5G + digital twinborn intelligent education platform management method

Abstract

The invention discloses a 5G + digital twinborn intelligent education platform management method, and relates to the technical field of teaching quality evaluation, and the method comprises the following steps: transmitting video data through 5G + data transmission, and constructing a classroom digital twinborn model based on the video data; accessing a classroom behavior identification monitoring model, and recording behavior data of students in a classroom; setting test questions for the course, and recording the answering condition of the student; the behavior data and the answering condition of the students in the course are analyzed, and the teaching quality of the course is evaluated; the method is used for solving the problem that an existing teaching quality evaluation technology cannot adjust the teaching level of teachers and the lecture attending behaviors of students in a course in real time and cannot analyze the teaching level and the lecture attending behaviors through student evaluation, so that the evaluation result of the teaching quality is seriously deviated.

Description

Technical Field

[0001] The present invention relates to the technical field of teaching quality assessment, and specifically to a 5G+digital twin smart education platform management method. Background Art

[0002] Teaching quality assessment technology refers to a technical system that uses systematic methods, tools and digital means to conduct multi-dimensional quantitative analysis and dynamic feedback on the teaching process, teacher performance, student learning outcomes and curriculum design. Its core goal is to objectively measure teaching effectiveness, identify teaching shortcomings, optimize the allocation of educational resources, and ultimately improve the quality of education and student learning outcomes through data-driven methods.

[0003] Smart education platform management includes many aspects of management content, including the evaluation of the teaching quality of the course. However, the existing teaching quality evaluation technology usually evaluates the teaching quality of the course after the course is completed, which makes it impossible to adjust the teaching level of the teacher and the listening behavior of the students in the course in real time. In addition, the existing teaching quality evaluation technology usually requires students to evaluate the teacher or the course, but usually students will give high scores to the teacher or the course out of respect for the teacher. Therefore, using students' evaluation of the teacher or the course for analysis will lead to serious deviations in the evaluation results of the teaching quality. For example, in the patent application with the publication number CN116823028A, a "teaching quality evaluation system and method" is disclosed. This scheme evaluates the teaching quality through student evaluation parameters, resulting in too strong subjective factors in the evaluation results. The existing teaching quality evaluation technology also has the problem of being unable to adjust the teaching level of the teacher and the listening behavior of the students in the course in real time and using student evaluation for analysis, resulting in serious deviations in the evaluation results of the teaching quality. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the prior art to a certain extent at least, by installing a camera in the classroom, transmitting video data through 5G+ data transmission, and constructing a classroom digital twin model based on the video data, and then connecting to the classroom behavior recognition monitoring model, monitoring the classroom digital twin model through the classroom behavior recognition monitoring model, recording the behavior data of students in the classroom, and the teacher sets test questions in the smart education platform, and then asks students to answer through the smart education platform, and records the students' answers. Then, based on the students' behavior data in this course and the corresponding behavior time period, the listening level of the students in this course is analyzed, and at the same time, the balance function of the listening level and the answering situation is analyzed through historical classroom monitoring and examinations. Finally, based on the balance function, the listening level and the answering situation are combined to analyze and evaluate the teaching quality of this course, so as to solve the problem that the existing teaching quality evaluation technology still has the inability to adjust the teaching level of teachers and students' listening behavior in the course in real time and adopt student evaluation for analysis, resulting in serious deviations in the evaluation results of teaching quality.

[0005] To achieve the above objectives, in the first aspect, the present application provides a 5G+digital twin smart education platform management method, comprising the following steps: Install cameras in the classroom, transmit video data through 5G+ data transmission, and build a classroom digital twin model based on the video data; Connect to the classroom behavior recognition monitoring model, monitor the classroom digital twin model through the classroom behavior recognition monitoring model, and record the behavior data of students in the classroom; Set test questions for this course, let students answer them through the smart education platform, and record their answers; Analyze the students' behavior data and answers in this course to evaluate the teaching quality of this course.

[0006] Furthermore, installing cameras in the classroom, transmitting video data through 5G+ data transmission, and building a classroom digital twin model based on the video data includes the following sub-steps: Install high-definition cameras in classrooms to ensure that the cameras can capture the faces of all students; Mark the images captured by the high-definition camera as video data, and transmit the video data in real time through 5G+ data transmission technology; A classroom digital twin model is constructed based on video data, and the behavior actions of students in the classroom digital twin model correspond to the video data in real time.

