The increasing use of automation in fishing vessels has improved trawling efficiency while directly affecting the fishing capacity and cost of fishing vessels. Among the various influencing factors, warp tension and warp length can be varied to automatically balance the retraction and release of warp control. We combined the two parameters and independently designed and developed the key equipment for fishing vessels—the warp dynamometer and meter counter—and control software. The accuracy of the warp tension and length measurements was improved. The designed equipment was applied to sea trials under different working conditions, and the test data records were exported. Next, filtered time-domain graphs of the required parameters were plotted through complex Fourier transform, first-order low-pass filtering, and inverse Fourier transform. The results of data processing using various parameters were compared and analysed to determine the variation trends of the parameters and verify the effects of their balance control. The results indicated that using an automatic balance control system that combines warp tension and warp length can be effective for the fishing operation of offshore double-deck trawlers. In addition, first-order low-pass filtering can be used to filter complex warp tension data. This study also determined the relationship between warp tension and experimental parameters such as warp length and ship speed during the release of control. After the balance control of warp tension and warp length, the net mouth area increased by 30.7% and 36.5%, respectively, and the fishing efficiency of the vessel improved considerably.

In underwater navigation, MUVs risk damage from obstacles and equipment. Effective damage management supports timely decisions and maximizes functionality recovery. Existing approaches can be roughly categorized into rule-based reasoning, case-based reasoning and expert systems. However, the primary limitation of the existing approaches is their inability to adapt to dynamically changing scenarios. In this paper, an auxiliary decision-making system (ADMS) for manned underwater vehicle (MUV) damage management based on deep reinforcement learning (DRL) is proposed to address the problem of cabin flooding. This system is designed to provide auxiliary decision-making in emergency situations and help preserve MUV vitality. Furthermore, a comprehensive States–Actions cluster encompassing various damage management measures for real damage scenarios is constructed and digitized. Moreover, several novel reward functions are developed to ensure the DRL model obtains a safe strategy with ADMS operations. Finally, the MUV buoyancy and stability vitality evaluation criteria are defined and analyzed. The simulation results show that the auxiliary decision-making measures given by the ADMS in the damage state are effective and rational. The evaluation criterion for buoyancy vitality can exceed 38%, while the criterion for stability vitality can surpass 92%, with an optimal value exceeding 99%.