Automated cloud hosted BMS archive and difference engine
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Solution Overview
Problem
Building management systems (BMS) face challenges in efficiently managing and analyzing configuration data across different time periods, leading to difficulties in identifying updates, equipment changes, and energy cost impacts.
Innovation Solution
A method involving a cloud server that receives configuration data from an on-premise server, uses a difference engine to determine deltas between current and previous configuration data sets, and provides this information to an interface for analysis and potential restoration of previous system implementations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If configuration data is stored locally in the BMS, then data access is fast and simple, but data storage capacity is limited and historical analysis is difficult
Solution Approach 1:
The patent moves BMS configuration data from local on-premise storage to cloud-based storage, transitioning from a single-location storage model to a distributed architecture. This allows the system to leverage the cloud's virtually unlimited storage capacity while maintaining local access points through web interfaces, effectively adding a spatial dimension to data storage and retrieval.
Solution Approach 2:
The patent introduces a web server and cloud server as intermediaries between the BMS and users. The web server receives requests from user devices, communicates with the cloud server to retrieve or store configuration data, and returns responses to users. This intermediary layer enables complex data management operations without increasing the complexity of the core BMS controller.
2Loss of information
If all configuration data is retained for historical analysis, then comprehensive analytics are possible, but data processing time and computational resources increase
Solution Approach 1:
The patent extracts the data analysis and comparison functions from the core BMS controller and places them in the cloud environment. The difference engine running on the cloud server performs historical comparisons and generates analytics, allowing the BMS to retain complete historical data without burdening the local system with heavy computational tasks. This separates data storage from data processing across different locations.
Solution Approach 2:
The system performs preliminary actions by continuously storing and organizing configuration data in the cloud as it is generated, rather than waiting for analysis requests. The difference engine is pre-configured to automatically compare new configuration data with historical data and identify changes, so that when users request analytics, the processing is already complete or near-complete, significantly reducing perceived wait time.
3Reliability
If the system provides detailed change analysis and restoration capabilities, then system control and recovery are improved, but computational requirements and processing overhead increase
Solution Approach 1:
The patent creates and maintains copies of BMS configuration data in the cloud, specifically maintaining historical versions and creating duplicate datasets for analysis purposes. The difference engine works on these copied datasets rather than the primary operational data, allowing comprehensive restoration capabilities while preserving the integrity and performance of the original BMS system. Users can restore to any previous configuration by retrieving the appropriate copy from cloud storage.
Data Source
AI summary
A method for determining updates in a building management system (BMS) is shown. The method includes receiving, via an on premise server, configuration data associated with at least part of the BMS. The method includes storing the configuration data within a cloud server communicably coupled with the on premise server. The method includes determining, via a difference engine within the cloud server, information comprising a delta between the configuration data and one or more previous sets of configuration data stored within the cloud server. The method includes providing, to the on premise server, the information to an interface.