Design and Implementation of Power Design Knowledge Graph Service Platform

doi:10.3969/j.issn.1006-2475.2025.10.010

Abstract

Abstract:
Abstract: With the explosive growth of data in the field of power engineering design， how to efficiently manage and utilize these data has become an urgent problem to be solved. Based on the knowledge graph technology， this paper focuses on the analysis of the characteristics of the data in the field of power design， and combines the specific needs of the professionals in the field of power design， and designs and implements a power design knowledge graph service platform to achieve efficient management， integration and deep mining of power design data. The platform adopts B/S architecture and follows the J2EE standard， and realizes the functions of power design knowledge search， knowledge graph visualization， intelligent question and answer， similar recommendation， and related data analysis. The test results show that the platform can effectively integrate and manage a large number of power design-related data， and can also deeply explore the correlation and value behind the data through knowledge graph technology， which provides strong support and innovation impetus for the research and application in the field of power design， and significantly improves the correlation and practicability of data. The application of the platform provides powerful knowledge support and decision aid for researchers and engineers in the field of power design， improving their efficiency and ability to process and analyze data.

Key words: Key words: power design, knowledge graph, knowledge search, intelligent question answering, visualization

CLC Number:

中图分类号：TP3

XU Yan1, BO Guanglin1, ZHANG Peng1, CHU Cheng2, LIU Wenwen2, ZHANG Bo2, XU Na3. Design and Implementation of Power Design Knowledge Graph Service Platform[J]. Computer and Modernization, 2025, 0(10): 57-66.

References

［1］蒋玮，周颖，陈舒淇，等. 电网基建工程数据挖掘及知识图谱构建技术研究［J］. 电力信息与通信技术， 2021，19（2）:15-22.
［2］高海翔，苗璐，刘嘉宁，等. 知识图谱及其在电力系统中的应用研究综述［J］. 广东电力， 2020，33（9）:66-76.
［3］杨东华，何涛，王宏志，等. 面向知识图谱的图嵌入学习研究进展［J］. 软件学报， 2022，33（9）:3370-3390.
［4］卢琪，谢艺菲，谢钧，等. 知识图谱在智能问答中的应用研究［J］. 计算机技术与发展， 2021，31（7）:13-20.
［5］蒲天骄，谈元鹏，彭国政，等.电力领域知识图谱的构建与应用［J］. 电网技术， 2021，45（6）:2080-2091.
［6］饶竹一，张云翔. 基于知识图谱的智能客服系统研究［J］. 电力信息与通信技术， 2017，15（7）: 41-45.
［7］杨强. 基于知识图谱的核电设备知识平台建设［C］// 数字中国能源互联——2018 电力行业信息化年会论文集. 中国电机工程学会电力信息化专业委员会， 2018:265-269.
［8］ FU B， QIU Y， TANG C， et al. A survey on complex question answering over knowledge base: Recent advances and challenges［J］. arXiv preprint arXiv:2007.13069， 2020.
［9］ BROWN T B. Language models are few-shot learners［J］. arXiv preprint arXiv:2005.14165， 2020.
［10］ AUGENSTEIN I， BALDWIN T， CHA M， et al. Factuality challenges in the era of large language models［J］. arXiv preprint arXiv:2310.05189， 2023.
［11］赵月，何锦雯，朱申辰，等.大语言模型安全现状与挑战［J］.计算机科学， 2024，51（1）:68-71.
［12］ LEWIS P， PEREZ E， PIKTUS A， et al. Retrieval-augmented generation for knowledge-intensive NLP tasks［C］// Proceedings of the 34th International Conference on Neural Information Processing Systems. ACM， 2020，33: 9459-9474.
［13］王学超，刘大伟，郑凯，等. 电力设计院知识库构建研究［J］. 电脑知识与技术， 2021，17（15）:222-225.
［14］王永胜，冯伟华，郑新章，等. 烟草科技知识图谱服务平台的设计与实现［J］. 计算机应用与软件， 2023，40（5）:37-43.
［15］刘峤，李杨，段宏，等. 知识图谱构建技术综述［J］. 计算机研究与发展， 2016，53（3）:582-600.
［16］付雷杰，曹岩，白瑀，等. 国内垂直领域知识图谱发展现状与展望［J］. 计算机应用研究， 2021，38（11）:3201-3214.
［17］刘锦隆，马进，邹双，等. 基于知识图谱的电力行业与外部数据融合研究［J］. 四川电力技术， 2020，43（6）:26-30.
［18］刘勤，陈世平，霍欢.基于知识图谱用户偏好传播的实体推荐模型［J］.计算机应用研究， 2020，37（10）:2926-2931.
［19］ QIAO J Y. Intelligent big data framework for the technical design of public management applications in sports［J］. Mathematical Problems in Engineering， 2022，2022（4）:1-9.
［20］ REYNOLDS O， GARCÍA-DOMÍNGUEZ A， BENCOMO N. Cronista: A multi-database automated provenance collection system for runtime-models［J］. Information and Software Technology， 2022，141. DOI: 10.1016/j.infsof.2021.
106694.
［21］ LI J， SUN A， HAN J， et al. A survey on deep learning for named entity recognition［J］. IEEE Transactions on Knowledge and Data Engineering， 2020，34（1）:50-70.
［22］ NASEER S， GHAFOOR M， BIN KHALID ALVI S， et al. Named entity recognition （NER） in NLP techniques， tools accuracy and performance［J］. Pakistan Journal of Multidisciplinary Research， 2021，2（2）:293-308.
［23］ JIANG J， LIN R， ZOU H. Power domain named entity recognition based on rules and dictionaries［C］// 2022 IEEE 22nd International Conference on Communication Technology （ICCT）. IEEE， 2022:1898-1902.
［24］ SHEN W， WANG J， HAN J. Entity linking with a knowledge base: Issues， techniques， and solutions［J］. IEEE Transactions on Knowledge and Data Engineering， 2014，27（2）:443-460.
［25］ REIMERS N， GUREVYCH I. Sentence-BERT: Sentence embeddings using siamese BERT-networks［J］. arXiv preprint arXiv:1908.10084， 2019.
［26］田星，郑瑾，张祖平. 基于词向量的Jaccard相似度算法［J］. 计算机科学， 2018，45（7）:186-189.
［27］郭胜国，邢丹丹. 基于词向量的句子相似度计算及其应用研究［J］. 现代电子技术， 2016，39（13）:99-102.
［28］张恩红，尹海燕，李高洁. 基于Elasticsearch的气象数据检索技术研究［J］. 计算机技术与发展， 2019，29（11）:154-158.
［29］王仁武，陈川宝，孟现茹. 基于词向量扩展的学术资源语义检索技术［J］. 图书情报工作， 2018，62（19）:111-119.
［30］马晖男，吴江宁，潘东华. 一种基于同义词词典的模糊查询扩展方法［J］. 大连理工大学学报， 2007，（3）:439-443.
［31］ ZHOU Y， MURESANU A I， HAN Z， et al. Large language models are human-level prompt engineers［J］. arXiv preprint arXiv:2211.01910， 2022.
［32］ ZHENG C， LIU Z， XIE E， et al. Progressive-hint prompting improves reasoning in large language models［J］. arXiv preprint arXiv:2304.09797， 2023.
［33］ SONG Y F， HE Y Q， ZHAO X F， et al. A communication theory perspective on prompting engineering methods for large language models［J］. arXiv preprint arXiv:2310.18358，
2023.
［34］文森，钱力，胡懋地，等. 基于大语言模型的问答技术研究进展综述［J］. 数据分析与知识发现， 2024，8（6）:16-29.
［35］张鹤译，王鑫，韩立帆，等. 大语言模型融合知识图谱的问答系统研究［J］. 计算机科学与探索， 2023，17（10）:2377-2388.

