Edge computing has wide application, such as in the construction of smart cities and smart homes, security monitoring, and the field of connected cars and autonomous driving. However, the most effective application of edge computing at present is in the SD Wan manufacturing.
In the field of industrial internet of things, there is a high requirement for real-time control and safety and privacy of edge devices, and generated data needs to be processed locally. Therefore, applying edge computing to the industrial internet of things has become the direction of industry development.
At present, edge computing is mainly applied in the following scenarios in manufacturing enterprises:
In the scenario of equipment monitoring, cloud and edge computing can support the transmission and processing of nearly one hundred million or even more data, ensuring the real-time and reliability of transmission. At the same time, combined with big data and artificial intelligence, edge computing can provide timely remote control of workshop equipment and improve the predictive ability of equipment failures, achieving predictive maintenance and minimizing the equipment lifespan and improving equipment utilization.
Edge computing is also applied in the daily use and operation of terminal products, such as excavators and smart cars. The remote control of unmanned excavators based on 5G communication technology, edge computing, and artificial intelligence can achieve accurate and rapid construction and improve work efficiency, cost savings, and reduce risk factors.
Traditional industrial robots can no longer meet the requirements of the intelligent manufacturing era for intelligence and multi-perception fusion. It is necessary to further build the next generation of industrial robots relying on Internet technology, deep learning, and robot operation system platforms, etc. Elevating the autonomous decision-making ability of robots as terminal execution devices through edge computing is a necessary foundation for achieving complex processes and coordinated control.
The information on the transmission state of robot intelligent operations, such as the position and speed of joints, not only has a large amount of information but also requires real-time information. The control command information has high requirements for safety and reliability. The cooperation of edge-side, remote control terminals and intelligent centers can achieve robot intelligent control. In addition, edge computing is also applied in multi-robot cooperative operations to ensure the safe and efficient completion of tasks by multiple robots.
The AI quality inspection system adopts advanced edge computing technology, sinking AI applications to the production workshop for machine vision analysis near the device, reducing the network bandwidth requirements for video transmission.
At present, the training stage of edge AI industrial quality inspection needs to be completed on the edge side, sd wan service provider using deep learning for data acquisition, labeling, training, testing, and deployment. Then, according to the product inspection requirements, feedback is provided on detection results such as product category information, defect location, and defect categories, issuing warnings and controlling on-site equipment for processing. Edge computing AI quality inspection has achieved high-precision recognition and analysis of industrial product appearance defects, shortened application response time, and improved business real-time performance.
As a new type of production method arising from the transformation and upgrading of the manufacturing industry, flexible production has been pursued by many enterprises. Due to slow data analysis and the low level of terminal informatization, achieving flexible production is not simple for traditional manufacturing. However, manufacturing enterprises can control different production equipment to work together through the sd wan managed services ability and achieve a flexible customized production mode, making the production line more intelligent.
Edge computing establishes a mathematical analysis model for "the impact of state changes on processing quality" through the monitoring of equipment processing status such as process parameters and production environment data, and predicts abnormal processing quality through trend analysis, adjusts equipment process parameters in a timely manner and forms a "monitoring-analysis-adjustment-optimization" closed loop to prevent the generation of waste and defect products.