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作 者:覃肖云 欧旭 TAN Xiao-yun;OU Xu(Information Center,Guangxi Medical University,Nanning Guangxi 530021,China)
出 处:《计算机仿真》2024年第9期500-504,共5页Computer Simulation
摘 要:高密度网络中的数据来源广泛,质量不一,存在数据噪声、缺失、错误等问题。上述问题会干扰随机风险预警过程,并降低预警的精度。为了解决以上问题,提出高密度网络海量数据访问随机风险预警方法。方法通过实时监测网络数据的运行状态,并采用基于非线性独立成分估计的增强方法处理网络数据,将主成分分析与线性判别分析法结合,得到鉴别主成分分析法,提取网络数据的特征。通过风险阈值判断网络数据中是否出现异常现象,从而实现高密度网络海量数据访问随机风险的预警。实验结果表明,所提方法的数据监测效果好、预警时间保持在90ms内,且预警精度高。The data sources in high-density networks are diverse and of varying quality,with issues such as data noise,missing data,and errors.The above issues will interfere with the random risk warning process and reduce the accuracy of the warning.Therefore,a random risk warning method for massive data access in high-density network was proposed.This method first monitored the running state of network data in real time.And then,the enhancement method based on nonlinear independent component estimation was adopted to process the network data.Moreover,principal component analysis was combined with linear discriminant analysis to obtain a discriminant principal component analysis method,thus extracting the characteristics of network data.Furthermore,risk thresholds were used to judge whether abnormal phenomena occurred in network data.Finally,the early warning of random risks in accessing massive data of high-density network was achieved.The experimental results show that the proposed method has good data monitoring effect and high warning accuracy.And the warning time can be controlled to be less than 90ms.
关 键 词:网络数据增强 鉴别主成分分析法 网络数据监测 在线预警 风险阈值
分 类 号:TP393.03[自动化与计算机技术—计算机应用技术]
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