Abstract: In order to promote the digital transformation of the shovel loading process in large open-pit mines, optimize key links, improve operating efficiency and safety, the equipment failures can be reduced, collisions between equipment can be prevented, ore size can be controlled, shoveling loads can be monitored, moreover, wear and fracture of bucket components can be warned to reduce maintenance costs and improve production continuity through the digital transformation. The research adopts methods such as process analysis, equipment coordination and risk identification, bucket components wear and fracture warning, establishment of a digital transformation framework, and integration of key functions, by studying the correlation between the shovel loading process and similar process flows in detail, the factors affecting operating efficiency and safety are identified. Using modern technologies such as visual analysis, pulsed laser and sensor technology, a digital transformation framework is established. Through the parallel structure hybrid model strategy, the integration of key functions such as bucket components missing detection, over-limit wear monitoring, equipment spacing detection, ore size analysis and dynamic load monitoring is achieved and constructs a monitoring and early warning system with prevention as the core. The system is closely integrated with the mining process, providing strong data support for management decisions and significantly improving the safety level of mine operations. The digital transformation practice of the Las Bambas Mining Site shows that through in-depth research and technological innovation, the efficiency and safety of open-pit mine shoveling and similar processes can be significantly improved. The digital transformation results of the mining site not only improve operating efficiency and safety, but also provide valuable reference for the intelligent development of other large open-pit mines at home and abroad.