Digital Twin Comparison for Automated Ecosystem Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automated ecosystems, such as those in industrial facilities, are complex and have proven difficult to optimize due to their intricate infrastructure and processes, leading to unsuccessful optimization efforts despite numerous attempts.
Innovation Solution
The use of digital twin simulations to compare and identify improvements in infrastructure and processes between a target automated ecosystem and a reference automated ecosystem, allowing for the adoption of superior implementations in the target ecosystem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional optimization methods are used on automated ecosystems, then optimization attempts are made, but the intricate infrastructure and processes make optimization unsuccessful
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the automated ecosystem to perform simulations and analysis. This copying approach allows optimization to be conducted on the virtual model without disrupting the actual complex infrastructure, thereby resolving the contradiction between optimization effectiveness and infrastructure complexity
Solution Approach 2:
The patent performs preliminary simulations and comparative analysis on the digital twin before implementing changes in the actual system. This preliminary action on the virtual model enables successful optimization by identifying improvements without being hindered by the complexity of the real infrastructure
2Reliability
If digital twin simulations are used to compare reference and target ecosystems, then improved infrastructure and processes can be identified, but simulation and analysis time is required
Solution Approach 1:
The digital twin simulation system serves multiple functions: it models the target ecosystem, implements reference ecosystem configurations, performs comparative analysis, and identifies optimizations. This multi-functionality consolidates multiple operations into a single simulation framework, achieving reliable optimization accuracy while managing time consumption through integrated processing
Data Source
AI summary
Described are techniques for optimizing a target automated ecosystem. Requirements of the target automated ecosystem are received. A digital twin simulation of one or more reference automated ecosystems implementing the requirements of the target automated ecosystem are created and executed. Furthermore, a digital twin simulation of the target automated ecosystem is created and executed to implement the requirements of the target automated ecosystem. A comparative analysis is then performed between the digital twin simulations of one or more reference automated ecosystems and the target automated ecosystem. Based on the comparative analysis, the infrastructure implemented and/or processes executed by the reference automated ecosystem(s) with an improvement over the infrastructure implemented and/or processes executed by the target automated ecosystem that exceeds a threshold value, which may be user-selected, are identified. Such identified infrastructure and/or processes are then provided to an expert to be adopted and implemented in the target automated ecosystem.