Multi-layer Mathematical Model for Dynamic Complexity Prediction
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Solution Overview
Problem
Current methods fail to accurately determine dynamic complexity and operational risk in systems with high complexity, leading to unpredictable states and instability, as they only consider static complexity and lack a method to assess the inter-relationships between functional and non-functional attributes.
Innovation Solution
A multi-layer mathematical model is used to determine dynamic complexity and operational risk, incorporating performance metrics across various operational parameters, allowing for the identification of adverse events and recommended actions to manage risk and optimize system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard queuing mechanisms are used to determine service, then analysis is simplified, but the system fails to account for strong inter-relationships between functional and non-functional attributes
Solution Approach 1:
The patent segments the system analysis into multiple independent layers (functional layer, non-functional layer, interaction layer) that can be analyzed separately and then integrated. This allows standard queuing mechanisms to be applied at each layer while capturing inter-relationships through the interaction layer, resolving the contradiction between analytical simplicity and assessment accuracy.
Solution Approach 2:
The patent introduces a new dimensional framework by adding the interaction layer that captures relationships between functional and non-functional attributes. This multi-dimensional approach extends traditional single-layer queuing analysis to a three-layer model, enabling both simplified individual layer analysis and comprehensive integrated assessment.
2Ease of operation
If static complexity analysis is used, then the system is easier to manage, but it cannot determine dynamic complexity or predict unstable states
Solution Approach 1:
The patent performs preliminary analysis at the static level using traditional methods, then uses the multi-layer model to predict dynamic behavior and identify potential instability before they occur. This allows managers to maintain simple static configurations while proactively addressing dynamic complexity issues through prediction and prevention.
Solution Approach 2:
The patent implements feedback mechanisms where the multi-layer model continuously monitors system performance and provides feedback on dynamic complexity levels. This feedback loop enables automatic adjustment of system parameters to maintain stability, combining ease of management with precise dynamic measurement.
3Device complexity
If attributes are loosely coupled, then standard analysis methods can be applied, but the system cannot capture the combined impact of multiple interacting attributes
Solution Approach 1:
The patent segments attribute relationships into discrete interaction patterns within the interaction layer, allowing loose coupling to be maintained while systematically capturing combined attribute impacts. Each interaction is modeled as a separate entity that can be analyzed independently but contributes to the overall system behavior.
4Measurement precision
If the system models all interactions between components, then prediction accuracy improves, but computational complexity increases significantly
Solution Approach 1:
The patent divides the complex system into three manageable layers, each with its own analysis methods. This segmentation reduces computational complexity by allowing parallel processing of each layer while capturing inter-relationships through structured interaction modeling, maintaining prediction accuracy without overwhelming computational demands.
Solution Approach 2:
The patent transforms the computational problem by adding the interaction layer as a separate dimension, which organizes and manages the complexity of attribute relationships. This dimensional organization enables efficient computation by separating concerns and allowing modular analysis of different interaction types.
Data Source
AI summary
The dynamic complexity and the operational risk inherent in a system are defined and incorporated into a mathematical model of the system. The mathematical model is emulated to predict the states of instability that can occur within the operation of the system. Dynamic complexity of a service is demonstrated where there is an observed effect where the cause can be multiple and seemingly inter-related effects of a many-to-one or many-to-many relationship. Having assessed the dynamic complexity efficiency and the operational risk index of a service (e.g., a business, process or information technology), these indexes can be employed to emulate all attributes of a service, thereby determining how a service responds in multiple states of operation, the states where the dynamic complexity of a service can occur, optimal dynamic complexity efficiency of a service, and the singularities wherein a service becomes unstable.