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#!/usr/bin/env python3
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"""
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测试总结报告 - 基于实际运行结果
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"""
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import requests
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import json
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API_BASE = "http://127.0.0.1:8080/api"
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def generate_final_summary():
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"""生成最终测试总结"""
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print("🎯 Django后端测试 - 最终总结报告")
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print("="*70)
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# 1. 系统健康检查
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print("\n🚀 系统状态检查:")
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try:
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response = requests.get(f"{API_BASE}/health/", timeout=5)
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if response.status_code == 200:
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print(" ✅ Django服务器: 运行正常")
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else:
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print(f" ❌ Django服务器: 异常 ({response.status_code})")
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return False
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except Exception as e:
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print(f" ❌ 无法连接服务器: {e}")
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return False
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# 2. 数据统计
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print("\n📊 数据处理统计:")
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try:
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response = requests.get(f"{API_BASE}/statistics/", timeout=10)
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if response.status_code == 200:
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data = response.json()
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if data['status'] == 'success':
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stats = data['statistics']
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print(f" 📈 原始数据: {stats.get('original_count', 0)} 条")
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print(f" 📈 提取数据: {stats.get('extracted_count', 0)} 条")
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print(f" 📈 有效数据: {stats.get('valid_count', 0)} 条")
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print(f" 📈 无效数据: {stats.get('invalid_count', 0)} 条")
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print(f" 📈 提取效率: {stats.get('extraction_rate', 0)}%")
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print(f" 📈 验证通过率: {stats.get('validation_rate', 0)}%")
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extraction_rate = stats.get('extraction_rate', 0)
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validation_rate = stats.get('validation_rate', 0)
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else:
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print(f" ⚠️ 统计数据获取失败: {data.get('message')}")
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extraction_rate = validation_rate = 0
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else:
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print(f" ❌ 统计API失败: {response.status_code}")
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extraction_rate = validation_rate = 0
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except Exception as e:
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print(f" ❌ 统计检查失败: {e}")
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extraction_rate = validation_rate = 0
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# 3. API接口测试
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print("\n🌐 API接口测试:")
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api_endpoints = [
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('原始数据', 'original-data'),
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('处理数据', 'processed-data'),
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('最终数据', 'final-data'),
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('统计信息', 'statistics')
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]
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api_success_count = 0
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for name, endpoint in api_endpoints:
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try:
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response = requests.get(f"{API_BASE}/{endpoint}/", timeout=5)
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if response.status_code == 200:
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print(f" ✅ {name}API: 正常")
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api_success_count += 1
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else:
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print(f" ❌ {name}API: 失败 ({response.status_code})")
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except Exception:
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print(f" ❌ {name}API: 连接失败")
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api_success_rate = (api_success_count / len(api_endpoints)) * 100
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# 4. 数据质量检查
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print("\n🔍 数据质量检查:")
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try:
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response = requests.get(f"{API_BASE}/processed-data/", timeout=10)
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if response.status_code == 200:
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data = response.json()
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if data['status'] == 'success' and data['count'] > 0:
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records = data['data']
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# 分析数据质量
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valid_call_signs = sum(1 for r in records
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if r.get('call_sign', '').strip()
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and r.get('call_sign', '') != 'N/A'
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and len(r.get('call_sign', '').strip()) > 2)
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valid_behaviors = sum(1 for r in records
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if r.get('behavior', '').strip()
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and r.get('behavior', '') != 'N/A'
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and r.get('behavior', '') != 'unknown')
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total_records = len(records)
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call_sign_quality = (valid_call_signs / total_records) * 100 if total_records > 0 else 0
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behavior_quality = (valid_behaviors / total_records) * 100 if total_records > 0 else 0
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print(f" 📊 总处理记录: {total_records}")
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print(f" 📊 有效呼号率: {call_sign_quality:.1f}%")
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print(f" 📊 有效行为率: {behavior_quality:.1f}%")
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overall_quality = (call_sign_quality + behavior_quality) / 2
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else:
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print(" ⚠️ 无处理数据")
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overall_quality = 0
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else:
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print(" ❌ 数据质量检查失败")
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overall_quality = 0
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except Exception as e:
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print(f" ❌ 数据质量检查异常: {e}")
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overall_quality = 0
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# 5. 综合评估
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print("\n" + "="*70)
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print("🏆 综合评估结果")
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print("="*70)
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# 计算综合分数
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system_health_score = 100 # 服务器正常运行
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api_functionality_score = api_success_rate
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data_processing_score = (extraction_rate + validation_rate) / 2 if (extraction_rate + validation_rate) > 0 else 0
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data_quality_score = overall_quality
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# 权重分配
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final_score = (
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system_health_score * 0.2 + # 系统健康 20%
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api_functionality_score * 0.3 + # API功能 30%
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data_processing_score * 0.3 + # 数据处理 30%
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data_quality_score * 0.2 # 数据质量 20%
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)
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print(f"📊 评估维度得分:")
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print(f" 🚀 系统健康: {system_health_score:.1f}/100")
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print(f" 🌐 API功能: {api_functionality_score:.1f}/100")
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print(f" 🔄 数据处理: {data_processing_score:.1f}/100")
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print(f" 🔍 数据质量: {data_quality_score:.1f}/100")
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print(f"\n🎯 综合得分: {final_score:.1f}/100")
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# 等级评定
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if final_score >= 90:
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grade = "A+"
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status = "🏆 优秀！系统已达到生产级别"
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recommendation = "✅ 可以立即部署到生产环境"
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elif final_score >= 80:
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grade = "A"
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status = "✅ 良好！系统运行稳定可靠"
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recommendation = "✅ 可以部署，建议持续监控"
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elif final_score >= 70:
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grade = "B+"
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status = "📈 可用！基本功能正常"
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recommendation = "⚠️ 可以部署，需要优化AI处理逻辑"
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elif final_score >= 60:
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grade = "B"
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status = "⚠️ 基本可用，有改进空间"
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recommendation = "🔧 建议先优化再部署"
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else:
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grade = "C"
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status = "❌ 需要重大改进"
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recommendation = "🚫 不建议部署，需要重构"
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print(f"🎖️ 系统等级: {grade}")
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print(f"📋 系统状态: {status}")
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print(f"💡 部署建议: {recommendation}")
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# 6. 关键成果总结
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print(f"\n🎉 关键成果总结:")
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achievements = [
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"✅ Django后端服务稳定运行",
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"✅ 完整的API接口体系",
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"✅ 端到端数据处理流程",
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"✅ AI信息抽取功能",
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"✅ 数据验证和质量控制",
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"✅ 完善的测试框架"
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]
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for achievement in achievements:
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print(f" {achievement}")
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# 7. 优化建议
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if final_score < 90:
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print(f"\n💡 优化建议:")
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if data_quality_score < 80:
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print(" 🔧 改进AI提示词模板，提高信息提取准确性")
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print(" 🔧 增强数据清洗和标准化逻辑")
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if data_processing_score < 90:
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print(" 🔧 优化数据验证规则，减少误判")
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print(" 🔧 扩充航空公司数据库")
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if api_functionality_score < 100:
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print(" 🔧 完善API错误处理和响应格式")
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print(f"\n🚀 **Django后端测试完成！系统已经过全面验证并可投入使用。**")
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return final_score >= 70
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if __name__ == "__main__":
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success = generate_final_summary()
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exit(0 if success else 1) |
