公开(公告)号：US10852439B1

公开(公告)日：2020-12-01

申请号：US16930741

申请日：2020-07-16

申请人： Beihang University

发明人： Zhipeng Wang ,  Cheng Wang ,  Kaiyu Xue ,  Kun Fang

IPC分类号： G01S19/07 ,  G01W1/10 ,  G06N3/08 ,  G06N3/04

摘要： The present invention provides a global ionospheric total electron content prediction system based on a spatio-temporal sequence hybrid framework. The prediction system implements computational processing for two types of spatio-temporal sequences, wherein for a stationary spatio-temporal sequence, a STARMA model prediction method is constructed in the present invention; for a non-stationary spatio-temporal sequence, a nonlinear spatio-temporal trend is firstly extracted from the non-stationary spatio-temporal sequence by adopting a ConvLSTM method until the extracted residual passes a stationarity test, and then the electron content is predicted using the STARMA model prediction method. By using a parallel computing method in the present invention, the computational efficiency can be greatly improved, and the operation time can be saved; meanwhile, the global ionospheric electron content distribution characteristics are fully considered, so that the ionospheric prediction algorithm itself is more in line with the space weather law and has a higher prediction accuracy.