Control Logic Generation From Industrial Process Narratives

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Solution Overview

Problem

Current control systems in industrial process plants require manual interpretation and conversion of control narratives into control logic, which is time-consuming, prone to errors, and subjective due to differences in expression by various personnel.

Innovation Solution

A method and system that automatically extract control logic from control narratives using predetermined regular expressions, models for named entity identification, and intent classifiers to generate control logic for industrial processes, enabling the automatic conversion of textual descriptions into machine-readable format for control systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual interpretation and conversion of control narratives into control logic is performed, then control logic can be generated for control systems, but the process is time-consuming and prone to errors

Engineering Contradiction:

Improveaccuracy of control logicVSAvoidtime for conversion process

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of interpreting control narratives with an automated natural language processing system. The NLP-based parser automatically extracts control logic from textual narratives, eliminating human intervention and thereby reducing both time consumption and human error while maintaining high accuracy through structured processing rules.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The control narrative parsing system is designed to be self-sufficient by automatically extracting control logic without requiring manual engineering intervention. The system uses predefined patterns and NLP techniques to autonomously convert textual requirements into executable control logic, making the process self-service oriented and significantly reducing dependency on human experts.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If manual interpretation of control narratives is performed by engineers, then control logic can be converted, but the process is subjective due to differences in expression by various personnel

Engineering Contradiction:

Improveflexibility in interpreting different expressionsVSAvoidconsistency of control logic

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent transforms the variable and subjective parameter of human interpretation into a fixed and objective computational process. By using NLP patterns and structured extraction rules, the system maintains consistent interpretation across different narratives regardless of expression variations, thereby ensuring reliable and reproducible control logic generation while adapting to different narrative styles through pattern matching.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If automated methods are used to extract control logic from control narratives, then time and error are reduced, but complexity of the system increases

Engineering Contradiction:

Improvespeed of control logic generationVSAvoidcomplexity of parsing system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the control narrative parsing process into distinct modular components: natural language processing module, pattern matching module, control logic extraction module, and validation module. This segmentation allows the system to achieve high productivity through automated processing while managing complexity by organizing functions into separate, manageable modules that can be independently developed and maintained.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12153406B2Method and system to generate control logic for performing industrial processes

Publication Date: 2024.11.26 ABB (SCHWEIZ) AG

AI summary

The invention relates to a method and system to generate control logic for performing industrial processes with a controller in a process plant. The method includes receiving a control narrative comprising one or more control requirements of the industrial process, and extracting a plurality of control entities and a plurality of set points, from the control narrative using one or more sets of predetermined regular expressions and one or more models. The method further includes identifying a set of inputs, outputs and control elements from the plurality of control entities using a domain dictionary, detecting a plurality of actions from the control narrative using an intent classifier, identifying a relationship between the set of inputs, outputs and control elements, the plurality of set points, and the plurality of actions, and generating based on the relationship identified the control logic for the controller to perform the process.