Digital Twin Agent Architecture for Building Time-Series Ingestion
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Solution Overview
Problem
Existing building management systems struggle to effectively analyze and manage the vast amounts of data generated in smart building environments, leading to inefficiencies in data processing and decision-making.
Innovation Solution
The implementation of an agent-entity based communication and control system, where agents are generated to communicate data from physical building entities through agent communication channels, enabling data ingestion, entity creation, and operation based on time series data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If building management systems collect data from all building entities and equipment, then the amount of available data increases, but the complexity of data processing and analysis increases exponentially
Solution Approach 1:
The patent segments the building management system into multiple independent agents, each responsible for specific building entities or equipment. This segmentation allows data processing to be distributed across multiple agents rather than centralized, reducing the complexity burden on any single processing unit while maintaining comprehensive data collection from all building elements.
Solution Approach 2:
The patent introduces an intermediary layer between data collection and analysis, where agents act as mediators that collect, process, and manage data from their respective building entities. This intermediary structure simplifies the overall system by breaking down the complex task of managing all building data into manageable agent-specific tasks, with standardized communication protocols facilitating coordination.
2Productivity
If traditional building management systems are used, then system structure is simple, but the ability to analyze vast amounts of data and make automated decisions is insufficient
Solution Approach 1:
The patent implements a dynamic agent-based architecture where agents can be created, activated, and configured based on real-time building needs and data requirements. This dynamic structure enables the system to scale its analytical capabilities flexibly, activating additional agents or increasing agent sophistication only when needed, thus improving data analysis efficiency without permanently increasing system complexity.
Solution Approach 2:
The patent changes the fundamental parameter of system organization from traditional hierarchical structures to agent-based distributed structures. This parameter change enables parallel data processing and analysis across multiple agents, dramatically improving productivity in handling vast amounts of building data while maintaining manageable complexity through standardized agent interfaces and communication protocols.
3Loss of time
If manual data analysis and decision-making processes are used, then system complexity is low, but decision-making speed and operational efficiency are reduced
Solution Approach 1:
The patent implements self-service capabilities where agents autonomously analyze their respective data, make decisions, and execute control actions without requiring manual intervention. Each agent independently processes data from its associated building entities, applies predefined or learned decision logic, and automatically implements control decisions, thereby dramatically reducing decision-making time while maintaining appropriate levels of automation that can be configured based on building needs.
Data Source
AI summary
A building management system includes one or more memory devices configured to store instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive a publication by an agent on an agent communication channel, the publication comprising timeseries data, identify, based on the publication, an object entity of an entity database associated with the agent, wherein the entity database includes one or more object entities and relationships between the one or more object entities and one or more data entities, identify a data entity related to the object entity based on a relationship of the relationships relating the object entity and the data entity, and ingest the timeseries data into the data entity.