Research On The Combination Generation Method Of Exercise Course Based On The Improved Artificial Raindrop Algorithm

The popularization of physical fitness knowledge has witnessed the growing attention that people paid to their own physical exercise in recent years.Exercise courses are being accepted by the public as a way of exercise instruction,however,the existing exercise course scheme is difficult to adapt to the current needs.A single exercise course will reduce people’s sports interest,and may cause sports injuries.In view of this,this thesis studies a combined generation method of exercise courses based on improved artificial raindrop algorithm.This method provides not only a targeted and effective exercise course selection scheme for the application of exercise prescription,but also the combination of exercise courses which can improve the diversity of exercise,thereby increasing the interest of exercise.On this basis,a personalized exercise course combination generation system is designed and implemented.The specific research contents are as follows:Firstly,most of the exercise programs provided by current sports and fitness platforms are monolithic exercise courses,which are difficult to adapt to the diverse and personalized exercise needs of different people.A sports curriculum combination generation method was designed to address the problems of current sports curriculum programs.The method constructs an exerciser model based on the information of the exerciser,then combines the core parameters in the exercise prescription on the basis of the exerciser model to construct an exercise course combination parameter optimization model,and optimizes the exercise course combination parameters that are more suitable for the exerciser’s own conditions by using the artificial raindrop algorithm.After that,the simlarity calculation method between parameters is used to combine the scientific,reasonable and suitable exercise course combination scheme.Secondly,to address the parameter optimization problem arising from the exercise course combination generation method,this thesis uses an improved artificial raindrop algorithm to optimize the parameters to ensure that the optimized exercise course parameters are more suitable for the exerciser’s own conditions.A hybrid collision strategy is added to the original artificial raindrop algorithm to expand the collision range of raindrops,thus improving the algorithm’s ability to traverse the solution space.At the same time,the raindrop pool is stretched to make it effectively guide the raindrop flow direction,thus ensuring the algorithm search efficiency.By comparing the other four algorithms,it is found that the improved artificial raindrop algorithm has higher solution accuracy and faster convergence speed than other comparison algorithms.Finally,as the exerciser’s physical fitness improves with the use of the exercise program combination program,the original exercise program combination parameters will no longer be applicable to the exerciser,so an adaptive adjustment strategy for the parameters was designed to ensure the continued effectiveness and safety of the program.In addition,based on the above research methods,this paper designs and implements an optimal combination generation system for personalized exercise courses.By implementing functional modules such as athlete information collection,exercise curriculum combination generation,and exercise curriculum combination parameter adjustment in the system,the usability and effectiveness of the exercise curriculum combination generation method are verified,which provides a reference for subsequent researchers to generate and recommend personalized exercise courses.In summary,the improved artificial raindrop algorithm proposed in this thesis optimizes the exercise course parameters to better fit the indicators of the exerciser,and the exercise course combination method based on the improved artificial raindrop algorithm can combine exercise course programs that better meet the individual needs of the exerciser.