Aeronautical Data Aggregation for Extended Weather Detection
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Solution Overview
Problem
Current onboard weather radars have limitations in detecting weather beyond 320 nautical miles, and some aircraft lack weather radar systems, leading to potential flight through adverse weather conditions, which can cause passenger discomfort and structural damage.
Innovation Solution
A system and method for aeronautical data aggregation and distribution that enables real-time data sharing between consumer and producer aircraft through a ground center, using a context builder module, producers locator module, data requests formatter, data responses validator, data predictor module, and data fusion module to provide unified and predicted weather data to pilots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If onboard weather radar is used, then weather detection capability is improved, but detection range is limited to about 320 nautical miles
Solution Approach 1:
The patent combines data from multiple sources including onboard weather radar, ground-based radar, satellite data, and reports from other aircraft to create a comprehensive weather picture that extends beyond the 320 nautical mile limitation of individual onboard radar systems
Solution Approach 2:
The system uses ground-based radar and satellite data as intermediary sources to extend weather detection capability beyond the range of onboard radar, allowing aircraft to receive weather information from distant sources through ground and space-based infrastructure
2Device complexity
If aircraft fly without onboard weather radar to reduce cost, then device complexity is reduced, but safety and reliability deteriorate due to inability to detect adverse weather
Solution Approach 1:
The system provides universal weather detection capability to all aircraft regardless of whether they have onboard radar, by receiving weather data from multiple external sources including ground-based radar, satellites, and other aircraft, making the safety function available to diverse aircraft types
Solution Approach 2:
Aircraft without onboard weather radar can obtain weather information services from the integrated system that aggregates data from ground-based radar, satellites, and other sources, allowing them to self-serve their weather detection needs without carrying expensive radar equipment
3Loss of information
If real-time weather data is not available from producers, then data completeness deteriorates, but system reliability can be maintained through predicted data
Solution Approach 1:
The system performs preliminary weather analysis and prediction using historical data and current conditions to generate forecasted weather information in advance, allowing it to provide predicted weather data when real-time measurements are unavailable from producer aircraft
Solution Approach 2:
The system continuously updates weather predictions by incorporating new real-time data when available from producer aircraft, using feedback loops to refine predictions and improve accuracy over time while maintaining reliable weather information during periods when real-time data is scarce
Data Source
AI summary
A system for data aggregation and distribution comprises a context builder that receives a data request from a consumer, and a producers locator that communicates with producers. A producers filter receives a list of producers and selects producers capable of providing data relevant to context information. A data requests formatter receives the context information, and sends the data request to the selected producers. A data responses validator validates data responses from producers, and a data responses processor processes validated data responses. A data predictor receives processed data responses and context information, and generates data prediction information. A data fusion module receives processed data responses, context information, data prediction information, and data history. The data fusion module combines processed data responses with data prediction information to generate a consolidated data response for the consumer. The data fusion module also considers data prediction information upon receiving a request for predicted data when real-time data is unavailable.