Remote data acquisition and analysis platform
Every process such as development, validation, production, and maintenance is digitized in the machine industry including automobiles, heavy equipment, construction equipment, farm equipment, and robots. Data acquisition, which is the main requirement in these areas, requires building an advanced data pipeline that can handle sensor data with high sampling rates and diverse fusion data. By collecting this high-definition mixed digital data from a remote location without human intervention through a public network such as a mobile network or the Internet, you can leverage data for numerical analysis, simulation, AI development, or other purposes more rapidly. In this way, remote data acquisition enabled by intdash assumes a very important role in industrial digitization.
You can remotely collect vehicle performance data and build a data pipeline in the cloud. This allows you to manage data more efficiently and share data with other processes such as simulation.
Measurement data such as CAN (Controller Area Network) data and various sensor signals is transmitted in real-time from the vehicle to the cloud at shakedown in the test course in the course of development. Measurement data can be rapidly distributed to multiple development sites.
Real-time transmission of mixed data such as machine data (J1939), GPS, and video from each machine to the cloud enables comparison analysis and real-time monitoring of each machine in a test field or test site.
You can instantaneously share driving data from an overseas development site for product validation by transmitting it to the global cloud in real-time.
Primary processing such as conversion of massive measurement data into physical values, resampling, and filtering is performed with the computing power of the cloud. You can build a data pipeline to seamlessly communicate data to analysis tools or external systems for secondary processing.
Remote data acquisition from industrial instruments to collect a large amount of time-series data brings to light many challenges in terms of IT (Information Technology) and OT (Operational Technology).
Proprietary high-performance sharding mechanism with linear scalability that can store massive amounts of time-series data at high speed (distributed management of time-series data)View Details
A variety of tool application suites help to manage measurement targets and edges, filter edge data, monitor and check the measurement status, and browse acquired data.View Details