Industrial Data Contextualization Using BIDT Metadata Tags
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Solution Overview
Problem
Industrial automation systems face challenges in collecting and formatting vast amounts of unstructured data from distributed industrial devices into meaningful presentations for users, requiring developers to define the meaning of each data item, which is burdensome and inefficient.
Innovation Solution
The implementation of basic information data types (BIDTs) such as state, rate, odometer, and event types, along with associated metadata, allows for structured data representation, enabling gateway devices and application servers to generate contextualized graphical presentations of industrial data by referencing these data types and their metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If developers manually define the meaning of each data item from distributed industrial devices, then data can be collected and formatted into meaningful presentations, but the process becomes burdensome and inefficient
Solution Approach 1:
The system enables self-service by allowing data to automatically carry its own meaning through standardized data type tags. Each data item is annotated with metadata indicating its type (e.g., temperature, pressure, flow rate), allowing the system to automatically interpret and contextualize data without requiring manual developer definition. This self-describing data approach eliminates the burdensome manual configuration process while preserving complete data meaning.
Solution Approach 2:
The invention changes the parameter of data representation by introducing standardized data type metadata alongside raw data values. Instead of treating all data as generic numbers or strings, the system attaches type parameters that automatically convey semantic meaning. This parameter enrichment allows the system to automatically understand data context, resolving the contradiction between maintaining data meaning and reducing developer time investment.
2Ease of manufacture
If unstructured data from distributed industrial devices is collected without standardized data types, then data collection is simple, but data integration and visualization become complex and inefficient
Solution Approach 1:
The system applies preliminary action by standardizing data types at the source before data collection occurs. Industrial devices are configured to output data with predefined standardized types (temperature, pressure, flow, etc.), and metadata is attached in advance. This preliminary structuring simplifies subsequent data integration and visualization tasks, as the system receives pre-organized, self-describing data rather than raw unstructured values requiring complex processing.
Solution Approach 2:
The invention implements universality by creating a standardized data type framework that works across all industrial devices and data sources. The same set of standardized data types (temperature, pressure, flow rate, etc.) can represent diverse physical quantities from different devices, enabling a single unified data integration approach to handle multiple data sources. This universal framework reduces system complexity by eliminating the need for device-specific data handling logic.
3Loss of information
If comprehensive data from industrial assets is visualized in detail, then user insights into industrial operations are enhanced, but the complexity of formatting and presenting the data increases
Solution Approach 1:
The data automatically provides its own contextual information through standardized type metadata, enabling the presentation system to self-configure visualizations without complex manual formatting rules. Each data item carries inherent type information that guides its presentation, allowing comprehensive data visualization with reduced system complexity.
Solution Approach 2:
Data is pre-tagged with standardized types and metadata before visualization, allowing the presentation system to automatically generate appropriate visual representations. This preliminary structuring enables detailed comprehensive visualizations while keeping the presentation system simple, as the heavy lifting of data organization occurs beforehand during data collection.
Data Source
AI summary
An industrial data presentation system leverages structured data types defined on industrial devices to generate and deliver meaningful presentations of industrial data. Industrial devices are configured to support structured data types referred to as basic information data types (BIDTs) comprising a finite set of structured information data types, including a rate data type, a state data type, an odometer data type, and an event data type. The BIDTs can be referenced by both automation models of an industrial asset and non-automation models of the asset, allowing data points of both types of models to be easily linked using a common data source nomenclature.