面向电力行业的标准动态知识服务系统

谢炜; 黄建业; 刘亚南; 郑艳蓉; 张新华

doi:10.3969/j.issn.1008-0198.2025.06.016

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面向电力行业的标准动态知识服务系统

电网运行与控制 | 更新时间：2026-02-06

- 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
- 湖南电力 2025年45卷第6期页码：120-125
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.06.016
  中图分类号： F49
- 网络首发：2026-01-13，
  
  纸质出版：2025
- 稿件说明：
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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.

谢炜, 黄建业, 刘亚南, et al. Research on Dynamic Knowledge Service System for Power Industry Standards[J]. 2025, 45(6): 120-125.
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. DOI： 10.3969/j.issn.1008-0198.2025.06.016.

谢炜, 黄建业, 刘亚南, et al. Research on Dynamic Knowledge Service System for Power Industry Standards[J]. 2025, 45(6): 120-125. DOI： 10.3969/j.issn.1008-0198.2025.06.016.

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。

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".

关键词

Keywords

references

中共中央,国务院.国家标准化发展纲要[Z]. 2021.

国家电网有限公司.国家电网有限公司技术标准发展纲要(2022—2025年)[Z]. 2022.

张冀,马也,张荣华,等. 数字孪生变电站框架设计与关键技术研究[J]. 工程科学与技术,2023,55(6):15-30.

马超,宋琛. 电力标准数字化:概念、核心挑战、治理路线图及发展趋势[J]. 电网技术,2024,48(2):480-497.

朱晓荣,彭柏,司羽,等. 基于知识图谱的配电网综合评价[J]. 现代电力,2022,39(6):677-684.

齐晓曼,刘恋,李文清,等. 标准化与电力科技创新的多维互动发展研究[J]. 中国标准化,2024(23):96-99,104.

马超. 面向机器可读标准的电力标准数字化述评与展望[J]. 中国电力,2023,56(8):216-229.

李春田. 标准化概论:6版[M]. 北京:中国人民大学出版社,2014:45-50.

BROWN T,MANN B,RYDER N,et al.Language models are few-shot learners[J]. Advances in Neural Information Processing Systems,2020,33:1877-1901.

张兰. 电网企业标准数字化发展现状及趋势分析[J]. 湖南电力,2023,43(1):95-98,102.

李俊平. 电力系统自动化控制中的标准化智能技术应用探讨[J]. 大众标准化,2023(21):137-139.

DEVLIN J,CHANG M W,LEE K,et al.BERT:pre-training of deep bidirectional transformers for language understanding[C] //North American Chapter of the Association for Computational Linguistics. San Diego,USA. 2019:3498-4195.

张金营,王哲峰,谢华,等. 基于知识图谱与大语言模型的电力行业知识检索分析系统研发与应用[J]. 中国电力,2024,57(12):198-205.

高龙梅. 基于知识服务的标准数字化研究:以英大传媒公司为例[D]. 北京:北京印刷学院,2025.

ZHANG B Y,LIU X Z,NIU N N,et al.Decision model for digital transformation of power industry standards[C] //2023 7th International Conference on Power and Energy Engineering (ICPEE). Chengdu,China. IEEE,2023:369-376.

GRIEVES M W.Virtually intelligent product systems:digital and physical twins[M] //Complex Systems Engineering:Theory and Practice. Reston,VA:AIAA,Inc.,2019:175-200.

LIAO W H,WANG J P,LI H L,et al.DocLayLLM:an efficient multi-modal extension of large language models for text-rich document understanding[C] //2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville,TN,USA. IEEE,2025:4038-4049.

ZHANG X,LI C,QIN N,et al. DocParser:a unified OCR-free framework for visually-rich document understanding viaZHANG X,LI C,QIN N, DocParser:a unified OCR-free framework for visually-rich document understanding via;joint text and layout modeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2024,46(5):3125-3140.

郑佳明,陈家宾,胡杰鑫,等. 基于大模型和知识图谱的标准领域融合应用方法研究[J]. 中国标准化,2023(23):39-46.

HUPPERZ M,GIEB A.The interplay of data-driven organizations and data spaces: unlocking capabilities for transforming organizations in the era of data spaces[J]. Proceedings of the Annual Hawaii International Conference on System Sciences,2024:4496-4505.

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