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Design And Implementation Of Rail Transit Energy Monitoring System Based On Android Mobile Terminal

Posted on:2022-05-02Degree:MasterType:Thesis
Country:ChinaCandidate:X HuFull Text:PDF
GTID:2532307070955599Subject:Electrical engineering
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In recent years,the construction speed of urban rail transit in my country has been accelerating,and energy consumption has become a key issue of people’s attention.Train traction energy is the main component of total energy consumption,and its related research has become an important basis for energy dispatching and routine maintenance of rail transit enterprises.At the same time,due to the popularization of mobile devices,subway operators require more flexibility in obtaining data.In order to be able to monitor energy consumption data in real time,understand its changing trends,and better analyze and manage energy consumption,this article conducts related research on the rail transit energy monitoring system.This paper comprehensively considers the functional and non-functional requirements of rail transit,and designs a rail transit energy monitoring system based on.The design of the system mainly includes system functions and software architecture design,database design,and client server software design.Through analysis of influencing factors,the energy consumption prediction model is built based on neural network,and the actual value is compared with the actual value for simulation verification.The client uses framework technology to achieve,and the server uses framework development,responsible for responding to client requests.Data communication is completed through technologies such as servers and communication protocols,and it is used as the data storage platform of the system.The energy monitoring system based on the monitoring,analysis and processing of energy consumption data is conducive to more scientific and efficient energy dispatching of rail transit operation organizations,and ultimately reduces energy consumption,thereby reducing operating costs.
Keywords/Search Tags:Rail Transit, Energy monitoring, Deep reinforcement learning, Load forecasting, Android system
PDF Full Text Request
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