Abstract
In view of the high computational cost and long computational time of IoT edge algorithm in traditional sports event evaluation, this paper optimizes IoT edge algorithm by introducing deep reinforcement learning technology. Set the IoT edge algorithm cycle through the IoT topology to obtain the data upload speed. In order to improve the evaluation efficiency of sports events, the process of edge algorithm is designed. The contribution rate of evaluation index is calculated, and the consistency, minimum deviation, and minimum difference of the results are taken as the standard to design the evaluation method of sports events. In order to verify the performance of the optimized edge algorithm, the test data set and test platform are set up and the comparative experiment is designed. Compared with the traditional methods, the edge algorithm based on DSLL has lower computational cost, shorter computational time, higher evaluation accuracy, and more practical results.
Highlights
As one of the important parts of the social subsystem, physical education has penetrated into every aspect of the society
The CNN model in deep reinforcement learning technology is used as the design basis to realize the resource allocation of the evaluation of sports events based on the edge algorithm of the Internet of ings
According to the parameter characteristics of the CNN model, the weights and biases in the allocation process are set as the connection number, and the number of parameters in the IoT edge algorithm can be expressed as
Summary
As one of the important parts of the social subsystem, physical education has penetrated into every aspect of the society. E above method calculates the cosine similarity between the evaluation index vector of each network node of sports events and the index vector of the ideal node, obtains the comprehensive ranking result of nodes, and selects the best nodes to complete the comprehensive evaluation of sports events. This method has a weak ability to resist the attack of the method nodes of sports events under the information condition.
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