Aviation Training Data Model for Simulator Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High fidelity aircraft simulators are costly and not necessary for all pilot training tasks, such as pre-flight operations and safety checks, which often require a trainer's presence, making them inefficient for training procedures that do not require high fidelity simulations.
Innovation Solution
A method and system that convert unstructured flight manual data into semi-structured data with actor-verb-object formats, allowing for the creation of training lessons executable in lower fidelity flight simulators, leveraging off-the-shelf flight simulators for GUIs and user interaction while using a higher fidelity plane simulator for accurate aircraft modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high fidelity aircraft simulators are used for all training tasks, then training accuracy is improved, but training cost increases significantly
Solution Approach 1:
The patent applies local quality by matching simulator fidelity to specific training task requirements. Different training tasks (e.g., pre-flight operations vs. emergency procedures) are assigned to appropriate simulator types based on their fidelity needs, rather than using high fidelity simulators for all tasks uniformly.
Solution Approach 2:
The patent changes the fidelity parameter of the simulation system based on the training task being performed. By adjusting simulation parameters and selecting appropriate simulator types for different training objectives, the system achieves cost-effective training while maintaining necessary accuracy for each specific task.
2Reliability
If high fidelity simulators are used for simple procedures, then training completeness is improved, but resource utilization deteriorates
Solution Approach 1:
The patent implements local quality by assigning different simulator fidelity levels to different procedural complexities. Simple procedures like pre-flight checks are trained using lower fidelity simulators, while complex emergency procedures use higher fidelity simulators, optimizing resource utilization while ensuring training completeness for each task type.
Solution Approach 2:
The patent applies partial action by using only the necessary level of simulation fidelity for each training task. Instead of always using maximum fidelity simulators, the system uses partial simulation capabilities appropriate to the training objective, improving resource efficiency while maintaining training effectiveness.
3Quantity of substance
If unstructured flight manual data is used directly, then data completeness is improved, but data usability deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-processing and structuring flight manual data before it is used in training simulations. The system automatically parses, structures, and organizes unstructured manual data into usable formats in advance, making it readily accessible and usable for training purposes without manual intervention during training operations.
Solution Approach 2:
The patent replaces manual data processing mechanisms with automated computational systems. Instead of manually structuring flight manual data, the system uses automated data processing algorithms to convert unstructured text into structured training content, significantly improving ease of operation while maintaining data completeness.
4Adaptability or versatility
If trainers manually create training lessons, then training customization is improved, but trainer workload increases
Solution Approach 1:
The patent implements self-service by enabling automated generation of training lessons from structured flight manual data. The system automatically creates customized training content based on the trained procedures and simulator capabilities, reducing trainer workload while maintaining training customization through automated adaptation to different training objectives.
Solution Approach 2:
The patent applies preliminary action by pre-structuring flight manual data and pre-configuring simulator parameters so that training lessons can be automatically generated when needed. This preliminary preparation eliminates the need for trainers to manually create each training lesson from scratch, reducing workload while preserving customization capabilities.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present disclosure provides ingesting unstructured data (e.g., a flight manual) containing procedures for operating an aircraft and converting that unstructured data into semi-structured data that lists the procedures along with the steps in those procedures. Further, the procedural steps are converted into data structures that have an actor-verb-object format. In addition, the data structures are mapped to components in the aircraft and the components in the aircraft are mapped to corresponding components in a software flight simulator.