Research And Application Of Urban Rail Transit System Based On Wireless Sensor Network

With the rapid development of network and information,urban rail transit industry has entered the era of intelligent information management.This paper focuses on three kinds of environmental factors such as temperature,humidity and air quality in the train cabin of rail transit,USES the multi-hop communication mode of wireless sensor network and combines with data fusion technology to finally realize the monitoring of the environmental data in the whole passenger room.The paper first designed the hardware part of the system,collected data information with multiple probes in the railway train passenger room,transmitted data through ZigBee wireless protocol,and completed the execution task with the main control module.The design of sensor module adopts DHT11 series sensor and MG811 type sensor.ZigBee wireless transmission module design USES CC2530 chip;The main control module adopts STM32F103C8T6 single chip microcomputer.Secondly,in order to combine the software design with the actual application requirements,IAR Embedded Workbench IDE integrated development environment of IAR System is adopted and the node data acquisition program is programmed with C language.The system adopts the node layout algorithm based on virtual diamond grid to arrange the terminal sensor nodes,which can save energy better.The program framework adopts z-stack protocol Stack with operating system principle.Finally,the login and monitoring functions of the system are realized through the client software system of the terminal.Thirdly,due to the large number of sensor nodes involved in the passenger room monitoring system of urban rail transit and the large external interference,the actual measured values will have some errors.In this paper,based on Pearson correlation calculation,the kalman filter algorithm is improved.Finally,the simulation test and result analysis of the proposed algorithm are carried out with the real-time data monitored by the system.Simulation results strongly prove the rationality of using kalman filter for data fusion and the correctness and availability of the improved kalman filter algorithm based on Pearson correlation processing.