#!/usr/bin/env python3
"""
自动化评估框架演示

演示自动化评估框架的核心功能：
1. 基准测试执行
2. 多维度性能指标计算
3. 趋势分析和预测
4. 自动化优化建议生成
5. 持续监控和报告

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

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

# 模拟导入（在实际环境中这些会是真实的导入）
try:
    from layers.intelligent_context.automated_evaluation_framework import (
        AutomatedEvaluationFramework, BenchmarkQuestion, BenchmarkCategory,
        EvaluationMetricType, TestResult
    )
except ImportError:
    # 模拟类定义用于演示
    from enum import Enum
    from dataclasses import dataclass
    
    class BenchmarkCategory(Enum):
        FPGA_BASICS = "fpga_basics"
        ADVANCED_DESIGN = "advanced_design"
        TROUBLESHOOTING = "troubleshooting"
        CREATIVE_TASKS = "creative_tasks"
    
    class TestResult(Enum):
        PASS = "pass"
        FAIL = "fail"
        WARNING = "warning"
    
    print("🔄 使用模拟模式运行演示...")

def print_section(title: str):
    """打印章节标题"""
    print(f"\n{'='*60}")
    print(f"🎯 {title}")
    print(f"{'='*60}")

def print_subsection(title: str):
    """打印子章节标题"""
    print(f"\n{'─'*40}")
    print(f"📋 {title}")
    print(f"{'─'*40}")

async def demo_benchmark_test_execution():
    """演示基准测试执行"""
    print_section("基准测试执行演示")
    
    # 模拟基准测试问题集
    benchmark_questions = [
        {
            'id': 'fpga_001',
            'category': 'fpga_basics',
            'query': '什么是FPGA？',
            'expected_source': 'local_kb',
            'expected_confidence': 0.85,
            'expected_cost': 0.2,
            'expected_latency': 1.0,
            'complexity': 'simple'
        },
        {
            'id': 'fpga_002',
            'category': 'fpga_basics',
            'query': 'FPGA和ASIC有什么区别？',
            'expected_source': 'local_kb',
            'expected_confidence': 0.80,
            'expected_cost': 0.3,
            'expected_latency': 1.5,
            'complexity': 'moderate'
        },
        {
            'id': 'fpga_003',
            'category': 'advanced_design',
            'query': '如何优化FPGA设计的时序性能？',
            'expected_source': 'ai_training',
            'expected_confidence': 0.75,
            'expected_cost': 1.5,
            'expected_latency': 3.0,
            'complexity': 'complex'
        },
        {
            'id': 'fpga_004',
            'category': 'troubleshooting',
            'query': 'FPGA综合失败，时序不满足怎么办？',
            'expected_source': 'local_kb',
            'expected_confidence': 0.80,
            'expected_cost': 0.5,
            'expected_latency': 2.0,
            'complexity': 'moderate'
        },
        {
            'id': 'fpga_005',
            'category': 'creative_tasks',
            'query': '设计一个创新的FPGA图像处理架构',
            'expected_source': 'ai_training',
            'expected_confidence': 0.65,
            'expected_cost': 3.0,
            'expected_latency': 5.0,
            'complexity': 'complex'
        }
    ]
    
    print("🧪 执行基准测试...")
    print(f"📚 测试问题集: {len(benchmark_questions)} 个问题")
    
    # 模拟测试执行结果
    test_results = []
    for question in benchmark_questions:
        # 模拟路由决策结果
        actual_result = simulate_routing_result(question)
        
        # 评估测试结果
        evaluation = evaluate_test_case(question, actual_result)
        test_results.append(evaluation)
        
        print(f"\n🔍 测试 {question['id']}:")
        print(f"   查询: {question['query']}")
        print(f"   期望源: {question['expected_source']} | 实际源: {actual_result['source']}")
        print(f"   期望置信度: ≥{question['expected_confidence']:.2f} | 实际: {actual_result['confidence']:.2f}")
        print(f"   期望成本: ≤${question['expected_cost']:.2f} | 实际: ${actual_result['cost']:.2f}")
        print(f"   期望延迟: ≤{question['expected_latency']:.1f}s | 实际: {actual_result['latency']:.1f}s")
        print(f"   📊 评分: {evaluation['score']:.3f}")
        print(f"   🎯 状态: {evaluation['status']}")
    
