基于OBS-MLP融合算法的船舶智能化锚位管理优化OA

[Objective]To address the low efficiency and high safety risks associated with multi-ship an-choring during offshore wind farm construction,an intelligent anchoring management method is proposed based on an optimal brain surgery-multi-layer perceptron(OBS-MLP)fusion algorithm.[Method]Initially,a three-level optimization framework of feature decoupling-dynamic pruning-preference decision was con-structed,integrating the advantages of decision trees and neural networks.This framework uses OBS pruning strategies to reduce model parameter redundancy,thus overcoming the curse of dimensionality faced by tradi-tional algorithms in high-dimensional decision spaces.Subsequently,a multi-modal feature fusion mechanism was established for the hierarchical extraction and collaborative optimization of static environmental features and dynamic interaction features.Finally,a preference decision-making model with mixed-integer program-ming constraints was designed to quantify the economic and safety weights of different adjustment strategies.[Results]Taking an offshore wind farm in Guangdong as an example,the average accuracy of the algorithm is 95.3%,which is 8.05%higher than that of the individual algorithm.The average response time is shortened to 0.9 seconds,a 30.77%reduction.The potential economic loss is reduced by about 240 000 yuan per year,thereby effectively reducing safety risks in ship anchoring operations.[Conclusion]The research provides an efficient solution to multi-ship anchoring collaborative optimization and promotes the digital trans-formation of maritime safety management.