To address the severe scouring challenges faced by offshore wind power infrastructure,

the scouring problem of the four-pile jacket foundation of offshore wind power under the action of ocean currents through numerical simulation. Using FLOW-3D software is studied, adopting the large eddy simulation (LES) turbulence model and the sediment transport model, the validity of the numerical model was verified through comparisons with experimental results, the scouring process of the four-pile jacket foundation under the action of a single steady flow is simulated. The development and changes in the scour pit morphology around the foundation over time are analyzed, and the effects of different flow velocities, incoming flow angles, and pile spacings on the scouring of the four-pile foundation are studied.

The results show that the group pile effect is central to the scouring characteristics of four-pile jacket foundations. The incident flow angle alters the shielding interactions among piles, leading to an asymmetric distribution of scour morphology. Pile spacing modulates the intensity of interference between adjacent piles; as the spacing increases, the group pile effect gradually weakens, and the scour pattern transitions from a unified, interconnected scour hole to relatively independent local scour holes. The maximum scour depth is primarily governed by flow velocity and exhibits only minor variation with changes in pile spacing.

The research findings provide a reference for the scouring of four-pile jacket foundations in offshore wind farms.

Aims to establish a cloud-edge-device collaborative intelligent management and control system to enhance production process controllability, shorten construction cycles, and strengthen decision support capabilities.

Driven by production plans and guided by process flows, a three-level cloud-edge-device collaborative architecture is designed. By constructing a physical-information fusion environment in the ship block workshop, a "plan-resource-execution" linkage mechanism is established. An improved genetic algorithm (IGA) combined with simulated annealing is proposed for the dynamic scheduling model, alongside the development of a multi-source heterogeneous data fusion engine to achieve full-factor visual management and control.

After system implementation, the ship block construction cycle is reduced by 19.7% compared to traditional models, production anomaly response time is shortened by 75%, and the equipment load balancing index is optimized by 28%.

The proposed cloud-edge-device collaborative management and control model effectively resolves the dynamic matching dilemma between planning and execution in ship block workshops. The established "perception-analysis-decision-execution" closed-loop system provides a reusable implementation framework for intelligent ship manufacturing, promoting the digital transformation of the shipbuilding industry.

To objectively and systematically understand the current status of reliability testing of maritime autonomous surface ships (MASS),

the current research status from three aspects: testing methods, testing technologies, and evaluation systems, and discusses the future development trends are analyzed. Specifically, it includes: conducting a visual analysis of 134 related papers using CiteSpace and VOSviewer to systematically sort out the research directions and development trends in the field of ship collision avoidance capability testing; sorting out the uses, advantages and disadvantages, and research status of the three major testing platforms: real ship testing, model testing, and virtual simulation testing; in-depth discussion on the development trends, feature comparisons, and challenges faced by the three mainstream testing scenario generation technologies based on expert knowledge, random sampling, and artificial intelligence; summarizing the evaluation indicators from four dimensions: data authenticity, scene complexity, risk, and generation efficiency; and on this basis, looking forward to future research directions.

The results show that virtual simulation testing has the advantages of low cost and high coverage and has become the main testing method. The ship collision avoidance capability testing method based on artificial intelligence has development potential in high-risk edge scenarios and ship interaction games, but the current research still faces challenges such as idealized motion models, lack of multi-ship dynamic game mechanisms, single evaluation indicators, and difficulties in virtual-to-real migration.

The research on testing scenario generation and deduction based on artificial intelligence has important research value and significance for promoting the testing of MASS.