摘要
基于云计算,结合食品安全检验检测的完备性与最小性原理,将影响食品安全的多维因素降维成平均含量(AVE)、限量标准(STA)、超限率(OUT)、超限程度(OUD)和最大值(MAX)5个因素,并建立食品有毒有害物质检验检测大数据的风险分析算法。利用云计算技术实现对地理上分布广泛、动态、复杂性高的海量数据进行存储,并运用云计算的MapReduce计算框架进行智能的并行数据处理及计算,最后得到风险分析结果。通过对在基于Web端的实验室管理系统采集的1 000 000条检验检测数据结果进行风险分析,得出食品安全指数IFS远小于1,表明消费者人群的食品安全状态良好。
Based on cloud calculation,combined with the completeness and minimum principle in food safety inspection and testing,multiple dimensional factors that affected the food safety were reduced into five factors：average content （AVE）, limit standard （STA）, overload rate （OUT） , out of limit degree （OUD）, and the maximum value （MAX）, and the poisonous and harmful substance risk analysis algorithm in food safety inspection and testing big data was established. The paper made use of cloud computing platform to achieve the data storage of massive extensive geographical distribution, dynamic, high complexity data, and applies MapReduce computational framework of cloud computing for intelligent parallel data processing and computing. Finally, we got the required risk analysis results. Through the risk analysis of collected 1 000 000 testing data results from the laboratory management information system based on web side, it was found that the food safety index was greater less than 1 ,which indicated that the food safety state was in good condition in consumer population.
作者
王雅洁
杨冰
代姣
何锦林
陈恺
罗艳
谭红
陶光灿
WANG Ya-jie YANG Bing DAI Jiao TAO Guang-can et al(Guizhou Academy of Testing and Analysis,Guiyang,Guizhou 55000)
出处
《安徽农业科学》
CAS
2017年第21期216-220,233,共6页
Journal of Anhui Agricultural Sciences
基金
"十二五"科技支撑计划项目(2015BAK36B04)
关键词
云计算
食品有毒有害物质检测
大数据
风险分析算法
Cloud computing
Food testing of poisonous and harmful substance
Big data
Risk analysis algorithm
