Autonomy Self Evolving Testing Loop

自主系统自进化仿真测试循环 (Autonomy Self-Evolving Testing Loop)

概述

AutonomyLens是一个自进化的基于仿真的测试循环框架，用于自主网络物理系统(如无人机)的验证。将场景设计、仿真执行和遥测分析整合为统一的自进化循环。

核心创新

1. 自进化测试循环

class AutonomyLens:
    def __init__(self, simulator, coverage_analyzer):
        self.sim = simulator
        self.coverage = coverage_analyzer
        self.scenario_db = ScenarioDatabase()
        
    def testing_loop(self, iterations=100):
        for i in range(iterations):
            # 1. 场景生成/选择
            scenario = self.generate_or_select_scenario()
            
            # 2. 仿真执行
            telemetry = self.sim.execute(scenario)
            
            # 3. 分析
            coverage = self.coverage.analyze(telemetry)
            failures = self.detect_failures(telemetry)
            
            # 4. 进化
            if coverage < threshold or failures:
                self.evolve_scenarios(scenario, failures)
            
            # 5. 更新知识库
            self.scenario_db.update(scenario, coverage, failures)
    
    def evolve_scenarios(self, base_scenario, failure_points):
        # 基于失败的场景进化
        mutations = [
            self.add_environment_complexity(base_scenario),
            self.stress_failure_conditions(base_scenario, failure_points),
            self.combine_scenarios(base_scenario)
        ]
        return mutations