Entity Graph Logic for Smart Building Timeseries Management
Find Innovative SolutionsGenerate Solutions
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
1Loss of information
If building management systems collect data from multiple sensors and devices to improve analysis capabilities, then the amount of available data increases, but the complexity of data management and processing increases
Solution Approach 1:
The patent segments the building management system into multiple independent components: edge computing devices that collect and pre-process data from sensors, a cloud-based platform that stores and manages data, and analytical tools that generate insights. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while maintaining comprehensive data collection capabilities
Solution Approach 2:
The patent introduces an intermediary layer (edge computing devices and data normalization services) between sensors and the central management system. This intermediary layer pre-processes, filters, and standardizes data before transmission, reducing the burden on the central system and simplifying data management while preserving complete information
2Loss of information
If building management systems process and analyze vast amounts of data to improve decision-making, then the quality of insights increases, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary data processing and filtering at the edge computing devices before data is transmitted to the cloud. Data is pre-aggregated, pre-filtered for relevance, and pre-formatted during off-peak hours or in parallel streams, reducing the computational burden during critical analysis periods and accelerating decision-making without sacrificing insight quality
Solution Approach 2:
The patent applies partial processing by focusing computational resources on the most critical data streams and high-priority analyses. Not all data is processed with equal depth - instead, the system applies varying levels of analysis based on data importance, allowing rapid processing of key metrics while maintaining thorough analysis of secondary data
3Loss of information
If building management systems store historical data from multiple sources to improve analysis, then the depth of historical analysis increases, but the storage requirements and data organization complexity increase
Solution Approach 1:
The patent implements a universal data storage architecture with standardized schemas and normalization rules that can accommodate multiple data sources (sensors, devices, systems) in a unified format. This universal approach allows historical data from diverse sources to be stored efficiently with consistent organization, reducing complexity while preserving complete historical information for deep analysis
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.