Research On Food Safety Early Warning Method Based On Detection Data

Food safety is an important social problem affecting the national economy and people's livelihood.Food safety incidents seriously damage the health of the people and have serious consequences for the economy and society.Therefore,carrying out food safety early warning research is of great theoretical and practical significance for reducing the occurrence of food safety accidents and realizing the prevention and prediction of food safety risks.This paper takes the food safety detection data as the research object,carries out the early warning research of the food safety risk of the detection data.The main research contents are as follows:1)In order to comprehensively and accurately assess the comprehensive risk status of food safety detection data,a comprehensive evaluation method of food safety risk based on differential evolution algorithm is proposed.In this method,the index weight is calculated by means of complex correlation coefficient method,entropy weight method and mean value method respectively,and the comprehensive weight of the index is obtained by using differential evolution algorithm,and the comprehensive risk value of the detected data is obtained,and the comprehensive evaluation of risk is realized.2)To accurately predict the safety risk of the food safety detection data,a food safety early warning method based on the AHC-RBF neural network algorithm is proposed.The algorithm uses AHC algorithm to adaptively obtain the central position of the hidden layer nodes of the RBF neural network,which overcomes the sensitivity of the traditional neural network model to the initial clustering center and improves the generalization accuracy of the model.It is used to construct the early warning model of food safety,to carry out the early warning modeling of detection data and to carry out early warning analysis.3)To mine the potential correlation between the detection information and the risk grade of samples in food safety detection data,this paper proposes a food safety early warning information mining method based on the bucket sorting-FP growth algorithm.Based on the bucket sorting algorithm,this algorithm improves the process of the traditional FP growth algorithm of obtaining frequent items list in the data set,and improves the efficiency of frequent pattern mining.Through the above algorithm to get early warning association rules and put forward the corresponding risk prevention and control recommendations.Finally,the above three methods are verified by using the food safety detection data as the research object.