    # 统计结果
    passed = sum(1 for r in test_results if r['status'] == 'PASS')
    warning = sum(1 for r in test_results if r['status'] == 'WARNING')
    failed = sum(1 for r in test_results if r['status'] == 'FAIL')
    
    print(f"\n📊 测试执行统计:")
    print(f"   总测试数: {len(test_results)}")
    print(f"   通过: {passed} ({passed/len(test_results):.1%})")
    print(f"   警告: {warning} ({warning/len(test_results):.1%})")
    print(f"   失败: {failed} ({failed/len(test_results):.1%})")
    
    return test_results

def simulate_routing_result(question: Dict[str, Any]) -> Dict[str, Any]:
    """模拟路由结果"""
    import random
    
    # 基于复杂度模拟结果准确性
    if question['complexity'] == 'simple':
        # 简单问题通常路由正确
        source_correct_prob = 0.9
        confidence_variance = 0.05
        cost_variance = 0.1
        latency_variance = 0.2
    elif question['complexity'] == 'moderate':
        # 中等问题有一定错误率
        source_correct_prob = 0.7
        confidence_variance = 0.1
        cost_variance = 0.2
        latency_variance = 0.5
    else:  # complex
        # 复杂问题错误率较高
        source_correct_prob = 0.6
        confidence_variance = 0.15
        cost_variance = 0.3
        latency_variance = 1.0
    
    # 模拟路由源选择
    if random.random() < source_correct_prob:
        actual_source = question['expected_source']
    else:
        # 错误路由
        sources = ['local_kb', 'ai_training', 'web_search']
        sources.remove(question['expected_source'])
        actual_source = random.choice(sources)
    
    # 模拟其他指标（加入随机变化）
    actual_confidence = max(0.1, min(1.0, 
        question['expected_confidence'] + random.uniform(-confidence_variance, confidence_variance)))
    actual_cost = max(0.01, 
        question['expected_cost'] + random.uniform(-cost_variance, cost_variance))
    actual_latency = max(0.1, 
        question['expected_latency'] + random.uniform(-latency_variance, latency_variance))
    
    return {
        'source': actual_source,
        'confidence': actual_confidence,
        'cost': actual_cost,
        'latency': actual_latency
    }

def evaluate_test_case(question: Dict[str, Any], actual: Dict[str, Any]) -> Dict[str, Any]:
    """评估测试用例"""
    score = 0.0
    reasoning = []
    
    # 路由准确性 (40%)
    if actual['source'] == question['expected_source']:
        score += 0.4
        reasoning.append("✅ 路由源正确")
    else:
        reasoning.append(f"❌ 路由源错误: 期望{question['expected_source']}, 实际{actual['source']}")
    
    # 置信度 (25%)
    if actual['confidence'] >= question['expected_confidence']:
        confidence_score = min(1.0, actual['confidence'] / question['expected_confidence'])
        score += 0.25 * confidence_score
        reasoning.append(f"✅ 置信度满足: {actual['confidence']:.3f}")
    else:
        reasoning.append(f"❌ 置信度不足: {actual['confidence']:.3f} < {question['expected_confidence']:.3f}")
    
    # 成本控制 (20%)
    if actual['cost'] <= question['expected_cost']:
        cost_score = max(0.0, 1.0 - actual['cost'] / question['expected_cost'])
        score += 0.2 * cost_score
        reasoning.append(f"✅ 成本控制: ${actual['cost']:.3f}")
    else:
        reasoning.append(f"❌ 成本超支: ${actual['cost']:.3f} > ${question['expected_cost']:.3f}")
    
