Train scheduling strategy self-adaptive selection method based on adjustment result

A technology of adaptive selection and scheduling strategy, applied in data processing applications, instruments, character and pattern recognition, etc., can solve the problems of immature theoretical research foundation, not very intuitive, and high computer performance requirements, to enhance the real-time performance of the system, The effect of overcoming computational complexity and speeding up algorithm processing

Active Publication Date: 2020-04-17
CASCO SIGNAL
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Problems solved by technology

This method makes the system state dynamically change with the continuous occurrence of events, but the theoretical research foundation is immature, not intuitive, and requires high computer performance

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  • Train scheduling strategy self-adaptive selection method based on adjustment result

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Embodiment 1

[0038] reference figure 1 , Describe the model of the present invention. The model of the present invention includes the following steps:

[0039] Step S 1 , Preprocessing of historical data, including data reconstruction, data cleaning and redefining the data for later analysis. Among them, the historical data includes but is not limited to historical operation scenario data, scheduling operation data, and scheduling result data.

[0040] Step S 2 , Use dimensionality reduction algorithm to process step S 1 The preprocessed data reduces the amount of data, only retains the most important characteristics of the data, and establishes a set of scheduling characteristics. Among them, the scheduling feature set includes but not limited to scene features, scheduling operation features, and operation result features.

[0041] Step S 3 , According to step S 2 The obtained feature set is subjected to cluster analysis to realize automatic classification of scheduling strategies and automatic...

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Abstract

The invention relates to a train scheduling strategy self-adaptive selection method based on an adjustment result. The method can be used for automatic generation and selection of scheduling strategies. The method comprises two parts of scheduling strategy generation and scheduling strategy identification. Wherein the scheduling strategy generation comprises a clustering result analyzed by utilizing big data; a scheduling strategy and a scheduling result feature set of the scheduling strategy are automatically generated, scheduling strategy recognition comprises the steps that a scheduling strategy selection problem is converted into a feature recognition problem, and the scheduling strategy is automatically matched through a machine learning algorithm according to result features of a train operation adjustment target. Compared with the prior art, the method has the advantages that the universality and result predictability of the method are enhanced, and the like.

Description

Technical field [0001] The invention relates to train operation control technology, in particular to an adaptive selection method of train dispatching strategy based on adjustment results. Background technique [0002] The main purpose of train operation adjustment is to make the train run in accordance with the pre-planned operation chart as much as possible, and to reduce the impact of emergencies on the service party and passengers. As the focus of the research on train operation organization, the method of train operation scheduling has relatively fruitful results in the research method and content. These studies mainly focus on the following aspects. [0003] Modeling based on operations research methods. This method is to give certain assumptions to the problems encountered in train adjustment, establish a mathematical model, and then use genetic algorithms and annealing algorithms to find the optimal solution to the model. The general logic of the mathematical norm of thi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06K9/62G06N20/00G06Q50/30
CPCG06Q10/0631G06N20/00G06Q50/30G06F18/23G06F18/253
Inventor 徐璟刘峰博颜红慧钱江周庭梁
Owner CASCO SIGNAL
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