Research On Temperature Compensation Of Mine Wind Pressure Sensor Based On Grasshopper Optimization Algorithm

Accurate monitoring of mine ventilation parameters is the basis of accurate measurement of ventilation resistance,which can provide accurate basic parameter support for real-time calculation of ventilation network and rapid and accurate positioning of the affected area of disaster.It is of great significance to intelligent decision and emergency control of mine disaster ventilation.Accurate monitoring of ventilation pressure drop is one of the keys to accurately measure ventilation resistance.Piezoresistive wind pressure sensor is the commonly used equipment to measure the ventilation pressure drop in the roadway,and its accuracy directly affects the measurement accuracy of ventilation resistance.In this paper,the temperature compensation algorithm of the wind pressure sensor is studied based on the research of the temperature characteristics of the wind pressure sensor.First of all,this paper takes the working principle and production process of the wind pressure sensor as the starting point of research,theoretically analyzes the causes of the temperature drift of the wind pressure sensor,and makes a comparative analysis of the existing temperature compensation methods,so as to select the best temperature compensation model to restrain the temperature drift of the wind pressure sensor.Secondly,RBPNN is used to compensate the temperature of piezoresistive wind pressure sensor,and GOA algorithm is introduced to optimize the parameters of RBFNN.In order to improve the GOA algorithm,which is easy to fall into the local optimal solution and the search speed is slow,a good point set strategy was introduced to ensure the diversity of the initial locust population.The strategy of elite grouping and multi-population updating was adopted to prevent the current situation of the optimal locust as the local optimal individual to guide the updating,so as to ensure that the diversity of the population was not reduced during the process of population updating and to improve the speed of population searching.Then combined with IGOA algorithm to optimize RBFNN,the optimized temperature compensation model of wind pressure sensor was established.Finally,the experimental platform of the wind pressure sensor was built,the temperature experiment of the wind pressure sensor designed in this project was carried out,and the IGOA-RBFNN temperature compensation algorithm was simulated and verified with the obtained experimental data.The simulation results show that the reference error of the air pressure sensor decreases from 3.93% to 0.11% after the compensation of the IGOA-RBFNN temperature compensation algorithm,which can effectively compensate the error of the air pressure sensor caused by temperature drift.The RBFNN compensation algorithm is applied to the air pressure sensor,and the maximum error of the air pressure sensor is 0.9Pa after testing,which can meet the accuracy requirements of the air pressure sensor in the mine ventilation system.