基于传统遗传和数据压缩算法的冗余光纤数据存储优化

Redundant optical fiber data storage optimization based on traditional genetic algorithm and data compression algorithm

传统基于节点分类的数据存储方法进行冗余光纤数据的存储优化,忽略了数据压缩的负荷开销评价,导致冗余光纤数据存储效率较低,提出基于传统遗传和数据压缩算法的冗余光纤数据存储优化方法。结合Dopplerlet变换寻找最佳基函数的全局优化性,进行光纤数据冗余特征分析与过滤;基于该结果采用传统遗传算法对冗余光纤数据进行初步压缩,在此基础上基于K-L特征进行光纤数据存储减负荷处理,完成冗余光纤数据压缩优化,实现冗余光纤数据的优化存储。实验结果表明,所提方法的冗余光纤数据压缩比重约为70%,冗余光纤数据存储优化速度高达211. 5 MB/ms,有效压缩冗余关光纤数据的同时具有高效率的优势。

Traditional data storage methods based on node classification optimize the storage of redundant optical fiber data,ignoring the evaluation of data compression load overhead,resulting in low storage efficiency of redundant optical fiber data. Combining with Doppler let transform to find the global optimization of the optimal basis function,the redundant characteristics of optical fiber data are analyzed and filtered. Based on the results,the redundant optical fiber data is compressed preliminarily by traditional genetic algorithm,and then the optical fiber data is stored and processed to reduce the load based on K-L characteristics,and the redundant optical fiber data compression is optimized.To optimize the storage of redundant optical fiber data. The experimental results show that the ratio of redundant optical fiber data compression is about 70%, and the optimization speed of redundant optical fiber data storage is211. 5 MB/ms. The proposed method has the advantage of high efficiency while effectively compressing redundant optical fiber data.