Shulou(Shulou.com)06/01 Report--

   Germany's Industrial 4.0is not very smooth in the process of promotion and construction, and has been forced to stagnate because of the technical obstacles of the information physics system CPS and the arrival of 5G. It is rumored that the "Industrial 4.0Project" has failed, but in fact, most people do not give up because of the failure of the Internet of things project. They think that the experiences and lessons learned from it have accelerated their IoT investment. We may face all kinds of entanglements and challenges in the short term, but we should still be resolutely optimistic in the long run.

   on the development path of the industrial Internet, more and more people rationally and objectively realize that this is not achieved overnight, but "point", "line", "surface" gradually carry out landing application. What attracts more attention is the business model of the industrial Internet. How can the combination of hardware and software make money? How to go from the one-shot sale of hardware to the sustainable profitability of software?

   from the current stage, the hardware products of the industrial Internet is a small and diverse long-tail market. While pursuing hardware profits, it is difficult to earn huge amounts of data, so many enterprises have begun a difficult journey of transformation. In line with this, end-users also begin to hope that the analysis and processing of operational data can be carried out not on the IIoT cloud platform, but on the industrial site. The main benefits include: realizing the closed loop from equipment analysis to enterprise decision-making; reducing (or eliminating) the cost of deploying and operating the IT system; sharing the operating load of the information platform; real-time requirements of data operation and processing; and localizing data processing to improve the information security of the system.

Enterprises with a good sense of smell and quick response to changes in the    market have launched an integrated data processing scheme of software and hardware on the edge side with similar functions. Using proven high reliability products, promote hardware standardization, increase artificial intelligence analysis capabilities, form differentiation through software and systems, and actively earn data, models and experience, promote the process from data to decision-making, based on service SaaS and long-term development.

   companies have developed a new generation of edge controllers, equipped with industrial artificial intelligence algorithms to meet the needs of users for predictive maintenance and analysis, and more and more enterprises have joined this track. For example: LogixAI analysis module, Sysmac AI controller, Simatic S7-1500 neural processor.

   LogixAI analysis module, added the ability to carry out predictive analysis of abnormal conditions. Users no longer need to install a separate application program in it, directly access the module through the Ethernet port, and then configure the parameters of the relevant application model on the Web browser to realize the function of analyzing and predicting the system data.

The    Sysmac AI controller is developed using the isolated Forest (Isolation Forest) machine learning engine on the anomaly detection algorithm. The memory requirement is very low, and the processing speed is very fast, and its time complexity is linear, so it is very suitable for high-speed real-time processing, and the detection accuracy and accuracy can be improved by fine-tuning. At the same time, it is also suitable for multimodal data and can be used for highly mixed production lines that require two or more modes of operation.

   SimaticS7-1500 neural processor can realize the efficient processing of neural network. The sensor data and the data from the CPU program are processed on the basis of neural network. With the help of machine learning algorithms, applications such as visual quality inspection of production factories or image-guided robot systems will be effectively realized.

   the above-mentioned products are still in a relatively early stage of market cultivation. At this stage, they seem to be more suitable for use in some scenarios which are closely related to economic benefits, such as production quality management, predictive maintenance of key equipment and so on. The final completion of the process from data to decision-making also needs to trigger a deep change in the business model.

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