Cloud Simulation Model Generation With Real-Time Asset Validation
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Solution Overview
Problem
Existing simulation model development in cloud computing environments is time-consuming, costly, and requires expert intervention, with models being specific to manufacturers and not meeting dynamic business requirements, and current methods lack validation of generated models.
Innovation Solution
A method and system for generating simulation models in a cloud computing environment that utilizes asset information from a plant environment, including real-time data, to automatically create, validate, and store models, using a cloud agent and database to derive mathematical models and output them on user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simulation models are developed manually by simulation experts, then model accuracy and reliability are improved, but development time and costs increase significantly
Solution Approach 1:
The system enables automatic self-generation of simulation models by extracting data directly from asset profiles and PLC programs without requiring manual intervention by simulation experts. The automated process transforms control logic and asset data into simulation models, eliminating the time-consuming manual development process while maintaining model quality through systematic data transformation.
Solution Approach 2:
The patent replaces the manual mechanical process of expert-driven model development with an automated computational system. The system uses algorithms to automatically extract data from asset profiles, transform PLC control logic into simulation-compatible formats, and generate models programmatically, substituting human expert labor with automated processing.
2Reliability
If simulation models are developed manually by experts, then model quality is improved, but development costs increase
Solution Approach 1:
The automated system performs model generation autonomously by systematically processing asset profile data and PLC programs through predefined transformation rules. This self-service approach eliminates the need to pay simulation experts for manual model development while maintaining consistent model quality through automated data extraction and transformation processes.
Solution Approach 2:
The system creates simulation models by copying and transforming existing asset profile data and control logic from PLC programs. Instead of manually recreating models from scratch, the system automatically copies relevant data elements and transforms them into simulation model formats, significantly reducing development costs while preserving model accuracy.
3Productivity
If simulation models are generated using existing methods, then model creation is automated, but validation and reliability are compromised
Solution Approach 1:
The system incorporates a validation mechanism that provides feedback on generated simulation models. The validation process checks whether the automatically generated models meet required quality standards and specifications, allowing the system to identify and correct issues in generated models, thereby maintaining reliability while preserving automation benefits.
4Manufacturing precision
If simulation models are developed locally and manually, then models are specific to manufacturers, but availability and adaptability decrease
Solution Approach 1:
The system generates simulation models in a standardized format that can be universally used across different platforms and applications. By automating model generation from standardized asset profiles and using consistent transformation rules, the system creates models that are not specific to individual manufacturers but can be broadly applied, enhancing availability and adaptability while maintaining the precision needed for specific applications.
Data Source
AI summary
A method and system for provisioning simulation model generation in a cloud computing environment is disclosed. The method includes receiving a request for generating a simulation model by a user device. The request includes asset information associated with an asset in a plant environment. Further, the method includes generating the simulation model associated with the asset based on the received asset information and a pre-stored asset information in a cloud database. The method further includes validating the simulation model associated with the asset based on real-time asset information received from the plant environment. Additionally, the method includes outputting the simulation model associated with the asset on a user interface of the user device.