• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

船舶异构模型的过程知识图谱建模技术与应用方法

Process knowledge graph modeling techniques and application methods for ship heterogeneous models.

作者信息

Dong Jianwei, Jing Xuwen, Lu Xiang, Liu Jinfeng, Li Haipeng, Cao Xuwu, Du Chenxiao, Li Jun, Li Lei

机构信息

School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, People's Republic of China.

China Merchants Cruise Shipbuilding Co., LTD., Nantong, 226000, People's Republic of China.

出版信息

Sci Rep. 2022 Feb 21;12(1):2911. doi: 10.1038/s41598-022-06940-y.

DOI:10.1038/s41598-022-06940-y
PMID:35190625
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8861156/
Abstract

In the process design and reuse of marine component products, there are a lot of heterogeneous models, causing the problem that the process knowledge and process design experience contained in them are difficult to express and reuse. Therefore, a process knowledge representation model for ship heterogeneous model is proposed in this paper. Firstly, the multi-element process knowledge graph is constructed, and the heterogeneous ship model is described in a unified way. Then, the multi-strategy ontology mapping method is applied, and the semantic expression between the process knowledge graph and the entity model is realized. Finally, by obtaining implicit semantics based on case-based reasoning and checking the similarity of the matching results, the case knowledge reuse is achieved, to achieve rapid design of the process. This method provides reliable technical support for the design of ship component assembly and welding process, greatly shortens the design cycle, and improves the working efficiency. In addition, taking the double-deck bottom segment of a ship as an example, the process knowledge map of the heterogeneous model is constructed to realize the rapid design of ship process, which shows that the method can effectively acquire the process knowledge in the design case and improve the efficiency and intelligence of knowledge reuse in the process design of the heterogeneous model of a ship.

摘要

在船用零部件产品的工艺设计与重用过程中,存在大量异构模型,导致其中蕴含的工艺知识和工艺设计经验难以表达与重用。因此,本文提出一种针对船舶异构模型的工艺知识表示模型。首先,构建多元素工艺知识图谱,以统一方式描述异构船舶模型。然后,应用多策略本体映射方法,实现工艺知识图谱与实体模型之间的语义表达。最后,通过基于案例推理获取隐含语义并检查匹配结果的相似度,实现案例知识重用,以实现工艺的快速设计。该方法为船舶零部件装配与焊接工艺设计提供了可靠的技术支持,大大缩短了设计周期,提高了工作效率。此外,以某船双层底分段为例,构建异构模型的工艺知识图谱以实现船舶工艺的快速设计,表明该方法能够有效获取设计案例中的工艺知识,提高船舶异构模型工艺设计中知识重用的效率和智能化程度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/319fce7f412e/41598_2022_6940_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/d6762b143340/41598_2022_6940_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/8539e9958faa/41598_2022_6940_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/639d3f60e85f/41598_2022_6940_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/8fde02f946a0/41598_2022_6940_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/480cf69559e2/41598_2022_6940_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/8ce71cfc1b68/41598_2022_6940_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/6b20fe4a4064/41598_2022_6940_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/656ee86994af/41598_2022_6940_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/1e2d54f2501d/41598_2022_6940_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/838861a1043b/41598_2022_6940_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/319fce7f412e/41598_2022_6940_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/d6762b143340/41598_2022_6940_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/8539e9958faa/41598_2022_6940_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/639d3f60e85f/41598_2022_6940_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/8fde02f946a0/41598_2022_6940_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/480cf69559e2/41598_2022_6940_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/8ce71cfc1b68/41598_2022_6940_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/6b20fe4a4064/41598_2022_6940_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/656ee86994af/41598_2022_6940_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/1e2d54f2501d/41598_2022_6940_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/838861a1043b/41598_2022_6940_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8385/8861156/319fce7f412e/41598_2022_6940_Fig11_HTML.jpg

相似文献

1
Process knowledge graph modeling techniques and application methods for ship heterogeneous models.船舶异构模型的过程知识图谱建模技术与应用方法
Sci Rep. 2022 Feb 21;12(1):2911. doi: 10.1038/s41598-022-06940-y.
2
Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion.基于路径的知识推理与文本语义信息融合的医疗知识图谱补全方法
BMC Med Inform Decis Mak. 2021 Nov 29;21(Suppl 9):335. doi: 10.1186/s12911-021-01622-7.
3
An effective knowledge graph entity alignment model based on multiple information.基于多源信息的知识图谱实体对齐模型
Neural Netw. 2023 May;162:83-98. doi: 10.1016/j.neunet.2023.02.029. Epub 2023 Feb 24.
4
Quality Prediction and Control of Assembly and Welding Process for Ship Group Product Based on Digital Twin.基于数字孪生的船舶组产品装配与焊接过程质量预测与控制
Scanning. 2020 Oct 18;2020:3758730. doi: 10.1155/2020/3758730. eCollection 2020.
5
Collaborative case-based reasoning for knowledge discovery of elders health assessment system.用于老年人健康评估系统知识发现的基于案例的协作推理
Open Biomed Eng J. 2014 Sep 29;8:68-74. doi: 10.2174/1874120701408010068. eCollection 2014.
6
Towards Semantic Sensor Data: An Ontology Approach.迈向语义传感器数据:本体论方法。
Sensors (Basel). 2019 Mar 8;19(5):1193. doi: 10.3390/s19051193.
7
A Tiny Model for Fast and Precise Ship Detection via Feature Channel Pruning.基于特征通道剪枝的快速精准船舶检测小模型
Sensors (Basel). 2022 Nov 30;22(23):9331. doi: 10.3390/s22239331.
8
Prediction and optimization method for welding quality of components in ship construction.船舶建造中部件焊接质量的预测与优化方法
Sci Rep. 2024 Apr 23;14(1):9353. doi: 10.1038/s41598-024-59490-w.
9
Construct a Knowledge Graph for China Coronavirus (COVID-19) Patient Information Tracking.构建中国新冠病毒(COVID-19)患者信息追踪知识图谱。
Risk Manag Healthc Policy. 2021 Oct 21;14:4321-4337. doi: 10.2147/RMHP.S309732. eCollection 2021.
10
A Knowledge Graph Entity Disambiguation Method Based on Entity-Relationship Embedding and Graph Structure Embedding.基于实体关系嵌入和图结构嵌入的知识图谱实体消歧方法。
Comput Intell Neurosci. 2021 Sep 23;2021:2878189. doi: 10.1155/2021/2878189. eCollection 2021.

本文引用的文献

1
Learning a Health Knowledge Graph from Electronic Medical Records.从电子病历中学习健康知识图谱。
Sci Rep. 2017 Jul 20;7(1):5994. doi: 10.1038/s41598-017-05778-z.