Lawn Lamp Design Based on Fuzzy Control and Secondary Optical Optimization

Abstract

With the emergence of new technologies, the design of urban infrastructure is constantly being innovated, and the lawn lamp as urban lighting infrastructure is an important part of urban infrastructure. For the current lawn lamp function, there are single, large power consumption, low light energy utilization and other shortcomings. Combined with deep learning and optical design, this paper constructs an adaptive lighting control system based on the technology of the Internet. Considering the nonlinear and time-varying characteristics of external factors, a fuzzy control model with ambient light level and pedestrian flow as input and dimming coefficient K (0 < K < 1) as output is proposed to adjust the brightness of the light source and achieve energy savings. In order to improve the light energy utilization of the luminaire and reduce the glare index of the luminaire, a free-form total internal reflection (TIR) lens was designed by finding the optimal curvature of the lens through the polycurved edge light principle. The light source of the lawn lamp was simulated by TracePro, and the results showed that the light energy utilization reached 90%. Finally, the ambient illumination and pedestrian flow data of Dalian ZT Park were measured for different time periods at the site, and the data were normalized using the min-max normalization algorithm. The adaptive dimming capability of the system was verified through simulation tests and field tests, and the results showed that the lighting energy efficiency under the control system was 38%.