Anti-Jamming Study Of The Automatic Monitoring System Of Ventilating Pressure

The stable ventilation system of mine is very important for preventing gasexplosion accident, dust explosion accident and spontaneous combustion of coal.It is the base of enhancing calamities-resistant capability, the important wayto prevent and reduce accidents, and the guarantee of coal mine production safety.Consequently, it is necessary to master the changes of air pressure of everyworking place. Then, the stability and variation of ventilation system can beanalyzed according to the changes of air pressure. Automatic air pressuremonitoring system can implement real-time and automatic monitoring on workingplaces. It can provide measured air pressure parameter and accurate data to avoidspontaneous combustion in time.But there are many problems when the monitoring system works, including:sometimes measured data is severely distorted; the sensors in return lanewayscan't work normally or even stop working because of great humidity; monitoringoutstation can't transfer data effectively; and the outputting and inputting ofintelligent terminal sensors is nonlinear. After analyzing the domestic andinternational research actuality of monitoring system, some advancedantidisturbance techniques are used to solve above problems. To solve the problemthat network transmission is easily interrupted, improve and optimize the networkstructure of monitoring system, and design the RS-485 network anti-transientpulse circuit after analyzing some neglected problems. To solve the problem thatsometimes measured data is severely distorted, after analyzing all kinds ofdigital filtering measures, make use of the compound filtering method to dealwith random pulse interference and severe errors, and improve the judgingprocedure of the t-testing criterion to raise the speed of dealing with errors;To solve the problem that the outputting and inputting of intelligent terminalsensors is nonlinear, after analyzing the nonlinear compensating process ofintelligent instruments, design the BP nerve net compensating model, and make use of the Levenberg-Marquardt algorithm to implement the nonlinear temperaturecompensating and raise the precision of air press sensors.