At present, ScanDisk is used to recover the data lost in network communication. But this method is limited in scope, and once the lost data is covered, it’s difficult or impossible to recover it, which results in low recovery degree. Accordingly, a recovery method for lost data in network communication based on RAID6 is proposed. Firstly, according to the mechanism of data loss in network communication, the missing data is divided into three categories: random loss, completely random loss and nonrandom loss, and then according to the results of classification, the recovery problem of the data loss in network communication is converted into the problem of matrix completion, finally, a low-rank decomposition model is proposed, according to the low rank characteristics of the matrix, the lost data in the matrix is recovered, thus the recovery of the lost data in network communication is finished. Experimental results show that the proposed method can easily recover the lost data in network communication with a simple operation, low computing complexity and strong applicability, and can be used as a universal recovery method for data lost in network communication.

1. [1] L. Cui and G. Zhu, “Research on move method for Earth mass recovery by gravity satellite data,” Science Technology and Engineering, Vol.15, No.14, pp. 106-109, 2015.
2. [2] J. Guo, T. Yuan, and Y. Li, “Data recovery algorithm in chemical process based on local weighting reconstruction,” J. of Computer Applications, Vol.36, No.1, pp. 282-286, 2016.
3. [3] J. Bai, H. Sun, and Z. Hu, “A recovery method of deleted data based on SQLite3 file format,” J. of Chinese Mini-Micro Computer Systems, Vol.37, No.3, pp. 505-509, 2016.
4. [4] Y. Chen, Y. Zhao, and T. Wang, “An all digital timing data recovery loop in isolated communications chip,” Microelectronics & Computer, Vol.33, No.12, pp. 117-120, 2016.
5. [5] G. Yang, W. Tan, H. Jin, et al., “Review wearable sensing system for gait recognition,” Cluster Computing, pp. 1-9, 2018.
6. [6] G. Yang, Y. Zhang, J. Yang, et al., “Automated classification of brain images using wavelet-energy and biogeography-based optimization,” Multimedia Tools and Applications, Vol.75, No.23, pp. 15601-15617, 2016.
7. [7] X. Zhao and J. Li, “Research on optimization simulation of cloud stored information query path of big data,” Computer Simulation, Vol.33, No.8, pp. 181-184, 2016.
8. [8] J. Zhou, X. Lou, W. Zhang, et al., “Research on recovery algorithm of FCM pulse compensation,” Computer Measurement & Control, Vol.24, No.3, pp. 244-247, 2016.
9. [9] F. Yue, W. Fan, and Y. Liu, “Design of high-speed data registration module based on ARM9 and FPGA,” Electronic Design Engineering, Vol.24, No.9, pp. 156-159, 2016.