Data management system based on live broadcast interaction platform

Abstract

The invention discloses a data management system based on a live broadcast interaction platform, which comprises a data acquisition module, an interaction management module and a feedback module, and is characterized in that the data acquisition module is used for collecting bullet screen data, comprehensive data of live broadcast and recorded broadcast watched by students at student ends and question data of the students in real time; collecting visual images and environment comprehensive data of students watching live broadcast during live broadcast teaching, and inputting historical live broadcast comprehensive data into the system; and the interaction management module is used for training an identification model by using historical live broadcast comprehensive data, performing identification statistics on live broadcast bullet screens according to the identification model, analyzing visual images of students watching live broadcast, analyzing iris data of the students and environment data of the students watching live broadcast, evaluating learning states of the students according to analysis results, and outputting the learning states of the students. The student bullet screens and the watching records of the students on the live broadcast recorded screens are analyzed, knowledge points doubted by the students are obtained, and the method has the advantages of being high in accuracy and interactivity.

Description

Technical Field

[0001] The present invention relates to the technical field of live teaching interaction, and in particular to a data management system based on a live interactive platform. Background Art

[0002] With the rapid development of Internet technology, live teaching has become an emerging educational method. It breaks through the time and space limitations of traditional teaching and allows students to receive education anytime and anywhere.

[0003] At present, most online education is generally conducted in the form of live broadcast or recorded broadcast. However, the existing technology only uses the camera to identify whether the student is facing the camera. It cannot accurately judge whether the student is listening carefully, nor can it accurately evaluate the student's class status, resulting in poor teaching effect. Moreover, the existing live teaching is relatively weak in interactivity, especially when there are a large number of students in the class and there are many barrages. The teacher cannot accurately read the students' questions, resulting in insufficient communication between students and teachers and poor teaching interactivity. Therefore, it is very necessary to design a data management system based on a live interactive platform with high accuracy and strong interactivity. Summary of the invention

[0004] The purpose of the present invention is to provide a data management system based on a live interactive platform to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a data management system based on a live interactive platform, comprising a data acquisition module, an interactive management module and a feedback module;

[0006] The data collection module is used to collect the bullet screen data, the comprehensive data of students watching live and recorded broadcasts on the student end, the students' question data in real time, collect the visual images and comprehensive environmental data of students watching live broadcasts during the live teaching period, and enter the comprehensive data of historical live broadcasts into the system;

[0007] The interactive management module is used to train the recognition model using the comprehensive data of historical live broadcasts, identify and count the live broadcast bullet screens according to the above recognition model, analyze the visual images of students watching live broadcasts, analyze the students' iris data and the environmental data when the students watch live broadcasts, evaluate the students' learning status according to the analysis results, analyze the students' bullet screens and the students' viewing records of live broadcast screen recordings, obtain the knowledge points that the students are confused about, match the information for the students according to the above knowledge points, and analyze and integrate the characteristics of the students' bullet screens;

[0008] The data management module is used to integrate the students' barrage data and live broadcast viewing data and transmit them to the teacher's end. Students establish a connection with the teacher's end through the student end, give feedback on questions and interact with the teacher.

[0009] According to the above technical solution, the data acquisition module includes a data collection module, a sensor module and a historical live comprehensive data entry module. The data collection module is used to collect barrage data and comprehensive data of students watching live and recorded broadcasts on the student side in real time. The sensor module is used to collect visual images and comprehensive environmental information of students watching live broadcasts during live teaching. The historical live comprehensive data entry module is used to enter the historical live comprehensive data of each subject into the system.

[0010] According to the above technical solution, the interaction management module includes a live broadcast supervision module and a live broadcast interaction module;

[0011] The live broadcast supervision module further includes an iris analysis submodule, a state evaluation submodule and an environment analysis submodule. The iris analysis submodule is used to identify the iris position of students when they are watching live broadcasts, and further analyze the impact of students' iris gaze deviation on their state. The environment analysis submodule is used to analyze the impact of comprehensive environmental data on students' state. The state evaluation submodule is used to evaluate students' state based on the above analysis results.

