基于ADF-PFT的体质监测中异常数据识别算法研究

Research on Abnormal Data Recognition Algorithm inPhysique Monitoring Based on ADF-PFT

为了保障体质监测的数据检测的准确性,提高体质监测的异常智能分析能力,需要进行体质监测中异常数据识别,提出基于ADF-PFT的体质监测中异常数据识别模型。通过体质监测的数据采集和诊断分析,构建体质监测数据信息流模型,提取体质监测中的异常数据特征量,采用匹配滤波分析方法进行体质监测中异常数据的干扰性滤波,去除多个已知干扰频率成分。采用ADF-PFT算法进行体质监测中异常数据识别和特征分析,建立异常数据识别的优化特征解,采用简化梯度算法进行异常数据识别的自适应寻优,结合自相关特征匹配方法实现体质监测中异常数据特征分析和数据优化识别。实验结果表明,采用该方法进行体质监测中异常数据识别的准确性较好,抗干扰能力较强。

In order to ensure the accuracy of data detection and identification of physical mass monitoring and improve the ability of abnormal intelligent analysis of physical mass monitoring,it is necessary to identify abnormal data in physical quality monitoring.Firstly,the data acquisition and diagnosis analysis model of physical mass monitoring is constructed,and the information flow model of the physical mass monitoring data is constructed.The characteristic quantity of abnormal data in the physical mass monitoring is extracted.ADFPFT algorithm is used to identify and analyze the abnormal data in the physical quality monitoring.The optimal feature solution of abnormal data identification is established.The simplified gradient algorithm is used to realize the adaptive optimization of abnormal data identification.Combined with the autocorrelation feature matching method,the analysis of abnormal data feature analysis and data optimization identification simulation result show that the method is more accurate and the anti-interference ability is strong.