Data Processing Apparatus for Analyzer File Tagging
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Solution Overview
Problem
Managing and efficiently extracting features from a wide variety of analysis data collected from multiple types of analyzers is challenging due to the difficulty in grouping analysis file sets of the same origin together, which affects the accuracy and efficiency of machine learning processes.
Innovation Solution
A data processing method and apparatus that collect analysis file sets from various analyzers and assign tags indicating the analyzer type, sample material, preprocessing conditions, and measurement conditions to each file set, allowing for efficient grouping and calculation of representative features from data of the same origin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If analysis file sets from multiple types of analyzers are collected and stored in a database, then the quantity and variety of analysis data increase, but the difficulty of managing and grouping analysis file sets of the same origin increases
Solution Approach 1:
The patent applies segmentation by dividing the management of analysis file sets into organized groups based on their origin. Analysis file sets are segmented according to the analyzer type, sample material, preprocessing conditions, and measurement conditions, allowing efficient grouping and management of large quantities of diverse data without increasing management complexity
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of a data processing apparatus that automatically extracts origin information from analysis file sets and groups them accordingly. This intermediary system mediates between the raw diverse data and the organized groups, enabling efficient management without direct manual intervention
2Productivity
If representative values are calculated from multiple analysis data of the same origin, then the computational load is reduced for machine learning, but the accuracy of feature extraction may be compromised without proper grouping
Solution Approach 1:
The patent applies preliminary action by automatically extracting origin information and grouping analysis file sets before the machine learning process. This preliminary organization ensures that representative values are calculated from properly grouped data of the same origin, maintaining feature extraction accuracy while enabling efficient computational processing
Solution Approach 2:
The patent implements feedback by verifying that analysis file sets are correctly grouped by their origin characteristics before calculating representative values. This feedback mechanism ensures that the grouping is accurate, thereby maintaining the precision of feature extraction while achieving computational efficiency
Data Source
AI summary
A data processing method includes a step for a computer to collect analysis file sets from a plurality of types of analyzers, the analysis file sets each including analysis data by the analyzer, and a step for the computer to tag each of a plurality of collected analysis file sets, the tag indicating a type of the analyzer, a material of a sample measured by the analyzer, preprocessing conditions of the sample, and measurement conditions of the sample.