Cloud Asset Modeling for Contextualized Industrial Data Services
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Solution Overview
Problem
Existing industrial automation systems face challenges in efficiently collecting, processing, and presenting industrial data across distributed industrial assets, due to the highly distributed nature of data sources and the need for contextualized data presentations.
Innovation Solution
A cloud-based industrial data services platform that registers industrial assets, defines asset models referencing data tags on industrial devices, and provides data service applications for processing industrial data, enabling efficient data collection, processing, and presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If industrial data services are provided across distributed industrial assets, then data accessibility and service capability are improved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent introduces a cloud-based platform as an intermediary between distributed industrial assets and end users. This platform provides centralized asset registration, model management, and data service delivery, thereby reducing the complexity of direct peer-to-peer integration while maintaining broad data accessibility and service capability across the distributed system.
2Loss of information
If asset models reference data tags on multiple industrial devices, then data contextualization and modeling capability are improved, but data collection and processing complexity increase
Solution Approach 1:
The patent creates virtual copies of industrial assets through asset models that reference data tags on physical devices. These digital models replicate the structure, hierarchy, and data relationships of physical assets, enabling contextualized data presentation and analysis without requiring direct access to or processing of the complex underlying device architecture.
3Productivity
If data service applications are deployed on a cloud platform, then service scalability and accessibility are improved, but data transmission and processing latency increase
Solution Approach 1:
The patent performs preliminary actions by registering assets, defining models, and configuring data service applications in advance on the cloud platform. Asset models are pre-established with references to data tags, and service applications are pre-deployed and configured. This advance preparation reduces real-time processing requirements and latency when actual data services are delivered to end users.
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.