基于深度学习的室内照明智能调节系统

为了实现室内照明的智能控制，根据室内人员实时位置进行跟踪式照明，通过人员覆盖区域完成自适应亮度调节，达到总体节能且局部照明舒适的目的。设计了基于光纤传感网络的智能照明系统，提出了基于深度学习的照明区块化设计与人员实时定位的照明调节算法。区块化设计将照明区域划分成多个等尺寸子区域，划分尺度由光源覆盖间距决定，实现对照明区域的离散处理。再通过状态采集模块对每个子区域进行照度量化分析，将检测结果作为卷积网络输入层的控制参数导入分析模型中。结合封闭体人员定位算法实现对照明子区域定位及照度调节。实验测试了100 m2照明范围内的四种典型情况，获得了各LED对测试位置的照度权值，并测试了不同高度对照度值的影响程度。区域内在x轴方向的人员定位精度最大误差为?96 cm，在y轴方向为91 cm，均小于预设照明区块的最小单元。文中算法在LED个数递增时，收敛时间略优于ANN算法，与对应LED体量的照明空间相比，收敛时间满足应用需求，验证了其具有大范围照明智能调节的能力。

Intelligent adjustment system of indoor lighting based on deep learning

To achieve intelligent control of indoor lighting, tracking lighting is performed according to the real-time position of indoor personnel, and adaptive brightness adjustment is completed through the coverage area of personnel, so as to achieve the purpose of overall energy savings and comfortable local lighting. An intelligent lighting system based on a fiber optic sensor network is designed, and a lighting adjustment algorithm based on deep learning lighting block design and real-time positioning of personnel is proposed. The block design divides the lighting area into multiple equal-sized subareas, and the division scale is determined by the light source coverage interval to realize the discrete processing of the lighting area. Then, through the state acquisition module to perform quantification analysis on each sub-region, the detection results are imported into the analysis model as the control parameters of the input layer of the convolutional network. The closed-body personnel positioning algorithm is combined to realize the positioning of the lighting subarea and the illuminance adjustment. The experiment tested four typical situations within the illumination range of 100 m2, obtained the illuminance weight value of each LED on the test location, and tested the degree of influence of the illuminance value of different heights. The maximum error of personnel positioning accuracy in the x-axis direction in the area is ?96 cm, and it is 91 cm in the y-axis direction, both of which are smaller than the minimum unit of the preset lighting block. When the number of LEDs increases, the convergence time of this algorithm is slightly better than that of the ANN algorithm. Compared with the lighting space of the corresponding LED volume, the convergence time meets the application requirements, verifying its ability to intelligently adjust lighting over a large range.