Research on Grid Monitoring Algorithm of Educational Resources Based on Economic Structure Transformation

Abstract

In order to overcome the problems of the traditional educational resource grid monitoring algorithm, such as high signal noise, high data acquisition time, and high monitoring error rate, an educational resource grid monitoring algorithm based on the transformation of economic structure was proposed. This paper analyzes the structure of educational resource grid and constructs the monitoring structure of educational resource grid by using hierarchical tree structure model. With the support of this architecture, information sensors and data collection detectors are used to collect relevant data, and convolutional neural networks are used to denoise the collected data. According to the processed data, the educational resource grid under the transformation of economic structure is monitored to obtain relevant monitoring results. Experimental results show that the maximum value and minimum value of signal noise are 15 dB and 9 dB, respectively, the data acquisition time is always lower than 0.3 s, and the monitoring error rate is always below 4%, which has high practical application value.