[0012] Among them, the after-class interaction module further includes an operation analysis submodule, a knowledge matching submodule and a model training submodule. The operation analysis submodule is used to analyze the time characteristics of students watching live broadcasts and screen recordings. The knowledge matching submodule is used to mark the live broadcast and screen recording videos according to the above time characteristics, further analyze the above marked videos and extract knowledge point features, and further match the corresponding teaching videos according to the knowledge point features. The model training submodule is used to train a recognition model based on historical live broadcast comprehensive data, use the above recognition model to identify barrage features and student doubt features, match knowledge points according to the above barrage features and student doubt features, and integrate them.

[0013] According to the above technical solution, the data management module includes a summary feedback module, which is used to feed back to the teacher side the knowledge points corresponding to the barrage features and the knowledge points corresponding to the students watching the marked video.

[0014] According to the above technical solution, the data management module also includes a question feedback module, and the question feedback module is used to feed back the students' question data to the system.

[0015] According to the above technical solution, the operation method of the data management system of the live interactive platform mainly includes the following steps:

[0016] Step S1: collect the bullet screen data, the comprehensive data of students watching live and recorded broadcasts on the student end, and the students' question data in real time, collect the visual images and comprehensive environmental data of students watching live broadcasts during the live teaching period, and enter the comprehensive data of historical live broadcasts into the system;

[0017] Step S2: When conducting live teaching, the system starts the live monitoring module, starts analyzing the comprehensive data of the environment in which the students are located, analyzes the iris gaze deviation of the students when watching, and evaluates the status of the students based on the above analysis results;

[0018] Step S3: During the live teaching process, the system starts the live interactive module, starts to train the recognition model using the historical live comprehensive data, identifies the bullet screen features and student question features according to the recognition model, matches the corresponding knowledge points according to the above features, further analyzes the students' live broadcast and screen recording records and extracts knowledge points, and further matches the corresponding teaching videos according to the above knowledge points;

[0019] Step S4: During the live teaching process, the system enters the questions fed back by the students into the system, and the system integrates the questions fed back by the students and the knowledge points involved in the barrage questions, and sends them to the teacher.

[0020] According to the above technical solution, step S2 further includes the following steps:

[0021] Step S21: construct a visual image database, obtain the visual images of students watching the live broadcast during the live teaching, identify the orientation features of the visual images, search the visual image database according to the orientation features, and match the corresponding shooting position and shooting distance L of the student-side camera device. 0 , identify the student's facial features, anchor the student's eye iris position, establish a coordinate system, identify the coordinates of the student's camera device and the coordinates of the student's left and right eye irises, and calculate the distance L between the camera device and the student's eye iris through the distance formula 1 and L 2 , set up the equation In the formula, θ 1 represents the angle between the line connecting the student camera and the student's right eye iris and the vertical plane where the student's eyes iris are located, θ 2 The angle between the line connecting the camera device at the student end and the student's left eye iris and the vertical plane where the student's eyes irises are located is shown;

[0022] Step S22: According to θ 1 and θ 2 Retrieve the corresponding influence coefficients on the student status score from the database, perform weighted fusion on the above influence coefficients to obtain the fused influence coefficient α, and then calculate the influence coefficient α according to θ 1 and θ 2 Retrieve the corresponding sight line intersection point in the database, identify the coordinates of the sight line intersection point, and retrieve the influence weight φ of the above influence coefficient corresponding to the database according to the coordinate position of the sight line intersection point i ;

[0023] Step S23: Obtain the shooting distance of the camera device at the student end, and retrieve the corresponding viewing angle area S in the database according to the shooting distance.1 , get the screen area S of the student device 2 , calculate the ratio of the screen area of ​​the student device to the viewing angle area corresponding to the shooting distance According to the ratio Retrieve the corresponding influence coefficient β on the student status score from the database.

[0024] According to the above technical solution, step S23 further includes the following steps:

[0025] Step S231: Obtain comprehensive environmental data, identify sound features in the environment, and compare the audio features in the live video. If the similarity is greater than the threshold, the system continues to detect. Otherwise, the sound volume in the environment is identified, and the corresponding influence coefficient ω on the student status score in the database is retrieved according to the sound volume;

[0026] Step S232: Obtain the student's status score P under the set conditions in the database 0 , calculate the student's actual status score P through the formula 1 =(α·φ i )·β·ω·P 0 , where P 1 Indicates the actual status score of the student. If the student status score P 1 When it is less than the threshold set by the system, the system obtains the student's IP and displays a pop-up reminder on the teacher's side.

