Blockchain-Based Decentralized Production Progress Early Warning in Social Manufacturing Paradigm

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2025-12-31 DOI:10.1109/TCSS.2025.3642911

Kangqian Zheng;Shaowei Zhi;Kai Ding;Qingdi Liu;Fuqiang Zhang;Jizhuang Hui;Felix T. S. Chan

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引用次数: 0

Abstract

Social manufacturing (SM) is a task-oriented production paradigm that enables coordinated scheduling across distributed manufacturing resources. However, its operational efficiency is often limited by reliance on centralized management systems. In this context, blockchain technology shows strong potential for decentralized management in SM. However; the corresponding models for early warnings of production progress, especially for task fulfillment and delay risks, remain largely unexplored. To address the limitations, this article first proposes a blockchain-based decentralized production progress early warning system in SM. Furthermore, a corresponding early warning workflow is developed to define the overall interaction logic among participants. Then, a hybrid state machine is implemented within the smart contract to construct a task fulfillment status early warning model. Afterward, a Bayesian network-based model is designed to assess the evolving delay risks of interdependent tasks. Finally, a case study on air-conditioner production is conducted to validate the proposed models. Results show that the prototype system maintains scalable performance, achieving up to 17.56 transactions/s under write-intensive workloads and 52.45 transactions/s under read-intensive workloads, demonstrating more stable scalability than traditional centralized and directed acyclic graph (DAG)-based systems.

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社会制造范式下基于区块链的去中心化生产进程预警

社会制造（SM）是一种面向任务的生产范式，它支持跨分布式制造资源的协调调度。然而，由于依赖集中管理系统，其运作效率往往受到限制。在这种情况下，区块链技术显示出在SM中分散管理的强大潜力。然而;相应的生产进度预警模型，特别是任务完成和延迟风险的预警模型，在很大程度上仍未得到探索。为了解决这些局限性，本文首先提出了一种基于区块链的SM分散生产进度预警系统。此外，开发了相应的预警工作流程来定义参与者之间的整体交互逻辑。然后，在智能合约内部实现混合状态机，构建任务完成状态预警模型。然后，设计了一个基于贝叶斯网络的模型来评估相互依赖任务的延迟风险。最后，以空调生产为例，对所提模型进行了验证。结果表明，原型系统保持了可扩展的性能，在写密集型工作负载下达到17.56事务/s，在读密集型工作负载下达到52.45事务/s，比传统的基于集中式和有向无环图（DAG）的系统表现出更稳定的可扩展性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Computational Social Systems Social Sciences-Social Sciences (miscellaneous)

CiteScore

10.00

自引率

20.00%

发文量

316

期刊介绍： IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.