Unmanned aerial vehicle inspection terminal data anomaly detection method and system based on cooperation of behavior coding and Transform-RVM

By combining behavior coding and Transformer-RVM, the problem of low accuracy in anomaly detection of UAV inspection terminal data was solved, and efficient and accurate anomaly detection of high-dimensional and nonlinear data was achieved.

CN121765576APending Publication Date: 2026-03-31ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID NINGXIA ELECTRIC POWER COMPANY
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Patent Information

Application Number
CN202511875104.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-12
Publication Date
2026-03-31

AI Technical Summary

Technical Problem

Existing technologies have low detection accuracy in anomaly detection of UAV inspection terminal data, especially in high-dimensional, nonlinear, and noisy data, where it is difficult to capture temporal correlation and spatial topological relationships, resulting in low anomaly recognition accuracy and poor generalization performance.

Method used

We employ a behavior-encoding-based Transformer-RVM approach. By preprocessing multi-source sensor data, we generate a regular temporal sample set. We then use graph convolutional networks to fuse device spatial topology information to generate spatiotemporal fusion features. We extract deep features using a Transformer editor and combine Relevant Vector Machine (RVM) and Neural Differential Equations for efficient training to construct a highly sparse RVM model for anomaly detection.

Benefits of technology

It effectively improves the accuracy and efficiency of anomaly detection in UAV inspection terminal data, and can efficiently capture dynamic behavior patterns and global temporal dependencies, thereby achieving accurate detection of anomalies in inspection data.

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Abstract

The invention provides an unmanned aerial vehicle inspection terminal data anomaly detection method and system based on behavior coding cooperating with Transform-RVM, and relates to the technical field of artificial intelligence, the method comprises the following steps: preprocessing sensor original data of an unmanned aerial vehicle inspection terminal, and generating a time sequence sample set; the time sequence samples are converted into behavior coding vectors, and space-time fusion features are generated by fusing equipment space topology information through a graph convolutional network; inputting the space-time fusion features into a Transform editor, and extracting deep features through a multi-head self-attention mechanism, residual connection, layer normalization and a feedforward network; constructing a probability classification model by adopting a relevance vector machine RVM, modeling a parameter optimization process by utilizing a neural differential equation, and performing efficient training in combination with a dynamic sparsity control and adjoint sensitivity method to obtain a highly sparse RVM model; and performing anomaly detection on the unmanned aerial vehicle inspection terminal data acquired in real time by using the highly sparse RVM model. According to the scheme, the anomaly detection precision of the unmanned aerial vehicle inspection terminal data can be improved.
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