[0027] According to the above technical solution, step S3 further includes the following steps:

[0028] Step S31: Obtain historical live broadcast comprehensive data, identify historical comprehensive live broadcast course features and barrage features, and use the historical comprehensive live broadcast course features and barrage features as sample data to train a recognition model;

[0029] Step S32: Acquire real-time bullet screen data, use the trained recognition model to identify bullet screen features in the real-time bullet screen data, identify the bullet screen features of each bullet screen and count them, identify the number of bullet screens with the same bullet screen features within the set recognition period T, if the number of bullet screens is greater than the system set threshold, the system will pop up a window to remind the teacher of the bullet screen feature, otherwise the system will continue to detect;

[0030] Step S33: When students are watching the screen recording, they identify the timestamp of the progress bar, mark the timestamp and enter it into the first table of the database, calculate the difference R between the adjacent first timestamp and second timestamp in the database, and when the difference R is greater than the system-set threshold, enter the second timestamp into the second table, identify the timestamp of the first-order progress bar and the timestamp of the last progress bar in each table, segment the video according to the above timestamps, identify the knowledge point features of each video clip respectively, retrieve the corresponding teaching video in the database according to the knowledge point features, mark the knowledge point features as the first doubtful knowledge point features and save them to the database.

[0031] According to the above technical solution, step S4 further includes the following steps:

[0032] Step S41: at the end of the live teaching, obtain student question data, identify student question features, and search the knowledge graph in the database according to the student question features to match the corresponding second question knowledge point features;

[0033] Step S42: Obtain student bullet screen features, first doubtful knowledge point features and second doubtful knowledge point features, integrate the features and send them to the teacher end.

[0034] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention can accurately determine whether students are concentrating on listening to live teaching by analyzing the angular offset between the pupil iris and the camera device on the student end when the students are watching live broadcasts, and can help teachers supervise students' listening. At the same time, according to the angular offset between the iris and the camera device, it can accurately determine the intersection of students' sight lines and whether students are concentrating on watching live videos, thereby greatly improving the accuracy of the system. By analyzing the proportion of the screen area in the viewing angle area when the students are watching live broadcasts, it can accurately determine whether students are concentrating on watching live videos, thereby further improving the accuracy of the system. By judging whether the clarity of the sound content in the environment is the same as the sound clarity in the live video, it can further analyze the impact of the sound volume in the environment on students' listening, thereby accurately Judge the students' listening status, thereby greatly improving the accuracy of the system. The above steps can be used to supervise students' online learning, avoid teachers wasting class time to monitor students' small windows, and greatly improve class efficiency. By detecting and counting the barrage features of the barrage in real time during the teacher's live teaching, the teacher can concentrate on teaching while also taking into account students' questions, thereby greatly improving the interactivity between teachers and students in online teaching. By identifying the knowledge point features of video clips that students watch repeatedly, the corresponding teaching videos can be automatically and accurately matched to users, thereby improving the accuracy and interactivity of the system. The above steps enable teachers to accurately grasp students' doubts, thereby greatly strengthening the interaction between teachers and students in online teaching, and at the same time making it convenient for teachers to formulate more accurate teaching plans for students' doubtful knowledge points. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0036] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 ,The present invention provides a technical solution: a data management system based on a live interactive platform, including a data acquisition module, an interactive management module and a feedback module;

[0039] The data collection module is used to collect the bullet screen data, the comprehensive data of students watching live and recorded broadcasts on the student end, the students' question data in real time, collect the visual images and comprehensive environmental data of students watching live broadcasts during live teaching, and enter the comprehensive data of historical live broadcasts into the system;

[0040] The interactive management module is used to train the recognition model using the comprehensive data of historical live broadcasts, identify and count the live broadcast bullet screens according to the above recognition model, analyze the visual images of students watching live broadcasts, analyze the students' iris data and the environmental data when the students watch live broadcasts, evaluate the students' learning status according to the analysis results, analyze the students' bullet screens and the students' viewing records of live broadcast screen recordings, obtain the knowledge points that the students are confused about, match the information for the students according to the above knowledge points, analyze the characteristics of the students' bullet screens and integrate them;

[0041] The data management module is used to integrate students' barrage data and live broadcast viewing data, and transmit them to the teacher's side. Students establish a connection with the teacher's side through the student side, provide feedback on questions and interact with teachers.

