Data Processing Method for Analyzer File Standardization
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Solution Overview
Problem
The existing data processing methods require significant time and effort to standardize and compare analysis data from multiple analyzers, as they need to perform preliminary tasks of summarizing and standardizing data that match specific conditions, which becomes cumbersome with increasing data types and volumes.
Innovation Solution
A data processing method and apparatus that collect and sort analysis file sets from multiple analyzers, forming subsets where analyzer type, preprocessing conditions, and measurement conditions match, allowing for efficient standardization and feature extraction for machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual preliminary tasks are performed to summarize and standardize analysis data from multiple analyzers, then data accuracy and comparability are improved, but time consumption and operational effort increase significantly
Solution Approach 1:
The system performs preliminary automated actions by collecting analysis data from multiple analyzers, storing it in a database with metadata, and pre-forming subsets based on matching conditions before standardization is needed. This automation of preliminary tasks reduces manual time consumption while maintaining data accuracy through systematic processing.
Solution Approach 2:
The system enables self-service by automatically collecting data, storing it with associated metadata, and forming subsets based on predefined matching conditions without requiring manual intervention. The automated subset formation process identifies and groups analysis data that share the same analyzer type, preprocessing conditions, and measurement conditions, eliminating the need for manual data summarization while ensuring accuracy.
2Reliability
If comprehensive standardization processing is performed on all analysis data to ensure comparability, then data reliability is improved, but processing complexity and operational effort increase
Solution Approach 1:
The system segments the large set of analysis data into smaller, manageable subsets based on matching conditions (analyzer type, preprocessing conditions, measurement conditions). Each subset contains only analysis data that can be reliably compared with each other, eliminating the need to perform comprehensive standardization on all data while maintaining reliability within each subset.
Solution Approach 2:
The system applies standardization processing locally to each formed subset rather than uniformly to all analysis data. Since each subset contains analysis data with identical matching conditions, the same standardization processing is appropriate and effective for all members of that subset, improving reliability while reducing overall processing complexity through targeted rather than universal processing.
3Productivity
If analysis data from multiple analyzers with different conditions are standardized using the same process, then processing efficiency is improved, but data comparability and accuracy deteriorate
Solution Approach 1:
The system performs preliminary action by automatically forming subsets based on matching conditions before standardization processing. This pre-grouping ensures that only analysis data with identical analyzer types, preprocessing conditions, and measurement conditions are placed together, enabling efficient batch processing while maintaining data comparability within each subset.
Solution Approach 2:
The system applies the same standardization processing to each subset locally, where it is appropriate and effective. Since each subset contains analysis data with matching conditions, the standardized processing maintains data comparability and accuracy within that local context, while the overall system achieves high processing efficiency through automated batch processing of multiple subsets.
4Measurement precision
If the database stores detailed metadata for each analysis file set to enable accurate subset formation, then subset accuracy is improved, but database complexity and storage requirements increase
Solution Approach 1:
The database stores metadata in a segmented and organized manner, with each analysis file set associated with specific metadata elements (analyzer type, preprocessing conditions, measurement conditions). This structured segmentation enables accurate subset formation by allowing the system to efficiently query and group data based on matching metadata conditions without requiring complex cross-referencing, thus improving subset accuracy while managing database complexity.
Data Source
AI summary
A data processing method includes a step for a computer to collect analysis file sets from multiple types of analyzers, the analysis file sets each including analysis data by the analyzer, a step for the computer to store the plurality of collected analysis file sets in a database with the analysis file sets sorted into corresponding collections for each material, and a step for the computer to form a subset by extracting an analysis file set in which a type of the analyzer, preprocessing conditions of a sample measured by the analyzer, and measurement conditions of the sample all match, from the collection.