用开关和摄像头学习用户习惯，实现无需预设逻辑的智能控设施
Logic-Free Building Automation: Learning the Control of Room Facilities with Wall Switches and Ceiling Camera
- 通过墙开关和天花板摄像头获取用户操作与环境数据，直接学习偏好
- 在多种场景下控制准确率达93%-98%，优于ViT、ResNet等模型
- 适合追求无代码、自适应控制的智慧建筑系统开发者
人工智能通过学习用户对设施控制的偏好，实现更智能的建筑自动化。强化学习（RL）虽是常用方法，但在实际部署中面临诸多挑战。本文提出一种无需预设逻辑的建筑自动化新架构（LFBA），利用深度学习（DL）直接控制房间设施。该方法不依赖强化学习，而是以墙开关作为监督信号，结合天花板摄像头监控环境，使深度学习模型从实际场景和开关状态中直接学习用户偏好。在自建测试平台上，系统在不同条件和用户活动下验证，结果表明，采用VGG模型时控制准确率达到93%-98%，优于Vision Transformer和ResNet等其他深度学习模型。这说明，通过观察场景和用户交互，LFBA能实现更智能、更人性化的设施控制。
原文摘要 · Abstract (English)
Artificial intelligence enables smarter control in building automation by its learning capability of users' preferences on facility control. Reinforcement learning (RL) was one of the approaches to this, but it has many challenges in real-world implementations. We propose a new architecture for logic-free building automation (LFBA) that leverages deep learning (DL) to control room facilities without predefined logic. Our approach differs from RL in that it uses wall switches as supervised signals and a ceiling camera to monitor the environment, allowing the DL model to learn users' preferred controls directly from the scenes and switch states. This LFBA system is tested by our testbed with various conditions and user activities. The results demonstrate the efficacy, achieving 93%-98% control accuracy with VGG, outperforming other DL models such as Vision Transformer and ResNet. This indicates that LFBA can achieve smarter and more user-friendly control by learning from the observable scenes and user interactions.
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