## 5 Discussion  
The generation of extremely-low-frequency magnetic fields in underwater platforms is complex, with diverse distribution patterns. The magnetic fields, which serve as the primary components, dynamically change with varying frequency bands, distances, environmental conditions, and operational states. To effectively implement active control of ELF magnetic fields, it is necessary to analyze the contribution rates of different types of ELF fields to the overall ELF magnetic environment, thereby providing a theoretical basis for the design of active control strategies. This study begins with magnetic field signals that share the same spatiotemporal reference, and extracts multi-modal features of the signals from aspects such as frequency-domain characteristics, time-domain characteristics, and time-frequency domain characteristics. Based on this, the Analytic Hierarchy Process (AHP) is utilized to establish a contribution rate model of ELF magnetic fields in underwater platforms based on multi-modal signal features. Finite element simulation methods are employed to analyze the contribution rates of ELF magnetic fields with different causes in typical scenarios. Due to the complexity of the causes of ELF magnetic fields in underwater platforms, there is currently a lack of systematic control strategies targeting different causes. To enhance the effectiveness of active control over ELF magnetic fields, this paper proposes a calculation \begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance** & **Magnetic Field Category** & **Contribution Rate** \\ \hline \multirow{3}{*}{10 m} & Wake magnetic field & 0.2335 \\  & Shaft frequency magnetic field & 0.7052 \\  & Equipment magnetic field & 0.0613 \\ \hline \multirow{3}{*}{50 m} & Wake magnetic field & 0.2707 \\  & Shaft frequency magnetic field & 0.6573 \\  & Equipment magnetic field & 0.0720 \\ \hline \multirow{3}{*}{100 m} & Wake magnetic field & 0.3488 \\  & Shaft frequency magnetic field & 0.5831 \\  & Equipment magnetic field & 0.0681 \\ \hline \multirow{3}{*}{150 m} & Wake magnetic field & 0.5317 \\  & Shaft frequency magnetic field & 0.3992 \\  & Equipment magnetic field & 0.0691 \\ \hline \multirow{3}{*}{200 m} & Wake magnetic field & 0.4901 \\  & Shaft frequency magnetic field & 0.4360 \\ \cline{1-1}  & Equipment magnetic field & 0.0740 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Contribution rates of the radial detection line. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance** & **Magnetic Field Category** & **Contribution Rate** \\ \hline \multirow{3}{*}{100 m} & Wake magnetic field & 0.7265 \\  & Shaft frequency magnetic field & 0.2083 \\  & Equipment magnetic field & 0.0652 \\ \hline \multirow{3}{*}{200 m} & Wake magnetic field & 0.7653 \\  & Shaft frequency magnetic field & 0.1571 \\  & Equipment magnetic field & 0.0776 \\ \hline \multirow{3}{*}{300 m} & Wake magnetic field & 0.7549 \\  & Shaft frequency magnetic field & 0.1726 \\  & Equipment magnetic field & 0.0725 \\ \hline \multirow{3}{*}{400 m} & Wake magnetic field & 0.7630 \\  & Shaft frequency magnetic field & 0.1615 \\  & Equipment magnetic field & 0.0756 \\ \hline \multirow{3}{*}{500 m} & Wake magnetic field & 0.7591 \\  & Shaft frequency magnetic field & 0.1648 \\ \cline{1-1}  & Equipment magnetic field & 0.0761 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Contribution rates of the stern detection line. model for the contribution rates of different types of ELF magnetic fields to the overall ELF magnetic environment, providing a theoretical foundation for future research and the design of active control strategies. This study establishes a multidimensional feature extraction method for ELF magnetic fields based on time-domain, frequency-domain, and time-frequency domain characteristics. Signals from ELF magnetic fields in underwater platforms, which arise from different causes, exhibit multidimensional and multimodal features, making it impractical to directly utilize the raw signals for contribution rate analysis.