Research On Temperature Compensation Of Ice Measurement System Based On Improved Particle Swarm Optimization Algorithm

As we all know,the problem of road icing has been troubling people’s travel.In recent years,in order to better detect the situation of road icing,a lot of research and development of road icing detection system have been carried out at home and abroad,but due to various reasons and the influence of temperature drift phenomenon,most of the ice measurement system has not achieved the expected effect.In view of the above problems,this paper designed a set of piezoelectric resonance sensor road icing monitoring system,and in view of the current piezoelectric icing sensor caused by the temperature drift problems of low accuracy,poor stability,designed an improved particle swarm optimization algorithm for temperature compensation.The design scheme of this system is divided into the appearance design,hardware circuit design and software algorithm design of the ice sensor.The hardware circuit design mainly includes the design of road sensor data detection terminal,signal transfer station and cloud platform server.The road sensor data detection terminal is mainly responsible for detecting road status information and then forwarding it to the signal transfer station.The signal transfer station mainly includes a wireless transmission module and a power supply module,which is responsible for supplying power to the system and sending the real-time pavement information data received to the cloud server.The server side of the cloud platform is divided into a cloud data processing module and a cloud detection module,which are responsible for processing and detecting the road information data received.Software algorithm design includes frequency scanning algorithm of measurement data,cloud data processing algorithm and cloud display interface algorithm.The main control chip of the ice measurement system receives measurement data through frequency scanning algorithm of measurement data and sends it to the cloud platform,and the cloud platform processes and converts the received measurement data according to the cloud data processing algorithm.And determine the road state and the thickness of ice or water on the road,and finally display real-time road state information in the user’s mobile phone or computer terminal through the cloud display interface algorithm.During the experimental study of ice measurement system,it was found that piezoelectric resonant icing sensor would produce a large temperature drift phenomenon under low temperature environment.In order to reduce the adverse effect of this phenomenon on the measurement results,the accuracy of the measurement data is improved,an improved particle swarm optimization algorithm ASA-PSO is proposed to compensate for the temperature of the icing sensor.This algorithm combines the simulated annealing algorithm and particle swarm algorithm,and selects the hyperbolic tangent function to adjust the inertia weight coefficientω,so that it can produce nonlinear adaptive changes.The linear change strategy is adopted to regulate the social learning factor c₁ and self-learning factor c₂,so that the optimization focus will change at different stages,which solves that the algorithm is easy to fall into local optimization solutions problem.In order to prove the advantages and disadvantages of the temperature compensation effect of this algorithm,several other particle swarm algorithms are specially selected to compare with the ASA-PSO,and it is analyzed that this algorithm has a better compensation effect on the temperature drift phenomenon of the piezoelectric icing sensor.The experimental results show that after the temperature compensation of ASA-PSO algorithm,the measurement error of the sensor in the iced state is reduced to about 3%,and the measurement accuracy is improved to 0.1mm,indicating that the compensation algorithm can effectively reduce the influence of-5℃～-55℃low temperature environment on the measurement results of the sensor,and improve the measurement accuracy.