Optimization Design of Perceptual Intention Modeling of Offshore Vessels Driven by Intelligent Assistance

HU Haowei; HU Jun; QIN Guixiang

doi:10.19554/j.cnki.1001-3563.2025.20.003

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Packaging Engineering ›› 2025, Vol. 46 ›› Issue (20) : 25-34. DOI: 10.19554/j.cnki.1001-3563.2025.20.003

Industrial Design

Optimization Design of Perceptual Intention Modeling of Offshore Vessels Driven by Intelligent Assistance

HU Haowei¹, HU Jun^1,*, QIN Guixiang²

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Abstract

To address partial gaps in the design of offshore vessel modeling in China, the work aims to develop a design optimization scheme aligning with the perceptual image of Anchor Handling Tug Supply vessels with Kansei Engineering and artificial intelligence generated content. The vessel type and functional structure were first analyzed to establish development priorities for AHTS vessels. Core perceptual image vocabulary and representative samples were then obtained through Kansei Engineering, guiding the AIGC-driven design process within an integrated workflow. A cloud model-based evaluation method was applied to screen design alternatives, with the highest-rated scheme selected for further development. With an in-service 8000 HP deep-water platform supply vessel in China as an example, a perceptual image-optimized design scheme was ultimately derived. Theoretically, the integrated workflow couples human cognition with AIGC methods, offering an advanced and practical approach to perceptual image-based design. Practically, the proposed design optimization helps bridge gaps in China's engineering vessel modeling, while high-quality design solutions can enhance the international profile of China's shipbuilding industry and reinforce the image of "critical national assets" in the maritime sector.

Key words

offshore vessels / Kansei Engineering / artificial intelligence generated content / modeling optimization design / Anchor Handling Tug Supply vessels / cloud model

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HU Haowei, HU Jun, QIN Guixiang. Optimization Design of Perceptual Intention Modeling of Offshore Vessels Driven by Intelligent Assistance[J]. Packaging Engineering. 2025, 46(20): 25-34 https://doi.org/10.19554/j.cnki.1001-3563.2025.20.003

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