Aircraft Flight Threat Pre-warning via Evolution Model and Crowdsourcing
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Solution Overview
Problem
Current air traffic control systems lack effective pre-warning methods for aircraft operation threats, such as dangerous weather and flight conflicts, due to the high dynamics and strong impact of these threats, making it challenging to accurately predict and perceive air traffic safety situations.
Innovation Solution
A method involving the acquisition of historical threat situation data, inputting it into a trained evolution model to predict evolution trends, assigning detection tasks to other aircraft through crowdsourcing, and using current flight route information to determine enhanced evolution data, which is then used to predict flight threats and send pre-warning information when conditions are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional air traffic control systems are used, then current operational control is maintained, but pre-warning capability for flight threats is insufficient
Solution Approach 1:
The system performs preliminary actions by training evolution models offline using historical threat data, and by proactively identifying potential threat evolution trends before actual threats materialize. The model pre-calculates evolution probabilities and trends for various threat scenarios, enabling early warning before dangerous situations develop.
Solution Approach 2:
The patent introduces evolution models as an intermediary between raw historical threat data and pre-warning decisions. These models act as mediators that process complex historical data, extract evolution patterns, and generate predictive warnings, thereby bridging the gap between data collection and actionable insights without requiring direct complex system restructuring.
2Measurement precision
If historical threat data is collected and analyzed, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent replaces complex manual data processing and analytical methods with automated evolution models. These models automatically ingest historical threat data, perform pattern recognition, calculate evolution probabilities, and generate predictions without requiring complex human intervention or manual analysis processes, thereby reducing processing complexity while maintaining or improving accuracy.
Solution Approach 2:
The system transforms raw historical threat data into structured evolution trends by changing parameters such as threat position, intensity, and temporal patterns. The evolution models process these parameter changes to predict future threat states, converting complex multi-dimensional historical data into simplified predictive parameters that are easier to process and interpret.
3Reliability
If real-time threat monitoring is implemented, then safety perception is improved, but response time to developing threats is insufficient
Solution Approach 1:
The system performs preliminary analysis of threat evolution trends using trained models to predict future threat states before they fully materialize. By analyzing historical patterns and projecting them forward, the system provides advance warning of developing threats, giving operators more time to respond before dangerous situations fully develop.
Solution Approach 2:
The patent implements feedback mechanisms where evolution model predictions are continuously updated with new real-time data. The system monitors actual threat developments, compares them with predicted evolution trends, and refines its predictions accordingly. This feedback loop improves both safety perception and response time by continuously adapting to actual threat patterns.
Data Source
AI summary
Embodiments of the present disclosure provide a method, an apparatus, a device and a storage medium for pre-warning of aircraft flight threat evolution. The method includes: inputting historical threat situation data to an evolution model that has been trained to convergence to output each evolution mode corresponding to the historical threat situation data and a probability corresponding to the evolution mode; obtaining evolution trend data corresponding to the historical threat situation data according to the evolution mode and the probability; assigning a detection task to other aircraft within a preset range of a target aircraft according to a crowdsourcing strategy, and acquiring current actual flight threat information detected by the other aircraft according to the detection task; sending pre-warning information to a pre-warning device if the flight threat meets a pre-warning condition.