Edge Data Filtering for Real-Time Facility Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing facility analytics systems struggle to effectively gather, interpret, and utilize data from various sources, leading to inefficiencies in energy management, maintenance, and cost optimization due to the complexity and volume of data.
Innovation Solution
A network edge device with a local and remote interface, processor, and memory that filters and transmits data based on threshold parameters, combined with a cloud-based platform using machine learning and analytics to provide real-time reporting, predictive maintenance, and customizable dashboards for energy management and cost optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple facility sources is gathered and transmitted without filtering, then complete data availability is improved, but data transmission complexity and network load increase
Solution Approach 1:
The system performs preliminary filtering and processing of facility data at the edge device before transmission. Data is pre-screened against thresholds and aggregated locally, so only relevant information is sent to the cloud platform, reducing network load while maintaining data completeness for analysis.
Solution Approach 2:
The system extracts and transmits only the most relevant data points that exceed defined thresholds or represent significant changes. By taking out only critical information from the full dataset, the system reduces transmission complexity while ensuring important facility data is captured for analytics.
2Productivity
If real-time data analysis is performed to identify savings opportunities, then energy management efficiency is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments data processing between edge devices and cloud platforms. Basic filtering and aggregation occur locally in real-time, while comprehensive analytics are performed on aggregated results. This segmentation enables efficient energy management through distributed processing, reducing both processing time and computational resource requirements.
3Measurement precision
If comprehensive facility data is collected from multiple sources, then analytics accuracy is improved, but data gathering difficulty increases
Solution Approach 1:
The edge device serves multiple functions: data collection, filtering, aggregation, and preliminary analysis. This multi-functional approach simplifies the overall data gathering process by consolidating multiple operations into a single device, while still enabling comprehensive facility analytics through its ability to handle diverse data types from multiple sources.
Data Source
AI summary
A network edge device includes a local network interface, a remote network interface, a memory and a processor. The local network interface receives data from local data sources over a local network. The remote network interface communicates with a remote computer over a remote network. The processor receives a filter parameter set for each local data source from the remote computer. Each filter parameter set includes a data rate parameter and at least one of a minimum threshold parameter, a maximum threshold parameter or a change-of-value (COV) parameter. For each local data source, the processor compare each data value received from the local data source to at least one of the filter parameters, and sends the data value and subsequent data values to the remote computer at a data rate that is based on the data rate parameter.