[0007] Furthermore, the classroom behavior recognition monitoring model is connected to monitor the classroom digital twin model through the classroom behavior recognition monitoring model, and the behavior data of students in the classroom are recorded, including the following sub-steps: The classroom behavior recognition monitoring model can identify students' behaviors in class, including raising hands, looking up, lowering heads, writing, and sleeping; The proportions of hand raising, head raising, head lowering, writing and sleeping identified in the classroom behavior recognition monitoring model are named as hand raising proportion, head raising proportion, head lowering proportion, writing proportion and sleeping proportion respectively to obtain behavior data; The behavioral data are all in percentage format, and the behavioral time period is additionally recorded. When the class starts, the built-in timer starts timing, and the behavioral data is recorded once per second. If the behavioral data is the same as the behavioral data of the previous second, the timing continues. If the behavioral data is different from the behavioral data of the previous second, the timing is stopped to obtain the behavioral time period, and the next timing starts at the same time until the end of the class.

[0008] Furthermore, test questions are set for this course, and students are asked to answer them through the smart education platform. Recording students' answers includes the following sub-steps: The teacher sets the test questions in the smart education platform; Let students answer questions through the smart education platform and record their answers.

[0009] Furthermore, the teacher sets the test questions in the smart education platform, including the following sub-steps: When the teacher is preparing for the next class, he / she names the next class as the class to be taught; The teacher sets corresponding questions in the smart education platform according to the knowledge to be explained in class, and names them as test questions.

[0010] Furthermore, allowing students to answer questions through the smart education platform and recording students' answers includes the following sub-steps: The teacher must ensure that the first test time is left for students to answer before the get out of class ends; During the first test time before get out of class ends, students answer test questions on the smart education platform through learning tablets; The students' answers are recorded, including the number of correct answers, the number of incorrect answers, and the number of blank answers.

[0011] Furthermore, the behavior data and answers of students in this course are analyzed, and the teaching quality of this course is evaluated, which includes the following sub-steps: Analyze the listening level of students in this course based on their behavior data and corresponding behavior time periods; A balance function between listening level and answering situation through historical classroom monitoring and examination analysis; Based on the balance function, the teaching quality of this course is analyzed and evaluated in combination with the listening level and answering situation.

[0012] Furthermore, analyzing the listening level of students in this course based on the behavior data of students in this course and the corresponding behavior time period includes the following sub-steps: Sleeping is classified as distracting behavior, while raising hands, raising heads, and writing are classified as listening behaviors. The head-raising ratio and head-lowering ratio in the behavior data are obtained. If the head-lowering ratio is less than the head-raising ratio, the head-lowering is classified as distracting behavior, otherwise, the head-lowering is classified as listening behavior. The judgment of whether the head-lowering is classified as distracting behavior or listening behavior is changed in real time according to the head-raising ratio and head-lowering ratio in different behavior time periods; The behavior time periods are numbered from early to late, using the symbol T n Indicates, where n is a positive integer and n is the serial number of T, for T n The behavior data in the data are numbered and marked as P(n,m) in the order of hand raising ratio, head raising ratio, head lowering ratio, writing ratio and sleeping ratio, where m is a positive integer and (n,m) is the sequence number of P, 1≤m≤5, and P(n,m) represents T n The mth behavior data in; For any T n , calculate the sum of P(n,m) belonging to the listening behavior, named the listening sum, represented by the symbol a, calculate the sum of P(n,m) belonging to the distraction behavior, named the distraction sum, represented by the symbol b, calculate a / (a+b) to get T n The proportion of lectures, represented by the symbol A n Indicates that, calculate b / (a+b) to get T n The distraction ratio is represented by the symbol B n express; Calculate T n The duration of the corresponding behavior time period is marked as the duration, represented by the symbol U n express; By formula Calculate the listening level of this course, where R is the listening level and max(n) is the maximum value of n.

