A Variable Granularity Migration Adaptation for Trajectory Simulation Model

In order to balance the model accuracy and real-time requirements of digital models in digital simulation systems, keep the granularity of various internal and external digital models at the same level, and solve the problem of simulation consistency synchronization, a dynamic trajectory model with variable granularity migration and adaptation method is proposed. It performs time series trajectory fitting prediction and the digital model to perform model correction by precise calculation by RBF neural network model. In practical application, this method has a good restoration effect on fine-grained trajectory simulation models. While reducing its simulation step size and improving timeliness by 3 times, the restoration degree can reach 90% compared with the real trajectory data, and it can be continuously corrected and improved in daily simulation deduction. The problem of inconsistent granularity when migrating external digital models to other digital simulation systems is solved, and a new idea and method is provided for the good compatibility of digital simulation systems with various digital models.