Data Selection System for Cross-Factory Application Reuse
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Solution Overview
Problem
The existing methods for using analysis applications in factories face significant challenges due to differing data structures across factories, leading to enormous man-hours spent searching for necessary data, especially when an entity-relationship diagram is not prepared, making it difficult to find and select the required data for analysis or application use.
Innovation Solution
A data selection system that identifies unconnected catalogs and their corresponding paths between different data objects, allowing for the easy selection of necessary data by using a processor unit and storage unit to manage catalogs and correspondence information across multiple tables, facilitating the generation of application data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data structures are customized for each factory, then data can be optimized for local needs, but it becomes difficult to reuse analysis applications across factories and requires enormous man-hours for data searching
Solution Approach 1:
The patent introduces correspondence information as an intermediary layer between factory-specific data structures and analysis application requirements. This correspondence information maps local data structures to standardized analysis data structures, enabling application reuse without requiring data structure standardization across factories. The intermediary layer translates between different data structures, solving both the adaptability need and the time loss problem.
Solution Approach 2:
The system performs preliminary action by pre-defining correspondence information that maps factory data structures to analysis application requirements. This correspondence information is prepared in advance, so when an analysis application needs to be deployed to a new factory, the system can automatically identify and select required data without manual searching. The preliminary mapping enables rapid data selection across different factories.
2Adaptability or versatility
If manual data searching is performed without entity-relationship diagrams, then flexibility is maintained, but the complexity and time required for data selection increases enormously
Solution Approach 1:
The correspondence information acts as an intermediary that automatically bridges the gap between factory data structures and analysis application requirements. Instead of manual searching or complex entity-relationship diagrams, the system uses pre-defined correspondence mappings to automatically identify required data, reducing both complexity and maintaining flexibility.
Solution Approach 2:
The system enables self-service by automatically identifying and selecting required data based on correspondence information. The data selection process does not require manual intervention or complex diagramming - the system autonomously matches factory data structures with analysis application needs using the pre-defined correspondence mappings, thereby reducing complexity while maintaining adaptability.
3Productivity
If analysis applications are reused across different factories, then productivity increases, but data structure differences require enormous manual effort for data preparation
Solution Approach 1:
The system performs preliminary action by pre-defining correspondence information that maps various factory data structures to standardized analysis data structures. This preliminary mapping is created once and can be reused across multiple factories, enabling rapid deployment of analysis applications without repetitive manual data preparation. The correspondence information serves as a reusable template that accelerates application reuse.
Solution Approach 2:
The correspondence information provides universality by serving multiple factories with different data structures through a single standardized mapping framework. Instead of creating separate data preparation processes for each factory, the system uses universal correspondence mappings that can be applied across all factories, enabling analysis application reuse while minimizing data preparation time regardless of local data structure variations.
Data Source
AI summary
An analysis management server stores a factory A catalog group including a plurality of catalogs in factory A, an application destination catalog including a plurality of catalogs in factory B, and an application source analysis data correspondence table associating columns between a target table of factory A and a defect factor analysis table. A CPU is configured to: identify an unconnected catalog for which a catalog of factory B having an identical correspondence does not exist among the plurality of catalogs of factory A included in the application source analysis data correspondence table; identify an unconnected correspondence, which is a correspondence of the unconnected catalog in the application source analysis data correspondence table, sensor data of factory B, and an identical path correspondence which is a correspondence of a catalog on a path including the unconnected catalog; and display information of the unconnected correspondence and the identical path correspondence.