Design and Development of an Intelligent Computer Room Management Platform Based on Artificial Intelligence and Big Data Analysis

The purpose of this paper is to design and develop an intelligent computer room management platform based on artificial intelligence and big data analysis. By integrating AI and big data technologies, the platform is able to realize real-time monitoring, intelligent prediction and alarm, and energy consumption optimization and management of the server room environment. This paper describes the implementation method of the LSTM (Long Short-Term Memory) network-based platform, and verifies the effectiveness of the platform through experiments. Among them, it is shown in the environment monitoring experiments that the mean temperature monitoring error of the platform is 0.12°C with a standard deviation of 0.98°C, and the mean humidity monitoring error is 0.15% with a standard deviation of 1.05%. In the fault response experiment, the average response time of the platform is about 5.88 minutes with a standard deviation of about 3.12 minutes. In the intelligent prediction performance evaluation, the LSTM model has MSE (Mean Squared Error) of 0.103 and MAE (Mean Absolute Error) of 0.256, which is better than ARIMA (Autoregressive Integrated Moving Average), simple RNN (Recurrent Neural Network) and linear regression models. The effectiveness and superiority of the platform in terms of environmental monitoring, fault response and predictive performance is evident from the data conclusions.