• Media type: E-Article
  • Title: Application of Edge Computing in Physical Education Teaching Management
  • Contributor: Nie, Dongfang; Yuan, Qin
  • imprint: Hindawi Limited, 2022
  • Published in: Wireless Communications and Mobile Computing
  • Language: English
  • DOI: 10.1155/2022/3719469
  • ISSN: 1530-8677; 1530-8669
  • Keywords: Electrical and Electronic Engineering ; Computer Networks and Communications ; Information Systems
  • Origination:
  • Footnote:
  • Description: <jats:p>In the traditional teaching mode, teachers have limited time and energy, but the emergence of AI technology-assisted teaching has greatly facilitated students and teachers. They can study and teach anytime and anywhere and can also solve the problem of lack of professional teachers or venues. With the continuous popularization of “Internet + education” technology, the teaching mode has changed, and online courses are gradually accepted by students regardless of time and geographical constraints. This article aims to study the optimization and application of the intelligent scheduling algorithm in the physical education management system based on blockchain technology. This article proposes a combination of Edge Computing technology and intelligent algorithm software and hardware to improve the current difficulties of domestic colleges and universities. The core content of integration, system functions, course scheduling algorithm, database, and other core contents are preliminary designed, hoping to provide ideas for the specific implementation of the college course scheduling system. The experimental results in this paper show that with the support of Edge Computing in solving the CSP problem, the efficiency of various algorithms is relatively high and comparable when the filling rate is less than 90%. However, when the filling rate exceeds 90%, the execution time of the IFS CBS algorithm is relatively longer, but it is generally acceptable. The algorithm and optimization strategy are also implemented, and the performance improvement of the algorithm is compared and analyzed through experiments, which proves the feasibility of the optimization strategy.</jats:p>
  • Access State: Open Access