    # 响应时间 (15%)
    if actual['latency'] <= question['expected_latency']:
        latency_score = max(0.0, 1.0 - actual['latency'] / question['expected_latency'])
        score += 0.15 * latency_score
        reasoning.append(f"✅ 响应及时: {actual['latency']:.2f}s")
    else:
        reasoning.append(f"❌ 响应过慢: {actual['latency']:.2f}s > {question['expected_latency']:.2f}s")
    
    # 确定状态
    if score >= 0.8:
        status = "PASS"
    elif score >= 0.6:
        status = "WARNING"
    else:
        status = "FAIL"
    
    return {
        'score': score,
        'status': status,
        'reasoning': reasoning,
        'question_id': question['id']
    }

async def demo_performance_metrics_calculation():
    """演示性能指标计算"""
    print_section("性能指标计算演示")
    
    # 模拟测试结果数据
    test_results = [
        {'score': 0.85, 'status': 'PASS', 'source_correct': True, 'confidence': 0.88, 'cost': 0.15, 'latency': 0.8},
        {'score': 0.72, 'status': 'WARNING', 'source_correct': True, 'confidence': 0.75, 'cost': 0.25, 'latency': 1.2},
        {'score': 0.45, 'status': 'FAIL', 'source_correct': False, 'confidence': 0.65, 'cost': 0.8, 'latency': 2.1},
        {'score': 0.90, 'status': 'PASS', 'source_correct': True, 'confidence': 0.92, 'cost': 0.12, 'latency': 0.6},
        {'score': 0.68, 'status': 'WARNING', 'source_correct': False, 'confidence': 0.78, 'cost': 0.35, 'latency': 1.8},
        {'score': 0.88, 'status': 'PASS', 'source_correct': True, 'confidence': 0.85, 'cost': 0.18, 'latency': 0.9},
    ]
    
    print("📊 计算核心性能指标...")
    
    # 1. 路由准确性指标
    print_subsection("路由准确性指标")
    correct_routes = sum(1 for r in test_results if r['source_correct'])
    route_accuracy = correct_routes / len(test_results)
    route_target = 0.85
    route_status = "PASS" if route_accuracy >= route_target else "FAIL"
    
    print(f"   正确路由数: {correct_routes}/{len(test_results)}")
    print(f"   路由准确率: {route_accuracy:.1%}")
    print(f"   目标准确率: {route_target:.1%}")
    print(f"   📊 状态: {route_status}")
    
    # 2. 响应质量指标
    print_subsection("响应质量指标")
    avg_confidence = sum(r['confidence'] for r in test_results) / len(test_results)
    quality_target = 0.80
    quality_status = "PASS" if avg_confidence >= quality_target else "FAIL"
    
    print(f"   平均置信度: {avg_confidence:.3f}")
    print(f"   目标置信度: {quality_target:.3f}")
    print(f"   置信度范围: {min(r['confidence'] for r in test_results):.3f} - {max(r['confidence'] for r in test_results):.3f}")
    print(f"   📊 状态: {quality_status}")
    
    # 3. 成本效率指标
    print_subsection("成本效率指标")
    avg_cost = sum(r['cost'] for r in test_results) / len(test_results)
    cost_target = 0.30
    cost_efficiency = max(0, 1 - avg_cost / cost_target) if cost_target > 0 else 0
    cost_status = "PASS" if avg_cost <= cost_target else "FAIL"
    
    print(f"   平均成本: ${avg_cost:.3f}")
    print(f"   目标成本: ≤${cost_target:.3f}")
    print(f"   成本效率: {cost_efficiency:.1%}")
    print(f"   📊 状态: {cost_status}")
    
    # 4. 系统性能指标
    print_subsection("系统性能指标")
    avg_latency = sum(r['latency'] for r in test_results) / len(test_results)
    latency_target = 1.5
    performance_score = max(0, 1 - avg_latency / latency_target) if latency_target > 0 else 0
    performance_status = "PASS" if avg_latency <= latency_target else "FAIL"
    
    print(f"   平均延迟: {avg_latency:.2f}s")
    print(f"   目标延迟: ≤{latency_target:.2f}s")
    print(f"   性能分数: {performance_score:.1%}")
    print(f"   📊 状态: {performance_status}")
    
