Method for predicting air inlet temperature of cabinet based on reinforcement learning model

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a method for predicting the air inlet temperature of the cabinet based on the reinforcement learning model. The method comprises the following steps: 1, collecting actual surface temperature data of the cabinet through a thermal imaging device, and collecting actual air inlet temperature data of the corresponding cabinet through a thermosensitive device; 2, calling a neural network model for training, and repeatedly training by taking the surface actual temperature data of the cabinet as input and the actual air inlet temperature data of the cabinet as output, so that the neural network model can predict air inlet simulation temperature data of the cabinet after training; 3, establishing a reinforcement learning model; 4, obtaining a neural network model under the optimal strategy of the reinforcement learning model to generate a new predictor; and 5, predicting the air inlet temperature of the cabinet by using the optimal predictor. According to the invention, the accuracy of the air inlet simulation temperature data of the cabinet is improved, the material is saved and labor cost is reduced,and use is convenient.

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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