物联网学报 ›› 2021, Vol. 5 ›› Issue (1): 62-71.doi: 10.11959/j.issn.2096-3750.2021.00204
韩青1, 高昆仑2, 赵婷2, 陈江琦2, 杨新宇1, 杨树森1
修回日期:
2021-01-26
出版日期:
2021-03-30
发布日期:
2021-03-01
作者简介:
韩青(1994- ),女,西安交通大学计算机学院博士生,主要研究方向为边缘计算、边云协同智能系统与算法基金资助:
Qing HAN1, Kunlun GAO2, Ting ZHAO2, Jiangqi CHEN2, Xinyu YANG1, Shusen YANG1
Revised:
2021-01-26
Online:
2021-03-30
Published:
2021-03-01
Supported by:
摘要:
随着电力物联网规模的不断扩大和部署在电力系统各环节的设备数量的快速增加,海量边缘设备所产生的数据呈指数级爆炸增长。海量边缘数据的高效、快速和安全处理与分析给传统的云计算智能技术带来极大挑战,而边云协同智能技术因节省带宽、减少时延、保护数据隐私等优点具有深度助力电力领域发展的巨大潜力。首先,对边云协同智能的概念和研究现状进行了介绍,阐述了边云协同智能的特征和优势,并对其赋能电力领域进行了适用性探讨。然后,结合电力系统的建设需求,讨论了面向电力场景的边云协同智能关键技术,接着针对电力领域的两个典型场景,分别给出了基于边云协同智能技术的解决方案,并搭建仿真实验进行效果验证。最后,对全文进行了总结并对下一步的研究方向进行了简要的展望。
中图分类号:
韩青, 高昆仑, 赵婷, 陈江琦, 杨新宇, 杨树森. 边云协同智能技术在电力领域的应用[J]. 物联网学报, 2021, 5(1): 62-71.
Qing HAN, Kunlun GAO, Ting ZHAO, Jiangqi CHEN, Xinyu YANG, Shusen YANG. Application of edge-cloud collaborative intelligence technologies in power grids[J]. Chinese Journal on Internet of Things, 2021, 5(1): 62-71.
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