Automation Domain Object Conversion Using Ontology Mapping
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Solution Overview
Problem
In industrial automation projects, manually converting one automation domain object with a specific specification to another with a different specification is labor-intensive and error-prone, requiring significant time and effort, and can be hazardous if not done correctly, due to the complexity of handling numerous data and metadata items with interrelationships.
Innovation Solution
A method and system that uses natural language processing and machine learning algorithms to generate an automation domain object with a given specification from another, by serializing data and metadata items, creating an ontology schema, and applying machine learning to convert the schema, thereby automating the conversion process and ensuring accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual conversion method is used to convert automation domain objects, then flexibility and adaptability are maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical conversion processes with an automated computer-based system that uses natural language processing and machine learning algorithms. The system automatically extracts data items and metadata from source automation domain objects, serializes them to intermediate representations, and generates target automation domain objects, eliminating the need for manual copying and modification of thousands of lines of code and design features.
Solution Approach 2:
The conversion system performs self-service by automatically analyzing the source automation domain object, identifying relevant data items and metadata, and generating the target object without human intervention. The system maintains interrelationships between data items autonomously through its automated processing pipeline, reducing dependency on manual engineering effort.
2Productivity
If manual conversion is performed without systematic approach, then conversion can be completed, but errors and faults increase in the generated object
Solution Approach 1:
The system performs preliminary actions by systematically identifying and extracting all relevant data items and metadata from the source automation domain object before conversion. It serializes these elements to intermediate representations and uses machine learning models to predict and maintain interrelationships, ensuring that all necessary information is prepared and validated before generating the target object, thereby preventing errors.
Solution Approach 2:
The system implements feedback mechanisms through machine learning models that analyze the source object's structure and relationships, predict appropriate mappings to the target object, and validate the converted result. The feedback loop ensures that interrelationships between data items are maintained correctly, and the system can identify and correct potential errors in the conversion process.
3Reliability
If all data items and metadata items are manually handled during conversion, then completeness is achieved, but complexity of the process increases
Solution Approach 1:
The patent segments the complex conversion process into distinct automated stages: extraction of data items and metadata, serialization to intermediate representations, machine learning-based mapping and transformation, and generation of the target automation domain object. This segmentation simplifies the overall complexity by breaking down the manual task into manageable automated steps, each handled by specialized software components.
Solution Approach 2:
The system introduces an intermediate representation layer that serializes data items and metadata from the source object. This intermediary format serves as a bridge between the source and target automation domain objects, allowing the complex conversion process to be simplified through structured transformation rules and machine learning models that operate on the standardized intermediate representation.
4Productivity
If automation domain objects are converted without maintaining interrelationships, then conversion speed increases, but safety and correctness decrease
Solution Approach 1:
The system performs preliminary analysis to identify and map interrelationships between data items and metadata before generating the target automation domain object. By predicting and maintaining these relationships in advance through machine learning models, the system ensures safety and correctness without sacrificing conversion speed, as the relationship maintenance is automated rather than manual.
Data Source
AI summary
A method and system for generating an automation domain object with a given specification from another automation domain object is provided. The method includes receiving a request to generate a first automation domain object which has a first specification. The method further includes generating a serialized intermediate representation of a second automation domain object. The serialized intermediate representation includes a description of a plurality of data items and a plurality of metadata items of the second automation domain object. The method further includes generating the first automation domain object which has the first specification, based on an analysis of an ontology schema associated with the second automation domain object with the second specification.