Urban low-altitude grid management process control method and system based on unmanned aerial vehicle analysis

By constructing models for building interference, meteorological interference, and flight risk assessment, the flight risks of drones in urban low-altitude environments are evaluated. This solves the problem that existing technologies cannot accurately model the impact of urban three-dimensional building structures and meteorological conditions, thereby improving the safety of drone flight paths.

CN120977150BActive Publication Date: 2026-06-09TUOHENG TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
TUOHENG TECH CO LTD
Filing Date
2025-07-29
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies fail to accurately model the impact of urban 3D building structures on signal propagation and flight feasibility in urban low-altitude environments, fail to assess the dynamic impact of meteorological conditions on flight paths in real time, and lack refined low-altitude grid division and local risk prediction, making it difficult to guarantee the safety of UAV flights.

Method used

By acquiring urban 3D data and meteorological data, we construct building interference assessment models, meteorological interference models, and flight risk assessment models to evaluate the impact of building density, reflection interference, rainfall interference, and wind disturbance on UAV flight and generate heat maps for path planning.

Benefits of technology

It improves the safety of drone flight paths, enabling accurate identification and avoidance of high-risk areas, thus enhancing the stability and safety of flight paths.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a city low-altitude gridding management process control method and system based on unmanned aerial vehicle analysis, belongs to the technical field of unmanned aerial vehicle flight management, acquires city three-dimensional data, divides city regions into multiple grid units, acquires meteorological data, constructs a building interference evaluation model, introduces the open angle and building enclosure rate of each grid unit into the building interference evaluation model to calculate building density, simultaneously evaluates the reflection interference of the unmanned aerial vehicle through the number and intensity of reflection paths, constructs a meteorological interference model, acquires the cloud cluster humidity distribution, raindrop particle size and wind parameters of the grid unit, introduces the meteorological data into the meteorological interference model to evaluate the rainfall interference condition and wind disturbance condition, constructs a flight risk evaluation model, introduces the building density, multi-path reflection error index, rainfall interference index and wind disturbance intensity into the flight risk evaluation model to evaluate the flight risk degree of the unmanned aerial vehicle, and the safety of the flight path of the unmanned aerial vehicle is improved.
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