Knowledge graph construction and Internet of Things optimisation for power grid data knowledge extraction
Problems exist in power grid data management that have unclear relationships, weak security and low accuracy. By analysing the knowledge graph construction characteristics of smart grid data information and knowledge extraction, the grid data management platform is reshaped architecturally, and the...
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Format: | Article |
Language: | English |
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Sciendo
2023-01-01
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Series: | Applied Mathematics and Nonlinear Sciences |
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Online Access: | https://doi.org/10.2478/amns.2021.2.00283 |
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author | Sun Xiangju Hao Ting Li Xing |
author_facet | Sun Xiangju Hao Ting Li Xing |
author_sort | Sun Xiangju |
collection | DOAJ |
description | Problems exist in power grid data management that have unclear relationships, weak security and low accuracy. By analysing the knowledge graph construction characteristics of smart grid data information and knowledge extraction, the grid data management platform is reshaped architecturally, and the knowledge graph construction technology is embedded in the grid data management framework. For the aforementioned problems, the knowledge graph construction and Internet of Things optimisation framework of power grid data knowledge extraction are proposed in this article. Firstly, the semantic search (KGSS) algorithm based on the knowledge graph is used. The KGSS algorithm extracts knowledge from structured, semi-structured and unstructured grid data through the massively parallel processing acquisition model and Hadoop database, and constructs knowledge entities, attributes and inter-entity relationships. Then, it optimises and predicts through the knowledge graph construction and Internet of Things optimisation framework extracted from power grid data knowledge. Finally, the experimental results show that the accuracy rate of the KGSS algorithm is 92%. The experimental results also show that it provides new ideas and research directions for power grid data under big data in the future. |
first_indexed | 2024-03-12T01:36:47Z |
format | Article |
id | doaj.art-fe488b52ea36421d8ef46ea330433071 |
institution | Directory Open Access Journal |
issn | 2444-8656 |
language | English |
last_indexed | 2024-03-12T01:36:47Z |
publishDate | 2023-01-01 |
publisher | Sciendo |
record_format | Article |
series | Applied Mathematics and Nonlinear Sciences |
spelling | doaj.art-fe488b52ea36421d8ef46ea3304330712023-09-11T07:01:08ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562023-01-01812729273810.2478/amns.2021.2.00283Knowledge graph construction and Internet of Things optimisation for power grid data knowledge extractionSun Xiangju0Hao Ting1Li Xing21Internet Division of State Grid Gansu Electric Power Company, Lanzhou, 730030, China1Internet Division of State Grid Gansu Electric Power Company, Lanzhou, 730030, China2Gansu Tongxing Intelligent Technology Development Co. LTD, Lanzhou, 730030, ChinaProblems exist in power grid data management that have unclear relationships, weak security and low accuracy. By analysing the knowledge graph construction characteristics of smart grid data information and knowledge extraction, the grid data management platform is reshaped architecturally, and the knowledge graph construction technology is embedded in the grid data management framework. For the aforementioned problems, the knowledge graph construction and Internet of Things optimisation framework of power grid data knowledge extraction are proposed in this article. Firstly, the semantic search (KGSS) algorithm based on the knowledge graph is used. The KGSS algorithm extracts knowledge from structured, semi-structured and unstructured grid data through the massively parallel processing acquisition model and Hadoop database, and constructs knowledge entities, attributes and inter-entity relationships. Then, it optimises and predicts through the knowledge graph construction and Internet of Things optimisation framework extracted from power grid data knowledge. Finally, the experimental results show that the accuracy rate of the KGSS algorithm is 92%. The experimental results also show that it provides new ideas and research directions for power grid data under big data in the future.https://doi.org/10.2478/amns.2021.2.00283grid dataknowledge extractionknowledge graphinternet of thingspower grid |
spellingShingle | Sun Xiangju Hao Ting Li Xing Knowledge graph construction and Internet of Things optimisation for power grid data knowledge extraction Applied Mathematics and Nonlinear Sciences grid data knowledge extraction knowledge graph internet of things power grid |
title | Knowledge graph construction and Internet of Things optimisation for power grid data knowledge extraction |
title_full | Knowledge graph construction and Internet of Things optimisation for power grid data knowledge extraction |
title_fullStr | Knowledge graph construction and Internet of Things optimisation for power grid data knowledge extraction |
title_full_unstemmed | Knowledge graph construction and Internet of Things optimisation for power grid data knowledge extraction |
title_short | Knowledge graph construction and Internet of Things optimisation for power grid data knowledge extraction |
title_sort | knowledge graph construction and internet of things optimisation for power grid data knowledge extraction |
topic | grid data knowledge extraction knowledge graph internet of things power grid |
url | https://doi.org/10.2478/amns.2021.2.00283 |
work_keys_str_mv | AT sunxiangju knowledgegraphconstructionandinternetofthingsoptimisationforpowergriddataknowledgeextraction AT haoting knowledgegraphconstructionandinternetofthingsoptimisationforpowergriddataknowledgeextraction AT lixing knowledgegraphconstructionandinternetofthingsoptimisationforpowergriddataknowledgeextraction |