The uncertainty of a moving target's speed and direction can lead to inaccurate target location and large energy consumption of network nodes when the target is covered and controlled by the sensor node in the wireless sensor network. This study proposed a method of threshold-controllable moving target coverage to accurately predict the target movement location, improve the energy consumption rate of the network, and clarify the main factors affecting the coverage of the moving target. First, a node participation threshold was introduced in the area of the moving target, and a scheduling mechanism of the node state was established. Second, the location prediction model of the moving target and the self-adjusting mechanism of the node data reporting frequency were established in accordance with the historical location information, movement direction, and speed of the moving target. Finally, the effect of node participation threshold on target location accuracy and network energy consumption during monitoring of the moving target was analyzed. Results show that when the participation threshold is set to decrease from 5 to 2, the target location error at different speeds decreases by 56.7%. When the participation threshold is equal to 3, the average energy consumption at different speeds decreases by 55% compared with consumption without a present threshold. Energy consumption decreases by an average of 41% in the model of moving target location prediction. Therefore, the threshold-controllable algorithm for moving target coverage shows excellent performance in terms of location accuracy and network energy utilization efficiency. The study can be utilized for sensor network environments with limited node energy to provide theoretical guidance for the dynamic coverage optimization of moving targets.

Football is one of the most popular sports in the world. As the popularity of football continues to grow worldwide, so does the number of incidents of violence on the pitch. Today, doping, match fixing, black whistles, and football hooliganism are ranked as the four most toxic aspects of sport. How to study the factors that cause aggressive behaviour of fans from a psychological perspective has become a key issue in the field of sports. Therefore, this study proposes a method for mining the psychological factors of sport fan community members based on machine learning clustering. Firstly, three different members of a large fan community, i.e., university students, office workers, and unemployed people, are used as research subjects to investigate the psychological factors influencing fans’ aggressive behaviour using a questionnaire method. Secondly, the data obtained were mined and analysed using the K-means clustering algorithm in machine learning techniques. At the same time, a K-means initial clustering centre optimization algorithm based on principal component analysis (PCA) was proposed for the data characteristics of the interaction of psychological factors. The results show that the new algorithm significantly improves the quality of clustering compared with other optimization algorithms and accurately identifies the multiple factors that contribute to the occurrence of fan attacks.