Edge Utilization Module for Low-Latency Industrial Data Processing
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Solution Overview
Problem
Conventional automated data acquisition and control systems in industrial processes are hindered by latency delays and disrupted connections due to their reliance on cloud-based servers, leading to inefficiencies in data processing and management.
Innovation Solution
Implementing an edge computing architecture that enables local processing and analysis of data by edge computing devices, which receive attributes and utilization data from cloud platforms, prepare comparisons, and send updates to human-machine interfaces for display, even when disconnected from the network, thereby reducing latency and improving data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based servers are used for data processing, then centralized data management is achieved, but latency delays and connection disruptions occur
Solution Approach 1:
The system segments data processing functions between edge computing devices (local processing) and cloud-based servers (centralized management). Edge devices perform real-time data acquisition, filtering, and preliminary analysis locally, while cloud servers handle comprehensive data management and long-term storage. This segmentation eliminates the need to transmit all raw data to the cloud, reducing latency and connection dependency while maintaining centralized oversight.
Solution Approach 2:
Edge computing devices act as intermediaries between sensors/controllers and cloud-based servers. These edge devices filter, aggregate, and pre-process data before transmission to the cloud, reducing the volume of data that requires network transmission and minimizing the impact of connection disruptions on overall system performance.
2Extent of automation
If all data is transmitted to cloud platform, then centralized analysis is achieved, but network dependency increases
Solution Approach 1:
The system divides data management responsibilities into two segments: edge computing devices handle local data filtering, aggregation, and real-time analysis, while cloud servers provide centralized configuration management and historical data storage. This segmentation reduces network dependency by enabling autonomous local operations while maintaining centralized automation capabilities.
Solution Approach 2:
Edge computing devices perform preliminary data processing actions (filtering, aggregation, anomaly detection) before data reaches the cloud platform. This preliminary action reduces the burden on network infrastructure and cloud resources, enabling centralized management with reduced network dependency.
3Productivity
If real-time data processing is implemented, then operational efficiency is improved, but data transmission volume increases
Solution Approach 1:
The system extracts and removes redundant or non-critical data elements at the edge computing level before transmission to the cloud. Edge devices filter out duplicate measurements, aggregate time-series data into meaningful summaries, and transmit only essential information, thereby maintaining real-time processing capabilities while minimizing data transmission volume.
Solution Approach 2:
The system implements partial data transmission by sending only the most critical or recently changed data to the cloud platform, while maintaining complete real-time data availability locally at edge devices. This approach achieves real-time operational efficiency without the overhead of transmitting entire datasets to centralized systems.
Data Source
AI summary
Embodiments include a server system including logic of an edge computing device. A network includes a cloud platform able to receive utilization change events from a utilization module, and execution of the program logic results in process steps of a method that include transmitting a plurality of attributes from the cloud platform to the at least one edge computing device, where the plurality of attributes can be associated with a device of a distributed environment coupled to the network. Another step includes receiving from the utilization module, by the edge computing device, current utilization data of the device, and a further step includes performing a comparison based on a set of rules or mappings of the attributes, by the edge computing device, of the current utilization data. Finally, based on the comparison, the method includes sending, by the edge computing device, an update to a human-machine-interface module.