Vehicle Operational Phase Detection With Trigger and Look-Ahead Logic
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Solution Overview
Problem
Existing systems for detecting operational phases in vehicles, such as aircraft, face challenges in efficiently adjusting and testing operational phase detection logic due to the need for extensive reprocessing of sensor data, which is time-consuming and computationally intensive.
Innovation Solution
A method and system that utilize a processor to identify operational phases and transitions by segmenting sensor data into intervals based on trigger and look-ahead conditions, allowing for real-time detection and adjustment of operational phases without requiring full reevaluation of data each time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If definition logic is changed or adjusted for new vehicles or updated components, then detection accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments sensor data into discrete intervals with assigned operational phases, creating a structured framework that can be efficiently updated. When definition logic changes, only the phase assignment rules need updating rather than reprocessing entire datasets, significantly reducing computational overhead while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary processing by assigning operational phases to data intervals upfront based on initial definition logic. This pre-assignment creates a baseline structure that allows future logic adjustments to be applied incrementally without requiring complete reprocessing of historical data, thereby reducing time loss when updating detection parameters.
2Measurement precision
If all sensor data is reprocessed to update operational phase detection logic, then detection accuracy is improved, but computational resources are excessively consumed
Solution Approach 1:
By dividing sensor data into discrete intervals and assigning phases segment-by-segment, the system enables selective reprocessing. When detection logic updates occur, only affected segments need recalibration rather than processing entire datasets, dramatically reducing computational energy consumption while preserving detection precision through targeted updates.
Solution Approach 2:
The system allows definition logic parameters to be changed and applied incrementally to data intervals. Instead of reprocessing all data with new parameters, the system can update phase assignments for specific intervals where changes are relevant, reducing computational energy while maintaining accurate detection through parameter-efficient updates.
3Adaptability or versatility
If definition logic is frequently adjusted to accommodate vehicle updates, then system adaptability is improved, but testing and validation time increase
Solution Approach 1:
The segmented data interval structure allows independent validation of phase assignments for different time periods. When adapting detection logic to new vehicles or components, testers can validate changes on specific data segments rather than entire datasets, reducing validation time while maintaining system adaptability through modular testing approaches.
Solution Approach 2:
The system enables partial reprocessing of data intervals when definition logic changes. Instead of validating all data comprehensively, the system can apply and test logic changes on representative subsets of data segments, reducing testing time while maintaining sufficient validation through selective sampling of critical operational phases.
Data Source
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AI summary
A method includes obtaining data associated with operation of a vehicle (102) and determining a first operational phase of the vehicle (102) based on the data (140). The method includes identifying a candidate operational phase transition from the first operational phase to a candidate operational phase based on a first portion of the data (140) satisfying a first condition associated with the candidate operational phase, the first portion of the data (140) associated with a first time. The method includes evaluating a second portion of the data (140) based on a second condition associated with the candidate operational phase, the second portion of the data (140) associated with a second time that is subsequent to the first time. The method further includes, based on the second condition being satisfied, generating an operational phase transition indication associated with the first time and that indicates an operational phase transition to the candidate operational phase.