Urban drainage pipe network intelligent health analysis system based on multi-mode twinborn deduction
Through the multimodal twin deduction of the urban drainage network intelligent health analysis system, the problem of inaccurate coupling between wellhead elevation and terrain elevation is solved. A high-precision three-dimensional model is constructed and dynamically adjusted, which improves the accuracy and stability of the drainage system, reduces the risk of flow state misjudgment, and improves the safety and management efficiency of the drainage system.
Patent Information
- Application Number
- CN202510650959.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-20
- Publication Date
- 2025-09-19
AI Technical Summary
In the three-dimensional modeling of urban drainage pipeline networks, the wellhead elevation is not accurately coupled with the terrain elevation, resulting in misjudgment of the wellhead height in the model, affecting the judgment of the free surface flow and pressure flow states. In particular, in low-lying areas, this may lead to misjudgment of the risks of backflow, overflow, or water hammer.
The intelligent health analysis system for urban drainage pipeline networks using multimodal twin deduction constructs a high-precision three-dimensional model through the three-dimensional modeling module, topological relationship construction module, characteristic parameter extraction module, coupling accuracy assessment module and flow state misjudgment risk prediction module, combined with GIS and BIM data, extracts the residual height gradient of the wellhead terrain and the height difference offset index of the historical waterlogging point, evaluates the accuracy of the coupling between the wellhead elevation and the DEM, and corrects the elevation parameters of highly sensitive wellheads through the dynamic adjustment module.
The precise coupling of wellhead elevation and DEM is achieved, which improves the accuracy and stability of the digital twin model of the drainage system, reduces the risk of misjudgment of flow state, and improves the safety and management efficiency of the drainage system.