    # 5. 总体评分
    print_subsection("总体评分")
    overall_score = (route_accuracy * 0.3 + avg_confidence * 0.25 + 
                    cost_efficiency * 0.2 + performance_score * 0.25)
    overall_status = "PASS" if overall_score >= 0.75 else ("WARNING" if overall_score >= 0.6 else "FAIL")
    
    print(f"   综合评分: {overall_score:.3f}")
    print(f"   评分构成:")
    print(f"     - 路由准确性 (30%): {route_accuracy:.3f}")
    print(f"     - 响应质量 (25%): {avg_confidence:.3f}")
    print(f"     - 成本效率 (20%): {cost_efficiency:.3f}")
    print(f"     - 系统性能 (25%): {performance_score:.3f}")
    print(f"   📊 总体状态: {overall_status}")
    
    return {
        'route_accuracy': route_accuracy,
        'response_quality': avg_confidence,
        'cost_efficiency': cost_efficiency,
        'system_performance': performance_score,
        'overall_score': overall_score
    }

async def demo_trend_analysis():
    """演示趋势分析"""
    print_section("性能趋势分析演示")
    
    # 模拟7天的历史数据
    historical_data = {
        'route_accuracy': [0.82, 0.85, 0.83, 0.87, 0.86, 0.88, 0.85],
        'response_quality': [0.78, 0.80, 0.82, 0.81, 0.83, 0.85, 0.84],
        'cost_efficiency': [0.72, 0.75, 0.73, 0.76, 0.78, 0.80, 0.77],
        'system_performance': [0.85, 0.83, 0.86, 0.88, 0.87, 0.89, 0.86]
    }
    
    print("📈 分析7天性能趋势...")
    
    for metric_name, values in historical_data.items():
        print(f"\n📊 {metric_name.replace('_', ' ').title()}:")
        
        # 计算趋势
        if len(values) >= 3:
            recent_avg = sum(values[-3:]) / 3
            early_avg = sum(values[:3]) / 3
            trend_change = recent_avg - early_avg
            
            if trend_change > 0.02:
                trend = "📈 改善"
            elif trend_change < -0.02:
                trend = "📉 下降"
            else:
                trend = "📊 稳定"
        else:
            trend = "📊 数据不足"
        
        print(f"   当前值: {values[-1]:.3f}")
        print(f"   7天平均: {sum(values)/len(values):.3f}")
        print(f"   最高值: {max(values):.3f}")
        print(f"   最低值: {min(values):.3f}")
        print(f"   趋势: {trend}")
        
        # 波动性分析
        if len(values) > 1:
            variance = sum((x - sum(values)/len(values))**2 for x in values) / len(values)
            volatility = variance ** 0.5
            stability = "高" if volatility < 0.02 else ("中" if volatility < 0.05 else "低")
            print(f"   稳定性: {stability} (波动: {volatility:.3f})")
    
    # 预测分析
    print_subsection("趋势预测")
    
    for metric_name, values in historical_data.items():
        if len(values) >= 5:
            # 简单线性趋势预测
            x = list(range(len(values)))
            y = values
            
            # 计算线性回归斜率
            n = len(values)
            sum_x = sum(x)
            sum_y = sum(y)
            sum_xy = sum(x[i] * y[i] for i in range(n))
            sum_x2 = sum(xi**2 for xi in x)
            
            slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x**2)
            intercept = (sum_y - slope * sum_x) / n
            
            # 预测明天的值
            next_day = len(values)
            predicted_value = slope * next_day + intercept
            
            print(f"   {metric_name}: 预测明天 {predicted_value:.3f} (趋势斜率: {slope:+.4f})")

async def demo_optimization_recommendations():
    """演示优化建议生成"""
    print_section("自动化优化建议演示")
    
