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Significance and Solution of Equipment Data Acquisition in Smart Factory
The solution is as follows:
Selection of appropriate sensors and monitoring equipment: According to the characteristics and requirements of the production process, select appropriate sensors and monitoring equipment, and collect data of equipment operation status, temperature, humidity, pressure, vibration and other parameters. These sensors can automatically upload data to the cloud or LAN server through network connection, which further facilitates the comprehensive collection and management of data.
Design and integration of data acquisition system: according to the specific needs of enterprises, design and integrate a big data acquisition system that meets the application scenarios of enterprises. For example, the Internet of Things technology can be used to build a data acquisition platform based on communication protocols such as Wifi, Bluetooth and NB-IoT.
Formulate data cleaning and archiving strategies: construct data cleaning and preprocessing algorithms to ensure fast and accurate storage of real-time data. At the same time, according to business requirements and industry standards, we will formulate a sound data archiving strategy to ensure timely backup and safe storage of data.
Data analysis and utilization: extract information from the collected equipment data, make statistical analysis, get the analysis results of production data such as defects and faults in the production process, and feed them back to production managers to help them solve problems in time, better optimize production processes and improve product quality.
To sum up, equipment data collection in smart factories is very important for enterprises. By selecting suitable sensors and monitoring equipment, an efficient data acquisition system is constructed, and efficient data management and analysis technology is adopted to realize automatic acquisition and rapid analysis of factory production data, and finally realize digitalization and intelligence of production process.
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