Sea ice poses a significant hazard to vessels navigating in high-latitude maritime regions. To effectively prevent the collision between ships and large sea ice (such as icebergs) and other similar accidents, it is crucial to identify sea ice and its types in a timely and accurate manner. However, traditional sea ice identification techniques, such as optical and radar remote sensing, are often limited by visibility conditions, suffering from poor accuracy, high measurement costs, and low identification efficiency. This paper proposes a sea ice identification method based on a vibration sensor network, combined with the PCA-BPMIV-GRU strategy, which achieves low cost, high efficiency, and high accuracy in sea ice type detection. Firstly, a cost-effective MEMS vibration sensor network is deployed to capture vibration information of sea ice. The data collected from six sensors at different locations at the same time are treated as independent samples to construct a dataset, which contains time-frequency domain characteristic data of three sea-ice types: broken ice, flat ice, and open water. Secondly, the dimension reduction and feature optimization of vibration data were carried out by combining Principal Component Analysis (PCA) and the improved Mean Influence Value Optimization algorithm (BPMIV). This integration of unsupervised and supervised algorithms effectively eliminated unnecessary feature information, significantly improving model identification efficiency. Finally, seven commonly used classification algorithms are compared for the identification of three types of sea ice, evaluating their identification accuracy and efficiency under different dimensionality reduction methods. The results confirm that the Gated Recurrent Unit (GRU) model achieves the best comprehensive performance. Experimental data shows that the proposed method reduces the number of features to 9, resulting in a 24.2 % decrease in parameters compared to the GRU model alone, while achieving a high identification accuracy of 93.2 %. The parameters and identification accuracy of the proposed method are superior to current sea ice identification technologies.

With the development of big data, people focus on data security and privacy, blockchain has been paid more and more attention. Compared to pre-2013, the number of blockchain literature publications has increased. Scholars from different research fields have deeply explored blockchain technology. In this paper, we collected blockchain literature data from 2013 to 2020, and then we conducted a quantitative analysis of publications, author groups, research content and future research content. For the current research status, we also discussed the problems facing blockchain. We started by mining representative authors, and revealed current status of blockchain research, author’s team cooperation, and blockchain’s development content. We can provide research objectives and inspiration for new researchers, and give research routes for the future development of blockchain.