National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China.
PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau 999078, China.
Int J Environ Res Public Health. 2020 Mar 29;17(7):2300. doi: 10.3390/ijerph17072300.
Current food traceability systems have a number of problems, such as data being easily tampered with and a lack of effective methods to intuitively analyze the causes of risks. Therefore, a novel method has been proposed that combines blockchain technology with visualization technology, which uses Hyperledger to build an information storage platform. Features such as distribution and tamper-resistance can guarantee the authenticity and validity of data. A data structure model is designed to implement the data storage of the blockchain. The food safety risks of unqualified detection data can be quantitatively analyzed, and a food safety risk assessment model is established according to failure rate and qualification deviation. Risk analysis used visual techniques, such as heat maps, to show the areas where unqualified products appeared, with a migration map and a force-directed graph used to trace these products. Moreover, the food sampling data were used as the experimental data set to test the validity of the method. Instead of difficult-to-understand and highly specialized food data sets, such as elements in food, food sampling data for the entire year of 2016 was used to analyze the risks of food incidents. A case study using aquatic products as an example was explored, where the results showed the risks intuitively. Furthermore, by analyzing the reasons and traceability processes effectively, it can be proven that the proposed method provides a basis to formulate a regulatory strategy for regions with risks.
当前的食品可追溯系统存在诸多问题，例如数据易被篡改，缺乏直观分析风险原因的有效方法。因此，提出了一种将区块链技术与可视化技术相结合的新方法，利用 Hyperledger 构建信息存储平台。其分布性和抗篡改性等特点能够保证数据的真实性和有效性。设计了数据结构模型来实现区块链的数据存储。对不合格检测数据导致的食品安全风险进行定量分析，并根据失效率和合格率偏差建立食品安全风险评估模型。风险分析采用可视化技术，如热力图，显示不合格产品出现的区域，使用迁移图和力导向图追踪这些产品。此外，使用食品抽样数据作为实验数据集来测试该方法的有效性。本方法使用了易于理解的食品抽样数据，而不是难以理解的、高度专业化的食品数据集，例如食品中的元素。使用 2016 年全年的食品抽样数据来分析食品事件的风险。以水产品为例进行了案例研究，直观地展示了风险。此外，通过有效分析原因和可追溯性过程，可以证明所提出的方法为风险地区制定监管策略提供了依据。
