导弹装备测试数据异常域检测方法研究

Research on Anomaly Domain Detection Method of Missile Equipment Test Data

随着导弹装备信息化水平的快速提高,对作战与保障的智能化应用要求不断增强,因此,如何快速处理导弹装备在长期使用、贮存、维护过程中积累的数据信息,提高数据质量,是数据时代背景下首要研究内容,也是数据智能化应用的根本途径;文章以大数据技术为基础,以统计理论、深度学习方法等手段展开测试数据异常域检测方法分析,将测试数据异常域划分为异常点、异常曲线以及异常簇,围绕导弹装备测试数据及数据特点,详细阐述不同特征数据条件下的测试数据异常域检测方法原理、检测模型、算法步骤等内容,并模拟导弹装备典型测试数据序列开展应用技术研究。

with the rapid improvement of the information level of Missile equipment,the requirements for intelligent application of operation and support are increasing.Therefore,how to quickly process the data information accumulated by Missile equipment in the processes of long-term use,storage and maintenance,and improving the data quality is the primary research content under the background of data time and the fundamental way of data intelligent application.Based on the big data technology,the detection method of test data anomaly domain is analyzed by means of statistical theory and deep learning method,the test data anomaly domain is divided into anomaly points,anomaly curves and anomaly clusters,and by the test data and data characteristics of Missile equipment,the detection method principle,detection model,algorithm steps of test data anomaly domain are elaborated under different characteristic data conditions,and the typical test data sequence of equipment to carry out application technology research is simulated.