Automated P&ID Digitization With Human-in-the-Loop Retraining
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Solution Overview
Problem
The manual digitization of P&IDs is labor-intensive and prone to errors, while automated methods face challenges due to data variability, lack of standardization, and model instability, making it difficult to achieve accurate and scalable symbol extraction using AI-based techniques.
Innovation Solution
An automated method that integrates human expertise through a continuous feedback loop, using an object detection model trained on an initial dataset, where user feedback adjusts the model's classifications and positions, and employs metrics like PSI and FSI to quantify data distribution shifts, enabling retraining for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated AI-based methods are used for symbol extraction, then productivity increases and manual labor is reduced, but model stability deteriorates due to data variability and lack of standardization
Solution Approach 1:
The patent implements a human-in-the-loop feedback mechanism where domain experts review and correct AI-generated symbol extractions. This feedback loop continuously improves model accuracy by training on corrected data, resolving the contradiction between automated high-speed processing and maintaining reliable accurate results.
Solution Approach 2:
The system dynamically adjusts model parameters and training data based on feedback from domain experts. By changing training parameters and retraining models with corrected annotations, the system maintains model stability and accuracy while preserving automated processing capabilities.
2Measurement precision
If manual conversion methods are used, then data quality and accuracy improve, but productivity decreases due to labor intensity and time consumption
Solution Approach 1:
Domain experts provide feedback on AI-generated extractions, correcting errors and improving accuracy. This feedback mechanism enables the system to achieve high data quality comparable to manual methods while maintaining automated processing speeds.
Solution Approach 2:
Instead of requiring complete manual review of all symbols, the system uses partial review focusing on correcting errors and edge cases. This partial action approach maintains high accuracy while preserving overall processing speed.
3Adaptability or versatility
If AI models are trained on limited and non-standardized symbol representations, then adaptability to different companies improves, but manufacturing precision of symbol extraction deteriorates
Solution Approach 1:
The system allows dynamic adjustment of model parameters and training data to adapt to different company-specific symbol representations. By changing training parameters based on feedback from different domains, the model maintains both adaptability and extraction precision across multiple companies.
Solution Approach 2:
The patent creates a universal training framework that can handle multiple companies and domains through a common platform. The system uses a universal model architecture that is fine-tuned for different companies based on their specific symbol representations, achieving both universality and precision.
Data Source
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AI summary
Disclosed is a method (100) and a system (200) for automated digitization of schematic diagrams. The method involves receiving a digital schematic diagram, processing it through the object detection model to extract symbols with preliminary classifications and positions, and displaying these for user review. User feedback, including corrections and identifications of inaccuracies, is utilized to quantify shifts in data distribution and retrain the model accordingly. The method leverages Population Stability Index (PSI) and Feature Stability Index (FSI) calculations to guide the retraining process and employs a recommendation engine to suggest model adaptation strategies based on the feedback and data shifts. The retrained model undergoes benchmarking to ensure performance improvements before deployment.