Ontology-Enhanced EPC Data Orchestration for Consistent Engineering Output
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data processing methods for engineering projects, such as P&IDs and CNs, are inefficient as they process data one-by-one, leading to loss of information and inconsistencies due to lack of comprehensive analysis.
Innovation Solution
A large language model-based autonomous agent with domain knowledge representation orchestrates the processing of multiple data types (images and text) using a mixture-of-experts system, ensuring comprehensive and consistent output by aligning and combining results with underlying domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If single engineering project data is processed one-by-one using AI/ML models, then processing speed and automation are improved, but information completeness and data consistency deteriorate
Solution Approach 1:
The patent combines multiple data sources (P&IDs, Control Narratives, IO Lists, Tag Lists, Single line diagrams, functional descriptions, logic diagrams, motor and consumer lists, and instrumentation lists) from the same engineering project into a unified processing framework. This merging allows the system to process all related data together, ensuring information completeness while maintaining automation through the LLM-based autonomous agent that coordinates the integrated processing workflow.
2Extent of automation
If single engineering project data is processed one-by-one, then automation is improved, but data consistency and contradiction detection deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the LLM-based autonomous agent comprehensively analyzes processed data to check for contradictions and optimization potential. The system uses this feedback to iteratively refine its processing, ensuring data consistency across all engineering documents. This feedback loop maintains high automation while improving reliability through continuous validation and correction.
Solution Approach 2:
The LLM-based autonomous agent acts as an intermediary that coordinates between different data sources and processing tools. It manages the integration of multiple data types, ensures consistent interpretation across documents, and mediates contradiction resolution by analyzing relationships between P&IDs, Control Narratives, IO Lists, and other engineering data, thereby maintaining data consistency throughout the automated process.
3Reliability
If comprehensive analysis of all engineering data is performed, then information completeness and consistency are improved, but processing complexity and resource requirements worsen
Solution Approach 1:
The patent segments the comprehensive data processing task into manageable components by using the LLM-based autonomous agent to selectively process different data types (P&IDs, Control Narratives, IO Lists, etc.) through appropriate specialized tools. This segmentation reduces processing complexity by breaking down the overwhelming task of analyzing all engineering data into structured, tool-specific processing steps while maintaining comprehensive coverage and data consistency.
Data Source
Figure 1
Figure 2
Figure 2
AI summary
There is disclosed a method for comprehensive engineering data processing in industrial plant. The method comprises providing to a large language model-based, LLM-based, autonomous agent access to domain knowledge representation associated with one engineering project. The method comprises applying the LLM-based autonomous agent provided with the access to orchestrate by the LLM-based autonomous agent at least the following based on and/or in alignment with the domain knowledge representation: 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; based on the applying, 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; comprehensively analysing the second data based on checking for contradictions and/or optimisation potential among the second data; and based on a result of the comprehensively analysing, obtaining third data indicative of a structured representation of the first data.