Cloud Simulation for Real-Time AI Model Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex data processing environments in industry settings face challenges in adapting to changing needs due to their resource-intensive nature, making it difficult to implement changes or integrate Artificial Intelligence modeling without significant resource allocation and long lead times.
Innovation Solution
A dynamically-constructed cloud-based real-time production data processing environment simulation system that uses event listeners to monitor production systems, process event data, and stand up cloud-based simulation environments for testing AI and ML models without affecting the production environment, allowing for real-time or near-real-time testing and adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If production systems use many functionally-specific processing sub components from different vendors, then the system can handle complex data processing needs, but the system becomes difficult to adapt to changing needs and requires significant resources to implement changes
Solution Approach 1:
The patent creates a simulation environment that copies the production system's architecture, data models, and processing logic. This digital twin allows testing and validation of changes without affecting the actual production system, enabling adaptability while maintaining system complexity intact in the production environment
Solution Approach 2:
The simulation environment enables preliminary testing of AI models, process changes, and scenario validations before implementing them in the production system. This preliminary action reduces the risk and resource requirements of implementing changes in the complex production system
2Adaptability or versatility
If the system implements AI modeling into decision-making processes, then decision-making capabilities are enhanced, but large amounts of processing power and data consumption are required
Solution Approach 1:
The simulation environment replicates the production system's data and processing logic, allowing AI models to be trained and validated on copied data rather than requiring the production system to continuously provide large amounts of data and processing power for model development
3Reliability
If the production system allocates more resources for testing and implementing new processes, then the quality and reliability of changes improve, but the lead time and cost increase
Solution Approach 1:
By creating a simulation copy of the production system, the patent enables comprehensive testing and validation of changes in the simulation environment before deployment. This eliminates the need for lengthy and costly pilot programs or phased rollouts in the production system, reducing lead time while maintaining reliability
Solution Approach 2:
The simulation environment performs preliminary validation of changes, AI models, and process modifications before they are implemented in production. This preliminary action ensures reliability by catching issues early while significantly reducing the time required compared to traditional implementation approaches
Data Source
AI summary
Systems and methods for dynamically-constructed cloud-based real-time (and/or near-real-time) production data processing environment simulation.