Marine oil spill path planning system and method based on multi-source data fusion

Through multi-source data fusion and deep Q network model, a collaborative closed-loop system is formed, which solves the lag and inefficiency problems of path planning in traditional marine oil spill treatment and realizes efficient and intelligent oil spill recovery and path planning.

CN120669699APending Publication Date: 2025-09-19WANJIANG INST OF TECH
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Patent Information

Application Number
CN202510808293.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-17
Publication Date
2025-09-19

AI Technical Summary

Technical Problem

Traditional marine oil spill treatment path planning relies on a single data source, resulting in delayed response, insufficient accuracy, high missed detection rate, and inability to achieve global optimal path planning, resulting in low oil spill recovery efficiency.

Method used

A multi-source data fusion mechanism is adopted to perform path decision-making through Kalman filtering weighted integration of drone boundary recognition, ship oil pollution parameters and meteorological diffusion prediction, combined with a deep Q network model, to form a collaborative operation closed-loop system and realize intelligent path planning.

Benefits of technology

Significantly improve oil spill recovery efficiency, shorten response time, reduce diffusion area, reduce energy consumption costs, and enhance oil spill recovery coverage and global optimization capabilities of path planning.

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Abstract

The invention discloses a marine oil spill path planning system and method based on multi-source data fusion, and belongs to the technical field of marine environment protection. According to the marine oil spill path planning method, real-time image data of an oil spill area is obtained through a multispectral camera carried by an unmanned aerial vehicle, oil spill thickness, flow velocity and oil contamination component data are collected through multiple sensors carried by a ship, meteorological data such as wind speed, wind direction and sea wave height are obtained, multi-source data are input into a reinforcement learning model, and a marine oil spill path is planned. And data fusion is carried out through dynamic weight distribution, a global optimal path is generated based on fused data, and ship-unmanned aerial vehicle cooperative operation is realized to execute an oil spill recovery task. The system comprises an unmanned aerial vehicle module, a ship module and a ground control module. Experiments show that compared with a ship oil cleaning method which does not adopt multi-source data fusion, the system and the method have the advantages that the path planning efficiency can be improved by 25%, the oil spill diffusion area can be reduced by more than 20%, the recovery efficiency can be improved by 30%, and the response time is less than or equal to 10 minutes.
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