Hierarchical network construction method for data compression storage of massive road network

Abstract

The invention discloses a hierarchical network construction method for the data compression storage of a massive road network. The method comprises the steps that hierarchy division is conducted on massive network data, the level of division can be set by parameters; based on the network layer division, a network overlay map is constructed, topology features of an upper layer network based on the shortest path are reconstructed, so that the upper layer network still has connectivity; the network is partitioned on the basis of hierarchical network overlay map; on the basis of the hierarchical network overlay map, partitioning is conducted on the network; on the basis of the hierarchical partition construction, the nodes in a region are compressed, and by calculating reachable nearest neighbor partition boundary nodes, a node is attached to the boundary nodes and relevant information is saved, so that the compression of the massive network data is achieved. The hierarchical network construction method is mainly used for the hierarchical construction and compression storage of a large-scale road network, the whole structure and topological features of the network can be kept well after the large-scale compression of the network, and the efficiency of the analysis algorithm for a sub network can be improved.

Description

technical field [0001] The invention relates to a computer information processing technology, in particular to a hierarchical network construction method for compressing and storing massive road network data. Background technique [0002] In the GIS application for large-scale road network data, the road network is complex and diverse, with a large amount of information. In the design of navigation systems, path finding for large-scale geographic networks often has high computational complexity, and cannot support the dynamic nature of the network, which increases query time and affects user experience. With the development of big data technology, massive road Network analysis under network data has attracted more and more attention. Traditional algorithms often have high computational complexity and memory usage when solving network analysis problems under the condition of massive network data. In response to this problem, on the basis of observation and research on real r...

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