Reservoir dam safety monitoring method and system based on edge calculation

By using edge computing nodes for data preprocessing and anomaly detection, combined with a Bayesian risk assessment model, the timeliness of data processing and spatial correlation analysis in the dam safety monitoring system were addressed, thereby improving the perception and early warning capabilities of dam safety status.

CN121148113AActive Publication Date: 2025-12-16NANJING R&D TECH GRP CO LTD +1
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Patent Information

Application Number
CN202511686342.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-18
Publication Date
2025-12-16
Estimated Expiration
2045-11-18

AI Technical Summary

Technical Problem

Existing dam safety monitoring systems suffer from insufficient timeliness in data processing and limited spatial correlation analysis capabilities, which affect the timely detection of abnormal conditions and the accuracy of safety status assessments.

Method used

An edge computing-based approach is adopted, which uses edge computing nodes to acquire monitoring data for preprocessing and anomaly detection, constructs node response feature matrix and spatial correlation matrix, and combines Bayesian risk assessment model for risk assessment and early warning.

Benefits of technology

It has improved the overall perception and early warning capabilities of dam safety status, realized the accurate extraction of response information at multiple time scales and the quantification of spatial correlation patterns, and provided forward-looking decision support.

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Abstract

The invention discloses a reservoir dam safety monitoring method and system based on edge calculation, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: obtaining monitoring data by each edge calculation node, carrying out the preprocessing, and generating a standardized data matrix; establishing a reference database, executing anomaly detection, and generating a labeling time sequence data matrix; performing multi-scale decomposition on the labeled time sequence data matrix, and constructing a node response feature matrix; the central processing unit receives data of each edge computing node, analyzes a multi-parameter spatial propagation mode by constructing a parameter-spatial correlation matrix, and obtains a spatial correlation feature matrix; establishing a Bayesian risk assessment model, predicting a dam risk level and outputting a risk evolution trend; and issuing a differentiated early warning instruction according to the risk assessment result. Through a distributed architecture combining edge calculation and central processing, real-time monitoring, intelligent analysis and accurate early warning of the dam safety state are realized, and the monitoring efficiency and the risk identification accuracy are improved.
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