Modular Analysis Technique Selection for Prediction Model Requests
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Solution Overview
Problem
Existing analysis techniques face limitations in efficiently creating prediction models for predicting abnormalities in facilities, as they require multiple models to be prepared in advance, which is not feasible for various analysis requests.
Innovation Solution
An analysis technique presenting system that includes a storage apparatus, identifying unit, replacing unit, and presenting unit, which stores and combines analysis modules to create flexible analysis packages according to the analysis purpose and model of the mechanism, allowing for the identification and replacement of analysis modules to generate appropriate analysis techniques on demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple prediction models are created in advance for various analysis requests, then the reliability of prediction is improved, but the device complexity and preparation time increase significantly
Solution Approach 1:
The prediction model is segmented into multiple independent analysis modules, each handling a specific analysis step. These modules can be independently selected and combined based on the specific analysis request, eliminating the need to prepare complete models in advance for every possible scenario.
Solution Approach 2:
The analysis modules are designed with universal interfaces and standardized data formats, allowing the same module to be used across different analysis techniques and prediction scenarios. This multi-functionality reduces the total number of modules needed while maintaining prediction reliability across various facility types.
2Adaptability or versatility
If all analysis techniques are prepared in advance, then the adaptability to different analysis requests is improved, but the loss of time for model creation and maintenance increases
Solution Approach 1:
The analysis technique selection process is made dynamic through automatic composition. When a prediction request is received, the system dynamically selects and combines the appropriate analysis modules based on the facility type, data characteristics, and analysis goals, rather than relying on pre-configured static models.
Solution Approach 2:
While complete models are not prepared in advance, the necessary analysis modules are pre-processed and made available in a standardized format. This preliminary preparation of modular components enables rapid assembly of complete analysis techniques when needed, reducing the time loss compared to creating models from scratch.
3Ease of operation
If analysis modules are flexibly combined to create analysis packages, then the ease of operation is improved, but the device complexity increases
Solution Approach 1:
The system performs automatic composition of analysis packages by selecting and combining modules based on the input prediction request. This self-service capability eliminates the need for users to manually configure complex module combinations, making the system easy to operate despite the underlying complexity of multiple analytically modules.
Solution Approach 2:
A standardized interface layer acts as an intermediary between the user's prediction request and the underlying analysis modules. This intermediary automatically translates high-level requests into appropriate module combinations, shielding users from the complexity of module integration while maintaining operational flexibility.
Data Source
AI summary
The analysis technique presenting system includes: a storage apparatus storing a plurality of analysis modules that are divided from the analysis processing into a plurality of steps, a plurality of analysis techniques that combine some of the analysis modules, analysis module information that associates the analysis module with the corresponding model of the mechanism, and analysis technique information that associates a list of the analysis modules belonging to the corresponding analysis technique with the model; an identifying unit identifies, when the analysis technique associated with an input model input is not present in the analysis technique information, the analysis module matching the input model from the analysis module information; a replacing unit replaces the analysis module, which is associated with the model different from the input model with the identified analysis module; and a presenting unit presents the analysis technique including the analysis module replaced.