Power transformation operation and maintenance knowledge frequent pattern mining method and device, equipment and storage medium

A technology of frequent patterns and electronic equipment, applied in character and pattern recognition, instruments, integrated learning, etc., can solve the problems of uneven knowledge level and operation and maintenance skills, extensive management of learning resources, imprecise portrayal of individual knowledge level and learning style, etc. question

Active Publication Date: 2021-05-11
CHINA ELECTRIC POWER RES INST +3
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] At present, there are a large number of substation operation and maintenance personnel, and their knowledge levels and operation and maintenance skills are uneven. The training of substation operation and maintenance personnel mainly adopts theoretical knowledge training, masters with apprentices, practical training using real equipment, and substation simulation training. Carrying out knowledge and skills training, although to a certain extent, can meet the training needs of substation operation and maintenance personnel, but the teaching form is mainly based on "instilling" passive learning, which is boring in form and content, lack of drills, and out of touch between learning and practice. The tra

Method used

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  • Power transformation operation and maintenance knowledge frequent pattern mining method and device, equipment and storage medium
  • Power transformation operation and maintenance knowledge frequent pattern mining method and device, equipment and storage medium
  • Power transformation operation and maintenance knowledge frequent pattern mining method and device, equipment and storage medium

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Embodiment approach

[0053] Explanation of related technical terms:

[0054] Let I={I 1 , I 2 , I 3 ,..., I n} is a knowledge item set under a certain knowledge module, a non-empty subset of I is called a knowledge item set, and a set containing k knowledge items is called a k-knowledge item set. Learning transaction database D={T 1 ,T 2 ,...,T n}, a learning transaction T is represented as a binary group T=(TID, X), wherein TID represents the learning transaction identifier of the learner;

[0055] 1. Support:

[0056] Let X As a knowledge item set, learning transaction T i Contains knowledge item set X if and only if The support degree of knowledge item set X is defined as: the percentage of learning transactions containing knowledge item set X in the learning transaction database D.

[0057] The formula for calculating the support degree of knowledge item set X is:

[0058]

[0059] 2. Minimum item support:

[0060] Let I={I 1 , I 2 ,..., I m} is a finite set composed of ...

Embodiment 1

[0073] The invention provides a method for mining frequent patterns of substation operation and maintenance knowledge, which is an improved method based on the FP-growth algorithm; in the method of the invention, each knowledge item in a certain learning module is allowed to calibrate different minimum supports according to the actual situation Degree, to realize frequent pattern mining under Multiple Item Support (Multiple Item Support), so that in the mining process, it can not only avoid generating too many meaningless rules, but also effectively mine low-frequency rules, thereby improving the performance of the algorithm. effectiveness. The technical solution of the invention will be described in detail below from the aspects of data structure design, frequent pattern tree construction, frequent pattern mining, calculation example application description, and experimental verification.

[0074] A. CMIS_Tree data structure design:

[0075] CMIS_Tree: Compact Multiple Item ...

Embodiment 2

[0125] The present invention also provides a device for mining frequent patterns of substation operation and maintenance knowledge, including:

[0126] The mining unit is configured to perform frequent pattern mining on the pre-built compact multi-item support tree CMIS_Tree, and output all frequent patterns obtained by mining.

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Abstract

The invention discloses a power transformation operation and maintenance knowledge frequent pattern mining method and device, equipment and a storage medium, and the mining method comprises: carrying out the frequent pattern mining of a pre-constructed compact multi-term support tree CMIS_Tree, and outputting all frequent patterns obtained through mining. According to the Invention, historical data such as the knowledge item learning frequency and the knowledge item learning path of the trainee in an online system are sorted, a frequent pattern mining method is adopted, the incidence relation of all the knowledge items in the historical data is mined, the structured knowledge association rule between all the knowledge items is established, and a solid foundation is laid for personalized recommendation.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a method, device, equipment and storage medium for mining frequent patterns of substation operation and maintenance knowledge. Background technique [0002] With the rapid development of social economy, power resources are gradually increasing, and the scale of power grid is expanding day by day. For the operation and maintenance of power substation, the State Grid Corporation of China has put forward new goals of safe, stable and efficient operation and maintenance, especially for the operation and maintenance personnel of power substation to speed up the mastery Operation and maintenance knowledge, rapid improvement of operation and maintenance quality, and efficient support for operation and maintenance actual combat drills put forward higher requirements. [0003] At present, there are a large number of substation operation and maintenance personne...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N20/20G06Q50/20G06Q50/06
CPCG06N20/20G06Q50/2057G06Q50/06G06F18/24323Y04S10/50
Inventor 李俊辉周海明王维洲林昌年刘璐赵喜兰高琦
Owner CHINA ELECTRIC POWER RES INST
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