Method for high-speed train operation and maintenance service knowledge fusion based on confidence calculation

Abstract

The invention provides a method for high-speed train operation and maintenance service knowledge fusion based on confidence calculation, and the method comprises the steps: building a data provider information library according to the basic information and confidence attribute information of a high-speed train operation and maintenance service provider, and building a confidence attribute score calculation table; according to the weight of each confidence coefficient attribute for the application, selecting the confidence coefficient attribute of the data provider from the data provider information base as a sub-attribute, and determining the score value of each confidence coefficient sub-attribute according to the score corresponding to each selected confidence coefficient sub-attribute and a confidence coefficient attribute score calculation table; according to the weight of each confidence coefficient sub-attribute for the application and the score value of each confidence coefficient sub-attribute, calculating the score of the high-speed rail train operation and maintenance service provider as the confidence coefficient of the high-speed rail train operation and maintenance service provider; and fusing the high-speed train operation and maintenance service knowledge according to the confidence of the high-speed train operation and maintenance service provider. The method can improve the accuracy of high-speed train operation and maintenance service field knowledge storage.

Description

technical field [0001] The invention relates to the technical field of high-speed rail train operation and maintenance, in particular to a method for knowledge fusion of high-speed rail train operation and maintenance services based on confidence calculation. Background technique [0002] High-speed train operation and maintenance data include on-board monitoring data, ground detection system data, train maintenance data, etc. Since these data are developed by different manufacturers and used online at different times, the detection methods and standards are also different, so it is inevitable that there will be conflicts. The name given by the same high-speed train entity object, the accuracy and granularity of the detection data (that is, the detection value) are different, and the stability and reliability of different systems are also different. Only with accurate data can the train run safely. When the operation and maintenance knowledge based on the above data extracti...

AI Technical Summary

Problems solved by technology

[0004] In the existing related technical solutions, when the data of multiple data providers is inconsistent and data selection is to be performed, it usually relies entirely on manual screening, without considering the "authority" of the data provider, that is, the credibility issue

Therefore, the correctness and credibility of the knowledge stored in the database are greatly affected by human factors, and it is very likely to get wrong results

In recent related studies, some materials mentioned methods to measure "authority", but these methods have yet to be perfected. For the field of high-speed rail train operation and maintenance services, these methods seem to be difficult to obtain satisfactory results

In the prior art, there is no method for identifying the "authority" of data providers. When the knowledge provided by multiple data providers is inconsistent, there is no quantitative calculation method to determine the trade-off, so that the knowledge stored in the database cannot be guaranteed to be correct. sex, affect the use effect

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