[0042] The data acquisition module includes a data collection module, a sensor module and a historical live comprehensive data entry module. The data collection module is used to collect barrage data in real time and comprehensive data of students watching live and recorded broadcasts on the student side. The sensor module is used to collect visual images and comprehensive environmental information of students watching live broadcasts during live teaching. The historical live comprehensive data entry module is used to enter the comprehensive data of historical live broadcasts of various subjects into the system.

[0043] The interactive management module includes a live broadcast supervision module and a live broadcast interactive module;

[0044] The live broadcast supervision module further includes an iris analysis submodule, a state evaluation submodule and an environment analysis submodule. The iris analysis submodule is used to identify the iris position of students when they are watching live broadcasts, and further analyze the impact of students' iris gaze deviation on their state. The environment analysis submodule is used to analyze the impact of comprehensive environmental data on students' state. The state evaluation submodule is used to evaluate students' state based on the above analysis results.

[0045] Among them, the after-class interaction module further includes an operation analysis submodule, a knowledge matching submodule and a model training submodule. The operation analysis submodule is used to analyze the time characteristics of students watching live broadcasts and screen recordings. The knowledge matching submodule is used to mark the live broadcast and screen recording videos according to the above time characteristics, further analyze the above marked videos and extract knowledge point features, and further match the corresponding teaching videos according to the knowledge point features. The model training submodule is used to train a recognition model based on historical live broadcast comprehensive data, use the above recognition model to identify barrage features and student doubt features, match knowledge points according to the above barrage features and student doubt features, and integrate them.

[0046] The data management module includes a summary feedback module, which is used to feed back to the teacher the knowledge points corresponding to the barrage features and the knowledge points corresponding to the marked videos watched by the students.

[0047] The data management module also includes a question feedback module, which is used to feed back students' question data to the system.

[0048] The operation method of the data management system of the live interactive platform mainly includes the following steps:

[0049] Step S1: collect the bullet screen data, the comprehensive data of students watching live and recorded broadcasts on the student end, and the students' question data in real time, collect the visual images and comprehensive environmental data of students watching live broadcasts during the live teaching period, and enter the comprehensive data of historical live broadcasts into the system;

[0050] Step S2: When conducting live teaching, the system starts the live monitoring module, starts analyzing the comprehensive data of the environment in which the students are located, analyzes the iris gaze deviation of the students when watching, and evaluates the status of the students based on the above analysis results;

[0051] Step S3: During the live teaching process, the system starts the live interactive module, starts to train the recognition model using the historical live comprehensive data, identifies the bullet screen features and student question features according to the recognition model, matches the corresponding knowledge points according to the above features, further analyzes the students' live broadcast and screen recording records and extracts knowledge points, and further matches the corresponding teaching videos according to the above knowledge points;

[0052] Step S4: During the live teaching process, the system enters the questions fed back by the students into the system, and the system integrates the questions fed back by the students and the knowledge points involved in the barrage questions, and sends them to the teacher.

[0053] Step S2 further comprises the following steps:

[0054] Step S21: construct a visual image database, obtain the visual images of students watching the live broadcast during the live teaching, identify the orientation features of the visual images, search the visual image database according to the orientation features, and match the corresponding shooting position and shooting distance L of the student-side camera device. 0 , identify the student's facial features, anchor the student's eye iris position, establish a coordinate system, identify the coordinates of the student's camera device and the coordinates of the student's left and right eye irises, and calculate the distance L between the camera device and the student's eye iris through the distance formula 1 and L 2 , set up the equation In the formula, θ 1 represents the angle between the line connecting the student camera and the student's right eye iris and the vertical plane where the student's eyes iris are located, θ 2The angle between the line connecting the camera device at the student end and the student's left eye iris and the vertical plane where the student's eyes irises are located is shown;

