#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Human干预系统测试脚本 """ import asyncio import json import time from datetime import datetime, timedelta from src.jixia.intervention.human_intervention_system import ( DebateHealthMonitor, HealthStatus, InterventionLevel, AlertType ) class TestHumanInterventionSystem: """Human干预系统测试类""" def __init__(self): self.monitor = DebateHealthMonitor() self.test_results = [] # 添加事件处理器用于测试 self.monitor.add_event_handler("alert_created", self._handle_alert_created) self.monitor.add_event_handler("intervention_executed", self._handle_intervention_executed) self.monitor.add_event_handler("human_notification", self._handle_human_notification) self.received_alerts = [] self.received_interventions = [] self.received_notifications = [] async def _handle_alert_created(self, alert): """处理警报创建事件""" self.received_alerts.append(alert) print(f"🚨 收到警报: {alert.alert_type.value} - {alert.message}") async def _handle_intervention_executed(self, action): """处理干预执行事件""" self.received_interventions.append(action) print(f"🛠️ 执行干预: {action.action_type} - {action.description}") async def _handle_human_notification(self, notification): """处理Human通知事件""" self.received_notifications.append(notification) print(f"👤 Human通知: {notification['message']}") async def test_basic_health_monitoring(self): """测试基本健康监控功能""" print("\n🧪 测试基本健康监控功能...") # 正常辩论数据 normal_debate_data = { "recent_messages": [ {"sender": "正1", "content": "我认为人工智能投资具有巨大潜力，因为技术发展迅速，市场需求不断增长。首先，AI技术在各行各业都有广泛应用前景。"}, {"sender": "反1", "content": "虽然AI投资有潜力，但我们也要考虑风险。技术泡沫、监管不确定性等因素都可能影响投资回报。"}, {"sender": "正2", "content": "反方提到的风险确实存在，但是通过合理的投资策略和风险管理，我们可以最大化收益同时控制风险。"}, {"sender": "反2", "content": "正方的观点有道理，不过我想补充一点：投资时机也很重要，现在可能不是最佳入场时机。"} ], "topic_keywords": ["人工智能", "AI", "投资", "风险", "收益", "技术", "市场"], "system_status": { "error_rate": 0.01, "avg_response_time": 1.2, "system_load": 0.5 } } score, status = await self.monitor.analyze_debate_health(normal_debate_data) success = score >= 70 and status in [HealthStatus.EXCELLENT, HealthStatus.GOOD] self.test_results.append(("基本健康监控", success, f"得分: {score:.1f}, 状态: {status.value}")) print(f"✅ 正常辩论健康度: {score:.1f}分 ({status.value})") return success async def test_quality_decline_detection(self): """测试质量下降检测""" print("\n🧪 测试质量下降检测...") # 低质量辩论数据 low_quality_data = { "recent_messages": [ {"sender": "正1", "content": "好"}, {"sender": "反1", "content": "不好"}, {"sender": "正2", "content": "是的"}, {"sender": "反2", "content": "不是"}, {"sender": "正1", "content": "对"}, {"sender": "反1", "content": "错"}, ], "topic_keywords": ["人工智能", "AI", "投资"], "system_status": { "error_rate": 0.01, "avg_response_time": 1.0, "system_load": 0.4 } } initial_alert_count = len(self.received_alerts) score, status = await self.monitor.analyze_debate_health(low_quality_data) # 检查是否触发了质量相关警报 quality_alerts = [alert for alert in self.received_alerts[initial_alert_count:] if alert.alert_type == AlertType.QUALITY_DECLINE] success = len(quality_alerts) > 0 and score < 50 self.test_results.append(("质量下降检测", success, f"得分: {score:.1f}, 警报数: {len(quality_alerts)}")) print(f"✅ 低质量辩论检测: {score:.1f}分, 触发警报: {len(quality_alerts)}个") return success async def test_toxic_behavior_detection(self): """测试有害行为检测""" print("\n🧪 测试有害行为检测...") # 包含有害行为的数据 toxic_data = { "recent_messages": [ {"sender": "正1", "content": "我认为这个观点是正确的，有充分的理由支持。"}, {"sender": "反1", "content": "你这个观点太愚蠢了！完全没有逻辑！"}, {"sender": "正2", "content": "请保持理性讨论，不要进行人身攻击。"