An unmanned aerial vehicle cluster-oriented intelligent computing node health state monitoring and fault early warning system

By collecting data in real time from the nodes of a drone swarm and constructing a dynamic weighted topology graph, fault modes are extracted and matched, solving the problem of insufficient accuracy in monitoring the collaborative relationship of multiple nodes in a drone swarm and achieving efficient fault early warning.

CN121963447BActive Publication Date: 2026-06-09GRADIENT TECH CO LTD +2
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
GRADIENT TECH CO LTD
Filing Date
2026-04-03
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing methods for monitoring the health of drone swarms cannot effectively detect abnormal correlations between multiple nodes within the swarm due to collaborative operations, resulting in insufficient accuracy and timeliness of fault warnings.

Method used

The system uses a cluster of UAV nodes to collect multi-dimensional operational status data and spatial location information in real time. After processing by a ground data aggregation unit, a standardized node dataset is generated. The cloud-based intelligent analysis platform constructs a dynamic weighted topology graph and divides it into collaborative unit subgraphs. Multi-source symptom features are extracted and matched with a fault mode knowledge base to generate fault warning information.

Benefits of technology

It enables dynamic modeling and refined monitoring of the collaborative relationships among multiple nodes within a drone swarm, and can automatically extract composite symptom features that reflect the overall operational status of the swarm, thereby improving the accuracy and timeliness of fault warnings.

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Abstract

This invention relates to the field of UAV swarm monitoring technology, providing an intelligent computing node health status monitoring and fault early warning system for UAV swarms. The system includes: real-time collection of multi-dimensional operational status data and spatial location information of each node in the UAV swarm node group; processing by a ground data aggregation unit to generate a standardized node dataset; construction of a dynamically weighted topology graph by a dynamic topology partitioning module in a cloud-based intelligent analysis platform, dividing the swarm into multiple collaborative unit subgraphs; extraction of operational status data change features from multiple nodes within each subgraph to generate structured symptom description data; matching of this description data with historical symptom feature sequences in a knowledge base; determination of the target fault mode based on the matching results; and generation of fault early warning information when warning conditions are met. Finally, the system is displayed through a user terminal. This effectively improves the accuracy and timeliness of fault early warning.
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