[0055] Step S22: According to θ 1 and θ 2 Retrieve the corresponding influence coefficients on the student status score from the database, perform weighted fusion on the above influence coefficients to obtain the fused influence coefficient α, and then calculate the influence coefficient α according to θ 1 and θ 2 Retrieve the corresponding sight line intersection point in the database, identify the coordinates of the sight line intersection point, and retrieve the influence weight φ of the above influence coefficient corresponding to the database according to the coordinate position of the sight line intersection point i By analyzing the angle offset between the pupil iris and the camera device on the student end when watching the live broadcast, it can accurately determine whether the student is concentrating on listening to the live broadcast teaching, which can help the lecturer supervise the student's listening situation. At the same time, according to the angle offset between the iris and the camera device, it can accurately determine the intersection of the student's line of sight and whether the student is concentrating on watching the live video, thereby greatly improving the accuracy of the system;

[0056] Step S23: Obtain the shooting distance of the camera device at the student end, and retrieve the corresponding viewing angle area S in the database according to the shooting distance. 1 , get the screen area S of the student device 2 , calculate the ratio of the screen area of ​​the student device to the viewing angle area corresponding to the shooting distance According to the ratio The corresponding influence coefficient β on the student status score is retrieved from the database. By analyzing the proportion of the screen area in the viewing angle area when the students are watching the live broadcast, it is possible to accurately determine whether the students are focused on watching the live video, thereby further improving the accuracy of the system.

[0057] Step S23 further includes the following steps:

[0058] Step S231: Obtain comprehensive environmental data, identify sound features in the environment, and compare them with audio features in the live video. If the similarity is greater than a threshold, the system continues to detect. Otherwise, the sound volume in the environment is identified, and the corresponding influence coefficient ω on the student status score in the database is retrieved according to the sound volume. By judging whether the clarity of the sound content in the environment is the same as that in the live video, the influence of the sound volume in the environment on the student's listening is further analyzed, so that the student's listening status can be accurately judged, thereby greatly improving the accuracy of the system;

[0059] Step S232: Obtain the student's status score P under the set conditions in the database 0 , calculate the student's actual status score P through the formula 1 =(α·φ i )·β·ω·P0 , where P 1 Indicates the actual status score of the student. If the student status score P 1 When it is less than the threshold set by the system, the system obtains the student's IP and pops up a reminder on the teacher's side. The above steps can be used to supervise students' online learning, avoid teachers wasting class time monitoring students' small windows, and thus greatly improve class efficiency.

[0060] Step S3 further comprises the following steps:

[0061] Step S31: Obtain historical live broadcast comprehensive data, identify historical comprehensive live broadcast course features and barrage features, and use the historical comprehensive live broadcast course features and barrage features as sample data to train a recognition model;

[0062] Step S32: Acquire real-time bullet screen data, use the trained recognition model to identify bullet screen features in the real-time bullet screen data, identify the bullet screen features of each bullet screen and count them, identify the number of bullet screens with the same bullet screen features within the set recognition period T, if the number of bullet screens is greater than the system set threshold, the system will pop up a window to remind the teacher of the bullet screen feature, otherwise the system will continue to detect, by real-time detection and counting of the bullet screen features during the teacher's live teaching, the teacher can concentrate on teaching while also taking into account the students' questions, thereby greatly improving the interactivity between teachers and students in online teaching;

[0063] Step S33: When students are watching the screen recording, they identify the timestamp of the progress bar, mark the timestamp and enter it into the first table of the database, calculate the difference R between the adjacent first timestamp and second timestamp in the database, and when the difference R is greater than the system-set threshold, enter the second timestamp into the second table, identify the timestamp of the first-order progress bar and the timestamp of the last progress bar in each table, segment the video according to the above timestamps, identify the knowledge point features of each video clip respectively, retrieve the corresponding teaching video in the database according to the knowledge point features, mark the knowledge point features as the first doubtful knowledge point features and save them to the database, and by identifying the knowledge point features of the video clips that students repeatedly watch, the corresponding teaching video can be automatically and accurately matched for the user, thereby improving the accuracy and interactivity of the system.

[0064] Step S4 further comprises the following steps:

[0065] Step S41: at the end of the live teaching, obtain student question data, identify student question features, and search the knowledge graph in the database according to the student question features to match the corresponding second question knowledge point features;

[0066] Step S42: Obtain student bullet screen features, first doubt knowledge point features and second doubt knowledge point features, integrate the features and send them to the teacher side. Through the above steps, teachers can accurately grasp students' doubts, thereby greatly strengthening the interaction between teachers and students in online teaching, and at the same time facilitating teachers to formulate more accurate teaching plans for students' doubt knowledge points.

