Freight train scheduling method and system based on rolling time domain event influence analysis

Abstract

The invention discloses a freight train scheduling method and system based on rolling time domain event influence analysis. According to the method, firstly, an initial train working diagram is compiled, and then when an emergency occurs, according to feature data corresponding to the emergency, train working diagrams corresponding to rolling time domains are sequentially calculated in a rolling mode through a train operation plan adjusting model. According to the method, adjustment on the train operation plan can be simulated in a rolling calculation mode until an output result capable of overcoming the influence of emergencies is obtained. Therefore, the most direct scheduling adjustment scheme can be automatically screened out according to the characteristic values of the emergencies. According to the train operation plan adjustment model, an ideal scheduling result can be quickly obtained by using simple calculation steps in an iteration mode.

Description

technical field [0001] The invention relates to a rail transportation system, in particular to a method and system for dispatching freight trains based on analysis of the impact of rolling time-domain events. Background technique [0002] During the running of the railway train, it is inevitable that various influencing factors will be encountered, causing the train to fail to run according to the map. Therefore, it is necessary to take adjustment measures to restore the normal running of the train as soon as possible. [0003] Under normal circumstances, after an emergency occurs, the emergency manager of the command center needs to combine the information fed back from the scene and use different accident consequence simulation software or related risk consequence analysis models according to the different nature of the accident to be able to calculate the consequences of the accident , determine potential accident consequences and losses, and predict on-site emergency res...

AI Technical Summary

Problems solved by technology

However, the existing algorithms often can only obtain the optimal scheduling scheme under certain conditions based on a single model

Existing technologies cannot respond to emergencies in a timely manner, nor do they have the learning ability to select optimal solutions from historical big data

The scheduling scheme provided by the existing scheduling system is often only applicable to the specific scenario of the scheduling model, so the scheduling efficiency and scheduling effect are not ideal

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