Smart meters enable real-time data collection, remote monitoring, and energy consumption management in the energy sector. This paper focuses on researching efficient methods for storing, processing, and visualizing smart meter data. Initially, a dataset containing hourly energy consumption of millions of residential units is generated using the ARIMA method. Leveraging multiple data dimensions, a distributed data file storage and visualization system with multiple nodes is constructed. A data query model is proposed based on data characteristics, comparing data query latency and data transfer speed between memory and disk access methods. The findings indicate that in scenarios involving iterative queries of large datasets, memory outperforms disk in terms of data access performance, while differences between the two are minimal for smaller datasets or non-iterative queries. This study contributes to optimizing energy system efficiency and advancing smart energy development. Further research is recommended to fully unlock the potential of smart meter data in energy management.