[0067] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0068] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data management system based on a live interactive platform, comprising a data acquisition module, an interactive management module and a feedback module, characterized in that: The data collection module is used to collect the bullet screen data, the comprehensive data of students watching live and recorded broadcasts on the student end, the students' question data in real time, collect the visual images and comprehensive environmental data of students watching live broadcasts during the live teaching period, and enter the comprehensive data of historical live broadcasts into the system; The interactive management module is used to train the recognition model using the comprehensive data of historical live broadcasts, identify and count the live broadcast bullet screens according to the above recognition model, analyze the visual images of students watching live broadcasts, analyze the students' iris data and the environmental data when the students watch live broadcasts, evaluate the students' learning status according to the analysis results, analyze the students' bullet screens and the students' viewing records of live broadcast screen recordings, obtain the knowledge points that the students are confused about, match the information for the students according to the above knowledge points, and analyze and integrate the characteristics of the students' bullet screens; The data management module is used to integrate the students' barrage data and live broadcast viewing data and transmit them to the teacher's end. Students establish a connection with the teacher's end through the student end, give feedback on questions and interact with the teacher.

2. A data management system based on a live interactive platform according to claim 1, characterized in that: The data acquisition module includes a data collection module, a sensor module and a historical live comprehensive data entry module. The data collection module is used to collect barrage data in real time and comprehensive data of students watching live and recorded broadcasts on the student side. The sensor module is used to collect visual images and comprehensive environmental information of students watching live broadcasts during live teaching. The historical live comprehensive data entry module is used to enter the historical live comprehensive data of each subject into the system.

3. A data management system based on a live interactive platform according to claim 2, characterized in that: The interactive management module includes a live broadcast supervision module and a live broadcast interactive module; The live broadcast supervision module further includes an iris analysis submodule, a state evaluation submodule and an environment analysis submodule. The iris analysis submodule is used to identify the iris position of students when they are watching live broadcasts, and further analyze the impact of students' iris gaze deviation on their state. The environment analysis submodule is used to analyze the impact of comprehensive environmental data on students' state. The state evaluation submodule is used to evaluate students' state based on the above analysis results. Among them, the after-class interaction module further includes an operation analysis submodule, a knowledge matching submodule and a model training submodule. The operation analysis submodule is used to analyze the time characteristics of students watching live broadcasts and screen recordings. The knowledge matching submodule is used to mark the live broadcast and screen recording videos according to the above time characteristics, further analyze the above marked videos and extract knowledge point features, and further match the corresponding teaching videos according to the knowledge point features. The model training submodule is used to train a recognition model based on historical live broadcast comprehensive data, use the above recognition model to identify barrage features and student doubt features, match knowledge points according to the above barrage features and student doubt features, and integrate them.

4. A data management system based on a live interactive platform according to claim 3, characterized in that: The data management module includes a summary feedback module, which is used to feed back to the teacher side the knowledge points corresponding to the bullet screen features and the knowledge points corresponding to the students watching the marked video.

5. A data management system based on a live interactive platform according to claim 4, characterized in that: The data management module also includes a question feedback module, which is used to feed back students' question data to the system.

6. A data management system based on a live interactive platform according to claim 5, characterized in that: The operation method of the data management system of the live interactive platform mainly includes the following steps: Step S1: collect the bullet screen data, the comprehensive data of students watching live and recorded broadcasts on the student end, and the students' question data in real time, collect the visual images and comprehensive environmental data of students watching live broadcasts during the live teaching period, and enter the comprehensive data of historical live broadcasts into the system; Step S2: When conducting live teaching, the system starts the live monitoring module, starts analyzing the comprehensive data of the environment in which the students are located, analyzes the iris gaze deviation of the students when watching, and evaluates the status of the students based on the above analysis results; Step S3: During the live teaching process, the system starts the live interactive module, starts to train the recognition model using the historical live comprehensive data, identifies the bullet screen features and student question features according to the recognition model, matches the corresponding knowledge points according to the above features, further analyzes the students' live broadcast and screen recording records and extracts knowledge points, and further matches the corresponding teaching videos according to the above knowledge points; Step S4: During the live teaching process, the system enters the questions fed back by the students into the system, and the system integrates the questions fed back by the students and the knowledge points involved in the barrage questions, and sends them to the teacher.

