Research on Intellectualized Network Fault Diagnosis System based on Big Data Analysis Technology

With the continuous progress of China’s science and technology, network technology has been widely applied in all walks of life. Various new types of network business expand the entire scale of network. The network environment is becoming complex, and users put forward higher requirements for the reliability and service quality of network system, thus traditional network system maintenance mode cannot satisfy the basic demand of current network application. This paper studies network fault diagnosis based on big data analysis technology, and designs a diagnosis system with high intellectualization, which can comprehensively analyze and process different network faults. It has good application effect and deserves popularization. At present, people increasingly depend on network, the scale of network is expanding, various new types of network business and equipment are widely applied, and the probability of network fault is simultaneously increasing, thus, network management faces great challenges. Scientific and efficient network management can constantly monitor the operation of various equipment in the system, timely find out fault problems, and remind network management staff of taking measures to ensure the stable operation of network system. Big data analysis technology, with good application effect, can monitor various types of fault, improve the overall performance and reliability of network, at the same time, enhance the work efficiency of network management staff and avoid serious fault problems. 1. Network Fault Diagnosis 1.1 Basic model and main background The basic task of network management is monitor network fault so as to comprehensively improve the reliability of network. Scientific and effective network fault monitoring can perform necessary diagnosis on fault in network system before users finding network fault, solve network problems and put forward suggestions and basic planning about the overall transformation of network system [1]. Network fault diagnosis is mainly to examine carefully relevant parameters such as basic performance of network equipment and network flow to judge the basic operation of network. Various problems in network performance and network system fault can bring big loss to users. Therefore, it is urgent for operators to take reasonable measures to realize accurate and reliable network system fault prediction by network system parameters. Traditional network fault diagnosis is mainly to perform visualized analysis and judgment based on causality. Many enterprises proposed solutions through this method. However, this method requires effective model built on propagation mode and specific causes of network fault, which requires enterprises to invest a large amount of capital and time, with higher cost and lower efficiency. Thus, it cannot adapt to current complex network business and network environment. With the progress of science and technology, big data is widely applied. The fault diagnosis and analysis based on this technology has been used in product testing, quality control and production process. It is not necessary for this method to model fault propagation and causes, which just requires the model of statistical analysis to determine the basic types of fault. The most commonly used method is threshold fault monitoring, which realizes set threshold value through specific network parameters and judge whether specific parameter is within the threshold scope. Main 2018 7th International Conference on Advanced Materials and Computer Science (ICAMCS 2018) Published by CSP © 2018 the Authors 240 problems of this method is that efficient processing of data below threshold value is lack, and many detailed problems related to network status are easily ignored. At the same time, the determination of threshold value always depends on the work experience of technicians, there is a lack of standard, and the uncertainty is too much high. In order to solve these problems, it is required to deeply study network anomaly and improve the utilization of monitored information of network system. Using statistical theory, the quantitative analysis is conducted on the network status. It can be assumed that the parameter set Xt monitored by the network parameter at time t is the same as the parameter set Xt+1 monitored at time t+1, and the final threshold is determined asE(X)=ΣXt. Once there is deviation in the network monitoring parameters, the statistics of the parameters will be obviously abnormal from the normal conditions. By analyzing the significance of this abnormality, the basic situation of the network fault can be determined [2]. 1.2 Basic method of network behavior fault diagnosis At present, network fault system can extensively collect network information, and various types of parameters will be stored in time sequence to form complete time series, which belongs to statistical method to explore inherent law of dynamic data resources and can analyze basic law existing in the system according to history of a certain period. To obtain information valuable for maintaining network safety from mass information is the research focus currently. If the abnormal network system parameters are detected, it indicates that there will be fault in network or related equipment. Using the random interference term and the data of a specific time period, a mathematical model can be established, and the network parameter set Xt is obtained according to the white noise and the response intensity. Once there is fault in network system, the mathematical model will generate a relatively large deviation. In this way, network fault can be effectively diagnosed and processed. This method requires simple calculation and small data volume, and it can realize the rapid diagnosis on fault based on stable operation of network system. Meanwhile, it can realize the pre-judgment on various faults, and perform comprehensive transformation and upgrading of network management system. Besides, operators can improve the processing efficiency of network fault and increase current utilization of network system [3]. 1.3 The performance verification of scheme The network fault diagnosis using time series analysis can help network managers find various anomalies in time and improve work efficiency. In the data collection of network equipment, the network anomaly early-warning system can comprehensively analyze the time series and realize the advance prediction of various faults. In the specific network anomaly early-warning, it is necessary to determine the basic parameters of the mathematical model reasonably, and establish a model in normal situation. Once there is deviation in network parameters, the system will alarm the first time [4]. In the actual operation process, the AR model can be used for mathematical modeling to determine whether the data result satisfies the basic requirements of stationarity, and analyze the residuals to finally determine the network fault. In order to verify the accuracy of the results, the Q-Q statistical graph can be used to perform detailed residual numerical analysis to determine whether it conforms to the normal distribution. If the residual has a strong linear characteristic after data analysis and processing, and the basic situation of the normal distribution is satisfied, it can be determined that there is no network abnormality in the data. At present, network operators require to determine various types of faults that may exist in the system in a short term, and also need to reduce false non-fault judgments to a certain extent. The use of big data analysis technology in fault diagnosis is correct significantly than traditional threshold detection mode. The false alarm rate is also lower than traditional detection method. Although the difficulty of the overall calculation has increased, the fault analysis effect is remarkable, and the work efficiency can be improved comprehensively, which is worthy of further study.