Building management system with artificial intelligence for unified agent based control of building subsystems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current building management systems (BMS) lack a unified, intelligent platform to dynamically react to changing situational data across disparate building systems, leading to inefficiencies and potential safety issues due to predefined scripted operations.
Innovation Solution
A BMS with artificial intelligence capabilities, incorporating data collectors, a learning engine, and cognitive agents that correlate data streams from various subsystems to generate control decisions, enabling dynamic and intelligent control of building conditions, such as responding to emergencies or optimizing environmental settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a unified AI-driven control platform is implemented, then building safety and operational efficiency are improved, but device complexity increases
Solution Approach 1:
The patent merges multiple discrete building subsystems (HVAC, lighting, security, fire safety) into a unified AI-driven control platform. The cognitive agent integrates data from diverse sources including sensors, building management systems, and external data feeds to provide centralized intelligent control, thereby improving reliability through coordinated system-wide responses while managing complexity through unified architecture.
Solution Approach 2:
The cognitive agent serves multiple functions simultaneously: it monitors building conditions, detects emergencies, generates control decisions, coordinates subsystem responses, and provides user notifications. This multi-functional approach improves building safety across various scenarios (fire, intrusion, HVAC failures) while avoiding the need for separate specialized systems for each function.
2Productivity
If real-time data correlation across multiple subsystems is performed, then operational efficiency is improved, but processing requirements and system complexity increase
Solution Approach 1:
The cognitive agent acts as an intermediary layer between raw data from multiple subsystems and the control decisions required. It receives data streams from HVAC systems, security systems, fire detection systems, and external sources, correlates this information using AI algorithms, and generates coordinated control decisions. This intermediary approach enables efficient real-time processing by centralizing correlation logic and avoiding direct point-to-point communication between all subsystems.
Solution Approach 2:
The system transforms raw sensor data and subsystem status information into meaningful building state parameters that the cognitive agent can process. Data from multiple sources is aggregated and converted into standardized parameters representing overall building conditions, enabling efficient real-time analysis and control decisions without processing every individual data point separately.
3Adaptability or versatility
If AI-based dynamic control is implemented, then adaptability to changing conditions is improved, but computational resources and system complexity increase
Solution Approach 1:
The cognitive agent implements dynamic control by continuously monitoring building conditions and adapting control decisions in real-time based on changing circumstances. Unlike static predefined scripts, the AI-based system adjusts its responses dynamically to emerging situations such as fire emergencies, security intrusions, or HVAC failures, improving adaptability while managing control complexity through centralized intelligent processing.
Data Source
AI summary
A building management system with artificial intelligence based control of a building includes data collectors are configured to receive data and generate data streams for subsystems of the building. The system includes a learning engine configured to identify a building state of the building by correlating data of the data streams for the subsystems and provide the identified building state to cognitive agents. The system includes the cognitive agents, each of the cognitive agents configured to receive the identified building state from the learning engine, generate a control decision based on the received building state, and operate at least one of the plurality of subsystems of the building to control a physical condition of the building based on the control decision.