[0013] Furthermore, the balance function of listening level and answering situation through historical classroom monitoring and examination analysis includes the following sub-steps: For any subject, obtain historical classroom monitoring and test scores, named as historical monitoring and historical scores respectively, the historical scores are the pass rate of students in the exam of the subject, the historical monitoring is the classroom monitoring between two adjacent historical scores, and the historical monitoring corresponds to the second historical score between the two adjacent historical scores, forming a historical analysis data; The historical analysis data is numbered by the symbol S i Represents, where i is a positive integer and i is the sequence number of S; For any S i ,Analyze historical monitoring through classroom behavior recognition monitoring model, analyze S i The listening level of each course in S is numbered and represented by the symbol H(i,j), where j is a positive integer and (i,j) is the serial number of H, H(i,j) represents S i The level of listening in the jth course; For any value of i, calculate the average value of H(i,j), marked as E i , for each S i Analyze and get S i The corresponding E i ; S i The corresponding historical grade is marked as F i , with E i is the X axis, F i Establish a plane rectangular coordinate system for the Y axis, named the classroom balance relationship diagram, according to E i and F i S i Enter the classroom balance relationship diagram; Perform regression analysis on the classroom balance relationship diagram and select the regression function with the smallest standard deviation as the balance function.

[0014] Furthermore, based on the weighing function, the teaching quality of this course is analyzed and evaluated in combination with the listening level and answering situation, including the following sub-steps: Get the answer status, mark the correct number, wrong number and blank number as N1, N2 and N3 respectively, calculate N1 / (N1+N2+N3), mark it as the answer correctness rate, and represent it by the symbol Z; Obtain the listening level, substitute the listening level into the balance function, and solve for the balance threshold; Compare the correct answer rate with the balance threshold. If the correct answer rate is less than the balance threshold, a low-quality signal is output; otherwise, a normal-quality signal is output. If the output quality signal is low, it indicates that the teaching quality of the teacher of this course is low.

[0015] Beneficial effects of the present invention: The present invention installs a camera in the classroom, transmits video data through 5G+ data transmission, builds a classroom digital twin model based on the video data, and then connects to the classroom behavior recognition monitoring model. The classroom digital twin model is monitored by the classroom behavior recognition monitoring model to record the behavior data of students in the classroom. The advantage is that the behavior of students in the classroom can be monitored in real time. At the same time, in order to ensure the privacy of students, the digital twin model is constructed through video data, and the students are converted into digital virtual characters, and then they are monitored by the classroom behavior recognition monitoring model, which can prevent the classroom behavior recognition monitoring model from collecting students' character information, improve the security of students' privacy in data collection, and ensure the objectivity of collected data; The present invention sets test questions in the smart education platform by the teacher, and then asks students to answer through the smart education platform, records the students' answers, and then analyzes the students' listening level in the current course based on the students' behavior data and the corresponding behavior time period in the current course, and analyzes the balance function of the listening level and the answering situation through historical classroom monitoring and examinations. Finally, based on the balance function, the teaching quality of the current course is analyzed and evaluated in combination with the listening level and the answering situation. The advantage is that in teaching, each class usually only teaches a small number of knowledge points. When preparing lessons, the teacher can set test questions according to the knowledge points, and then use a small amount of time at the end of the class to test the mastery of the knowledge points, and then analyze and evaluate the teaching quality of the current course through the listening level and the answering situation. At the same time, although there is no historical data on the listening level and the answering situation, the model can be trained through historical monitoring and historical grades. Finally, after each class, it can be judged through the evaluation results of the teaching quality whether the poor teaching quality is a problem of teacher preparation or a problem of classroom management, thereby improving the accuracy and effectiveness of the teaching quality evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the steps of the method of the present invention; Figure 2 A flow chart of the steps of calculating the listening level of the present invention; Figure 3 It is the classroom balance relationship diagram of the present invention; Figure 4 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] Example 1, please refer to Figure 1 As shown, the present application provides a 5G+digital twin smart education platform management method, comprising the following steps: Step S1, installing a camera in the classroom, transmitting video data through 5G+ data transmission, and building a classroom digital twin model based on the video data; Step S1 includes the following sub-steps: Step S101, installing a high-definition camera in the classroom to ensure that the high-definition camera can capture the faces of all students; Step S102, marking the images captured by the high-definition camera as video data, and transmitting the video data in real time through 5G+ data transmission technology; Step S103, constructing a classroom digital twin model based on the video data, wherein the student's behavior and actions in the classroom digital twin model correspond to the video data in real time; In specific implementation, in daily life, high-definition cameras have been installed in classrooms of various schools, which can be directly connected to transmit video data through 5G+ data transmission technology to ensure the real-time nature of video data, and then build a classroom digital twin model through the existing digital twin technology. The classroom digital twin model converts students in the video data into virtual characters and replicates the students' movements, which can protect students' privacy to a certain extent.

