基于知识图谱与反事实提示的跨境供应链韧性增强与可解释性决策研究

Research on Cross-Border Supply Chain Resilience Enhancement and Explainable Decision-Making Based on Knowledge Graph and Counterfactual Prompting

摘要: 全球贸易格局的动荡与不确定性，使得构建具有韧性的跨境供应链成为企业生存与发展的关键。传统AI决策模型在跨境供应链管理中存在“黑箱”操作、知识滞后与动态适应性不足等问题，为此提出了一种创新的融合框架——供应链语义网络与Prompt可解释性增强系统，通过构建结构化的跨境供应链领域知识图谱，为AI提供可推理的领域知识基础；进而设计“图谱增强Prompt”范式，实现自然语言交互下的复杂网络查询与风险诊断；并创新引入“反事实提示”机制，通过假设性场景推演生成具有因果解释的韧性优化方案，显著提升决策透明度与可审计性。基于该框架开发的系统原型，不但能够提升供应链风险定位效率，降低潜在损失，也为人机协同、可信赖的供应链智能决策提供了有效工具与方法。

Abstract: The turbulence and uncertainty of the global trade landscape have made building resilient cross-border supply chains a critical factor for enterprise survival and development. Traditional AI decision-making models in cross-border supply chain management suffer from issues such as “black-box” operations, knowledge lag, and insufficient dynamic adaptability. To address these, this paper proposes an innovative integrated framework—the Supply Chain Semantic Network and Prompt Explainability Enhancement System. By constructing a structured cross-border supply chain domain knowledge graph, it provides AI with a foundational layer of domain knowledge for reasoning. Subsequently, a “Graph-Enhanced Prompt” paradigm is designed to enable complex network queries and risk diagnostics through natural language interaction. Furthermore, a “Counterfactual Prompt” mechanism is innovatively introduced to generate resilient optimization solutions with causal explanations through hypothetical scenario simulations, significantly improving decision transparency and auditability. The system prototype developed based on this framework can not only enhance the efficiency of supply chain risk identification and reduce potential losses but also provide an effective tool and method for human-machine collaborative and trustworthy intelligent decision-making in supply chains.