Study Of A Smart Classroom Thermal Environment Regulation System Considering Learning Efficiency

The smart classroom is a materialisation of a smart learning environment,a new type of classroom built with the help of the Internet of Things and cloud computing,which can regulate the indoor environment of the smart classroom according to actual needs.Traditional research on thermal control in smart classrooms has focused on comfort,however,one of the most basic functions of a classroom is to provide a good learning environment for students,so the design of thermal control systems in smart classrooms must take into account the learning efficiency of the students studying in them.Therefore,how to balance comfort while further improving student learning efficiency is a key issue in the design process of the regulation scheme.Based on thermal comfort theory,generative learning theory,BP neural network theory and intelligent control theory,this paper designs a thermal control scheme for smart classrooms that takes into account both comfort and student learning efficiency.The main research contents and innovations of this paper are as follows:(1)The evaluation method of indoor thermal environment and learning efficiency is studied,and the human thermal comfort index(Predicted Mean Vote,PMV)and total learning performance are selected as the quantitative evaluation parameters of the two respectively,so that the thermal environment comfort situation and PMV correspond to each other numerically and make the study more accurate;(2)The quantitative relationship between indoor thermal environment and learning efficiency was studied,and the correspondence between the two was obtained by collecting the learning efficiency of students under different thermal environments and comparing the analysis,so as to clarify the thermal environment when learning efficiency is at its highest,and provide reference for the design of regulation schemes;(3)To address the problem that PMV is affected by the coupling of multiple variables and the existence of multiple complex functions in direct calculation,this paper proposes a BP neural network model prediction method based on particle swarm optimization.Experimental results show that compared with the traditional BP neural network model,the improved prediction model has improved performance in terms of convergence speed,training time and prediction accuracy;(4)To address the problem that the parameters of the traditional PID controller are fixed and difficult to adjust,this paper proposes a PID controller based on BP neural network optimization,which optimizes and adjusts the parameters through BP neural network.The experimental results show that the proposed BP-PID controller reduces the adjustment time by74.25% and 73.63% respectively compared with the traditional PID controller and the fuzzy PID controller under the condition that the accuracy meets the conditions,and achieves the purpose of fast response;(5)In order to find a balance between thermal comfort and learning efficiency,this paper proposes a thermal control scheme for the classroom that takes into account learning efficiency.By building a model structure for the control scheme and simulating it with data collected in the laboratory,the learning efficiency of students in the controlled environment can be increased to 107.36% of the original one,providing a comfortable and efficient learning environment for students.In summary,this paper finds a balance between the comfort of the thermal environment in the classroom and the thermal environment to maintain a high level of learning efficiency,and designs a scheme to regulate the indoor thermal environment.