Research On Data Mining In Mobile Base Station Failure Prediction

In recent years,the mobile communication industry has developed vigorously in our country,especially the 5G communication technology represented by Huawei has been in the forefront of the world,and the realization of these technologies requires a huge group of equipment to support,the continuous normal operation of various parts of communication equipment will undoubtedly improve the competitiveness of enterprises,zero-fault operation is also a constant goal of communication companies.The most serious communication failure is the base station back-up failure.How to accurately predict the occurrence of faults is one of the hot research directions in various countries.Fault diagnosis in our country has become mature,but the fault prediction technology is not mature enough.The current fault prediction method is an expert system,which has been widely developed and applied in the fault diagnosis of base stations,but it has deficiencies in the establishment of knowledge base and self-learning.With the wide application of data mining technology in all walks of life,it is not only prospective in the subject,but also of great significance to apply it to the prediction of base station failure.Based on the base station operation and maintenance data of a mobile company,this paper analyses the characteristics of the data from various angles,and puts forward the short-term and long-term prediction methods for the base station according to the characteristics of the base station data.Base station operation and maintenance data is long and complex.After full analysis of the data,association rules mining method is used in short-term base station failure prediction.Apriori algorithm can effectively mine frequent item sets in data.According to the frequent item sets,association rules can be quickly and accurately found.In the rules,alarm data and accidents can be obtained in a short time.The relationship between obstacles is successfully predicted in a short time.In the long-term fault prediction,association rules and principal component analysis are used.Firstly,operation and maintenance data are pre-processed,including data cleaning,feature screening and networkelement clustering.Based on the principal component analysis of clustered data,a regression equation is established between the principal component and the failure of the base station.After obtaining the expression of principal component which is highly correlated with the occurrence of the failure,the key network optimization index is obtained by referring to the linear relationship between the principal component index and the network optimization index.Key network optimization indicators will have different values before the failure occurs.Through the analysis of threshold value,we can get several kinds of characteristics of index values.For example,some network optimization indicators before the failure of base station return to service tend to decrease,some to increase,and some to have obvious fluctuation trend.Through the observation of network optimization index,the long-term prediction of base station failure can be achieved.Improving the success rate of base station failure prediction is the relentless pursuit of communication companies.Fault prediction not only saves costs,but also improves customer satisfaction with a communication company.Data mining is used in the research of base station failure prediction,which greatly improves the success rate of fault prediction.Through engineering practice,31.3% of base station equipment failure events can be accurately measured.At the same time,it also improves customer satisfaction with a communication company.The next research on fault prediction broadens the thinking and lays a solid foundation.