#!/usr/bin/env python3
"""
高级融合策略演示

演示高级融合策略的核心功能：
1. 时间感知融合 - 基于信息新鲜度的融合
2. 语义融合 - 基于语义相似性的聚类融合
3. 成本感知融合 - 基于成本效益的优化融合
4. 质量驱动融合 - 基于权威性和质量的融合
5. 多视角融合 - 提供全面的多角度分析

Author: ADC Team
Date: 2024-12-19
Version: 1.0.0
"""

import asyncio
from datetime import datetime, timedelta
from typing import List, Dict, Any

def print_section(title: str):
    """打印章节标题"""
    print(f"\n{'='*70}")
    print(f"🎯 {title}")
    print(f"{'='*70}")

def print_subsection(title: str):
    """打印子章节标题"""
    print(f"\n{'─'*50}")
    print(f"📋 {title}")
    print(f"{'─'*50}")

async def demo_temporal_fusion():
    """演示时间感知融合"""
    print_section("时间感知融合演示")
    
    # 模拟不同新鲜度的知识源
    knowledge_sources = [
        {
            'source_type': 'local_kb',
            'content': 'FPGA是一种可编程逻辑器件，广泛应用于数字信号处理...',
            'confidence': 0.90,
            'cost': 0.1,
            'authority_level': 0.95,
            'freshness_score': 0.6,  # 较旧但权威
            'timestamp': datetime.now() - timedelta(days=30)
        },
        {
            'source_type': 'ai_training',
            'content': 'FPGA技术在2024年有了新的发展，特别是在AI加速领域...',
            'confidence': 0.85,
            'cost': 1.0,
            'authority_level': 0.80,
            'freshness_score': 0.8,  # 较新
            'timestamp': datetime.now() - timedelta(days=7)
        },
        {
            'source_type': 'web_search',
            'content': '最新的FPGA市场报告显示，2024年FPGA市场增长了15%...',
            'confidence': 0.75,
            'cost': 0.5,
            'authority_level': 0.70,
            'freshness_score': 0.95,  # 最新
            'timestamp': datetime.now() - timedelta(hours=2)
        }
    ]
    
    query = "FPGA的最新发展趋势是什么？"
    
    print(f"🔍 查询: {query}")
    print("\n📚 可用知识源:")
    
    for i, source in enumerate(knowledge_sources, 1):
        days_ago = (datetime.now() - source['timestamp']).days
        hours_ago = (datetime.now() - source['timestamp']).seconds // 3600
        
        time_desc = f"{days_ago}天前" if days_ago > 0 else f"{hours_ago}小时前"
        
        print(f"\n{i}. {source['source_type']} ({time_desc}):")
        print(f"   内容: {source['content'][:60]}...")
        print(f"   置信度: {source['confidence']:.2f}")
        print(f"   权威性: {source['authority_level']:.2f}")
        print(f"   新鲜度: {source['freshness_score']:.2f}")
        print(f"   成本: ${source['cost']:.2f}")
    
    print_subsection("时间感知融合分析")
    
    # 计算时间权重
    freshness_weight = 0.3
    print(f"🕐 新鲜度权重: {freshness_weight:.1%}")
    print(f"🏛️ 权威性权重: {1-freshness_weight:.1%}")
    
    weighted_scores = []
    for source in knowledge_sources:
        time_weight = source['freshness_score'] * freshness_weight
        authority_weight = source['authority_level'] * (1 - freshness_weight)
        total_weight = time_weight + authority_weight
        weighted_scores.append(total_weight)
        
        print(f"\n📊 {source['source_type']} 权重计算:")
        print(f"   时间权重: {source['freshness_score']:.2f} × {freshness_weight:.1%} = {time_weight:.3f}")
        print(f"   权威权重: {source['authority_level']:.2f} × {1-freshness_weight:.1%} = {authority_weight:.3f}")
        print(f"   总权重: {total_weight:.3f}")
    
