基于多Agent的实时自适应数据判读方法

针对目前已有数据判读方法在有效载荷地面集成测试中不适应测试环境变化、实时判读不连续、错误率高的问题，提出一种基于多Agent框架的实时自适应判读（MARAD）方法。首先，依据"感知-决策-执行"的设计理念，构建四个具有独立任务又互相协同工作的智能Agent，以适应测试环境的改变；其次，采用面向活动建模的方式，以C语言集成产生式系统（CLIPS）作为推理机，取消判读规则对测试序列的依赖，保证判读过程的连续性；最后，在判读规则中引入容错机制，在不改变正确性的前提下减少误判和漏判。测试验证结果表明，在判读数据相同的条件下，MARAD方法的实时判读结果与已有的状态模型方法的三次事后判读的均值结果相比，参数漏判率均为0%，但活动误判率降低10.54个百分点；与人工判读相比，参数漏判率降低5.97个百分点，活动误判率降低3.02个百分点，且无需人员参与判读。所提方法能够有效提高判读系统的自适应测试环境能力、实时判读的持续性和正确性。

Multi-Agent-based real-time self-adaptive discrimination method

Concerning the problem that existing data discrimination methods can not adapt to changeable test environment and realize continuous real-time discriminating process with low error rate when applied in ground integrated test of payload, a Multi-Agent-based Real-time self-Adaptive Discrimination (MARAD) method was proposed. Firstly, based on the design principle of "sensing-decision-execution", four Agents which had own tasks but also interact and cooperate with each other were adopted in order to adapt the changeable test situation. Secondly, an activity-oriented model was constructed, and the C Language Integrated Production System (CLIPS) was used as an inference engine to make the discrimination rules independent of test sequences and assure the continuity of discrimination. Finally, fault-tolerant mechanism was introduced to the discrimination rules to decrease fault positive rate without changing the correctness. With the same test data, compared with the state modeling method with the average result of three times after discriminating, MARAD method has the same parameter missing rate 0% but decreases the activity false-positive rate by 10.54 percentage points; compared with the manual method, MARAD method decreases the parameter missing rate by 5.97 percentage points and activity false-positive rate by 3.02 percentage points, and no person is needed to participate in the discrimination. The proposed method can effectively improve the environment self-adaptability, real-time discriminating continuity and correctness of the system.