Contextual AI Flight Warning Prediction for Stable Approach Safety
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Solution Overview
Problem
Existing technologies lack an effective means to identify probable causes of aircraft performance events and provide real-time predictions for stable flight conditions using machine learning techniques.
Innovation Solution
A contextual artificial intelligence model is developed to analyze flight data, incorporating aircraft operator background, environmental factors, and airport data, using machine learning approaches like logistic regression to predict safety-related events and provide proactive warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are applied to flight data analysis, then prediction accuracy for safety events is improved, but system complexity increases
Solution Approach 1:
The system segments flight data analysis into multiple specialized machine learning models, each targeting specific safety events (e.g., unstable approach prediction, runway overrun prediction, go-around prediction). This segmentation allows complex prediction tasks to be divided into manageable components while maintaining high accuracy for each specific event type.
Solution Approach 2:
The patent introduces contextual models as intermediary layers between raw flight data and prediction outputs. These contextual models process and interpret flight parameters, environmental factors, and aircraft state data before feeding them into prediction algorithms, thereby managing system complexity while preserving prediction accuracy.
2Reliability
If contextual data from multiple sources is integrated, then prediction reliability is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary processing and filtering of contextual data from multiple sources (flight management systems, weather data, airport information) before predictions are required. Flight parameters are continuously monitored and pre-processed during normal flight operations, so that when prediction is needed, the data is already prepared and validated, reducing actual prediction processing time.
Solution Approach 2:
The contextual models continuously process flight data throughout the flight, maintaining an ongoing analysis of aircraft state and environmental conditions. This continuous processing ensures that prediction reliability is improved through comprehensive data integration, while the system is already prepared for rapid prediction when safety events are detected.
3Loss of time
If real-time predictions are provided during flight, then operator response time is improved, but computational load increases
Solution Approach 1:
The system implements partial real-time prediction by continuously monitoring flight parameters and providing predictions only when specific triggering conditions are met (e.g., when approach parameters indicate potential instability). This selective prediction approach reduces computational load during normal flight while maintaining rapid response capability when safety events are likely, balancing operator response time needs with energy consumption constraints.
Data Source
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AI summary
A method for creating and using a contextual Artificial Intelligence (Al) model to analyze flight data for one or more aircraft, by a central computer system, is provided. The method obtains a set of aggregate contextual data comprising at least aircraft and flight-specific data, airport and air traffic control (ATC) data, weather data, and human factor data associated with flight crew members of the one or more aircraft; creates the contextual AI model using the set of aggregate contextual data, by the at least one processor; applies the contextual AI model to a set of flight data, to perform a statistical analysis; generates a set of results based on the statistical analysis, by the at least one processor, wherein the set of results comprises at least one of probable causes of aircraft performance events and probable aircraft performance events resulting from current conditions; and presents the set of results.