Adaptive Real-Time Service Execution in Avionic Systems
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Solution Overview
Problem
Existing real-time avionic systems face challenges in dynamically adjusting service quality and response times to accommodate new clients or connections without degrading overall system performance or requiring software/hardware modifications, limiting the evolution of aircraft operations.
Innovation Solution
A method for real-time service execution by a server that adjusts calculation parameters based on client-defined constraints, prioritizing parameters to meet response time expectations while maintaining system performance, allowing for flexible quality of service adaptation without modifying the server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system statically allocates operational functions to physical systems during architecture production, then system performance and reliability are guaranteed for defined use cases, but the system cannot accommodate new clients or connections without costly requalification
Solution Approach 1:
The patent implements dynamic adjustment of calculation parameters at runtime based on client requirements and system load. The server can adaptively modify parameters such as calculation precision, data sampling rates, and processing depth to accommodate new clients without requalification, transforming the static architecture into a dynamically adaptable system.
Solution Approach 2:
The system changes operational parameters (calculation precision, data resolution, processing depth) to adjust service quality dynamically. By modifying these parameters rather than the system architecture itself, new clients can be integrated without costly requalification while maintaining performance for existing clients.
2Reliability
If the system guarantees quality of service for all clients, then response time and accuracy requirements are met, but adding new clients degrades overall system performance
Solution Approach 1:
The patent applies different quality levels to different clients based on their specific requirements. Each client receives customized service quality (calculation precision, response time) matched to their needs, rather than uniform high quality for all. This allows the system to maintain high productivity while ensuring each client gets adequate service quality.
Solution Approach 2:
The system performs partial calculations or uses reduced precision data for clients who don't require full accuracy, while allocating full computational resources to clients who do. This selective approach maintains overall system productivity while meeting the quality requirements of individual clients.
3Adaptability or versatility
If the system uses fixed calculation parameters for services, then execution time and quality are predictable, but the system cannot adapt to different client constraints
Solution Approach 1:
The system dynamically adjusts calculation parameters at runtime based on client-specific constraints such as maximum response time requirements. The server evaluates client needs and modifies parameters like calculation depth, data sampling frequency, and processing complexity to meet individual response time constraints while maintaining service quality.
Data Source
AI summary
The system executes services by an application called “server” for at least one application called “client.” A preliminary step establishes for each service a list of calculation parameters that can be varied in a given range, called “adjustable parameters” as well as time and quality of the said service information according to the value of the said parameters. At the request of a client for a given service, the method adjusts the value of the adjustable calculation parameters as a function of a given constraint, the service being executed using the adjusted values of the said parameters.