摘要
目的:研究构建一种时空趋势与风险食品类别相关的多维分析模型,旨在实现对食品安全风险的高效预警,并为监督管理的智能化和精准化提供科学依据和决策支持。方法:依托江苏省市场监督管理局2019年至2024年间的食品抽检数据,设计一种基于时空Transformer的多分类预测模型。该模型深度挖掘食品安全事件在时间与空间维度的演变规律,并建立与风险类别的内在关联,实现对全时空点风险食品类别的预测。为解决多类别数据量差异导致的长尾分布问题,引入加权均方误差损失函数以优化训练,增强模型对尾部类别数据的敏感性。结果:采用4种最高风险类别分类指标与3种全类别风险分类指标综合评估模型的准确性,不仅在已发生风险区域的预测精度上与统计方法相当,而且在全时空范围内实现了全类别风险的高效预测。结论:时空Transformer多分类预测模型为食品安全监管部门提供了一种新颖且有效的工具,能够优化随机抽检策略并提升监管效率。
Objectives:This study aimed to develop a multidimensional analytical model for spatiotemporal trends and risk food categories,with the objective of achieving efficient early warning of food safety risks and providing a scientific basis and decision support for the intelligent and precise management of supervision.Methods:Based on the food sampling data from Jiangsu Market Supervision and Administration Bureau from 2019 to 2024,a spatio-temporal Transformer based multi-class prediction model was proposed.This model deeply explored the evolution patterns of food safety incidents in both temporal and spatial dimensions and established intrinsic associations with risk categories.To mitigate the long-tail distribution problem caused by disparities in data volume across multiple categories,a weighted mean squared error loss function was introduced to optimize model training,thereby enhancing the sensitivity of the model to tail categories.Results:The accuracy of the model was comprehensively evaluated using four high-risk category classification metrics and three all-category risk classification metrics.It not only achieved prediction accuracy comparable to statistical methods in regions where risks had occurred,but also efficiently predicted all-category risks across the entire spatiotemporal scope.Conclusions:The spatio-temporal Transformer multi-class prediction model provides a novel and effective tool for food safety regulatory authorities,enabling the optimization of random inspection strategies and enhancing regulatory efficiency.
作者
罗晓清
郭林
杨雨萌
黄耐云
吴小俊
Luo Xiaoqing;Guo Lin;Yang Yumeng;Huang Naiyun;Wu Xiaojun(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,Jiangsu;Sino-UK Joint Laboratory on Artificial Intelligence of Ministry of Science and Technology,Jiangnan University,Wuxi 214122,Jiangsu)
出处
《中国食品学报》
EI
CAS
CSCD
北大核心
2024年第11期1-9,共9页
Journal of Chinese Institute Of Food Science and Technology
基金
国家重点研发计划项目(SQ2023YFF1100111)
国家自然科学基金项目(61772237)。
关键词
时空Transformer
加权均方误差
食品抽检
风险预警
多分类预测
食品安全
spatio-temporal Transformer
weighted mean squared error
food sampling inspection
risk early warning
multi-class prediction
food safety
