Tunnel inrush water pressure monitoring method and system based on double collection multi-modal data
By combining multimodal data monitoring from high-definition and infrared cameras, and employing technologies such as U-Net, RAFT, ConvLSTM, and Transformer, the environmental adaptability and accuracy issues of monitoring sudden water inrush in tunnels have been resolved. This has enabled multidimensional information fusion and early warning, thereby improving the monitoring effect of sudden water pressure in tunnels.
CN121113176BActive Publication Date: 2026-07-03SHANDONG UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANDONG UNIV
- Filing Date
- 2025-09-05
- Publication Date
- 2026-07-03
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Figure CN121113176B_ABST
Abstract
The application provides a tunnel inrush water pressure monitoring method and system based on double collection multi-modal data, and belongs to the technical field of inrush water pressure monitoring; the method comprises the following steps: collecting video data by using a high-definition camera; sequentially performing intelligent water flow area segmentation, optical flow calculation and ConvLSTM time sequence prediction on the video data to predict water flow velocity; collecting temperature data by using an infrared camera; sequentially performing adaptive temperature gradient calculation and temperature anomaly detection on the temperature data to predict water flow temperature; aligning and fusing the predicted data of the water flow velocity and the water flow temperature, and constructing a water pressure calculation model to obtain a water pressure prediction value; and determining an early warning level according to the water pressure prediction value to adjust the monitoring mode of the tunnel inrush water pressure. The application combines infrared and visual range technologies, can realize accurate prediction of the tunnel inrush water pressure, and provides corresponding early warning monitoring modes according to the prediction results.
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Citation Information
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