1. The concept of edge computing
Edge computing originated in the media field. It refers to an open platform that integrates network, computing, storage, and application core capabilities on the side close to the source of things or data to provide the nearest end service. In the Internet of Things, edge computing is local computing and analysis performed on the side of the end device close to the data source. Because we are close to the terminal, our calculations will be more real-time.
In essence, edge computing and cloud computing were both born to deal with data computing problems, but they are implemented in different ways. Cloud computing gathers data together for calculations, while edge computing performs calculations on the terminal. So what are the advantages over cloud computing?
We can see from the figure that cloud computing needs to transmit all data through the network and respond, while edge computing processes data on edge devices to respond in a timely manner, and at the same time sends the processed valuable data to the cloud.
Today’s self-driving cars have hundreds of sensors and generate 40TB of data every 8 hours of driving. Most of this data is not important, and it is impractical to transfer such a large amount of data to the cloud. . Edge computing solves this problem very well. It calculates locally first, and then uploads important calculation results to the cloud.
What can we learn from this example?
Autonomous driving requires the system to have real-time response capabilities. You can imagine the consequences on the road if the system does not respond in time. So this reflects the real-time nature and necessity of edge computing.
Such a large amount of data must contain a large amount of useless data. It is impractical to transmit all of it to the cloud. Edge computing solves this problem. After calculation, important calculation results are transmitted to the cloud, which greatly saves bandwidth resources.
Of course, edge computing is not intended to replace cloud computing, but as a complement to cloud computing. Because the results of edge computing will still be sent in whole or in part to the cloud for storage and analysis.
2. Mobile Edge Computing (MEC)
I have to mention MEC here, because with the arrival of the 5G era, our mobile life will be further changed. Future communication base stations will have computing capabilities, and the mobile APP we use will perform calculations at the nearest base station, which I believe will bring us a faster user experience.
3. Intelligent manufacturing applications
The purpose of intelligent manufacturing is to realize a smart factory and make machines and equipment self-aware. Replace human decision-making. How to achieve this goal? Edge computing is a good means. By deploying an edge computing AI platform around the device and running machine learning algorithms, the edge computing platform becomes the brain of the machine and calculates and analyzes data from various sensors. Later, it is determined whether the robot status is normal and what action to perform.
As smart manufacturing continues to deepen, more and more devices are connected to the Internet, and more and more data are generated. Edge computing devices will gradually become popular to process this data. At the same time, the ultimate goal of intelligent manufacturing, the self-awareness of equipment requires artificial intelligence (AI) algorithms to make decisions, and the production site has high requirements for real-time control of equipment, so in the future these algorithms will definitely be calculated on edge devices. , and then control the device in real time, truly becoming the brain of the device.
4. Edge computing gateway
Die Edge-Gateway ZHL4911 has strong edge computing capabilities, shares computing resources deployed in the cloud, and implements data optimization, real-time response, agile connection, model analysis and other services at the edge nodes of the Internet of Things, taking the digital Internet of Things further in the AI era.
Die Edge-Gateway ZHL4911 serves as a communication hub on the industrial application site side and the platform server side, realizing data collection, communication protocol conversion and data transmission at the industrial site, and providing an efficient and reliable data channel for equipment informatization and industrial big data applications in the industrial field.
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