Server fault monitoring method and system based on neural network

A neural network and fault monitoring technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as failure diagnosis and monitoring of servers, and achieve the effect of preventing local optimum and improving stability

Inactive Publication Date: 2020-05-12
SHANDONG CHAOYUE DATA CONTROL ELECTRONICS CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This technical solution can more fully monitor server failures, and at the same time significantly improve the accuracy of locating the location of server failures, and then timely and effectively diagnose and analyze the causes of corresponding server failures, but it cannot realize neural network-based Server fault diagnosis and monitoring of server status to improve server stability

Method used

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  • Server fault monitoring method and system based on neural network
  • Server fault monitoring method and system based on neural network
  • Server fault monitoring method and system based on neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0075] as attached figure 1 As shown, the neural network-based server fault monitoring method of the present invention is to use BMC to obtain server information, analyze and predict whether the server will fail through the neural network, and feed back the fault to the webpage for display, and monitor the server status at the same time , to improve the stability of the server; the details are as follows:

[0076] S1. The BMC system obtains server health information data through I2C, and the next step is to perform step S2; wherein, the server health information data includes server-side voltage, server-side current and server-side temperature;

[0077] S2. Perform preprocessing on the acquired health information data, and then perform step S3 in the next step; details are as follows:

[0078] S201. Convert character data into data data;

[0079] S202. Perform normalization processing on the data to prevent data differences from affecting the prediction result.

[0080] S3....

Embodiment 2

[0108] Based on the neural network-based server failure monitoring method of embodiment 1, the specific steps are as follows:

[0109] 1), BMC system obtains server health information through I2C;

[0110] 2) To process the health information data, the string type data is encoded into a numerical type, and the numerical type is normalized. The training data set needs to mark the current server status, and the status is divided into failures and numerical data corresponding to various types of failures. ;

[0111] 3) Neural network training and testing, complete the determination of neural network weights and thresholds through training, and fine-tune neural network weights and thresholds through testing; 70% of the marked data are used as training data sets, and 30% are used as test data Set, take the training data set as input, encode the initial value of the weight threshold of the BP neural network, and use the error obtained by the BP neural network training as the fitnes...

Embodiment 3

[0115] The neural network-based server fault monitoring system of the present invention, the system includes,

[0116] The data acquisition module is used for the BMC system to obtain the health information data of server-side voltage, server-side current and server-side temperature through I2C;

[0117] The data processing module is used to convert character data into numerical data, and to normalize the data in order to prevent the impact of data differences on the prediction results;

[0118] The fault prediction module based on the neural network is used to apply the neural network that has completed the training and determined the parameters of each node to the BMC system. The preprocessed data is used as the input of the neural network, and the output is the predicted server fault type;

[0119] The failure warning module is used to display alarm information on the web page to prompt the user when the prediction result is a failure, and to monitor whether the server will...

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Abstract

The invention discloses a server fault monitoring method and system based on a neural network. The invention belongs to the field of equipment fault diagnosis analysis. The technical problem to be solved by the invention is how to realize server fault diagnosis based on a neural network and monitor a server state. According to the technical scheme, the method comprises the steps that server information is obtained through a BMC, whether a server breaks down or not is analyzed and predicted through a neural network, the fault is fed back to a webpage to be displayed, meanwhile, the state of theserver is monitored, and therefore the stability of the server is improved. The system comprises a data acquisition module, a data processing module, a fault prediction module based on a neural network and a fault early warning module. The fault prediction module based on the neural network applies the neural network which completes training and determines parameters of each node to a BMC system,the preprocessed data is used as input of the neural network, and output of the neural network is a predicted server fault type.

Description

technical field [0001] The invention relates to the field of equipment fault diagnosis and analysis, in particular to a neural network-based server fault monitoring method and system. Background technique [0002] A server is a computer with faster operation, higher load and stronger performance. Long-term reliable operation is an important feature of the server. Monitoring the operation status of the server is an important method to ensure the long-term reliable operation of the server. However, once the server If there is a fault and cannot run, it is necessary to reset the server with the help of the remote controller BMC of the server. [0003] At present, the machine learning algorithm as the core of artificial intelligence has an endless number of layers. There is a neural network based on supervised learning, which learns a function from a given training data set. When new data is input, the result can be predicted according to this function. The main application For...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/30G06F11/32G06N3/08G06N3/12
CPCG06F11/3058G06F11/321G06N3/084G06N3/126
Inventor 杨柳王朝晖陈亮甫
Owner SHANDONG CHAOYUE DATA CONTROL ELECTRONICS CO LTD
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