Cross-Platform Digital Twin Model Deployment via Adapter Patterns
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Solution Overview
Problem
The lack of industry standards in IoT platforms leads to time-consuming, platform-specific development, deployment, and maintenance of digital twin models, which require integration of physical behavior models, data analytics, and integration modules, resulting in increased latency and inefficiency due to platform-specific approaches.
Innovation Solution
A method and apparatus for extending, customizing, and validating Simulation-Based Digital Twin (SBDT) models using general-purpose programming languages, allowing direct integration with IoT platforms, reducing data transfer latency, and enabling direct data exchange within a local runtime environment, thus streamlining development, maintenance, and deployment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If platform-specific approaches are used for digital twin model development and deployment, then integration with IoT platforms is achieved, but development time and maintenance complexity increase significantly
Solution Approach 1:
The patent creates a universal digital twin model structure that can operate across multiple IoT platforms through standardized interfaces and abstraction layers. The model separates platform-specific code from core simulation logic, allowing the same digital twin to be deployed on different platforms without complete reconfiguration, thereby reducing development time while maintaining reliable integration.
Solution Approach 2:
The patent introduces intermediate layers including configuration files, standardized data interfaces, and platform adapter patterns that mediate between the core digital twin model and specific IoT platforms. These intermediaries translate platform-specific requirements into a unified model structure, reducing direct coupling and development complexity.
2Productivity
If platform-specific customization is performed for each IoT platform, then platform optimization is achieved, but model complexity and maintenance difficulty increase
Solution Approach 1:
The patent segments the digital twin model into modular components: core simulation engine, platform-specific adapters, configuration parameters, and data interfaces. Each segment can be independently developed, optimized for specific platforms, and maintained separately, reducing overall model complexity while allowing platform-specific performance optimization.
Solution Approach 2:
The patent applies local quality by allowing platform-specific optimizations only in designated adapter sections while keeping the core model universal. Each platform adapter can be customized for optimal performance on its target platform without affecting the core simulation logic, maintaining low complexity in critical path components.
3Adaptability or versatility
If extensive configuration and resource deployment are performed for each platform, then model functionality is enhanced, but deployment time and resource overhead increase
Solution Approach 1:
The patent implements preliminary action through pre-configured template models, predefined adapter patterns, and standardized interface definitions that are prepared in advance. When deploying to a new platform, developers can select and configure pre-built templates rather than creating models from scratch, significantly reducing deployment time while maintaining full functionality through configuration parameters.
Solution Approach 2:
The patent uses parameter-driven configuration where platform-specific behavior is controlled through configurable parameters rather than hard-coded logic. The same model structure can be adapted to different platforms by changing configuration parameters and selecting appropriate adapters, enabling rapid deployment without extensive reconfiguration or additional resource allocation.
Data Source
AI summary
A method and apparatus for extending, customizing and validating a simulation-based digital twin model is described. In an exemplary embodiment, the device transmits a model to a client, where the model is a simulation-based digital twin model. In addition, the device receives a customization to the model, the where the customization adds a functionality to the model. Furthermore, the device deploys the model in a model platform, where the model is used in a simulation with the model platform and the model is coupled with the model platform.