    # 归一化权重
    total_weight = sum(weighted_scores)
    normalized_weights = [w / total_weight for w in weighted_scores]
    
    print_subsection("融合结果")
    
    print("🔄 时间感知融合内容:")
    total_cost = 0
    for i, (source, weight) in enumerate(zip(knowledge_sources, normalized_weights)):
        if weight > 0.1:  # 只显示权重显著的源
            freshness_label = "最新" if source['freshness_score'] > 0.8 else ("较新" if source['freshness_score'] > 0.5 else "较旧")
            print(f"\n【{freshness_label}信息 - 权重{weight:.1%}】")
            print(f"{source['content'][:80]}...")
            total_cost += source['cost']
    
    # 计算融合指标
    weighted_confidence = sum(source['confidence'] * weight 
                            for source, weight in zip(knowledge_sources, normalized_weights))
    
    print(f"\n📊 融合效果:")
    print(f"   融合置信度: {weighted_confidence:.3f}")
    print(f"   总成本: ${total_cost:.2f}")
    print(f"   时间平衡: 新鲜度与权威性的最优平衡")
    print(f"   信息完整性: 涵盖历史、现状、趋势三个时间维度")

async def demo_semantic_fusion():
    """演示语义融合"""
    print_section("语义融合演示")
    
    # 模拟不同语义角度的知识源
    knowledge_sources = [
        {
            'source_type': 'official_docs',
            'content': 'FPGA（现场可编程门阵列）的基本原理是通过配置存储器来定义逻辑功能...',
            'confidence': 0.95,
            'cost': 0.1,
            'relevance_score': 0.90,
            'semantic_category': '理论基础'
        },
        {
            'source_type': 'tutorial',
            'content': '学习FPGA设计的最佳实践包括：1. 掌握HDL语言 2. 理解时序概念...',
            'confidence': 0.85,
            'cost': 0.2,
            'relevance_score': 0.88,
            'semantic_category': '实践指南'
        },
        {
            'source_type': 'code_examples',
            'content': '以下是一个简单的FPGA计数器设计示例：\nmodule counter(clk, rst, count)...',
            'confidence': 0.80,
            'cost': 0.15,
            'relevance_score': 0.85,
            'semantic_category': '代码示例'
        },
        {
            'source_type': 'expert_knowledge',
            'content': 'FPGA设计中需要特别注意时序约束，建议使用流水线技术优化性能...',
            'confidence': 0.88,
            'cost': 0.8,
            'relevance_score': 0.92,
            'semantic_category': '专家经验'
        }
    ]
    
    query = "如何学习FPGA设计？"
    
    print(f"🔍 查询: {query}")
    print("\n📚 语义分析的知识源:")
    
    for i, source in enumerate(knowledge_sources, 1):
        print(f"\n{i}. {source['source_type']} - {source['semantic_category']}:")
        print(f"   内容: {source['content'][:60]}...")
        print(f"   置信度: {source['confidence']:.2f}")
        print(f"   相关性: {source['relevance_score']:.2f}")
        print(f"   成本: ${source['cost']:.2f}")
    
    print_subsection("语义聚类分析")
    
    # 按语义类别分组
    semantic_groups = {}
    for source in knowledge_sources:
        category = source['semantic_category']
        if category not in semantic_groups:
            semantic_groups[category] = []
        semantic_groups[category].append(source)
    
    print("🔍 语义聚类结果:")
    for category, sources in semantic_groups.items():
        avg_relevance = sum(s['relevance_score'] for s in sources) / len(sources)
        print(f"   📂 {category}: {len(sources)}个源, 平均相关性{avg_relevance:.2f}")
    
    print_subsection("语义融合结果")
    
    print("🔄 语义融合内容:")
    total_cost = 0
    source_contributions = {}
    
    for category, sources in semantic_groups.items():
        # 选择该类别中最佳代表
        best_source = max(sources, key=lambda x: x['confidence'] * x['relevance_score'])
        group_weight = sum(s['relevance_score'] for s in sources) / len(sources)
        
        print(f"\n【{category} - 相关性{group_weight:.1%}】")
        print(f"{best_source['content'][:100]}...")
        
        source_contributions[best_source['source_type']] = group_weight
        total_cost += best_source['cost']
    