[1]	JIANG Sulun1, 2, 3, YUAN Decheng1, GUO Qingda2, 3, LIU Jian3, YU Guangping2, 3. Survey of Application of Knowledge Graph in Field of Intelligent Manufacturing [J]. Computer and Modernization, 2025, 0(05): 48-59.
[2]	ZHANG Yue, LI Huayu, ZHANG Zhikang, SHEN Xinyi. Academic Recommendation System Based on Knowledge Graph and Semantic Information [J]. Computer and Modernization, 2025, 0(01): 50-58.
[3]	ZHANG Kun1, ZHANG Yongwei1, WU Yongcheng1, ZHANG Xiaowen2, ZHAI Shichen2. An LLM-based Method for Automatic Construction of Equipment Failure Knowledge Graphs [J]. Computer and Modernization, 2024, 0(11): 46-53.
[4]	QI Jun1, 2, QU Ruiting2, JIAO Chuanming2, ZHOU Qiaoni2, GUO Yanliang3, TAN Wenjun3. Enhanced Big Language Model Dual Carbon Domain Services Based on Knowledge Graph [J]. Computer and Modernization, 2024, 0(09): 8-14.
[5]	WANG Xianshun, XIONG Qingzhi, WAN Lei, LI Xiang, LIN Chongshan, JIN An’an. Regional Enterprise Association Visualization and Relationship Mining Based on#br# Knowledge Graph [J]. Computer and Modernization, 2024, 0(08): 11-16.
[6]	LIU Yang1, HUANG Zhi2, XU Juan1, GAO Peng1, CHEN Xuhui1. Hardware Monitoring and Warning System of Provincial Meteorological Data Center Based on Distributed [J]. Computer and Modernization, 2024, 0(07): 41-46.
[7]	YANG Meng, YANG Jin, CHEN Bu-qian. Multiple Objective Explainable Recommendation Based on Knowledge Graph [J]. Computer and Modernization, 2024, 0(03): 34-40.
[8]	HUANG Zheng-lin, DONG Bao-liang. Temporal Knowledge Graph Question Answering Method Based on#br# Semantic and Structural Enhancement [J]. Computer and Modernization, 2024, 0(03): 15-23.
[9]	WANG Yun-qian, WANG Yi-song, CHEN Pan-feng, ZOU Long. Named Entity Recognition of Medicinal Plant Texts Integrated with Attention Mechanism [J]. Computer and Modernization, 2021, 0(11): 100-105.
[10]	WANG Yu-ying, WANG Yong. Recommendation Algorithm Based on Knowledge Graph and Bi-LSTM [J]. Computer and Modernization, 2021, 0(09): 90-98.
[11]	GENG Hua-cong, LIANG Hong-tao, LIU Guo-zhu. Recipe Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering [J]. Computer and Modernization, 2021, 0(08): 24-29.
[12]	ZHANG Zi-xuan, LU Jia-min, JIANG Xiao, FENG Jun. Construction and Application of Intelligent Question Answering System#br# for Water Conservancy Information Resources [J]. Computer and Modernization, 2020, 0(03): 65-.
[13]	XIANG Wei. Reviews on Event Knowledge Graph Construction Techniques and Application [J]. Computer and Modernization, 2020, 0(01): 10-.
[14]	FENG Jun, XU Xin, LU Jia-min. Construction and Application of Water Conservancy Information Knowledge Graph [J]. Computer and Modernization, 2019, 0(09): 35-.
[15]	FENG Jun, LIU Jing-hua, KONG Sheng-qiu. Chinese Collective Entity Linking Method Based on Multiple Features [J]. Computer and Modernization, 2019, 0(01): 69-.