Semantic Description Generation for Industrial IoT Entities
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Solution Overview
Problem
Current methods for developing composite interactions among industrial entities require semantic expertise and coding, making it challenging for non-experts and limiting the re-use of existing semantic models in alternative environments.
Innovation Solution
A method that uses entity icons and semantic configuration templates to visually define composite interactions, allowing non-experts to create semantic descriptions without coding, and employs a semantic reasoner to discover and adapt existing descriptions for generating machine-interpretable digital twins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If semantic models are configured and coded manually by experts, then the precision and reliability of semantic descriptions are improved, but the device complexity and difficulty of operation increase significantly
Solution Approach 1:
The patent introduces an intermediary system that automatically generates semantic descriptions by mapping industrial entity data to predefined semantic models. This intermediary process eliminates the need for manual expert configuration while maintaining high precision through automated reasoning and model matching algorithms.
Solution Approach 2:
The system enables self-service by allowing industrial entities to automatically generate their own semantic descriptions through data-driven model instantiation. The entities themselves participate in the semantic description generation process by providing their data, which is then automatically mapped to appropriate semantic models without requiring external expert intervention.
2Manufacturing precision
If expert knowledge is required for semantic model configuration, then the manufacturing precision is maintained, but the ease of operation and accessibility to non-experts deteriorates
Solution Approach 1:
The patent employs an intermediary automated system that bridges the gap between non-expert users and complex semantic models. The system handles the translation of industrial data into semantically accurate descriptions through automated mapping and reasoning, making the process accessible to users without semantic expertise while maintaining high accuracy.
Solution Approach 2:
The system segments the complex semantic configuration process into automated data mapping steps and predefined model instantiation. By breaking down the monolithic expert-driven configuration into smaller automated components, the system enables non-experts to develop semantic interactions through simple data provision rather than complex model configuration.
3Adaptability or versatility
If semantic models are customized for specific environments, then the adaptability to specific use cases is improved, but the loss of time for model development and reconfiguration increases
Solution Approach 1:
The patent implements universal semantic models that can be instantiated across multiple industrial environments and use cases. These models are designed to be environment-agnostic and can be automatically adapted to different contexts through data-driven instantiation, eliminating the need for time-consuming customizations while maintaining versatility across diverse applications.
Solution Approach 2:
The system performs preliminary actions by pre-defining reusable semantic models that can be directly instantiated in different environments. Rather than starting from scratch for each new environment, the system has already prepared versatile model templates that can be quickly adapted through automatic mapping, significantly reducing development and reconfiguration time.
4Manufacturing precision
If manual configuration methods are used, then the manufacturing precision of semantic descriptions is maintained, but the productivity and development speed decrease
Solution Approach 1:
The patent enables self-service automated generation of semantic descriptions where industrial entities automatically provide their data and the system instantiates appropriate semantic models. This self-service process maintains consistency through automated mapping rules while dramatically increasing development speed by eliminating manual configuration steps.
Solution Approach 2:
The system introduces an intermediary automated processing layer that maintains semantic model consistency through standardized mapping procedures while accelerating development. The intermediary automatically handles the transformation from industrial data to semantic descriptions, ensuring consistency through rule-based mapping while improving productivity by removing manual intervention bottlenecks.
Data Source
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AI summary
The embodiments proposed herein provide a simplified configuration of an industrial entity and an application design development using a visual programming environment. The concept is based on graphical representation of semantic models as configurable entity icons. An entity icon corresponding to a semantic configuration template acts as a template for an atomic capability of an industrial entity ensuring a configuration that is consistent with preconfigured solutions of the semantic model. The entity icon can be customized according to a device specification of the industrial entity by configuring one or more attributes according to the device specifications. The configuration step is guided and validated. One or more configured entity icons can be used to describe capabilities of an industrial entity or to create an IoT application template.