用大模型+区块链让医疗物资分配更透明高效
Resilient Multi-Agent Negotiation for Medical Supply Chains:Integrating LLMs and Blockchain for Transparent Coordination
- 大模型驱动多智能体自主协商，快速响应突发需求
- 区块链确保决策不可篡改，审计追踪全程可查
- 适合应急物资调度、供应链透明化研究者使用
全球健康危机（如新冠疫情）暴露出传统医疗供应链在资源调配效率、透明度和应对动态中断方面的严重缺陷。本文提出一种融合区块链与去中心化大语言模型（LLM）驱动的多智能体协商系统的新框架，以增强危机期间医疗供应链的韧性与问责性。系统中，代表制造商、分销商和医疗机构的自治智能体，在大模型支持下开展结构化、情境感知的协商与决策，实现稀缺医疗资源的快速且合乎伦理的分配。离链智能体层支持自适应推理与本地决策，链上区块链层通过智能合约确保决策的不可篡改、透明与可审计。框架还引入正式的跨层通信协议，连接去中心化协商与制度化执行。模拟疫情场景评估显示，该系统在协商效率、分配公平性、供应链响应速度和可审计性方面均有显著提升。本研究创新性地结合区块链的信任保障与大模型的自适应智能，为不确定性下的关键供应链协调提供稳健可扩展的解决方案。
原文摘要 · Abstract (English)
Global health emergencies, such as the COVID-19 pandemic, have exposed critical weaknesses in traditional medical supply chains, including inefficiencies in resource allocation, lack of transparency, and poor adaptability to dynamic disruptions. This paper presents a novel hybrid framework that integrates blockchain technology with a decentralized, large language model (LLM) powered multi-agent negotiation system to enhance the resilience and accountability of medical supply chains during crises. In this system, autonomous agents-representing manufacturers, distributors, and healthcare institutions-engage in structured, context-aware negotiation and decision-making processes facilitated by LLMs, enabling rapid and ethical allocation of scarce medical resources. The off-chain agent layer supports adaptive reasoning and local decision-making, while the on-chain blockchain layer ensures immutable, transparent, and auditable enforcement of decisions via smart contracts. The framework also incorporates a formal cross-layer communication protocol to bridge decentralized negotiation with institutional enforcement. A simulation environment emulating pandemic scenarios evaluates the system's performance, demonstrating improvements in negotiation efficiency, fairness of allocation, supply chain responsiveness, and auditability. This research contributes an innovative approach that synergizes blockchain trust guarantees with the adaptive intelligence of LLM-driven agents, providing a robust and scalable solution for critical supply chain coordination under uncertainty.
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