Manual dependency in P&ID I/O extraction
The extraction of Input/Output (I/O) data from Piping and Instrumentation Diagrams (P&IDs) is a critical phase in control system design. Traditionally, this process relied heavily on manual interpretation, requiring engineers to painstakingly identify and extract data from complex schematics. This manual dependency created a constant bottleneck, requiring significant senior engineering hours and extensive training for junior staff. Furthermore, identifying control points and inter-equipment connectivity requires understanding the specific context and visual patterns of a drawing—complexities that historically limited the effectiveness of standard automation.
Vision AI-driven automated P&ID I/O analysis
To overcome these limitations, we developed an automated extraction system utilizing Vision AI and OCR-based data extractors capable of recognizing symbols, text, and layouts in both digital and scanned PDFs. The system incorporates Vision-Language Model (VLM) logic to analyze drawings at a granular “tile” level, enabling context-aware identification of instrument tags. We built an end-to-end automated pipeline—from initial drawing upload to structured data extraction and validation—and integrated optimization logic for I/O card allocation alongside a dedicated visualization application.
Over 90% reduction in I/O design resources
The implementation transformed the I/O allocation design process, reducing a workflow that previously took months down to mere minutes, effectively slashing design resource requirements by over 90%. By establishing a modular extraction system that can be reused even when drawings are updated, we minimized repetitive design tasks and significantly enhanced overall engineering efficiency.
