Distributed Flight Route Management System
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Solution Overview
Problem
Existing flight and route management systems are limited in scope, primarily considering static weather and traffic data localized to a single aircraft, and do not dynamically prioritize routing based on diverse constraints such as passenger comfort or mission objectives, lacking real-time and predictive awareness of changing weather and atmospheric conditions.
Innovation Solution
A distributed flight and route management system that combines onboard and ground-based processing, using portable devices with situation modelers and rerouters to generate and modify flight plans based on real-time and predictive data, prioritizing constraints such as comfort, cost, and safety, and propagating these modifications across a network of aircraft.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing FARM systems use only static weather and traffic data localized to a single aircraft, then system complexity is reduced, but situational awareness and routing effectiveness deteriorate
Solution Approach 1:
The system divides the FARM functionality into ground-based components (weather radar, traffic management servers) and onboard components (situation modeler, rerouter). This segmentation allows comprehensive data collection without requiring all processing capacity onboard, thus improving situational awareness while managing system complexity through distributed architecture.
Solution Approach 2:
A ground-based server acts as an intermediary between weather radar systems and aircraft onboard systems. The server aggregates weather data from multiple sources, processes it into predictive models, and distributes it to multiple aircraft. This intermediary approach improves information availability without requiring direct complex connections between all system components.
2Adaptability or versatility
If FARM systems consider only flight distance and fuel consumption as routing priorities, then routing decisions are simplified, but adaptability to diverse constraints deteriorates
Solution Approach 1:
The situation modeler dynamically adjusts routing priorities based on real-time weather conditions, traffic patterns, and aircraft-specific constraints. The system can shift priorities between fuel efficiency, passenger comfort, and safety depending on current situational factors, providing adaptability without requiring manual reconfiguration of complex constraint hierarchies.
Solution Approach 2:
The system changes operational parameters (routing priorities, constraint weights) based on situational models. Different constraint sets can be activated depending on flight phase, weather severity, and aircraft type, allowing versatile adaptability through parameter adjustment rather than structural system changes.
3Speed
If onboard FARM devices process all flight plan modifications independently, then real-time responsiveness is improved, but data synchronization and coordination with ground control deteriorate
Solution Approach 1:
The ground-based situation modeler prepares predictive weather models and traffic forecasts in advance, distributing them to onboard systems before flights begin or during pre-flight briefings. This preliminary action allows onboard systems to make rapid local decisions without real-time ground communication, while maintaining data synchronization through pre-coordinated information.
Solution Approach 2:
The onboard rerouter generates flight plan modifications based on local situational assessment, then communicates these changes back to ground control for coordination. Ground control receives feedback from multiple aircraft and adjusts overall traffic management accordingly, maintaining synchronization while enabling rapid onboard decision-making.
4Loss of information
If the system uses localized weather data only, then data processing requirements are reduced, but predictive awareness of developing weather patterns deteriorates
Solution Approach 1:
The ground-based server merges weather radar data from multiple aircraft and ground-based weather stations into a comprehensive regional weather model. This combined data set provides predictive awareness of developing weather patterns that no single localized system could detect, while the server processes the aggregated data centrally to reduce individual onboard processing requirements.
Solution Approach 2:
The system transitions from two-dimensional localized weather maps to three-dimensional predictive weather models that show the evolution and movement of weather systems over time. This dimensional expansion provides predictive awareness of developing patterns while the ground-based processing handles the computational complexity of multi-dimensional data analysis.
Data Source
AI summary
A distributed system for flight and route management (FARM) of one or more aircraft of the system may include onboard processing devices for combining sensor data local to an aircraft with cloud-based data received through the system from other aircraft or from ground-based processing devices, thereby generating situation models of each aircraft relative to its flight path and in the context of current and predictive conditions (weather, traffic, terrain, threats, etc.). The FARM system may evaluate situation models against prioritized constraint sets of business rules or policies associated with each aircraft's flight plan to determine, crosscheck, and implement possible modifications to the flight plan. Localized aircraft data and flight plan modifications may be propagated through the system via a variety of communications networks to provide synchronized, holistic airspace data portraits to aircraft and ground control alike.