Modular Digital Twin Architecture for Faster IIoT Event Analytics
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Solution Overview
Problem
Current digital twin-based solutions for Industrial Internet of Things (IIoT) applications are labor-intensive, time-consuming, and lack effective actionable business insights, necessitating a composable on-demand digital twin architecture that is modular and scalable.
Innovation Solution
A composable modular architecture comprising four modules: Analytics Solution Cores, Sensor Cores, Asset Cores, and Policy Cores, which can compose inferencing and training pipelines on demand for complex event processing, enabling quick development of adaptive machine learning-based business solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current digital twin-based solutions are used for IIoT applications, then the system can monitor and analyze sensor data, but the deployment is labor-intensive and time-consuming
Solution Approach 1:
The digital twin architecture is segmented into four independent modules: Analytics Solution Cores, Sensor Cores, Asset Cores, and Policy Cores. Each module can be developed, tested, and deployed independently, significantly reducing the overall deployment time and labor requirements while maintaining the full analytical capability of the system.
Solution Approach 2:
The Analytics Solution Cores are pre-configured with machine learning algorithms and analytics capabilities before deployment. This preliminary preparation allows the system to be deployed quickly without requiring extensive on-site configuration and setup, thereby reducing deployment time while preserving analytical precision.
2Adaptability or versatility
If a modular architecture is implemented, then the system becomes composable and scalable, but the device complexity increases
Solution Approach 1:
The system is divided into four standardized modules (Analytics Solution Cores, Sensor Cores, Asset Cores, Policy Cores) that can be composed in different configurations. While the modular structure increases architectural complexity, each module has a well-defined interface and function, making the overall system manageable and scalable through standardized composition rather than ad-hoc integration.
Solution Approach 2:
The modular architecture allows dynamic composition and configuration of digital twins based on specific application requirements. Modules can be added, removed, or reconfigured without affecting the entire system, enabling scalability and adaptability while managing complexity through flexible, dynamic assembly rather than fixed rigid structures.
Data Source
AI summary
Systems and methods described herein which can involve, for receipt of a composed digital twin, processing the composed digital twin through a policy core process that determines a policy for the digital twin; executing an asset core process that determines an asset hierarchy of physical assets represented by the digital twin based on metadata of the physical assets retrieved from a metadata database and the determined policy; executing a sensor core process that determines a sensor hierarchy to be associated with the asset hierarchy based on metadata of sensors retrieved from the metadata database of the sensors and the asset core process; executing an analytics solution core that determines analytics solutions for the physical assets; constructing pipelines to facilitate the analytics solutions across a policy core layer, asset core layer, sensor core layer, and analytics solution core layer of the digital twin; and executing the pipelines.