Optimization of indoor thermal environment based on sensor networks and multimedia assisted physics teaching

Abstract

Multimedia technology combines various media elements such as text, images, sound, and video, making classroom teaching more vivid, intuitive, and interesting. This article introduces a multimedia courseware assisted physics teaching application based on sensor networks and deep learning technology. This application has two functional modules: user user end and cluster control end. On the user end, obtain user commands through the virtual machine end and share the original multimedia files using the CIFS protocol. The user command is redirected to the user end, and then the corresponding command is executed on the user end to directly transmit and play the original multimedia file. At the same time, the user end will also provide data feedback and recording for subsequent data analysis and evaluation. At the cluster control end, based on the information collected by the sensor network, an adaptive linear regression algorithm is used to predict the reference value. By analyzing and processing the collected information, the cluster control end can reasonably arrange the playback content of multimedia courseware to meet the learning needs of students. This multimedia courseware assisted physics teaching application based on sensor networks and deep learning technology provides new help and support for physics teaching.