[0019] Step S2, accessing the classroom behavior recognition monitoring model, monitoring the classroom digital twin model through the classroom behavior recognition monitoring model, and recording the behavior data of students in the classroom; Step S2 includes the following sub-steps: Step S201, the classroom behavior recognition monitoring model can recognize students' behaviors in the classroom including raising hands, raising heads, lowering heads, writing and sleeping; Step S202, the proportions corresponding to hand raising, head raising, head lowering, writing and sleeping identified in the classroom behavior recognition monitoring model are named as hand raising proportion, head raising proportion, head lowering proportion, writing proportion and sleeping proportion, respectively, to obtain behavior data; Step S203, the behavior data are all in percentage format, and the behavior time period is additionally recorded. When the class starts, the built-in timer starts timing, and the behavior data is recorded once per second. If the behavior data is the same as the behavior data of the previous second, the timing continues. If the behavior data is different from the behavior data of the previous second, the timing is stopped to obtain the behavior time period, and the next timing starts at the same time until the class ends; In the specific implementation, looking up means that the students are looking at the blackboard, and looking down means that the students are looking at the desktop without holding a pen in their hands. Actions such as raising hands, raising heads, lowering heads, writing and sleeping are all recognized by the existing classroom behavior recognition and monitoring model, and will not be described in detail in this embodiment; for example, timing is started at the beginning of the class, and the hand-raising ratio, head-raising ratio, head-lowering ratio, writing ratio and sleeping ratio at this time are 0%, 80%, 20%, 0% and 0% respectively, and they remain unchanged for 3 seconds. At the 4th second, the hand-raising ratio, head-raising ratio, head-lowering ratio, writing ratio and sleeping ratio change to 0%, 60%, 40%, 0% and 0%, then the timing is stopped, and the behavior time periods of 0%, 80%, 20%, 0% and 0% are recorded as [0s, 4s], and the timing of the next behavior time period is started at the same time.

[0020] Step S3, setting test questions for this course, allowing students to answer through the smart education platform, and recording students' answers; Step S3 includes the following sub-steps: Step S301, the teacher sets the test questions in the smart education platform; Step S301 includes the following sub-steps: Step S3011, when the teacher is preparing for the next class, the next class is named as the class to be taught; Step S3012, the teacher sets corresponding questions in the smart education platform according to the knowledge to be explained in class, and names them as test questions; In the specific implementation, the test questions are set by the teacher in the smart education platform when preparing lessons, and will not be described in detail in this embodiment; Step S302, allowing students to answer questions through the smart education platform and recording their answers; Step S302 includes the following sub-steps: Step S3021, the teacher must ensure that the first test time is left for students to answer before the get out of class ends; Step S3022: During the first test time before the end of get out of class, the student answers the test questions on the smart education platform through the learning tablet; Step S3023, recording the student's answers, including the number of correct answers, the number of wrong answers, and the number of blank answers; In specific implementation, the learning tablet is a learning tool provided by the school for students. Existing learning that supports the smart education platform is usually equipped with a learning tablet. The learning tablet is only connected to the smart education platform and cannot access other functions. The first test time is set by the teacher according to the lesson preparation content and the difficulty of the test questions. The first test time of each course is independent. After the students finish answering, if the test questions are multiple-choice questions or other questions without calculation steps, they will be directly corrected through the smart education platform. If they include calculation steps, the teacher will correct them in the smart education platform. The number of correct answers is the number of students who answered correctly, the number of errors is the number of students who answered incorrectly, and the number of blanks is the number of students who did not answer.

