Coupling Model Analysis of Multi-disciplinary Systems for Fully Automatic Rail Transit
In the context of the rapid development of fully automated unmanned driving technology in rail transit,the coupling and collaboration between multiple specialized systems have become increasingly crucial.To provide effective operational decision support in emergency and fault scenarios under a fully automated unmanned system,The coupling relationships and models of various specialized systems are innovatively analyzed from a multi-system coupling perspective,with the coupling relationships and weight changes among the subsystems being considered.Firstly,the coupling weight indices among subsystems at the highest automation level,GoA4,were analyzed.The Analytic Hierarchy Process was employed to construct a coupling judgment matrix and assess the rationality of the coupling weights.Secondly,in scenarios where the automation level degrades,a Markov state-space transition model was used to analyze and model the coupling relationships and state transitions of these systems.The study found that as the GoA automation level decreases,the coupling relationships among the specialized subsystems gradually weaken and the weights become more balanced.This confirms that higher GoA automation levels result in closer coupling relationships among subsystems.Theoretical support for the prediction and decision-making processes in future fully automated unmanned rail transit systems is provided using digital parallel simulation neural networks and other large models.
Automated urban rail transitMarkovMulti-disciplinaryCouplingGrade of Automation
NETL
NSTL
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