一种用于时空体元编解码存储的低计算量优化方法

针对时空网格体对象的编解码占用存储空间大的问题，提出了一种用于时空体元编解码存储的低计算量优化方法。首先以十六叉树索引结构为基础，构建了时空网格体元编解码的数学模型，实现体元对象标识和时空位置索引，并借助3DGIS的自动编解码方法，实现了时空网格体元对象编解码存储表示的换算；其次，采用伽罗华有限域理论，构建了网格体元的二进制编码矩阵和存储的低计算量优化算法，实现了体元对象编解码存储过程中的优化计算；最后，以某矿山的矿床空间块体数据为例，对网格体元编解码模型、存储表示换算以及低计算量优化算法进了实际应用，并与八叉树索引结构的Morton码进行比较和分析，结果表明：该方法可有效降低30%的编解码存储计算量，提高了存储网格体元对象的时空效率。

A low computational optimization method forspatiotemporal voxel encoding and decoding storage

Regarding the large storage space occupied by the encoding and decoding data of spatiotemporal grid voxels, we propose a low computational method to store the encoding and decoding of spatiotemporal voxels based on hexadecimal tree index structure, and establish a mathematical model of encoding and decoding to realize the identification and location index of voxels. We employ the automatic encoding and decoding method of 3DGIS to achieve the conversion of encoding and decoding storage representation of spatiotemporal voxels. Secondly, using the Galois finite field theory, we build a binary encoding and decoding matrix of grid voxels as well as a low computational optimization algorithm to realize calculation optimization in the storage process of voxel encoding and decoding. Finally, taking the deposit block data of a mine as an example, the grid voxel codec model, storage representation conversion and low computational optimization algorithm are put into practical application. The proposal is compared with the Morton code of the octree index structure. The results show that the proposal can effectively reduce computational cost of encoding and decoding storage by about 30%, as well as the spatiotemporal efficiency for storing grid voxels.