[0021] Step S4, analyzing the behavior data and answer status of students in this class to evaluate the teaching quality of this class; Step S4 includes the following sub-steps: See also Figure 2 As shown, step S401, analyzing the listening level of students in this course based on the behavior data of students in this course and the corresponding behavior time period; Step S401 includes the following sub-steps: Step S4011, classifying sleeping as distracting behavior, and classifying raising hands, raising heads, and writing as listening behaviors, obtaining the head-raising ratio and head-lowering ratio in the behavior data, and classifying the head-lowering as distracting behavior if the head-lowering ratio is less than the head-raising ratio, otherwise classifying the head-lowering as listening behavior, and determining whether the head-lowering is a distracting behavior or a listening behavior is changed in real time according to the head-raising ratio and head-lowering ratio in different behavior time periods; Step S4012: number the time periods in order from early to late, using the symbol T n Indicates, where n is a positive integer and n is the serial number of T, for T n The behavior data in the data are numbered and marked as P(n,m) in the order of hand raising ratio, head raising ratio, head lowering ratio, writing ratio and sleeping ratio, where m is a positive integer and (n,m) is the sequence number of P, 1≤m≤5, and P(n,m) represents T n The mth behavior data in; In specific implementation, usually, teachers will ask students to look at the blackboard or read books when giving lectures. If most students look up at the blackboard, it means that the teacher is explaining the content on the blackboard at this time. If students look down and do not look at the blackboard, it means that they are distracted. If most students look down and read books, it means that the teacher is explaining the content in the textbook at this time. Looking down at this time is not distraction. If they look up at this time, distraction behavior usually does not look up at the teacher, otherwise it is easy to be noticed by the teacher. Therefore, looking up will not be included in distraction behavior. If students look down and write, they will be identified as writing by the classroom behavior recognition monitoring model. In this embodiment, a mathematics course is listed, in which 236 behavior data are recorded, that is, 236 behavior time periods, and the number is obtained by T. n , 1≤n≤236; Step S4013, for any T n , calculate the sum of P(n,m) belonging to the listening behavior, named the listening sum, represented by the symbol a, calculate the sum of P(n,m) belonging to the distraction behavior, named the distraction sum, represented by the symbol b, calculate a / (a+b) to get T n The proportion of lectures, represented by the symbol A n Indicates that, calculate b / (a+b) to get T n The distraction ratio is represented by the symbol B n express; Step S4014, calculate T n The duration of the corresponding behavior time period is marked as the duration, represented by the symbol U n express; Step S4015, by formula Calculate the listening level of this course, where R is the listening level and max(n) is the maximum value of n; In a specific implementation, for example, T 1 In the figure, P(1,1) to P(1,5) are 0%, 80%, 20%, 0% and 0% respectively. Among them, the proportion of looking up is 80%, and the proportion of looking down is 20%. Therefore, looking down is a distracting behavior. The statistics show that a=0%+80%+0%=80%, b=20%+0%=20%, and the listening ratio A is calculated. 1 80%, distraction ratio B 1 is 20%, while T 1 The behavior time period is [0s, 4s], and the duration U is calculated. 1 is 4s, for all T n Perform the same process and obtain A n , B n and U n, and then calculate the listening level R of this course through the formula. Due to the large amount of data, it is inconvenient to show it in detail in this embodiment. Only the listening level calculated in the end is given. The listening level R of this course is calculated to be 95%, which means that from the overall point of view, 95% of the people in this course are listening carefully, while 5% of the people are not listening carefully. However, the number of students who are not listening carefully is not fixed, but the result obtained from the global perspective; Step S402, analyzing the balance function of lecture level and answering situation through historical classroom monitoring and examination; Step S402 includes the following sub-steps: Step S4021, for any subject, obtain historical classroom monitoring and test scores, named historical monitoring and historical scores respectively, the historical score is the pass rate of students in the exam of the subject, the historical monitoring is the classroom monitoring between two adjacent historical scores, and the historical monitoring corresponds to the second historical score between the two adjacent historical scores, forming a historical analysis data; In specific implementation, usually, schools set up different small tests, midterm exams and final exams. Assuming that the school does not set small tests and only includes midterm exams and final exams, the classroom monitoring from the start of school to the midterm exam and the classroom monitoring from the end of the midterm exam to the final exam are two different historical monitoring. At the same time, the pass rate of students in different subjects in the midterm exam and the final exam is the historical score. If small tests are included, the small tests, midterm exams and final exams are divided in the same way to obtain historical monitoring and historical scores. The historical monitoring and historical scores form a historical analysis data, which can obtain all the historical analysis data saved by the school, and different subjects can be analyzed independently; Step S4022: number the historical analysis data, using the symbol S i Represents, where i is a positive integer and i is the sequence number of S; In the specific implementation, the school in this embodiment sets a small test from the beginning of the school to the midterm exam, and sets a small test from the end of the midterm exam to the final exam, that is, the period from the beginning of the school to the first small test is a historical analysis data, and the first small test to the midterm exam is a second historical analysis data, and so on. For example, for mathematics, the school saves historical analysis data for 3 years from 2021 to 2024, two semesters per year, and 4 historical analysis data for each semester, so a total of 24 historical analysis data for mathematics are obtained, and the number is S i , 1≤i≤24; Step S4023, for any S i ,Analyze historical monitoring through classroom behavior recognition monitoring model, analyze S iThe listening level of each course in S is numbered and represented by the symbol H(i,j), where j is a positive integer and (i,j) is the serial number of H, H(i,j) represents S i The level of listening in the jth course; Step S4024, for any value of i, calculate the average value of H(i,j), marked as E i , for each S i Analyze and get S i The corresponding E i ; See also Figure 3 As shown, step S4025, S i The corresponding historical grade is marked as F i , with E i is the X axis, F i Establish a plane rectangular coordinate system for the Y axis, named the classroom balance relationship diagram, according to E i and F i S i Enter the classroom balance relationship diagram; Step S4026, performing regression analysis on the classroom balance relationship diagram, and selecting the regression function with the smallest standard deviation as the balance function; In the specific implementation, each subject is analyzed separately. i All belong to the subject of mathematics, S i The analysis process of the listening level H(i,j) is the same as step S4, and will not be described in detail in this embodiment. 1 There are 17 courses in the dataset, so we get H(1,1) to H(1,17). We calculate their average and get E 1 , for each S i Analyze and get S i The corresponding E i ; Construct the classroom balance relationship diagram as follows Figure 3 As shown in the figure, in the regression analysis, the standard deviation of the linear regression function is the smallest, so the linear regression function is selected as the balance function, and the balance function is Y=1.0638×X-10.249, where Y is F i , X is E i ; Step S403, based on the balance function, analyzing and evaluating the teaching quality of the course in combination with the listening level and answering situation; Step S403 includes the following sub-steps: Step S4031, obtaining the answer status, marking the correct number, wrong number and blank number as N1, N2 and N3 respectively, calculating N1 / (N1+N2+N3), marking it as the answer correctness rate, represented by the symbol Z; Step S4032, obtaining the listening level, substituting the listening level into the balance function, and solving to obtain the balance threshold; Step S4033, comparing the answer accuracy with the balance threshold, if the answer accuracy is less than the balance threshold, outputting a low quality signal, otherwise outputting a normal quality signal; Step S4034, if the output quality signal is low, then mark the teaching quality of the teacher of this course as low; In the specific implementation, after the course is over, the students' answers are obtained, and the answer accuracy Z is calculated to be 88%, while the listening level is 95%. Substituting X=95 into Y=1.0638×X-10.249, the balance threshold is calculated to be 90.8%. The calculation result is rounded to one decimal place. By comparison, it is found that the answer accuracy is less than the balance threshold, and a low quality signal is output, marking the low teaching quality of the teacher of this course, and notifying the teacher to optimize the explanation content when preparing for the next class. The listening level is 95%, and the balance threshold is 90.8%, which means that when the listening level is 95%, 90.8% of the students should be able to master the knowledge points, but the actual answer accuracy is only 88%, which means that the teacher's explanation is not clear enough and students find it difficult to understand after listening carefully. The listening level reflects the teacher's management of classroom order. The school can also remind teachers to manage classroom order by setting requirements for the listening level. For example, the school sets the listening threshold to 90%. When the listening level is less than 90%, the teacher is reminded to strengthen the management of classroom order.

