Abstract: In recent years, China's new energy vehicle charging infrastructure has entered a phase of large-scale and systematic development, with significantly improved coverage density and network integrity, gradually forming a three-dimensional supply pattern of "point-line-area" collaboration. However, the traditional charging station model that solely relies on grid power supply is straightforward, but has notable drawbacks: during peak charging periods, it imposes substantial pressure on regional power grids; moreover, the operational costs of charging stations are entirely subject to the time-of-use electricity tariffs set by the grid, where high peak-time electricity prices severely constrain their profit margins. To address the aforementioned issues, this study proposes an integrated photovoltaic and energy storage system, establishing a new model of "photovoltaic-storage-charging integration" smart power stations. The research focuses on constructing a cloud-edge collaborative intelligent energy management system. Through big data analytics and AI forecasting technologies, it dynamically optimizes key processes such as the immediate utilization of photovoltaic power generation, energy storage during off-peak periods, and orderly discharging during peak periods, thereby maximizing comprehensive energy efficiency and operational revenue. This model not only enhances the spatial utilization and economic benefits of charging stations but also actively participates in grid regulation through coordinated control strategies, mitigating load fluctuations and providing flexible support for stable grid operation and efficient renewable energy.