Deep learning-based intelligent monitoring system and method for bank collapse risk of reservoir area

By using a deep learning-based intelligent monitoring system for reservoir bank collapse risks, high-risk key points are screened, a dynamic correlation network is constructed, the correlation degree is calculated, and risk communities are divided. This solves the problem of inaccurate monitoring in existing technologies, realizes real-time dynamic monitoring and precise prevention and control, and meets the needs of intelligent water conservancy management.

CN122022504APending Publication Date: 2026-05-12浙江中易慧能科技有限公司
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Patent Information

Application Number
CN202610492959.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-15
Publication Date
2026-05-12

AI Technical Summary

Technical Problem

Existing reservoir bank collapse risk monitoring technologies cannot achieve real-time dynamic monitoring and accurate risk level output, nor can they accurately assess the radiation impact of key risk points on surrounding monitoring areas, resulting in insufficient comprehensiveness and accuracy of risk assessment.

Method used

The deep learning-based intelligent monitoring system for reservoir bank collapse risk identifies high-risk key points, constructs a dynamic correlation network, calculates direct and indirect correlations, introduces correction functions and background risk attributes, and employs a graph neural network community detection algorithm to divide risk communities, thereby achieving accurate risk classification and prevention.

Benefits of technology

It enables precise classification and prevention of bank collapse risks in the opposite bank area, adapts to the needs of smart water conservancy management under the digital economy, reduces disaster losses, and supports optimal resource allocation and early warning.

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Abstract

The invention provides a deep learning-based intelligent monitoring system and method for reservoir bank collapse risk, and relates to the technical field of bank monitoring, and the method comprises the steps: obtaining and preprocessing all monitoring point data of a reservoir bank monitoring network, and constructing a total monitoring point set; screening key base points and quantifying the inherent risk characterization quantity of the key base points; a dynamic association network of the common monitoring points and the key base points is established, and the final clustering association degree of the common monitoring points relative to the key base points is obtained; calculating a radiation risk value of each key base point to the associated common monitoring points in combination with the inherent risk characterization quantity and the final clustering association degree; fusing the radiation risk values of all the key base points to obtain a comprehensive risk value of the common monitoring point; constructing a directed weighted graph by taking the key base point as a source point, and dividing risk communities by adopting a graph neural network community discovery algorithm; and based on the community comprehensive risk value statistical distribution and the highest risk level of the contained key base points, assigning a unified risk level to each monitoring point and outputting the risk level.
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