    # 计算语义融合指标
    avg_relevance = sum(source['relevance_score'] for source in knowledge_sources) / len(knowledge_sources)
    semantic_coverage = len(semantic_groups)
    
    print(f"\n📊 语义融合效果:")
    print(f"   语义覆盖度: {semantic_coverage} 个主题维度")
    print(f"   平均相关性: {avg_relevance:.3f}")
    print(f"   内容多样性: {len(source_contributions)} 个不同源类型")
    print(f"   总成本: ${total_cost:.2f}")
    print(f"   融合优势: 提供了从理论到实践的完整学习路径")

async def demo_cost_aware_fusion():
    """演示成本感知融合"""
    print_section("成本感知融合演示")
    
    # 模拟不同成本效益的知识源
    knowledge_sources = [
        {
            'source_type': 'local_kb',
            'content': 'FPGA基础知识：可编程逻辑器件的工作原理...',
            'confidence': 0.85,
            'cost': 0.1,
            'efficiency': 8.5  # 置信度/成本
        },
        {
            'source_type': 'cached_response',
            'content': 'FPGA设计流程包括：需求分析、架构设计、RTL编码...',
            'confidence': 0.80,
            'cost': 0.001,  # 缓存成本极低
            'efficiency': 800  # 极高效益
        },
        {
            'source_type': 'ai_training',
            'content': '深度分析FPGA设计的关键技术要点和实现策略...',
            'confidence': 0.90,
            'cost': 1.2,
            'efficiency': 0.75  # 高质量但成本高
        },
        {
            'source_type': 'expert_consultation',
            'content': '专家建议：FPGA设计需要考虑的高级优化技巧...',
            'confidence': 0.95,
            'cost': 5.0,  # 专家咨询成本很高
            'efficiency': 0.19  # 质量最高但效益低
        }
    ]
    
    cost_budget = 2.0
    query = "FPGA设计的完整流程是什么？"
    
    print(f"🔍 查询: {query}")
    print(f"💰 成本预算: ${cost_budget:.2f}")
    print("\n📚 成本效益分析的知识源:")
    
    for i, source in enumerate(knowledge_sources, 1):
        print(f"\n{i}. {source['source_type']}:")
        print(f"   内容: {source['content'][:60]}...")
        print(f"   置信度: {source['confidence']:.2f}")
        print(f"   成本: ${source['cost']:.3f}")
        print(f"   效益比: {source['efficiency']:.1f} (置信度/成本)")
    
    print_subsection("成本感知融合分析")
    
    # 按效益排序
    sorted_sources = sorted(knowledge_sources, key=lambda x: x['efficiency'], reverse=True)
    
    print("📊 按成本效益排序:")
    for i, source in enumerate(sorted_sources, 1):
        efficiency_label = "极高效" if source['efficiency'] > 10 else ("高效" if source['efficiency'] > 1 else "低效")
        print(f"   {i}. {source['source_type']}: 效益{source['efficiency']:.1f} ({efficiency_label})")
    
    # 选择在预算内的最优组合
    selected_sources = []
    cumulative_cost = 0.0
    
    print(f"\n💡 在${cost_budget:.2f}预算内的最优选择:")
    
    for source in sorted_sources:
        if cumulative_cost + source['cost'] <= cost_budget:
            selected_sources.append(source)
            cumulative_cost += source['cost']
            print(f"   ✅ 选择 {source['source_type']}: +${source['cost']:.3f} (累计: ${cumulative_cost:.3f})")
        else:
            print(f"   ❌ 跳过 {source['source_type']}: ${source['cost']:.3f} 超预算")
    
    print_subsection("成本感知融合结果")
    
    if selected_sources:
        print("🔄 成本优化融合内容:")
        
        total_efficiency = sum(s['efficiency'] for s in selected_sources)
        source_contributions = {}
        
        for source in selected_sources:
            weight = source['efficiency'] / total_efficiency
            source_contributions[source['source_type']] = weight
            
            efficiency_label = "极高效" if source['efficiency'] > 10 else ("高效" if source['efficiency'] > 1 else "一般")
            print(f"\n【{efficiency_label}源 - 权重{weight:.1%}】")
            print(f"{source['content'][:80]}...")
        
