Intelligent Computing Resource Management and Optimization Support for AI Applications in the Power Industry

To address the heterogeneous computing resources with multi-tier and multi-hardware architecture in the power industry, an intelligent orchestration and unified management framework is proposed, enabling resource pooling and intelligent scheduling to enhance utilization efficiency and provide computing power services. In response to fluctuating operational workloads, a deep learning-based load prediction and elastic scaling mechanism is designed, leveraging container technology and hybrid cloud to achieve rapid capacity expansion and reduction, ensuring business continuity while reducing costs. Considering the requirements for model latency and energy efficiency, a model-hardware co-design approach is adopted, incorporating model lightweighting, compilation optimization, and priority-based scheduling. The implementation of these technologies has achieved positive results in applications such as real-time defect detection in transmission line inspections and intelligent hazard identification in construction safety, thereby contributing to the digital foundation of the new-generation power system.