Online education monitoring picture intelligent analysis system
Through the AI analysis model built by the deep convolutional inverse graph network, the real-time monitoring images of online education are intelligently analyzed, which solves the problem of difficult to monitor the physical contact of students in online education and improves the ability to maintain teaching order.
Patent Information
- Application Number
- CN202411496615.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-25
- Publication Date
- 2025-02-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In online education, it is difficult to monitor and synchronize the contact between every two adjacent student objects in real time, making it difficult to effectively maintain teaching order.
The deep convolutional inverse graph network is used for training to build an AI analysis model. By intelligently analyzing multiple visual information and configuration information of the real-time monitoring screen of online education, we can judge whether the two adjacent student objects are in close contact.
It realizes an intelligent analysis of the contact between every two adjacent student objects in the real-time monitoring screen of online education, provides a reliable monitoring basis, and improves the management level of online education.
Smart Images
Figure CN119418276A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of online education, and in particular to an intelligent analysis system for online education monitoring images. Background Art
[0002] Online education refers to learning in an electronic environment composed of communication technology, microcomputer technology, computer technology, artificial intelligence, network technology and multimedia technology. It is technology-based learning. Generally speaking, online education is defined as a method of content dissemination and rapid learning through the application of information technology and Internet technology. It adopts electronic learning, efficient learning, exploratory learning, experiential learning, expanded learning, extended learning, easy-to-use learning and enhanced learning. For example, the online education of an enterprise is to provide personalized, time- and geographically-free continuous education and training for its employees through the Internet that penetrates deep into the enterprise. The teaching content is an innovative course that has been planned, related to the future of the enterprise, and related to the current work performance and future career development goals of employees.
[0003] Everything has its pros and cons. While online education has improved teaching efficiency and facilitated the interaction between teachers and students, it also has the problem of difficulty in monitoring teaching order. For example, it is difficult to synchronously monitor and highlight the physical contact between every two adjacent student objects in the real-time monitoring screen of online education, resulting in the inability to effectively maintain the teaching order of online education. Summary of the invention
[0004] In order to solve the technical problems in the prior art, the present invention provides an intelligent analysis system for online education monitoring images, which obtains the deep convolutional inverse graphics network after each training by training the deep convolutional inverse graphics network multiple times and uses it as an AI analysis model. It is used to perform intelligent analysis based on multiple visualization information and configuration information on whether the contact between every two adjacent student object entities in the real-time monitoring screen of online education is too close, thereby providing a reliable basis for the key monitoring of online education and improving the management level of online education.
[0005] According to the present invention, there is provided an online education monitoring screen intelligent analysis system, the system comprising: The target extraction mechanism is used to output image regions corresponding to two adjacent student objects in the real-time monitoring screen of online education as a first image region and a second image region, wherein the two adjacent student objects are composed of a first student object and a second student object; a first capturing mechanism, connected to the target extracting mechanism, and used to obtain various visual information of the first image area and various visual information of the second image area, wherein the various visual information of the first image area is a depth of field value, a horizontal coordinate value, and a vertical coordinate value of each edge pixel point of the first image area, and the various visual information of the second image area is a depth of field value, a horizontal coordinate value, and a vertical coordinate value of each edge pixel point of the second image area; A second capture mechanism, connected to the target extraction mechanism, is used to obtain various associated data of the real-time monitoring screen, wherein the various associated data of the real-time monitoring screen include the number of pixel rows in the real-time monitoring screen, the number of pixel columns in the real-time monitoring screen, and the number of student objects in the real-time monitoring screen; A target construction device is used for an AI analysis model corresponding to a deep convolutional inverse graphics network mapping, wherein the AI analysis model corresponding to the deep convolutional inverse graphics network mapping includes: training the deep convolutional inverse graphics network for each time to obtain a deep convolutional inverse graphics network after each training and outputting it as an AI analysis model, wherein the number of times the deep convolutional inverse graphics network is trained is positively correlated with the sum of the number of pixel rows in the real-time monitoring screen and the number of pixel columns in the real-time monitoring screen; An intelligent analysis device, connected to the first capture mechanism, the second capture mechanism and the target construction device respectively, for using an AI analysis model to intelligently analyze whether two adjacent student objects are in close physical contact based on the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area and various associated data of the real-time monitoring screen; Among them, using the AI analysis model to intelligently analyze whether two adjacent student objects are in close physical contact based on the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area, and various associated data of the real-time monitoring screen includes: the physical contact of two adjacent student objects is too close means that the closest distance between the two student entities corresponding to the two adjacent student objects is less than or equal to the set distance limit; Among them, the image areas corresponding to two adjacent student objects in the real-time monitoring screen of online education are output as the first image area and the second image area respectively, and the two adjacent student objects are composed of a first student object and a second student object, including: the first student object corresponds to the first image area, and the second student object corresponds to the second image area.
