Industrial Data Contextualization With BIDTs and Hierarchical Asset Models
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Solution Overview
Problem
Industrial automation systems face challenges in collecting and formatting vast amounts of unstructured data from various industrial devices into meaningful presentations for users, as the data is highly distributed and often lacks context, burdening developers to define its meaning.
Innovation Solution
The implementation of a system that utilizes structured data types, specifically basic information data types (BIDTs) including rate, state, odometer, and event types, which are configured on industrial devices and can be discovered by external systems, allowing for the creation of asset models that group these data types hierarchically and generate graphical presentations based on user-defined metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If unstructured data from industrial devices is collected and formatted into meaningful presentations, then data visualization quality is improved, but device complexity and developer burden increase
Solution Approach 1:
The patent changes the data structure parameters by introducing standardized data types (state, rate, odometer, event) that transform unstructured industrial data into structured formats with inherent semantic meaning. This allows data to be self-descriptive without requiring complex external context definitions, resolving the contradiction between information quality and system complexity
Solution Approach 2:
The patent segments data into distinct standardized types (state, rate, odometer, event) with specific characteristics and metadata requirements. This segmentation allows each data type to be handled independently with appropriate formatting rules, reducing the overall complexity of data management while preserving contextual information
2Ease of operation
If structured data types are implemented on industrial devices, then data presentation quality is improved, but ease of manufacture decreases
Solution Approach 1:
The patent creates universal data type definitions that can be applied across multiple industrial devices and applications. The standardized data types (state, rate, odometer, event) serve multiple purposes: data collection, contextualization, validation, and presentation formatting, thereby improving data management while requiring configuration effort only once per device type
3Loss of information
If hierarchical asset models are created to group data types, then data understanding is enhanced, but device complexity increases
Solution Approach 1:
The patent implements a nested hierarchical structure where asset models contain multiple data types, each data type contains specific parameters and metadata. This nesting organizes data from general to specific levels (asset → data type → parameter), enhancing data meaning through hierarchical context while reusing parent-level definitions to minimize overall complexity
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 are discoverable by a gateway device on which models of industrial assets can be defined, where the models reference the BIDTs defined on the industrial devices. The gateway device can retrieve industrial data from the data tags, formatted in accordance with the data types and associated user-defined metadata specific to the respective data types. The gateway device or a separate application server system can then generate graphical presentations of the industrial data in accordance with the model and the metadata.