近海航运中船舶吃水精准控制对安全与效率至关重要，传统人工观测精度不足，单一测量技术受环境制约。本文对船舶三维吃水模型动态匹配技术进行研究，构建包含多源数据采集预处理、多层感知机神经网络模型设计及动态反馈机制的技术架构，实现船舶载重、水文环境与航行任务的动态耦合建模，在算法层面，采用多层感知机（MLP）神经网络，有效处理多变量非线性耦合关系，其三维吃水预测精度较传统方法提升70%–80%。本文构建的船舶三维吃水动态匹配模型对保障船舶安全航行具有重要作用。
Accurate control of ship draft is very important for safety and efficiency in offshore shipping. Traditional manual observation accuracy is insufficient, and single measurement technology is restricted by environment. This paper studies the dynamic matching technology of ship's three-dimensional draft model, builds a technical framework including multi-source data acquisition and pre-processing, multi-layer perceptron neural network model design and dynamic feedback mechanism, and realizes the dynamic coupling modeling of ship's load, hydrological environment and navigation tasks. At the algorithm level, multi-layer perceptron (MLP) neural network is adopted. The accuracy of 3D draft prediction is improved by 70%～80% compared with the traditional method. The ship's three-dimensional draft dynamic matching model constructed in this paper plays an important role in ensuring ship's safe navigation.
2025,47(10): 176-180 收稿日期：2025-4-15
DOI：10.3404/j.issn.1672-7649.2025.10.030
分类号：U667.65
作者简介：王霞（1983-），女，硕士，副教授，研究方向为虚拟现实技术应用
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