Optimization Method of Spatial Layout of Marine Industry Based on Cloud Computing

Fazhi Wang, Xiaoyu Qi, Jing Dong, Yongzeng Jiang and Ali Asghar Rahmani Hosseinabadi

Mathematical Problems in Engineering, 2022, vol. 2022, 1-10

Abstract: With the continuous expansion of the marine economy, a large number of problems have been encountered in the spatial layout of the marine industry, such as space intensification, low space utilization, unreasonable industrial structure, poor management system, inadequate scientific research and innovation capabilities, poor ecological environment, poor load balance, and large resource consumption, etc. In response to these problems, this study proposes to reoptimize the spatial layout of the marine industry on the basis of cloud computing. On the basis of collecting the Hilbert values of all data, all nodes are arranged in the order of construction time to improve the Hilbert R-tree structure. Then the idea of clustering is introduced to avoid data overlap. Using the spatial correlation coefficient, the access control model of the marine industry spatial layout platform is established to optimize the marine industry spatial layout under the background of cloud computing. When using the method proposed in this study, the time consumption was about 4 seconds when the number of blocks increased to 5000. Compared with 11 seconds and 7 seconds when using the other two methods, the proposed method greatly reduces the time consumed by the number of pieces of ocean remote sensing images in different query ranges under the ocean information cloud platform. The proposed method also shows good performance in the encryption and decryption test process of the pairing method. In summary, the feasibility and effectiveness of the proposed method in the optimization of the marine industry spatial layout is confirmed, which provides a suitable theoretical basis for the optimization of the marine industry spatial layout.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3072291

DOI: 10.1155/2022/3072291

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