Runway Condition Model for Landing Safety
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Solution Overview
Problem
Current runway condition assessment and vehicle operation safety techniques fail to accurately account for real-time variability in runway surface conditions, leading to increased risks of runway overruns and veering off due to inadequate data and manual input reliance.
Innovation Solution
A machine-learning model-based system that aggregates data from multiple sources to generate adjusted runway condition codes, determining required landing parameters for each section of the runway, and provides real-time alerts and interface updates for safe landing procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual runway condition assessment methods are used, then system complexity is reduced, but measurement precision and reliability of runway condition data deteriorate
Solution Approach 1:
The patent replaces manual mechanical assessment methods with an automated machine learning model that processes runway condition data. The system uses computational algorithms to analyze multiple data sources and generate adjusted runway condition codes, eliminating the need for manual assessment while significantly improving measurement precision and reliability of runway condition data.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw runway condition data and final assessment results. This intermediary layer processes and synthesizes data from multiple sources including sensors, weather stations, and historical records, transforming raw data into accurate adjusted runway condition codes that reflect real-time runway conditions.
2Reliability
If real-time data aggregation from multiple sources is implemented, then reliability of runway condition assessment is improved, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional machine learning model that simultaneously performs data aggregation from multiple sources, data validation, trend detection, and adjusted runway condition code generation. This universal system handles diverse data types including sensor readings, weather data, and historical records through a single integrated platform, improving reliability while managing complexity through functional consolidation.
Solution Approach 2:
The patent divides the runway into multiple sections and processes data for each section independently through the machine learning model. This segmentation allows the system to handle complex multi-source data aggregation at a granular level, generating specific adjusted runway condition codes for each section while maintaining overall system manageability through modular processing.
3Productivity
If adjusted runway condition codes with confidence scores are generated, then productivity of landing safety assessment is improved, but loss of information increases due to data processing
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model generates adjusted runway condition codes along with confidence scores that indicate the reliability of each assessment. This feedback loop allows operators to understand the certainty level of each code, enabling informed decision-making while maintaining data fidelity through transparent representation of assessment confidence levels.
Solution Approach 2:
The patent transforms raw runway condition data into adjusted runway condition codes with associated confidence scores, effectively changing the parameter representation from raw sensor readings to standardized, interpretable codes. This parameter transformation maintains essential information by preserving confidence metrics that reflect the quality and reliability of the underlying data, enabling efficient productivity improvement without significant information loss.
Data Source
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AI summary
Embodiments of the present disclosure are directed in part toward a runway assessment and landing safety (RALS) service comprising a runway condition model configured to provide enhanced runway condition assessment and vehicle landing safety for one or more vehicles. Embodiments are configured to process runway condition data aggregated from a plurality of runway condition data sources that describe a surface condition associated with a plurality of sections of a runway in order to generate adjusted runway condition codes for the plurality of sections of the runway. The adjusted runway condition codes are generated by the runway condition model based on a confidence associated with initial runway condition codes associated with the plurality of sections of the runway. Embodiments are configured to generate required landing parameters for the plurality of sections of the runway in order to determine a required runway length for a respective vehicle.