Building Management Space Graph for Dynamic Entity Relationship Adaptation
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Solution Overview
Problem
Traditional building management systems require manual reconfiguration and specific programming to adapt to changes in relationships between spaces, assets, and people, leading to cumbersome exception handling and limited ability for multi-dimensional analysis, which hinders natural adaptation to usage and operating conditions.
Innovation Solution
A building management system that utilizes a space graph data structure to dynamically generate and update relationships between entities based on incoming data, allowing for automatic adaptation and efficient information retrieval and control algorithm updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional building management systems use pre-defined hierarchical relationships, then the system structure is simple and stable, but the system cannot automatically adapt to changes in usage and operating conditions
Solution Approach 1:
The patent applies the dynamics principle by transforming the static, pre-defined hierarchical relationships into dynamic, automatically generated relationships. The system continuously discovers and updates relationships between building entities based on real-time data patterns, allowing the relationship structure to adapt organically to changing usage and operating conditions without manual reconfiguration.
Solution Approach 2:
The system implements self-service by automatically discovering and generating relationships between entities without requiring external user intervention. The relationship generation engine autonomously analyzes data patterns, identifies new relationships, and updates the building information model, enabling the system to self-adapt to changes in usage and operating conditions.
2Productivity
If manual reconfiguration is used to adapt relationships, then the system maintains control and stability, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system applies preliminary action by continuously and proactively discovering relationships in the background before they are needed. The relationship generation engine operates continuously to identify and establish relationships between entities, so when changes occur in the building environment, the system has already prepared the appropriate relationship structures, eliminating reconfiguration delays.
Solution Approach 2:
The system performs self-service by automatically generating and updating relationships without human intervention. The relationship generation engine autonomously monitors data patterns, discovers new relationships, and updates the building information model in real-time, eliminating the time-consuming manual reconfiguration process entirely.
3Reliability
If specific programming paradigms are built into the system, then the system maintains reliability, but the exception handling becomes complicated and error-prone
Solution Approach 1:
The system implements self-service through the relationship generation engine that autonomously validates and verifies discovered relationships. The engine uses multiple verification mechanisms including data consistency checks, pattern matching validation, and cross-referencing with existing building information to ensure relationship accuracy, maintaining reliability without complex exception handling procedures.
Solution Approach 2:
The system applies feedback by implementing continuous verification and validation loops in the relationship generation process. The relationship generation engine receives feedback from data quality metrics, relationship consistency checks, and operational performance data, using this feedback to refine and improve relationship discovery accuracy, thereby maintaining high data reliability.
4Adaptability or versatility
If linear associations are used for data organization, then the data structure is simple, but multi-dimensional dynamic analysis is not enabled
Solution Approach 1:
The patent applies the dimensionality change principle by transitioning from linear, hierarchical data associations to multi-dimensional relationship networks. The system creates relationships across multiple dimensions including spatial, temporal, functional, and contextual dimensions, enabling comprehensive multi-dimensional analysis of building data while maintaining manageable complexity through structured relationship generation.
Data Source
AI summary
A building system including one or more memory devices configured to store instructions that cause one or more processors to store a graph data structure in a data storage device including a plurality of nodes representing a plurality of entities and a plurality of edges between the plurality of nodes representing a plurality of relationships between the plurality of entities, wherein the plurality of entities include a first entity representing one of a person, place, or piece of equipment of the building, wherein a second entity of the plurality of entities represents a software component, wherein the software component performs operations for the person, place, or piece of equipment of the building indicated by one or more edges of the plurality of edges relating the first entity to the second entity and cause the software component to execute and perform the operations for the person, place, or piece of equipment.