Hierarchical Model Analysis for System Manageability and Risk
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Solution Overview
Problem
Current modeling and analysis procedures for real-world systems lack the ability to adequately represent complexity while generating high-level information for meaningful analysis and control, often becoming bogged down in details.
Innovation Solution
A computer-implemented method using a hierarchical computer model that represents real-world systems with artefacts, risk, and action artefacts, allowing for the determination of a roadmap for target tasks, manageability, feasibility, and risk assessment, and visual representation of resilience, manageability, and feasibility indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed modeling of real-world systems is performed, then representation accuracy is improved, but analysis complexity increases and high-level information generation becomes difficult
Solution Approach 1:
The system divides the complex real-world system into multiple hierarchical levels (e.g., system level, subsystem level, component level), each with its own model representation. This segmentation allows detailed modeling at lower levels while maintaining abstract representations at higher levels, resolving the contradiction between representation accuracy and analysis complexity.
Solution Approach 2:
The patent introduces intermediate abstraction layers that mediate between detailed system models and high-level analysis requirements. These intermediate representations aggregate detailed information into meaningful summaries, enabling both accurate representation and simplified analysis without getting bogged down in details.
2Measurement precision
If detailed modeling of real-world systems is performed, then representation accuracy is improved, but control capability deteriorates
Solution Approach 1:
The control function is segmented across hierarchical levels, with each level responsible for control decisions relevant to its scope. Detailed models at lower levels provide accurate representation for component-level control, while aggregated information at higher levels enables system-level control strategies, maintaining both representation accuracy and control capability.
Solution Approach 2:
The system implements feedback mechanisms that aggregate detailed information from lower hierarchical levels back to higher levels. This feedback loop enables control decisions at any level to be informed by accurate detailed representations below, while the summarized information above provides context for strategic control, resolving the contradiction between representation accuracy and control capability.
3Loss of information
If high-level information generation is pursued, then analysis meaningfulness is improved, but representation of system complexity deteriorates
Solution Approach 1:
The system segments the representation of complexity across hierarchical levels, where each level models only the complexity relevant to its scope. Detailed complexity is represented at lower levels while higher levels aggregate this into meaningful patterns, generating high-level information without losing the representation of underlying complexity.
Solution Approach 2:
Intermediate abstraction layers serve as mediators that transform detailed complexity representations into meaningful high-level information. These layers aggregate and summarize detailed data while preserving essential complexity characteristics, enabling both high-level information generation and accurate complexity representation.
4Loss of information
If comprehensive system analysis is performed, then decision-making quality is improved, but computational resources and time increase
Solution Approach 1:
The analysis process is segmented across hierarchical levels, allowing computational resources to be allocated according to the importance and complexity of different system aspects. Critical detailed analysis is performed at relevant levels while less critical areas use simplified representations, improving decision-making quality without requiring comprehensive analysis of all system details.
Solution Approach 2:
The system applies partial analysis to the extent necessary for meaningful decisions, rather than performing complete comprehensive analysis. By identifying which levels and aspects require detailed analysis and which can use summaries, the system achieves sufficient decision-making quality with reduced computational time and resources.
Data Source
AI summary
A method for the analysis, based on a computer model of a real-world system whose entities are represented by software objects (artefacts) for: risks of real-world events occurring (risk artefacts); actions in the real-world system (action artefacts), associated with tasks, resources and conditions; and in which a subset of the model is represented by a system context. The method comprises defining actions required to accomplish a target task, thereby allocating resources to actions; computing a manageability index of the at least one system context, representing an extent to which resources are available; computing a feasibility index of the at least one system context, representing an extent to which actions are possible; computing an aggregated risk of the at least one system context; creating and displaying, on a display device, a visual representation of the aggregated risk, the manageability index and the feasibility index of the at least one system context.