Entity Graph Logic for Smart Building Timeseries Management
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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 derived timeseries through relational objects, which define relationships between object entities and data entities, allowing for the identification of virtual data points and the creation of shadow entities to store historical values, thereby enhancing data management and analysis capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If building management systems collect data from multiple sensors and devices, then the amount of available data increases, but the efficiency of data management and processing deteriorates
Solution Approach 1:
The patent segments the building management system into multiple specialized components: a data collection module that gathers raw data from sensors, a data processing module that transforms and analyzes the data, a database module that stores processed information, and a user interface module that presents insights. This segmentation allows each component to handle specific tasks efficiently, preventing the system from becoming overwhelmed by the total data volume while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The patent introduces intermediary components including data normalization layers that standardize data from different sources, processing queues that manage data flow rates, and caching mechanisms that buffer data between collection and analysis. These intermediaries act as mediators between the high-volume data input and the processing capabilities, preventing bottlenecks and maintaining processing efficiency despite increasing data quantities.
2Loss of information
If building management systems process vast amounts of data from various sources, then the comprehensiveness of analysis improves, but the complexity of the system increases
Solution Approach 1:
The system segments data processing into distinct functional modules: data ingestion, validation, transformation, analysis, and visualization. Each module handles a specific aspect of the data pipeline, reducing the complexity any single component must manage while collectively maintaining comprehensive analysis capabilities across all building operations.
Solution Approach 2:
The patent implements universal data processing components that can handle multiple data types and sources through standardized interfaces. The processing engine is designed to work with various sensor inputs (temperature, humidity, occupancy, energy consumption) using the same analytical frameworks, reducing system complexity by avoiding the need for separate specialized processing paths for each data type.
3Loss of time
If building management systems store historical data values, then the capability for real-time monitoring improves, but the data management complexity increases
Solution Approach 1:
The patent implements preliminary data processing and organization before full storage, including data validation, normalization, and initial aggregation. Historical data is pre-processed and structured in standardized formats with metadata tags, making it readily accessible for real-time monitoring queries without requiring complex search and retrieval operations, thus reducing data management complexity while enabling fast access.
Solution Approach 2:
The system organizes historical data in multi-dimensional structures with time, location, device type, and parameter type as indexing dimensions. This dimensional organization allows real-time monitoring to query specific data slices efficiently (e.g., all temperature readings from a specific zone at a given time) without scanning the entire historical dataset, reducing the complexity of real-time data retrieval.
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.