微型电脑应用

针对网络信息资源中冗余数据提取精度差、非线性冗余数据难以提取,导致冗余数据的检测查全率及准确率较低的问题,设计了网络信息资源的冗余数据检测算法。针对网络信息资源中的线性冗余数据,采用经验模态分解方法提取冗余数据特征,通过特征时间序列得出其状态特征分布函数,据此构建线性冗余数据检测模型;针对非线性冗余数据难以检测的问题,重构非线性冗余数据特征,采用高阶累积特征后置聚焦搜索方法构建特征时间序列的指向性波束模型,实现非线性冗余数据的准确检测。实验结果表明,该算法能够准确检测网络信息资源冗余信息,对冗余数据的查全率为98%,检测准确率为95%,证明该算法性能优异。

Aiming at the problems of poor accuracy of redundant data extraction in network information resources and difficulty in extracting non-linear redundant data, which lead to low detection recall rate and accuracy of redundant data, a redundant data detection algorithm for network information resources is designed. For linear redundant data in network information resources, empirical mode decomposition method is used to extract redundant data features, and its state feature distribution function is obtained through feature time series, and a linear redundant data detection model is constructed accordingly. The problem of data is difficult to detect, reconstruct the characteristics of nonlinear redundant data, and we use the high-order cumulative feature post-focus search method to construct a directional beam model of feature time series to achieve accurate detection of nonlinear redundant data. Experimental results show that the algorithm can accurately detect redundant information of network information resources. The recall rate of redundant data is 98%, and the detection accuracy rate is 95%, which proves that the algorithm has excellent performance.