Research on shipping statistics method based on AIS big data mining analysis

Longfei Zhao; Xiaoyi Jiang; Wenhu Lu; Linchong Kang; Yi Wang

doi:10.1117/12.3019611

23 January 2024 Research on shipping statistics method based on AIS big data mining analysis

Longfei Zhao, Xiaoyi Jiang, Wenhu Lu, Linchong Kang, Yi Wang

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 1297819 (2024) https://doi.org/10.1117/12.3019611
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

In order to solve the problems of traditional shipping statistics, this paper puts forward the method of shipping statistics based on AIS big data, and gives complete technical process and technical scheme including big data platform construction, data access, data cleaning, data warehouse construction, navigation event analysis, voyage number mining and generation of statistical index and systematically applies the above processes and schemes. Firstly, based on the distributed storage and computing framework, the four-layer system architecture of data acquisition and processing layer, data storage layer, data analysis layer and service encapsulation layer is used to put forward the construction technology method of AIS big data platform, which meets the requirements of real-time AIS stream data acquisition, historical AIS file extraction, data cleaning, data storage management, data mining analysis, statistical analysis, visual display and application service; Secondly, AIS big data mining analysis model including electronic fence analysis, berth supplement, berth commodity identification, navigation event analysis, voyage number mining, voyage cargo capacity calculation and other model algorithms is constructed and integrated into the big data platform to meet the needs of AIS big data mining analysis and statistical analysis; Finally, taking the application of AIS data of global bulk cargo ships from March to May of 2019 as an example, it shows that this method can provide statistical indicators of shipping big data in three aspects including port, sea passage and bulk cargo, and provides a new idea for realizing real-time, accurate and refined statistical analysis and display of shipping.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Longfei Zhao, Xiaoyi Jiang, Wenhu Lu, Linchong Kang, and Yi Wang "Research on shipping statistics method based on AIS big data mining analysis", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 1297819 (23 January 2024); https://doi.org/10.1117/12.3019611

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KEYWORDS

Artificial intelligence

Statistical analysis

Data archive systems

Data storage

Data conversion

Data modeling

Data processing

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