Risk Analysis Device for Service Availability
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Solution Overview
Problem
Existing systems for improving service availability fail to effectively present and remove multiple risk factors simultaneously, as they lack a method to account for the interdependencies between risk factors influencing service execution.
Innovation Solution
A risk analysis device and method that compute influence degrees for each risk factor based on its relation to other components and services, and generate risk groups by identifying similarities among these factors, allowing for targeted removal of interdependent risk factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems analyze risk factors independently without considering interdependencies, then the analysis process is simple, but the ability to improve service availability is insufficient
Solution Approach 1:
The patent segments risk factors into distinct entities with independent characteristics (failure rate, recovery rate) while maintaining relationships between them. Each risk factor is analyzed as a separate unit with defined parameters, allowing systematic evaluation of interdependencies without overwhelming complexity.
Solution Approach 2:
The patent introduces an intermediary analysis layer that computes influence degrees and similarity measures between risk factors. This intermediary layer translates complex interdependencies into quantifiable metrics (influence degree, similarity score) that facilitate systematic risk group formation while maintaining analytical tractability.
2Measurement precision
If multiple risk factors are analyzed simultaneously considering their interdependencies, then the accuracy of availability improvement is improved, but the computational complexity increases
Solution Approach 1:
The patent transforms the analysis by changing parameters into standardized metrics: influence degree (quantifying impact on service availability) and similarity score (measuring relationship strength between risk factors). These parameter transformations enable precise multi-factor analysis while keeping computations manageable through normalized scales.
Solution Approach 2:
The patent applies partial action by focusing computational resources on risk factors with high influence degrees and strong similarity scores. Instead of uniformly analyzing all risk factors equally, the system prioritizes those most critical to service availability, reducing overall computational burden while maintaining precision for key risks.
3Productivity
If risk factors are presented and removed individually, then the management process is simple, but the efficiency of availability improvement is low
Solution Approach 1:
The patent merges risk factors into groups based on their similarity scores and influence degrees. Risk factors with high similarity (e.g., similar failure patterns, interconnected components) are combined into risk groups, allowing administrators to address multiple risks simultaneously through unified management actions, thereby improving efficiency.
Solution Approach 2:
The patent performs preliminary analysis to compute influence degrees and similarity scores before actual risk removal decisions are made. This preliminary grouping of risk factors into manageable sets enables administrators to prepare and execute comprehensive availability improvement plans more efficiently, rather than reacting to individual risks in isolation.
Data Source
AI summary
An information processing device includes: a unit configured to compute a service influence degree for each risk factor with respect to each service, on the basis of information which indicates a relation between components which have the risk factors and other components which are influenced by the state of the components, information which denotes characteristics of the respective risk factors, and information which denotes a correspondence between the services and these components; and a unit configured to compute, on the basis of the computed service influence degrees, similarities between specific risk factors and other risk factors, and for generating and outputting a set of component identification information on the basis of the computed similarities.