Design Of Intelligent Monitoring System For Base Station Distribution Equipment

Power distribution equipment is an important device for receiving and distributing electrical energy.According to research,the current distribution system of communication small and micro base stations still relies on manual inspection and operation,with high maintenance costs.Moreover,due to the inability to accurately and timely determine the health status of distribution equipment,it is easy to cause unexpected power outages.To this end,based on the operation and maintenance stage of distribution equipment from put into use to retirement,an intelligent monitoring system with comprehensive management functions has been designed.The device status data is collected through sensors,and cloud edge collaboration and health management technology are used,combined with real-time and historical data,to intelligently evaluate and analyze the device status to meet the needs of intelligent monitoring.The main research content is summarized as follows:(1)According to the hierarchical design of the cloud edge collaboration framework,using a domestically produced SOC core processor,equipped with an open-source embedded Linux operating system,the system hardware and software have been developed.The hardware part includes data acquisition,storage,and network communication circuits.The software section includes driver software configuration,application development,and other logical functions such as data collection and cloud deployment.The cloud platform designs the interfaces of various functional modules,realizes data visualization,alarm processing,equipment management and status evaluation,and sets data tables to store data.(2)Taking the residual life prediction of energy storage lithium batteries in DC distribution systems as the research content,a residual life prediction model based on POA-ELM algorithm is constructed.The model is trained and predicted using public datasets,and combined with output results and error index analysis,it is proven that the model has high prediction accuracy,thereby verifying the feasibility of "predictive maintenance" for equipment.(3)By building an experimental platform and conducting on-site equipment debugging,the system’s data collection and processing,local data management,cloud edge collaboration,and other functions and related performance were tested.Compared with similar monitoring systems,it has advantages such as short sampling cycle,high accuracy,low packet loss rate,and short communication delay.In summary,the intelligent monitoring system built in this article integrates functions such as data lifecycle services,cloud edge collaboration,and device operation and maintenance management.It provides diversified services such as situation presentation,monitoring and warning,linkage command,analysis and decision-making,and trend prediction.The edge introduces data lifecycle services,and in combination with the introduction of intelligent algorithm technology in the cloud,cloud edge data aggregation and backup are achieved,improving the efficiency of base station distribution equipment operation and maintenance,And a model was built to achieve life prediction,which made up for the lack of health assessment in the existing system and verified the feasibility of predictive maintenance.After on-site testing,the overall system runs relatively stable and has high practical application value,which can be widely used in internal power distribution stations,communication base stations,etc.of enterprises.