Automated Data Structure Creation for Technical Components
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Solution Overview
Problem
Existing data management systems in mechanical engineering and production sectors face challenges in creating error-free data structures for technical components, often resulting in costly errors due to misclassification and inconsistent naming conventions.
Innovation Solution
A method for automatically creating a new data structure that accounts for technical dependencies between components, using a computer-assisted approach to parse and classify information technology objects, providing a selection menu for valid component identifiers, and assigning them to classes based on entity-specific semantic-technical relationships, thereby avoiding errors and ensuring data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a predetermined data structure is used for classification, then the structure is established and can be maintained, but the data structure cannot be amended from the basic structure and may lead to misclassification errors
Solution Approach 1:
The patent implements a dynamic data structure that can automatically adapt and modify itself based on the input data characteristics. The system analyzes the data and dynamically adjusts the classification structure during the processing process, allowing the structure to evolve from a basic predetermined form to an optimized structure that fits the specific data, thereby resolving the contradiction between maintaining a stable structure and adapting to data-specific requirements
Solution Approach 2:
The system changes the parameters of the data structure dynamically during processing. By analyzing frequency distributions and occurrence patterns of objects to be classified, the system modifies structural parameters such as hierarchy levels, classification criteria, and data organization forms, enabling the structure to adapt to the specific characteristics of the input data while maintaining reliability
2Reliability
If manual classification methods are used, then data can be assigned to existing structures, but errors and inconsistencies occur leading to costly consequences
Solution Approach 1:
The system performs self-service by automatically analyzing and classifying data without requiring manual intervention. The computer-assisted method automatically processes the data, identifies patterns, and creates appropriate data structures, eliminating human errors and inconsistencies while maintaining high processing efficiency and accuracy
Solution Approach 2:
The system incorporates feedback mechanisms where the classification results are continuously monitored and evaluated. The system uses frequency distributions and occurrence patterns as feedback to adjust and optimize the classification process, ensuring high accuracy while maintaining efficiency through automated iterative processing
3Quantity of substance
If multiple information technology objects refer to the same technical item, then comprehensive data coverage is achieved, but duplicate entries and misclassification errors are generated
Solution Approach 1:
The system extracts and separates duplicate and inconsistent data entries through automated analysis. By identifying frequency distributions and occurrence patterns, the system extracts the essential information from multiple references to the same technical item and consolidates them into a single consistent entry, maintaining comprehensive data coverage while eliminating duplicates and errors
Solution Approach 2:
The system merges multiple information technology objects that refer to the same technical item into a unified data structure. Through automated classification, the system combines redundant information and creates a consistent representation of each technical item, achieving both comprehensive coverage and data consistency
Data Source
AI summary
Data is analyzed by a computer for the automated creation of a new data structure for information technology objects. The objects represent technical components from the mechanical engineering sector or the electrical industry and are assigned to a company. The objects to be structured are captured and then subjected to a parsing. Technical relationships are then created between the parsed objects to construct technical metrics. The data structure is derived from the technical metrics.