摘要
本研究基于深度强化学习与域适应迁移学习框架,构建了“感知-决策-执行”全链条智能照明系统。实验平台集成毫米波雷达(TI IWR6843)、环境光传感器(BH1750)及Jetson Xavier NX边缘节点,采集办公/商业/工业三类场景的12.7万组多模态数据。算法层面,设计多目标奖励函数(能效权重α=0.6,舒适度β=0.3,设备寿命γ=0.1),通过NSGA-Ⅲ优化实现帕累托最优解。系统在办公场景单位面积能耗降至8.2 W/m^(2)(较PID控制降低30%),动态响应延迟1.8秒,用户满意度达88%。研究为复杂场景下的照明系统自主优化提供了可复用的技术路径。
Traditional lighting control relies on preset rules and static thresholds,making it difficult to cope with the compound effects of personnel mobility uncertainty,natural light volatility,and time-varying equipment aging,leading to an imbalance between energy efficiency and user experience.Currently,although AI technology has made breakthroughs in the field of industrial control,there are still key bottlenecks in the application of AI technology in lighting scenarios:the semantic gap of heterogeneous sensor data restricts the generalization ability of decision-making models;a quantitative trade-off mechanism has not yet been established for the conflict between long-term energy efficiency goals and short-term comfort needs;and the contradiction between privacy protection and model effectiveness in cross-scene knowledge migration needs to be resolved.This paper discusses the paradigm of autonomous optimization of lighting systems driven by artificial intelligence,in order to build a smart lighting technology system that takes real-time response,long-term reliability and cross-scene generalization into account.
作者
韦靖靖
杜盼盼
WEI Jingjing;DU Panpan(Zhengzhou University of Science and Technology,Zhengzhou 450000,China)
出处
《中国照明电器》
2025年第6期168-171,共4页
China Light & Lighting
关键词
人工智能
智能照明
照明系统
artificial intelligence
intelligent lighting
lighting system
作者简介
韦靖靖,硕士,助教。研究方向:人工智能,数据挖掘。