    # 模拟当前性能数据
    current_metrics = {
        'route_accuracy': 0.75,      # 低于目标85%
        'response_quality': 0.82,    # 达到目标80%
        'cost_efficiency': 0.65,     # 低于目标75%
        'system_performance': 0.88,  # 超过目标85%
        'cache_hit_rate': 0.60,      # 低于目标70%
        'user_satisfaction': 0.78    # 接近目标80%
    }
    
    # 模拟测试失败分布
    failure_analysis = {
        'fpga_basics': 2,
        'advanced_design': 1,
        'troubleshooting': 3,
        'creative_tasks': 1
    }
    
    print("🔍 分析当前系统状态...")
    
    for metric, value in current_metrics.items():
        status = "✅" if value >= 0.80 else ("⚠️" if value >= 0.70 else "❌")
        print(f"   {metric.replace('_', ' ').title()}: {value:.1%} {status}")
    
    print("\n🧠 生成优化建议...")
    
    recommendations = []
    
    # 基于指标生成建议
    if current_metrics['route_accuracy'] < 0.85:
        recommendations.append({
            'priority': 'HIGH',
            'category': '路由准确性',
            'issue': f"路由准确性偏低 ({current_metrics['route_accuracy']:.1%})",
            'suggestion': '调整路由权重，增加领域匹配权重从0.40到0.45',
            'expected_improvement': '+8%',
            'implementation_effort': '低'
        })
    
    if current_metrics['cost_efficiency'] < 0.75:
        recommendations.append({
            'priority': 'HIGH',
            'category': '成本控制',
            'issue': f"成本效率需要改善 ({current_metrics['cost_efficiency']:.1%})",
            'suggestion': '提高缓存命中率，降低相似度阈值从0.85到0.80',
            'expected_improvement': '+15%',
            'implementation_effort': '中'
        })
    
    if current_metrics['cache_hit_rate'] < 0.70:
        recommendations.append({
            'priority': 'MEDIUM',
            'category': '缓存优化',
            'issue': f"缓存命中率偏低 ({current_metrics['cache_hit_rate']:.1%})",
            'suggestion': '增加缓存时间，实施更激进的缓存策略',
            'expected_improvement': '+12%',
            'implementation_effort': '中'
        })
    
    # 基于失败分布生成建议
    max_failures = max(failure_analysis.values())
    problem_category = max(failure_analysis.items(), key=lambda x: x[1])[0]
    
    if max_failures >= 2:
        recommendations.append({
            'priority': 'MEDIUM',
            'category': '专项优化',
            'issue': f"{problem_category} 类别问题频繁失败 ({max_failures}次)",
            'suggestion': f'针对 {problem_category} 优化知识库内容和路由策略',
            'expected_improvement': '+6%',
            'implementation_effort': '高'
        })
    
    # 显示建议
    for i, rec in enumerate(recommendations, 1):
        priority_icon = "🔴" if rec['priority'] == 'HIGH' else ("🟡" if rec['priority'] == 'MEDIUM' else "🟢")
        
        print(f"\n{i}. {priority_icon} {rec['priority']} 优先级 - {rec['category']}")
        print(f"   问题: {rec['issue']}")
        print(f"   建议: {rec['suggestion']}")
        print(f"   预期改善: {rec['expected_improvement']}")
        print(f"   实施难度: {rec['implementation_effort']}")
    
    # 实施路线图
    print_subsection("实施路线图")
    
    high_priority = [r for r in recommendations if r['priority'] == 'HIGH']
    medium_priority = [r for r in recommendations if r['priority'] == 'MEDIUM']
    
    print("📅 建议实施顺序:")
    print(f"   第1周: 实施 {len(high_priority)} 个高优先级改进")
    for rec in high_priority:
        print(f"     - {rec['category']}: {rec['suggestion'][:50]}...")
    
    print(f"   第2-3周: 实施 {len(medium_priority)} 个中优先级改进")
    for rec in medium_priority:
        print(f"     - {rec['category']}: {rec['suggestion'][:50]}...")
    
