摘要
近年来地理标志农产品质量安全事故被频频爆出,为有效规避与控制质量安全风险,提出一种基于随机森林(RF)与深度置信网络(DBN)的质量安全风险评估及预警模型。将全面质量管理理论中“人、机、料、法、环”5要素与种植、加工、物流、销售4阶段相结合,构建质量安全风险评估体系;运用RF模型对指标体系进行降维,确定出13个评价指标;运用DBN理论构建质量安全风险评估及预警模型,并以云南普洱茶为例进行实证研究。结果表明,将RF和DBN模型应用于地理标志农产品风险评估的精度高达96.67%,明显优于反向传播神经网络(BP)的90%和支持向量机(SVM)的85%。该成果可为降低地理标志农产品质量安全风险、保证其品牌影响力提供一定参考。
In recent years,quality safety accidents of geographical indication agricultural products have been frequently reported.In order to effectively avoid and control quality safety risks,a quality safety risk assessment and early warning model of random forest(RF)and deep confidence network(DBN)was proposed.First of all,the quality safety risk assessment system is constructed by combining the five elements of total quality management(TQM),namely"human,machine,material,method and environment",with the four stages of planting,processing,logistics and sales.Secondly,the stochastic forest model is used to reduce the dimension of the index system,and 13 evaluation indexes are determined.Finally,DBN is used to construct the quality safety risk assessment and early warning model,and Yunnan Pu’er tea is selected as an example for empirical study.The results show that the accuracy of RF and DBN model applied to the risk assessment of agricultural products with geographical indication can be as high as 96.67%,which is significantly better than 90%of reverse propagation neural network(BP)and 85%of support vector machine(SVM).This result provides a reference basis for reducing the quality and safety risk of agricultural products with geographical indications and ensuring their brand influence.
作者
张彪
胡晨钰
李晶
ZHANG Biao;HU Chen-yu;LI Jing(Faculty of Management and Economics,Kunming University of Science and Technology,Kunming 650500,China)
出处
《软件导刊》
2022年第6期11-18,共8页
Software Guide
基金
云南省社会科学基金项目(YB2019036)。
关键词
RF
DBN
地理标志农产品
风险评估
预警
RF
DBN
geographical indications of agricultural products
risk assessment
early warning
