Design of intelligent control system for public art light environment based on BP neural network

Abstract:
The traditional public art light environment control system usually has a fixed control mode, which can’t be flexibly adjusted according to the actual environment. This paper proposes a design scheme to predict the temperature by using the change of temperature data as the input parameter of the BP neural network, and combining the accommodating number of people in the public space and the daily traffic level as the system control factor. In this way, the light environment mode of the public art space can be automatically preset according to the ambient temperature and the flow of people information. And it uses different modes to meet people's entertainment spiritual and cultural needs. Compared with the traditional lighting control system, this design has higher flexibility and adjustability. At the same time, the preset mode can also make a more changeable design according to the actual situation of public space.