AI Asset Tagging for Similar Point Grouping in BMS and SCADA
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Solution Overview
Problem
Current building management systems (BMS) and Supervisory Control and Data Acquisition (SCADA) systems face challenges in managing complex environments due to their large size and dynamic changes, requiring labor-intensive manual semantic tagging of assets, which hinders the realization of benefits such as enhanced energy efficiency and prolonged equipment lifespan.
Innovation Solution
A computer tool and method for automated grouping of assets in BMS and SCADA systems using AI techniques to identify and group similar assets based on embedded numerical values of textual attributes, simplifying the asset tagging process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual semantic tagging of assets is performed, then asset management accuracy is improved, but labor time and operational complexity increase significantly
Solution Approach 1:
The system enables self-service automated asset tagging by utilizing existing sensor data and device metadata to automatically generate semantic tags without human intervention. The computer tool autonomously processes device information, extracts meaningful attributes, and applies appropriate tags to assets, eliminating the need for manual tagging while maintaining high accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of semantic tagging with an automated computational system. The computer tool uses data processing algorithms and machine learning models to substitute human operators, transforming the tagging process from a labor-intensive manual operation to an automated digital process that leverages existing sensor networks and device data.
2Measurement precision
If manual semantic tagging of assets is performed, then asset management accuracy is improved, but operational complexity increases
Solution Approach 1:
The computer tool is designed with multi-functionality to handle various asset tagging scenarios across different building types and sensor configurations. It can process diverse device data formats, apply multiple tagging methodologies, and adapt to different building management requirements, thereby reducing the need for separate manual processes and simplifying the overall system architecture.
Solution Approach 2:
The system creates digital copies of device metadata and sensor data to generate semantic tags, eliminating the need for physical manual tagging operations. By working with digital representations of asset information, the system simplifies the tagging process while maintaining accuracy, as the computer tool can replicate and process device characteristics automatically.
3Productivity
If automated grouping of assets is implemented, then productivity is improved, but measurement precision of asset characteristics may be reduced
Solution Approach 1:
The computer tool incorporates feedback mechanisms that continuously evaluate asset characteristics and refine grouping decisions. By monitoring device performance data and tag accuracy, the system adjusts its automated grouping algorithms to maintain precision while improving productivity. The feedback loop ensures that automated grouping results are validated and corrected when necessary.
Solution Approach 2:
The automated grouping system employs dynamic algorithms that adapt to changing asset characteristics and building requirements. The computer tool can dynamically adjust grouping criteria, reclassify assets as new data becomes available, and modify tag assignments based on evolving operational needs, thereby maintaining measurement precision while enhancing management efficiency.
Data Source
AI summary
A computer method and system for grouping like points in an automated and industrial control system (AIC). A point is identified as a reference for grouping with other similar points in the AIC. Captured from the AIC are textual attributes associated with the identified point. The captured textual attributes are embedded into at least one numeral value. Captured from the computer database are textual attributes associated with other points in the AIC. The captured textual attributes for each other point are then embedded into at least one respective numerical value. The numerical value of the identified point is compared with each numerical value of the other points. Points from the other points are grouped with the identified point that are determined to have a similar embedded numerical value to that of the identified point. Based upon a labelling technique, a semantic tag is assigned to each of the grouped points.