}, {"sender": "反2", "content": "闭嘴！你们这些白痴根本不懂！"}, {"sender": "正1", "content": "让我们回到正题，理性分析这个问题。"} ], "topic_keywords": ["观点", "逻辑", "分析"], "system_status": { "error_rate": 0.02, "avg_response_time": 1.5, "system_load": 0.6 } } initial_alert_count = len(self.received_alerts) score, status = await self.monitor.analyze_debate_health(toxic_data) # 检查是否触发了有害行为警报 toxic_alerts = [alert for alert in self.received_alerts[initial_alert_count:] if alert.alert_type == AlertType.TOXIC_BEHAVIOR] success = len(toxic_alerts) > 0 self.test_results.append(("有害行为检测", success, f"警报数: {len(toxic_alerts)}, 文明度分数: {self.monitor.health_metrics['interaction_civility'].value:.1f}")) print(f"✅ 有害行为检测: 触发警报: {len(toxic_alerts)}个") return success async def test_emotional_escalation_detection(self): """测试情绪升级检测""" print("\n🧪 测试情绪升级检测...") # 情绪激动的数据 emotional_data = { "recent_messages": [ {"sender": "正1", "content": "我强烈反对这个观点！！！"}, {"sender": "反1", "content": "你们完全错了！！！这太愤怒了！！！"}, {"sender": "正2", "content": "我非常生气！！！这个讨论让我很讨厌！！！"}, {"sender": "反2", "content": "大家都冷静一下！！！不要这么激动！！！"} ], "topic_keywords": ["观点", "讨论"], "system_status": { "error_rate": 0.01, "avg_response_time": 1.0, "system_load": 0.5 } } initial_alert_count = len(self.received_alerts) score, status = await self.monitor.analyze_debate_health(emotional_data) # 检查是否触发了情绪升级警报 emotion_alerts = [alert for alert in self.received_alerts[initial_alert_count:] if alert.alert_type == AlertType.EMOTIONAL_ESCALATION] success = len(emotion_alerts) > 0 self.test_results.append(("情绪升级检测", success, f"警报数: {len(emotion_alerts)}, 情绪稳定性: {self.monitor.health_metrics['emotional_stability'].value:.1f}")) print(f"✅ 情绪升级检测: 触发警报: {len(emotion_alerts)}个") return success async def test_participation_imbalance_detection(self): """测试参与不平衡检测""" print("\n🧪 测试参与不平衡检测...") # 参与不平衡的数据 imbalanced_data = { "recent_messages": [ {"sender": "正1", "content": "我有很多观点要分享..."}, {"sender": "正1", "content": "首先，我认为..."}, {"sender": "正1", "content": "其次，我们应该..."}, {"sender": "正1", "content": "最后，我建议..."}, {"sender": "正1", "content": "总结一下..."}, {"sender": "正1", "content": "补充一点..."}, {"sender": "正1", "content": "再说一遍..."}, {"sender": "反1", "content": "好的"} ], "topic_keywords": ["观点", "建议"], "system_status": { "error_rate": 0.01, "avg_response_time": 1.0, "system_load": 0.5 } } initial_alert_count = len(self.received_alerts) score, status = await self.monitor.analyze_debate_health(imbalanced_data) # 检查是否触发了参与不平衡警报 balance_alerts = [alert for alert in self.received_alerts[initial_alert_count:] if alert.alert_type == AlertType.PARTICIPATION_IMBALANCE] success = len(balance_alerts) > 0 self.test_results.append(("参与不平衡检测", success, f"警报数: {len(balance_alerts)}, 平衡度: {self.monitor.health_metrics['participation_balance'].value:.1f}")) print(f"✅ 参与不平衡检测: 触发警报: {len(balance_alerts)}个") return success async def test_auto_intervention(self): """测试自动干预功能""" print("\n🧪 测试自动干预功能...") # 触发多种问题的数据 problematic_data = { "recent_messages": [ {"sender": "正1", "content": "你们都是白痴！！！"}, {"sender": "反1", "content": "愚蠢！！！"}, {"sender": "正2", "content": "垃圾观点！！！"}, {"sender": "反2", "content": "讨厌！！！"