【摘要】 针对在集成数据交互中心进行数据随机转发时,散乱数据易出现信息丢帧的问题,提出一种基于大数据分析的散乱缺损信息无损恢复方法.首先通过在云存储数据交互中心采集散乱缺损信息组成大数据比特序列,分析数据在云计算中心的存储结构模型;然后利用联合稀疏分解方法进行散乱缺损信息的特征分解,并采用压缩感知方法进行散乱缺损信息的特征压缩及信息自适应特征聚类处理,结合大数据信息融合方法进行散乱缺损信息的关联特征挖掘;最后采用相空间重构方法进行散乱缺损信息的特征重组,在重构的相空间中进行散乱缺损信息的无损信息恢复.仿真实验结果表明,采用该方法进行散乱缺损信息无损恢复的误差较低,数据重构的精度较高,运算开销较小,有效提高了数据的信息恢复能力.更多还原

【Abstract】 Aiming at the problem that the scattered data was easy to lose frame when the data was randomly forwarded in the integrated data interaction center, the author proposed a lossless restoration method based on big data analysis. Firstly, big data bit sequence was formed by collecting scattered defect information in the data interaction center of cloud storage, and the storage structure model of data in cloud computing center was analyzed. Secondly, the feature decomposition of scattered defect information was carried out by using the method of joint sparse decomposition, the feature compression and adaptive feature clustering processing of information of scattered defect information were carried out by using compression perception method, combined with big data information fusion method, and the association feature mining of scattered defect information was carried out. Finally, the method of phase space reconstruction was used to reconstruct the features of scattered defect information, and the lossless information of scattered defect information was restored in the reconstructed phase space. The simulation results show that the error of lossless restoration of scattered information is lower, the precision of data reconstruction is higher, the computation cost is small, and the ability of information restoration is improved effectively.更多还原