IoT Digital Twin Simulation for Scalable Industrial Asset Validation
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Solution Overview
Problem
Existing IoT technologies face challenges in gathering, processing, and utilizing data from widely distributed assets for designing or validating further assets or operations due to complexities with connectivity and data storage, especially when dealing with hundreds or thousands of connected assets.
Innovation Solution
A cloud-implemented method and system for performing closed-loop simulations in an IoT environment, which includes receiving real-time IoT data from industrial plants, configuring digital twins, generating data packages, and enabling user devices to perform simulations for validating asset designs and production processes, thereby overcoming bottlenecks and optimizing design parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is gathered from widely distributed assets for design validation, then asset design reliability is improved, but system complexity increases due to connectivity and data storage challenges
Solution Approach 1:
The patent introduces an edge computing device as an intermediary between distributed assets and cloud systems. This edge device aggregates data from multiple assets, performs preliminary processing, and manages local storage, thereby reducing the complexity of direct cloud-asset connectivity while enabling reliable design validation through comprehensive data collection
2Quantity of substance
If hundreds or thousands of assets are connected for data gathering, then data comprehensiveness is improved, but scalability becomes complex
Solution Approach 1:
The patent segments the system into hierarchical layers: edge computing devices that serve local asset groups, regional aggregation points, and cloud-based central systems. This segmentation allows the system to scale by adding more edge devices independently, enabling hundreds or thousands of assets to be connected without creating monolithic complexity
3Measurement precision
If physical prototypes are used for asset testing, then validation accuracy is improved, but time-to-market and costs increase
Solution Approach 1:
The patent creates virtual copies (digital twins) of physical assets that replicate their behavior and characteristics. These digital copies can be tested extensively without physical constraints, providing sufficient validation accuracy while eliminating the need for multiple physical prototypes, thereby reducing time-to-market and costs
4Measurement precision
If real-time data processing is implemented for closed loop simulations, then simulation accuracy is improved, but computational requirements and costs increase
Solution Approach 1:
The patent divides computational tasks between edge devices that handle real-time data preprocessing and filtering, and cloud systems that perform intensive simulation computations. This segmentation allows real-time processing with improved accuracy while distributing computational energy consumption across multiple devices rather than concentrating it in one system
Data Source
AI summary
An IoT environment including at least one source configured to provide IoT data associated with at least one asset of at least one industrial plant, at least one user device; and a cloud computing system communicatively coupled to the at least one source and the at least one user device. The cloud computing system configures a digital twin corresponding to at least one of the asset and a production process associated with the industrial plant based on the IoT data received from the source. Further, at least one data package is generated based on the configured digital twin upon receiving a request for accessing the digital twin from the user device. The at least one data package is transmitted to the user device, for enabling the user device to perform one or more simulations based on the data package.