LLM Process Control Analytics Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing process control systems face challenges in efficiently generating customized visualizations and reports for operational statistics, as this process is often costly and time-consuming, requiring programmer intervention and involving long delays between user requests and visualization delivery.
Innovation Solution
The implementation of an AI-driven system that uses a Large Language Model (LLM) to generate instructions for creating analytics information, such as visualizations and reports, based on user requests. This system includes components for data collection, prompt generation, obfuscation, and instruction validation and execution, enabling rapid and customized data visualization without the need for extensive programmer intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a traditional process control system is used to generate customized visualizations and reports, then the system can provide operational statistics, but the process is costly and time-consuming requiring programmer intervention
Solution Approach 1:
The system enables users to directly interact with the process control data through natural language queries, allowing the system to serve itself by automatically generating visualizations and reports without requiring programmer intervention. The AI-driven architecture translates user requests into executable analytics instructions autonomously.
Solution Approach 2:
The patent replaces the mechanical process of manual programming and programmer intervention with an AI-driven system that uses natural language processing and automated instruction generation. This substitution eliminates the need for mechanical coding operations while achieving the same analytics visualization goals.
2Loss of time
If customized visualizations are generated through traditional methods, then specific operational insights can be obtained, but long delays occur between user requests and visualization delivery
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing process control data in a structured format, enabling rapid retrieval and visualization generation when users make requests. The AI model is pre-trained to quickly translate natural language queries into executable analytics instructions, reducing response time.
Solution Approach 2:
The patent replaces slow mechanical data processing and manual visualization creation with an AI-driven automated system that uses natural language processing and instant data querying. This substitution dramatically reduces the time between user requests and visualization delivery by eliminating manual intervention steps.
3Loss of time
If an AI-driven system with LLM is implemented to generate analytics instructions, then time and cost are reduced, but data confidentiality and security must be maintained
Solution Approach 1:
The patent introduces an intermediary layer between the AI-driven analytics system and the process control data. This intermediary architecture includes secure data access controls, authentication mechanisms, and isolation layers that protect confidential data while enabling AI processing. The system acts as a mediator that maintains security boundaries while facilitating analytics operations.
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods to perform process control analytics are disclosed. An example method includes generating a prompt based on the request for analytics data, providing the prompt to a large language model for generation of analytics instructions, validating the analytics instructions to determine whether the analytics instructions are to be executed, and in response to the determination that the analytics instructions are to be executed, executing the analytics instructions to generate the analytics data.