Buoys, serving as crucial platforms for ocean observation, require precise predictions of their motion states, which are essential for buoy structure design, testing, and directly related to the stability and reliability of data collection. Leveraging data-driven methods instead of traditional software modeling analysis enables efficient analysis of the ocean environment’s impact on buoys. However, the coupling mechanisms between the ocean and the atmosphere complicate the pre-diction of buoy attitudes. In response to these challenges, this paper systematically analyzes the key ocean surface elements that affect buoy attitudes and innovatively applies the Pearson correlation coefficient to quantify the potential coupling relationships between these elements. The Recursive Feature Elimination with Cross-Validation (RFECV) algorithm is employed to select the optimal feature subset from a large number of raw features. Based on this, a Convolutional Neural Networks-Bidirectional Gated Recurrent Unit (CNN-BiGRU) buoy attitude prediction model is constructed. Experimental results demonstrate that the optimized prediction model, when combined with the feature selection algorithm, achieves a minimum prediction accuracy of 95.7%. This model not only reduces the dimensionality of the original data but also precisely captures the dynamics of ocean elements and their effects on buoy attitudes, leveraging the powerful feature extraction and fusion capabilities of CNN.

Significant wave height (SWH) is a key physical parameter influencing the safety of shipping, fisheries, and marine engineering projects, and is closely related to climate change and marine disasters. Existing models struggle to balance a high prediction accuracy with low parameter counts, and are challenging to deploy on platforms such as buoys. To address these issues, this study proposes an innovative method for SWH prediction by combining Singular Spectrum Analysis (SSA) with a residual correction mechanism in a Long Short-Term Memory (LSTM) network. This method utilizes SSA to decompose SWH time series, accurately extracting its main feature modes as inputs to the LSTM network and significantly enhancing the model’s ability to capture time-series data. Additionally, a residual correction module is introduced to fine-tune the prediction results, effectively improving the model’s 12 h forecasting accuracy. The experimental results show that for 1, 3, 6, and 12 h SWH predictions, by incorporating SSA and the residual correction module, the model reduces the Mean Squared Error (MSE), Root-Mean-Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) by 60–95%, and increases the coefficient of determination (R²) by 2–60%. The proposed model has only 10% of the parameters for LSTM based on Variational Mode Decomposition (VMD), striking an excellent balance between prediction accuracy and computational efficiency. This study provides a new methodology for deploying SWH prediction models on platforms such as buoys, and holds significant application value in marine disaster warning and environmental monitoring.

The application of internal wave recognition to the buoy system is of great significance to enhance the understanding of the ocean internal wave phenomenon and provide more accurate data and information support. This article proposes an automatic internal wave recognition algorithm based on convolutional neural networks (CNN), which is used in the tight-profile intelligent buoy system. The sea profile temperature data were collected using the Bailong buoy system in the Andaman Sea in 2018. The CNN network structure is applied to feature compression of ocean temperature profile data, reducing the input feature amount of the feature recognition network, thereby reducing the overall algorithm parameters and computational complexity. By adjusting the number of convolution kernels and the length of convolution steps, the original data features in the time domain and the space domain are compressed, respectively. The experimental results show that the identification accuracy and robustness of this method are clearly superior to those of other methods. Additionally, the parameter number and calculation amount of this algorithm are very tiny, which greatly improves the possibility of its deployment in the buoy system.

The marine ecosystem is one of the most extensive and abundant ecosystems on Earth. Marine plankton is an important component, and its abundance, number of species, and dominant species are regarded as important monitoring indicators. Aiming at the problems of low accuracy and high complexity in identifying plankton based on convolutional neural networks, this study proposes a lightweight identification algorithm for plankton images based on the improved MobileNetV2. Firstly, the network layer structure is extracted by redesigning features to balance the depth and width of the network to reduce the model parameters; secondly, the lightweight coordinate attention (CA) mechanism is introduced to strengthen the attention and extraction ability of key areas; in addition, the structure of the network classifier is optimized to improve the utilization efficiency of the model parameters. The results show that the model achieves a 95.46% accuracy and 94.48% recall in 12 kinds of images. Compared with the initial MobileNetV2, the parameters and calculation amount are reduced by 72.47% and 52.09%, respectively, and the reasoning time for a single image is 6.15 ms. The model realizes the accurate identification of plankton in situ under the premise of ensuring it is lightweight. Combining time information and depth data, it is of great significance for marine ecological environment monitoring and prediction to obtain the abundance of various plankton.

To address the limitations in identifying complex anomaly patterns and the heavy reliance on manual labeling in traditional oceanographic data quality control (QC) processes, this study proposes an intelligent QC method that integrates Gated Recurrent Units (GRU) with a Mean Teacher–based semi-supervised learning framework. Unlike conventional deep learning approaches that require large amounts of high-quality labeled data, our model adopts an innovative training strategy that combines a small set of labeled samples with a large volume of unlabeled data. Leveraging consistency regularization and a teacher–student network architecture, the model effectively enhances its ability to learn anomalous features from unlabeled observations. The input incorporates multiple sources of information, including temperature, salinity, vertical gradients, depth one-hot encodings, and seasonal encodings. A bidirectional GRU combined with an attention mechanism enables precise extraction of profile structure features and accurate identification of anomalous observations. Validation on real-world profile datasets from the Bailong (BL01) moored buoy and Argo floats demonstrates that the proposed model achieves outstanding performance in detecting temperature and salinity anomalies, with ROC-AUC scores of 0.966 and 0.940, and precision–recall AUCs of 0.952 and 0.916, respectively. Manual verification shows over 90% consistency, indicating high sensitivity and robust generalization capability under challenging scenarios such as weak anomalies and structural profile shifts. Compared to existing fully supervised models, the proposed semi-supervised QC framework exhibits superior practical value in terms of labeling efficiency, anomaly modeling capacity, and cross-platform adaptability.