基于多源数据的重金属污染扩散预测方法、装置、设备及介质
By constructing a river network map structure and combining it with a graph neural network spatiotemporal prediction model, the problem of insufficient data utilization in the study of heavy metal pollution diffusion was solved, and accurate dynamic prediction and control of heavy metal pollution in mining area watersheds were achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CENT SOUTH UNIV
- Filing Date
- 2026-05-15
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies rely on a single data source in the study of heavy metal pollution diffusion, fail to fully utilize multi-source environmental information, make it difficult to achieve real-time or near-real-time prediction, and have failed to effectively construct a physical propagation mechanism under the constraints of river network topology, thus failing to meet the needs of accurate early warning and prevention and control of heavy metal pollution in mining areas and watersheds.
A river network map structure is constructed by collecting multi-source environmental data. Standardized node feature vectors are generated through data preprocessing. Multi-source fusion feature vectors are generated and a multi-factor coupled propagation weight matrix is constructed. A spatiotemporal dynamic model of pollution diffusion is established and a spatiotemporal prediction model of graph neural network is used for prediction.
It has enabled accurate and dynamic prediction of the spread of heavy metal pollution in mining areas and watersheds, strengthened the coupling mechanism of rainfall, topography and hydrology, and provided efficient technical support for pollution prevention and control and risk early warning.
Smart Images
Figure CN122197743B_ABST