Design of Intelligent Home Environment Monitoring System Based on Deep Learning

With the rapid development of smart home technology, environmental monitoring has become an important part of improving the level of home intelligence. Traditional home environment monitoring systems often lack intelligent prediction capabilities, while the introduction of deep learning technology enables the system to analyze and predict environmental data more accurately, thus improving the comfort and safety of home life. In this paper, a smart home environment monitoring system based on deep learning is designed and implemented. By integrating an LSTM neural network and a self-attention mechanism, an intelligent decision-making module with temporal prediction ability is constructed to realize accurate monitoring of home environment parameters and adaptive regulation of equipment. The system provides a high-precision, low-delay solution for smart home environment monitoring, and has significant application value in improving residential comfort and energy-saving efficiency.