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低碳供应链物流风险预测使用基于元学习的图形卷积网络在原型空间上.

Yueqi Wang1, Yonghe Sun2

  • 1Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China. wangyueqi2025@163.com.

Scientific reports
|December 23, 2025
PubMed
概括

本研究介绍了一种基于Meta-Learning的原型空间图形卷积网络 (ML-GCNPS),用于预测低碳供应链 (LCSC) 中的罕见风险. ML-GCNPS模型提供了准确且具有成本效益的早期预警系统.

关键词:
图表 卷积网络 卷积网络超级学习 (Meta-learning) 是一种学习方式.太空空间原型模型从顶端到边缘的顶端.

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科学领域:

  • 供应链管理 供应链管理
  • 风险分析 风险分析
  • 机器学习 机器学习

背景情况:

  • 预测低碳供应链 (LCSCs) 中的罕见,高影响风险对于可持续性至关重要.
  • 数据失衡和复杂的相互依赖对现有的风险预测模型构成重大挑战.

研究的目的:

  • 开发一个先进的模型,用于准确和成本效益的LCSC风险预警.
  • 在LCSC风险预测中解决数据不平衡和复杂的相互依赖.

主要方法:

  • 提出了基于元学习的原型空间图形卷积网络 (ML-GCNPS).
  • 使用多模式,企业级碳排放和供应链数据,以图形形式建模LCSC.
  • 采用自适应的顶点到边缘 (V2E) 网络,通过GCN构建图形拓和风险传播.

主要成果:

  • ML-GCNPS显著超过了最先进的基线.
  • 在精度回忆曲线 (AUPRC) 下的面积达到0.850.
  • 将假负率 (FNR) 降至0.080,加权平均成本 (WAC) 为45.

结论:

  • ML-GCNPS模型证明了在LCSC中准确且具有成本效益的风险预警的实际价值.
  • 提出的方法有效地处理数据不平衡和复杂的相互依赖.
  • 结果证实了该模型具有强大的LCSC风险预测能力.