The invention discloses a
large model driven automobile model modeling method based on a
Modelica language, and belongs to the technical field of
intelligent modeling and automobile
simulation. The method comprises the following steps: firstly, accurately analyzing a
natural language demand into a structured triple by adopting a BERT-CRF (
domain knowledge enhanced) multi-task model; matching an optimal component combination through a multi-objective optimization
algorithm driven by a graph neural network, and cooperatively predicting an interdisciplinary parameter feasible region in combination with symbolic mathematical derivation and
machine learning; a topological connection matrix is innovatively optimized by using a graph
attention network, and intelligent generation and dynamic
verification of
simulation codes are realized by fusing a template engine and
syntax tree analysis; and finally, constructing a multi-target reward
function optimization control strategy through
reinforcement learning, and establishing a closed-loop knowledge iteration mechanism. Compared with a traditional modeling method, through deep combination of the
large model and
Modelica, the technical difficulty of automobile
system modeling is remarkably reduced while the preciseness of physical modeling is kept, and the method is particularly suitable for complex scenes such as
new energy vehicle
model development and intelligent driving
system integration.