领域知识引导的作物模型参数智能优化框架研究

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1、万方数据relativeerroriswithin7％ofparameteroptimization；2)SelectingLAI，organsbiomass(stemandleaf,spike)asatargetvariableiseffective．Spikebiomass，totalbiomass，LAlwasreducedby0．32％．1．52％and0．32％respectively,andNRMSERMSErespectivelyreduced8kg／haand123．1kg／haand0．08；MADwerereduced5．4

2、4kg／ha，105．1kg／haand0．07；3)PhaseparametersoptimizationstrategyforBGA—CMPOFeffectisnotobvious，thegapbetweenthetargetNRMSE<1％；4)MECAalgorithmismoreaccuracythanIAGAalgorithm，butittakeslongertime．Soitisnotconducivetorapidoptimizationofmodelparameters．(2)Propsoethecropgrowthperio

3、dmodelparametersoptimizationmethodbasedongeneticalgorithmguidebydomainknowledge．Constraintandguidethecropmodelparameteroptimizationframeworkthroughtheconstraintinitialparameters，determiningkeyphenophase，extendingphenologicalphaseofthemeasuredvalues，refineddirectionoperator．T

4、heresultsshowedthat：1)Foursitepreliminarytestresultswereachieved：RMSEis1．51d，2．05dand0．72d，时>0．99，MADwere1．1d，1．6d，0．55dand0．8d，simulationeffectisgood，buttherearesomeparametersisnotinconformitywiththebiologicalcharacteristicsofparametervalues；2)Byconstrainingtheinitialscopeo

5、fPVTparameters(30，40)，maketheoptimizationresultsmeethalfofwintervailetycharacteristicsofjinanno．13；3)theverificationresultsofxuzhou，jining，weifang，jiaozhouRMSEwere1．24d、1．56d、1．22d和1．48d，《is0．997、O．993、0．997and0．991，MADisld、1．2d、1．8and1．2d，respectively,andtheparametervariety

6、IEcomply、Ⅳimbiologicalcharacteristics．4)Afterjoiningdirectionoperator,IAGAalgorithmconvergencealgebraarereduced8and3generation，directionoperatorcanacceleratetheconvergencespeedofthealgorithm．(3)DevelopcropmodelparameterIntelligentoptimizationanduncertaintyanalysistool(CMPOAT

7、)CMPOATisbasedontheframeworksoftwaredevelopmethod．ItCanrealizedynamicassemblyofcropmodel，theevolutionaryalgorithm，dataprocessingandotherbusinessaccordingtouserneeds．Systemhasrealized：datamanagementandanalysis，cropmodelanalysis，parametercalculation，expertknowledgebasemanageme

8、nt，componentlibrarymanagement，andotherfunctions．ApplicationcasesshowthatCMP