AI-based low-altitude airspace information center collaborative management and control method

By using multimodal sensor data fusion, graph attention network to assess aircraft interaction risks, multidimensional environmental voxel model trajectory replanning, and communication parameter optimization, a high-precision and robust intelligent control system for low-altitude airspace was constructed. This system solves the problem of lagging decision-making mechanisms in existing technologies and achieves dynamic adaptive collaborative control.

CN122024533AActive Publication Date: 2026-05-12BEIJING XUNAO TECH
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Patent Information

Application Number
CN202610192491.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-10
Publication Date
2026-05-12
Estimated Expiration
2046-02-10

AI Technical Summary

Technical Problem

The existing low-altitude airspace management system lacks a closed-loop intelligent decision-making mechanism from multimodal perception to collaborative execution. It is unable to dynamically and adaptively identify high-risk aircraft interactions and complex environmental constraints, resulting in lagging response speed and decision-making accuracy. It is also difficult to cope with abnormal situations such as complex changes in meteorological conditions, sudden electromagnetic interference, and close-range aircraft conflicts in the low-altitude airspace.

Method used

Initial sensing data is collected by multimodal sensors, spatiotemporal calibration and multi-source feature fusion are performed, graph attention network is used to assess the interaction risks of the aircraft, trajectory replanning is performed by combining a multidimensional environmental voxel model, communication channel quality is evaluated by a directional antenna array, collaborative control execution strategy is generated, and a classifier and Neo4j graph database are integrated for multi-department collaborative response.

Benefits of technology

It achieves high-precision and robust intelligent control of low-altitude airspace, dynamically identifies high-risk interactions, optimizes communication parameters, avoids communication interruptions, enables emergency response with multi-department resource linkage, and avoids chain conflicts caused by local scheduling.

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Abstract

The invention relates to the technical field of airspace control, and discloses an AI-based low-altitude airspace information center collaborative control method, which comprises six core steps of multi-source sensing fusion, abnormal behavior discrimination, dynamic risk assessment, environmental constraint re-planning, communication collaborative optimization and collaborative execution decision-making to construct a closed-loop intelligent decision-making mechanism. Microsecond-level time synchronization is realized through a Beidou or GPS satellite time service system, and the target identification precision is improved in combination with a weighted evidence fusion theory; an R-tree and space-time bounding box dual rule engine is combined to accurately detect abnormal flight; the collision risk between the aircrafts is quantified by using a three-layer stacked multi-head map attention network, and trajectory re-planning under environmental constraints is realized through a multi-dimensional environment voxel model; a ground and satellite hybrid networking architecture is adopted for communication collaborative optimization, so that terrain shielding and electromagnetic interference are avoided in management and control communication, global coverage and accurate adjustment are realized, and the accuracy, real-time performance and safety of management and control are ensured.
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