A method for predicting cabinet inlet air temperature based on reinforcement learning model

Abstract

The present invention relates to the technical field of artificial intelligence, in particular to a method for predicting the inlet air temperature of a cabinet based on a reinforcement learning model. The method includes the following steps: the method includes the following steps. For temperature data, the actual air inlet temperature data of the corresponding cabinet is collected through the thermal device; step 2 calls the neural network model for training, using the actual surface temperature data of the cabinet as input, and the actual air inlet temperature data of the cabinet as output for repeated training, so that the neural network After the network model is trained, it can predict the air intake simulation temperature data of the cabinet; step 3 establishes a reinforcement learning model; step 4 obtains the neural network model under the optimal strategy of the reinforcement learning model to generate a new predictor; step 5 uses the optimal prediction The controller predicts the inlet air temperature of the cabinet. The method improves the accuracy of the air intake simulation temperature data of the cabinet, saves material and labor costs, and is easy to use.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a method for predicting the inlet air temperature of a cabinet based on a reinforcement learning model. Background technique [0002] The measurement of the air inlet temperature of the data center cabinet in the computer room is a topic that cannot be avoided in the design of the computer room. In the process of energy-saving renovation of the computer room, the temperature field of the computer room needs to be measured before the renovation. The existing method is to use a hand-held thermal temperature measuring device. The sequence of measuring the airflow temperature of each cabinet in turn is time-consuming and costly, and the temperature field of the data center is constantly changing. If the state of the air conditioner changes during the measurement process, the measurement The obtained temperature field is not the result we want, and the method of infrar...

AI Technical Summary

Problems solved by technology

[0002] The measurement of the air inlet temperature of the data center cabinet in the computer room is a topic that cannot be avoided in the design of the computer room. In the process of energy-saving renovation of the computer room, the temperature field of the computer room needs to be measured before the renovation. The existing method is to use a hand-held thermal temperature measuring device. The sequence of measuring the airflow temperature of each cabinet in turn is time-consuming and costly, and the temperature field of the data center is constantly changing. If the state of the air conditioner changes during the measurement process, the measurement The obtained temperature field is not the result we want, and the method of infrared thermal imaging is used for measurement. The advantage of infrared thermal imaging is that it can collect the temperature information of the entire computer room in a short period of time, but it measures the surface of the cabinet. The temperature data cannot represent the temperature data of the air inlet airflow. The data center is a complex time-varying environment. The previous method based on BP neural network cannot solve the problem of predicting the air inlet temperature of the cabinet very well.

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