Indoor energy conservation and intelligent elderly care rehabilitation based on ambient light sensing in the Internet of Things

With the aging of the population, the quality of life and health management of the elderly have received more and more attention. Indoor thermal environment has an important impact on the comfort and health of the elderly, but the traditional indoor environment control methods are often unable to adjust flexibly according to the needs of different individuals. This study aims to explore the indoor thermal environment optimization scheme based on artificial intelligence technology to improve the living comfort and quality of life of the elderly, especially in the elderly rehabilitation environment. By combining environmental sensors, intelligent temperature control system and machine learning algorithm, an intelligent heat management model is established. The model can monitor indoor temperature, humidity and air quality in real time, and automatically regulate the indoor thermal environment according to the individual needs and preferences of the elderly. Data is collected through a smart home platform, and machine learning algorithms are used to analyze historical data and optimize environmental regulation strategies. The experimental results show that the proposed intelligent thermal environment optimization system has remarkable performance in providing personalized comfort, the fluctuation range of indoor temperature is reduced, and the satisfaction of the elderly is increased. The system also effectively saves energy consumption and improves the overall energy efficiency of the environment. The indoor thermal environment optimization scheme based on artificial intelligence provides a more comfortable and healthy living environment for the elderly, and has a good application prospect.