摘要
为了解决监测系统无法及时检测发现异常数据存在,导致正常数据无法正常完成数据交换的问题,利用信号滤波原理,采用卡尔曼滤波算法确定系统硬件框架,采用电子信息数据正常滑动窗口模型、卡尔曼滤波下的异常数据特征识别与异常数据识别结果输出,在数据层彻底优化异常数据检测机制,达到提升检测精准度的目的。通过模拟数据导入的3组测试系统对比表明系统检测效果与检测精度较好于另外两种检测系统,具备提升异常数据检测精准度的能力。
In order to solve the problem that the monitoring system cannot detect the existence of abnormal data in time,which leads to the failure of normal data to complete the data exchange,the signal filtering principle is used,the Kalman filtering algorithm is used to determine the hardware framework of the system,and the electronic information data normal sliding window model,Kalman The abnormal data feature identification under filtering and the abnormal data identification result output,thoroughly optimize the abnormal data detection mechanism at the data layer to achieve the purpose of improving the detection accuracy.The compari-son of the three groups of test systems with simulated data import shows that the system has better detection effect and detection accuracy than the other two detection systems,and has the ability to improve the detection accuracy of abnormal data.
作者
顾亚文
GU Ya-wen(School of Artificial Intelligence and Information Engineering,Jinken College of Technology,Nanjing 211156 China)
出处
《自动化技术与应用》
2023年第12期95-98,共4页
Techniques of Automation and Applications
关键词
卡尔曼滤波
电子信息
数据特征识别
检测系统
Kalman filter
electronic information
data feature identification
detection system