        # 计算融合效果
        weighted_confidence = sum(source['confidence'] * source_contributions[source['source_type']] 
                                for source in selected_sources)
        
        print(f"\n📊 成本感知融合效果:")
        print(f"   融合置信度: {weighted_confidence:.3f}")
        print(f"   实际成本: ${cumulative_cost:.3f} / ${cost_budget:.2f}")
        print(f"   预算利用率: {cumulative_cost/cost_budget:.1%}")
        print(f"   平均效益: {sum(s['efficiency'] for s in selected_sources)/len(selected_sources):.1f}")
        print(f"   成本节省: ${sum(s['cost'] for s in knowledge_sources) - cumulative_cost:.2f}")
    
    else:
        print("❌ 预算不足，无法选择任何源")

async def demo_quality_driven_fusion():
    """演示质量驱动融合"""
    print_section("质量驱动融合演示")
    
    # 模拟不同质量等级的知识源
    knowledge_sources = [
        {
            'source_type': 'ieee_paper',
            'content': 'IEEE论文：FPGA架构优化的理论基础和数学模型...',
            'confidence': 0.95,
            'authority_level': 0.98,  # 极高权威性
            'cost': 2.0,
            'quality_score': 0.965
        },
        {
            'source_type': 'vendor_docs',
            'content': 'Xilinx官方文档：UltraScale架构的设计指南和最佳实践...',
            'confidence': 0.90,
            'authority_level': 0.95,  # 很高权威性
            'cost': 0.3,
            'quality_score': 0.925
        },
        {
            'source_type': 'community_wiki',
            'content': '社区Wiki：FPGA设计经验分享和常见问题解答...',
            'confidence': 0.75,
            'authority_level': 0.60,  # 中等权威性
            'cost': 0.05,
            'quality_score': 0.675
        },
        {
            'source_type': 'blog_post',
            'content': '技术博客：我的FPGA学习心得和项目经验分享...',
            'confidence': 0.70,
            'authority_level': 0.40,  # 较低权威性
            'cost': 0.02,
            'quality_score': 0.550
        }
    ]
    
    query = "FPGA架构优化的最佳方法？"
    user_role = "expert"  # 专家用户，重视质量
    
    print(f"🔍 查询: {query}")
    print(f"👤 用户角色: {user_role} (重视质量)")
    print("\n📚 不同质量等级的知识源:")
    
    for i, source in enumerate(knowledge_sources, 1):
        authority_label = "极高" if source['authority_level'] > 0.9 else ("很高" if source['authority_level'] > 0.8 else ("中等" if source['authority_level'] > 0.6 else "较低"))
        
        print(f"\n{i}. {source['source_type']} - {authority_label}权威性:")
        print(f"   内容: {source['content'][:60]}...")
        print(f"   置信度: {source['confidence']:.2f}")
        print(f"   权威性: {source['authority_level']:.2f}")
        print(f"   质量分数: {source['quality_score']:.3f}")
        print(f"   成本: ${source['cost']:.2f}")
    
    print_subsection("质量驱动融合分析")
    
    authority_weight = 0.4
    print(f"🏛️ 权威性权重: {authority_weight:.1%}")
    print(f"🎯 置信度权重: {1-authority_weight:.1%}")
    
    # 计算质量权重
    quality_weights = []
    for source in knowledge_sources:
        quality_score = (source['confidence'] * (1 - authority_weight) + 
                        source['authority_level'] * authority_weight)
        quality_weights.append(quality_score)
        
        print(f"\n📊 {source['source_type']} 质量权重:")
        print(f"   置信度贡献: {source['confidence']:.2f} × {1-authority_weight:.1%} = {source['confidence'] * (1-authority_weight):.3f}")
        print(f"   权威性贡献: {source['authority_level']:.2f} × {authority_weight:.1%} = {source['authority_level'] * authority_weight:.3f}")
        print(f"   质量分数: {quality_score:.3f}")
    
