Aeronautical Data Provider Switching for Flight Accuracy
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Solution Overview
Problem
Existing aeronautical data providers offer varying quality and coverage, making it difficult to integrate their information seamlessly into flight management systems, and current SBAS selection criteria do not address interoperability issues in overlapping service regions, leading to potential loss of user service.
Innovation Solution
A system utilizing a processor and memory to receive multiple streams of real-time aeronautical data, generate a machine learning model trained on historical data, and dynamically switch to the stream with higher accuracy based on a decision model that considers factors like message latency and update frequency, with a scaling parameter for real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If aeronautical data from multiple providers is integrated, then data coverage and availability improve, but data quality consistency and integration difficulty worsen
Solution Approach 1:
The system segments the data integration problem by evaluating each data provider independently through a decision model that assesses individual provider accuracy, coverage, and reliability metrics, then selectively integrates only the most suitable providers for specific geographical areas or flight phases
Solution Approach 2:
The system dynamically changes evaluation parameters such as accuracy thresholds, coverage requirements, and reliability weights based on flight conditions, geographical location, and data type needs, allowing flexible optimization of data integration without fixed integration rules
2Reliability
If data from multiple providers is simultaneously received, then data redundancy and coverage improve, but processing complexity and decision-making difficulty worsen
Solution Approach 1:
The decision model implements continuous feedback by monitoring data accuracy, latency, and provider performance in real-time, automatically adjusting data source selection based on observed performance metrics and flight conditions
Solution Approach 2:
The system performs self-service by automatically evaluating multiple data providers and selecting the most appropriate source without requiring manual intervention, using predefined criteria and machine learning algorithms to make autonomous data source decisions
3Device complexity
If a single data provider is used, then system simplicity is maintained, but data accuracy and coverage in specific areas worsen
Solution Approach 1:
The system transitions from static single-provider selection to dynamic multi-provider evaluation, where the decision model continuously assesses data provider performance and automatically switches between providers based on real-time accuracy predictions and flight conditions
Data Source
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AI summary
Apparatuses, methods, systems, and program products are disclosed for dynamic selection of an aeronautical data provider. An apparatus includes a processor and a memory that stores code executable by the processor to receive a first stream of real-time aeronautical data from a first aeronautical data provider, receive at least one second stream of real-time aeronautical data from at least one second aeronautical data provider simultaneously with the first stream, provide data from the first stream and the at least one second stream to a decision model for predicting which data has a higher accuracy, and switch using data from the first stream to data from one of the at least one second streams in response to the one of the at least one second streams having a higher predicted accuracy than the first stream.