Title: Balanced scheduling method of ideological and political teaching resources based on cluster analysis algorithm

Authors: Wenbin Liu

Addresses: Department of Management, Hunan City University, Hunan, Yiyang, 413000, China

Abstract: In order to overcome the problems of poor scheduling accuracy, low recall rate and long scheduling time in the traditional scheduling method of ideological and political resources, the paper proposes a balanced scheduling method of resources based on clustering analysis algorithm. First, the load time series of the teaching resource server is determined and pre-processed. Secondly, cluster analysis is used to classify the data. Finally, according to the classification results, the balanced scheduling function of resources is constructed, and the particle swarm optimisation algorithm is used to solve the scheduling function to obtain the final scheduling strategy. The results show that the scheduling accuracy of the proposed method is 99.12%, the recall rate is up to 95%, the scheduling time is controlled within 7 s, and the resource balance scheduling effect is good.

Keywords: clustering analysis algorithm; balanced scheduling; time series; particle swarm optimisation.

DOI: 10.1504/IJRIS.2025.145056

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.1, pp.1 - 8

Received: 17 Feb 2023
Accepted: 27 Apr 2023
Published online: 18 Mar 2025 *

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