Optimizing Risk Management Critical Values via Iterative Combination Scoring
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Solution Overview
Problem
Traditional methods for determining critical values in risk management systems rely on manual experience, which is inadequate for comprehensive decision-making in complex systems, lacking an objective criterion to differentiate between high and low risks.
Innovation Solution
A method and apparatus that generate and optimize combinations of critical values through relation functions between features and index values, using a cyclic process of scoring, combination exchange, and probability-based selection to identify the best combination of critical values, ensuring objective and efficient risk management decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual experience-based methods are used to determine critical values, then the implementation process is simple, but the comprehensiveness and objectivity of risk assessment deteriorates
Solution Approach 1:
The patent replaces manual experience-based determination with an automated computer-implemented method. The system uses processors to automatically generate, evaluate, and optimize combinations of critical values through algorithmic processes, substituting human subjective judgment with objective computational analysis that comprehensively evaluates multiple features and index values.
Solution Approach 2:
The system performs self-optimization by automatically generating combinations of critical values, evaluating them through relation functions, and iteratively improving the combination based on score comparisons. The method autonomously determines optimal critical values without requiring manual intervention or subjective expertise, allowing the system to self-adjust and self-optimize the risk assessment parameters.
2Device complexity
If manual experience-based methods are used to determine critical values, then the system complexity is low, but the ability to comprehensively reflect multiple requirements deteriorates
Solution Approach 1:
The patent creates a universal system that can handle multiple features (M features) and evaluate them against multiple index values (N index values) simultaneously. The relation functions and combination generation mechanism are designed to work with any number of features and requirements, making the system adaptable to various risk management scenarios while maintaining a unified computational framework.
Solution Approach 2:
The system dynamically adjusts parameters by generating K combinations of critical values, each combination representing different parameter settings. Through iterative evaluation and score comparison, the system identifies the optimal parameter combination that best reflects multiple requirements. The method allows flexible modification of the number of features, index values, and combinations based on specific application needs.
3Measurement precision
If automated combination optimization is implemented, then the objectivity and comprehensiveness of critical value determination is improved, but the computational complexity increases
Solution Approach 1:
The patent divides the complex optimization problem into manageable segments: generating K combinations, evaluating each combination through N relation functions, comparing scores, and iteratively selecting optimal combinations. This segmentation allows the system to handle computational complexity through structured, modular processing steps rather than attempting to solve the entire problem simultaneously.
Solution Approach 2:
The system evaluates K combinations (where K is a preset value) rather than exhaustively checking all possible combinations. This partial action approach provides a practical balance between computational feasibility and optimization quality, generating sufficiently many combinations to achieve objectivity and comprehensiveness without requiring prohibitively complex computational resources.
Data Source
AI summary
A method for determining critical values includes obtaining N relation functions between M features and N index values, the N relation functions taking the M features as inputs and the N index values as outputs; generating K combinations, each comprising M critical values of the M features; repeating following steps until a preset stop condition is satisfied: determining a score for each of the K combinations based on the N relation functions to represent an overall quality of the N index values; repeatedly selecting two combinations and exchanging critical values between the two combinations to generate two new combinations until a quantity of the new combinations reaches a preset number; and selecting K combinations from the new combinations; and after the preset stop condition is satisfied, associating the M features with the M critical values in a combination with a highest score.