中国舰船研究2025，Vol.20Issue(2)：118-130,13.DOI:10.19693/j.issn.1673-3185.04113
基于数字孪生与改进KD树算法的船舶运维知识推理与策略优化
Knowledge reasoning and strategy optimization for ship operation and maintenance based on digital twin and improved KD tree algorithm
摘要
Abstract
[Objective]With the continuous development of industrial technology,the intelligence of mod-ern ship processes has been continuously advancing.The propulsion system,auxiliary power system,etc.of ships have become increasingly intelligent,and ship maintenance work has become ever more complex.Dif-ferent from land equipment,the environment in which ships are located is more severe.When a problem oc-curs,it will not only affect the stability of the ship during operation,but also bring huge safety hazards.To this end,this paper focuses on a knowledge reasoning method for ship operations and maintenance(O&M)based on digital twin technology.[Method]Based on the physical entity of the ship,the ship operation and main-tenance process is analyzed,and a digital twin model for ship O&M is constructed from the multi-dimensions of"geometry-physics-behavior-rule".Aiming at the early warning information in the ship O&M knowledge model,by using previous ship O&M cases,a ship O&M case database containing ship dynamic monitoring data and maintenance methods is established.Based on the database,a method for ship O&M knowledge reas-oning and strategy generation using an improved KD tree algorithm is proposed.Neighboring cases are weighted using Gaussian distance weighting,and the whale optimization algorithm(WOA)is used to optim-ize the characteristic attributes of ship equipment to achieve accurate knowledge reasoning.[Results]The experimental results show that the proposed improved KD tree algorithm(ω-KDtree-WOA)achieves an infer-ence accuracy of 0.928 when the K value is 4 and the population size is 400,which is approximately 3.2%higher than that of the traditional KD tree algorithm under the same conditions.In addition,compared with the classification confidence weighted and distance weighted K-nearest neighbor algorithm(CCW-WKNN)and the smoothing weight distance to solve K-nearest neighbor(SDWKNN)algorithm,etc.,the algorithm pro-posed in this paper has significant advantages in accuracy,recall,precision,and F1 score,especially showing stronger stability when the K value is larger.[Conclusion]The proposed method can be effectively applied to the O&M process of ship gas turbines.关键词
船舶运维/数字孪生/知识推理/知识工程/KD树算法/鲸鱼优化算法Key words
ship operations and maintenance/digital twin/knowledge reasoning/knowledge-based engin-eering/KD-tree algorithm/whale optimization algorithm分类
交通工程引用本文复制引用
张立尧,郭梓芊,李瑞芳,叶勋,马涛..基于数字孪生与改进KD树算法的船舶运维知识推理与策略优化[J].中国舰船研究,2025,20(2):118-130,13.基金项目
国防基础科研计划资助项目(JCKY2021206A008) （JCKY2021206A008）
