毕业生知识库

As one of the most exciting events in the Winter Olympics, speed skating has received extensive attention from various national sports bureaus. The use of intelligent analysis and decision-making technology to research speed skating events is of great significance for assisting the improvement of competition performance. This paper studies the intelligent analysis and decision-making technology for speed skating. The main research contents are as follows:

(1) Aiming at the problems of poor accuracy and rough establishment granularity of traditional data analysis methods, a comprehensive analysis and evaluation method of speed skaters' state performance is proposed. This method builds a rich feature set through feature modeling, and then designs an importance analysis algorithm based on the decision tree model to realize the importance analysis of the features that affect the game performance. Further, a comprehensive evaluation model is designed to evaluate athletes' competitive state and sports performance, which improves the accuracy of evaluation.

( 2) Aiming at the problems of complex decision-making rules, poor decision-making effect and poor environmental coupling in the traditional speed skating tactical decision-making method, a speed skating tactical decision-making method based on deep reinforcement learning is proposed. First, this method builds a speed skating competition training environment considering the real scene, physical constraints and competition rules. Next, we use deep reinforcement learning algorithm to make tactical decision-making in speed skating competitions, and design reward functions such as skating reward, physical fitness reward and performance reward. Then, this method realizes the learning of tactical decision-making strategies that can effectively improve the game performance.

(3) Aiming at the problems of athletes' collision constraints, speed constraints and other competition rules in speed skating competitions, a knowledge-driven deep reinforcement learning algorithm is proposed to optimize speed skating tactical decisions. It is mainly realized through data-driven knowledge imitation, knowledge-based reward function design, and knowledge-based action space constraints. This method performs knowledge imitation through supervised learning based on historical game data. The rationality and effectiveness of tactical decisions can be improved by designing reward functions that take into account collision constraints and speed constraints.

(4) According to the tactical strategy obtained by deep reinforcement learning training, the tactical strategy guidance and analysis of the athletes are carried out. By comparing the tactical strategy learned by the agent with the real strategy of the athlete in time and space, an easy-to-understand tactical strategy guidance can be formed. Through the interpretable expression of tactical strategies generated by the model, the guiding role of the decision-making model for athletes' skating training is improved.