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.

针对飞机健康管理所面临的模糊信息系统中的大数据，模糊性和数据采集与传输的实时性的特点，在机载计算资源有限的情况下，减轻机载数据处理与传输系统的负担。由于时间紧迫，机载系统收集的数据首先被压缩，传输前减少数据量，传输后进行重构。针对大型模糊系统无法满足系统传输要求的情况，主要数据压缩的压缩率太小，压缩时间过长的问题，本文结合了有损压缩方法的优点，该方法消耗较少。时间和无损压缩方法，压缩率更高；并且创新地提出了一种结合有损压缩和无损压缩的两级数据压缩和重构框架。进行优化分析。以一个真实的航空发动机健康样本为例，通过与基于冗余稀疏表示和压缩感知的数据压缩和重构算法进行比较，验证了该框架的有效性，科学性和鲁棒性。