Edge State Module Server for Low-Latency Industrial HMI Updates
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Solution Overview
Problem
Conventional automated data acquisition and control systems heavily depend on cloud-based servers and databases, leading to latency delays, slow, intermittent, and disrupted connections, which hinder efficient processing and visualization of large volumes of industrial data.
Innovation Solution
A server system utilizing edge computing devices that process data locally, enabling analysis and visualization of industrial data without constant internet connectivity, using edge computing devices to perform operations such as data comparison, generate visual representations, and update human-machine interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based servers and databases 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 and cloud-based servers. Edge devices perform local data processing and caching, while cloud servers handle centralized management. This segmentation allows continuous local operation during connection disruptions, eliminating latency for critical functions and maintaining connection reliability.
Solution Approach 2:
An edge computing device acts as an intermediary between industrial data sources and cloud-based servers. The edge device includes a caching mechanism that stores data locally, enabling it to mediate data processing operations independently when cloud connections are disrupted, thus ensuring continuous operation and reducing latency.
2Productivity
If cloud-based servers are used, then centralized control is maintained, but slow and intermittent connections reduce system efficiency
Solution Approach 1:
The edge computing device performs preliminary data processing and caching actions before cloud connection is needed. By pre-processing data locally and maintaining a cache of recent data, the system eliminates the need for slow cloud connections during critical processing operations, thereby improving productivity without being constrained by connection speed.
3Loss of time
If edge computing devices are used for local data processing, then latency is reduced and continuous operation is enabled, but device complexity increases
Solution Approach 1:
The edge computing device is designed with multi-functionality, serving as a local processor, cache storage, and communication intermediary. By consolidating these functions into a single device, the system reduces latency and enables continuous operation without proportionally increasing overall system complexity, as the edge device replaces multiple separate components.
Data Source
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AI summary
Embodiments include a server system including logic of an edge computing device. A network includes a cloud platform able to receive state change events from a state 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. A further step includes receiving from the state module, by the edge computing device, current state data of the device, and a subsequent step includes performing a comparison based on a set of rules of the attributes, by the edge computing device, of the current state data. Further, based on the comparison, the method includes sending, by the edge computing device, an update to a human-machine-interface module.