发表机构
IIT Ropar（印度理工学院罗帕尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对冷链系统仅靠阈值报警、无法关联产品降解并生成物流决策的问题，提出融合质量状态表示、混合建模与Phi-4+RAG推理的QADI框架，在多场景下显著提升货架期预测精度与决策最优率。
AI 中文摘要
冷链物流在技术上已取得进展，但多数已部署的系统仍属于被动式监控工具，而非决策智能体：阈值会触发警报，但既没有机制将违规情况与产品累积降解关联起来，也无法将降解信号转化为物流决策。我们针对这一空白提出了质量感知决策智能（Quality-Aware Decision Intelligence，QADI）框架，该框架整合了三项核心能力：一是结构化质量状态表示$S_q = [L, Q, U, R]$，分别对应剩余货架期、降解速率、估计不确定性和操作风险，所有参数均可通过框架方程推导和计算；二是混合质量建模层，将基于物理的微生物动力学模型与数据驱动校正项相结合；三是基于Microsoft Phi-4构建的推理层，在结构化领域知识库上实现检索增强生成（retrieval-augmented generation，RAG）。 我们以巴氏杀菌乳为主要研究案例，在8种冷链场景下将该框架与5个基线方法（阈值监控、纯物理模型、物理加噪声模型、基于优化的决策方法、基于规则的专家系统）进行基准测试，货架期真值取自独立于本模型的已发表乳制品研究数据。对比采用经Holm校正的Wilcoxon符号秩检验。在牛奶和西兰花场景中，该框架的货架期平均绝对误差为7.2小时（纯物理模型为30.9小时，$p<0.001$），腐败率为14.5%（纯物理模型和基于规则的系统为16.6%，p=0.08），在99.5%的场景中达到最优决策水平。移除大语言模型（LLM）推理组件后，决策最优率降至45.5%（$p<0.001$）。专家评分的解释质量达83%（$\u03ba=0.71$）。消融实验表明，混合建模与LLM推理分别带来了不同的性能提升，而RAG检索主要作用于提升解释质量。代码开源地址：https://bit.ly/4d6t44C。
英文摘要
Cold chain logistics has advanced technologically, yet most deployed systems remain reactive monitors, not decision-making agents: thresholds trigger alerts, but nothing relates violations to cumulative product degradation or converts degradation signals into logistics decisions. We address this gap with a Quality-Aware Decision Intelligence (QADI) framework combining three capabilities: a structured quality state representation, $S_q = [L, Q, U, R]$ -- remaining shelf life, degradation rate, estimation uncertainty, and operational risk, all derived and computable from the framework equations; a hybrid quality modeling layer combining physics-based microbial kinetics with a data-driven correction term; and a reasoning layer built on Microsoft Phi-4~\cite{Phi4} with retrieval-augmented generation over a structured domain knowledge base. We benchmark against five baselines -- threshold monitoring, physics-only, physics-plus-noise, optimisation-based decisions, and a rule-based expert system -- across eight cold chain scenarios, using pasteurised milk as the primary case, with ground truth shelf-life drawn from published dairy studies~\cite{Singh1994, Smigic2015} independent of our model. Comparisons use Wilcoxon signed-rank tests with Holm correction. Across milk and broccoli scenarios, the framework attains mean absolute shelf-life error of 7.2 hours (versus 30.9 hours, physics-only; $p<0.001$), spoilage rate of 14.5% (versus 16.6%, physics-only and rule-based; p=0.08), and oracle-optimal decisions in 99.5% of scenarios. Removing the LLM reasoning component drops optimality to 45.5% ($p<0.001$). Expert-rated explanation quality reaches 83% ($κ= 0.71$). Ablations show hybrid modeling and LLM reasoning contribute distinct gains, while RAG retrieval mainly drives explanation quality. Code: https://bit.ly/4d6t44C.