[0006] It can be seen that the present invention has at least the following important inventive concepts: Invention concept 1: An intelligent analysis based on multiple visualization information and configuration information is performed on whether two adjacent student objects in a real-time monitoring screen of online education are too close to each other, thereby providing a reliable basis for key monitoring of online education, wherein the close contact of two adjacent student objects means that the closest distance between two student entities corresponding to the two adjacent student objects is less than or equal to a set distance limit; Inventive concept 2: The deep convolutional inverse graphics network is trained each time to obtain the deep convolutional inverse graphics network after each training and output as an AI analysis model, wherein the number of times the deep convolutional inverse graphics network is trained is positively correlated with the sum of the number of pixel rows in the real-time monitoring screen and the number of pixel columns in the real-time monitoring screen, thereby constructing an artificial intelligence model for intelligent analysis; Inventive concept three: Introduce multiple basic data for performing intelligent analysis, wherein the multiple basic data include the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area, and various related data of the real-time monitoring screen, thereby ensuring the reliability and stability of the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1 Schematic diagram of the internal structure of the online education monitoring screen intelligent analysis system according to the first embodiment of the present invention.
[0008] Figure 2 Schematic diagram of the internal structure of an online education monitoring screen intelligent analysis system according to the second embodiment of the present invention.
[0009] Figure 3 Schematic diagram of the internal structure of an online education monitoring screen intelligent analysis system according to the third embodiment of the present invention. DETAILED DESCRIPTION
[0010] The following is a detailed description of an embodiment of the online education monitoring screen intelligent analysis system of the present invention with reference to the accompanying drawings.
[0011] Figure 1 This is a schematic diagram of the internal structure of an online education monitoring screen intelligent analysis system according to a first embodiment of the present invention, wherein the system comprises: The target extraction mechanism is used to output image regions corresponding to two adjacent student objects in the real-time monitoring screen of online education as a first image region and a second image region, wherein the two adjacent student objects are composed of a first student object and a second student object; Specifically, the target extraction mechanism is used to output the image areas corresponding to two adjacent student objects in the real-time monitoring screen of online education as the first image area and the second image area, and the two adjacent student objects are composed of the first student object and the second student object, including: selecting an ASIC device to implement the target extraction mechanism, which is used to output the image areas corresponding to two adjacent student objects in the real-time monitoring screen of online education as the first image area and the second image area, and the two adjacent student objects are composed of the first student object and the second student object; a first capturing mechanism, connected to the target extracting mechanism, and used to obtain various visual information of the first image area and various visual information of the second image area, wherein the various visual information of the first image area is a depth of field value, a horizontal coordinate value, and a vertical coordinate value of each edge pixel point of the first image area, and the various visual information of the second image area is a depth of field value, a horizontal coordinate value, and a vertical coordinate value of each edge pixel point of the second image area; A second capture mechanism, connected to the target extraction mechanism, is used to obtain various associated data of the real-time monitoring screen, wherein the various associated data of the real-time monitoring screen include the number of pixel rows in the real-time monitoring screen, the number of pixel columns in the real-time monitoring screen, and the number of student objects in the real-time monitoring screen; A target construction device is used for an AI analysis model corresponding to a deep convolutional inverse graphics network mapping, wherein the AI analysis model corresponding to the deep convolutional inverse graphics network mapping includes: training the deep convolutional inverse graphics network for each time to obtain a deep convolutional inverse graphics network after each training and outputting it as an AI analysis model, wherein the number of times the deep convolutional inverse graphics network is trained is positively correlated with the sum of the number of pixel rows in the real-time monitoring screen and the number of pixel columns in the real-time monitoring screen; An intelligent analysis device, connected to the first capture mechanism, the second capture mechanism and the target construction device respectively, for using an AI analysis model to intelligently analyze whether two adjacent student objects are in close physical contact based on the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area and various associated data of the real-time monitoring screen; Among them, using the AI analysis model to intelligently analyze whether two adjacent student objects are in close physical contact based on the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area, and various associated data of the real-time monitoring screen includes: the physical contact of two adjacent student objects is too close means that the closest distance between the two student entities corresponding to the two adjacent student objects is less than or equal to the set distance limit; The image areas corresponding to two adjacent student objects in the real-time monitoring screen of online education are output as the first image area and the second image area, and the two adjacent student objects are composed of the first student object and the second student object, including: the first student object corresponds to the first image area, and the second student object corresponds to the second image area; Among them, using the AI analysis model to intelligently analyze whether two adjacent student objects are in close physical contact based on the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area, and various associated data of the real-time monitoring screen also includes: synchronously inputting the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area, and various associated data of the real-time monitoring screen into the AI analysis model; Among them, using the AI analysis model to intelligently analyze whether two adjacent student objects are in close physical contact based on the imaging focal length of the real-time monitoring screen, the visual information of the first image area, the visual information of the second image area, and the related data of the real-time monitoring screen also includes: executing the AI analysis model to obtain a status identification of whether the two adjacent student objects are in close physical contact output by the AI analysis model.
