On-Board Weather Data Pre-Processing for Aircraft Latency Reduction
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Solution Overview
Problem
Current weather data processing systems face significant delays in transmitting and processing data from aircraft in flight, leading to inadequate real-time information for adverse weather conditions, which can compromise aircraft safety and performance, especially during takeoffs and landings.
Innovation Solution
A computer-implemented method for local transmission and processing of weather sensor data and performance data between airborne aircraft, involving pre-processing of raw data into a standardized format, transmission to nearby aircraft, and post-processing using machine learning models to generate alerts and recommendations for adverse conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If ground-based sensors and measurement devices are used to provide weather data, then comprehensive weather coverage is achieved, but significant delays occur in transmitting and processing data from aircraft in flight
Solution Approach 1:
The system performs preliminary actions by pre-processing weather sensor data and aircraft performance data on-board during flight, generating a context of the weather data before transmission. This preliminary processing ensures that when data is transmitted to ground-based systems or other aircraft, it is already prepared and contextualized, reducing subsequent processing delays and information loss.
2Reliability
If weather data is transmitted from aircraft during flight to ground-based models, then real-time weather information is obtained, but the data processing and transmission back to aircraft creates significant lag time
Solution Approach 1:
The system introduces an intermediary layer by implementing on-board weather data processing and local transmission capabilities. Instead of direct transmission between aircraft and ground-based models, the on-board systems act as intermediaries that pre-process data locally and can transmit to other aircraft directly, reducing the lag time in receiving weather information while maintaining reliability through contextual processing.
Solution Approach 2:
The system adds another dimension to weather data transmission by enabling peer-to-peer transmission between aircraft in addition to the traditional aircraft-to-ground transmission path. This creates multiple transmission dimensions, allowing aircraft to receive real-time weather information from nearby aircraft without waiting for ground-based model processing and re-transmission.
3Productivity
If ground-based weather models process weather data, then comprehensive weather analysis is provided, but the information reaches aircraft too late to prevent adverse conditions during takeoffs and landings
Solution Approach 1:
The system performs preliminary processing of weather sensor data and aircraft performance data on-board during flight, generating a context of the weather data before transmission. This preliminary action reduces the time required for ground-based models to process and re-transmit data, as the data arrives pre-processed and contextualized.
Solution Approach 2:
The system introduces intermediary on-board processing systems that can analyze weather data locally and transmit alerts directly to other aircraft without waiting for ground-based model processing. This intermediary layer enables faster dissemination of weather alerts during critical phases like takeoffs and landings.
4Reliability
If weather sensor data is collected and processed on-board aircraft, then real-time weather information is available to nearby aircraft, but additional processing resources are required on aircraft
Solution Approach 1:
The system uses copying by transmitting pre-processed weather sensor data and aircraft performance data from one aircraft to other aircraft within vicinity. Instead of each aircraft having full processing capabilities, the transmitting aircraft performs the processing and creates copies of the processed data for distribution to receiving aircraft, reducing on-board processing requirements while maintaining real-time information availability.
Data Source
AI summary
A computer-implemented method operating on a first aircraft and performed by one or more processors collects sensor data and aircraft performance data generated by the first aircraft during flight. The method pre-processes the sensor and performance data generating a context with respect to the first aircraft. The method transmits the pre-processed sensor data and first aircraft performance data to a set of aircraft within a region of the first aircraft. The method receives pre-processed sensor data and aircraft performance data from the set of aircraft within the region of the first aircraft. The method processes the received pre-processed sensor and performance data from the set of aircraft, and the method generates alerts associated with adverse conditions determined based on the analysis of the pre-processed sensor data and performance data received from the set of aircraft in the context of the performance data of the first aircraft.