Automated Data Structure Transformation for Industrial Plant Integration
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Solution Overview
Problem
In industrial plants, integrating data from multiple engineering applications is cumbersome and error-prone, requiring manual export and import of data between applications, which is time-consuming and inefficient due to mismatched data structures and the need for user identification of relevant data.
Innovation Solution
A method and system that automatically extracts, transforms, and merges source data from one application into a target application by determining data structure compatibility, performing hierarchical checking, and updating, deleting, or adding data as necessary to align with the target application's structure, thereby streamlining the data integration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual export and import of data is used between applications, then data can be transferred between applications with different data structures, but the process is time-consuming and error-prone
Solution Approach 1:
The patent introduces an intermediary data transformation system that acts as a mediator between source and target applications. This system automatically maps data fields between different data structures, transforming source data into the target application's format without requiring manual intervention. The intermediary handles data structure compatibility issues, thereby improving both accuracy and reducing time compared to manual methods.
Solution Approach 2:
The patent replaces the manual mechanical process of exporting and importing data with an automated computer-based system. The system uses software to automatically extract, transform, and load data between applications, substituting human operators with an automated data integration platform that performs field mapping and data transformation operations.
2Ease of operation
If user manually identifies data to be imported, then relevant data can be selected for integration, but the process is tedious and requires user knowledge
Solution Approach 1:
The patent implements self-service functionality where the data integration system automatically performs field identification and mapping without requiring user intervention. The system autonomously analyzes the source data structure, identifies relevant fields based on naming conventions and data types, and maps them to the target application's data structure, eliminating the need for users to manually identify and select data.
Solution Approach 2:
The patent performs preliminary data analysis and field identification automatically before the user initiates the import process. The system pre-processes the source data, identifies potential matching fields, and prepares the mapping configuration in advance, so that when the user starts the integration, the heavy lifting of data identification has already been completed.
3Quantity of substance
If all data from source application is imported, then complete data transfer is achieved, but irrelevant data increases complexity and confusion
Solution Approach 1:
The patent extracts only the relevant data fields from the source application based on automated field mapping rules. The system analyzes the source data structure, identifies fields that correspond to the target application's data model, and extracts only those relevant fields for import, leaving out unnecessary data. This selective extraction reduces the volume of imported data and simplifies data management.
4Adaptability or versatility
If multiple import files are used, then comprehensive data coverage is achieved, but file management becomes difficult
Solution Approach 1:
The patent merges multiple source data files into a single integrated data structure before importing to the target application. The system consolidates data from multiple files, applies unified field mapping rules, and creates a single coherent data set that covers all necessary information. This merging approach maintains comprehensive data coverage while simplifying file management by eliminating the need to handle multiple separate import files.
Data Source
AI summary
A control system in an industrial plant is configured to determine whether the source data extracted from the source application is not matched in data structure with the target application, and to transform the source data structure to be matched with the target data structure in accordance with a data structure requirement of the target application if the source data is not matched in data structure with the target data in the target application. The industrial plant control system is configured to perform a hierarchical checking of the data structure between the target application and the source application in a correlated definition status, and perform at least one of updating, deleting and adding the source data from the source application, based at least in part on a result of the hierarchical checking for merging the source data into the target data in the target application.