AIC Asset Classification Using Ontology and ML Queries
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Solution Overview
Problem
The process of providing standardized semantic classification of assets in automated and industrial control systems is highly time-consuming and labor-intensive, making it difficult to monitor and understand the impact of changes over time and compare different systems effectively.
Innovation Solution
A computer tool and method that provides automated semantic classification of assets using artificial intelligence techniques, such as machine learning and neural networks, to streamline the classification process according to standards like Brick or Haystack, by analyzing asset attributes and user information to generate classification queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of assets is performed to ensure accuracy and standardization, then classification precision is improved, but productivity deteriorates due to the highly time-consuming and labor-intensive nature of the process
Solution Approach 1:
The patent replaces the manual mechanical classification process with an automated computer-based system that uses machine learning algorithms and natural language processing to classify assets. The system automatically analyzes asset information, determines appropriate classification categories, and assigns classifications without human intervention, thereby maintaining precision while dramatically improving productivity.
Solution Approach 2:
The classification system is designed to autonomously perform classification tasks by itself, using automated data processing and decision-making algorithms. The system self-manages the entire classification workflow from data ingestion to classification output, eliminating the need for manual labor while maintaining consistent and accurate classification results.
2Productivity
If automated classification methods are used to improve productivity, then classification speed is improved, but classification precision may deteriorate due to lack of human judgment
Solution Approach 1:
The system incorporates feedback mechanisms where classification results are continuously evaluated and used to refine and improve the classification algorithms. The system learns from past classifications and adjusts its decision-making processes to maintain or improve precision over time, ensuring that automated classification achieves both speed and accuracy.
Solution Approach 2:
The system performs preliminary analysis and processing of asset data before final classification, preparing structured information that enhances the accuracy of automated decision-making. By pre-processing and organizing data in advance, the system ensures that the classification algorithms receive high-quality input, thereby maintaining precision while operating at automated speeds.
3Measurement precision
If detailed asset information is collected to improve classification accuracy, then measurement precision is improved, but device complexity increases due to additional data collection and processing requirements
Solution Approach 1:
The system segments the data collection and processing functions into modular components, each handling specific aspects of asset information. This segmentation allows the system to collect detailed information when needed while maintaining a manageable and scalable architecture that doesn't become overly complex as data requirements increase.
Data Source
AI summary
Classifying one or more assets in an automated and industrial control system (AIC) according to a classification standard. In a computer monitoring tool, a classification query is received for an asset managed by the AIC. Responsive to this classification query, the computer monitor tool retrieves a listing of candidate ontology classes for the queried asset utilizing information received from a semantic data model of known assets. The computer monitor tool then captures, preferably from a database coupled to the AIC, certain classification attribute variables associated with the queried asset. Additionally, the computer monitor tool receives user information describing certain building information associated with the queried asset. The computer monitor tool then generates a computer query configured for requesting results from a machine learning (ML) algorithm indicative of one or more classification standards for the queried asset.