Training Course Harmonization via Fragment Segmentation
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Solution Overview
Problem
Existing training programs for flight personnel are not adaptable for quick updates, making it difficult to address changes in regulatory documents and incorporate dynamic work experience, leading to inefficiencies in updating training and certification programs, which affects flight safety and increases financial and computational burdens.
Innovation Solution
A method that breaks down training course content into fragments with unique identification attributes, allowing for automatic modification and substitution of fragments to reflect changes in regulatory documents, reducing the need for manual intervention and enhancing the efficiency of updating training programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training programs use conventional knowledge units compiled from regulatory documents, then the training content is comprehensive and accurate, but the update process is time-consuming and requires manual intervention
Solution Approach 1:
The training program content is divided into discrete functional units, each with a unique identifier. These functional units can be independently updated without affecting the entire training program, enabling rapid updates while maintaining content accuracy through systematic organization of information.
Solution Approach 2:
The system automatically detects changes in regulatory documents and triggers updates of affected functional units. This feedback mechanism ensures training content remains accurate and current while reducing manual intervention time through automated monitoring and update processes.
2Reliability
If training programs are manually updated to reflect changes in regulatory documents, then the content remains accurate, but the financial and computational resources required increase significantly
Solution Approach 1:
The system performs self-updates by automatically detecting changes in regulatory documents, identifying affected functional units, and updating them without requiring manual intervention. This self-service capability maintains content accuracy while reducing the complexity and resource requirements of the update process.
Solution Approach 2:
Manual mechanical processes of reviewing and updating training content are replaced with automated computational processes. The system uses algorithms to detect changes, match them to relevant functional units, and perform updates automatically, reducing both complexity and resource consumption.
3Stability of the object's composition
If the training program structure is rigid and fixed, then the implementation is simple and stable, but the ability to quickly adapt to changes in regulatory documents is limited
Solution Approach 1:
The training program is segmented into modular functional units with unique identifiers, allowing the structure to remain stable at the program level while enabling flexible updates at the unit level. This modular architecture provides both stability and adaptability simultaneously.
Solution Approach 2:
The system introduces dynamic elements that allow functional units to be added, modified, or removed based on regulatory changes while maintaining the overall program structure. This dynamic capability enables quick adaptation to changes without disrupting the stability of the training program as a whole.
Data Source
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AI summary
The invention relates to a method for harmonizing the content of functional units of training courses. The method includes the steps of uploading, comparing, updating comparison results, processing fragments, and generating a content tree, an instructional course and an examination. The invention allows the automatic adjustment of training programme parameters.