Evaluation Model Generation Based on Raw Material Similarity
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Solution Overview
Problem
Existing systems face increased processing load and time loss due to the frequent generation of new evaluation models for varying crude oil properties, which are machine-learned to evaluate the state of equipment, particularly in oil refineries, as properties of crude oil vary by region and time of production.
Innovation Solution
An evaluation model generating apparatus determines whether to generate a new evaluation model based on the similarity of crude oil properties using machine learning, clustering raw materials, and generating models only when necessary, thereby reducing processing load and time loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If evaluation models are frequently generated for varying crude oil properties, then model accuracy for different raw materials is improved, but processing load and time loss increase
Solution Approach 1:
The patent changes the parameter of model generation frequency from frequent to conditional based on raw material property variations. The determining unit evaluates whether property data variations exceed a threshold before triggering model generation, thus reducing unnecessary processing while maintaining accuracy when needed.
Solution Approach 2:
The system performs self-evaluation by automatically determining whether new evaluation models are needed based on property data comparisons. The determining unit autonomously assesses property variations and triggers model generation only when necessary, eliminating the need for continuous external intervention.
2Measurement precision
If evaluation models are frequently generated for varying crude oil properties, then model accuracy for different raw materials is improved, but time loss increases
Solution Approach 1:
The patent changes the parameter of model generation timing from continuous/frequent to conditional/event-driven. By monitoring property data variations and triggering model generation only when changes exceed thresholds, the system reduces time loss while maintaining model accuracy for varying crude oil properties.
Solution Approach 2:
The system implements periodic evaluation of property data against existing models, generating new models only when periodic checks reveal significant variations. This periodic action replaces continuous model generation, reducing time loss while ensuring accuracy when raw material properties change.
3Productivity
If evaluation models are generated based on raw material properties, then processing load is reduced, but determination complexity increases
Solution Approach 1:
The patent segments the model generation process into two distinct parts: a determining unit that evaluates whether model generation is needed, and a model generation unit that creates models only when triggered. This segmentation reduces overall processing load by separating the lightweight determination step from the resource-intensive model generation step.
Solution Approach 2:
The determining unit acts as an intermediary between property data input and model generation. It mediates by evaluating property variations and deciding whether to trigger model generation, thus reducing unnecessary processing load while managing determination complexity through a dedicated intermediate component.
Data Source
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AI summary
SOLUTION: An evaluation model generating apparatus, including: a property data obtaining unit which obtains property data representing a property of a raw material to be used in equipment that manufactures a product from the raw material; a determining unit which determines, based on the property data, whether to generate an evaluation model that outputs an indicator obtained by evaluating a state of the equipment; and an evaluation model generating unit which generates, according to a result of the determining, the evaluation model by machine learning, is provided. In the evaluation model generating apparatus, the determining unit may determine, if it is judged that a target raw material is not similar in property to a raw material that has been used in the past based on the property data, to generate the evaluation model.