Food safety risk level prediction method and device and electronic equipment

Abstract

The invention provides a food safety risk grade prediction method and device and electronic equipment, and the method comprises the steps: dividing food safety risk grades based on food safety historical detection data, and obtaining food safety risk grade historical data; based on Daubechies wavelet basis, carrying out the wavelet decomposition on the food safety risk level historical data to obtain a plurality of food safety risk level historical data components; inputting the plurality of food safety risk grade historical data components into the LSTM model, and predicting the food safety risk grade to obtain a predicted value of the food safety risk grade. According to the food safety risk level prediction method and device and the electronic equipment provided by the invention, the prediction of the food risk level can be effectively realized.

Description

technical field [0001] The invention relates to the technical field of food safety, in particular to a food safety risk level prediction method, device and electronic equipment. Background technique [0002] Stewed meat products with sauce are one of the traditional meat products in my country. They have unique flavor and high nutritional value, and are deeply loved by consumers. Their safety issues directly affect the health of the broad masses of the people. Food safety involves the entire process of the food supply chain. Potential factors that threaten food safety exist in all links from raw material supply, food production and processing, and food circulation. Food safety risk assessment and supervision need to comprehensively consider risk factors in each link. Therefore, it is very necessary to mine and analyze these factors, make full use of these complex data, extract potential valuable information, identify potential safety risks according to different food safety ...

AI Technical Summary

Problems solved by technology

[0003] At present, the main research methods for food safety risk prediction are BP artificial neural network and support vector machine, but BP artificial neural network and support vector machine have long training time in food safety prediction, unstable network training efficiency, and low accuracy

Regulatory agencies usually use a simple statistical analysis of the food safety historical sampling data set to obtain the unqualified rate of this type of food for the massive supervision and sampling data, and then use this index to evaluate the safety status of this type of food. Post-event analysis of the food safety situation. Then, there are often a large number of null values ​​in the food safety testing data over the years, which means that an item has not been tested or there is no test result after the test. Mathematical statistics methods cannot perform risk assessment on the null value and find that there is contact

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