Operation Model Evaluation for Human-Guided Equipment Control

The patent introduces an evaluation unit as an intermediary component that bridges the automated operation model and human expertise. The evaluation unit evaluates the operation model based on model evaluation values derived from actual equipment operation results, and this evaluation information is fed back to improve the model. This mediator ensures that automated control maintains reliability by continuously aligning with human expertise and actual performance.

The patent implements a feedback mechanism where the evaluation unit assesses the operation model using model evaluation values obtained from actual equipment operation. The evaluation results are used to improve the operation model, creating a closed-loop system that continuously enhances both productivity and reliability by incorporating real-world performance data and human expertise.

The patent implements self-service through automated learning processing where the operation model performs reinforcement learning using operation data acquired from actual equipment operation. The system automatically optimizes control parameters without requiring complex external intervention or manual tuning, thereby achieving manufacturing precision improvement while managing device complexity through automation.

The patent applies preliminary action by performing learning processing in advance to build an optimized operation model before actual equipment operation. The model is trained using historical operation data and reinforcement learning, so that when deployed, it can immediately provide optimized control parameters without requiring complex real-time adjustments during equipment operation.

The patent uses copying by creating an operation model that replicates human operation expertise. The model is trained using operation data that captures human judgment and decision-making patterns. This copy of human expertise enables automated control to respond quickly without losing the nuanced information contained in human judgment, as the model has learned from and replicated human operational knowledge.

The evaluation unit provides feedback that prevents loss of information by continuously assessing whether the operation model's decisions align with human expertise. The model evaluation values are derived from actual equipment operation results, ensuring that the automated system maintains access to human judgment information while achieving fast response times through automated decision-making.