Flight Data Matching Module for Multi-Source Correlation
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Solution Overview
Problem
Determining relevant flight data from multiple sources is challenging due to incomplete and erroneous data, making it difficult to correlate and match aircraft data effectively.
Innovation Solution
A method involving the comparison of data features from parametric flight data (PFD) and operational flight data (OFD) sets, using a matching module to determine matches and link data sets based on a total score that satisfies predetermined threshold values, while considering error estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If flight data from multiple sources is collected and correlated, then data completeness and analysis accuracy are improved, but data matching difficulty and processing complexity increase due to incomplete and erroneous data
Solution Approach 1:
The system transforms the data matching problem by changing parameters: it converts multiple data sources into standardized data sets with common features, transforms error-prone data into scored matches using probability thresholds, and changes the matching criterion from exact equality to statistical likelihood. This allows incomplete and erroneous data to be processed effectively while maintaining matching accuracy.
Solution Approach 2:
The patent introduces an intermediary matching module that acts as a mediator between multiple data sources. This module compares data features across different sources, calculates match probabilities, and determines correlations based on predefined thresholds. The intermediary handles data inconsistencies and errors by using statistical methods rather than direct comparison, thereby reducing processing complexity despite data imperfections.
2Measurement precision
If strict data matching criteria are applied, then data accuracy is improved, but data loss increases due to incomplete or erroneous entries
Solution Approach 1:
The system applies partial matching by comparing only common features between data sets rather than requiring complete data alignment. It uses probability thresholds that allow matches even when not all data points are present or perfectly accurate. This partial action approach maintains accuracy for the features that are available while avoiding data loss from overly strict criteria.
Solution Approach 2:
The patent changes the matching parameter from binary (match/no match) to probabilistic (match probability with threshold). This allows the system to accommodate incomplete and erroneous data by accepting matches above a certain probability threshold, thereby reducing data loss while maintaining acceptable accuracy levels through statistical rather than deterministic matching.
3Reliability
If multiple data sources are integrated, then information reliability is improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments the data integration process into distinct steps: receiving individual data sets, extracting common features, comparing features across sources, calculating match probabilities, and determining correlations. This segmentation allows for efficient processing by handling each data source and comparison independently, reducing overall processing time while maintaining reliability through systematic multi-source validation.
Solution Approach 2:
The system performs preliminary actions by pre-defining common features to compare and establishing probability thresholds before actual matching occurs. This preparation reduces processing time during data integration by avoiding ad-hoc analysis and enabling rapid comparison against predetermined criteria, thus improving efficiency while maintaining information reliability through consistent multi-source correlation.
Data Source
AI summary
A method of matching flight data from multiple sources, including receiving a parametric flight data set for a given flight where the parametric flight data set includes a number of data features, receiving operational flight data sets related to a number of flights where the operational flight data sets include a number of data features, and determining a matching operational flight data set for the parametric flight data set.