Research On Operation Parameter Decision Support System Of Key Parts Of Sugarcane Harvester

How to improve the harvesting quality of sugarcane is an urgent problem to be solved now.There are three key components that affect the quality of sugarcane harvest: sugarcane cutting device,cutting device and fan device.Their mechanical structure parameters and operating parameters will affect the quality of sugarcane harvest.In this paper,a decision-making support system for operation parameters of key components of sugarcane harvester was developed,the system is based on the exploration of key factors which affect the harvest quality of sugarcane,the collection of key signals and the establishment of prediction model.The specific research contents are as follows:1.The key influencing factors of sugarcane harvest quality were explored through orthogonal experiment of sugarcane breakage rate and impurity rate;Select the appropriate data acquisition system,speed sensor,pressure sensor and other hardware to build the sugarcane harvester signal acquisition test platform.Signal acquisition test is carried out for each key influencing factor,and butterworth filtering is carried out for the collected data.The analysis of variance was used to obtain the significant influence of each factor on the breaking rate and the impurity rate of sugarcane,which provided the data base for the data instance base of the prediction and decision system of sugarcane harvest quality.2.Put forward a prediction model based on least squares support vector machine(LSSVM)of sugarcane harvest quality prediction model,and using particle swarm optimization(PSO)and genetic algorithm(GA)to optimize the model,the mean absolute error(MAE)and the mean square error(MSE)are chosen as an evaluation index,evaluation the model based on least squares support vector machine(SVM)of particle swarm optimization(PSO-LSSVM)and the model based on the genetic algorithm(GA)to optimize the least squares support vector machine(LSSVM).The PSO-LSSVM harvest quality prediction model with better prediction effect is selected to provide algorithm support for the system.3.On the basis of functional requirements,the overall structure of decision support system is designed.The system includes man-machine interface,management system,knowledge acquisition,knowledge base,decision support and other functional modules.The system adopts kernel principal component analysis(KPCA)to realize semi-automatic knowledge acquisition,and adopts generative knowledge representation.The knowledge base mainly includes system information database,harvest instance base,rule base,prediction model,etc.,and the decision support results are obtained by combining data instance and algorithm.4.On LabVIEW 2018 development platform,G language and My SQL data management software were applied to develop the operation parameter decision-making support system for key components of sugarcane harvester.The system can realize the analysis of influencing factors of sugarcane harvest quality,the prediction of sugarcane harvest quality,the decision-making support of key components of sugarcane harvester.The reliability of the system decision was verified by comparing the harvesting quality before and after decision in field experiment.