Computer Room Monitoring And Early Warning Platform Based On Internet Of Things

With the rapid development and popularization of information technology,the degree of social informatization continues to increase,and the monitoring and management of the data center computer room to ensure the construction of informatization have become particularly important.The data center computer room stores various network equipment,servers,and data storage equipment that maintain a stable network connection and ensure the normal operation of the school's information system.When the environmental parameters of the equipment room exceed the threshold for normal operation of the device,it will affect the service life of the device and the stability of performance.Once the equipment fails,it will directly affect the smooth operation of various information systems and the normal connection of the network,which may lead to the stagnation of business operations of various units.Therefore,it is of great significance to strengthen the monitoring of the data center computer room environment and establish an environmental abnormality early warning mechanism to ensure the safety and stable operation of network equipment.This paper combines the Internet of Things technology and designs and implements a computer room monitoring and early warning platform according to the actual needs of a data center computer room in a university.The platform consists of two parts:the data acquisition terminal of the computer room and the monitoring terminal of the computer room.Among them,the data collection terminal of the computer room is deployed on the site of the computer room,using the Raspberry Pi as the main control unit,and collecting and uploading environmental parameters by connecting various sensors and communication modules.When it is found that the environment of the equipment room is abnormal,the communication module is used to send an alarm message to the staff of the equipment room.In addition,in order to prevent the leakage of the computer room image,the digital image encryption algorithm based on chaotic system is used to encrypt the computer room image.The computer room monitoring terminal is deployed on the server side to implement various business logics,which are mainly divided into Web applications,main program services,and database services.The Web application provides visualization services for the staff;the main program service implements the calculation and analysis of the data uploaded by the collection end;the database service is used to realize the storage and query of various types of data.For computer room environment prediction,this paper proposes a nonlinear combination time series prediction model.The combined prediction model combines the prediction results of the two models LSTM and Prophet nonlinearly through the BP neural network to obtain the final prediction value.The computer room temperature data and three sets of public data sets in different fields were used to verify the prediction effect of the combined prediction model.Experimental results show that the mean absolute deviation and root mean squared error of the combined forecasting model are the best compared to other models.Therefore,this paper proposes that the combined forecasting model has better forecasting effect and higher forecasting accuracy,and is an effective time series forecasting model.Through this model,the abnormality of the computer room environment can be predicted in advance,and timely warnings can be provided to effectively ensure the smooth operation of various equipment in the computer room and escort the school's information construction.