并行处理技术在全球海量地理信息数据质量控制中的应用

针对全球海量地理信息数据成果数据量大、数据类型丰富、质量检查内容多的特点，本文将分布式并行计算技术、多线程技术应用到地理信息数据质量控制体系中，基于MapReduce框架实现了多源多时相海量数据并行质量控制，把算法结构由一个周期执行一个操作改造为一个周期执行多个操作的并行处理，从根本上解决重复操作多、计算慢的质量检查难题。选取核心矢量要素、DOM成果、DEM成果作为典型数据案例开展效率对比试验。试验结果表明，该技术方案的处理效率比传统技术方案提高2~3倍，有效地压缩了任务执行时间，节约了任务执行成本，实现了对海量地理信息数据的快速质量控制，保障了全球地理信息数据的成果质量。

Application of parallel processing technology in quality control of mass geographic information data

Aiming at the characteristics of huge data volume, rich types and abundant inspection contents, based on MapReduce framework, this paper applies the distributed parallel computing technology and multi-thread technology to the quality control system to build the parallel quality control framework of multi-source and multi-temporal mass data. The algorithm structure of one operation per cycle is transformed into the parallel algorithm of multiple operations per cycle to solve fundamentally the problem of multiple operations and slow calculation. Then we select core vector elements, DOM results and DEM results as the experimental data. The results show that the built quality control technology based on parallel processing increases the efficiency by 2~3 times, effectively compresses the task execution time, saves the cost, realizes the rapid quality check and ensures the timely quality control of the geographic information resources.