Design Of Street Lamp Management And Control System Based On 5G

With the proposal and development of the policy of ecological civilization construction in China.Industrial upgrading,energy conservation and emission reduction have become an important goal of our development.Urban streetlights are the basic construction of a city.As the number of urban roads increases,the management of urban streetlights becomes more and more complex.In order to maintain the brightness of streetlights at night,the cost of urban operation will increase.In order to reduce operating costs,respond to the "carbon neutral" policy and the goal of energy conservation and emission reduction,China is now fully implementing the construction of smart cities,only by maintaining good urban lighting services and making effective management methods can we reflect the "smart and efficient" of a smart city.Therefore,this paper proposes the design of street lamp management and control system of smart city,which changes the control mode of traditional street lamps,and the intelligent control of time and brightness of street lamps is of great significance to the energy conservation of urban street lamp management.The research contents of this paper are as follows:This paper completes the design of the overall framework of the system according to the functional requirements of the system.Firstly,the whole system is divided into five parts: application layer,data layer,transmission layer,control layer and perception layer.Secondly,each part is analyzed and studied.For the transmission layer,in order to improve the stability and efficiency of data transmission,5G communication network is adopted.Its core is the design of the control method of the system.A scheme of combining neural network and fuzzy control to complete the street lamp control is proposed.Firstly,the classification and prediction of the street lamp control data are completed through the neural network,and then the control instructions are issued through the fuzzy control.BP(back propagation)neural network is selected as the neural network in this paper.By analyzing the propagation mode and process of BP neural network,the key technologies of BP neural network,such as the number of hidden layers and neurons,the selection of initial weights and thresholds,learning rate and transfer function,are improved to make BP neural network faster in training time,lower in error and more accurate in prediction results.The improved BP neural network algorithm is verified and simulated by MATLAB program.The simulation results show that the improved training error attenuation is faster,the error is more stable,and the fitting degree of training simulation results is higher.STM32H7 is selected as the main control chip in the hardware design of the street lamp control terminal,and a simple test is carried out after the completion of the hardware control circuit.Compared with the traditional control mode,the feasibility and energy-saving effect of the street lamp management control system are verified.