蔬菜种子技术泄露的全周期风险因素识别与静态贝叶斯网络建模  

Identification of Full-Cycle Risk Factors and Static Bayesian Network Modeling for Vegetable Seed Technology Leakage

作　　者：张鹏[1] 肖必燕 郑小京 孙杰 戴荣 Peng ZHANG;Biyan XIAO;Xiaojing ZHENG;Jie SUN;Rong DAI(Weifang University of Science and Technology;Jiangxi Institute of Fashion Technology)

机构地区：[1]潍坊科技学院,山东省寿光市261700 [2]江西服装学院,江西省南昌市330201

出　　处：《社会科学理论与实践》2025年第2期30-40,共11页

摘　　要：蔬菜种子技术泄露风险对我国种业安全与农业可持续发展构成严峻挑战。本研究围绕内部泄密、生物窃取等5类核心风险及24项基本事件,构建故障树模型,解析风险事件的逻辑因果关系,引入模糊集理论量化专家语义评价以确定基本事件先验概率,并将故障树映射为贝叶斯网络,建立静态风险评估模型。通过多维致因分析与概率推理,揭示了风险传导路径与关键致因节点,为蔬菜种子技术泄露风险的主动防控与决策优化提供理论支撑。The risk of vegetable seed technology leakage poses a significant challenge to the security of China’s seed industry and the sustainable development of agriculture.This study focuses on five core risks,including internal leakage and biotheft,and identifies 24 fundamental risk events.A fault tree model is constructed to analyze the logical causal relationships of risk events.Fuzzy set theory is introduced to quantify expert semantic evaluations and determine the prior probabilities of fundamental events.The fault tree is then mapped into a Bayesian network to establish a static risk assessment model.Through multi-dimensional causation analysis and probabilistic inference,this study reveals the risk transmission pathways and key causal nodes,providing theoretical support for proactive risk prevention and decision optimization regarding vegetable seed technology leakage.

关 键 词：蔬菜种子技术泄露 故障树分析 贝叶斯网络 模糊集理论 风险评估 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]