Online Education Oriented Design and Estimation of Ideological and Political: An Edge Computing Approach
Ziqiao Wang, Zhefeng Yin · Journal of Multimedia Information System · 2023
With the rapid development of information technology, online education has become a new teaching mode in colleges and universities, and has made great contributions to China’s education reform. The online education of ideological and political education for college students is still in its infancy. With the deepening and popularization of the mobile Internet, the use of mobile ends to carry out online ideological and political education courses is becoming a mainstream choice. However, the large number of click-to-play demands caused the problem of response delay. In order to solve this problem, this paper proposes an online ideological and political education architecture based on edge computing. In order to effectively utilize the edge computing architecture and optimize the transmission delay, this paper introduces a caching approach for the Genetic Algorithm utilized in Long and Short-term Memory Network, referred to as LSTM-GA. First, in the face of the fast refresh of video requests when user ends move between different edge computing servers, based on the "device-edge-cloud" system collaborative architecture, to reduce transmission delay and optimize QoE to build a network model for the target. Furthermore, the genetic algorithm optimized Long-Short-Term Memory Network (LSTM-GA) model is applied to forecast the trajectory of the user’s endpoint, and seek the optimal solution with the minimum transmission delay, so as to ensure that the video content is cached in an appropriate location. According to the simulation results, the technique presented in this paper can significantly decrease transmission delays and enhance the quality of user experience.