Research On Food Safety Network Public Opinion Warning Based On Storm

Food safety is related to the national economy and the people’s livelihood.All countries in the world are paying close attention to it.At present,food safety incidents in our country occured frequently,and the data on public opinion generated by it has also undergone tremendous changes in terms of quantity,speed,and complexity.Under the big data environment,how to construct an effective food safety big data public opinion warning model is imperative.The main research of this paper is the analysis of food safety incident network public opinion,and the proposal of the back-propagation(BP)neural network(AHP-BP)warning method based on Analytic Hierarchy Process(AHP).Design and implement a food safety network public opinion warning system based on Storm.The main work is as follows:1.The rapid development of the current Internet industry,the number of Internet public opinion is increasing sharply.Network public opinion monitoring and early warning are inseparable from the public opinion analysis.A brief overview of food safety incidents was conducted,and the factors affecting the stage division and evolution of the Internet public opinion were analyzed.Based on this,the general rules of food safety network public opinion were summarized,and the scenes of food safety network public opinion at different stages of development were analyzed...2.The establishment of a scientific and reasonable network public opinion early warning indicator system is the key to effective early warning.It deeply analyzes the characteristics of food safety network public opinion and the principles of constructing an Internet public opinion alert system,and serves as an important reference for building a food safety network public opinion alert system.Based on this,using other research results and combining the data source of this paper,a network public opinion early warning index system based on microblog data and news data was constructed.3.There is no good method for early warning of Internet public opinion for food safety.Therefore,this paper proposes a Back Propagation Neural Network(AHP-BP)early warning method based on Analytic Hierarchy Process(AHP).The index data of news and news is used as the input of BP neural network,and four kinds of early warning levels are obtained as the output of BP neural network through AHP fusion index data,and a food security network public opinion early warning model is established.4.Under the big data environment,in order to be able to timely and effectively monitor and alert food safety incidents,the AHP-BP early warning model was deployed under the Storm distributed framework,and a Storm-based food safety network public opinion early warning system was designed and introduced.The regulatory data,quality control data,test data and public opinion data are matched and matched to achieve comprehensive detection and analysis of food safety incidents.