Abstract: County-level distributed photovoltaic (PV) power stations serve as a crucial carrier for the integrated development of new energy in rural areas, and the quality of their operation and maintenance (O&M) directly determines the stability and economy of clean energy supply. To address the problems such as low O&M efficiency, slow fault response, and persistently high costs caused by the “small-scale, scattered, and remote” characteristics of current county-level PV power stations, this paper systematically constructs a “five-in-one” intelligent O&M theoretical system of “inspection, perception, analysis, decision, and execution”. It also designs an overall architecture for the intelligent O&M of county-level PV power stations based on UAV inspection, artificial intelligence, and big data technologies, which covers the UAV mobile perception layer, fixed monitoring layer, data service layer, and application service layer. Taking a PV cluster in a mountainous county in East China as the research object, the analysis shows that the system improves the accuracy of component defect identification to over 97% and reduces O&M costs by approximately one-third. This effectively solves the key pain points in county-level PV O&M and provides a reference model for the efficient operation of similar county-level PV power stations.