With the rapid increase in mobile device usage and the popularity of 5G communication technology, rich Big spatiotemporal Data has become an important research resource. How to quickly synchronize the Big spatiotemporal Data collected by various types of devices to multiple distributed data centers has become the key to further utilizing spatio-temporal data. Therefore, in this paper, a new lightweight Big Spatiotemporal Data synchronization scheme called SPsync, based on a traditional file synchronization algorithm, is proposed. The contributions of SPsync are as follows. (1) SPsync leads to the optimization of the file processing strategy, changing the algorithm from a serial algorithm to a parallel one. (2) Combined with Spark, a distributed Big Data processing framework is advanced to achieve optimization of distributed tasks and further improvement of algorithm performance. Further, we demonstrate the excellent performance of SPsync through rich experiments using multiple simulated and real datasets. Compared to the best incremental synchronization algorithms currently available, the synchronization speedup is 30% faster and CPU usage 20% lower. The algorithm itself is 20% faster and CPU usage reduced by more than 25% in a multi-node scenario.

随着移动设备使用量的快速增加和5G通信技术的普及，丰富的时空大数据成为重要的研究资源。如何将各类设备采集的时空大数据快速同步到多个分布式数据中心，成为进一步利用时空数据的关键。因此，本文在传统文件同步算法的基础上，提出了一种新的轻量级大时空数据同步方案SPsync。SPsync 的贡献如下。(1) SPsync导致文件处理策略的优化，将算法从串行算法变为并行算法。(2) 结合Spark，提出分布式大数据处理框架，实现分布式任务的优化，进一步提升算法性能。此外，我们通过使用多个模拟和真实数据集的丰富实验展示了 SPsync 的出色性能。与目前最好的增量同步算法相比，同步提速快30%，CPU占用率低20%。在多节点场景中，算法本身的速度提高了 20%，CPU 使用率降低了 25% 以上。