    # 归一化权重
    total_quality = sum(quality_weights)
    normalized_weights = [w / total_quality for w in quality_weights]
    
    print_subsection("质量驱动融合结果")
    
    print("🔄 质量驱动融合内容:")
    total_cost = 0
    source_contributions = {}
    
    # 按质量权重排序，只选择高质量源
    quality_ranked = list(zip(knowledge_sources, normalized_weights))
    quality_ranked.sort(key=lambda x: x[1], reverse=True)
    
    for source, weight in quality_ranked:
        if weight > 0.15:  # 只包含高权重源
            authority_label = "权威" if source['authority_level'] > 0.8 else ("可信" if source['authority_level'] > 0.6 else "一般")
            confidence_label = "高信度" if source['confidence'] > 0.8 else ("中信度" if source['confidence'] > 0.6 else "低信度")
            
            print(f"\n【{authority_label}源 - {confidence_label}, 权重{weight:.1%}】")
            print(f"{source['content'][:90]}...")
            
            source_contributions[source['source_type']] = weight
            total_cost += source['cost']
    
    # 计算质量指标
    included_sources = [source for source, weight in quality_ranked 
                       if source['source_type'] in source_contributions]
    avg_authority = sum(s['authority_level'] for s in included_sources) / len(included_sources)
    avg_confidence = sum(s['confidence'] for s in included_sources) / len(included_sources)
    
    print(f"\n📊 质量驱动融合效果:")
    print(f"   平均权威性: {avg_authority:.3f}")
    print(f"   平均置信度: {avg_confidence:.3f}")
    print(f"   质量一致性: {min(s['confidence'] for s in included_sources):.3f}")
    print(f"   总成本: ${total_cost:.2f}")
    print(f"   质量保证: 专家级用户获得最高质量的融合内容")

async def demo_multi_perspective_fusion():
    """演示多视角融合"""
    print_section("多视角融合演示")
    
    # 模拟多个不同视角的知识源
    knowledge_sources = [
        {
            'source_type': 'local_kb',
            'content': '从技术角度看，FPGA相比CPU具有并行处理优势，适合特定算法加速...',
            'confidence': 0.88,
            'cost': 0.1,
            'perspective': '技术视角'
        },
        {
            'source_type': 'ai_training',
            'content': '从商业角度分析，FPGA虽然开发成本高，但在特定应用中ROI显著...',
            'confidence': 0.82,
            'cost': 1.0,
            'perspective': '商业视角'
        },
        {
            'source_type': 'web_search',
            'content': '从市场趋势看，FPGA在AI推理、5G通信、自动驾驶等领域需求激增...',
            'confidence': 0.78,
            'cost': 0.5,
            'perspective': '市场视角'
        },
        {
            'source_type': 'academic_research',
            'content': '从学术研究角度，FPGA在量子计算、神经形态计算等前沿领域有重要应用...',
            'confidence': 0.85,
            'cost': 1.5,
            'perspective': '学术视角'
        }
    ]
    
    query = "FPGA相比CPU有什么优势？"
    
    print(f"🔍 查询: {query}")
    print("\n📚 多视角知识源:")
    
    for i, source in enumerate(knowledge_sources, 1):
        print(f"\n{i}. {source['perspective']} ({source['source_type']}):")
        print(f"   内容: {source['content'][:60]}...")
        print(f"   置信度: {source['confidence']:.2f}")
        print(f"   成本: ${source['cost']:.2f}")
    
    print_subsection("多视角融合分析")
    
    # 检查视角多样性
    perspectives = set(source['perspective'] for source in knowledge_sources)
    diversity_score = len(perspectives) / len(knowledge_sources)
    
    print(f"🔍 视角多样性分析:")
    print(f"   总视角数: {len(perspectives)}")
    print(f"   总源数: {len(knowledge_sources)}")
    print(f"   多样性分数: {diversity_score:.2f}")
    print(f"   多样性评估: {'优秀' if diversity_score > 0.8 else ('良好' if diversity_score > 0.6 else '一般')}")
    
    # 计算视角权重
    perspective_weights = {}
    for source in knowledge_sources:
        # 视角权重 = (置信度 + 权威性) / 2
        # 这里用置信度代替权威性进行演示
        perspective_weight = (source['confidence'] + source['confidence']) / 2
        perspective_weights[source['perspective']] = perspective_weight
    
    print("\n📊 视角权重分配:")
    total_perspective_weight = sum(perspective_weights.values())
    for perspective, weight in perspective_weights.items():
        normalized_weight = weight / total_perspective_weight
        print(f"   {perspective}: {normalized_weight:.1%}")
    
    print_subsection("多视角融合结果")
    
    print("🔄 多视角融合内容:")
    
    source_contributions = {}
    total_cost = 0
    
    for source in knowledge_sources:
        perspective_weight = perspective_weights[source['perspective']] / total_perspective_weight
        
        print(f"\n【{source['perspective']} - 权重{perspective_weight:.1%}】")
        print(f"{source['content'][:90]}...")
        
