基于大数据和智能识别的食品安全预警研究

Research on food safety early warning based on big data and intelligent identification

针对当前食品安全预警性能差的问题,基于大数据和人工智能技术构建出新型食品安全溯源及预警系统,通过大数据技术对食品流通各个环节所收集的原始数据进行数据过滤和抽样分析,获取宏观的数学模型和预警信息;并通过深度学习等人工智能技术,对复杂场景下的高维数据进行深入挖掘分析,获取数据背后的社会行为的微过程,从而获得一个精确的风险预警模型,化解群体性的食品安全事故。通过合理分工,解决了数据收集、上报、分析到预警的全环节技术难题,提高溯源数据的全覆盖性、实时性、预警准确性和智能化程度。试验表明,该方案时间误差小,正确率高,安全性能好。

Aiming at the current problem of poor food safety early warning performance,a new type of food safety traceability and early warning system was constructed based on big data and artificial intelligence technology.The big data technology was used to filter and sample the raw data collected in various links of food distribution to obtain macro Mathematical models and early-warning information;and through deep learning and other artificial intelligence technologies,in-depth mining and analysis of high-dimensional data in complex scenarios,to obtain the micro-process of social behavior behind the data,so as to obtain an accurate risk Early warning model to resolve mass food safety accidents.Through reasonable division of labor,this research solved the technical difficulties of data collection,reporting,analysis,and early warning,and improved the full coverage,real-time,early warning accuracy,and intelligence of traceable data.Experiments show that the scheme has small time error,high accuracy and good safety performance.