摘要
食品供应链中的供给侧种类多、业务杂,监督管理人员难以制定统一模板对上链节点进行安全评估,导致食品安全监管效率差、质量低。Ego SF是一个结合超级账本2.0去中心化和随机森林集成化的基于授权机制的企业级许可食品安全监管集成模型,交易确认时间短且具有高鲁棒性。通过在食品溯源产品中应用EgoSF模型,证明EgoSF可以在食品安全监管过程中提升监督管理人员的工作效率和工作质量,为进一步研究区块链技术与机器学习、数据挖掘的结合和探索区块链技术在食品溯源领域的应用奠定良好基础。
The first mock exam is that the supply side of food supply chain is various and mixed.It is difficult for supervisors to make unified template to evaluate the safety of the upper chain nodes,which leads to poor efficiency and low quality of food safety supervision.EgoSF is an enterprise licensed food safety supervision integration model based on authorization mechanism,which combines super ledger 2.0 decentralization and random forest integration.It has short transaction confirmation time and high robustness.The EgoSF can improve the work efficiency and quality of supervisors in the process of food safety supervision.This work can lay a good foundation for further research on the combination of blockchain technology,machine learning and data mining,and exploring the application of blockchain technology in the field of food traceability.
作者
张乐
冷基栋
吕学强
田驰
姜阳
李果林
ZHANG Le;LENG Jidong;Lü Xueqiang;TIAN Chi;JIANG Yang;LI Guolin(Beijing Key Laboratory of Internet Culture and Digital Dissemination Research,Beijing Information Science and Technology University,Beijing 100101;Sunego(Beijing)Technology Limited Company,Beijing 100036)
出处
《食品工业》
CAS
2022年第3期195-200,共6页
The Food Industry
基金
国家自然科学基金项目(项目编号:61671070)
青海省藏文信息处理与机器翻译重点实验室/藏文信息处理教育部重点实验室开放课题基金(项目编号2019Z002)
教育部科技司项目(项目编号:MCM2020_4_2)。
关键词
食品安全
供应链
区块链
超级账本
机器学习
food safety
supply chain
blockchain
hyperledger fabric
machine learning
