River ecological management method, device, equipment and medium

By constructing a water, sediment, and temperature threshold prediction model and using neural differential equations for continuous manifold learning, the problem of the inability to effectively quantify the synergistic thresholds of water, sediment, and temperature in existing technologies has been solved, thereby improving the precision and systematic nature of river ecological management.

CN121881114AActive Publication Date: 2026-04-17TSINGHUA UNIVERSITY
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Patent Information

Application Number
CN202610343122.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-19
Publication Date
2026-04-17
Estimated Expiration
2046-03-19

AI Technical Summary

Technical Problem

Existing river management methods have failed to effectively unify and quantify the synergistic thresholds of water, sediment, and temperature, and lack dynamic correlation and feedback on ecological process response mechanisms, resulting in severe ecological degradation.

Method used

By constructing a water, sediment, and temperature threshold prediction model and using neural differential equations for continuous manifold learning, the threshold ranges for runoff, sediment content, and water temperature are determined based on a comprehensive ecological health index, thereby achieving precise ecological management.

Benefits of technology

It improves the precision of river ecological management, provides accurate, continuous threshold ranges that conform to ecological laws, and enhances the precision and systematic nature of ecological management.

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Abstract

The invention provides a river ecology management method and device, equipment and a medium, and the method comprises the steps: obtaining an ecological data set, wherein ecological data in the ecological data set comprises a comprehensive ecological health index of a target river reach in a target historical period, and four-dimensional data formed by runoff, sand content and water temperature in river monitoring data; training the water and sand temperature threshold prediction model based on the plurality of ecological data, and obtaining an implicit function of the description model after the training is completed; the implicit function comprises a continuous function of comprehensive ecological health indexes about runoff volume, sand content and water temperature, and the continuous function is obtained by performing continuous manifold learning and fitting on a plurality of discrete ecological data through a neural differential equation in a water and sand temperature threshold prediction model; and solving the implicit function to obtain a runoff threshold range, a sand content threshold range and a water temperature threshold range. According to the embodiment of the invention, the method for quantitatively evaluating the river health degree and determining the threshold value is provided, and river ecological management can be performed in a targeted manner.
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