流体机械2026，Vol.54Issue(2)：100-108,9.DOI:10.3969/j.issn.1005-0329.2026.02.011
基于深度学习与热成像技术的热舒适度预测方法研究
Research on thermal comfort prediction method based on deep learning and thermal imaging technology
摘要
Abstract
With the advancement of electrification and intelligent technologies,higher demands have been placed on human-centric air conditioning systems.Based on image recognition technology,a deep learning model using ResNet50 as the backbone network and incorporating a spatial attention module(Attention-ResNet50)was constructed for human-centric air conditioning systems.By collecting infrared image data under different thermal comfort conditions and classifying them into three categories,cold,comfort,and hot,for training and testing,this method significantly improved prediction accuracy.Experimental results showed that the introduction of the spatial attention mechanism increased the overall model accuracy by approximately 5%.The best model accuracy exceeded 94%for male subjects and surpassed 96%for female subjects.The study confirms the effectiveness of the attention mechanism in feature extraction,providing a reliable and universal technical pathway for personalized thermal comfort control.关键词
深度学习/热成像/注意力集中机制/个性化热舒适性模型Key words
deep learning/thermal imaging/attention mechanism/personalized thermal comfort model分类
信息技术与安全科学引用本文复制引用
严锦君,胡家云,王佳韵,李康..基于深度学习与热成像技术的热舒适度预测方法研究[J].流体机械,2026,54(2):100-108,9.基金项目
国家自然科学基金面上项目(52376203) （52376203）
