Semantic Binding for OPC UA Migration of Legacy Autonomous Models
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Solution Overview
Problem
Existing engineering tools lack proper information modeling capabilities, leading to inefficiencies and errors when migrating legacy automation systems to standardized information models like OPC UA, as they often lack structural or contextual information, resulting in poor interoperability and increased productive work time for engineers.
Innovation Solution
A knowledge-based information modeling service platform with a semantic binding engine and adapter that generates binding configurations based on a semantic model extracted from a legacy model and a knowledge graph, enabling the transformation of flat models into contextualized, standard-compliant models by mapping legacy system components to new model nodes using domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing engineering tools are used to export variables to OPC UA server, then the migration from legacy automation systems is enabled, but the exported models lack structural and contextual information resulting in poor interoperability
Solution Approach 1:
The system performs preliminary action by automatically generating contextual information and semantic annotations during the model export process, rather than requiring engineers to add this information manually later. The binding configuration and semantic model are created in advance, ensuring contextual information is preserved before the interoperability issue arises.
Solution Approach 2:
The patent introduces an intermediary layer (binding configuration and semantic model) between the legacy automation system variables and the OPC UA information model. This intermediary preserves contextual information by mapping variables with their semantic meanings, data types, and relationships, thereby preventing information loss while enabling interoperability.
2Reliability
If engineers manually recover and understand variable meanings from documents, then contextual information can be obtained, but productive work time is significantly increased
Solution Approach 1:
The system implements self-service by automatically generating binding configurations and semantic models without requiring engineer intervention. The tool extracts variable information from legacy systems, binds it to OPC UA nodes, and generates contextual information automatically, making the system self-sufficient and eliminating the need for manual document review and variable interpretation.
Solution Approach 2:
The patent replaces the mechanical process of manual information recovery (engineers reading documents, guessing, and consulting) with an automated computational system. The binding configuration engine and semantic model generator substitute human cognitive activities with algorithmic processes, dramatically reducing time while maintaining or improving accuracy through systematic extraction and binding of variable contextual information.
3Ease of manufacture
If OPC UA server directly exports shell model from legacy projects, then the export process is simple, but the model lacks compliance with domain relevant companion specifications
Solution Approach 1:
The system segments the model export process into distinct components: variable extraction, binding configuration generation, semantic model creation, and compliance validation. This segmentation allows each component to be optimized independently - the binding configuration engine handles compliance with companion specifications while the overall process remains automated and simple, resolving the contradiction between simplicity and precision.
4Adaptability or versatility
If engineers use visual tools to manually label OPC UA information model components, then the model can be customized, but standard conformance cannot be guaranteed
Solution Approach 1:
The system implements dynamics by providing an adaptive binding configuration that can be customized for different companion specifications and domain requirements. The binding configuration engine dynamically adjusts the semantic model and node bindings based on the target standard, allowing customization while maintaining conformance. Engineers can select different companion specifications and the system automatically adapts the model generation process accordingly.
Data Source
AI summary
A system and method for knowledge based information modeling using a semantic binding engine that generates a binding configuration based on a legacy domain semantic model extracted from a controller of an autonomous system and a knowledge graph extracted from a knowledge repository of domain knowledge related to the autonomous system. The binding configuration represents a mapping of standardized model instance components to a component of the legacy domain semantic model. An adapter with a server processes communications related to a standardized information model, including information requests that are translated to a set of process variables of the legacy domain semantic model using the binding configuration.