Study On Visualization Techniques In Main Characteristics Of Stored Grain’s Ecological

Along with our country grain storage scale expands unceasingly as well as morestringent quality requirements of stored grain,the management technology of grain storageand the storage conditions of storage facilities improved continuously,the new grain storageenvironment put forward higher requirements in the field of grain storage information andintelligence.Especially to the main characteristics of ecological parameters of grain storage——the temperature and humidity of the heap of the grain.It’s a serious threat to thesecurity of stored grain that the main characteristic parameters of stored grain exceedssafety standards.Therefore,we need to establish the appropriate visual model of grain heapto monitoring the situation of grain storage in a real-time,intuitive and effective wayaccording to the nature of the heap of the grain.To this end,this paper analyzed main factors affecting grain storage grain storagesecurity environment,including the temperature and humidity of the heap of grain,the grainstorage pests,the air flow of grain storage and so on.Then I study the changes of themechanism of the temperature and humidity of the heap of grain and the corresponding ruleof macroscopic variation on the basis of these factors,to provide theoretical support for theset up of the visualization model of grain storage ecological main characteristicparameters.Then this paper analyzed and studied the principle of temperature and humiditytransfer of the heap of grain on the microcosmic angle,and derivation of the correspondingdifferential equation of hot and humid of grain heap.After that,this paper suing the finiteelement method to establish the corresponding finite element model of grain heaptemperature through combined with a large number of measured temperature data and thecorresponding equations and boundary conditions. By using the measured data to validatethe model,the results have the same overall trend with the results measured,and there aresome obvious errors between them. Artificial neural network has excellent capability in thenon-liner approximation,epically in the optimization calculation and the aspects of thefitting function.This paper analyzed the principle and structure of the artificial neuralnetwork and corresponding learning method.Then the RBF neural network which has agood approximation ability of any function was choose to be researched to analyzed thestructures and training methods,and validated the fitting ability.Finally,this paper useconventional finite element simulations to establish the appropriate grain storagetemperature contours, however optimized conventional finite element model improvementswith the RBF neural network, and set up the improved grain storage temperaturecontours.After comparison,the effect of the improved grain storage temperature contours isbetter than conventional temperature contours, and the precision is higher.