Model-based systems engineering(MBSE)is one of the most important methods for today's digital design of products.However,due to the high specialization of systems engineering and the complex interrelationships within products,the application of MBSE to complex products has proven challenging.To address this problem,an intelligent design method based on retrieval-augmented large language model was proposed for the first time.The method first established an object-oriented multi-modal vector representation for models,leveraging retrieval-augmented generation techniques that incorporate domain knowledge and modeling rules to guide the model in more accurately generating MBSE model diagrams.Secondly,a diagram optimization method based on the MBSE model relations was proposed,cross-validating the model accuracy through the results of contextual interaction.Thirdly,the large language model was employed to call modelling APIs and to select the proper materials to generate design models and eBOM.Finally,a dataset containing 24 scenario models was constructed for method validation.Experimental results showed that the approach possessed high accuracy and usability.A case study with water jet propulsion as the modelling object further demonstrated that the approach can effectively enhance the modelling efficiency while maintaining usability,marking an important step toward intelligent application of model-based systems engineering.
model based systems engineeringlarge language modelintelligent designprompt engineering,computer-aided design
维普
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