关键词: 有效载荷, 自适应判读, 多Agent框架, 面向活动建模

Abstract: 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.

Key words: effective payload, self-adaptive discrimination, multi-Agent framework, activity-oriented modeling