Building Automation Point Classification Using Substring Probabilities

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Solution Overview

Problem

Current building automation systems face challenges in efficiently classifying and identifying data points due to non-standard and semantically rich descriptions, requiring manual intervention and lengthy commissioning processes, especially in heterogeneous systems with varying naming conventions and languages, which hinder automated processing and relationship identification.

Innovation Solution

A computerized method and system that utilize a processing circuit to automatically classify building automation system points by generating frequency matrices and probabilistically assigning point types based on substring probabilities, using techniques like naive Bayes classification and latent semantic indexing, without the need for manual word-breaking rules or lexical analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual point classification methods are used, then classification accuracy can be maintained through human judgment, but commissioning time increases significantly

Engineering Contradiction:

Improveclassification accuracyVSAvoidcommissioning time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by automatically analyzing point names, descriptions, and attributes before manual review, pre-classifying points using machine learning models trained on naming conventions, thereby reducing the time required for manual commissioning while maintaining accuracy through human verification of automated results

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

An intermediary automated classification system is introduced between the raw point data and final application use, using natural language processing and machine learning to bridge the gap between non-standard point descriptions and standardized classifications, reducing both time and manual effort

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If automated processing is implemented, then commissioning time is reduced, but difficulty in handling non-standard naming conventions increases

Engineering Contradiction:

Improvecommissioning speedVSAvoidhandling non-standard descriptions

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The system changes parameters by transforming non-standard point names and descriptions into standardized formats using learned naming conventions, converting unstructured text into structured classification data that can be processed automatically, thereby enabling high-speed processing while handling diversity

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system performs self-service by automatically adapting to different naming conventions through machine learning models that are trained on existing point data, enabling the system to self-adjust and handle non-standard descriptions without requiring manual configuration for each new building or system

Inventive Principle:

Principle #25Self-service

3Reliability

If manual investigation of naming conventions is performed, then relevant points can be identified, but the process becomes extremely lengthy for large numbers of points

Engineering Contradiction:

Improvepoint identification accuracyVSAvoidinvestigation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system applies partial action by focusing automated classification on the most critical point attributes and using probabilistic methods to identify relevant points without requiring complete manual investigation of all naming conventions, achieving sufficient accuracy for application needs while dramatically reducing time

Inventive Principle:

Principle #16Partial or excessive action

4Adaptability or versatility

If heterogeneous building automation systems are integrated, then system versatility is improved, but complexity of classification and mapping increases

Engineering Contradiction:

Improvesystem integration capabilityVSAvoidclassification complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system achieves universality by developing a unified classification approach that works across multiple building automation systems and protocols, using standardized point type classifications that can be applied regardless of the source system, thereby integrating heterogeneous systems without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12474679B2Syntax translation from first syntax to second syntax based on string analysis

Publication Date: 2025.11.18 TYCO FIRE & SECURITY GMBH

AI summary

A computerized method of assigning a building automation system point type to a plurality of unclassified data points is provided. The method includes receiving unclassified data points and attributes for each data point. The method includes receiving classifications for a first subset of the unclassified data points. Each classification associates a data point with a building automation system point type. The method includes generating a term set containing substrings that appear in the attributes. The method includes generating a first matrix describing a frequency that the substrings appear in the attributes. The method includes calculating an indicator of a probability that the presence of the selected substring results in the data point belonging to the selected point type. The method includes assigning a point type to a second subset by finding the substring and potential point type pair having the greatest indication of probability.