The traditional data mining method of students’ physical health has some problems, such as low recall rate of data mining, long mining time, and poor mining accuracy. Therefore, this paper proposes a data mining method of college students’ physical health for physical education reform. Using association rules to construct the correspondence between the fitness test data, the fitness test data can be classified and the data training model can be built. The decision tree of data attribute was built, and the physical health data was segmented by the segmentation technology. The information entropy of health data was calculated by the decision tree, and the information gain of health data sample set was obtained. The C4.5 algorithm was used to improve the ID3 algorithm. The improved decision tree was used to obtain the physique data splitting attribute, and the information gain rate was obtained by the ID3 algorithm correction. The k -means algorithm is used to divide the data into clusters, according to which the physical health data mining of college students is realized. Experimental results show that the recall rate of the physical health data mining method proposed in this paper is as high as 96%, the data mining time is only 3 s, and the accuracy of data mining is as high as 98%, indicating that the method proposed in this paper can improve the physical health data mining effect.
Based on computing cluster and intelligent sensor network technology, in view of network delay, this paper uses first-in-first-out buffers to be built at the node sending and receiving ports to convert the random delay of the physical exercise behavior network control system into a fixed delay. First, we analyze and model the controller design of the physical exercise behavior network control system. Through the analysis and synthesis of the current situation and methods of the physical exercise behavior network control system controller at home and abroad, the sensor is driven by time, and the controller and actuator are used. In the event-driven method, the sending and receiving buffers are set on the network ports of the nodes, the delay is changed from random to fixed at the same time, and the problem of data packet timing disorder is improved. Secondly, through the analysis of the internal control system node, the internal AD, DA conversion, data storage, CPU internal tasks, and task scheduling algorithm modules are implemented in the model. Experimental simulations show that, in view of the difficulty of unsatisfactory tracking effect caused by the aliasing of multiple target signals collected by sensor nodes, a combined tracking strategy is adopted; that is, multiple tracking dynamic clusters are combined into one for tracking when the sports behavior is close. In order to avoid the heavy communication and computing requirements in the centralized mode, mobile sensor networks usually adopt a distributed fusion architecture. The dynamic cluster maintenance and positioning strategy are given. In the stage of separation of multiple sports behaviors, a dynamic cluster decomposition algorithm based on boundary search is proposed, which can effectively determine the degree of separation of sports behaviors and provide a basis for establishing new dynamic clusters for follow-up tracking. The results show that the algorithm can effectively realize the merging and decomposition of dynamic clusters of multiple sports behaviors and effectively realize the dynamic tracking of multiple sports behaviors.
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