A hybrid task scheduling method for railway locomotive operation and control system based on reinforcement learning

Abstract

The invention provides a railway locomotive operation control system hybrid task scheduling method based on reinforcement learning, and the method is an offline learning process. According to the method, first hybrid task set data during actual operation or in a simulation experiment of a locomotive operation control system, a hybrid task set is formed and regularization marking is performed on each task in the hybrid task set. Then the task set after regularization marking serves as input of a reinforcement learning system, and a reinforcement learning environment is formed. The reinforcement learning system applies a reinforcement learning algorithm, inspects scheduling objectives of the locomotive operation control system to perform an iterative learning process, a <state-rule> corresponding relation table corresponding to the hybrid task set is generated; and the <state-rule> corresponding relation table is stored in a database. The rule whose frequency of occurrence is the highest is selected from the database as the optimal rule of a current state, and a final <state-rule> corresponding relation table is formed. During operation of the locomotive control system, generation of a real-time scheduling sequence of hybrid tasks is guided according to the <state-rule> corresponding relation table, thereby realizing task scheduling.

Description

technical field [0001] The invention relates to a mixed task scheduling method for a railway locomotive operation control system, in particular to a reinforcement learning-based mixed task scheduling method for a railway locomotive operation control system. Background technique [0002] A real-time system means that when an external event or data is generated, it can accept and process it at a fast enough speed, and the processing result can control the production process or respond quickly to the processing system within the specified time, and schedule all possible events. Utilize resources to complete real-time tasks and control a system in which all real-time tasks run in unison. Such systems are widely used in various fields of social life, such as workshop real-time scheduling system, train operation energy-saving optimization control system, flight simulator, etc. The railway locomotive operation and control system is a typical real-time system, and the system will g...

AI Technical Summary

Problems solved by technology

This type of method has the following two disadvantages: First, it is not flexible enough. Once the scheduling table is determined, the scheduling order cannot be changed during the scheduling process; second, it requires the arrival time of all tasks to be obtained before the system runs. Period, running time and other information, so it is difficult to apply this strategy to aperiodic real-time task scheduling

[0006] Due to the dynamic variability of the locomotive operating environment, a single rule has certain limitations in dealing with real-time scheduling problems

However, all the above-mentioned algorithms are currently unable to select the appropriate scheduling rules in real-time under complex and dynamically changing environments, and algorithms with strong adaptability usually have relatively large time and computational overhead.

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