With the rapid advancement of artificial intelligence, big data, the Internet of Things, and other emerging intelligent technologies, with their deep integration into the military domain, modern naval warfare is undergoing an accelerated transformation from information-centric warfare to intelligent warfare. The intelligentization of surface warships has become a core development direction for naval equipment modernization and major naval powers worldwide. Focusing on the demand for full-life-cycle intelligent upgrading of surface warships, this paper systematically identifies their core development trends and provides an in-depth analysis of the application status and evolutionary pathways of key technologies. The research indicates that the intelligentization of surface warships is characterized by four major development trends: First, design and manufacturing are shifting from experience-driven paradigms to data- and knowledge-driven collaborative approaches, enabling full-process intelligent iteration through generative design, physics-informed neural networks, and digital twin technologies. Second, operation and maintenance is transitioning from manual inspection and scheduled maintenance to minimally manned and autonomous modes, achieving condition awareness, fault diagnosis, and predictive maintenance through ship-shore integrated intelligent systems. Third, perception and decision-making are evolving from traditional sequential architectures into integrated closed loop systems featuring multi-source fusion, autonomous cognition, and real-time decision-making, thereby supporting autonomous navigation and rapid battlefield response of surface warships. Fourth, maritime warfare is advancing from platform-centric confrontation to distributed and networked system-of-systems operations, forming a globally integrated combat architecture characterized by manned-unmanned collaboration, edge autonomy, and high resilience. This paper focuses on key technologies across four major domains: intelligent design and manufacturing, minimally manned operation and maintenance, integrated perception and decision-making, and system-of-systems maritime warfare. The coverage includes parametric design, performance simulation and prediction, digital welding, intelligent fault diagnosis, condition-based maintenance, multi-source data fusion, intelligent decision-making and planning, cluster coordination, and system security. This study provides theoretical support and technical references for the evolution of surface warships in China from localized intelligence toward global system-level intelligence, promotes the overall improvement of the intelligent level of surface warships, and supports the development of future maritime intelligent warfare capabilities.
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This study proposes a strategy algorithm based on the fuzzy inference method for the rapid optimization of unmanned underwater vehicle (UUV) hull design parameters.
The initial solutions generated by the genetic algorithm are first fuzzified during the fuzzification stage, then used as training samples, and the antecedent parameters of the fuzzy rules are obtained using an equal interval fuzzy partition strategy with the membership values of all calculated UUV solutions. Next, a least learning machine (LLM) is employed to solve the consequent parameters of the fuzzy rules. Based on the generated antecedent and consequent parameters, new UUV solutions are created and the evaluation membership values for speed and range are calculated. Finally, these new UUV solutions are tested against the constraint conditions to obtain optimized and compliant UUV design parameters.
The experimental results show that within 20 seconds, the intelligent fuzzy inference method can infer multiple UUV hull parameter schemes with a combined evaluation membership degree score for speed and range of over 170 points based on the initial UUV hull parameters generated by genetic algorithms.
The proposed method effectively enhances design efficiency and balances speed and range. The findings of this study can provide valuable references for the intelligent and rapid generation of UUV hull parameters.
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