Building automation system with live memory management
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Solution Overview
Problem
Current HVAC system control algorithms require iterative rebuilding and testing, leading to inefficient development and deployment processes, as changes often necessitate entire algorithm rebuilds, which is time-consuming and inefficient.
Innovation Solution
A method for executing computations in parallel within a building management system, utilizing computing threads to determine property values based on dependencies, allowing for efficient generation and deployment of control algorithms, and optimizing resource allocation across multiple nodes for cost-effective and reliable operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative rebuilding and testing of control algorithms is performed, then algorithm functionality can be improved, but development time and efficiency deteriorate
Solution Approach 1:
The control algorithm is divided into multiple independent computing threads, each responsible for specific computations. This segmentation allows individual threads to be modified and tested independently without requiring complete algorithm rebuilds, thus improving development efficiency while maintaining functionality.
Solution Approach 2:
The system implements dynamic thread management where computing threads can be created, modified, and executed flexibly during runtime. This dynamic approach enables iterative testing and improvement of algorithm components without static rebuild constraints, resolving the contradiction between functionality improvement and development time.
2Speed
If parallel computing threads are executed, then computational speed is improved, but system complexity increases
Solution Approach 1:
The computational task is segmented into independent threads with clear division of labor. Each thread handles specific property computations, reducing inter-thread dependencies and simplifying the management of parallel execution despite the increased number of concurrent processes.
Solution Approach 2:
A property model serves as an intermediary layer that manages thread creation, execution, and coordination. This mediator abstracts the complexity of parallel computing management, allowing threads to execute in parallel while the property model handles synchronization and data flow, thus maintaining speed improvement without proportionally increasing observable system complexity.
3Measurement precision
If property dependencies are tracked and managed, then computation accuracy is improved, but processing overhead increases
Solution Approach 1:
Property dependencies are pre-defined and stored in the property model before computation begins. This preliminary structuring of dependency relationships eliminates the need for dynamic dependency analysis during execution, ensuring computation accuracy through proper dependency tracking while minimizing processing overhead by avoiding repeated dependency resolution.
Data Source
AI summary
A method for reducing the memory usage of a memory device of a building management system of a building includes receiving a request for a property of a data model stored in the memory device, wherein the data model includes relationships, wherein each relationship links one of multiple properties to serialized building data stored within the memory device, receiving, by the building management system, a request for a property of the data model, and generating, by the building management system, de-serialized data for the requested property in response to receiving the request by retrieving the serialized data from the memory device based on the relationships of the data model and de-serializing the retrieved serialized data.