Industrial Plant Data Evaluation Using Ontology Embeddings
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Solution Overview
Problem
Industrial plant data evaluation is hindered by the lack of machine-understandable formats, requiring extensive manual processing due to diverse and unrelated data modalities such as images, audio, and text, which prevents efficient analysis and automation of plant operations and engineering.
Innovation Solution
A computer-implemented method transforms data records of various modalities into a unified embedding space using a trained encoder, allowing comparison with semantic information models to evaluate the semantic meaning of plant components and operations, thereby facilitating automatic data evaluation and improving data integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple modalities (images, audio, text, time series) are processed independently in their original formats, then each data type can be analyzed with specialized tools, but the overall system requires extensive manual processing and cannot efficiently integrate diverse data types for automated evaluation
Solution Approach 1:
The patent merges multiple independent data processing streams (images, audio, text, time series) into a single unified evaluation framework by transforming all modalities into a common embedding space, where they can be jointly analyzed against the semantic information model without requiring separate manual processing pipelines
Solution Approach 2:
The embedding space serves as a universal interface that can accommodate and process multiple data modalities simultaneously. The same evaluation architecture works for images, audio recordings, textual narratives, and time series data, eliminating the need for modality-specific processing complexity
2Productivity
If all data is converted to a unified embedding space for automated evaluation, then manual processing effort is reduced and data integration is improved, but the complexity of training the encoder and creating the semantic information model increases
Solution Approach 1:
The semantic information model and encoder are trained in advance before actual data evaluation begins. This preliminary action creates ready-to-use transformation components that can then rapidly process production data without requiring complex real-time processing, shifting the complexity burden to an offline setup phase
Solution Approach 2:
The embedding space acts as an intermediary layer between raw multi-modal data and the semantic information model. This mediator transforms diverse data formats into a standardized representation that simplifies subsequent evaluation operations, making the overall system more productive despite the initial complexity of establishing the intermediary
3Ease of operation
If diverse data modalities are processed in their original independent formats, then data processing is simpler for each individual type, but the ability to automatically evaluate and integrate information across different data types is lost
Solution Approach 1:
The patent changes the fundamental parameter of data representation by transforming all modalities from their native formats into a common embedding space with unified dimensional characteristics. This parameter change enables automated evaluation while maintaining processing efficiency, as the transformed data retains essential information in a standardized form suitable for machine understanding
Data Source
AI summary
A computer-implemented method for evaluating at least one record of data characterizing at least one component of an industrial plant, comprising: transforming by a trained encoder the at least one record of data into a representation in an embedding space; comparing this representation to representations of semantic entities from a semantic information model in the same embedding space, wherein each semantic entity carries a semantic meaning with respect to the structure, the construction, and/or the functioning, of the industrial plant, and the semantic information model comprises relationships between the semantic entities; and evaluating from the result of the comparison the semantic meaning of the record of data with respect to the structure, the construction, and/or the functioning, of the industrial plant.