} ], "topic_keywords": ["观点"], "system_status": { "error_rate": 0.05, "avg_response_time": 3.0, "system_load": 0.9 } } initial_intervention_count = len(self.received_interventions) score, status = await self.monitor.analyze_debate_health(problematic_data) # 检查是否执行了自动干预 new_interventions = self.received_interventions[initial_intervention_count:] success = len(new_interventions) > 0 self.test_results.append(("自动干预功能", success, f"执行干预: {len(new_interventions)}次")) print(f"✅ 自动干预: 执行了 {len(new_interventions)} 次干预") for intervention in new_interventions: print(f" - {intervention.action_type}: {intervention.description}") return success async def test_human_notification(self): """测试Human通知功能""" print("\n🧪 测试Human通知功能...") # 设置较低的通知阈值以便测试 original_threshold = self.monitor.monitoring_config["human_notification_threshold"] self.monitor.monitoring_config["human_notification_threshold"] = InterventionLevel.MODERATE_GUIDANCE # 严重问题数据 critical_data = { "recent_messages": [ {"sender": "正1", "content": "你"}, {"sender": "反1", "content": "我"}, {"sender": "正2", "content": "他"}, {"sender": "反2", "content": "她"} ], "topic_keywords": ["重要话题"], "system_status": { "error_rate": 0.1, "avg_response_time": 5.0, "system_load": 0.95 } } initial_notification_count = len(self.received_notifications) score, status = await self.monitor.analyze_debate_health(critical_data) # 恢复原始阈值 self.monitor.monitoring_config["human_notification_threshold"] = original_threshold # 检查是否发送了Human通知 new_notifications = self.received_notifications[initial_notification_count:] success = len(new_notifications) > 0 self.test_results.append(("Human通知功能", success, f"发送通知: {len(new_notifications)}次")) print(f"✅ Human通知: 发送了 {len(new_notifications)} 次通知") return success async def test_health_report_generation(self): """测试健康报告生成""" print("\n🧪 测试健康报告生成...") report = self.monitor.get_health_report() required_fields = ["overall_score", "health_status", "metrics", "active_alerts", "recent_interventions", "monitoring_enabled", "last_check"] success = all(field in report for field in required_fields) success = success and len(report["metrics"]) == 6 # 6个健康指标 self.test_results.append(("健康报告生成", success, f"包含字段: {len(report)}个")) print(f"✅ 健康报告生成: 包含 {len(report)} 个字段") print(f" 整体得分: {report['overall_score']}") print(f" 健康状态: {report['health_status']}") print(f" 活跃警报: {report['active_alerts']}个") return success async def test_alert_resolution(self): """测试警报解决功能""" print("\n🧪 测试警报解决功能...") # 确保有一些警报 if not self.monitor.active_alerts: # 创建一个测试警报 from src.jixia.intervention.human_intervention_system import InterventionAlert test_alert = InterventionAlert( id="test_alert_123", alert_type=AlertType.QUALITY_DECLINE, severity=InterventionLevel.GENTLE_REMINDER, message="测试警报", affected_participants=[], metrics={"test": 50}, timestamp=datetime.now() ) self.monitor.active_alerts.append(test_alert) # 解决第一个警报 if self.monitor.active_alerts: alert_id = self.monitor.active_alerts[0].id success = self.monitor.resolve_alert(alert_id, "测试解决") # 清理已解决的警报 initial_count = len(self.monitor.active_alerts) self.monitor.clear_resolved_alerts() final_count = len(self.monitor.active_alerts) success = success and (final_count < initial_count) else: success = True # 没有警报也算成功 self.test_results.append(("警报解决功能", success, f"解决并清理警报")) print(f"✅ 警报解决: 功能正常") return success async def test_monitoring_control(self): """测试监控控制功能""" print("\n🧪 测试监控控制功能...") # 测试禁用监控 self.monitor.disable_monitoring() disabled_state = not self.monitor.monitoring_enabled # 测试启用监控 self.monitor.enable_monitoring() enabled_state = self.monitor.monitoring_enabled success = disabled_state and enabled_state self.test_results.append(("监控控制功能", success, "启用/禁用功能正常")) print(f"✅ 监控控制: 启用/禁用功能正常") return success async