As an essential means to eliminate redundant data, data deduplication technology significantly affects today’s era of massive data growth. In recent years, due to the rapid development of a series of related industries, such as marine monitoring, the marine monitoring data has exploded, leading to higher storage costs for marine observation stations. In the face of the surge in data size, we first think of using data deduplication technology to reduce the stored data to save storage costs. However, we have many choices for data deduplication technology. Because-block level data deduplication technology can better complete the task, and the core technology of block-level data deduplication technology is how to cut data blocks, this paper proposes a dual sliding window-based segmentation technology. The structure of double sliding windows makes the divided data block size more average to reduce the consumption of the fingerprint table in memory. At the same time, we add a prediction algorithm to the data deduplication system to predict the cutting point of the data block to improve the cutting efficiency. In addition, we propose a more accurate calculation method of the deduplication ratio, which can more accurately compare the algorithm’s performance and obtain the final experimental results of this paper by using this calculation method. Moreover, we propose a model based on Markov prediction to store massive ocean data, which can save more resources. At the end of the article, we compared the commonly used segmentation algorithms through careful experiments. Finally, we obtained and will use the public dataset experiment to compare the same checking rate at the end of this article.

With the in-depth study of the internal process mechanism of the global ocean by oceanographers, traditional ocean observation methods have been unable to meet the new observation requirements. In order to achieve a low-cost ocean observation mechanism with high spatio-temporal resolution, this paper introduces mobile crowd sensing technology into the field of ocean observation. Firstly, a Transformer-based vessel trajectory prediction algorithm is proposed, which can monitor the location and movement trajectory of vessel in real time. Secondly, the participant selection algorithm in mobile crowd sensing is studied, and based on the trajectory prediction algorithm, a dynamic participant selection algorithm for ocean mobile crowd sensing is proposed by combining it with the discrete particle swarm optimization (DPSO) algorithm. Thirdly, a coverage estimation algorithm is designed to estimate the coverage of the selection scheme. Finally, the spatio-temporal resolution of the vessel's driving trajectory is analyzed through experiments, which verifies the effectiveness of the algorithm and comprehensively confirms the feasibility of mobile crowd sensing in the field of ocean observation.

Precise vessel trajectory prediction is of great importance to the maritime safety. However, most of the existing methods only utilize Automatic Identifica

Sea surface temperature (SST) serves as a critical indicator of oceanic thermodynamic processes and climate variability, exerting essential influence on ocean fronts, typhoon tracks, and monsoon evolution. Nevertheless, owing to the highly nonlinear and complex multi-scale characteristics of SST, achieving accurate spatiotemporal forecasting remains a formidable challenge. To address this issue, we proposed an enhanced Transformer architecture that incorporates a Coordinate Attention (CA) module and an Adaptive Fusion (AD) module, enabling the joint extraction and integration of temporal and spatial features. The proposed model is evaluated through SST prediction experiments over a localized region of the South China Sea with lead times of 1, 7, 15, and 30 days. Results indicate that our approach consistently outperforms baseline models across multiple evaluation metrics. Moreover, generalization experiments conducted on datasets from regions with diverse latitudes and climate regimes further demonstrate the model’s robustness and adaptability in terms of both accuracy and stability.

Due to the complexity of ocean sensing tasks, buoy detection in traditional ocean observation methods has the disadvantages of high cost and insufficient real-time performance. Ocean mobile crowd sensing technology collects high-resolution data in real-time through shipborne sensors and has significant potential for future development in the ocean Internet of Things. In this article, we consider a cooperative vessel activity that performs sensing tasks, incentivizing multiple vessels through dynamic external task profits. To spontaneously incentivize the team to take part in tasks and maximize the total profit of the vessels, we propose a multiagent task allocation model (MATM-SN) based on deep reinforcement learning (DRL). This model uses a generative adversarial network to predict the trajectory of the vessels, and STGCN is used to extract synergistic features between vessels effectively. DRL training provides an optimal task allocation strategy for vessels. Finally, we compared four baselines and demonstrated that the MATM-SN model outperformed other baselines regarding task completion rate and total efficiency.

As an important technology for eliminating redundant data, data deduplication significantly impacts today’s era of explosive data growth. In recent years, due to the rapid development of a series of related industries, such as ocean observation, ocean observation data has also shown a speedy growth trend, leading to the continuous increase in storage costs of ocean observation stations. Faced with the constant increase in data scale, our first consideration is to use data deduplication technology to reduce storage costs. While using duplicate data deletion technology to achieve our goals, we also need to pay attention to some of the actual situations of ocean observation stations. The fingerprint retrieval process in duplicate data deletion technology plays a key role in the entire process. Therefore, this paper proposes a fast retrieval strategy based on locally sensitive hashing. The fast retrieval algorithm based on locally sensitive hashing can enable us to quickly complete the retrieval process in duplicate data deletion technology and achieve the goal of saving computing resources. At the same time, we proposed a bucket optimization strategy for retrieval algorithms based on locally sensitive hashing. We utilized visual information to address the bottleneck problem in duplicate data deletion technology. At the end of the article, we conducted careful experiments to compare hash retrieval algorithms and concluded the strategy’s feasibility.