Custom Query Models for Industrial Process Data Retrieval
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Solution Overview
Problem
Users of industrial process control systems require SQL expertise to create custom queries, leading to reliance on R&D teams for query development and manual data consolidation, which is time-consuming and inefficient.
Innovation Solution
A computer-implemented method using a machine learning custom query engine that builds a training dataset from system data, generates domain models, and executes custom queries based on user input, eliminating the need for SQL expertise by auto-generating queries and providing visualization and analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually create custom queries using SQL or Advanced Query tools, then query functionality is achieved, but user expertise requirements and time consumption increase significantly
Solution Approach 1:
The system enables users to create and execute custom queries independently through natural language input without requiring SQL expertise or manual data consolidation. The automated query engine processes user requests directly, eliminating the need for R&D team assistance and manual operations.
Solution Approach 2:
The patent replaces manual SQL query construction and mechanical data consolidation processes with an automated machine learning-based query engine. This substitution transforms the mechanical process of writing SQL code into an automated natural language processing system that generates and executes queries automatically.
2Reliability
If R&D teams manually develop and validate custom queries, then query accuracy is improved, but development time and resource requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing system data into organized domains and training the query engine in advance. This preparation enables the engine to automatically generate accurate queries without requiring real-time manual validation by R&D teams, thus improving both accuracy and speed.
Solution Approach 2:
The query engine utilizes feedback mechanisms to continuously learn from executed queries and improve future query generation. This feedback loop enables the system to maintain high query accuracy while operating autonomously, eliminating the need for continuous manual validation.
3Productivity
If users consolidate and export custom query results manually, then data analysis is achieved, but operational complexity and time requirements increase
Solution Approach 1:
The patent merges multiple separate operations (query execution, result consolidation, data export, and analysis preparation) into a single automated process. The query engine executes queries and automatically consolidates results from multiple sources, eliminating the need for separate manual operations and reducing operational complexity.
Solution Approach 2:
The automated query engine performs multiple functions including query generation, execution, result consolidation, and data preparation in a single system. This multi-functionality replaces the need for separate tools and manual operations for each step of the data analysis process.
Data Source
AI summary
Monitoring an industrial process by building a training dataset of system data representative of status of industrial process parameters and training a custom query engine based on the training dataset. Models are generated using the custom query engine for matching query terms to the system data in response to user input representative of the system data that the user intends to access. Executing one of the models based on the input from the user generates an output retrieving the selected system data from the data tables for visualization.