一种基于图学习的试飞试验点关联性挖掘算法

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中图分类号：TN99-34 文献标识码：A 文章编号：1004-373X（2025）17-0160-07

引用格式：，.一种基于图学习的试飞试验点关联性挖掘算法[J].现代电子技术，2025，48（17）：160-166.

Agraph-basedflighttestpointsrelationshippredictionalgorithm

LIU Peng，DENG Xiaozheng （ChineseFlightTestEstablishment，Xi'an71oO89，China）

Abstract：Thearrangementofflighttestpointsisfundamentalinflighttestig.Howtosientificallandefectivelyarange thesetestpointsandformarationalflighttestplanplaysacrucialroleinensuringsafetyficiencyandcost-ectivenes throughouttheentireflighttestlifecycle.Amongthem，theanalysisofthecorrelationbetweentestpoints，especiallthe determinationoftheprecedencerelationship，representstheexecutionorderofthetestpointsandisakeyfactorinthe arrangement oftheflighttest plan.Therefore，aknowledgemining algorithmbasedongraph convolutional neural networksis proposed tomeetthedemandforpredictingtheprecedencerelationshipof testpoints.Theentirealgorithmmodelisdevelopedin a knowledge graphbasedonthestructuralrepresentatioof test points.Subsequently，modulessuchasgraph knowledgeelement extraction，testpoints‘deepfeatureminingbasedongraphconvolution，andtestpointpairs'logicalrelationshipegreionare designedto explorethecorelationrelationshipbetweentestpointpairsandachieveaccurate precedencerelationshipprediction. Intheflighttest，thetestpointdatasetwastested，andmultipleclassicalmodelswerecompared.Theproposedalgorithm demonstratessignificantadvantagesintermsofaccracyandstability.Theeffectivenessof theproposedalgorithmisverified.

Keywords：flight test;test pointexecutionorder;graph learning;graphconvolutional network；autoencoder;knowledge graph

0 引言

近年来，随着我国航空装备的飞速发展，对飞行试验的安全和效率提出了更高的要求。（剩余10086字）