MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||6|
|Section||Smart Algorithms and Recognition|
|Published online||04 March 2020|
Grid text classification method based on DNN neural network
Guangdong Power information Technology Co., Ltd. Yuedian Buliding, 6-8 Shuijun Road, Yuexiu District, Guangzhou, Guangdong, China
* Corresponding author: firstname.lastname@example.org
With the rapid development of network technology, the electric power Internet of Things needs to face a large number of electronic texts and a large number of distributed data access and analysis requirements. If the system wants to complete accurate and efficient data analysis and build an existing data and service standard system covering the entire chain of energy and power business on the existing basis, it must implement massive electronic text retrieval, information extraction and classification in the power grid system. In order to achieve this purpose, a DNN neural network classification model is constructed to classify the text information of the power grid, and the effectiveness of the method is verified by experiments based on data from the substation information system.
Key words: Natural language processing / Text classification / DNN network
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.