Entity Graph Commands for Smart Building Time-Series Data
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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 time-series data from various objects, devices, and sensors, leading to inefficiencies in data management and decision-making.
Innovation Solution
A building management cloud computing system that processes time-series data by generating input timeseries from raw data points, identifying object and data entities, and creating derived timeseries through processing workflows, while also storing historical values and virtual data points, enabling efficient data management and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing building management systems are used to manage time-series data from various objects, devices, and sensors, then the system can collect and store data, but the system struggles to efficiently process and analyze the increasing amount of data, leading to data management inefficiencies
Solution Approach 1:
The patent segments the building management system into multiple specialized components: a data collection module that gathers raw data from sensors and devices, a data processing module that transforms and analyzes the data, and a data storage module that archives processed information. This segmentation allows each module to handle specific tasks efficiently, preventing the system from being overwhelmed by the increasing volume of data while maintaining high processing productivity.
2Loss of information
If more data is collected from smart building objects and devices, then better analysis and decision-making can be achieved, but the complexity of managing and processing this data increases
Solution Approach 1:
The patent introduces intermediary components including a standardized data interface layer that mediates between diverse data sources and the processing system, and a data normalization module that acts as an intermediary to standardize different data formats. These intermediaries simplify data management complexity by providing uniform interfaces and processing routines, allowing the system to handle increased data volume without proportionally increasing management complexity.
Solution Approach 2:
The patent implements a universal data processing framework that can handle multiple types of data from various sources (sensors, devices, systems) through a single integrated processing pipeline. This multi-functional approach reduces management complexity by eliminating the need for separate processing routines for each data type, while still enabling comprehensive data analysis for improved decision-making.
3Productivity
If real-time data processing is implemented for monitoring and decision-making, then operational efficiency improves, but the system requires more sophisticated data processing capabilities
Solution Approach 1:
The patent implements preliminary data processing steps including data validation, filtering, and preliminary analysis before main processing occurs. This preliminary action prepares data in advance, reducing the computational complexity required for real-time processing while maintaining high operational efficiency. The system performs initial data quality checks and transformations upfront, so that subsequent real-time processing can focus on analysis rather than basic data preparation.
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.