人工智能赋能关键矿产供应链风险治理：作用机制与工程实现路径

Empowering Key Mineral Supply Chain Risk Governance with Artificial Intelligence: Mechanism and Engineering Implementation Path

摘要: 关键矿产供应链具有资源地理分布和加工能力高度集中、价格金融化及开发周期较长等特征，传统治理在信息感知、传导分析、风险预警和应对决策方面存在局限。本文从工程应用视角出发，构建风险感知、风险判断、风险预警和风险应对四阶段分析框架，结合自然语言处理、知识图谱、机器学习与智能优化等技术，分析人工智能在多源信息识别、风险传导分析、动态预警和方案优化中的工程应用，并讨论模型可靠性、数据安全、专业适配和组织协同等约束。研究认为，应完善模型验证与数据治理机制，强化行业知识与人工智能融合，健全人机协同和跨主体联动机制，推动智能分析成果向风险治理工程实践转化，提升关键矿产供应链风险治理能力。

Abstract: Critical mineral supply chains are characterized by highly concentrated resource distribution and processing capacity, price financialization, and long development cycles. Traditional governance approaches face limitations in information perception, transmission analysis, risk warning, and response decision-making. From an engineering application perspective, this paper develops a four-stage analytical framework comprising risk perception, risk assessment, risk warning, and risk response. By integrating technologies such as natural language processing, knowledge graphs, machine learning, and intelligent optimization, it examines the engineering applications of artificial intelligence in multi-source information identification, risk transmission analysis, dynamic warning, and response optimization, while also discussing constraints related to model reliability, data security, domain adaptability, and organizational coordination. The study suggests that model validation and data governance mechanisms should be improved, the integration of industry knowledge and artificial intelligence should be strengthened, human-AI collaboration and cross-actor coordination mechanisms should be enhanced, and intelligent analytical outputs should be translated into engineering practices for risk governance, thereby improving the risk governance capacity of critical mineral supply chains.