ML Weather Data Compression for Low-Bandwidth Distribution
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Solution Overview
Problem
Existing methods for managing and distributing meteorological data, particularly in aviation and air traffic systems, face challenges with large storage requirements and limited bandwidth, leading to complex data handling and inefficient data transmission.
Innovation Solution
Utilizing machine learning encoding models, such as autoencoders and proper orthogonal decomposition, to compress meteorological data sets while preserving data integrity, allowing for efficient storage and distribution with minimal information loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional meteorological data storage and transmission methods are used, then complete weather information is preserved, but storage space and bandwidth requirements become excessively large
Solution Approach 1:
The patent extracts only the most relevant and impactful weather parameters for each aircraft type, rather than transmitting complete meteorological datasets. Different aircraft receive different subsets of weather data based on their specific operational needs, significantly reducing storage and bandwidth requirements while maintaining information completeness for each application.
Solution Approach 2:
The patent applies local quality by customizing weather data transmission for different aircraft types and flight conditions. Each aircraft receives weather information tailored to its specific route, altitude, speed, and operational characteristics, rather than uniform complete datasets, optimizing the balance between information completeness and resource consumption.
2Measurement precision
If complete meteorological data sets are transmitted, then data accuracy is maintained, but bandwidth consumption and transmission time increase
Solution Approach 1:
The system extracts and transmits only the critical weather parameters needed for each aircraft's safe and efficient operation, such as wind speed and direction for flight path planning, temperature for performance calculations, and turbulence data for comfort and safety. This extraction approach maintains necessary accuracy while dramatically reducing bandwidth consumption.
Solution Approach 2:
The patent implements partial action by transmitting a subset of weather data that is sufficient for operational decision-making without providing the complete meteorological dataset. The selected parameters represent the minimum necessary information for accurate flight operations, reducing transmission energy while maintaining functional accuracy.
3Reliability
If high-resolution weather data is processed and stored, then forecast accuracy improves, but computational complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the essential weather parameters and processed results needed for flight operations, such as derived wind fields, temperature profiles, and turbulence predictions. By extracting and transmitting only these processed outputs rather than raw complete datasets, the system maintains forecast reliability while reducing computational complexity at the receiving end.
Solution Approach 2:
The system performs preliminary processing and extraction of critical weather information before transmission. Weather data is pre-processed to extract meaningful parameters and predictions in advance, reducing the computational burden on receiving systems and simplifying data handling while maintaining forecast accuracy for operational use.
4Loss of information
If uncompressed meteorological data is transmitted, then data integrity is preserved, but transmission time and network load increase
Solution Approach 1:
The patent extracts and transmits only the essential weather parameters needed for operational decision-making, such as wind velocity, temperature, pressure, and turbulence data. This selective extraction maintains data integrity for critical parameters while significantly reducing transmission time and network load compared to uncompressed complete datasets.
Data Source
AI summary
Apparatuses, methods, systems, and program products are disclosed for compression and distribution of meteorological data using machine learning. An apparatus includes a processor and a memory that stores code executable by the processor to receive a raw meteorological data set for a time frame, the raw meteorological data set comprising a plurality of dimensions. The code is executable by the processor to compress the raw meteorological data set using a machine learning encoding model to create an encoded meteorological data set that has a storage size that is smaller than a storage size of the raw meteorological data set, wherein the encoded meteorological data set can be decoded to create a decoded meteorological data set that is substantially similar to the raw meteorological data set. The code is executable by the processor to make the encoded meteorological data set accessible to one or more end users.