Integration Mechanism of Heterogeneous Foreign Language Education Resources Based on Time Series Analysis in IIoT

Abstract

Industrial Internet of Things (IIoT) has attracted much attention from global researchers and has been applied into many fields, such as medical treatment, transportation, and education. This paper pays attention to an IIoT-oriented education problem and gives the corresponding solution. Heterogeneous educational resources have multisource target data, so it is necessary to integrate the repetitive data and data with the same attributes. However, due to the poor tracking effect of the model constructed by traditional methods, the mining technology loses a part of the data characteristics and affects the multisource foreign language education data integration. So this article studies the integration mechanism of foreign language heterogeneous educational resources based on time series analysis. The mechanism adopts a data cleaning and fusion method based on the time series similarity measurement. This method uses approximate symbol aggregation, European algorithm, and similar sequences with adjusted similarity weights to complete the data cleaning of foreign language heterogeneous educational resources. After that, it uses multiple heterogeneous data fusion algorithms to complete data integration. Experiments with foreign language education resources at all levels in a certain city show that the mechanism can detect abnormal data of foreign language education resources, fill in vacant data, reduce data redundancy, and integrate heterogeneous data. After the data are cleaned by multisource heterogeneous data fusion algorithm, the credibility of the measurement data is reflected, and the mean absolute percentage error is only 6.25%. The data quality is improved as a whole, and it provides reliable basic data for the application of foreign language education resources.