Smart Entity Timeseries Mapping for Scalable Building Data
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Solution Overview
Problem
The increasing amount of data being produced in smart building environments poses a challenge for effective analysis, as existing building management systems struggle to efficiently manage and process the vast amounts of data from various sources, including sensors and devices, leading to inefficiencies in data management and decision-making.
Innovation Solution
A building management cloud computing system that processes and analyzes timeseries data by generating input timeseries from raw data points, identifying object and data entities, and creating derived timeseries through processing workflows, allowing for the storage of historical values and virtual data points, thereby enhancing data management and analysis capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If building management systems collect data from multiple sensors and devices to improve monitoring capabilities, then the amount of data produced increases exponentially, but the efficiency of data management and analysis deteriorates
Solution Approach 1:
The patent segments the monolithic data management system into a distributed architecture with multiple data collectors, processors, and storage nodes. Each data collector independently gathers data from specific sensors and devices, then data is distributed across multiple processing nodes that handle specific analysis tasks, and finally results are stored in a distributed database. This segmentation allows the system to scale horizontally to handle exponentially increasing data volumes while maintaining management efficiency.
Solution Approach 2:
The patent introduces intermediary components including data normalization layers that standardize data from diverse sources, message queues that buffer and route data between producers and consumers, and abstraction layers that shield upper-level applications from low-level data collection details. These intermediaries decouple different system components, allowing independent optimization and improving overall data management efficiency despite increased data volume.
2Loss of information
If building management systems process and analyze vast amounts of data in real-time to improve decision-making, then the analysis effectiveness increases, but the system complexity increases
Solution Approach 1:
The patent implements preliminary data processing steps including data validation, normalization, and filtering at the collection stage, before data enters the main analysis pipeline. Aggregate and pre-computed metrics are calculated in advance and stored for quick retrieval. This preliminary action reduces the complexity of real-time analysis by ensuring data is ready-to-use when analysis is needed, maintaining analysis effectiveness without proportionally increasing system complexity.
Solution Approach 2:
The patent applies different processing qualities and levels of detail to different data types and analysis needs. Critical data receives real-time processing with high resource allocation, while less critical data undergoes batch processing. Different analysis algorithms are selected based on local data characteristics and requirements. This local quality approach optimizes resource usage and manages complexity by not applying uniform high-level processing to all data.
Data Source
AI summary
One or more non-transitory computer readable media contain program instructions that, when executed, cause one or more processors to: receive first raw data including one or more first data points generated by a first object of a plurality of objects associated with one or more buildings; generate first input timeseries according to the one or more data points; access a database of interconnected smart entities, the smart entities including object entities representing each of the plurality of objects and data entities representing stored data, the smart entities being interconnected by relational objects indicating relationships between the smart entities; identify a first object entity representing the first object from a first identifier in the first input timeseries; identify a first data entity from a first relational object indicating a relationship between the first object entity and the first data entity; and store the first input timeseries in the first data entity.