Design Of Mobile Vehicle Exhaust Detection System Based On Internet Of Things

With the popularization of automobiles,influence of automobile exhaust pollution to air is becoming more and more serious.At present,the chassis dynamometer is used in our country to simulate the vehicle running condition to detect vehicle exhaust emissions.However,this method of inspection could not accurately copy the emission status of vehicles running on the road and need the examination clerks to have high technology level.Through the investigation of automobile exhaust detection stations in Urumqi and the tracking study in one exhaust detection station,and in view of the shortcomings of current inspection methods,at the same time,according to the Regulations on the experimental discharge of actual driving pollutants of " Emission limits and measuring methods of light vehicles(sixth stage of China)" standards,we design a mobile vehicle exhaust gas detection system based on Internet of things for In use light car using sensor technology,embedded development technology,platform construction technology.Using the system,the exhaust detection of automobile actual road is realized.And we also studied the misfire fault diagnosis using Matlab neural network toolbox.This paper designs the system according to the three layer structure of the Internet of things: the perception layer,the network layer and the application layer.(1)Taking the ARM processor which transplanted the Linux system as the core,a perceptual terminal for collecting emission related data was constructed.Perceptual terminal collects the emission related parameters through emission analysis system,OBD module,navigation and positioning system,environmental sensors.(2)We build the remote management platform and using J2 EE platform technology.And the exhaust gas detection database is constructed.(3)By using 4G module,the communication between the perceptual terminal and the remote management platform is realized through the mobile communication network.(4)The fire fault diagnosis is studied using BP neural network algorithm.