Data-Driven Operational Assessment Method and Digital Twin System for Unmanned Surface Vehicles

To address the challenge of effectively leveraging multi-source data for automated operational assessment of Unmanned Surface Vehicles (USVs) and utilizing digital technologies for monitoring and control, this paper proposes a data-driven state assessment method for surface unmanned systems and develops a digital twin system tailored for USVs. First, a dual-channel feature modeling mechanism is constructed by integrating physically interpretable statistical features with temporal convolutional features. Second, a complementary modeling strategy is adopted using CatBoost for static classification and GRU for dynamic modeling, while a Covariance Intersection (CI) fusion strategy is introduced to enhance the classification performance and adaptability of the model. Finally, a digital twin system is designed that incorporates Position Estimation, Attitude Estimation, and State Evaluation, enabling real-time monitoring and multidimensional visualization of USV operational states. Experimental results demonstrate that the proposed method outperforms baseline approaches in terms of accuracy, F1-score, and other key metrics, exhibiting strong generalization capability and promising potential for practical deployment.