Transformer-Based Production Control With Secure Layered Integration
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Solution Overview
Problem
Current technologies face challenges in generating training data suitable for retraining pre-trained transformer-based models for monitoring and controlling distributed production environments, such as chemical production, and integrating these models into environments with heightened security and safety concerns.
Innovation Solution
The solution involves providing historic production data accumulated over more than 150 years to train transformer-based models, and using these models to analyze plant-based data patterns, identify anomalies, and generate operating instructions for improving control and monitoring of distributed production environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-trained transformer-based models are used for analyzing production data, then data analysis capability and anomaly identification are improved, but the difficulty of integrating these models into secure production environments increases
Solution Approach 1:
The system is divided into multiple secure processing layers (first processing layer for process data, second processing layer for plant-specific data, third processing layer for analytics) that can be independently deployed and secured. The transformer model is integrated as a separate analytics component that receives processed data through defined interfaces, reducing integration complexity while maintaining high analysis capability.
Solution Approach 2:
The patent introduces intermediary processing layers that act as mediators between the production environment and the transformer-based model. These layers preprocess data, manage security protocols, and facilitate communication without requiring direct integration between the model and production systems, thereby reducing integration complexity.
2Reliability
If historic production data accumulated over 150 years is used to train transformer-based models, then model accuracy and reliability are improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing and filtering in the first and second processing layers before data reaches the transformer model. Historic production data is preprocessed, validated, and structured in advance, reducing the computational burden during actual analysis while maintaining model accuracy through high-quality training data.
Solution Approach 2:
The patent extracts only the relevant features and patterns from 150 years of historic production data using the intermediary processing layers. Instead of processing raw historical data in full, the system extracts essential plant-specific data and patterns that are fed to the transformer model, reducing processing time while preserving model accuracy.
3Productivity
If transformer-based models are deployed in distributed production environments with heightened security concerns, then monitoring and control efficiency are improved, but system security requirements and complexity increase
Solution Approach 1:
The monitoring system is segmented into multiple isolated processing layers with defined communication interfaces. The transformer-based model operates in a separate analytics layer that receives processed data through secure interfaces, allowing high monitoring efficiency while maintaining security through architectural isolation and controlled data flow between layers.
Data Source
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AI summary
The disclosure relates to providing plant-based data for re-training a pre-trained transformer-based model for production. The disclosure further relates to using the trained transformer-based model for controlling and/or monitoring distributed production environment such as chemical production in a safe manner.