A water supply pipeline leakage point identification and early warning method and system based on a neural network algorithm
By collecting multimodal data through distributed sensors and processing and fusing features using neural network algorithms, the problems of high false alarm rate and insufficient positioning accuracy in water supply network leak detection have been solved, achieving high-precision leak identification and real-time early warning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HONGJI JUNYE ENVIRONMENTAL PROTECTION TECH CO LTD
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for detecting leaks in water supply networks suffer from high false alarm rates, high false negative rates, and insufficient location accuracy. In particular, they are difficult to accurately describe the propagation pattern of leakage signals in complex environments, resulting in large location errors.
A multimodal data collection model for water supply pipeline leaks is constructed by using distributed deployment of acoustic, pressure, and flow sensors, preprocessing, feature extraction, and fusion through neural network algorithms, and utilizing a cyclic cross-modal attention mechanism to achieve information interaction.
It improves the accuracy and robustness of leak detection, reduces the false detection rate, and achieves high-precision leak location and real-time early warning.
Smart Images
Figure CN122407993A_ABST