[0012] Figure 2 Schematic diagram of the internal structure of an online education monitoring screen intelligent analysis system according to the second embodiment of the present invention.
[0013] and Figure 1 different, Figure 2 The online education monitoring screen intelligent analysis system can also include the following components: A fault self-detection device, connected to the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device, respectively, for measuring the current fault codes inside the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively; Wherein, the fault self-checking device is respectively connected with the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device, and is used to respectively measure the current fault codes inside the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device, including: the fault self-checking device includes a plurality of fault self-checking units, which are respectively connected with the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device, so as to respectively measure the current fault codes inside the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device; The fault self-checking device includes a plurality of fault self-checking units, which are respectively connected to the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device to respectively measure the current fault codes inside the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device, including: the internal structures of the plurality of fault self-checking units respectively used by the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are the same; Among them, the fault self-detection device includes multiple fault self-detection units, which are used to connect with the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively, so as to respectively measure the current fault codes inside the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively. It also includes: the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively use multiple fault self-detection units to perform internal self-detection operations.
[0014] Figure 3 Schematic diagram of the internal structure of an online education monitoring screen intelligent analysis system according to the third embodiment of the present invention.
[0015] and Figure 1 different, Figure 3 The online education monitoring screen intelligent analysis system can also include the following components: A signal identification device is arranged near the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device and is respectively connected to the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device, and is used for real-time measurement of the operation utilization percentage of the respective operators of the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device.
[0016] Next, the specific structure of the online education monitoring screen intelligent analysis system of the present invention will be further explained.
[0017] In the online education monitoring screen intelligent analysis system according to any embodiment of the present invention: The intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device all include a signal input unit and a signal output unit, and the signal input unit and the signal output unit all include a ground terminal.
[0018] In the online education monitoring screen intelligent analysis system according to any embodiment of the present invention: The intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are respectively implemented by using different CPLD chips, and the CPLD chips are designed by using VHDL language.
[0019] In the online education monitoring screen intelligent analysis system according to any embodiment of the present invention: The housings of the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are each provided with a plurality of heat dissipation holes, and the plurality of heat dissipation holes of any device of the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are evenly distributed on the housings; Among them, the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are each provided with a plurality of heat dissipation holes on their respective shells, and the plurality of heat dissipation holes of any device of the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are evenly distributed on their shells, including: the aperture values of each of the plurality of heat dissipation holes of the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are the same.
[0020] In addition, in the online education monitoring screen intelligent analysis system, the target construction device is used to map the corresponding AI analysis model based on the deep convolutional inverse graphics network. The AI analysis model based on the deep convolutional inverse graphics network mapping includes: training the deep convolutional inverse graphics network for each time to obtain the deep convolutional inverse graphics network after each training and output it as the AI analysis model. The number of times the deep convolutional inverse graphics network is trained is positively correlated with the sum of the number of pixel rows in the real-time monitoring screen and the number of pixel columns in the real-time monitoring screen, including: using a dual-input and single-output information analysis function to represent the numerical correspondence between the number of times the deep convolutional inverse graphics network is trained and the sum of the number of pixel rows in the real-time monitoring screen and the number of pixel columns in the real-time monitoring screen.
[0021] The online education monitoring screen intelligent analysis system of the present invention solves the technical problem in the prior art that it is difficult to use an intelligent analysis mechanism to identify in real time whether every two adjacent student object entities in the real-time monitoring screen of online education are in too close contact. An AI analysis model with a customized structure design is used to perform intelligent analysis based on multiple visual information and configuration information on whether every two adjacent student object entities in the real-time monitoring screen of online education are in too close contact, thereby solving the above technical problem.
[0022] Although the present invention has been described with reference to the preferred embodiments, it is obvious that various modifications and variations can be made to the present invention by those skilled in the art without departing from the spirit and scope of the present invention. Therefore, various modifications and variations of the present invention are covered by the appended claims and their equivalents.
