Cloud Emulation Runtime Engine for Industrial Controller Integration
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Solution Overview
Problem
Current industrial analytics systems lack an integrated framework for performing enterprise-level modeling, validation, and analytics of industrial systems, requiring separate configuration tools and expertise, and struggle to seamlessly interface with both on-premise and cloud-based infrastructure.
Innovation Solution
A multi-tier cyber analytics system is implemented on a cloud platform, comprising an emulation component for virtualizing industrial controllers, a simulation component for executing simulations, and an analytics component that generates output data through an emulation data exchange interface, enabling seamless interaction with distributed simulations and hardware controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If separate configuration tools and expertise are used for industrial analytics systems, then system functionality can be achieved, but system complexity and difficulty of operation increase
Solution Approach 1:
The patent combines multiple separate configuration tools and functionalities into a single integrated configuration environment. This unified interface allows users to configure emulation, simulation, and analytics components together without needing multiple separate tools, thereby reducing operational complexity while maintaining full system functionality.
Solution Approach 2:
The configuration environment is designed to be universal, supporting multiple configuration tasks and component types within a single interface. It can configure virtualized controllers, simulations, data exchange interfaces, and analytics components all through one system, eliminating the need for specialized expertise for each separate tool.
2Reliability
If cloud-based virtualized controllers are implemented, then enterprise-level analytics capability is improved, but integration complexity with existing infrastructure increases
Solution Approach 1:
The patent introduces an emulation data exchange component as an intermediary layer between the cloud-based virtualized controller and existing on-premise infrastructure. This intermediary handles data translation and communication protocols, simplifying integration by absorbing the complexity of interfacing with diverse existing systems while maintaining clean, standardized interfaces on both sides.
Solution Approach 2:
The system architecture is segmented into distinct modular components: the cloud-based virtualized controller, the emulation data exchange component, simulation components, and analytics components. This segmentation allows each component to be configured and integrated independently, reducing overall integration complexity while enabling enterprise-level analytics capability.
3Measurement precision
If comprehensive emulation and simulation components are integrated, then analytics accuracy is improved, but system configuration complexity increases
Solution Approach 1:
The patent merges the configuration of emulation components, simulation components, and analytics components into a single unified configuration environment. This integration allows all components to be configured together in one place, reducing configuration complexity while maintaining the comprehensive functionality needed for accurate analytics.
Solution Approach 2:
The configuration environment is designed to perform preliminary configuration actions automatically, such as setting up data exchange interfaces and establishing communication channels between components. This preliminary automation reduces the manual configuration effort required while ensuring that all necessary components are properly integrated for accurate analytics.
Data Source
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AI summary
A cloud-based multi-tier cyber analytics system is provided for integration of cloud-side and on-premise analytics for industrial systems. The analytics system includes an emulation runtime engine that executes a virtualized controller on a cloud platform. The runtime engine serves as a core analytics component by providing a control-level analytics engine with application programming interfaces (APIs) that enable seamless interaction of distributed simulations, cloud level services, and hardware industrial controllers. A cloud-based framework integrates soft control, hard control, and simulation with cloud-level services, and includes components that facilitate near real-time data streaming from the plant floor to the cloud platform to yield an industrial Internet of Things (IoT).