发表机构
Zhejiang University; Rutgers University（浙江大学; 罗格斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于Transformer的框架，整合多类数据与Wilson区间预训练，在稀疏数据下细粒度预测城市食品安全风险，实验及浙江实地测试显示其性能优于基线，可提升检测率与检查资源分配效率。
AI 中文摘要
保障食品安全是一项关键的公共卫生挑战，尤其是在检查资源有限且区域抽样数据稀疏的情况下。本研究提出了一种基于Transformer的框架，该框架通过整合超过1100万条检查记录与从统计年鉴中提取的补充人口、经济和环境指标，能够预测城市级别的细粒度食品安全风险。三阶段预训练设计利用Wilson区间（同时捕捉安全性和风险排名）的部分监督，结合半监督标签优化，即使在本地样本量不足时也能有效利用历史记录。对2022年数据的实验评估表明，所提方法显著优于基线模型。与浙江省市场监督管理局合作开展的后续实地实验进一步证明，与人工制定的计划相比，该方法提高了检测率并更高效地分配了检查资源。对监管决策的观察显示，检查员采用基于阈值的启发式方法，表明额外的培训或决策支持界面可进一步增强AI生成风险评分的效果。总体而言，这些发现强调，将大规模公共检查数据、基于Wilson区间的置信度建模与先进深度学习严格整合，可促进更早、更细粒度地识别食品安全威胁。通过减少对被动措施的依赖，所提框架有望推进对全球食品供应链的主动、数据驱动的监督。
英文摘要
Ensuring food safety represents a critical public health challenge, particularly when inspection resources are limited and regional sampling data are sparse. This study proposes a Transformer-based framework capable of forecasting fine-grained, city-level food safety risks by unifying over 11 million inspection records with supplemental demographic, economic, and environmental indicators extracted from the Statistical Yearbook. A three-stage pretraining design leverages partial supervision from the Wilson interval (capturing both safety and risk rankings), together with semi-supervised label refinement, to effectively utilize historical records even when local sample sizes are insufficient. Experimental evaluations on data from 2022 show that the proposed approach outperforms baselines significantly. A subsequent field experiment in collaboration with the Zhejiang Provincial Administration for Market Regulation further demonstrates improved detection rates and more efficient allocation of inspection resources compared to a manually developed plan. Observations of regulatory decision-making reveal a threshold-based heuristic employed by inspectors, hinting that additional training or decision-support interfaces could further enhance the impact of AI-generated risk scores. Overall, these findings underscore that a rigorous integration of large-scale public inspection data, Wilson interval-based confidence modeling, and advanced deep learning can facilitate earlier and more granular identification of food safety threats. By reducing reliance on reactive measures alone, the proposed framework has the potential to advance proactive, data-driven oversight of the global food supply.
