Smart Manufacturing Data Profiles for Cross-Vendor Interoperability
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Solution Overview
Problem
Existing smart manufacturing systems face challenges in seamlessly integrating different machines and software applications due to varying protocols, data formats, and vendor-specific implementations, leading to difficulties in data reuse and interoperability.
Innovation Solution
A data-centric smart manufacturing system is developed, utilizing reusable data model profiles that ingest raw data, contextualize it, and define data connectivity protocols, along with data transformation models that provide higher-level operational insights, enabling software interoperability and reusability across different systems and vendors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vendor-specific data formats and protocols are used for each machine, then each vendor's system operates reliably, but interoperability between different vendors' systems deteriorates
Solution Approach 1:
The patent introduces data model profiles as intermediary components that mediate between vendor-specific data formats and a universal data representation. These profiles translate and contextualize proprietary data formats into standardized structures, enabling interoperability while preserving the reliability of original vendor systems. The profiles act as adapters that bridge different data ecosystems without requiring changes to the underlying vendor systems.
Solution Approach 2:
The patent transforms data parameters by changing their representation format through data model profiles. Raw data from various vendors is converted into contextualized data with standardized parameters, attributes, and relationships. This parameter transformation enables different vendor systems to communicate using a common language while maintaining their original operational characteristics.
2Ease of operation
If custom software applications are developed for each specific machine or process, then the application meets specific operational requirements, but software reusability and ease of deployment deteriorates
Solution Approach 1:
The patent segments software functionality into modular components based on data model profiles. Instead of developing monolithic custom applications, the system divides functionality into reusable software modules that can be independently developed, tested, and deployed. Each module operates on contextualized data from specific data model profiles, enabling selective reuse across different manufacturing contexts.
Solution Approach 2:
The patent creates universal data model profiles that can serve multiple software applications and different vendor systems simultaneously. These profiles define common data structures and relationships that enable a single software component to function across diverse manufacturing scenarios, reducing the need for custom development while maintaining operational specificity.
3Adaptability or versatility
If extensive data contextualization and transformation are performed for each vendor's system, then data interoperability improves, but system complexity and implementation time increases
Solution Approach 1:
The patent performs data contextualization and transformation in advance by pre-defining data model profiles that encapsulate vendor-specific data formats and relationships. These profiles are created and validated beforehand, so that during actual system integration, the complex transformation logic is already prepared and can be applied automatically without manual intervention for each new vendor or system.
Solution Approach 2:
The patent creates reusable templates and copies of data model profiles that can be instantiated for different vendors and systems. Instead of building transformation logic from scratch for each integration scenario, the system copies and adapts existing profiles, significantly reducing implementation complexity while maintaining comprehensive interoperability coverage.
Data Source
AI summary
Data centric smart manufacturing systems are described. In an embodiment, the data centric smart manufacturing system includes several systems providing raw data, several reusable data model profiles that ingest the raw data and contextualize the raw data, where a data model profile defines contextualized data, configurations, and data connectivity protocols for a particular type of system, and several data transformation models integrated with the data model profiles and instantiated with contextualized data to provide higher level operational insights.