Research on Entity Recognition Technology in Power Grid Dispatching Field

doi:10.12204/j.issn.1000-7229.2021.10.008

Abstract

Abstract:

In recent years, with the increasing demand of data automation and intelligent management in the field of power grid dispatching, knowledge graph has become an important technology to provide knowledge management, intelligent query, auxiliary decision-making and other functions. As the core element of knowledge graph, the accuracy of entity recognition will directly affect the quality of knowledge graph. Aiming at the field of power grid dispatching, this paper firstly analyzes the research status of entity recognition in power grid dispatching field, and defines the task objective of entity recognition. Then, according to the text data features of power grid dispatching, an algorithm structure is designed to meet the requirements of local and global feature extraction, and a named entity recognition model based on BiLSTM-CNN-CRF is constructed. Finally, the experimental results show that the recognition accuracy of this method reaches 93.1%, and the F₁ value reaches 86.05%, which can effectively support the development of entity recognition in the field of power grid dispatching.

Key words: entity recognition, knowledge graph, bi-directional long short-term memory (BiLSTM), convolutional neural networks (CNN), conditional random field(CRF)

CLC Number:

TM734

XU Huifang, ZHANG Zhonghao, TAN Yuanpeng, HAN Fujia. Research on Entity Recognition Technology in Power Grid Dispatching Field[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(10): 71-77.

Figures/Tables 9

Fig.1 BiLSTM model

Fig.2 BiLSTM-CNN-CRF model

Table 1 Examples of entity recognition

Fig.3 Loss value in different model training

Table 2 Entity recognition results of Softmax classifier %

Table 3 Entity recognition results of CRF classifier %

Fig.4 The corresponding F1 of classifier

Fig.5 Result comparison of optimal models

Table 4 Entity recognition results %

References 20

[1]	李新鹏, 徐建航, 郭子明, 等. 调度自动化系统知识图谱的构建与应用[J]. 中国电力, 2019, 52(2): 70-77,157.
	LI Xinpeng, XU Jianhang, GUO Ziming, et al. Construction and application of knowledge graph of power dispatching automation system[J]. Electric Power, 2019, 52(2): 70-77,157.
[2]	周帆, 叶健辉, 肖林朋, 等. 基于知识图谱的电网模型本体智能问答系统研究[J]. 中国科技信息, 2019(16): 85-86.
[3]	闪鑫, 陆晓, 翟明玉, 等. 人工智能应用于电网调控的关键技术分析[J]. 电力系统自动化, 2019, 43(1): 49-57.
	SHAN Xin, LU Xiao, ZHAI Mingyu, et al. Analysis of key technologies for artificial intelligence applied to power grid dispatch and control[J]. Automation of Electric Power Systems, 2019, 43(1): 49-57.
[4]	刘梓权, 王慧芳. 基于知识图谱技术的电力设备缺陷记录检索方法[J]. 电力系统自动化, 2018, 42(14): 158-164.
	LIU Ziquan, WANG Huifang. Retrieval method for defect records of power equipment based on knowledge graph technology[J]. Automation of Electric Power Systems, 2018, 42(14): 158-164.
[5]	ZHOU P, SHI W, TIAN J, et al. Attention-based bidirectional long short-term memory networks for relation classification[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016: 207-212.
[6]	XU K, FENG Y S, HUANG S F, et al. Semantic relation classification via convolutional neural networks with simple negative sampling[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015: 536-540.
[7]	吴超. 电力调度知识图谱中知识抽取系统的设计与实现[D]. 沈阳: 中国科学院大学(中国科学院沈阳计算技术研究所), 2020.
	WU Chao. Design and implementation of knowledge extraction system in knowledge graph of electric power dispatch[D]. Shenyang: Shengyang Institute of Computing Technology, University of Chinese Academy of Sciences, 2020.
[8]	王适乾. 电力调度控制系统中语义解析技术研究[D]. 济南: 山东大学, 2018.
	WANG Shiqian. Research on semantic parsing technology in power control system[D]. Jinan: Shandong University, 2018.
[9]	LIU Y, WEI F R, LI S J, et al. A dependency-based neural network for relation classification[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015.
[10]	REI M, CRICHTON G, PYYSALO S. Attending to characters in neural sequence labeling models[C]// Proceedings of the 26th International Conference on Computational Linguistics. Osaka,Japan: ICCL, 2016:309-318.
[11]	王莉军, 周越, 桂婕, 等. 基于BiLSTM-CRF的中医文言文文献分词模型研究[J]. 计算机应用研究, 2020, 37(11): 3359-3362,3367.
	WANG Lijun, ZHOU Yue, GUI Jie, et al. Chinese medicine literature word segmentation based on BiLSTM-CRF[J]. Application Research of Computers, 2020, 37(11): 3359-3362,3367.
[12]	JI Z X, WANG X H, CAI C Y, et al. Power entity recognition based on bidirectional long short-term memory and conditional random fields[J]. Global Energy Interconnection, 2020, 3(2): 186-192. doi: 10.1016/j.gloei.2020.05.010 URL
[13]	刘宇鹏, 栗冬冬. 基于BLSTM-CNN-CRF的中文命名实体识别方法[J]. 哈尔滨理工大学学报, 2020, 25(1): 115-120.
	LIU Yupeng, LI Dongdong. Chinese named entity recognition method based on Bi-directional LSTM-CNN-CRF[J]. Journal of Harbin University of Science and Technology, 2020, 25(1): 115-120.
[14]	高翔, 张金登, 许潇, 等. 基于LSTM-CRF的军事动向文本实体识别方法[J]. 指挥信息系统与技术, 2020, 11(6): 91-95.
	GAO Xiang, ZHANG Jindeng, XU Xiao, et al. Military trend text entity recognition method based on LSTM-CRF[J]. Command Information System and Technology, 2020, 11(6): 91-95.
[15]	郑诚, 洪彤彤, 薛满意. 用于短文本分类的BLSTM_MLPCNN模型[J]. 计算机科学, 2019, 46(6): 206-211.
	ZHENG Cheng, HONG Tongtong, XUE Manyi. BLSTM_MLPCNN model for short text classification[J]. Computer Science, 2019, 46(6): 206-211.
[16]	REKIA KADARI. 基于深度学习模型的CCG超标注[D]. 哈尔滨: 哈尔滨工业大学, 2018.
	REKIA KADARI. CCG supertagging based on deep learning models[D]. Harbin: Harbin Institute of Technology, 2018.
[17]	AN C, HUANG J M, CHANG S F, et al. Question similarity modeling with bidirectional long short-term memory neural network[C]// 2016 IEEE First International Conference on Data Science in Cyberspace (DSC). Changsha, China: IEEE, 2016: 318-322.
[18]	RAY A, RAJESWAR S, CHAUDHURY S. Text recognition using deep BLSTM networks[C]// 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR). Kolkata, India: IEEE, 2015: 1-6.
[19]	石民, 李斌, 陈小荷. 基于CRF的先秦汉语分词标注一体化研究[J]. 中文信息学报, 2010, 24(2): 39-45.
	SHI Min, LI Bin, CHEN Xiaohe. CRF based research on a unified approach to word segmentation and POS tagging for pre-Qin Chinese[J]. Journal of Chinese Information Processing, 2010, 24(2): 39-45.
[20]	张金龙, 王石, 钱存发. 基于CRF和规则的中文医疗机构名称识别[J]. 计算机应用与软件, 2014, 31(3): 159-162,198.
	ZHANG Jinlong, WANG Shi, QIAN Cunfa. Crf and rules-based recognition of medical institutions name in Chinese[J]. Computer Applications and Software, 2014, 31(3): 159-162,198.