        source_contributions[source['source_type']] = perspective_weight
        total_cost += source['cost']
    
    # 计算多视角融合指标
    weighted_confidence = sum(source['confidence'] * perspective_weights[source['perspective']] / total_perspective_weight 
                            for source in knowledge_sources)
    
    viewpoint_balance = min(perspective_weights.values()) / max(perspective_weights.values()) if perspective_weights else 0
    
    print(f"\n📊 多视角融合效果:")
    print(f"   视角多样性: {len(perspectives)} 个不同视角")
    print(f"   视角平衡度: {viewpoint_balance:.3f}")
    print(f"   综合置信度: {weighted_confidence:.3f}")
    print(f"   总成本: ${total_cost:.2f}")
    print(f"   融合优势: 提供了技术、商业、市场、学术的全方位分析")

async def demo_intelligent_fusion_decision():
    """演示智能融合决策"""
    print_section("智能融合决策演示")
    
    # 模拟不同场景的融合决策
    test_scenarios = [
        {
            'query': '最新的FPGA技术发展趋势？',
            'user_role': 'researcher',
            'source_count': 3,
            'confidence_variance': 0.15,  # 置信度差异小
            'has_temporal_keywords': True,
            'expected_strategy': 'temporal_fusion'
        },
        {
            'query': '如何实现复杂的FPGA状态机设计？',
            'user_role': 'expert',
            'source_count': 4,
            'confidence_variance': 0.25,  # 置信度差异大
            'has_semantic_complexity': True,
            'expected_strategy': 'semantic_fusion'
        },
        {
            'query': 'FPGA设计入门指南',
            'user_role': 'beginner',
            'source_count': 3,
            'cost_budget': 1.0,  # 预算有限
            'cost_sensitive': True,
            'expected_strategy': 'cost_aware_fusion'
        },
        {
            'query': 'FPGA vs ASIC vs GPU的全面比较',
            'user_role': 'expert',
            'source_count': 4,
            'needs_multiple_perspectives': True,
            'expected_strategy': 'multi_perspective_fusion'
        }
    ]
    
    print("🧠 智能融合决策分析...")
    
    for i, scenario in enumerate(test_scenarios, 1):
        print(f"\n{i}. 场景分析:")
        print(f"   查询: {scenario['query']}")
        print(f"   用户角色: {scenario['user_role']}")
        print(f"   可用源数: {scenario['source_count']}")
        
        # 模拟决策过程
        decision_result = make_fusion_decision(scenario)
        
        print(f"   🎯 决策策略: {decision_result['strategy']}")
        print(f"   📊 置信度: {decision_result['confidence']:.3f}")
        print(f"   💰 预期成本: ${decision_result['expected_cost']:.2f}")
        print(f"   🔍 决策理由: {decision_result['reasoning']}")
        print(f"   ✅ 符合预期: {scenario['expected_strategy']}")
    
    print_subsection("融合策略统计")
    
    strategy_usage = {}
    for scenario in test_scenarios:
        decision = make_fusion_decision(scenario)
        strategy = decision['strategy']
        strategy_usage[strategy] = strategy_usage.get(strategy, 0) + 1
    
    print("📊 融合策略使用分布:")
    for strategy, count in strategy_usage.items():
        percentage = count / len(test_scenarios) * 100
        print(f"   {strategy.replace('_', ' ').title()}: {count} 次 ({percentage:.1f}%)")
    
    print(f"\n🎯 决策智能化效果:")
    print(f"   策略多样性: {len(strategy_usage)} 种不同策略")
    print(f"   决策准确性: 100% (所有决策符合预期)")
    print(f"   自适应能力: 根据查询特征自动选择最优策略")

def make_fusion_decision(scenario: Dict[str, Any]) -> Dict[str, Any]:
    """模拟智能融合决策"""
    
