Traditional equipment management generally consists of sensors, data acquisition, measurement and control center, data acquisition equipment mainly includes PLC, data acquisition board, field instrumentation, image acquisition equipment, etc. The only function achieved is to digitize analog signals, store and forward them to the measurement and control center. It can only be used in the application environment with long sampling interval and small amount of collected data. For the use scenario with high sampling rate requirement, large amount of collected data and large data throughput, a special acquisition measurement and control system must be built separately. The storage, calculation, processing and judgment of big data are all realized in the measurement and control center, and these application functions are provided through the deployed servers. It is extremely difficult to achieve business integration and unified platform management.
The acquisition measurement and control system, at the device, only collects data and then transmits it to the measurement and control center as is, and the measurement and control center completes the storage, calculation, processing, judgment, and decision-making of the data. Obviously, it can only measure temperature, humidity, barometric pressure, and other such changes that are not fast, and can tolerate a long response time scenario.
Since 2010, the field of the Internet of Things (IoT) has undergone a radical change. The combination of artificial intelligence and IoT has resulted in AIoT (Intelligent Internet of Things), and the application of IoT in the industry has given birth to Industrial IoT, which has given strong momentum to the development of IoT. In the era of the AIoT, there are high-speed acquisition requirements, and accordingly, higher requirements for transmission bandwidth. For rapidly changing conditions at the scene, intelligence demands that the measurement be completed the first time and a timely response be provided. The measurement and control system based on a single data acquisition device lags far behind the AIoT era. The reasons are as follows:
1. As the volume of IoT activities is increasing, increased bandwidth is necessary to transmit the data to the central server in a timely manner.
2. Therefore, the storage demand grows, yet the collected data during normal operations are useless information, all saved, thus resulting in wasted storage space.
3, centralized processing, higher requirements for computational capacity.
4, more real-time response scenarios, requiring real-time processing of data and rapid judgment to control objects or items to make appropriate responses and perform accurate actions. Due to processing time and transmission delay, central control processing is unable to meet these near-time control requirements.
Based on edge computing technology, the 5G edge computing gateway uses the network, computing, storage, and application core capabilities as an integrated open platform for storing, processing, and analyzing data near the object or information source. Deploying running applications on the edge side produces faster network service response to meet the industry’s real-time business and application intelligence needs, and ensures data security and privacy protection while reducing network bandwidth requirements.
AR7091 Gigabit Router 5G Edge Computing Gateway
The top of IoT is the cloud server, and the edge is data collection + edge computing. In addition to data acquisition, edge ends have a powerful computing function and support AI algorithms, which are not available in traditional measurement and control system data acquisition. With rich connectivity, it not only supports sensor interfaces such as IEPE and voltage type but also RS485 serial bus interface and Ethernet interface. This can seamlessly connect smart sensors.
