Research On Smart Street Light Control Module And Applications

With the development of technology,automated street lamp control has become an important requirement for smart street lamp management.At present,the light sensor can be used to effectively control the single lamp automatically,but due to some aging,damage,pollution and other factors,the problem of high complaint rate in the popularization of lightcontrolled street lamps has been caused.Solving the practical problems in street light control and dimming on the basis of regional data through fog computing has certain promotion and practical significance for the development of smart street light control systems.At present,many citizens in street lamp management feedback the street lamp situation through channels through the 12345 hotline or Weibo and the management department manually handles the corresponding problems.There are many business process links,low efficiency and high labor cost.The focus of this study is to integrate the fog computing concept into traditional light control,optimize the light control model and using Natural Language Processing(NLP)technology build an integrated platform in conjunction with regional public opinion to provide a reference for the comprehensive digital development of smart street light control.The main content of this thesis includes the following aspects: 1.Proposing a street lighting control module based on fog computing: a new intelligent1.Proposing a street lighting control module based on fog computing: a new intelligent street lighting control model is used to comprehensively use edge nodes to solve the problems of automatic control caused by dust,fallen leaves,aging and damage of photoresistors in existing smart street lighting control equipment.The area data is obtained through the area coding positioning area,and the joint calculation is used to correct or even replace the street light electrical control system to solve the impact of the sensor failure on the street light system.This model can effectively reduce street lamp complaints,reduce the frequency of operation and maintenance,improve citizen satisfaction and reduce the operating costs of managers.2.Proposing a faulty street lamp identifying method by Transfer learning Bi LSTM-CRF: analyze various necessary factors of street lamp management,use Transfer learning Bi LSTM-CRF technology to identify faulty street lamp events from public opinion,extract relevant address information in the event and determine the validity of the information.By accurately locating the faulty street lamp in the address,the system can solve the problem that the traditional street lamp fault location information needs to be confirmed by the operation and maintenance personnel,improve the system efficiency,and reduce the cost of human resources.3.Implementation of intelligent street lamp control module based on microservice architecture and management system integration: based on micro-service architecture,using Spring Cloud,Kubernets,Docker and other technologies,the control module is integrated with the smart street light control system to realize the street light control function based on fog computing and the traditional street light management function based on Transfer learning Bi LSTM-CRF to improve development efficiency and enhance the system.Usability,expand the scope of system management,and improve citizen satisfaction.In the test,the performance and effect evaluation of the system have reached expectations.Improve the level of automatic control of street lamps and reducing labor costs are important goal of the development of smart street lamp control systems.This thesis studies traditional street lighting control methods and proposes a control optimization model;with the help of Transfer learning Bi LSTM-CRF technology,the management capabilities of smart street lighting platforms are improved.