基于CNN-MLP-ThermoAttention的数据中心三维温度场预测OA

The temperature field distribution in data centers directly affects operational safety,equipment performance,and cooling energy consumption.While the mainstream computational fluid dynamics(CFD)simulation can obtain 3D temperature contour maps,it is constrained by computing power,with a single simulation taking hours to days,failing to meet the real-time requirements of actual data center operation scenarios.To address this,this paper used CFD data as training samples and proposed the thermodynamics-constrained attention(ThermoAttention)mechanism,which embedded thermodynamic laws into the multi-head attention mechanism and coupled it with convolutional neural networks and multi-layer perceptrons to construct a neural network framework capable of real-time prediction of 3D temperature fields.Combined with point cloud technology and particle systems,dynamic reconstruction and visualization of temperature field contour maps were realized in Unity 3D.Experimental results showed that the model's prediction error was≤2％and a single prediction was shortened to minutes or even seconds,providing efficient support for real-time operation and maintenance of data centers and offering a new approach to real-time prediction of high-dimensional physical fields.