Automatic text classification method for power grid user fault repair work orders

An automatic classification and user-friendly technology, applied in the direction of neural learning methods, electrical digital data processing, biological neural network models, etc., can solve the problems of automatic processing and classification of fault repair work orders for difficult users, so as to shorten fault maintenance time and improve service quality effect

Inactive Publication Date: 2019-09-13
SHANGHAI UNIVERSITY OF ELECTRIC POWER
View PDF4 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The present invention is aimed at the problem that it is difficult for the power grid department to automatically process and classify user fault repair work orders, and proposes a text automatic classification method for power grid user fault repair work orders to realize automatic classification

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Automatic text classification method for power grid user fault repair work orders
  • Automatic text classification method for power grid user fault repair work orders

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0015] like figure 1 Shown is a schematic flow chart of the automatic text classification method based on word2vec and CNN for the power grid user's fault repair work order. The implementation of the method of the present invention includes the following steps:

[0016] 1. Data preprocessing stage based on word2vec: Word2vec is a group of related models used to generate word vectors, which are used for training to reconstruct linguistic word texts. At this stage, firstly, word segmentation is performed on the data of the text data set of the power grid user fault repair work order, filtering stop words and training word vectors. The Word2vec method is used to train the word vector. Word2vec learns the distributed representation of the text, vectorizes the text, and uses the word vector to represent the text.

[0017] 2. Text classification based on CNN (Convolutional Neural Networks, convolutional neural network). The trained word vector is loaded into the CNN for feature ex...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to an automatic text classification method for power grid user fault repair work orders. The automatic text classification method comprises the steps: firstly, conducting word segmentation on data of a text data set of a power grid user fault repair work order, filtering stop words and training word vectors, wherein a Word2vec method is adopted to train word vectors and Word2vec learns distributed expression of the text, vectorizing the text, and expressing the text through the word vectors; and loading the trained word vectors into a convolutional neural network for feature extraction and model training, and finally outputting probability distribution on each label to obtain a classification result. Compared with a traditional text classification method, the automatic text classification method obviously improves the accuracy, and greatly shortens the classification time. Meanwhile, the automatic text classification method can help the power grid department morereasonably configure various emergency maintenance resources, thus greatly shortening the fault maintenance time, and facilitating the improvement of the service quality of the power grid department.

Description

technical field [0001] The invention relates to a text classification technology, in particular to a text automatic classification method for a power grid user's fault repair work order. Background technique [0002] In recent years, with the rapid development of economy and informatization, the scale of the power grid has been continuously expanding, and the number of users has also been increasing. Power grid user fault repair work orders are important information for recording user feedback. These information are mainly presented in the form of Chinese text, with short content and sparse features. Traditionally, power grid fault repair work orders mainly rely on manual classification. However, this processing method is not only inefficient, but also extremely error-prone. Contents of the invention [0003] The invention aims at the problem that it is difficult for the current power grid department to automatically process and classify user fault repair work orders, an...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06F40/289G06F40/30G06N3/045G06F18/241
Inventor 赵田曹渝昆
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap