Design And Implementation Of Intelligent Classroom Control Model Based On Deep Reinforcement Learning

In recent years,with the continuous development of IoT-related technologies,smart classrooms,as one of the important applications of IoT technology,have received extensive attention from academia and industry.The constantly improving smart classroom related products have brought great convenience to the layout,control and use of classrooms,and have attracted many universities to start building smart classroom projects.In use,it was found that with the centralized and remote control of equipment,the equipment in the classroom still requires users to manually control according to personal experience.The core of intelligent classroom control,that is,the intelligent control of indoor equipment,must effectively deal with the challenges of the changing environment in the classroom,the diversification of equipment,and user behavior habits.In order to solve the problem of intelligent classroom control,this study first collected the target environmental data through the intelligent classroom data collection system,and combined with the university database related information,pre-processed and analyzed the data to form a structured classroom original data set.Based on the original data set,this paper proposes to use Deep Reinforcement Learning(DRL)to solve the problem of intelligent classroom control.With the help of reinforcement learning platform and deep learning framework,a variety of intelligent classroom environment parameters are integrated,and deep Q-Learning(DQN)training methods are used to train intelligent classroom control models.The trained model can effectively control the equipment in the classroom according to the current environmental parameters of the classroom and the current state of the classroom,and then play the role of intelligently controlling the classroom equipment and optimizing the indoor environment.This article first introduces the background and significance of this study,analyzes the development prospects of the smart classroom industry and the problems that need to be solved,outlines the progress and achievements of related research at home and abroad,and quotes and elaborate the objectives and main contents of this study.After that,this article introduces the relevant technical background and explains the relevant research foundation.Next,this article focuses on explaining the data sources,the relevant data preprocessing methods,and the related analysis of the data set.Then it analyzes and models the feasibility of applying deep reinforcement learning to intelligent classroom control model training.Finally,this paper focuses on the structure and training methods of the intelligent classroom control model based on deep reinforcement learning,related training platforms and deployment schemes,proposes a training method that combines the training set with the real environment,and describes the model at different stages The loss,prediction accuracy,and performance of actual application scenarios demonstrate the effectiveness and feasibility of the model and its training methods.