Tyson Polygon Construction Based on Spatio-temporal Data Network

With the rapid development of space technology and the exponential growth of data generated by a large number of space satellite equipment every day, these huge spatio-temporal data have the characteristics of complex information, heterogeneous, real-time receiving and receiving, scattered storage and long storage period. Based on this situation, spatio-temporal data networks are facing the severe challenges of confusion and complexity of data management and uncertainties in network topology structure. Therefore, it is very important to manage spatio-temporal data effectively and to solve the problem of topological change caused by node movement in spatio-temporal data network. In this paper, we will construct the network of spatio-temporal data based on the improved Tyson polygon topology algorithm. In the construction, we mainly use Hadoop architecture as the processing architecture of massive spatial large data. At the same time, we construct the cluster structure of the network based on the improved Tyson polygon theory, and creatively propose a hierarchical spatio-temporal data network architecture. In the last part of the experiment, a number of experiments are carried out to verify and analyze the proposed architecture. The experimental results show that the proposed architecture and algorithm have obvious advantages in the management of spatio-temporal large data network and in solving the problem of network topology change.