Machine-Learned Operating System for Avionics Processor Updates
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Solution Overview
Problem
The development and implementation of updates for mission-critical avionics processing systems are time-consuming and costly, requiring extensive testing and reprogramming, leading to significant downtime and inefficiencies.
Innovation Solution
A machine-learned operating system and processor configuration that uses machine-learning processors to identify operational parameters and determine modifications to program instructions, allowing for continuous processor improvement and quicker update implementation without the need for manual revisions or replacement of mission computing processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual reprogramming and testing methods are used for mission-critical avionics processors, then system reliability is maintained through thorough verification, but development time and implementation downtime increase significantly
Solution Approach 1:
The system employs machine-learning processors that automatically analyze operational parameters, generate modifications to program instructions, and optimize processor performance without requiring manual reprogramming or extensive testing. The machine-learning analyzer independently identifies issues and implements corrections, enabling the system to self-improve while maintaining reliability
Solution Approach 2:
The patent replaces traditional manual mechanical reprogramming processes with automated machine-learning-based analysis and modification systems. The machine-learning processor substitutes human engineers' manual verification and reprogramming work with automated algorithms that can rapidly analyze and optimize processor instructions without physical reprogramming steps
2Manufacturing precision
If extensive testing and verification are performed on processor updates, then quality and reliability are ensured, but implementation costs and downtime increase
Solution Approach 1:
The machine-learning analyzer continuously monitors operational parameters and provides feedback on processor performance. This feedback mechanism enables the system to automatically identify deviations from optimal performance and generate targeted modifications, ensuring quality improvements while reducing the need for extensive manual testing cycles
Solution Approach 2:
The system performs preliminary analysis of operational parameters using machine-learning algorithms before implementing modifications. The machine-learning processor pre-identifies potential issues and prepares optimized program instructions in advance, allowing for quicker implementation with reduced testing requirements while maintaining quality standards
3Adaptability or versatility
If frequent processor updates are implemented to keep pace with technology changes, then system adaptability improves, but development complexity and resource requirements increase
Solution Approach 1:
The machine-learning analyzer autonomously identifies operational parameters and generates modifications without requiring complex manual development processes. This self-service capability enables frequent updates to be implemented with reduced development complexity, as the system automatically adapts to new requirements through machine-learning-based analysis rather than traditional lengthy development cycles
Data Source
AI summary
A computing device may include operational processors, configured to execute a set of program instructions, wherein the set of program instructions is configured to cause the operational processors to: receive input signals indicative of input conditions; determine input conditions based input signals; determine output signals based on the determined input conditions; and provide determined output signals. The computing device may further include machine-learning processors, wherein the machine-learning processors are configured to develop machine-learning analyzers, wherein the machine-learning analyzers are configured to: identify operational parameters of the operational processors; determine modifications to the set of program instructions, wherein the modifications satisfy a selected quality metric; and provide the modifications to the operational processors.