Industrial Data Gateway Semantic Mapping for OPC UA Interoperability
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Solution Overview
Problem
Mapping from a vendor-proprietary data model to an OPC UA standardized information model is a complex, time-consuming, and error-prone task, often requiring manual intervention and significant expertise.
Innovation Solution
A method and system for exchanging data between a server and a client in an industrial data network, involving the conversion of information models into machine-interpretable descriptions, deducing similarities between elements, proposing and implementing a mapping, and employing a gateway entity for data exchange, thereby facilitating semantic mapping across different information models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mapping from vendor-proprietary data model to OPC UA standardized information model is performed, then mapping accuracy can be maintained, but time consumption and error rate increase significantly
Solution Approach 1:
The patent introduces an intermediary mapping system that includes a vendor-proprietary data model interface, a standardized information model interface (OPC UA), and a mapping engine. This intermediary layer automatically translates between proprietary data models and standardized OPC UA information models, eliminating the need for manual mapping while maintaining accuracy through structured translation rules and validation mechanisms.
Solution Approach 2:
The patent replaces the manual mechanical process of mapping (where engineers manually configure data points, tags, and relationships) with an automated electronic system. The mapping engine uses computer algorithms to automatically match vendor-specific data structures with OPC UA information models, substituting human cognitive work with automated pattern recognition and transformation rules.
2Manufacturing precision
If manual mapping is performed to ensure accuracy, then mapping quality can be maintained, but error rate increases due to human factors
Solution Approach 1:
The mapping system performs self-validation through automated consistency checks, data type verification, and relationship validation. The system automatically detects and reports mapping errors, ensuring high quality without human intervention. The standardized OPC UA interface provides built-in validation rules that automatically verify mapping correctness.
Solution Approach 2:
The patent implements feedback mechanisms where the mapping engine continuously validates mappings against OPC UA information model constraints and vendor-specific data model requirements. Error feedback is automatically generated and can be corrected through the system interface, creating a closed-loop quality assurance process that eliminates human error.
3Productivity
If automated mapping is implemented to reduce time consumption, then productivity increases, but adaptability to different vendor models decreases
Solution Approach 1:
The patent creates a universal mapping framework that can handle multiple vendor-specific data models through a common proprietary data model interface. The standardized OPC UA information model interface provides a universal target structure that accommodates various industrial automation data types. This universal architecture enables the system to adapt to different vendors while maintaining automated mapping capabilities.
Solution Approach 2:
The mapping engine uses configurable parameters and transformation rules that can be adjusted for different vendor data models. By changing mapping parameters, data type conversions, and relationship definitions, the system adapts to various proprietary models without requiring manual reconfiguration, maintaining both automation and versatility.
4Adaptability or versatility
If complex OPC UA information models are used to ensure standardization, then interoperability improves, but mapping complexity increases
Solution Approach 1:
The patent segments the complex OPC UA information model into manageable components through the standardized information model interface. The mapping engine processes vendor-specific data models by breaking them down into discrete data points, tags, and relationships that can be systematically mapped to corresponding OPC UA nodes, attributes, and references, reducing overall mapping complexity.
Data Source
AI summary
A system and a method for exchanging data between a server and a client in an industrial data network, wherein the server employs a first information model for information interchange and the client employs a second information model for information interchange, where the method includes converting the first and the second information models in a first and a second machine-interpretable description, deducing similarities between elements of the first and the second machine-interpretable description, proposing and implementing a mapping of at least one element of the first information model to an element of the second information model based on the deduced similarities in text and in structure and, employing, by a gateway entity, the mapping for a data exchange between the server and the client such that the semantic mapping of virtually any input, vendor-specific metadata, and any output model (including OPC UA-based models) is achieved.