River pollutant tracing system and method based on digital twinning

Through digital twin technology, a river pollutant traceability system is built, and a traceability path heat map is generated using graph convolution neural network and Bayesian inference, which solves the efficiency and accuracy of traditional river pollutant traceability technology in complex environments, realizes real-time response and dynamic correction of pollution sources, and improves identification efficiency and accuracy.

CN120355435AActive Publication Date: 2025-07-22NORTH CHINA UNIV OF WATER RESOURCES & ELECTRIC POWER
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Patent Information

Application Number
CN202510431642.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-08
Publication Date
2025-07-22
Estimated Expiration
2045-04-08

AI Technical Summary

Technical Problem

Traditional river pollutant traceability technology is difficult to achieve rapid and accurate pollution source identification in complex dynamic environments. The existing technology consumes time, is limited in accuracy, and has strong data heterogeneity, making it difficult to meet the needs of efficient governance.

Method used

The river pollutant traceability system based on digital twins is adopted to obtain the current water quality data through the water body sampling module, and a digital twin model is constructed. The traceability path heat map is generated using graph convolution neural network and Bayesian inference. The pollution abnormal areas are marked in real time with the drone remote sensing technology to dynamically correct the probability distribution of pollution source location.

Benefits of technology

Real-time response and dynamic correction of pollution sources have been achieved, which significantly improves the efficiency and accuracy of pollution sources identification in complex dynamic environments, and provides technical support for river basin ecological security and precise governance.

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Abstract

The invention relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.
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