Filter Criterion Generator for Aviation Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing volume of meteorological and flight path data limits the availability of time, processing resources, and data storage for training predictive aviation models, making it difficult to analyze and utilize this data effectively.
Innovation Solution
A method and system that access historical timestamped meteorological and flight path data to identify relevant conditions and time periods with high air traffic volume, generating a filter criterion to focus analysis on specific geographic regions and conditions, thereby reducing data complexity and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all available meteorological and flight path data is used for analysis, then the completeness and accuracy of predictive models is improved, but the processing time and resource requirements increase significantly
Solution Approach 1:
The patent extracts and filters only the most relevant data subsets from the complete dataset based on predefined criteria such as aircraft type, flight phase, meteorological conditions, and geographic regions. This extraction process removes unnecessary data while retaining the essential information needed for accurate model training, thereby reducing processing time without significantly compromising model accuracy
Solution Approach 2:
The patent segments the large dataset into multiple smaller, specialized subsets categorized by different flight phases (takeoff, climb, cruise, descent, landing), aircraft types, and meteorological conditions. Each segmented dataset can be processed independently and used for training specific aspects of predictive models, enabling parallel processing and reducing overall computation time
2Reliability
If the complete historical data set is processed, then comprehensive model training is achieved, but processing resources and computational power are overwhelmed
Solution Approach 1:
The patent applies local quality by selecting data subsets that are locally optimal for specific training objectives. Instead of uniformly processing all data, the system identifies and processes only the data portions that are most relevant to particular flight conditions, aircraft types, or meteorological scenarios, thereby maintaining model reliability while reducing overall processing resource requirements
Solution Approach 2:
The patent implements partial action by processing only a carefully selected portion of the complete dataset that is sufficient for achieving reliable predictive models. The filtering criteria are designed to retain the most informative data samples while discarding redundant information, enabling model training with partial data that maintains reliability without overwhelming processing resources
3Adaptability or versatility
If all meteorological data from all geographic regions is analyzed, then the coverage and applicability of the model is improved, but data storage requirements become prohibitive
Solution Approach 1:
The patent performs preliminary filtering and selection of geographic regions and meteorological conditions based on expected model applicability requirements. By pre-identifying and retaining only the data from relevant geographic regions and weather conditions before model training, the system reduces storage requirements while maintaining the versatility needed for various flight scenarios
Data Source
AI summary
A method includes accessing historical data including timestamped meteorological data and flight path data. The timestamped meteorological data indicates meteorological conditions detected in each of a plurality of geographic regions. The flight path data indicates air traffic volume in each of the plurality of geographic regions during particular time periods. The method also includes, based on the historical data, identifying at least one meteorological condition and at least one time period corresponding to higher than threshold air traffic volume in a particular geographic region of the plurality of geographic regions. The method further includes generating a filter criterion based on the particular geographic region, the at least one meteorological condition, and the at least one time period. The method also includes generating a flight plan based on a portion of the historical data that satisfies the filter criterion.