AI Flight Vehicle Prediction Model for Collision Avoidance
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Solution Overview
Problem
Existing flight vehicle control systems face challenges in accurately predicting and preventing collisions due to sensor malfunctions and inadequate communication, leading to inefficiencies in collision avoidance measures.
Innovation Solution
A method and apparatus utilizing AI-trained operation information prediction models to generate future flight vehicle operation data, displayed on multiple control screens, which predict potential collisions and provide selectable collision avoidance alternatives, enhancing user awareness and efficiency in collision prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor-based collision prediction systems are used, then real-time collision detection is achieved, but system reliability deteriorates due to sensor malfunctions and communication failures
Solution Approach 1:
The patent introduces an AI-based operation information prediction model as an intermediary between raw sensor data and collision prediction results. This model processes and interprets sensor data, compensating for sensor malfunctions and communication failures by predicting expected operation information, thereby maintaining system reliability without requiring additional complex sensor hardware
Solution Approach 2:
The system performs preliminary prediction of flight vehicle operation information using the AI model before actual collisions occur. By predicting future operation states and potential collision risks in advance, the system enables proactive collision prevention measures, improving reliability by not depending solely on real-time sensor accuracy
2Loss of information
If multiple control screens with detailed operation information are provided, then user awareness of flight vehicle states is improved, but information processing time increases
Solution Approach 1:
The patent extracts and displays only the most critical operation information and collision risk indicators on the control screens, rather than presenting all available data. The AI model identifies and highlights key parameters such as predicted collision risks, abnormal operation patterns, and essential flight vehicle states, reducing information processing time while maintaining completeness of crucial operational awareness
Solution Approach 2:
Different control screens display operation information with varying levels of detail based on local needs and priorities. The system provides comprehensive detailed views for specific flight vehicles when necessary, while offering summarized overviews for multiple vehicles, optimizing the balance between information completeness and processing efficiency for different operational contexts
3Measurement precision
If AI prediction models are implemented to forecast future operation information, then collision prediction accuracy is improved, but computational requirements and system complexity increase
Solution Approach 1:
The AI prediction model creates virtual copies or simulated versions of flight vehicle operation information to predict future states. Instead of requiring complex physical simulations, the system generates predicted operation data that mirrors actual flight dynamics, achieving high collision prediction accuracy through data-driven modeling rather than computationally intensive physics calculations
Solution Approach 2:
The system optimizes the AI model by selecting and focusing on key operation parameters that most significantly impact collision prediction accuracy. By identifying and processing only the most influential parameters (such as position, velocity, and trajectory) rather than all possible flight parameters, the system maintains high prediction accuracy while reducing computational complexity and model complexity
Data Source
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AI summary
A method of providing flight vehicle operation information is provided. The method may comprise providing a first control screen, which displays first operation information of a plurality of monitored flight vehicles, inputting the first operation information and context information to a pre-established operation information prediction model and generating second operation information of the monitored flight vehicles at a future time point based on data output from the operation information prediction model, and providing a second control screen, which displays the second operation information.