一种基于人工智能的水质异常检测与污染源溯源系统
By constructing an AI-based water quality anomaly detection and pollution source tracing system, and utilizing spatiotemporal graph neural networks and knowledge graph technology, the system solves the problems of high false alarm rate and low tracing efficiency in existing water quality monitoring systems, and achieves efficient detection and rapid tracing of water quality anomalies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHUIFA PLANNING & DESIGN CO LTD
- Filing Date
- 2026-04-10
- Publication Date
- 2026-07-17
AI Technical Summary
Existing water quality monitoring systems suffer from high false alarm rates, low efficiency, and limited coverage in anomaly detection and pollution source tracing. They are unable to effectively identify multi-parameter coupled anomalies and cross-regional progressive pollution, and lack refined modeling of water system topology and probabilistic assessment of multiple potential pollution sources.
An AI-based water quality anomaly detection and pollution source tracing system is adopted, including a spatiotemporal graph neural network anomaly detection module and an intelligent tracing module that integrates knowledge graphs. By constructing a water system topology map, graph attention convolutional network, multi-scale temporal coding and Bayesian probabilistic inference decision network, combined with hydrodynamic inverse model and pollution source knowledge graph, the system can achieve real-time monitoring of water quality parameters and rapid location of pollution sources.
It significantly improves the detection accuracy of complex pollution events, reduces the false alarm rate, shortens the response time for abnormal early warnings, and provides clear source tracing results and probability confidence levels, supporting rapid and accurate emergency response for environmental supervision.
Smart Images
Figure CN122020490B_ABST