Claims
1. An online education monitoring screen intelligent analysis system, characterized in that: The system comprises: The target extraction mechanism is used to output image regions corresponding to two adjacent student objects in the real-time monitoring screen of online education as a first image region and a second image region, wherein the two adjacent student objects are composed of a first student object and a second student object; a first capturing mechanism, connected to the target extracting mechanism, and used to obtain various visual information of the first image area and various visual information of the second image area, wherein the various visual information of the first image area is a depth of field value, a horizontal coordinate value, and a vertical coordinate value of each edge pixel point of the first image area, and the various visual information of the second image area is a depth of field value, a horizontal coordinate value, and a vertical coordinate value of each edge pixel point of the second image area; A second capture mechanism, connected to the target extraction mechanism, is used to obtain various associated data of the real-time monitoring screen, wherein the various associated data of the real-time monitoring screen include the number of pixel rows in the real-time monitoring screen, the number of pixel columns in the real-time monitoring screen, and the number of student objects in the real-time monitoring screen; A target construction device is used for an AI analysis model corresponding to a deep convolutional inverse graphics network mapping, wherein the AI analysis model corresponding to the deep convolutional inverse graphics network mapping includes: training the deep convolutional inverse graphics network for each time to obtain a deep convolutional inverse graphics network after each training and outputting it as an AI analysis model, wherein the number of times the deep convolutional inverse graphics network is trained is positively correlated with the sum of the number of pixel rows in the real-time monitoring screen and the number of pixel columns in the real-time monitoring screen; An intelligent analysis device, connected to the first capture mechanism, the second capture mechanism and the target construction device respectively, for using an AI analysis model to intelligently analyze whether two adjacent student objects are in close physical contact based on the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area and various associated data of the real-time monitoring screen; Among them, using the AI analysis model to intelligently analyze whether two adjacent student objects are in close physical contact based on the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area, and various associated data of the real-time monitoring screen includes: the physical contact of two adjacent student objects is too close means that the closest distance between the two student entities corresponding to the two adjacent student objects is less than or equal to the set distance limit; Among them, the image areas corresponding to two adjacent student objects in the real-time monitoring screen of online education are output as the first image area and the second image area respectively, and the two adjacent student objects are composed of a first student object and a second student object, including: the first student object corresponds to the first image area, and the second student object corresponds to the second image area.
2. The online education monitoring screen intelligent analysis system according to claim 1, characterized in that: Using the AI analysis model to intelligently analyze whether two adjacent student objects are in close physical contact based on the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area, and various associated data of the real-time monitoring screen further includes: synchronously inputting the imaging focal length of the real-time monitoring screen, various visual information of the first image area, various visual information of the second image area, and various associated data of the real-time monitoring screen into the AI analysis model; Among them, using the AI analysis model to intelligently analyze whether two adjacent student objects are in close physical contact based on the imaging focal length of the real-time monitoring screen, the visual information of the first image area, the visual information of the second image area, and the related data of the real-time monitoring screen also includes: executing the AI analysis model to obtain a status identification of whether the two adjacent student objects are in close physical contact output by the AI analysis model.
3. The online education monitoring screen intelligent analysis system according to claim 2, characterized in that: The system comprises: A fault self-detection device, connected to the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device, respectively, for measuring the current fault codes inside the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively; Among them, the fault self-detection device is connected to the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively, and is used to respectively measure the current fault codes inside the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively. The fault self-detection device includes multiple fault self-detection units, which are used to respectively connect to the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device to respectively measure the current fault codes inside the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively.
4. The online education monitoring screen intelligent analysis system according to claim 3, characterized in that: The fault self-detection device includes multiple fault self-detection units, which are used to connect to the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively to measure the current fault codes inside the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively. The internal structures of the multiple fault self-detection units used by the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are the same.
5. The online education monitoring screen intelligent analysis system according to claim 4, characterized in that: The fault self-detection device includes multiple fault self-detection units, which are used to connect to the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively to measure the current fault codes inside the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively. It also includes: the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device respectively use multiple fault self-detection units to perform internal self-detection operations.
6. The online education monitoring screen intelligent analysis system according to claim 2, characterized in that: The system further comprises: A signal identification device is arranged near the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device and is respectively connected to the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device, and is used for real-time measurement of the operation utilization percentage of the respective operators of the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device.
7. The online education monitoring screen intelligent analysis system according to any one of claims 2 to 6, characterized in that: The intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device all include a signal input unit and a signal output unit, and the signal input unit and the signal output unit all include a ground terminal.
8. The online education monitoring screen intelligent analysis system according to any one of claims 2 to 6, characterized in that: The intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are respectively implemented by using different CPLD chips, and the CPLD chips are designed by using VHDL language.
9. The online education monitoring screen intelligent analysis system according to any one of claims 2 to 6, characterized in that: The housings of the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are each provided with a plurality of heat dissipation holes, and the plurality of heat dissipation holes of any device of the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are evenly distributed on the housings; Among them, the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are each provided with a plurality of heat dissipation holes on their respective shells, and the plurality of heat dissipation holes of any device of the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are evenly distributed on their shells, including: the aperture values of each of the plurality of heat dissipation holes of the intelligent analysis device, the first capture mechanism, the second capture mechanism and the target construction device are the same.