现代信息科技2025，Vol.9Issue(7)：114-119,6.DOI:10.19850/j.cnki.2096-4706.2025.07.021
基于非平衡数据的深度再分片算法
Deep Re-segmentation Algorithm Based on Unbalanced Data
赵鹏 1李军 1卢波 1郭赟泽 1陈伟1
作者信息
- 1. 太原师范学院计算机科学与技术学院,山西 晋中 030619
- 折叠
摘要
Abstract
With the popularization of blockchain technology,the surge in transaction volume poses challenges to network performance.Traditional segmentation methods based on account transactions perform poorly when dealing with unbalanced data,which leads to an increase in cross-segmentation transactions and affects performance.In response to this situation,a Deep Traversal Re-segmentation Algorithm based on transaction frequency is proposed.This algorithm reduces cross-segmentation transactions through initial segmentation,and then conducts deep traversal re-segmentation on the segmentation with dense transactions to solve the problem of data imbalance.By calculating the transaction frequency of accounts,it ensures load balancing and attempts to allocate accounts with frequent transactions to the same segmentation as much as possible.Experimental results show that this algorithm can effectively balance the load,reduce cross-segmentation transactions,and improve network throughput and efficiency.关键词
区块链技术/深度遍历再分片算法/交易频率/跨分片交易Key words
blockchain technology/Deep Traversal Re-segmentation Algorithm/transaction frequency/cross-segmentation transaction分类
信息技术与安全科学引用本文复制引用
赵鹏,李军,卢波,郭赟泽,陈伟..基于非平衡数据的深度再分片算法[J].现代信息科技,2025,9(7):114-119,6.
