        )
        
        strategy = {
            "重点投资": [],
            "适度投资": [],
            "观望": []
        }
        
        for app, data in sorted_apps:
            if data["推荐投资"] and data["投资回报率"] > 100:
                strategy["重点投资"].append({
                    "应用": app,
                    "建议投资": total_budget * 0.4,  # 40%预算
                    "预期ROI": data["投资回报率"],
                    "回收期": data["回收期"]
                })
            elif data["推荐投资"]:
                strategy["适度投资"].append({
                    "应用": app,
                    "建议投资": total_budget * 0.2,  # 20%预算
                    "预期ROI": data["投资回报率"],
                    "回收期": data["回收期"]
                })
            else:
                strategy["观望"].append(app)
        
        return strategy

# 使用示例
fintech = FintechAIAnalysis()
roi_analysis = fintech.calculate_roi(investment=25)  # 2500万美元投资

print("💰 金融科技AI投资回报分析:")
for app, data in roi_analysis.items():
    print(f"\n{app}:")
    print(f"  年化收益: ${data['年化收益']:.1f}M")
    print(f"  投资回报率: {data['投资回报率']:.1f}%")
    print(f"  回收期: {data['回收期']:.1f}年")
    print(f"  推荐投资: {'✅' if data['推荐投资'] else '❌'}")

strategy = fintech.generate_investment_strategy(100)
print("\n🎯 投资策略建议:")
print("重点投资领域:")
for item in strategy["重点投资"]:
    print(f"  • {item['应用']}: ${item['建议投资']}M, 预期ROI: {item['预期ROI']:.1f}%")

class RegionalAIAnalysis:
    def __init__(self):
        self.regions = {
            "北美": {
                "研发实力": 0.95,
                "产业生态": 0.90,
                "资本投入": 0.85,
                "人才储备": 0.80,
                "政策支持": 0.75
            },
            "欧洲": {
                "研发实力": 0.80,
                "产业生态": 0.75,
                "资本投入": 0.70,
                "人才储备": 0.85,
                "政策支持": 0.90
            },
            "中国": {
                "研发实力": 0.85,
                "产业生态": 0.95,
                "资本投入": 0.90,
                "人才储备": 0.75,
                "政策支持": 0.95
            },
            "亚太其他": {
                "研发实力": 0.70,
                "产业生态": 0.65,
                "资本投入": 0.75,
                "人才储备": 0.80,
                "政策支持": 0.85
            }
        }
    
    def calculate_composite_score(self):
        """计算综合得分"""
        scores = {}
        weights = {
            "研发实力": 0.30,
            "产业生态": 0.25,
            "资本投入": 0.20,
            "人才储备": 0.15,
            "政策支持": 0.10
        }
        
        for region, metrics in self.regions.items():
            composite_score = 0
            for metric, weight in weights.items():
                composite_score += metrics[metric] * weight
            
            scores[region] = {
                "综合得分": composite_score,
                "优势领域": max(metrics, key=metrics.get),
                "劣势领域": min(metrics, key=metrics.get),
                "各项得分": metrics
            }
        
        return scores
    
    def predict_2030_landscape(self):
        """预测2030年发展格局"""
        growth_rates = {
            "北美": 0.08,   # 8%年增长
            "欧洲": 0.10,   # 10%年增长
            "中国": 0.15,   # 15%年增长
            "亚太其他": 0.12  # 12%年增长
        }
        
        current_scores = self.calculate_composite_score()
        future_landscape = {}
        
        for region, data in current_scores.items():
            current_score = data["综合得分"]
            growth_rate = growth_rates[region]
            years = 2030 - 2026
            
            future_score = current_score * ((1 + growth_rate) ** years)
            
            future_landscape[region] = {
                "当前得分": current_score,
                "预测得分": future_score,
                "增长率": growth_rate,
                "排名变化": "待计算"
            }
        
