Student activity multi-dimensional management system and management method based on behavior analysis

Abstract

The invention provides a student activity multi-dimensional management system and management method based on behavior analysis. According to the application, the corresponding identity recognition module is used for recognizing student identity information and clothing features at the entrance and exit positions of the student activity area, and then the monitoring system interface is triggered tocorrespondingly collect student images for the behavior monitoring module to perform behavior recognition. The behavior monitoring module performs corresponding statistics on specific state characteristics in student images, and can directly obtain participation conditions of students through simple operation. Therefore, according to the participation conditions obtained through statistics, the participation conditions of the students in different types of activities can be obtained by comparing the participation conditions with the corresponding statistical models, the performance of the students is comprehensively evaluated, and corresponding prompts are given. The extraction mode of the student state features is simple and convenient, occupation of on-campus operation resources can beeffectively reduced, accurate comprehensive evaluation data can be provided, and reference is provided for teaching management.

Description

technical field [0001] This application relates to the field of behavior analysis technology, in particular to a behavior analysis-based multi-dimensional management system and management method for student activities. Background technique [0002] Existing student behavior analysis systems usually can only adapt to the detection needs of a single application environment, and can only analyze and record a single activity of students, such as classroom activities. However, students' life and study in school are not limited to classrooms. Students' self-study and participation in club activities in their spare time will affect the development of students' comprehensive quality. Therefore, it is also necessary to evaluate the participation of students in these activity scenarios. However, when analyzing student behavior in these scenarios, because the behavior patterns of students in the scenarios are completely different from those in the classroom scenario, if the evaluation...

AI Technical Summary

Problems solved by technology

However, when analyzing student behavior in these scenarios, because the behavior patterns of students in the scenarios are completely different from those in the classroom scenario, if the evaluation model in the classroom scenario is directly applied, it will be difficult due to the convergence speed of the model itself, data processing capabilities, and representation of characteristic data. and screening bias lead to erroneous evaluation results

[0003] Moreover, due to the large number of students in the school, the existing analysis models based on machine vision have slow extraction and convergence speeds, and the deep learning algorithms used in them require huge computing resources, so they are deployed in the entire campus environment. , it will be difficult to realize the proper computing functions through the school's existing computing resources

Under the existing methods, it is difficult to achieve multi-dimensional comprehensive management of different activities in the entire campus environment

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