摘要
变电图纸通常包含复杂的数据结构和业务逻辑,在分布式环境中,图纸数据更新操作在不同节点上不一致,导致数据丢失和更新耗时增加。本文提出了一种基于分布式数据库的变电图纸频繁项目集更新方法。通过构建深度卷积神经网络(DCNN)和深度哈希函数,将图纸数据特征转化为二进制哈希码,实现高效分类。引入滑动窗口机制,实时刷新图纸数据的最高频项集,实现变电图纸内容的动态更新。采用S2索引技术和GeoMesa工具,构建高效的时空索引,提升查询效率。实验结果表明,所提方法能够在较短时间内完成变电图纸的更新,提高数据处理的效率和实时性,确保数据的完整性和准确性,为电力行业的信息化和数字化转型提供了有力支持。
Substation drawings usually contain complex data structures and business logic.In a distributed environment,the update operation of drawing data is inconsistent on different nodes,resulting in data loss and increased update time.This article proposes a method for frequent project set updates of substation drawings based on distributed databases.By constructing a deep convolutional neural network(DCNN)and deep hash function,the features of drawing data are converted into binary hash codes to achieve efficient classification.Introducing a sliding window mechanism to refresh the highest frequency itemset of drawing data in real-time,achieving dynamic updates of substation drawing content.Using S2 indexing technology and GeoMesa tool to build efficient spatiotemporal indexes and improve query efficiency.The experimental results show that the proposed method can complete the update of substation drawings in a relatively short period of time,improve the efficiency and real-time performance of data processing,ensure the integrity and accuracy of data,and provide strong support for the informationization and digital transformation of the power industry.
作者
杨禄清
杨铭
董治洲
段晓
赵腾藻
王俊丰
Yang Luqing;Yang Ming;Dong Zhihzou;Duan Xiao;Zhao Tengzao;Wang Junfeng(Yunnan Power Grid Co.,Ltd.,Baoshan Power Supply Bureau,Baoshan 678000,Yunnan,China)
出处
《云南电力技术》
2025年第1期81-85,共5页
Yunnan Electric Power
关键词
分布式数据库
变电图纸
频繁项目集
更新方法
Distributed Database
Substation Drawings
Frequent Project Portfolio
Update Method
