A method and system for scheduling unmanned aerial vehicle tasks based on traffic abnormal events

By using road risk prediction and drone resource pool management, and employing the Xgboost model to screen and optimize drone resources, the problems of misjudgment and low resource utilization in drone scheduling were solved, achieving efficient and accurate drone scheduling for traffic anomalies.

CN122176925APending Publication Date: 2026-06-09SHENZHEN TUOBIDA TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN TUOBIDA TECH CO LTD
Filing Date
2026-03-23
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing drone scheduling methods suffer from problems such as misjudgment, low resource utilization, high operating costs, and insufficient intelligence and precision in handling traffic anomalies, and cannot effectively match task characteristics with drone equipment capabilities.

Method used

By employing a road risk prediction model and a drone resource pool management system, information on abnormal traffic events is acquired, and the Xgboost model is used for risk assessment. Drone resources are then screened and prioritized to generate optimal flight routes and task scheduling instructions, ensuring the intelligence and accuracy of drone selection and scheduling.

Benefits of technology

It improves the accuracy and efficiency of drone dispatching, reduces the omission and delay in the collection of key information, enhances resource utilization and operational efficiency, and meets the requirements of rapid, accurate, and efficient emergency response.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to the technical field of unmanned planes, in particular to an unmanned plane task scheduling method and system based on traffic abnormal events, which comprises the following steps: acquiring first traffic abnormal event information and road equipment information; predicting the risks of the road equipment information by using a road risk prediction model to obtain risk prediction result information; determining a first unmanned plane from an unmanned plane resource pool according to the first traffic abnormal event information; and generating a task scheduling instruction according to the initial route information of the first unmanned plane, the risk prediction result information and the first traffic abnormal event information, and delivering the task scheduling instruction to the first unmanned plane, so that the accuracy of unmanned plane scheduling for traffic abnormal events is improved.
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