摘要
智能照明系统的动态调节需兼顾节能效益与用户体验,传统控制策略因环境感知维度单一与用户行为建模不足难以实现精准适配。文章提出基于机器学习的自适应调节方法,构建融合多模态数据采集、用户行为识别与强化学习决策的系统架构。通过多光谱传感器与视觉模块的协同感知,建立光照强度、用户位置及活动特征的联合表征模型。实验表明,该系统在保证视觉舒适度的前提下,显著降低能耗并提升环境适应性。
The dynamic adjustment of intelligent lighting systems must balance energy efficiency and user experience.Traditional control strategies struggle to achieve precise adaptation due to their single environmental perception dimension and insufficient modeling of user behavior.This paper proposes an adaptive regulation method based on machine learning,constructing a system architecture that integrates multi-modal data collection,user behavior recognition,and reinforcement learning decision-making.By leveraging the collaborative perception of multi-spectral sensors and visual modules,a joint representation model is established for light intensity,user position,and activity characteristics.Experiments show that this system significantly reduces energy consumption and enhances environmental adaptability while ensuring visual comfort.
作者
陈晓冬
CHEN Xiaodong(Henan Industrial and Trade Vocational College,Zhengzhou 451191,China)
出处
《中国照明电器》
2025年第8期101-103,共3页
China Light & Lighting
关键词
机器学习
智能照明
自适应调节
machine learning
intelligent lighting
adaptive regulation
