Industrial Data Platform Using BIDTs for Contextualized Asset Data
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Solution Overview
Problem
Industrial automation systems face challenges in collecting and formatting data from distributed industrial devices into meaningful presentations for users, as much of the data is unstructured and lacks context, burdening developers to define its meaning.
Innovation Solution
The implementation of Basic Information Data Types (BIDTs) within industrial devices, which are structured data types representing rates, states, and events, along with user-configurable metadata, and a gateway device that references these BIDTs in asset models to generate contextualized data presentations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If unstructured data from industrial devices is collected without structured data types, then data collection is simple, but data lacks context and meaning requiring developer burden to define
Solution Approach 1:
The patent applies parameter changes by transforming raw industrial device data into structured Basic Information Data Types (BIDTs) with defined parameters such as rates, states, and events. This transformation adds contextual meaning to the data through standardized parameter definitions while maintaining compatibility with existing industrial devices, thereby resolving the contradiction between preserving data context and avoiding excessive structural complexity.
Solution Approach 2:
The patent introduces BIDTs as an intermediary layer between raw industrial device data and application-level data processing. These BIDTs serve as standardized mediators that provide contextual meaning without requiring direct complex structuring at the device level, thus preserving information while avoiding the burden of defining detailed data structures at each device.
2Loss of information
If developers manually define data meaning for each industrial device, then data becomes meaningful and contextualized, but development time and effort increase significantly
Solution Approach 1:
The patent implements universality by creating a standardized set of BIDTs that can be applied across multiple industrial devices and applications. These universal data types (rates, states, events) provide pre-defined contextual meaning that can be reused across different devices and scenarios, eliminating the need for developers to manually define data meaning for each device and significantly reducing development time while preserving data semantics.
3Ease of operation
If structured data types are implemented in industrial devices, then data presentations are simplified and contextualized, but integration complexity between automation and non-automation models increases
Solution Approach 1:
The patent applies segmentation by dividing complex industrial data into distinct, manageable BIDT categories (rates, states, events). This segmentation allows each data type to be handled independently with specific processing rules, simplifying data presentation while making integration between automation and non-automation models more manageable through modular, category-based integration approaches.
Data Source
AI summary
A cloud-based industrial data services (IDS) architecture leverage smart tags, asset models, and data service applications to facilitate secure transaction and exchange of contextualized factory data between different parties as part of a combined technology and commerce platform, or to perform provide asset owners with insights into operation of their industrial assets. The IDS platform supports a set of services that connect providers of smart industrial devices to plant floor and systems owned by the end users of these devices. The cloud-based platform allows asset providers to publish data service applications for purchase and use by end users of their assets, and allows equipment owners to control remote access to selected sets of their industrial data via the cloud platform.