让冷链系统从报警变决策，智能评估食品变质风险并自动优化物流。
Beyond Thresholds: A Quality-Aware Decision Intelligence Framework for Cold Chain IoT Systems
- 构建四维质量状态模型，融合保质期、变质速率等参数实时评估食品品质。
- 混合建模结合物理规律与数据修正，预测误差仅7.2小时，显著优于传统方法。
- 引入大模型推理+知识检索，解释性强且99.5%决策接近最优，适合工业落地。
冷链物流虽技术进步，但多数系统仍为被动监控：阈值触发警报后无后续动作，无法将异常与累积变质关联或转化为物流决策。本文提出质量感知决策智能（QADI）框架，包含三部分：结构化质量状态表示 $S_q = [L, Q, U, R]$（剩余保质期、变质率、估计不确定性、运营风险），基于物理的微生物动力学与数据驱动修正项相结合的混合建模层，以及基于 Microsoft Phi-4 的推理层，通过结构化知识库的检索增强生成实现可解释决策。在巴氏奶和西兰花共八个场景下，对比五种基线方法（阈值监测、纯物理模型、含噪声物理模型、优化决策、规则专家系统），使用独立于模型的乳品研究数据作为真实保质期基准。结果表明，该框架平均绝对保质期误差为7.2小时（纯物理模型为30.9小时，$p<0.001$），腐败率为14.5%（纯物理与规则系统为16.6%，$p=0.08$），99.5%场景达近似最优决策；移除大模型后决策最优性降至45.5%（$p<0.001$）。专家评估解释质量得分为83%（κ=0.71）。消融实验显示混合建模与大模型推理各贡献独立优势，而RAG主要提升解释质量。代码开源：https://bit.ly/4d6t44C。
原文摘要 · Abstract (English)
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.
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