Cloud Data Label Processing for Wind Turbine Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The evaluation of data from similar systems, such as wind turbines, is hindered by the use of different data identifiers, requiring manual intervention by human experts, which is time-consuming and prone to errors, leading to incomplete data evaluation and missed optimization opportunities.
Innovation Solution
An automated method using an analysis unit in a cloud platform to process data identifiers, determining confidence values for associated data streams and summarizing them under a higher-level variable name, enabling automatic recognition and grouping of related data identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification by human experts is used to identify related data identifiers, then data can be accurately classified, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent introduces an analysis unit with functional units that act as an intermediary between raw data identifiers and human expert review. This automated analysis unit processes data identifiers, generates confidence values, and pre-classifies data before human intervention, reducing both time and errors while maintaining accuracy for borderline cases
Solution Approach 2:
The system enables self-service by automatically classifying data identifiers using the analysis unit without requiring human expert intervention for clear cases. The automated confidence value generation and threshold-based classification allow the system to handle itself, reserving human expertise only for ambiguous cases that fall within a confidence threshold range
2Loss of information
If complete manual evaluation of all transmitted data is performed, then comprehensive data analysis is achieved, but the complexity and time required become unmanageable
Solution Approach 1:
The patent segments the data processing task into multiple functional units within the analysis unit, each handling specific aspects of data identifier analysis. This segmentation allows comprehensive evaluation to be divided into manageable components, reducing overall system complexity while maintaining completeness of analysis
Solution Approach 2:
The system applies local quality by using different analysis approaches and confidence thresholds for different types of data identifiers. Rather than applying a uniform complex analysis to all data, the system tailors the analysis depth and methods to the specific characteristics of each data identifier, achieving comprehensive evaluation with reduced complexity
3Productivity
If automated processing without human review is implemented, then processing speed increases, but reliability decreases due to potential errors in automatic classification
Solution Approach 1:
The patent implements feedback by generating confidence values for automated classifications and using these to determine whether human review is needed. High confidence results are accepted automatically for speed, while lower confidence results trigger human review to ensure reliability, creating a feedback loop that balances speed and accuracy
Solution Approach 2:
The system applies partial automation by automatically processing only those data identifiers that meet confidence thresholds, while partially retaining human review for borderline cases. This partial action approach achieves high productivity for clear cases while maintaining reliability through selective human intervention
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
The invention is a method, an apparatus and a computer program for the automatic processing of data identifiers (20, 22), wherein data (12) associated with data identifiers (20, 22) is transmitted to a cloud platform (16) and to an analysis unit (32) there having at least one functional unit (45-50), wherein each functional unit (45-50) performs an analysis of the data (12) and/or associated data identifier (20, 22) and outputs a confidence value as an analysis result, wherein the or each confidence value is mapped to an overall confidence value (60), wherein the overall confidence value encodes a correlation between two data identifiers (20, 22), wherein data identifiers (20, 22) determined as correlated are combined under a higher-order variable name (66), and wherein the higher-order variable name (66) can be used for a database query, which supplies data relating to all data identifiers (20, 22) combined under the variable name (66), wherein data identifiers (20, 22) having an overall confidence value (60) above a predefined or predefinable threshold value are correlated.