    # 时间感知融合触发条件
    if scenario.get('has_temporal_keywords', False):
        return {
            'strategy': 'temporal_fusion',
            'confidence': 0.85,
            'expected_cost': 1.6,
            'reasoning': '查询包含时间敏感关键词，选择时间感知融合'
        }
    
    # 语义融合触发条件
    if scenario.get('has_semantic_complexity', False):
        return {
            'strategy': 'semantic_fusion',
            'confidence': 0.88,
            'expected_cost': 2.2,
            'reasoning': '复杂语义查询，选择语义聚类融合'
        }
    
    # 成本感知融合触发条件
    if scenario.get('cost_sensitive', False):
        return {
            'strategy': 'cost_aware_fusion',
            'confidence': 0.80,
            'expected_cost': 0.8,
            'reasoning': '成本敏感用户，选择成本优化融合'
        }
    
    # 多视角融合触发条件
    if scenario.get('needs_multiple_perspectives', False):
        return {
            'strategy': 'multi_perspective_fusion',
            'confidence': 0.90,
            'expected_cost': 2.5,
            'reasoning': '需要多角度分析，选择多视角融合'
        }
    
    # 默认策略
    return {
        'strategy': 'quality_driven_fusion',
        'confidence': 0.82,
        'expected_cost': 1.2,
        'reasoning': '默认选择质量驱动融合'
    }

async def demo_fusion_performance_comparison():
    """演示融合性能对比"""
    print_section("融合性能对比演示")
    
    # 模拟不同融合策略的性能数据
    strategy_performance = {
        'temporal_fusion': {
            'avg_confidence': 0.83,
            'avg_cost': 1.6,
            'avg_latency': 2.8,
            'user_satisfaction': 0.87,
            'use_cases': ['时间敏感查询', '趋势分析', '最新信息需求']
        },
        'semantic_fusion': {
            'avg_confidence': 0.88,
            'avg_cost': 2.2,
            'avg_latency': 3.2,
            'user_satisfaction': 0.91,
            'use_cases': ['复杂概念查询', '技术对比', '深度分析']
        },
        'cost_aware_fusion': {
            'avg_confidence': 0.80,
            'avg_cost': 0.8,
            'avg_latency': 1.5,
            'user_satisfaction': 0.82,
            'use_cases': ['预算限制', '高频查询', '成本敏感场景']
        },
        'quality_driven_fusion': {
            'avg_confidence': 0.92,
            'avg_cost': 2.8,
            'avg_latency': 3.5,
            'user_satisfaction': 0.94,
            'use_cases': ['专家用户', '关键决策', '高质量要求']
        },
        'multi_perspective_fusion': {
            'avg_confidence': 0.86,
            'avg_cost': 2.5,
            'avg_latency': 4.0,
            'user_satisfaction': 0.89,
            'use_cases': ['对比分析', '全面评估', '决策支持']
        }
    }
    
    print("📊 融合策略性能对比:")
    
    print(f"\n{'策略':<20} {'置信度':<8} {'成本':<8} {'延迟':<8} {'满意度':<8}")
    print("-" * 60)
    
    for strategy, perf in strategy_performance.items():
        strategy_name = strategy.replace('_', ' ').title()[:18]
        print(f"{strategy_name:<20} {perf['avg_confidence']:<8.3f} ${perf['avg_cost']:<7.2f} {perf['avg_latency']:<8.1f}s {perf['user_satisfaction']:<8.1%}")
    
    print_subsection("策略适用场景")
    
    for strategy, perf in strategy_performance.items():
        strategy_name = strategy.replace('_', ' ').title()
        print(f"\n🎯 {strategy_name}:")
        print(f"   适用场景: {', '.join(perf['use_cases'])}")
        
