[1] PEARL J. Fusion, propagation and structuring in belief networks[J]. Artificial Intelligence, 1986, 29(3): 241-288.[2] MIROSLAV S, JANA M, DIMITRIS D. Bayesian network application for the risk assessment of existing energy production units[J]. Pathology and Immunopathology Research, 2018, 169: 312-320.[3] ZHANG D, LIU Q, YAN H, et al. A matrix analytic approach for Bayesian network modeling and inference of a manufacturing system[J]. Acta Oceanological Sinica, 2021,60: 202-213.[4] 张勤. DUCG: 一种新的动态不确定因果知识的表达和推理方法( Ⅰ ): 离散、静态、证据确定和有向无环图情况[J]. 计算机学报, 2010, 33(4): 625-651.[5] ZHANG Q, DONG C, CUI Y. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: statistics base, matrix, and application[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(4): 645-663.[6] ZHANG Q. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning: directed cyclic graph and joint probability distribution[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(7): 1503-1517.[7] Zhang Q. Dynamic uncertain causality graph for knowledge representation and reasoning: continuous variable, uncertain evidence, and failure forecast[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45(7): 990-1003.[8] ZHANG Q, GENG S. Dynamic uncertain causality graph applied to dynamic fault diagnoses of large and complex systems[J]. IEEE Transactions on Reliability, 2015, 64(3): 910-927.[9] 李晓宾, 李淑珍. 模糊动态贝叶斯网络防御态势感知模型[J]. 探测与控制学报, 2017, 39(3): 124-129.[10] 董春玲, 赵越, 张勤. 动态故障诊断中的立体因果建模与不确定性推理方法[J]. 清华大学学报 ( 自然科学版), 2018, 58(7): 614-622.[11] 胡婷婷, 王洪春, 吴成姚. 基于M-DUCG 中条件作用事件展开的一般形式[J]. 重庆师范大学学报( 自然科学版),2019,36(02):75-80.