应用于供应链的区块链PBFT共识算法优化

Optimization of Blockchain PBFT Consensus Algorithm for Supply Chain

目前,区块链在供应链领域中的应用越来越受到业界的广泛关注.但由于供应链中存在大量复杂性的事务,这给可信的主节点选取工作带来了挑战.因此,在机器学习分类算法与PBFT(practical Byzantine fault tolerance)共识算法的基础上,提出一种应用于供应链的区块链PBFT共识算法优化方法.对构建供应链与区块链的集成框架进行分析,根据供应链中参与共识的节点属性特征,运用K-近邻(K-nearest neighbors)来优化PBFT共识算法的主节点选取规则.实验结果表明,对共识节点进行信任评估分类可以较好地解决因视图切换所引发的效率问题,从而提升区块链的吞吐量、时延、容错性等共识性能,具有一定的实用性,也给区块链在其他行业的应用提供了思路.

Currently,the application of blockchain in the supply chain is receiving increasing attention from the industry.However,due to the presence of a large number of complex transactions in the supply chain,selecting trustworthy primary nodes poses a challenge.Therefore,based on the machine learning classification algorithms and PBFT(practical Byzantine fault tolerance),this study proposes a blockchain PBFT optimization method applied to the supply chain.The integrated framework for the supply chain and blockchain is analyzed,and K-nearest neighbors(K-NN)is applied to optimize the primary node selection rules of the PBFT consensus algorithm based on the features of participating nodes in the supply chain consensus.Experimental results show that trust evaluation classification of consensus nodes can effectively address efficiency issues caused by view switching,thereby improving the consensus performance of blockchain in terms of throughput,latency,fault tolerance,and other aspects.The proposed method is practical and provides ideas for the application of blockchain in other industries.