Site Data Meaning Estimation for Manufacturing Process Models
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Solution Overview
Problem
Existing technologies struggle to create a process model that defines the data structure of site data generated in manufacturing processes when the meaning of data items is unknown, as they require prior knowledge of data item meanings.
Innovation Solution
An information processing system estimates the meaning of data items in site data by selecting start-to-completion achievement data, estimating feature amounts, and comparing candidates to determine data items indicating predetermined meanings, enabling the creation of a process model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior knowledge of data item meanings is required to create a process model, then the accuracy of the process model is improved, but the complexity of the system increases and the ease of operation deteriorates
Solution Approach 1:
The system enables automatic estimation of data item meanings by analyzing temporal correlations between data items without requiring manual input of meaning information. The processor automatically compares feature amounts and determines meanings based on start-to-completion achievements, allowing the system to serve itself rather than requiring external knowledge input
Solution Approach 2:
The system performs preliminary analysis of data item correlations by calculating feature amounts and identifying temporal patterns before the actual process model creation. This preliminary action of estimating meanings from data patterns prepares the necessary information structure in advance, eliminating the need for subsequent manual meaning assignment
2Reliability
If manual specification of data item meanings is required, then the reliability of the process model is improved, but the productivity deteriorates due to increased time consumption
Solution Approach 1:
The system replaces the manual mechanical process of specifying data item meanings with an automated information processing mechanism. The processor automatically estimates meanings by analyzing temporal correlations and comparing feature amounts, substituting human cognitive work with computational analysis that processes data patterns systematically and rapidly
Solution Approach 2:
The system uses feedback from temporal correlation analysis between data items to automatically refine and determine data item meanings. By continuously analyzing the relationships between data items and their temporal patterns, the system feedback-drivenly estimates meanings with increasing accuracy without requiring manual verification of each data item
Data Source
AI summary
An information processing system holds: start-to-completion achievement data which is selected from site data relating to a process including tasks and in which a data item indicating a predetermined meaning is specified; and meaning estimation target data selected from the site data; estimates a candidate of a data item indicating the predetermined meaning from the data items of the meaning estimation target data based on a feature amount of the data item of the start-to-completion achievement data indicating the predetermined meaning and a feature amount of each data item included in the meaning estimation target data; and estimate a data item indicating the predetermined meaning from the candidates based on a result of the comparing the start-to-completion achievement data and the meaning estimation target data in a case in which the candidate is the data item indicating the predetermined meaning.