Tire modeling method based on data driving and medium

The invention discloses a tire modeling method based on data driving. The tire modeling method specifically comprises the following steps: determining an input variable and an output variable of tire modeling; carrying out a tire experiment, and collecting input data and output data of the tire on different road surfaces and different working conditions; processing the collected input and output data, constructing a data set for tire modeling, and dividing a database into training samples and verification samples; selecting a proper data driving model, determining a driving model structure and method, initializing, training a tire model by using a training sample, and testing and verifying the model by using a verification sample. According to the method, on the basis of experimental data, deep learning is utilized to train a Koopman operator and search a high-dimensional function space at the same time, a dictionary set is updated, and tire modeling is automatically carried out. The tire modeling method can