%0 Journal Article
%T 数字经济背景下生鲜农产品冷链绿色物流风险评估<br>Risk Assessment of Green Logistics in the Cold Chain of Fresh Agricultural Products under the Background of the Digital Economy
%A 丁博
%A 张人龙
%A 刘小红
%J Operations Research and Fuzziology
%P 2990-3005
%@ 2163-1530
%D 2023
%I Hans Publishing
%R 10.12677/ORF.2023.134300
%X 随着数字经济日渐融入农业，显著加速了农产品现代化进程。然而，整个生鲜农产品绿色物流过程，仍然存在风险识别问题，导致新鲜农产品的腐烂和损失，不仅产生的气体对生态环境有较大的破环，而且对食用腐烂农产品的人易患食源性疾病。本文对现有的物流风险因素研究进行分析总结，使用分解分析法划分绿色物流流程，结合事故致因相关理论，建立科学合理的风险评估体系和分级标准；增加压缩变量和反正切函数学习变量以及个体之间的交叉与自身的变异率，改进粒子群算法，克服由人为因素或客观数据差异带来的影响，建立IPSO-SVR模型；并对一家草莓物流运输公司数据进行风险评估，通过不同的模型对比，验证了本文模型的准确性和可靠性，为农产品绿色物流风险评估提供了科学依据。<br />
With the digital economy increasingly integrating into agriculture, the modernization process of agricultural products has been significantly accelerated. However, the logistics risk identification problem still exists, which leads to the decay and loss of fresh agricultural products. The gas generated not only significantly damages the ecological environment but also is vulnerable to food-borne diseases for people who eat rotten agricultural products. This paper analyzes and summarizes the existing research on logistics risk factors, uses the decomposition analysis method to divide the logistics process, and establishes a scientific and reasonable risk assessment system and grading standards based on the theory of accident causes. It Increases the compression variables, arctangent function learning variables, and the crossover between individuals and their mutation rate, improves the particle swarm optimization algorithm, overcomes human factors or objective data differences, and establishes the impact of human factors or objective data differences in the IPSO-SVR model. The data of a strawberry logistics transportation company is evaluated for risk. By comparing different models, the accuracy and reliability of this model are verified, which provides a scientific basis for the risk assessment of green logistics of agricultural products.
%K 风险评估体系，支持向量机，改进的粒子群算法，生鲜农产品<br>Risk Assessment System
%K Support Vector Machine
%K Improved Particle Swarm Optimization Algorithm
%K Fresh Agricultural Products
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=70256