Imported cold chain food traceability management platform and innovative application
Journal of Agricultural Big Data ; 4(1):69-76, 2022.
Article in Chinese | CAB Abstracts | ID: covidwho-2145868
ABSTRACTIn 2020, the COVID-19 pandemic broke out globally, and cold-chain food has become the key point of the task to prevent the coronavirus from spreading within the city/region or beyond. Using information tools like establishing a national cold-chain traceability management platform to control the risk of imported cold-chain food has become an important topic at present. The paper focuses on the technology used by provinces and cities in cold-chain traceability, and combined with the actual needs, analyzes the advantages of heterogeneous identification technology. This paper starts from the present situation, introducing the technical roadmap selecting of traceability of imported cold-chain food and elaborating the data circulation structure of traceability of imported cold-chain food. Based on that, this paper introduces the national traceability platform of imported cold-chain food and its core functions based on heterogeneous identification. Through the construction of a three-level framework with the national cold-chain traceability management platform, to make sure the cold chain food can be traceable and be manageable. Meanwhile, through the two applications of inter-provincial confirmation and data verification, the national management effect of imported cold chain food is improved. [Outlook] Currently, the platform mainly focuses on imported livestock, poultry and aquatic products. In the future, the traceability scope may be gradually expanded to help improve the management effect. Establishing the imported cold-chain food traceability platform has a good effect on improving the information management level and promoting the traceability efficiency of problematic food, which is conducive to ensuring food safety.
Full text: Available Collection: Databases of international organizations Database: CAB Abstracts Type of study: Prognostic study Language: Chinese Journal: Journal of Agricultural Big Data Year: 2022 Document Type: Article