        # 计算排名变化
        current_ranking = sorted(
            current_scores.items(),
            key=lambda x: -x[1]["综合得分"]
        )
        future_ranking = sorted(
            future_landscape.items(),
            key=lambda x: -x[1]["预测得分"]
        )
        
        for i, (region, data) in enumerate(future_ranking):
            current_rank = [r[0] for r in current_ranking].index(region) + 1
            future_rank = i + 1
            change = current_rank - future_rank
            
            if change > 0:
                rank_change = f"上升{change}位"
            elif change < 0:
                rank_change = f"下降{-change}位"
            else:
                rank_change = "保持不变"
            
            future_landscape[region]["排名变化"] = rank_change
        
        return future_landscape

# 使用示例
regional = RegionalAIAnalysis()
scores = regional.calculate_composite_score()

print("🌍 全球AI发展区域分析:")
for region, data in scores.items():
    print(f"\n{region}:")
    print(f"  综合得分: {data['综合得分']:.3f}")
    print(f"  优势领域: {data['优势领域']}")
    print(f"  劣势领域: {data['劣势领域']}")

future = regional.predict_2030_landscape()
print("\n🔮 2030年发展格局预测:")
for region, data in future.items():
    print(f"  {region}: 得分{data['预测得分']:.3f} ({data['排名变化']})")

class AIRiskAssessment:
    def __init__(self):
        self.risks = {
            "技术风险": {
                "模型偏见": {"概率": 0.7, "影响": 0.8, "缓解措施": ["数据清洗", "公平性评估", "多样化训练"]},
                "安全漏洞": {"概率": 0.6, "影响": 0.9, "缓解措施": ["安全测试", "对抗训练", "输入验证"]},
                "技术依赖": {"概率": 0.5, "影响": 0.7, "缓解措施": ["技术多元化", "自主可控", "备份方案"]}
            },
            "商业风险": {
                "投资过热": {"概率": 0.8, "影响": 0.6, "缓解措施": ["理性投资", "价值评估", "长期规划"]},
                "竞争加剧": {"概率": 0.9, "影响": 0.7, "缓解措施": ["差异化竞争", "技术创新", "生态建设"]},
                "市场变化": {"概率": 0.7, "影响": 0.8, "缓解措施": ["敏捷响应", "多元化布局", "风险对冲"]}
            },
            "监管风险": {
                "政策变化": {"概率": 0.8, "影响": 0.9, "缓解措施": ["政策跟踪", "合规建设", "政府沟通"]},
                "数据隐私": {"概率": 0.9, "影响": 0.8, "缓解措施": ["隐私保护", "数据脱敏", "合规审计"]},
                "伦理争议": {"概率": 0.6, "影响": 0.7, "缓解措施": ["伦理审查", "透明公开", "社会对话"]}
            }
        }
    
    def calculate_risk_score(self):
        """计算风险评分"""
        risk_scores = {}
        
        for category, risks in self.risks.items():
            category_score = 0
            category_risks = []
            
            for risk_name, risk_data in risks.items():
                # 风险值 = 概率 × 影响
                risk_value = risk_data["概率"] * risk_data["影响"]
                category_score += risk_value
                
                category_risks.append({
                    "风险": risk_name,
                    "概率": risk_data["概率"],
                    "影响": risk_data["影响"],
                    "风险值": risk_value,
                    "缓解措施": risk_data["缓解措施"]
                })
            
            # 按风险值排序
            category_risks.sort(key=lambda x: -x["风险值"])
            
            risk_scores[category] = {
                "平均风险值": category_score / len(risks),
                "最高风险": category_risks[0],
                "所有风险": category_risks
            }
        
        return risk_scores
    
    def generate_mitigation_plan(self):
        """生成风险缓解计划"""
        risk_scores = self.calculate_risk_score()
        mitigation_plan = {}
        
        for category, data in risk_scores.items():
            top_risk = data["最高风险"]
            
            mitigation_plan[category] = {
                "最高风险项": top_risk["风险"],
                "风险值": top_risk["风险值"],
                "紧急程度": "高" if top_risk["风险值"] > 0.6 else "中" if top_risk["风险值"] > 0.4 else "低",
                "缓解策略": top_risk["缓解措施"],
                "实施时间": "立即" if top_risk["风险值"] > 0.6 else "近期" if top_risk["风险值"] > 0.4 else "规划中"
            }
        
        return mitigation_plan

# 使用示例
risk_assessor = AIRiskAssessment()
risk_scores = risk_assessor.calculate_risk_score()

print("⚠️ AI发展风险评估:")
for category, data in risk_scores.items():
    print(f"\n{category}:")
    print(f"  平均风险值: {data['平均风险值']:.2f}")
    print(f"  最高风险: {data['最高风险']['风险']} (风险值: {data['最高风险']['风险值']:.2f})")