    # 预期效果
    total_improvement = sum(int(rec['expected_improvement'].rstrip('%')) for rec in recommendations)
    print(f"\n🎯 预期总体改善: +{total_improvement}% 综合性能提升")

async def demo_continuous_monitoring():
    """演示持续监控"""
    print_section("持续监控演示")
    
    print("🔄 持续监控系统特性:")
    print("   ⏰ 自动定期评估 - 每24小时执行一次完整评估")
    print("   📊 实时指标收集 - 持续收集系统性能数据")
    print("   🚨 异常检测 - 自动检测性能异常和降级")
    print("   📈 趋势预测 - 基于历史数据预测未来趋势")
    print("   🔧 自动优化 - 根据分析结果自动调整参数")
    
    print_subsection("监控Dashboard数据")
    
    # 模拟实时监控数据
    monitoring_data = {
        'system_status': 'HEALTHY',
        'last_evaluation': '2024-12-19 14:30:00',
        'next_evaluation': '2024-12-20 14:30:00',
        'alerts': [
            {'level': 'WARNING', 'message': '缓存命中率连续3天低于70%', 'time': '2024-12-19 12:00:00'},
            {'level': 'INFO', 'message': '路由准确性提升至87%', 'time': '2024-12-19 10:15:00'}
        ],
        'key_metrics': {
            'overall_score': 0.82,
            'route_accuracy': 0.85,
            'cost_efficiency': 0.68,
            'cache_hit_rate': 0.65,
            'avg_response_time': 1.2
        },
        'daily_stats': {
            'total_queries': 1247,
            'successful_routes': 1059,
            'cache_hits': 810,
            'total_cost': 89.34,
            'avg_satisfaction': 0.84
        }
    }
    
    status_icon = "🟢" if monitoring_data['system_status'] == 'HEALTHY' else "🔴"
    print(f"   系统状态: {status_icon} {monitoring_data['system_status']}")
    print(f"   上次评估: {monitoring_data['last_evaluation']}")
    print(f"   下次评估: {monitoring_data['next_evaluation']}")
    
    print("\n📊 关键指标:")
    for metric, value in monitoring_data['key_metrics'].items():
        if isinstance(value, float):
            print(f"   {metric.replace('_', ' ').title()}: {value:.3f}")
        else:
            print(f"   {metric.replace('_', ' ').title()}: {value}")
    
    print("\n📈 今日统计:")
    for stat, value in monitoring_data['daily_stats'].items():
        if isinstance(value, float):
            print(f"   {stat.replace('_', ' ').title()}: {value:.2f}")
        else:
            print(f"   {stat.replace('_', ' ').title()}: {value:,}")
    
    print("\n🚨 系统警报:")
    for alert in monitoring_data['alerts']:
        level_icon = "⚠️" if alert['level'] == 'WARNING' else "ℹ️"
        print(f"   {level_icon} [{alert['time']}] {alert['message']}")
    
    print_subsection("自动化响应")
    
    print("🤖 自动化响应机制:")
    print("   📉 性能下降 → 自动调整权重参数")
    print("   💰 成本超标 → 激活成本优先路由策略")
    print("   🐌 响应过慢 → 增加缓存激进度")
    print("   ❌ 错误率高 → 降级到更保守的路由策略")
    print("   📊 数据异常 → 发送警报通知管理员")

async def demo_report_generation():
    """演示报告生成"""
    print_section("评估报告生成演示")
    
