A heterogeneous model quantization and similarity matching approach based on structural features for MBSE

Abstract

With the development of Model-Based Systems Engineering (MBSE), model reuse technology is an important method to improve the modeling efficiency and credibility, and the research difficulties and hotspots in the field of complex system simulation. However, model reuse still faces challenges due to the existence of text structure bias and feature mismatch. To this end, this paper presents a structure-based model similarity quantization algorithm to obtain the similarities among models. In the first stage, the properties of the structure between the models are obtained by the structural vectorization method, and different module data are accounted. In the second stage, weights are assigned to the module data using the AHP–CRITIC assignment method. The experiment is based on the circuit amplifier model as a case study, and the experimental results verify the feasibility of structure-based model similarity quantification.