Smart Entity Graphs for Scalable Building Data Analysis
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Solution Overview
Problem
The increasing amount of data from smart building environments poses a challenge for effective analysis, as existing building management systems struggle to efficiently manage and process 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 is developed, which generates a database of interconnected smart entities, allowing for the reception, processing, and analysis of data from multiple objects such as sensors, spaces, and persons, by creating shadow entities to store historical values and calculating average and abnormal 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 complexity of data management and processing increases
Solution Approach 1:
The patent segments data management by creating separate data entities for different types of information (sensor data, device data, environmental data) and organizing them into a structured database schema. This segmentation allows the system to handle large quantities of diverse data without overwhelming complexity, as each data type can be managed independently according to its specific requirements.
Solution Approach 2:
The patent introduces an intermediary layer (the building management platform with its entity-relationship database) between the sensors/devices and the analysis systems. This intermediary standardizes data collection, storage, and retrieval processes, simplifying management while enabling comprehensive data collection from multiple sources.
2Measurement precision
If building management systems process and analyze all collected data, then decision-making accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The patent extracts and stores historical data values in a separate shadow entity structure, allowing the system to retain comprehensive data for accurate analysis while enabling efficient querying. By separating historical archival from active processing data, the system maintains decision-making accuracy without the overhead of processing entire historical datasets in real-time.
Solution Approach 2:
The patent performs preliminary data organization and validation when data is first collected, structuring it according to the entity-relationship schema before analysis is needed. This preliminary structuring reduces the computational burden during actual analysis, as data is already organized and ready for processing, thereby reducing analysis time while maintaining accuracy.
3Loss of information
If building management systems store historical data values, then data analysis capabilities improve, but the storage requirements and database complexity increase
Solution Approach 1:
The patent implements a nested database structure where shadow entities contain historical values nested within the main entity framework. This nested organization allows historical data to be stored systematically within the existing entity-relationship structure, providing comprehensive analysis capability while maintaining a manageable database architecture through hierarchical organization.
4Speed
If building management systems create shadow entities for historical data, then data retrieval efficiency improves, but the initial data setup and maintenance complexity increase
Solution Approach 1:
The patent merges the shadow entity management into the existing building management platform architecture, combining historical data storage with current operational data management. This integration allows the system to benefit from unified data access and management procedures, improving retrieval efficiency while avoiding the need for separate complex maintenance systems for historical data.
Data Source
AI summary
One or more non-transitory computer readable media contain program instructions that, when executed by one or more processors, cause the one or more processors to perform operations including: generating a database of interconnected smart entities including object entities representing each of a plurality of objects associated with one or more buildings and the plurality of objects each representing a space, person, building subsystem, and/or device, and data entities representing data generated by the objects, the smart entities being interconnected by relational objects indicating relationships between the object entities and the data entities; receiving data from a first object of the plurality of objects; determining a second object from a relational object for the first object based on the received data; and modifying a data entity connected to an object entity of the second object within the database of smart entities based on the received data for the first object.