Digital twin reservoir flood control scheduling method

By generating a digital twin reservoir base and utilizing deep temporal neural networks and scheduling optimization models, the problem of disconnect between multi-source data fusion and the prediction stage was solved, realizing dynamic optimization control of reservoir flood control scheduling and improving the adaptability and safety of flood forecasting and scheduling.

CN122304315APending Publication Date: 2026-06-30JILIN WATER RESOURCE & HYDROPOWER CONSULTATIVE CO OF P R CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
JILIN WATER RESOURCE & HYDROPOWER CONSULTATIVE CO OF P R CHINA
Filing Date
2026-05-29
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

In existing reservoir flood control scheduling and control schemes, the lack of spatiotemporal alignment of multi-source data makes it difficult to seamlessly integrate monitoring data, and the forecasting stage is disconnected from the scheduling generation, making it difficult to achieve dynamic optimal control.

Method used

By generating a digital twin reservoir base plate, data topology binding and prediction are performed using the reservoir spatiotemporal grid and deep temporal neural network. Combined with the scheduling optimization model, group iterative optimization is carried out to generate the target scheduling sequence and control the physical reservoir to perform flood control scheduling.

Benefits of technology

It achieves spatiotemporal alignment and dynamic optimization control of multi-source data, improves the adaptability of flood forecasting and the safety of reservoir flood control, and ensures the real-time performance and effectiveness of scheduling strategies.

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Abstract

This application provides a digital twin reservoir flood control scheduling method, comprising: acquiring multi-source reservoir monitoring data and a reservoir spatiotemporal grid; performing spatial topology binding on the multi-source reservoir monitoring data according to the three-dimensional spatial coordinate system, grid time reference, and spatial topology binding rules in the reservoir spatiotemporal grid to generate a digital twin reservoir base plate; generating time-series grid data based on the digital twin reservoir base plate, and using a deep temporal neural network to perform time series prediction on the time-series grid data to obtain a predicted water level sequence and a predicted inflow sequence; inputting these into a scheduling optimization model, performing candidate scheduling encoding processing and group iterative optimization on a preset reservoir gate opening range and scheduling time unit to obtain a target scheduling sequence; and converting the target scheduling sequence into physical reservoir control commands to control the physical reservoir. This method effectively improves the spatiotemporal alignment effect of multi-source data and the decision-making adaptability of flood control scheduling.
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