## 1 Introduction
This visual modeling method not only simplifies the work for programmers but also provides more convenient solutions for the management and maintenance of the ship's piping system, further ensuring the safety and efficient operation of the ship. To improve the programming efficiency of ship piping systems, significant efforts have been made by programmers. In 2005, [PERSON] et al. used a radial basis function (RBF) neural network to model the marine diesel generator system. The RBF neural network has strong nonlinear approximation capabilities, making it ideal for handling complex multi-input, multi-output (MIMO) systems. In their study, they modeled the generator system by inputting physical quantities such as the diesel engine's torque, excitation current, and voltage and outputting the generator's voltage, current, and frequency. The system was thoroughly analyzed. During model training, the authors used supervised learning, training the RBF neural network with extensive experimental data. The results showed that the model accurately predicted the generator's dynamic response, demonstrating high precision and reliability. The study highlighted the advantages of neural network models in handling nonlinear, complex systems, especially their robustness and adaptability during transient conditions such as sudden load changes. The innovation of this research lies in combining neural networks with traditional diesel generator modeling methods, solving the challenges traditional models face in accurately addressing complex conditions. By using the RBF neural network, the model trained quickly and could be used for real-timesimulation of ship power systems. This research offers a new approach to marine diesel generator modeling and lays the foundation for further optimization and expansion [15]. In 2010, [PERSON] et al. explored how to achieve neutral model visualization of ship piping systems using X3D technology, particularly how to utilize 3D technology to more intuitively display complex piping systems. The researchers built a lightweight 3D visualization framework using X3D, an XML-based 3D graphics standard, to show the layout and connections of ship pipelines. This visualization model could run in a browser, achieving cross-platform display and significantly facilitating the system's application and dissemination. The paper provided a detailed discussion of the visualization process, including geometric modeling of the pipeline system, texture mapping, and dynamic display of pipeline connections. The research also noted that 3D visualization technology could enhance fault detection, pipeline maintenance, and pipeline improvement design. This provides ship engineers with a crucial tool to help them understand complex piping network systems. Furthermore, the study highlighted the system's strong scalability, supporting future integration of additional functions, such as fluid dynamics simulation. The innovation of this work lies in utilizing open-source X3D technology to achieve a 3D display of ship piping systems, reducing system complexity and providing a highly operable solution for practical applications [16]. In 2015, [PERSON] et al. summarized the modeling and simulation of ship fuel supply systems, proposing a new sparse-matrix-solving algorithm capable of efficiently handling the flow and pressure distribution in large-scale fuel supply piping systems. The paper discussed in detail the mathematical models of various fuel system components, such as fuel pumps, pipelines, and valves and, based on this, established a dynamic simulation model of the entire supply system. The model is characterized by its ability to handle the system's dynamic response under different fuel conditions, particularly with adaptive adjustment to fuel viscosity changes as temperature varies. The authors verified the model's accuracy and real-time performance by integrating the simulation system with an actual ship fuel supply system.