Building Automation Component Mapping With Uniform Naming

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Solution Overview

Problem

Current building automation systems face challenges in efficiently identifying and mapping components from different manufacturers, due to varying naming protocols, leading to time-consuming and error-prone manual processes for establishing relationships between components, which hampers maintenance, fault diagnosis, and control operations.

Innovation Solution

A context generation system utilizing learning algorithms to recognize patterns in name and telemetry data, along with component schematics, to predict and identify equipment types, roles, and relationships, enabling the application of uniform names and establishing relationships independently of specific naming conventions, thereby automating the mapping process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual processes are used to establish relationships between building automation system components from different manufacturers, then component relationships can be established, but the process becomes time-consuming and error-prone

Engineering Contradiction:
Improveaccuracy of component relationship establishmentVSAvoidtime required to map components
Core Design Contradiction:
ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processes with an automated machine learning system. The system uses trained machine learning models to automatically identify component types, assign uniform names, and establish relationships between building automation components, eliminating the need for manual data entry and relationship mapping while significantly improving accuracy and reducing time requirements.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables the building automation system to automatically identify and map its own components without external manual intervention. The machine learning models process component data, determine component types based on naming patterns and telemetry data, and automatically establish relationships, allowing the system to self-organize and self-documented its architecture.

Inventive Principle:
Principle #25Self-service

2Adaptability or versatility

If components from different manufacturers are integrated into a building automation system, then system functionality is enhanced, but varying naming protocols increase system complexity

Engineering Contradiction:
Improveability to integrate multi-manufacturer componentsVSAvoidcomplexity of naming protocols
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a universal naming system that works across components from different manufacturers. The machine learning models analyze various manufacturer-specific naming patterns and translate them into a unified naming convention, allowing the system to handle diverse component types (HVAC, lighting, security, etc.) from multiple manufacturers through a single consistent interface.

Inventive Principle:
Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system changes the naming parameter from manufacturer-specific protocols to a standardized uniform naming protocol. By training machine learning models to recognize and transform various naming patterns into a consistent format, the system maintains adaptability to different manufacturers while reducing the effective complexity presented to users and operators.

Inventive Principle:
Principle #35Parameter changes

3Ease of operation

If uniform names are applied to building automation system components, then system operation is facilitated, but processing and storing data requires additional computational resources

Engineering Contradiction:
Improveease of controlling building automation systemVSAvoidcomputational resources for data processing
Core Design Contradiction:
Ease of operationVSUse of energy by moving object

Solution Approach 1:

The system performs the computationally intensive task of applying uniform names and establishing relationships in advance, during an initial processing phase. The machine learning models are trained offline and then used to pre-process component data, creating a standardized foundation that simplifies subsequent operational tasks. This preliminary action reduces the computational burden during real-time system operation.

Inventive Principle:
Principle #10Preliminary action

Data Source

PatentEP3935452B1Systems and approaches for establishing relationships between building automation system components
Publication Date: 2023.09.13 HONEYWELL INTERNATIONAL INC
  • EP3935452B1 patent drawingFigure 1
  • EP3935452B1 patent drawingFigure 2
  • EP3935452B1 patent drawingFigure 3

AI summary

Systems and methods for establishing relationships between building automation system components and controlling building automation system components. Data for a building automation system components may be received from the building automation system components and one or more models may be applied to the received data to determine types of the building automation system components and relationships between building automation system components. Once the types of building automation system components have been determined or identified, uniform names may be applied to the building automation system components. The received data may include, among other data, naming data and telemetry data from the building automation system components.