Two-level fusion big data compression and reconstruction framework combining second-generation wavelet and lossless compression

Abstract

In view of the characteristics of big data, fuzziness, and real time of data acquisition and transmission in the fuzzy information system faced by aircraft health management, to reduce the load of airborne data processing and transmission system under the condition of limited airborne computing resources and strong time constraints, the data collected by the airborne system are first compressed, and the amount of data are reduced before transmission and reconstructed after transmission. In view of the situation that the compression ratio of primary data compression is too small and the compression time is too long for large-scale fuzzy systems to meet the transmission requirements of the system, this paper combines the advantages of lossy compression method which consumes less time and lossless compression method which has higher compression ratio, and innovatively proposes a two-level data compression and reconstruction framework combining lossy compression and lossless compression. The optimization analysis is carried out. Taking a real aero-engine health sample as an example, the validity, scientificity, and robustness of the proposed framework are verified by comparing with data compression and reconstruction algorithm based on redundant sparse representation and compressed sensing.