Computing Cluster and Intelligent Sensor Network in the Analysis and Application of College Students’ Physical Exercise Behavior

Abstract

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.