考虑故障恢复的产业互联“智造”供需网级联失效模型
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国家自然科学基金(71871144); 上海理工大学科技发展项目(2020KJFZ046); 上海市哲学社会科学规划一般课题(2023BGL009)


Cascading Failure Model of Industrial Interconnection Intelligent Manufacturing Supply and Demand Network Considering Fault Recovery
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    摘要:

    大规模、数字化的产业互联“智造”供需网相比于传统供应链具有更强的响应调节能力和风险预防与恢复能力, 但可能面临的风险种类更多且风险传播路径更广, 导致其鲁棒性更易受到威胁, 准确描述网络中故障风险的动态传播过程是提高其鲁棒性的基础. 首先, 构建具有多个产业社团的产业互联“智造”供需网模型; 其次, 结合业务节点间相对关联度, 构建具有相对故障概率的风险传播模型, 再根据节点重要程度, 建立同时考虑恢复概率和恢复周期的故障恢复模型; 最后, 基于改进引力模型构建网络, 以网络相对连通率R作为指标, 对不同故障和恢复场景下的级联失效进行仿真分析. 仿真结果表明: 在4组不同故障和恢复场景下均存在临界值导致R值长期处于不稳定状态; 参数ημR值的影响均具有一定边际效应; 当网络故障传播能力一定, 恢复能力越弱则R值振荡越明显, 网络受影响规模越大, 而恢复能力一定, 故障强度越强R值振荡越明显, 网络受影响规模越大.

    Abstract:

    Compared to traditional supply chains, the large-scale, digitalized industrial interconnected intelligent manufacturing supply and demand network has stronger response and adjustment capabilities as well as risk prevention and recovery capabilities. However, it also faces a greater variety of risks and has broader risk transmission pathways, more easily threatening its robustness. Accurately describing the dynamic propagation process of fault risks within the network is fundamental to enhancing its robustness. Firstly, a model of the industrial interconnected intelligent manufacturing supply and demand network with multiple industrial communities is constructed. Secondly, by considering the relative correlation between business nodes, a risk propagation model with relative fault probabilities is built. Then a fault recovery model that considers both the recovery probability and recovery period is established based on the importance of nodes. Finally, a network is constructed based on an improved gravity model, and the relative connectivity rate R of the network is used as an indicator to simulate and analyze cascading failures under different fault and recovery scenarios. The simulation results indicate that there are critical values in all four sets of different fault and recovery scenarios that lead to R being in an unstable state in the long run. The parameters η and μ have certain marginal effects on the value of R. When the network’s fault propagation capability is fixed, the weaker the recovery capability, the more pronounced the oscillation of R, and the larger the scale of network impact. Conversely, with a certain recovery capability, the stronger the fault intensity, the more pronounced the oscillation of R, and the larger the scale of network impact.

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邓子玥,何建佳.考虑故障恢复的产业互联“智造”供需网级联失效模型.计算机系统应用,,():1-8

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  • 收稿日期:2024-01-02
  • 最后修改日期:2024-03-05
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  • 在线发布日期: 2024-05-31
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