def test_data_persistence(self): """测试数据持久化""" print("\n🧪 测试数据持久化...") try: # 保存监控数据 test_filename = "test_monitoring_data.json" self.monitor.save_monitoring_data(test_filename) # 检查文件是否存在并包含正确数据 import os if os.path.exists(test_filename): with open(test_filename, 'r', encoding='utf-8') as f: data = json.load(f) required_sections = ["health_metrics", "active_alerts", "intervention_history", "monitoring_config", "monitoring_enabled", "export_time"] success = all(section in data for section in required_sections) # 清理测试文件 os.remove(test_filename) else: success = False except Exception as e: print(f" 数据持久化错误: {e}") success = False self.test_results.append(("数据持久化", success, "保存/加载功能正常")) print(f"✅ 数据持久化: 功能正常") return success async def test_performance(self): """测试性能""" print("\n🧪 测试性能...") # 准备测试数据 test_data = { "recent_messages": [ {"sender": f"用户{i%4}", "content": f"这是第{i}条测试消息，包含一些内容用于分析。"} for i in range(20) ], "topic_keywords": ["测试", "性能", "分析", "消息"], "system_status": { "error_rate": 0.01, "avg_response_time": 1.0, "system_load": 0.5 } } # 性能测试 iterations = 100 start_time = time.time() for _ in range(iterations): await self.monitor.analyze_debate_health(test_data) end_time = time.time() total_time = end_time - start_time avg_time = total_time / iterations analyses_per_second = iterations / total_time # 性能要求：平均处理时间 < 100ms success = avg_time < 0.1 self.test_results.append(("性能测试", success, f"平均处理时间: {avg_time*1000:.2f}ms, 处理速度: {analyses_per_second:.1f}次/秒")) print(f"✅ 性能测试: 平均处理时间 {avg_time*1000:.2f}ms, 处理速度 {analyses_per_second:.1f}次/秒") return success async def run_all_tests(self): """运行所有测试""" print("🚀 开始Human干预系统测试...") print("=" * 60) test_functions = [ self.test_basic_health_monitoring, self.test_quality_decline_detection, self.test_toxic_behavior_detection, self.test_emotional_escalation_detection, self.test_participation_imbalance_detection, self.test_auto_intervention, self.test_human_notification, self.test_health_report_generation, self.test_alert_resolution, self.test_monitoring_control, self.test_data_persistence, self.test_performance ] passed_tests = 0 total_tests = len(test_functions) for test_func in test_functions: try: result = await test_func() if result: passed_tests += 1 except Exception as e: print(f"❌ 测试失败: {test_func.__name__} - {e}") self.test_results.append((test_func.__name__, False, f"异常: {e}")) # 输出测试结果 print("\n" + "=" * 60) print("📊 测试结果汇总:") print("=" * 60) for test_name, success, details in self.test_results: status = "✅ 通过" if success else "❌ 失败" print(f"{status} {test_name}: {details}") success_rate = (passed_tests / total_tests) * 100 print(f"\n🎯 总体测试结果: {passed_tests}/{total_tests} 通过 ({success_rate:.1f}%)") if success_rate >= 90: print("🎉 Human干预系统测试优秀！") elif success_rate >= 80: print("👍 Human干预系统测试良好！") elif success_rate >= 70: print("⚠️ Human干预系统测试一般，需要改进。") else: print("❌ Human干预系统测试较差，需要重大改进。") # 输出系统状态 print("\n📋 系统状态报告:") report = self.monitor.get_health_report() print(f"监控状态: {'启用' if report['monitoring_enabled'] else '禁用'}") print(f"活跃警报: {report['active_alerts']}个") print(f"近期干预: {report['recent_interventions']}次") print(f"收到警报: {len(self.received_alerts)}个") print(f"执行干预: {len(self.received_interventions)}次") print(f"Human通知: {len(self.received_notifications)}次") return success_rate >= 80 async def main(): """主函数""" tester = TestHumanInterventionSystem() await tester.run_all_tests() if __name__ == "__main__": asyncio.run(main())