A large model-based ship intelligence research and judgment analysis method and platform

By adopting a ship intelligence analysis method based on a large model, integrating multimodal data and combining industrial cloud computing and knowledge graphs, and optimizing feature extraction and model training, the problem of low efficiency in multimodal data processing in existing technologies is solved, and ship intelligence analysis with high adaptability and high accuracy is achieved.

CN122173887APending Publication Date: 2026-06-09BEIJING LITONG XINYUAN TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING LITONG XINYUAN TECH CO LTD
Filing Date
2026-02-05
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing ship intelligence analysis methods rely on single-modal data processing and lack systematic integration of multimodal data, resulting in one-sided analysis results. Furthermore, incomplete model training data affects the model's generalization ability and analysis accuracy, failing to meet the high requirements of maritime management and ship operation safety.

Method used

A large-model-based ship intelligence analysis method is adopted. Multimodal ship intelligence data is acquired and processed in sections, and combined with ship maneuvering characteristics and sea area attributes for fusion analysis. An enhanced ship model is constructed, and industrial cloud computing and knowledge graphs are used to optimize feature extraction and model training, realizing the full-process processing of multimodal data.

Benefits of technology

It has improved the accuracy and real-time nature of ship intelligence analysis, ensured the reliability of intelligence analysis, and provided strong support for maritime management and safe ship operation.

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Abstract

The application provides a ship intelligence analysis method and platform based on a large model, and belongs to the technical field of large model analysis, and comprises the following steps: acquiring AIS data, radar images, ship communication messages and other multi-modal historical data and processing the multi-modal historical data in a mode, combining ship maneuvering characteristics and sea area attributes, generating a supplementary sample set through time-space correlation analysis; constructing a single modal feature set according to a mode matching feature extraction mode, outputting an analysis index and a fusion confidence after analyzing a feature distribution vector; integrating an index set, a sample set and a ship field knowledge graph, optimizing a preset large model hierarchical structure, and obtaining a ship enhanced model through multi-stage training, so that structured intelligence can be output by inputting current multi-modal data. The application improves sample integrity and model adaptability, and guarantees analysis accuracy and real-time performance.
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