Concrete Dam Partition Monitoring via Graph Attention Networks
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Solution Overview
Problem
Current safety monitoring systems for concrete dams struggle to dynamically identify key parts under various loads and accurately diagnose operational behavior using data from a single type of monitoring instrument, lacking comprehensive analysis across multiple types of instruments.
Innovation Solution
A partition monitoring method that divides concrete dam operation key parts into partitions, processes time series measurement data from different types of instruments, constructs graph structures, and uses attention networks to capture temporal and spatial correlations, ultimately calculating an abnormal score to detect anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-type monitoring instruments are deployed to comprehensively analyze structural behavior, then measurement precision and reliability improve, but device complexity and data processing difficulty increase
Solution Approach 1:
The patent segments the complex monitoring system into modular components: data acquisition module, graph construction module, attention network module, and anomaly detection module. Each module handles specific tasks independently, making the overall system more manageable despite processing multi-type instrument data.
Solution Approach 2:
The patent introduces graph structures as an intermediary representation layer between raw monitoring data and anomaly detection. The graph construction module transforms multi-type instrument data into graph representations with nodes and edges, facilitating integrated analysis while simplifying the complexity of direct multi-instrument data processing.
2Ease of manufacture
If traditional multi-measuring point correlation models are used for same-type instruments, then implementation simplicity is maintained, but the ability to comprehensively verify structural behavior using multiple instrument types is insufficient
Solution Approach 1:
The patent creates a universal graph-based monitoring model that can process and integrate data from multiple types of monitoring instruments simultaneously. The graph structure serves as a multi-functional framework that handles different instrument types through unified node and edge representations, enabling comprehensive structural verification while maintaining implementation feasibility.
Solution Approach 2:
The patent combines data from different monitoring instrument types into a composite graph structure, where each instrument type contributes different features to the overall model. This composite approach integrates diverse data sources (displacement, stress, strain, etc.) into a unified analysis framework, enhancing comprehensive verification capability.
3Adaptability or versatility
If dynamic partitioning of key parts is implemented to adapt to various loads, then adaptability improves, but computational complexity and processing time increase
Solution Approach 1:
The patent implements dynamic partitioning of the dam structure into key parts based on real-time monitoring data and graph attention mechanisms. The system dynamically identifies and focuses computational resources on critical regions where anomalies are detected, adapting to varying load conditions while optimizing processing efficiency by not uniformly analyzing the entire structure.
Solution Approach 2:
The patent applies local quality by using graph attention networks to assign different importance weights to different parts of the structure. Rather than uniformly processing all areas, the system focuses computational attention on locally critical regions identified through the graph model, reducing overall processing time while maintaining high adaptability to various load scenarios.
Data Source
AI summary
The partition monitoring method for concrete dam operation key parts provided by the disclosure firstly utilizes the extracted monitoring data time-frequency vector to partition the concrete dam key parts, and on this basis, obtains time series measurement data of different types of monitoring instruments with high temporal and spatial correlation, so as to establish a graph structure. Then, the dependence of time dimension and variable dimension of multivariate time series data is captured, and the relationship between further learning and representation of graph attention network is provided. Furthermore, the final feature representation of time series measured data is obtained, and finally the anomaly score is calculated through the final feature representation to detect anomalies. The complementary mutual verification of multiple measuring points of monitoring instruments with various types is realized. The structural integrity and spatial distribution law of concrete dams are fully embodied.