Intelligent BMC Cooling Fan Control via AI Prediction

The patent introduces an AI model as an intermediary component between the BMC and the cooling fan control system. The AI model receives monitoring data from the BMC, performs intelligent prediction of CPU temperature trends, and generates optimized control instructions for the cooling fan. This intermediary layer enables advanced power consumption reduction without requiring complete redesign of the BMC architecture, thus resolving the contradiction between energy efficiency and system complexity.

The AI model performs preliminary prediction of future CPU temperature based on historical and current monitoring data before the temperature actually reaches critical levels. This allows the BMC to proactively adjust cooling fan speeds in advance, optimizing power consumption by avoiding unnecessary high-speed fan operation while ensuring temperature thresholds are not exceeded. The preliminary action enables more efficient energy management compared to reactive control.

Instead of increasing the computing power of the BMC itself, the patent introduces an external AI model as a mediator that performs the complex computational tasks. The AI model runs on separate hardware (such as a GPU or specialized AI accelerator) and communicates with the BMC through standardized interfaces. This approach achieves advanced power consumption reduction effectiveness without requiring the BMC to have high computing power, thus resolving the contradiction between energy efficiency and computing power requirements.

The patent replaces the traditional mechanical/control-based fan speed adjustment with an AI-driven predictive control system. The AI model uses machine learning algorithms to analyze patterns in monitoring data and predict future temperature trends, substituting simple threshold-based control mechanisms with intelligent prediction. This substitution achieves superior power consumption reduction without requiring the BMC's computing resources to be increased.

The AI model performs preliminary prediction of CPU temperature trends based on current monitoring data, workload patterns, and historical information. By predicting future temperature behavior in advance, the system can determine whether high fan speeds are actually necessary or if lower speeds will suffice. This preliminary action enables the BMC to avoid unnecessary high-power fan operation while ensuring CPU temperature remains within safe thresholds, thus resolving the contradiction between temperature control and power consumption.

The system implements a closed-loop feedback mechanism where the AI model continuously receives monitoring data from the BMC, predicts future temperature trends, adjusts fan speed recommendations, and validates the effectiveness of these adjustments. The feedback loop allows the system to learn from actual temperature outcomes and refine its predictions, enabling increasingly accurate optimization of the balance between CPU temperature control and power consumption over time.