一种基于轨迹大数据的刺网渔船油耗分析方法
By using multi-source data processing and dynamic behavior perception models, the problem of insufficient accuracy in fishing vessel fuel consumption analysis under complex sea conditions has been solved, enabling real-time monitoring and optimization suggestions, and improving the accuracy and adaptability of fuel consumption prediction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- FISHERY ENG RES INST CHINESE ACAD OF FISHERY SCI
- Filing Date
- 2025-11-11
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies lack sufficient accuracy in analyzing fuel consumption of fishing vessels under complex sea conditions, and lack the ability to fuse multi-source data and process it in real time, making it difficult to formulate and implement fuel consumption optimization strategies.
Data is collected from multiple sources of sensors, preprocessed, and deep hidden features are extracted to construct social force maps, operational mode maps, and environmental impact maps. These maps are then processed using a spatiotemporal multi-graph convolutional network and combined with a dynamic behavior perception model for fuel consumption prediction and optimization.
It improves the accuracy and adaptability of fuel consumption analysis, enables real-time monitoring and optimization suggestions under complex sea conditions, enhances the accuracy and reliability of fuel consumption prediction, and supports fisheries supervision and energy efficiency improvement.
Smart Images
Figure CN121435144B_ABST