Aircraft Flight Parameter Merging With Equation-Based Validity Scoring
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Solution Overview
Problem
Current data merging methods for aircraft flight parameters are limited to processing measurements of a single parameter and do not effectively handle the integrity and availability of data across all user systems, leading to potential errors and system failures.
Innovation Solution
A method and system for merging measurements of all flight parameters linked by equations, involving acquisition, scoring, determination, and consolidation steps to assess validity and transmit accurate data to user systems, using validity scores and fuzzy logic for error handling and redundancy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data merging is performed for all flight parameters using equations and validity scoring, then measurement accuracy and system reliability are improved, but computational complexity and processing time increase
Solution Approach 1:
The data merging process is segmented into distinct functional modules: acquisition modules for collecting parameter data, scoring modules for evaluating validity, determination modules for assessing measurement reliability, and consolidation modules for integrating results. This modular segmentation allows each component to handle specific tasks independently, improving overall system reliability while making the computational complexity more manageable through distributed processing.
Solution Approach 2:
Validity scores are computed in advance for each measurement before the actual data merging occurs. The scoring modules pre-evaluate the reliability of each acquisition module's measurements using the equation-based relationships between flight parameters. This preliminary action ensures that when consolidation is needed, the system already has pre-computed validity metrics, reducing real-time computational burden while maintaining high reliability.
2Reliability
If multiple redundant sensors are installed for all flight parameters, then data availability is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system employs universal acquisition modules, scoring modules, determination modules, and consolidation modules that can process any flight parameter through the same standardized workflow. Each module type serves multiple functions across different parameters (speed, altitude, heading, etc.), allowing redundant sensors to be integrated systematically without proportionally increasing overall system complexity. The same modular architecture handles all parameters uniformly.
Solution Approach 2:
The determination module acts as an intermediary between the multiple redundant sensors and the final consolidated output. It receives measurements from various acquisition modules, uses pre-computed validity scores to assess their reliability, and determines which measurements to trust. This intermediary layer manages the complexity of multiple redundant sources by providing a standardized evaluation and selection process, ensuring data availability while controlling system complexity.
3Measurement precision
If validity scoring and determination steps are implemented for all measurements, then measurement accuracy is improved, but processing time and computational load increase
Solution Approach 1:
Validity scores are computed in advance during normal operation for all flight parameter measurements. These pre-computed scores represent the expected accuracy of each measurement based on the equation-based relationships between parameters. When actual data merging or consolidation is needed, the system retrieves these pre-computed scores rather than recalculating them, significantly reducing processing time while maintaining measurement accuracy.
Solution Approach 2:
The system performs self-validation by using the inherent equation-based relationships between flight parameters to automatically assess measurement validity. Each measurement essentially validates itself against the physical relationships it represents (e.g., speed-altitude-heading relationships), without requiring external verification. This self-service approach improves measurement accuracy through continuous validation while minimizing additional processing time since the validation uses the same data already being collected.
Data Source
AI summary
Method and system for merging measurements of flight parameters of an aircraft. The system is for merging measurements of flight parameters of an aircraft, linked to one another by at least one equation, and includes at least one acquisition module for acquiring at least one measurement of flight parameters, a scoring module for attributing a first validity score to the equations by checking whether the measurement or measurements of flight parameters are a solution or solutions of the equation or equations, a scoring module for attributing a second validity score to failure scenarios involving one or more acquisition modules from the first validity scores, a determination module for determining the validity of the measurements of flight parameters from the second validity scores, consolidation module for consolidating the measurements of flight parameters, a transmission module for transmitting the consolidated measurements of flight parameters to a user device.