High-precision Humidity Measurement System Suitable For Low-temperature Meteorological Environments

Automatic weather stations generally use capacitive humidity sensors when measuring relative humidity on the ground.However,these sensors are prone to relatively significant measurement errors in harsh environments such as low temperatures or high humidity,which has become a bottleneck restricting the performance improvement of humidity sensors.To improve humidity measurement accuracy,a humidity measurement system that features error correction using air pressure control and sensor heating technology was proposed.The system includes four humidity sensors,four heating resistors and its measurement and control circuit system.It makes use of the measurement data at different air pressure and rational quadratic Gaussian regression process to perform error correction.The geometric design and heating resistance type of the humidity sensor array were investigated,and the 3D model of the sensor was built with Creo software.The distance between each adjacent sensor was 1 cm to avoid the influence of heating resistance of adjacent sensors.Then,a finite element analysis model of the sensor structure was established by ANSYS,and the structure was simplified into a one-dimensional solid element.The heating and cooling times of polyimide and nichrome heating resistors at different altitudes were calculated by computational fluid dynamics(CFD)when the temperature was increased by 50 ? and decreased by 50 ? at 0.5 W.According to the simulation results,the heating and cooling time of the two heating materials decreases with the increase of altitude.Finally,a polyimide heating film with a heating and cooling time of 3.5 ? 5 s near the ground was selected.To ensure the stable operation of the measurement system,the heating strategy was studied.The four sensors were divided into two groups for heating in turn,and each group consists of two sensors and two heating resistors.This configuration can improve the error tolerance of the measurement system.In order to verify the performance of the humidity measurement system,an experiment platform was built with a variable-pressure temperature chamber system.This thesis proposed a method that the air pressure parameter participates in the measurement error correction.A conjugate gradient method,a Levenberg-Marquardts neural network algorithm and a rational quadratic Gaussian process regression were used to establish the correction equations.After comparing the correction results of these different correction equations,it was found that the correction equation established by the rational quadratic Gaussian process regression had the smallest error and was able to reduce most of the measurement errors to within ±3%.For comparison,a conventional correction method was also used in this thesis,and the correction equation was established by rational quadratic Gaussian process regression to compare the correction results,which verifies that the air pressure parameters participate in the error correction with higher accuracy.Finally,a data display interface was built to illustrate measurement data and correction data in real time,while the historical data can be queried according to time.