When discussing the differences between edge collection and edge computing, we need to conduct in-depth analysis from multiple dimensions such as their definitions, functions, application scenarios, and technical characteristics. Here is a detailed comparison between the two:
Definition: Edge collection refers to the process of collecting data from external devices such as sensors and PLCs through specific protocols and converting it into internal data points.
Data acquisition: Connect with external devices through various communication protocols such as Modbus and OPC UA to collect data from sensors or devices in real time.
Data point configuration: In the edge collection device, by configuring data points, the data of external devices is associated with internal data points to ensure the accuracy and consistency of the data.
Data reporting: Based on preset parameters such as reporting mode, reporting time, and change range, data is reported to the cloud or central server. At the same time, reporting conditions can be set to reduce unnecessary data transmission and optimize the use of network resources.
Definition: Edge computing is a distributed computing model that brings computing and data storage closer to the data source or user. It is located at the edge of the network near the source of objects or data, integrating core capabilities of network, computing, storage, and applications to provide edge intelligent services nearby.
Low-latency processing: Data processing is performed on edge devices close to the data source, reducing the distance and time of data transmission and reducing latency, which is crucial for applications with high real-time requirements.
High bandwidth efficiency: Process large amounts of data locally, and only transmit key data or processing results to the cloud, reducing the need for network bandwidth.
High reliability: Redundant storage and processing of data are implemented locally, ensuring the continuous operation of critical applications even in the event of network interruptions or cloud failures.
Privacy protection: Processing sensitive data locally and avoiding transmitting data to the cloud improves data security and privacy protection.
It is mainly used in scenarios where real-time data collection from various external devices is required, such as device monitoring and environmental monitoring in industrial IoT.
Through edge collection, real-time monitoring and data collection of equipment status can be achieved, laying the foundation for subsequent data analysis and application.
It is widely used in scenarios that require low latency, high bandwidth efficiency, and high reliability, such as autonomous driving, telemedicine, and smart factories.
In autonomous driving, edge computing can complete data processing in milliseconds, ensuring the safe driving of vehicles; In smart factories, edge computing devices can monitor the running status of production equipment in real time, predict equipment failures, and automatically adjust production plans.
It focuses on real-time data collection and preliminary processing, providing an accurate and consistent data foundation for subsequent data analysis and application.
By configuring data points and reporting conditions, the use of network resources can be optimized and unnecessary data transmission can be reduced.
Emphasis is placed on data processing and analysis on edge devices close to the data source to achieve low latency, high bandwidth efficiency, and high reliability.
Through local processing and redundant storage, the security and privacy protection capabilities of data can be improved.
There are significant differences between edge collection and edge computing in terms of definition, function, application scenarios, and technical characteristics. Both play their unique roles in networking applications such as industrial IoT, and together promote the continuous development and innovation of IoT technology.
