Research on automatic construction of a multi-resolution 3D design knowledge graph for maritime vessels

Yongjin Lu; Ke Lin; Hairong Han

doi:10.1117/12.3033029

5 July 2024 Research on automatic construction of a multi-resolution 3D design knowledge graph for maritime vessels

Yongjin Lu, Ke Lin, Hairong Han

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131842E (2024) https://doi.org/10.1117/12.3033029
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In order to establish a unified data source for ship design, effectively integrate 3D design into each stage of the design process, and achieve lightweight models while systematically organizing modelling knowledge, this article primarily conducts research on automated construction of a knowledge graph for multi-resolution 3D ship design. Firstly, the concept and process of multi-resolution modelling are proposed, with detailed explanation provided through an example focusing on hull structure. Subsequently, the technology for automatic construction of a knowledge graph for multi-resolution 3D ship design is discussed. The vertical domain-specific knowledge graph is automatically constructed using a deep neural network based on BERT-LSTM-Softmax. Finally, the effectiveness of the proposed method is validated by utilizing the Neo4j graph database storage to represent and query the constructed knowledge graph. The resulting knowledge graph facilitates acquisition of 3D modelling expertise by design and construction personnel, enhances their capabilities, and improves overall design quality

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yongjin Lu, Ke Lin, and Hairong Han "Research on automatic construction of a multi-resolution 3D design knowledge graph for maritime vessels", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131842E (5 July 2024); https://doi.org/10.1117/12.3033029

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KEYWORDS

3D modeling

Design

Modeling

Data modeling

Education and training

3D acquisition

Neural networks

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