基于深度学习的建筑自动化技术研究与发展

Research and development of building automation technology based on deep learning

作为智能建筑的核心组成部分,建筑自动化通过集成各种智能设备和技术实现了对建筑环境的自动调节、监控和管理。然而,传统建筑自动化技术通常依赖于预设的规则和模型,缺乏灵活性和适应性。近年来,深度学习技术的快速发展为建筑自动化提供了新的解决方案。通过模拟人脑神经网络的工作方式,深度学习可以从大量的数据中学习并提取出有用的信息,从而实现对建筑环境的智能调节和管理。

As a core component of smart buildings,building automation realizes the automatic regulation,monitoring and management of the built environment through the integration of various smart devices and technologies.However,traditional building automation technologies often rely on preset rules and models,which lack flexibility and adaptability.In recent years,the rapid development of deep learning technology has provided new solutions for building automation.By simulating the way human brain neural networks work,deep learning can learn from large amounts of data and extract useful information to achieve intelligent regulation and management of the built environment.