With the deepening of digital transformation, edge computing has begun to enter the implementation stage.
Reporter of Caijing Han Shulin | Written by Mark | Edited by
This year’s Hannover Industrial Fair in Germany and the China International Industry Fair in Shanghai have gradually made concepts like Industry 4.0 and digital twins well-known and no longer novel. The digital transformation needs of the manufacturing industry are gradually delving into more segmented and in-depth aspects, one typical trend being that edge computing is beginning to take root.
Almost all major players are talking about edge computing. Just as the digital transformation market in manufacturing not only attracts traditional industrial giants but also sees internet and IT giants deeply involved, in the field of edge computing, leading figures from various sectors are also present and have started launching dedicated products while collaborating deeply with each other.
Edge computing is a concept relative to centralized cloud computing. Different organizations define it differently, but generally speaking, it is a development platform with computing, storage, and networking capabilities provided for developers at the network edge, close to users and data sources.
Devices with edge computing capabilities can vary widely, such as Industrial PCs (IPC), gateways, and Programmable Logic Controllers (PLC), which can all become edge computing devices. These devices themselves need to have storage and computing capabilities, built-in specific algorithms and software, and various communication methods. One side of the edge computing device connects to the physical devices that generate data, while the other side can upload processed data to the cloud.
Edge computing devices often need to have strong data processing capabilities. For example, in the case of industrial computer products, different models vary in size from about the size of a computer host to about the size of a mobile phone box; the latest typical configurations of industrial computers already include Intel i7 processors.
The existence of edge computing is because it undertakes different functions from cloud computing.
In the manufacturing field, the data generated is diverse and complex, but not all data needs to be uploaded to the cloud for analysis and processing. On one hand, this brings demand for traffic and bandwidth, which is tied to the cost of using cloud services; additionally, companies have concerns about the data security of uploading all data to the cloud.
More importantly, how to utilize the data is crucial. Data on site can contain ineffective information; for example, data during production intervals does not need to be fully uploaded but requires selective processing. Some data needs to be processed immediately, such as real-time detection and warning information feedback for certain hardware products, which requires millisecond-level response times, while uploading to the cloud for processing may result in delays of seconds, greatly slowing production efficiency if all analysis is done through the cloud. This processing needs to be done by edge devices close to the data source.
Generally, production data that does not require high real-time response and is related to reports over a certain period is often uploaded to the cloud for analysis; while data analysis closely linked to production cycles and requiring high real-time responses usually needs to be completed at the edge computing stage.
As the digital transformation of manufacturing deepens, the different analyses of data in various scenarios to excavate its value are gradually becoming clear, thus highlighting the importance of edge computing.
Edge computing technology spans multiple fields including IT (Information Technology), OT (Operational Technology), and CT (Communication Technology), and its implementation cannot be separated from close cooperation between companies in different fields, leveraging each other’s strengths. Over the past year, leaders from various related industries have started launching products specifically targeting edge computing; this concept, which is not new, has just begun to enter the implementation stage.
Traditional industrial giants like Siemens and Mitsubishi have launched products and platforms specifically for edge computing this year. In April, Siemens launched the Industrial Edge digital platform, pre-configured with numerous applications for data processing, visualization, and transmission; Siemens’ own industrial computers can use this platform and can also be deployed on third-party hardware. In June, Mitsubishi introduced its industrial computer (MELIPC series) aimed at edge computing, which was observed by Caijing reporters at the Industry Fair in September, demonstrating rapid diagnostics and warnings for real-time production waveforms through built-in AI algorithms.
Software companies are also making moves; the two major global public cloud infrastructure service providers (IaaS), Amazon and Microsoft, have accelerated the deployment of edge computing this year. In June 2017, Amazon publicly released AWS Greengrass, software that enables data computation and transmission on locally connected devices, effectively Amazon’s edge computing platform; in June 2018, Microsoft open-sourced its Azure IoT Edge service platform, which aims to transfer cloud analytics and business logic to local devices.
However, both Amazon and Microsoft focus only on software and do not involve hardware products. Their services cannot be implemented without hardware companies that produce edge devices, such as Siemens and Mitsubishi mentioned earlier, as well as Huawei, which produces communication devices like gateways. In the field of edge computing, the trend of integrating software and hardware to promote digital transformation is once again evident.
In addition to edge devices, built-in software, and algorithms, a crucial factor is communication. Due to the higher real-time requirements for communication and the complex and diverse bus protocol standards on site, for edge computing to function effectively, communication must meet cross-platform and low-latency requirements.
During Hannover in April this year, over 20 international organizations and manufacturers, including Huawei, jointly released the TSN (Time-Sensitive Networking) + OPC UA smart manufacturing testbed, where OPC UA is a cross-platform industrial standard transmission protocol that can solve the incompatibility of data transmission standards between products produced by different automation manufacturers. Huawei’s TSN switch provides a highly reliable and low-latency industrial control network. The combination of the two enables data collaboration between different devices, which Huawei refers to as bridging the “last mile” of the industrial internet.
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