        # 性能特点
        if perf['avg_cost'] < 1.0:
            print(f"   💰 成本优势: 低成本 (${perf['avg_cost']:.2f})")
        if perf['avg_latency'] < 2.0:
            print(f"   ⚡ 速度优势: 快速响应 ({perf['avg_latency']:.1f}s)")
        if perf['avg_confidence'] > 0.9:
            print(f"   🎯 质量优势: 高置信度 ({perf['avg_confidence']:.1%})")
        if perf['user_satisfaction'] > 0.9:
            print(f"   😊 体验优势: 高满意度 ({perf['user_satisfaction']:.1%})")
    
    print_subsection("融合策略选择建议")
    
    print("💡 智能选择建议:")
    print("   🕐 时间敏感查询 → temporal_fusion (新鲜度优先)")
    print("   🧠 复杂技术问题 → semantic_fusion (语义聚类)")
    print("   💰 预算限制场景 → cost_aware_fusion (成本优化)")
    print("   👨‍💼 专家用户查询 → quality_driven_fusion (质量保证)")
    print("   📊 对比分析需求 → multi_perspective_fusion (全面视角)")
    
    # 性能综合评估
    print("\n🏆 性能综合排名:")
    
    # 计算综合分数（置信度40% + 满意度30% + 成本效益20% + 速度10%）
    strategy_scores = {}
    for strategy, perf in strategy_performance.items():
        cost_efficiency = 1 / max(perf['avg_cost'], 0.1)  # 成本效益
        speed_score = 1 / max(perf['avg_latency'], 0.1)   # 速度分数
        
        # 归一化分数
        normalized_cost_efficiency = min(1.0, cost_efficiency / 10)
        normalized_speed = min(1.0, speed_score / 1)
        
        comprehensive_score = (
            perf['avg_confidence'] * 0.4 +
            perf['user_satisfaction'] * 0.3 +
            normalized_cost_efficiency * 0.2 +
            normalized_speed * 0.1
        )
        
        strategy_scores[strategy] = comprehensive_score
    
    ranked_strategies = sorted(strategy_scores.items(), key=lambda x: x[1], reverse=True)
    
    for i, (strategy, score) in enumerate(ranked_strategies, 1):
        strategy_name = strategy.replace('_', ' ').title()
        medal = "🥇" if i == 1 else ("🥈" if i == 2 else ("🥉" if i == 3 else "🏅"))
        print(f"   {medal} {i}. {strategy_name}: {score:.3f}")

async def main():
    """主演示函数"""
    print("🔄 高级融合策略完整演示")
    print("=" * 70)
    
    try:
        # 1. 时间感知融合
        await demo_temporal_fusion()
        
        # 2. 语义融合
        await demo_semantic_fusion()
        
        # 3. 成本感知融合
        await demo_cost_aware_fusion()
        
        # 4. 质量驱动融合
        await demo_quality_driven_fusion()
        
        # 5. 多视角融合
        await demo_multi_perspective_fusion()
        
        # 6. 智能融合决策
        await demo_intelligent_fusion_decision()
        
        # 7. 性能对比
        await demo_fusion_performance_comparison()
        
        print_section("演示总结")
        
        print("🎊 高级融合策略核心价值:")
        print("   ✅ 时间感知 - 智能平衡新鲜度与权威性")
        print("   ✅ 语义聚类 - 自动分组相关内容，避免重复")
        print("   ✅ 成本优化 - 在预算约束下获得最佳效益")
        print("   ✅ 质量保证 - 专家级用户获得最高质量内容")
        print("   ✅ 多视角 - 提供全面的多角度分析")
        print("   ✅ 智能决策 - 根据查询特征自动选择最优策略")
        
        print("\n💡 技术创新:")
        print("   🧠 自适应策略选择 - 基于查询特征智能匹配")
        print("   📊 多维度权重计算 - 综合考虑质量、成本、时间")
        print("   🔄 动态内容组织 - 智能聚类和分层展示")
        print("   🎯 个性化融合 - 基于用户角色的定制化策略")
        print("   📈 持续优化 - 基于历史数据的策略调优")
        
        print("\n🚀 商业价值:")
        print("   💰 成本控制: 智能预算管理，避免不必要的高成本查询")
        print("   🎯 质量保证: 专家用户获得权威性和准确性保证")
        print("   ⚡ 效率提升: 语义聚类避免信息重复，提高阅读效率")
        print("   🌐 全面性: 多视角融合提供更全面的信息覆盖")
        print("   🔮 前瞻性: 时间感知融合确保信息的时效性")
        
    except Exception as e:
        print(f"❌ 演示过程中出现错误: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    asyncio.run(main()) 