With the rapid development of intelligent manufacturing,industrial big data play an increasingly crucial role in the digital transformation of enterprises.However,current industrial big data platforms still face chall...With the rapid development of intelligent manufacturing,industrial big data play an increasingly crucial role in the digital transformation of enterprises.However,current industrial big data platforms still face challenges in data acquisition,processing,and visualization,including data processing inefficiencies,suboptimal storage solutions,and insufficient visualization experiences,which are often exacerbated by inherent data quality issues such as noise and outliers.To address these problems,this study proposes an industrial big data processing framework based on Flink and builds a data presentation system by combining Grafana and ECharts.The system collects data through enterprise sensors,utilizes Kafka message queues for data buffering,and uses Flink for efficient real-time data processing,incorporating foundational data cleansing techniques and strategies for mitigating common noise and anomalies.For data storage,MySQL is employed for static data,and InfluxDB is used for real-time data to improve storage efficiency.In terms of data visualization,Grafana displays real-time data,whereas ECharts is used for static data,offering users an intuitive and comprehensive data display interface.This study aims to provide an efficient and customizable industrial big data solution,with an emphasis on improving data reliability for visualization,to help enterprises monitor equipment information in real time,obtain effective information,and accelerate their intelligent transformation process.展开更多
文摘With the rapid development of intelligent manufacturing,industrial big data play an increasingly crucial role in the digital transformation of enterprises.However,current industrial big data platforms still face challenges in data acquisition,processing,and visualization,including data processing inefficiencies,suboptimal storage solutions,and insufficient visualization experiences,which are often exacerbated by inherent data quality issues such as noise and outliers.To address these problems,this study proposes an industrial big data processing framework based on Flink and builds a data presentation system by combining Grafana and ECharts.The system collects data through enterprise sensors,utilizes Kafka message queues for data buffering,and uses Flink for efficient real-time data processing,incorporating foundational data cleansing techniques and strategies for mitigating common noise and anomalies.For data storage,MySQL is employed for static data,and InfluxDB is used for real-time data to improve storage efficiency.In terms of data visualization,Grafana displays real-time data,whereas ECharts is used for static data,offering users an intuitive and comprehensive data display interface.This study aims to provide an efficient and customizable industrial big data solution,with an emphasis on improving data reliability for visualization,to help enterprises monitor equipment information in real time,obtain effective information,and accelerate their intelligent transformation process.
