时空图神经网络在物联网中的应用综述  

Review of Application of Spatiotemporal Graph Neural Networks in Internet of Things

作　　者：张建伟 陈旭 王叔洋[2] 景永俊[1] 宋吉飞 ZHANG Jianwei;CHEN Xu;WANG Shuyang;JING Yongjun;SONG Jifei(School of Computer Science and Engineering,North Minzu University,Yinchuan 750030,China;School of Electrical and Information Engineering,North Minzu University,Yinchuan 750030,China;National New Internet Exchange Center,Zhongwei,Ningxia 755001,China)

机构地区：[1]北方民族大学计算机科学与工程学院,银川750030 [2]北方民族大学电气信息工程学院,银川750030 [3]国家新型互联网交换中心,宁夏中卫755001

出　　处：《计算机工程与应用》2025年第5期43-54,共12页Computer Engineering and Applications

基　　金：宁夏回族自治区重点研发项目(2023BDE02017);北方民族大学中央高校基本科研业务费专项资金(2022PT_S04)。

摘　　要：随着物联网在各个领域物理设备的发展,产生的大量数据给当前数据处理方法带来了挑战。深度学习模型具备处理大规模和高维度数据的能力,已逐渐应用于物联网不同领域。时空图神经网络作为一种处理图结构数据的深度学习模型,能够对物联网中的拓扑结构和时间信息进行建模,并在物联网预测任务中展现出优秀性能。介绍了物联网中的时间相关性和空间相关性,以及不同时空网络架构的构建方法,并基于空间相关性的不同,将时空图神经网络分为时空图卷积网络和时空图注意力网络。进一步分析了时空图卷积网络和时空图注意力网络在物联网中的应用,主要包括交通、环境和能源领域。最后,探讨了时空图神经网络在物联网应用中面临的挑战和未来的研究方向。With the development of physical devices in various fields of the Internet of things(IoT),the large amount of data generated has brought challenges to current data processing methods.Deep learning models have the ability to process large-scale and high-dimensional data,and have gradually been applied to different fields of the Internet of things.As a deep learning model for processing graph structured data,spatiotemporal graph neural network can model the topological structure and temporal information in the Internet of things and show excellent performance in the prediction tasks of the Internet of things.Firstly,the temporal correlation and spatial correlation in the Internet of things,as well as the construction methods of different spatiotemporal network architectures are introduced.Based on the difference in spatial correlation,the spatiotemporal graph neural network is divided into spatiotemporal graph convolutional network and spatiotemporal graph attention network.Then,the application of spatiotemporal graph convolutional network and spatiotemporal graph attention network in the Internet of things is further analyzed,mainly including the fields of transportation,environment and energy.Finally,the challenges faced by spatiotemporal graph neural network in the application of the Internet of things and the future research directions are discussed.

关 键 词：物联网 深度学习 时空图神经网络 图结构数据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]