BMS Operator Automation Using Learned Alarm Response Patterns
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Solution Overview
Problem
Building management systems (BMS) face challenges in efficiently processing and responding to high volumes of event and alarm data, making it difficult to automate user interactions and diagnose issues such as faults, overrides, and misconfigured settings.
Innovation Solution
A system that includes a processing circuit with a processor and memory, capable of receiving BMS data, generating models to replicate user responses, and sending control signals to automate responses to events and diagnose problems, while also configuring user interfaces based on user interest and interaction patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a BMS processes and responds to high volumes of event and alarm data manually, then the system can handle diverse situations with human judgment, but the workload is excessive and response efficiency is reduced
Solution Approach 1:
The system enables automated self-service by having the BMS automatically respond to events and alarms based on learned user behavior patterns. The automation engine replicates user actions without human intervention, allowing the system to handle itself for routine situations while reducing manual workload.
Solution Approach 2:
The system creates copies of user response patterns by analyzing historical user actions and generating automated responses that replicate human decision-making. The automation engine copies the essence of user behavior to automatically handle events, maintaining the quality of human judgment while eliminating manual effort.
2Extent of automation
If the BMS implements comprehensive automation to reduce workload, then response efficiency improves, but the system complexity increases
Solution Approach 1:
The system uses feedback mechanisms by continuously monitoring user responses to events and using this information to refine and update automated response patterns. The automation engine learns from actual user actions and adjusts its behavior, creating a self-improving system that manages complexity through adaptive learning rather than rigid programming.
Solution Approach 2:
The system changes parameters by dynamically adjusting automation based on event category, user behavior patterns, and system state. Rather than implementing fixed complex automation rules, the system adapts its automation level and response strategies based on learned parameters from actual user interactions, simplifying the overall system architecture.
3Productivity
If the system replicates user responses automatically, then the workload is reduced and response efficiency improves, but the ability to handle novel situations may be limited
Solution Approach 1:
The system implements dynamic automation by continuously learning and updating user response patterns based on actual user actions. The automation engine is not static but adapts its behavior over time, allowing it to handle novel situations by incorporating new user responses into its learned patterns, thus maintaining adaptability while improving efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-learning common user response patterns for different event categories. By anticipating typical user responses based on historical data, the system can automatically handle routine situations efficiently while remaining prepared to learn and adapt to novel situations as they occur.
Data Source
AI summary
A system to replicate user interaction with a building management system (BMS), the system including a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to receive first BMS data describing a BMS event having a first category, receive second BMS data describing a user response to the BMS event, generate a model, based on the first and second BMS data, describing response patterns associated with the user response, wherein the response patterns include actions to replicate the user response, and generate, in response to receiving third BMS data having the first category, one or more control signals based the actions in the model to control the BMS to replicate the user response.