7. A data management system based on a live interactive platform according to claim 6, characterized in that: The step S2 further comprises the following steps: Step S21: construct a visual image database, obtain visual images of students watching live broadcasts during live teaching, identify the orientation features of the visual images, search the visual image database according to the orientation features, match the corresponding shooting position and shooting distance L0 of the student-side camera device, identify the student's facial features, anchor the position of the student's eye iris, establish a coordinate system, identify the coordinates of the student-side camera device and the coordinates of the student's left and right eye irises, calculate the distances L1 and L2 between the camera device and the student's eye irises through the distance formula, and set up the equation Wherein, θ1 represents the angle between the line connecting the camera device at the student end and the iris of the student's right eye and the vertical plane where the irises of both eyes of the student are located, and θ2 represents the angle between the line connecting the camera device at the student end and the iris of the student's left eye and the vertical plane where the irises of both eyes of the student are located; Step S22: According to θ1 and θ2, the corresponding influence coefficients on the student status score in the database are retrieved respectively, and the above influence coefficients are weighted and fused to obtain the fused influence coefficient α. According to θ1 and θ2, the corresponding sight line intersection point in the database is retrieved, the coordinates of the sight line intersection point are identified, and the influence weight φ of the above influence coefficient corresponding to the database is retrieved according to the coordinate position of the sight line intersection point. i ; Step S23: Obtain the shooting distance of the camera device at the student end, retrieve the corresponding viewing angle area S1 in the database according to the shooting distance, obtain the screen area S2 of the student end device, and calculate the ratio of the screen area of ​​the student end device to the viewing angle area corresponding to the shooting distance According to the ratio Retrieve the corresponding influence coefficient β on the student status score from the database.

8. A data management system based on a live interactive platform according to claim 7, characterized in that: The step S23 further comprises the following steps: Step S231: Obtain comprehensive environmental data, identify sound features in the environment, and compare the audio features in the live video. If the similarity is greater than the threshold, the system continues to detect. Otherwise, the sound volume in the environment is identified, and the corresponding influence coefficient ω on the student status score in the database is retrieved according to the sound volume; Step S232: Obtain the student's status score P0 under the set conditions in the database, and calculate the student's actual status score P1 by the formula = (α·φ i )·β·ω·P0, where P1 represents the student’s actual status score. If the student’s status score P1 is less than the threshold set by the system, the system obtains the student’s IP and pops up a window to remind the teacher.

9. A data management system based on a live interactive platform according to claim 8, characterized in that: The step S3 further comprises the following steps: Step S31: Obtain historical live broadcast comprehensive data, identify historical comprehensive live broadcast course features and barrage features, and use the historical comprehensive live broadcast course features and barrage features as sample data to train a recognition model; Step S32: Acquire real-time bullet screen data, use the trained recognition model to identify bullet screen features in the real-time bullet screen data, identify the bullet screen features of each bullet screen and count them, identify the number of bullet screens with the same bullet screen features within the set recognition period T, if the number of bullet screens is greater than the system set threshold, the system will pop up a window to remind the teacher of the bullet screen feature, otherwise the system will continue to detect; Step S33: When students are watching the screen recording, they identify the timestamp of the progress bar, mark the timestamp and enter it into the first table of the database, calculate the difference R between the adjacent first timestamp and second timestamp in the database, and when the difference R is greater than the system-set threshold, enter the second timestamp into the second table, identify the timestamp of the first-order progress bar and the timestamp of the last progress bar in each table, segment the video according to the above timestamps, identify the knowledge point features of each video clip respectively, retrieve the corresponding teaching video in the database according to the knowledge point features, mark the knowledge point features as the first doubtful knowledge point features and save them to the database.

10. A data management system based on a live interactive platform according to claim 9, characterized in that: The step S4 further comprises the following steps: Step S41: at the end of the live teaching, obtain student question data, identify student question features, and search the knowledge graph in the database according to the student question features to match the corresponding second question knowledge point features; Step S42: Obtain student bullet screen features, first doubtful knowledge point features and second doubtful knowledge point features, integrate the features and send them to the teacher end.

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