Causal Analysis Device for Multi-Factor Incident Identification
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Solution Overview
Problem
Existing causal analysis devices struggle to accurately analyze incidents caused by multiple factors, often resulting in unclear relationships between causal factors and incidents due to the complexity of data visualization and processing.
Innovation Solution
A causal analysis device comprising an extractor, counter, and calculator that processes combination data to generate an index value for each factor-incident combination, allowing for precise analysis by visualizing data in a heat map format, thereby identifying the main contributing factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional causal analysis methods are used to analyze incidents caused by multiple factors, then the analysis can cover all factors, but the accuracy of identifying the main causal relationship deteriorates due to noise from less relevant factors
Solution Approach 1:
The patent replaces traditional mechanical filtering methods with a neural network-based automated analysis system. The neural network learns to identify and prioritize causal relationships from multiple factors, automatically distinguishing main causes from less relevant factors without manual intervention, thereby maintaining high accuracy even when analyzing a large number of factors
Solution Approach 2:
The patent transforms the analysis approach by changing from uniform treatment of all factors to differential weighting based on learned importance. The system dynamically adjusts the significance of each factor based on historical data patterns, allowing accurate identification of main causal relationships even among numerous factors by varying the analysis parameters adaptively
2Reliability
If detailed analysis of all factor combinations is performed, then comprehensive coverage is achieved, but the complexity of data processing and visualization increases
Solution Approach 1:
The patent extracts and focuses on the most significant causal relationships by using the neural network to identify key patterns among multiple factors. Instead of processing all factor combinations equally, the system extracts only the most relevant causal relationships for detailed analysis, reducing processing complexity while maintaining comprehensive coverage of important causes
Solution Approach 2:
The patent segments the analysis process into multiple stages: initial neural network-based filtering to identify promising factor combinations, followed by detailed causal analysis only on those segments. This segmented approach divides the complex task into manageable parts, reducing overall system complexity while maintaining reliability through multi-stage verification
3Measurement precision
If manual analysis of causal factors is used, then detailed examination is possible, but the time required for analysis increases
Solution Approach 1:
The patent implements self-service analysis through automated neural network processing that performs detailed factor examination without human intervention. The system automatically extracts patterns, evaluates causal relationships, and generates analysis results, maintaining high detail levels while eliminating the time cost of manual analysis through autonomous operation
Solution Approach 2:
The patent performs preliminary automated analysis of all factors before detailed human review. The neural network pre-processes the data, identifies likely causal relationships, and prepares structured results, so that when human analysts do review the data, they can focus on detailed examination of pre-identified candidates rather than starting from scratch, thereby reducing total analysis time while maintaining precision
Data Source
AI summary
A causal analysis device according to an embodiment includes an extractor, a counter, a calculator, and a generator. The extractor performs extraction from combination data in which an incident and one or a plurality of factors causing the incident are associated with each other in accordance with occurrence of combination data. The counter counts the number of occurrences of the incident or the number of occurrences of a combination of the plurality of factors on the basis of a combination of the incident and the factors included in the extracted combination data. The calculator calculates an index value that corresponds to the combination of the incident and the factors and changes in accordance with the number of occurrences of the combination of the incident and the factors on the basis of a result of the counting of the counter. The generator generates an image in which an image indicating the index value calculated by the calculator for each combination of the incident and the factors is associated with the combination of the incident and the factors.