面向电力行业的标准动态知识服务系统

doi:10.3969/j.issn.1008-0198.2025.06.016

湖南电力 ›› 2025, Vol. 45 ›› Issue (6): 120-125.doi: 10.3969/j.issn.1008-0198.2025.06.016

面向电力行业的标准动态知识服务系统

谢炜¹, 黄建业¹, 刘亚南², 郑艳蓉², 张新华³

1.国网福建省电力有限公司电力科学研究院,福建福州 350003;
2.英大传媒投资集团有限公司,北京 100005;
3.北京印刷学院,北京 102600

收稿日期:2025-09-04 修回日期:2025-10-09 出版日期:2025-12-25 发布日期:2026-01-13
通信作者: 张新华（1973）,博士,教授,主要研究方向为数字出版、文化产业、计算传播。
作者简介:谢炜（1994）,硕士,工程师,主要研究方向为电力人工智能、数据应用。黄建业（1986）,硕士,高级工程师,主要研究方向为电力人工智能。刘亚南（1985）,学士,编辑,主要研究方向为标准数字化。郑艳蓉（1978）,学士,编审,主要研究方向为标准数字化。
基金资助:
国家电网有限公司总部管理科技项目（5700-202366831A-4-2-KJ）

Research on Dynamic Knowledge Service System for Power Industry Standards

XIE Wei¹, HUANG Jianye¹, LIU Yanan², ZHENG Yanrong², ZHANG Xinhua³

1. State Grid Fujian Electric Power Company Limited Research Institute, Fuzhou 350003, China;
2. Yingda Media Investment Group Co., Ltd., Beijing 100005, China;
3. Beijing Institute of Graphic Communication, Beijing 102600, China

Received:2025-09-04 Revised:2025-10-09 Online:2025-12-25 Published:2026-01-13

摘要/Abstract

摘要： 为解决电力标准管理中的信息实时性差、知识关联性弱与服务被动性突出等关键难题,提出一种基于分布式监测、自然语言处理与知识图谱协同驱动的动态知识服务系统。系统依托分布式监测引擎实现多源异构标准数据秒级采集,标准更新发现时延由人工巡检的120 h压缩至5.8 h;通过深度学习驱动的动态知识图谱构建技术,突破传统关键词检索的语义边界限制,知识关联查全率提升至96.2%;提出图谱推理赋能的主动预警机制,推动服务模式从被动响应转向按需推送。经1 752次/s并发压力测试,系统95%请求响应时间稳定低于300 ms,在时效性、完整性与鲁棒性层面实现协同优化。该系统为电力行业构建企业级“数字标准馆”提供了可复用的技术路径。

关键词: 动态知识服务, 电力标准, 分布式监测, 知识图谱, 实时更新

Abstract: To address the critical challenges in power standard management including poor real-time information, weak knowledge correlation, and prominent service passivity, a dynamic knowledge service system based on the collaborative drive of distributed monitoring, natural language processing, and knowledge graph is proposed. The system relies on a distributed monitoring engine to achieve second-level data collection from multiple source heterogeneous sources, reducing the standard update detection delay from 120 hours (with manual inspection) to 5.8 hours. Through dynamic knowledge graph construction technology driven by deep learning, the system breaks through the semantic limitations of traditional keyword search, increasing the knowledge association recall rate to 96.2%. It also pioneers a proactive early warning mechanism empowered by graph reasoning, promoting the service model to shift from passive response to on-demand push. After a high-concurrency pressure test of 1 752 requests per second, the system's 95% request response time remains stably below 300 ms, achieving coordinated optimization in terms of timeliness, completeness, and robustness. This system provides a reusable technical path for the power industry to build an enterprise-level "digital standard library".

Key words: dynamic knowledge service, power standards, distributed monitoring, knowledge graph, real-time updating

中图分类号:

谢炜, 黄建业, 刘亚南, 郑艳蓉, 张新华. 面向电力行业的标准动态知识服务系统[J]. 湖南电力, 2025, 45(6): 120-125.

XIE Wei, HUANG Jianye, LIU Yanan, ZHENG Yanrong, ZHANG Xinhua. Research on Dynamic Knowledge Service System for Power Industry Standards[J]. Hunan Electric Power, 2025, 45(6): 120-125.

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面向电力行业的标准动态知识服务系统

Research on Dynamic Knowledge Service System for Power Industry Standards

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