Journal of Shanghai Jiaotong University >
Construction of a Regulation Ontology Label System for Intelligent Search of Power Grid Regulation Micro Scene
Received date: 2021-09-01
Online published: 2022-01-24
With the rapid development of the smart grid technology and the advent of the big data era of informatization, data labels have become a new evaluation standard to measure the informatization level of the industry, which also have a great impact on the construction direction of industry informatization, automation, and intelligent system in the later stage. With the promotion of new technology, the power grid dispatching industry is also carrying out a new generation of digital reform around data and business. This paper proposes a construction method of regulation ontology label system suitable for the search application of power grid regulation micro scene, which starts with the power grid regulation micro scene, divides and automatically reorganizes the business scene of the power grid. On this basis, by creating corresponding regulatory ontology labels and corresponding label dimensions, it innovatively proposes the construction method of label library suitable for regulatory ontology, and puts forward the construction process of regulatory ontology label system with sharing, security, service, and statistics as the core. Finally, the rationality and correctness of the construction method are verified by the application of regulation ontology label system in power grid micro scene intelligent search.
Key words: power grid regulation; labeling system; intelligent search
QU Gang, XIAO Linpeng, ZHANG Liang . Construction of a Regulation Ontology Label System for Intelligent Search of Power Grid Regulation Micro Scene[J]. Journal of Shanghai Jiaotong University, 2021 , 55(S2) : 92 -97 . DOI: 10.16183/j.cnki.jsjtu.2021.S2.015
[1] | 周玲, 钟璐, 黄渊军, 等. 用户画像和标签在电力服务系统中的应用研究[J]. 自动化仪表, 2021, 42(5): 10-13. |
[1] | ZHOU Ling, ZHONG Lu, HUANG Yuanjun, et al. Research on the application of user portrait and label in power service system[J]. Automation Instrument, 2021, 42(5): 10-13. |
[2] | 徐婷婷. 关于智能化检索功能的设计应用和技术探索[J]. 数字技术与应用, 2021, 39(7): 44-46. |
[2] | XU Tingting. Design, application and technical exploration of intelligent retrieval function[J]. Digital Technology and Application, 2021, 39(7): 44-46. |
[3] | 田少骅, 胡琦瑶, 蒙泽新, 等. 基于提取标签显著性区域的深度学习图像检索方法[J]. 物联网技术, 2020, 10(9): 54-57. |
[3] | TIAN Shaohua, HU Qiyao, MENG Zexin, et al. Deep learning image retrieval method based on extracting tag saliency region[J]. Internet of Things Technology, 2020, 10(9): 54-57. |
[4] | 陈世聪, 袁得嵛, 黄淑华, 等. 基于结构深度网络嵌入模型的节点标签分类算法[J/OL]. (2021-08-20) [2021-08-28].http://kns.cnki.net/kcms/detail/50.1075.TP.20210819.1726.040.html. |
[4] | CHEN Shicong, YUAN Deyu, HUANG Shuhua, et al. Node label classification algorithm based on structural depth network embedding model[J/OL]. (2021-08-20) [2021-08-28].http://kns.cnki.net/kcms/detail/50.1075.TP.20210819.1726.040.html. |
[5] | 李鹏, 阮晓钢, 朱晓庆, 等. 基于深度强化学习的区域化视觉导航方法[J]. 上海交通大学学报, 2021, 55(5): 575-585. |
[5] | LI Peng, RUAN Xiaogang, ZHU Xiaoqing, et al. Regional visual navigation method based on deep reinforcement learning[J]. Journal of Shanghai Jiao Tong University, 2021, 55(5): 575-585. |
[6] | 吴倩红, 韩蓓, 冯琳, 等. “人工智能+”时代下的智能电网预测分析[J]. 上海交通大学学报, 2018, 52(10): 1206-1219. |
[6] | WU Qianhong, HAN Bei, FENG Lin, et al. Prediction and analysis of smart grid in the era of “artificial intelligence +”[J]. Journal of Shanghai Jiao Tong University, 2018, 52(10): 1206-1219. |
[7] | 陆泉, 何超, 陈静, 等. 基于两阶段迁移学习的多标签分类模型研究[J]. 数据分析与知识发现, 2021, 5(7): 91-100. |
[7] | LU Quan, HE Chao, CHEN Jing, et al. Research on multi label classification model based on two-stage transfer learning[J]. Data Analysis and Knowledge Discovery, 2021, 5(7): 91-100. |
/
〈 |
|
〉 |