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.