    # 模拟生成评估报告
    report_data = {
        'report_id': 'eval_20241219_143000',
        'timestamp': '2024-12-19T14:30:00',
        'overall_score': 0.823,
        'execution_time': 12.5,
        'test_summary': {
            'total_tests': 15,
            'passed': 10,
            'warning': 3,
            'failed': 2,
            'pass_rate': 0.667
        },
        'metric_summary': {
            'route_accuracy': {'value': 0.85, 'target': 0.85, 'status': 'PASS'},
            'response_quality': {'value': 0.82, 'target': 0.80, 'status': 'PASS'},
            'cost_efficiency': {'value': 0.68, 'target': 0.75, 'status': 'FAIL'},
            'system_performance': {'value': 0.88, 'target': 0.85, 'status': 'PASS'}
        },
        'recommendations': [
            '提高缓存命中率以改善成本效率',
            '优化troubleshooting类别的路由策略',
            '考虑降低相似度阈值到0.80'
        ]
    }
    
    print(f"📋 评估报告 - {report_data['report_id']}")
    print(f"🕐 生成时间: {report_data['timestamp']}")
    print(f"⏱️ 执行时间: {report_data['execution_time']}秒")
    print(f"📊 总体评分: {report_data['overall_score']:.3f}")
    
    print_subsection("测试结果摘要")
    summary = report_data['test_summary']
    print(f"   总测试数: {summary['total_tests']}")
    print(f"   通过: {summary['passed']} ({summary['passed']/summary['total_tests']:.1%})")
    print(f"   警告: {summary['warning']} ({summary['warning']/summary['total_tests']:.1%})")
    print(f"   失败: {summary['failed']} ({summary['failed']/summary['total_tests']:.1%})")
    print(f"   通过率: {summary['pass_rate']:.1%}")
    
    print_subsection("指标评估结果")
    for metric, data in report_data['metric_summary'].items():
        status_icon = "✅" if data['status'] == 'PASS' else "❌"
        print(f"   {metric.replace('_', ' ').title()}: {data['value']:.3f} / {data['target']:.3f} {status_icon}")
    
    print_subsection("优化建议")
    for i, recommendation in enumerate(report_data['recommendations'], 1):
        print(f"   {i}. {recommendation}")
    
    print_subsection("报告导出")
    print("📄 支持的导出格式:")
    print("   📋 JSON格式 - 结构化数据，便于程序处理")
    print("   📊 HTML报告 - 可视化报告，便于人工阅读")
    print("   📈 CSV数据 - 指标数据，便于Excel分析")
    print("   📧 邮件摘要 - 自动发送给相关人员")

async def main():
    """主演示函数"""
    print("🔬 自动化评估框架完整演示")
    print("=" * 60)
    
    try:
        # 1. 基准测试执行
        test_results = await demo_benchmark_test_execution()
        
        # 2. 性能指标计算
        metrics = await demo_performance_metrics_calculation()
        
        # 3. 趋势分析
        await demo_trend_analysis()
        
        # 4. 优化建议生成
        await demo_optimization_recommendations()
        
        # 5. 持续监控
        await demo_continuous_monitoring()
        
        # 6. 报告生成
        await demo_report_generation()
        
        print_section("演示总结")
        
        print("🎊 自动化评估框架核心价值:")
        print("   ✅ 全面基准测试 - 6类测试场景，15个标准测试用例")
        print("   ✅ 多维度指标 - 路由准确性、响应质量、成本效率、系统性能")
        print("   ✅ 智能趋势分析 - 7天历史数据分析和未来预测")
        print("   ✅ 自动化建议 - 基于数据的精准优化建议")
        print("   ✅ 持续监控 - 24/7实时监控和异常检测")
        print("   ✅ 丰富报告 - 多格式导出和可视化展示")
        
        print("\n💡 业务价值:")
        print("   🎯 质量保证: 确保系统性能始终满足预期")
        print("   📈 持续改进: 数据驱动的系统优化")
        print("   💰 成本控制: 及时发现和解决成本问题")
        print("   🚀 效率提升: 自动化减少人工监控成本")
        print("   📊 可观测性: 全面的系统健康度监控")
        
        print("\n🔮 技术优势:")
        print("   🤖 完全自动化: 无需人工干预的评估流程")
        print("   📊 数据驱动: 基于真实数据的客观评估")
        print("   🎯 精准建议: 针对性的优化建议和实施路线")
        print("   🔄 持续学习: 基于历史数据的趋势分析")
        print("   🛡️ 预防性维护: 提前发现潜在问题")
        
    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()) 