A fish behavior dynamic monitoring and data analysis method for a fish passage of a water conservancy project
By constructing a multimodal fusion fish behavior monitoring system, the problems of insufficient multimodal data fusion and coarse behavior recognition in fish behavior monitoring in water conservancy projects have been solved. This system achieves high-precision fish behavior monitoring and ecological event analysis, has adaptive and proactive early warning capabilities, and enhances scientific decision support for aquatic ecosystem management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- POWER CHINA KUNMING ENG CORP LTD
- Filing Date
- 2025-10-14
- Publication Date
- 2026-07-24
AI Technical Summary
Existing technologies for monitoring fish behavior in water conservancy projects suffer from several problems, including insufficient multimodal data fusion, coarse identification of individual and group behaviors, difficulty in achieving spatiotemporal continuity and efficient anomaly detection, and a lack of ecological causal mechanism modeling and intelligent feedback loop.
An integrated monitoring system is constructed that combines multi-source feature collaborative extraction, trajectory autonomous shaping, swarm intelligence aggregation, real-time early warning of behavioral anomalies, and in-depth ecological causal analysis. Through multimodal synchronous acquisition and preprocessing, fusion feature extraction, target aggregation and local behavior recognition, large-scale behavioral event recognition, and ecological causal modeling, a high-precision, full-process, and self-evolving intelligent monitoring and management of fish passage behavior is achieved.
It significantly improves the accuracy and timeliness of fish behavior event identification, possesses high versatility, strong adaptability and proactive early warning capabilities, can adaptively integrate multi-source data, accurately track the spatiotemporal dynamics of individual fish and groups, identify complex ecological events in real time, and combine environmental parameters to deeply analyze the ecological driving forces of behavior, providing reliable and efficient intelligent technical support.
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Figure CN121542787B_ABST