mitigation = risk_assessor.generate_mitigation_plan()
print("\n🛡️ 风险缓解计划:")
for category, plan in mitigation.items():
    print(f"\n{category}:")
    print(f"  最高风险: {plan['最高风险项']}")
    print(f"  紧急程度: {plan['紧急程度']}")
    print(f"  实施时间: {plan['实施时间']}")
    print(f"  缓解策略: {', '.join(plan['缓解策略'][:2])}")

class AIStrategyAdvisor:
    def __init__(self, company_type):
        self.company_type = company_type
        self.strategies = {
            "科技巨头": {
                "重点": "基础研究和平台建设",
                "投资方向": ["大模型研发", "AI芯片", "开发者生态"],
                "关键行动": ["建立AI实验室", "开源核心模型", "构建云AI平台"],
                "风险控制": ["避免垄断争议", "加强AI安全", "推动行业标准"]
            },
            "创业公司": {
                "重点": "垂直领域应用创新",
                "投资方向": ["行业解决方案", "产品差异化", "客户获取"],
                "关键行动": ["聚焦细分市场", "快速迭代产品", "建立合作伙伴"],
                "风险控制": ["控制烧钱速度", "保护知识产权", "应对巨头竞争"]
            },
            "传统企业": {
                "重点": "数字化转型和效率提升",
                "投资方向": ["流程自动化", "数据分析", "客户体验"],
                "关键行动": ["设立AI转型部门", "培训现有员工", "试点项目先行"],
                "风险控制": ["管理变革阻力", "确保数据安全", "衡量投资回报"]
            },
            "投资机构": {
                "重点": "价值发现和生态布局",
                "投资方向": ["早期技术公司", "AI基础设施", "应用场景落地"],
                "关键行动": ["建立专家网络", "分散投资组合", "投后增值服务"],
                "风险控制": ["避免估值泡沫", "关注技术成熟度", "退出策略规划"]
            }
        }
    
    def generate_strategy(self, timeframe="3年"):
        """生成战略规划"""
        if self.company_type not in self.strategies:
            return {"error": "未知公司类型"}
        
        base_strategy = self.strategies[self.company_type]
        
        # 根据时间框架调整
        if timeframe == "1年":
            focus = "快速验证和试点"
            actions = base_strategy["关键行动"][:2]
        elif timeframe == "3年":
            focus = "规模化和生态建设"
            actions = base_strategy["关键行动"]
        else:  # 5年
            focus = "行业领导和持续创新"
            actions = base_strategy["关键行动"] + ["国际化扩张", "技术并购"]
        
        strategy = {
            "公司类型": self.company_type,
            "时间框架": timeframe,
            "战略重点": focus,
            "投资优先级": base_strategy["投资方向"],
            "关键行动计划": actions,
            "风险控制措施": base_strategy["风险控制"],
            "成功指标": self._generate_kpis(timeframe)
        }
        
        return strategy
    
    def _generate_kpis(self, timeframe):
        """生成关键绩效指标"""
        if timeframe == "1年":
            return {
                "技术指标": ["模型准确率提升", "处理速度优化", "成本降低"],
                "业务指标": ["试点项目成功", "客户满意度", "团队建设"],
                "财务指标": ["研发投入", "成本控制", "收入增长"]
            }
        elif timeframe == "3年":
            return {
                "技术指标": ["专利申请数量", "技术壁垒建立", "生态合作伙伴"],
                "业务指标": ["市场份额增长", "客户数量增长", "产品线完善"],
                "财务指标": ["收入增长率", "利润率提升", "投资回报率"]
            }
        else:  # 5年
            return {
                "技术指标": ["行业标准参与", "核心技术领先", "全球影响力"],
                "业务指标": ["市场领导地位", "品牌价值提升", "国际化程度"],
                "财务指标": ["市值/估值增长", "盈利能力", "资本运作能力"]
            }

# 使用示例
advisor = AIStrategyAdvisor("创业公司")
strategy = advisor.generate_strategy("3年")

print("🎯 AI发展战略规划 (创业公司 - 3年):")
print(f"\n战略重点: {strategy['战略重点']}")
print(f"\n投资优先级:")
for item in strategy["投资优先级"]:
    print(f"  • {item}")

print(f"\n关键行动计划:")
for action in strategy["关键行动计划"]:
    print(f"  • {action}")

print(f"\n成功指标:")
for category, kpis in strategy["成功指标"].items():
    print(f"  {category}: {', '.join(kpis[:2])}")