| Education has always been in the most important position in our country.With the development of Artificial Intelligence,distance education shows us a vigorous development trend.With the large increasing number of students who choose distance education,lots of log information has been produced in online education platform.How to reasonably use the data generated by online education platform in order to improve the utilization rate of distance education platform to provide theoretical basis for the safe and reliable operation of distance education platform has become a hot research topic.The paper is based on the number of log online information and sorts out the number in the online examination platform per hour,forecasting the online number so as to achieve the goal of improving the utilization rate of system resources and the quality of examination service.It’s particularly important how to improve the accuracy of population forecast.Based on this point,the main contents of the paper will be summarized as follows:Firstly,this paper extracts the number of online examination from the log information of the online examination platform and collates the hourly real time data of the number in the system after preprocessing in order to provide the real data for the later research.Secondly,the data of online examination population will be transformed into a stationary time series by differential processing,SARIMA is used to model it at the meantime.The validity of the model is verified by a residual test and the parameters in SARIMA are optimized by grid search algorithm.The experimental result shows that SARIMA model is able to effectively predict the number in the future,but it is sensitive to parameter setting and produces some subjectivity in the parameter selection which will affect the prediction effect of this model.Thirdly,time window technology is used to transform the time series problem into a supervised regression problem in the paper,which uses the single-layer LSTM model to prevent the problem of over fitting caused by the model over complexity.According to the actual situation of the prediction,the Relu activation function is used in the output layer to avoid negative prediction results.The parameters of LSTM are adjusted to determine the LSTM model by the way of experimental verification and the "early stop" technology is used to prevent the model from over fitting for the data is too little in the process of training.Compared with SARIMA model,the accuracy of LSTM model is improved by 35%in the test set.Finally,combined with the scene of online test platform,the characteristics of online test data are as follows:the number of test cycles is large,the number of data in each test cycle is little,the data correlation is large during the test week.These characteristics will lead to the problems of "cold start" and small amount of training data in online test population prediction.In this paper,different transfer learning schemes are designed to solve these problems.To solve the problem of "cold start",the paper uses the pre-training model to train only the last full connection layer in order to improve the model accuracy.To solve the problem of less training data,the paper uses the pre-training model loading scheme to reduce the model error.Compared with the LSTM model which only uses data for training by experimental comparison,the performance of the LSTM model with migration learning improves by 60%and 18%respectively on the test set.To sum up,the scheme designed in the paper effectively solves the problem of online examination number prediction,which makes practical significance for rational use of service resources and improvement of the service level. |
