Subway fault association recommendation method based on prior weight and multi-layer TFP algorithm

A recommendation method and failure technology, applied in the direction of calculation, response error generation, structured data retrieval, etc., can solve the problem that the algorithm cannot be well adapted and cannot be suggested by users.

Inactive Publication Date: 2019-11-12
SOUTHWEST JIAOTONG UNIV +1
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Problems solved by technology

However, these single algorithms cannot be well adapted to the multi-layer data structure of the subway catenary fault data, and it is very likely to return a large number of strong association rules to the user, so it cannot provide good advice to the user effect

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  • Subway fault association recommendation method based on prior weight and multi-layer TFP algorithm

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Embodiment Construction

[0034] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0035] The subway fault association recommendation method based on prior weight and multi-layer TFP algorithm is characterized in that: comprising the following steps:

[0036]The basic data processing step is to analyze the catenary fault data collected on site, and encode all the fault data. In this embodiment, when encoding the fault data, it can be encoded according to the user-defined encoding rules, and after the encoding is completed , forming a fault database divided into three layers, the three layers of data layers in the fault database are respectively: fault type layer, fault equipment layer and fault attribute layer; the data in the fault database completed by coding is carried out according to the needs of users set generation operation to obtain the preprocessed transaction set data; in this embodiment, the encoding rules can ...

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Abstract

The invention discloses a subway fault association recommendation method based on prior weight and a multilayer TFP algorithm, and relates to the technical field of tractive power supply systems in rail transit. The method comprises the following steps: firstly, analyzing contact network fault data collected on site; coding all fault types according to corresponding coding rules; dividing the fault data into three layers of fault databases; wherein the layers comprise a fault type layer, a fault equipment layer and a fault attribute layer; aiming at hierarchical characteristics for data, formulating corresponding weights according to the on-site conditions of the subway faults, and calculating the weights of all the layers; and generating a corresponding association rule path, mining top-kmost frequent modes on the basis of the generated strong association rule base to achieve the purpose of reducing the number of modes returned by mining, and finally returning k strong association fault rules with the highest support degree to the user.

Description

technical field [0001] The invention relates to the technical field of traction power supply systems in rail transit, and more specifically relates to a subway fault association recommendation method based on prior weight multi-layer TFP algorithm. Background technique [0002] From the perspective of the entire current catenary research field, the correlation analysis for catenary fault data is still in its infancy. As far as the existing technical literature is concerned, there is no public technical solution in this field that can effectively analyze the catenary of the subway. Therefore, it is impossible to form a relatively complete association rule base for various faults of the subway, and thus it is impossible to provide good maintenance suggestions for the subway operation site. [0003] From the perspective of fault data, most of the current research stays at the stage of coding standard research, and cannot establish a relatively standardized and systematic data s...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/26G06F11/07
CPCG06F11/0766G06F11/0781G06F16/26
Inventor 于龙刘兰刘一谷赖声钢李政蒋中志李正国康学剑
Owner SOUTHWEST JIAOTONG UNIV
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