Big Data Parsing and Dynamic Monitoring of Parameters During Atomization Process
This thesis discusses the big data analysis and parameter dynamic monitoring techniques in the fogging process.First,data acquisition techniques are analyzed,emphasizing the importance of sensors and data acquisition systems in real-time monitoring.Second,a variety of data analysis methods are discussed,including regression analysis,time series analysis,and machine learning models.Further,key parameters,such as operating pressure,liquid temperature,and flow rate,were identified,and the effect of dynamic monitoring on atomization effectiveness was explored.The results show that the dynamic monitoring technique significantly improves the efficiency of the atomization equipment and the quality of the product,which is of great practical significance to the related industries.
atomization processbig data analysisparameter dynamic monitoringdata acquisition technologydata analysis methods
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