[1]董 言,努尔兰别克·哈巴斯,谷 峥,等.基于可验证随机函数与PBFT共识算法的供应链管理方案研究[J].南京师大学报(自然科学版),2025,48(05):121-128.[doi:10.3969/j.issn.1001-4616.2025.05.014]
　Dong Yan,Nurlanbek Hapas,Gu Zheng,et al.Research on Supply Chain Management Scheme Based on Verifiable Random Function and PBFT Consensus Algorithm[J].Journal of Nanjing Normal University(Natural Science Edition),2025,48(05):121-128.[doi:10.3969/j.issn.1001-4616.2025.05.014]
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基于可验证随机函数与PBFT共识算法的供应链管理方案研究()

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
48
期数:
2025年05期
页码:
121-128
栏目:
计算机科学与技术
出版日期:
2025-10-20

文章信息/Info

Title:
Research on Supply Chain Management Scheme Based on Verifiable Random Function and PBFT Consensus Algorithm
文章编号:
1001-4616(2025)05-0121-08
作者:
董 言1; 努尔兰别克·哈巴斯1; 谷 峥2; 藏哈尔·努尔兰别克3
(1.新疆大学经济与管理学院,新疆 乌鲁木齐 830099)
(2.新疆工程学院控制工程学院,新疆 乌鲁木齐 830023)
(3.上海交通大学农业与生物学院,上海 200240)
Author(s):
Dong Yan1; Nurlanbek Hapas1; Gu Zheng2; Zanghar Nurlanbek3
(1.School of Economics and Management, Xinjiang University, Urumqi 830099, China)
(2.Control Engineering College, Xinjiang Institute of Engineering, Urumqi 830023, China)
(3.School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China)
关键词:
区块链; 机器学习; 共识算法; 供应链
Keywords:
blockchain; machine learning; consensus algorithm; supply chain
分类号:
TP301
DOI:
10.3969/j.issn.1001-4616.2025.05.014
文献标志码:
A
摘要:
近年来,随着供应链企业的迅猛发展,如何构建高效的供应链管理策略模型成为研究的热点. 当前,供应链管理面临着库存成本高昂、通信开销巨大的问题. 针对这些挑战,本文提出了一种融合机器学习与改进实用拜占庭容错(practical Byzantine fault tolerance,PBFT)共识算法的优化方案. 该方案采用遗传算法与梯度算法相结合的混合优化策略,兼具全局寻优和局部精细调整的优势,从而显著降低了库存管理成本. 同时,PBFT共识机制通过引入Honest-peer信誉模型和可验证随机函数有效提升了节点选举过程的效率和系统的整体可靠性. 仿真实验结果表明,与传统方法相比,该优化方案在降低供应链管理成本方面取得了显著效果,具体表现为库存成本平均下降了4.6%,通信开销减少了约50%,具有广泛的应用前景和实用价值.
Abstract:
In recent years, with the rapid development of supply chain enterprises, how to build an efficient supply chain management strategy model has become a hot research topic. Currently, supply chain management faces the problems of high inventory cost and huge communication overhead. To address these challenges, an optimization scheme that integrates machine learning and improved practical Byzantine fault tolerance(PBFT)consensus algorithm is proposed. The scheme adopts a hybrid optimization strategy combining genetic algorithm and gradient algorithm, which has the advantages of both global optimization and local fine tuning, thus significantly reducing the inventory management cost. Meanwhile, the PBFT consensus mechanism effectively improves the efficiency of the node election process and the overall reliability of the system by introducing the Honest-peer reputation model and verifiable random function. The experimental results show that, compared with the traditional method, the optimization scheme achieves significant results in reducing the supply chain management cost, as evidenced by an average decrease of 4.6% in inventory cost and a reduction of about 50% in communication overhead, which has a wide range of application prospects and practical value.

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备注/Memo

备注/Memo:
收稿日期:2024-10-16.
基金项目:新疆维吾尔自治区自然科学基金项目(2020D01B20).
通讯作者:努尔兰别克·哈巴斯,博士,副教授,研究方向:人工智能与大数据,经济管理. E-mail:3501490154@qq.com
更新日期/Last Update: 2025-10-20