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Text classification method based on distribution network automation terminal text classification model

A distribution network automation and text classification technology, applied in the field of neural network text classification model, can solve problems such as lack of classification accuracy, low efficiency and incompleteness of in-depth information and historical information, so as to improve the level of intelligence and good text classification ability and the effect of generalization ability

Pending Publication Date: 2021-09-24
STATE GRID ZHEJIANG ELECTRIC POWER CO LTD HANGZHOU POWER SUPPLY CO
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Problems solved by technology

The structures of CNN and LSTM have their own advantages in text classification tasks, but the power equipment defect texts are highly specialized, and the existing methods are inefficient and incomplete in extracting depth information and historical information in long sequences. lacking

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  • Text classification method based on distribution network automation terminal text classification model
  • Text classification method based on distribution network automation terminal text classification model
  • Text classification method based on distribution network automation terminal text classification model

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Embodiment Construction

[0050] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0051] A text classification method based on the distribution network automation terminal text classification model, such as figure 1 As shown, the method includes:

[0052] Step 1, build a layered comprehensive context modeling network model, the network model includes an input integration module, a TCN residual module, a self-attention layer and an output layer;

[0053] Step 2, preprocessing the power defect text data set, converting the original Chinese text into a preset input format;

[0054] Step 3. In the input integration module, the process of extracting the context information and integrating the original input is performed;

[0055] Step 4. The processed information enters the TCN module with the self-attention layer as input to extract temporal features and other long-term historical information;

[0056] Step 5. The i...

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Abstract

The invention relates to a text classification method based on a distribution network automation terminal text classification model, and the method comprises the steps: collecting power defect text data, and carrying out the data cleaning and text segmentation of a data set; and secondly, combining a long-short-term memory (LSTM) network with a time convolutional network (TCN), connecting with a residual error and an attention mechanism, establishing a layered comprehensive context network structure, and hierarchically extracting deep context information, long-term historical information and more comprehensive time features from a defect text. Compared with other neural network classification models, the model provided by the invention has good text classification capability and generalization capability, and can improve the intelligent level of distribution network terminal debugging.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to a neural network text classification model based on an advanced mechanism. Background technique [0002] With the deepening of power big data applications and power information management, the effective use of data assets is related to the reliable operation of the power grid. In the daily operation and maintenance process of power enterprises, a large number of distribution network terminal commissioning work texts are retained, including information such as joint commissioning date, equipment type, fault content, and defect elimination time, which are important indicators that affect the safe and stable operation of the power system. However, these historical text data are often in an idle state after they are entered into the Open5200 system. In addition, manual classification of fault content has the interference of human factors. Therefore, the research on the au...

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Application Information

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IPC IPC(8): G06F16/35G06K9/62G06F40/289G06N3/04G06N3/08
CPCG06F16/353G06F40/289G06N3/08G06N3/044G06F18/2415Y04S10/50
Inventor 姜建郑伟彦吴靖刘宏伟何雨微卢家驹顾建炜严性平刘兴业江端袁喆沈蕴华蔡剑彪朱理宋佳
Owner STATE GRID ZHEJIANG ELECTRIC POWER CO LTD HANGZHOU POWER SUPPLY CO
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