Ontology-Enhanced Agent Orchestration for Cross-Format Engineering Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data processing methods for engineering projects, such as P&IDs and Control Narratives, often result in incomplete information due to processing individual data types independently, missing insights from cross-analysis, and potential errors from manual human intervention.
Innovation Solution
An Ontology-enhanced Autonomous Agent and Mixture-of-Experts (MoE) system that utilizes large language models (LLMs) to orchestrate comprehensive data processing, integrating domain knowledge and expert models for image, text, and table data, ensuring consistency and generating structured representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processing by human experts is used, then flexibility and adaptability are maintained, but processing time and potential for errors increase
Solution Approach 1:
The system segments the complex data processing task into multiple specialized expert models, each handling specific data types (P&ID images, control narratives, IO lists, etc.). This segmentation enables parallel processing of different data types while maintaining specialized expertise for each, thus reducing overall processing time while preserving adaptability through targeted expert selection.
Solution Approach 2:
The patent introduces an intermediary orchestration layer that coordinates between human experts and AI expert models. This intermediary manages task allocation, combines results, and ensures quality control, allowing the system to leverage both human flexibility and machine efficiency without fully replacing human involvement.
2Ease of manufacture
If single data type processing is used, then processing simplicity is maintained, but information completeness and cross-analysis insights are lost
Solution Approach 1:
The system merges results from multiple expert models that process different data types (P&ID images, control narratives, IO lists, tag lists, etc.) into a unified project understanding. The orchestration layer integrates these diverse processing outcomes to create comprehensive insights that leverage cross-analysis between different engineering data types.
Solution Approach 2:
The patent creates a universal processing framework that handles multiple data types through a common orchestration architecture. This multi-functional system can adaptively select and coordinate various expert models based on the specific project requirements, maintaining processing simplicity through standardized interfaces while achieving information completeness through diverse data type coverage.
3Loss of information
If comprehensive multi-data type processing is implemented, then information completeness improves, but system complexity increases
Solution Approach 1:
The system segments comprehensive data processing into specialized expert models, each handling specific data types independently. This segmentation reduces system complexity by allowing each component to be developed, tested, and maintained separately while still achieving comprehensive processing through their coordinated integration.
Solution Approach 2:
The orchestration layer acts as an intermediary that manages the complexity of coordinating multiple expert models. It provides a standardized interface for task allocation, result aggregation, and quality control, thereby hiding the underlying system complexity from users while enabling comprehensive multi-data type processing.
4Productivity
If automated AI/ML processing is used, then processing speed and consistency improve, but domain knowledge integration and contextual understanding may be reduced
Solution Approach 1:
The system introduces domain knowledge representations and project context as intermediaries between the AI expert models and the processing tasks. These intermediaries provide contextual guidance to the automated models, ensuring that processing speed is maintained while contextual understanding and domain-specific accuracy are enhanced through structured knowledge integration.
Data Source
AI summary
A method for an industrial plant includes providing to a large language model-based (LLM)-based, autonomous agent access to domain knowledge representation associated with one engineering project; obtaining first data indicative of engineering data associated with the one engineering project; based on one or more types of information provided in the engineering data and/or based on a type of the one engineering project, selecting one or more processing tools for processing the first data; applying the selected one or more processing tools on the first data; obtaining second data indicative of one or more intermediate results from a processing of the first data by the selected one or more processing tools; analysing the second data based on checking for contradictions and/or optimisation potential among the second data; and obtaining third data indicative of a structured representation of the first data.