Research And Implementation Of Efficient Spatial Data Processing Technology For Tiles

With the development of map mapping technology,the amount of original map data has exploded,which puts higher demands on the space and time efficiency of map data processing.In response to this demand,this paper proposes an efficient parallel slice technique based on vector data for vector tiles based.There is a significant effect on the efficient processing of massive vector data,this topic proposes a feasible method.The experimental results show that this parallel processing technology improves the vector data slice.Firstly,this paper discusses the vector data model and the vector feature processing strategy.The distributed vector data storage model and the processing strategy for the point-line space entity elements are determined.On this basis,the high-performance storage model of vector tiles,vector data parallel processing and Map-Reduce-based parallel processing support technology are researched in this paper.Based on the above research on models,strategies and algorithms,the fourth chapter proposes the overall architecture of vector tile efficient processing services,which are designed from the underlying support platform,intermediate service layer design and application layer multithread/process execution.The aspects are described,and the vector data,vector tile storage model,distributed parallel tiling scheme and vector tile upload service implementation are designed in the following.Finally,the traditional cut graph experiment and multi-machine parallel cut graph experiment are carried out,and the experimental results are compared and analyzed.The data source of the experiment is the map original vector data,the vector data is sliced and the tile service uploads the tile to the data storage platform(Hbase)for display,while the traditional slicing tool,the massive vector data cut often takes weeks or even more.A lot of time,then reasonable task partitioning for vector data characteristics,multi-machine multi-process cut graph through parallel distributed processing service,and parallel upload on the server side,can greatly improve the overall processing efficiency of vector data.