基于深度学习的智能室内空气净化节能系统设计研究  

Design Research of Intelligent Indoor Air Purification Energy Saving System Based on Deep Learning

作　　者：刘俊鸣 杜瑞行 LIU Junming;DU Ruixing(Xi’an University of Architecture and Technology Huaqing College,Xi’an Shaanxi 710043,China)

机构地区：[1]西安建筑科技大学华清学院,陕西西安710043

出　　处：《信息与电脑》2025年第6期103-105,共3页Information & Computer

基　　金：2024年陕西省大学生创新训练计划“纯净守护——智能家居室内空气质量监测与净化系统”(项目编号:S202413679030)。

摘　　要：随着工业化进程的加速和人们生活水平的提升,室内空气质量问题逐渐成为关注的焦点。传统的空气净化系统往往无法根据实时空气质量变化进行动态调整,导致能效低下。文章提出了一种基于深度学习的智能室内空气净化节能系统。其结合变分自编码器(Variational Autoencoder,VAE)和深度强化学习(Deep Reinforcement Learning,DRL)技术,能够实时监测室内空气质量并动态优化空气净化器的工作状态,从而实现更高效的能耗控制。系统通过精确的数据采集和处理、智能控制策略,显著提升了空气质量管理的灵活性,增强了节能效果,具有广泛的应用前景。With the acceleration of industrialization and the improvement of people’s living standards,the issue of indoor air quality has gradually become a focus of attention.Traditional air purification systems often fail to dynamically adjust according to real-time air quality changes,resulting in low energy efficiency.The article proposes an intelligent indoor air purification energy saving system based on deep learning.It combines variational autoencoder(VAE)and deep reinforcement learning(DRL)technologies to monitor real-time indoor air quality and dynamically optimize the working state of the air purifier,thus achieving more efficient energy consumption control.The system significantly improves the flexibility of air quality management and enhances the energy saving effect through accurate data acquisition and processing and intelligent control strategies,which has a wide range of application prospects.

关 键 词：深度学习 智能室内空气净化 节能系统 空气质量优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]