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大规模光纤激光传感云数据具有维数高的特点,激光数据本身存储开销较大,需要对其存储方法进行优化设计。提出一种基于冗余激光数据删除和特征空间降维压缩的大规模光纤激光传感云数据优化存储方法。构建大规模光纤激光传感云数据存储结构模型,进行云激光数据的特征抽取,采用冗余数据删除滤波方法降低光纤网络中的光栅干扰,采用特征压缩方法降低对大激光数据的存储开销。测试结果表明,该存储方法能提高对大规模光纤激光传感云数据的存储效能,节省了存储开销,提高了存储空间。
Large-scale fiber laser sensing cloud data has the characteristics of high dimensionality, and the laser data itself has a large storage cost and needs to be optimized for its storage method. This paper presents a method for optimizing the storage of large-scale fiber laser sensing cloud data based on redundant laser data deletion and feature space reduction. A large-scale fiber laser sensing cloud data storage structure model is constructed to extract the features of the cloud laser data. Redundant data deletion filtering is used to reduce the grating interference in the optical fiber network. Feature compression is used to reduce the storage overhead for large laser data. The test results show that the storage method can improve the storage performance of large-scale fiber laser sensing cloud data, save the storage overhead and improve the storage space.