Development and Application of a Knowledge Retrieval and Analysis System for the Power Industry Based on Knowledge Graph and Large Language Model

doi:10.11930/j.issn.1004-9649.202409084

Abstract

Abstract:

With the rapid development of artificial intelligence technology, knowledge retrieval systems in the power industry are facing technological updates and iterations. A knowledge retrieval and analysis system for the power industry based on knowledge graph and big language model has been proposed. Firstly, using big language models to mine user needs and understand their intentions; Then, for knowledge information with different structures, structured knowledge graphs are constructed through strategies such as knowledge modeling, knowledge extraction, and knowledge fusion; Finally, utilizing a large language model based on user requests and professional knowledge obtained from knowledge subgraphs, and visualizing the generated content for display to users, provides a new approach for knowledge retrieval systems in the power industry.

Key words: large language model, knowledge graph, knowledge extraction

Jinying ZHANG, Zhefeng WANG, Hua XIE, Changying YAO, Yanli MIN, XINYing WANG. Development and Application of a Knowledge Retrieval and Analysis System for the Power Industry Based on Knowledge Graph and Large Language Model[J]. Electric Power, 2024, 57(12): 198-205.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202409084

https://www.electricpower.com.cn/EN/Y2024/V57/I12/198

Figures/Tables 11

References 29

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任务	条数/条	备注
训练集	2000	随机采集各电力指标不同难度下的数据
	1000	开源中文数据集
	1000	开源英文数据集
	1000	开源数学推理数据集
验证集	300	简单问题、中等问题与推理问题各随采100条
测试集	300	简单问题、中等问题与推理问题各随采100条
系统测试集	300	简单问题、中等问题与推理问题各随采100条

任务	条数/条	备注
训练集	2000	随机采集各电力指标不同难度下的数据
	1000	开源中文数据集
	1000	开源英文数据集
	1000	开源数学推理数据集
验证集	300	简单问题、中等问题与推理问题各随采100条
测试集	300	简单问题、中等问题与推理问题各随采100条
系统测试集	300	简单问题、中等问题与推理问题各随采100条

序号	配置名称	配置详情
1	中央处理器	Intel(R) Xeon(R) Silver 4110 CPU @ 2.10 GHz x86_64
2	内存	219 G
3	显卡	NVIDIA A800 80 GB PCIe 2块
4	操作系统	Centos 7
5	开发框架	Python3.8+Pytorch2.1+py2 neo2021.2.4
6	大语言模型	chatglm3
7	图谱	Neo4 j 3.5

序号	配置名称	配置详情
1	中央处理器	Intel(R) Xeon(R) Silver 4110 CPU @ 2.10 GHz x86_64
2	内存	219 G
3	显卡	NVIDIA A800 80 GB PCIe 2块
4	操作系统	Centos 7
5	开发框架	Python3.8+Pytorch2.1+py2 neo2021.2.4
6	大语言模型	chatglm3
7	图谱	Neo4 j 3.5

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[2]	LIU Dong, ZHANG Yue, PI Junbo, SHAN Lianfei, LIU He, SONG Pengcheng, JIANG TAO. Construction and Application of Knowledge Graph for Intelligent Retrieval of Power Grid Dispatching and Control Information [J]. Electric Power, 2023, 56(7): 78-84.
[3]	MEI Bingxiao, ZHOU Jinhui, SUN Xiang. Analysis of Distribution Network Information Risks Based on Knowledge Graph and Cellular Automata [J]. Electric Power, 2022, 55(10): 23-31.
[4]	LI Xiaolu, ZUO Xuan, LIU Riliang, LU Yiming, LI Congli, LIN Shunfu. SHACL-Based Validation Method of Knowledge Graph for Power System Model [J]. Electric Power, 2022, 55(1): 119-125,228.
[5]	LI Xinpeng, XU Jianhang, GUO Ziming, LI Junliang, NING Wenyuan, WANG Zhenxue. Construction and Application of Knowledge Graph of Power Dispatching Automation System [J]. Electric Power, 2019, 52(2): 70-77,157.