Cyber Physical Human System Modeling via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Creating high-fidelity models of complex infrastructure systems is costly and complex, often requiring specialized expertise and intimate knowledge of machinery and human operations, making it rare for infrastructure systems to have accurate and reliable models of their behavior.
Innovation Solution
A CPHS modeling system that uses intelligent automation to generate high-fidelity models by obtaining information from libraries, receiving user-provided schemas and operational data, and employing machine learning to refine parameters, thereby reducing the need for extensive expertise and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional specialized expertise and intimate knowledge methods are used to create high-fidelity models, then model accuracy and reliability are improved, but cost and complexity increase significantly
Solution Approach 1:
The patent replaces manual expert knowledge integration with automated machine learning systems. The ML module automatically learns system dynamics and parameters from operational data, substituting the mechanical process of expert analysis and model construction with an automated computational approach that maintains accuracy while reducing complexity and cost.
Solution Approach 2:
The system enables self-service modeling where the machine learning module autonomously generates high-fidelity models from operational data without requiring specialized expert intervention. The system serves itself by automatically identifying parameters, learning dynamics, and constructing accurate models that would traditionally require expert knowledge to develop.
2Reliability
If traditional specialized expertise methods are used to create high-fidelity models, then model accuracy is improved, but resource requirements increase
Solution Approach 1:
The patent substitutes resource-intensive expert manual processes with automated machine learning computation. The ML module processes operational data to extract system dynamics and parameters, replacing the need for extensive expert time and resources while maintaining or improving model accuracy through automated pattern recognition and parameter identification.
3Device complexity
If automated machine learning approaches are used instead of traditional mathematical modeling, then cost and complexity are reduced, but model fidelity may be compromised
Solution Approach 1:
The patent employs machine learning to substitute traditional mathematical modeling approaches. The ML module learns system dynamics directly from operational data, capturing complex nonlinear behaviors that would be difficult to model with traditional methods, thereby maintaining high fidelity while reducing the complexity of model construction and parameter identification.
Solution Approach 2:
The system changes the parameters from manually specified expert knowledge to data-driven learned parameters. The ML module automatically identifies and learns system parameters from operational data, transforming the modeling approach from parameter-specification-based to parameter-learning-based, which maintains accuracy while reducing the burden of expert knowledge input.
4Reliability
If extensive expert knowledge is required for modeling, then model accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service modeling where the machine learning module automatically generates accurate models from operational data without requiring expert intervention. Users can obtain high-fidelity models by simply providing operational data, eliminating the need for specialized knowledge while maintaining model accuracy through automated learning processes.
Solution Approach 2:
The patent replaces the manual expert-driven modeling process with an automated machine learning system. The ML module handles parameter identification, dynamics learning, and model construction automatically, substituting the complex mechanical process of expert analysis with an automated computational approach that is easier to operate while maintaining accuracy.
Data Source
AI summary
A system for building a high fidelity model of a cyber physical human system (CPHS) is disclosed. The CPHS modelling system may comprise a database containing database information and an interface configured to receive a schema and operational data from a user. The CPHS modelling system may comprise an assembler configured to: receive the schema and database information; and generate a generic model of the CPHS. The CPHS modelling system may comprise a machine learning module configured to: receive the operational data; execute a machine learning process; and use the operational data to generate the high fidelity model.