Neural Network Pilot Support System for Flight Context Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Pilots face challenges in quickly interpreting and responding to complex flight conditions due to the vast amount of irrelevant information from various sensors, which can lead to delayed responses and increased risk during critical situations like turbulence or system failures.
Innovation Solution
A neural network-based pilot support system that analyzes sensor data and control inputs to categorize flight contexts, highlighting relevant instruments and suggesting control actions, thereby improving situational awareness and response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If pilots integrate information from a wide variety of sensors and instruments to maintain situational awareness, then the completeness of flight information is improved, but the complexity of information processing increases and response time decreases
Solution Approach 1:
The patent introduces an intermediary system (the pilot support system with neural network) that acts as a mediator between the multiple sensors/instruments and the pilot. This intermediary automatically integrates and processes information from various flight instruments, presenting synthesized results to the pilot, thereby maintaining complete situational awareness while reducing the cognitive burden and response time.
Solution Approach 2:
The patent replaces the mechanical cognitive process of manual information integration with an automated electronic system. The neural network-based pilot support system automatically processes and integrates data from multiple sensors, substituting the pilot's manual information synthesis with automated computational processing, thus reducing response time while maintaining information completeness.
2Measurement precision
If pilots integrate information from multiple sensors to maintain situational awareness, then the accuracy of flight context understanding is improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal pilot support system that handles multiple flight contexts and instrument types through a single integrated neural network architecture. This multi-functional system processes data from various sensors and instruments uniformly, achieving accurate flight context understanding across different flight phases without requiring separate specialized systems for each function.
Solution Approach 2:
The patent merges multiple information sources and processing functions into a single integrated pilot support system. The neural network combines inputs from various sensors, instruments, and flight phases into a unified context understanding, reducing overall system complexity while maintaining measurement precision through consolidated processing.
3Loss of information
If the system provides comprehensive information from all instruments, then the completeness of flight data is improved, but the ease of operation decreases due to information overload
Solution Approach 1:
The patent extracts and highlights only the most relevant flight information from the comprehensive sensor data, presenting it prominently to the pilot. The system identifies critical flight parameters and contextual information, extracting these from the full data set and presenting them in an easily accessible format, thus maintaining data completeness while improving ease of operation through selective presentation.
Solution Approach 2:
The patent applies local quality by providing different levels of information detail in different contexts. The system adapts the presentation of flight data based on the current flight phase and situation, providing detailed information where needed and summarized information where sufficient, thereby maintaining completeness while optimizing ease of operation for each specific context.
4Measurement precision
If the neural network processes all sensor inputs to categorize flight contexts, then the accuracy of context detection is improved, but the use of energy increases
Solution Approach 1:
The patent applies partial action by processing only the most critical sensor inputs and flight parameters through the neural network at any given time. The system identifies and processes key parameters necessary for accurate context detection while potentially using simplified processing for less critical data, thereby maintaining detection accuracy while reducing overall energy consumption through selective processing.
Data Source
AI summary
A pilot support system includes: a processing circuit; and memory storing instructions that, when executed by the processing circuit, cause the processing circuit to: receive input data regarding a current state of a vehicle; encode the input data to generate encoded input data; supply the encoded input data to a trained statistical model; compute a current context of the vehicle based on the input data using the trained statistical model; compute one or more pilot feedback indicators based on the current context using the trained statistical model; and provide the one or more pilot feedback indicators to a cockpit of the vehicle.