From distribution centers to clean logistics in precision manufacturing processes, hundreds of thousands of vibration and PLC sensor data are generated per second for control of hundreds of thousands of autonomous mobile robots. Real time logistics tracking and facility status management eliminates various logistics risks and saves analysis time and network cost.
In unmanned transportation, data generation and exchange takes place in all possible logistics chains, including intelligent containers, smart warehousing, smart ports and smart shelves.
Like Industry 4.0, edge intelligence is required to play a key role in the IIoT supply chain.
Hundreds of thousands of autonomous moving robots (AGVs) are moving along the rails installed on the ceiling. If there is a problem in the track, such as small pieces, cracks, wear etc., there should be precautions in real time. Sensor data of autonomous mobile robots transpire more than 100,000 per second, and real-time response to this data is required.
IoT gateways are embedded with real-time data that is close to 100,000 per second. The sensor data must be saved in the edge intelligence devices locally and archived for more than a week. Edge intelligence devices, such as LattaPanda, ARTIK, or NVIDIA Jetson series, must be capable of collecting and storing data in real time in order to respond to anomalies, and to be able to judge the presence of data anomalies.
Effect of application
Machbase was implemented on local edge intelligence devices, (Intel Atom 4 Core, 64 -bit and Window 10 environment) achieving over 100,000 inputs per second. Machbase CPU utilization was only 10-15% making data storage more efficient.
In addition, the Machbase Edge Edition transfers abnormal data to the Machbase Fog Server. The Fog Server is now able to store full history for abnormalities gathered from the 500+ Edge Intelligence devices