[0022] Example 2, please refer to Figure 4 As shown, Figure 4 The structural diagram of an electronic device is illustrated, and the electronic device may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a 5G+ digital twin smart education platform management method are executed to achieve the following functions: transmit video data through 5G+ data transmission, and build a classroom digital twin model based on video data; access the classroom behavior recognition monitoring model to record the behavior data of students in the classroom; set test questions for this course and record the students' answers; analyze the behavior data and answers of students in this course to evaluate the teaching quality of this course.

[0023] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0024] Example 3. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by the processor, the steps in the above 5G+digital twin smart education platform management method are executed to achieve the following functions: transmit video data through 5G+data transmission, and build a classroom digital twin model based on the video data; access the classroom behavior recognition monitoring model to record the behavior data of students in the classroom; set test questions for this course and record the students' answers; analyze the students' behavior data and answers in this course to evaluate the teaching quality of this course.

[0025] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0026] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0027] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A 5G+digital twin smart education platform management method, characterized in that: The steps include: Install cameras in the classroom, transmit video data through 5G+ data transmission, and build a classroom digital twin model based on the video data; Connect to the classroom behavior recognition monitoring model, monitor the classroom digital twin model through the classroom behavior recognition monitoring model, and record the behavior data of students in the classroom; Set test questions for this course, let students answer them through the smart education platform, and record their answers; Analyze the students' behavior data and answers in this course to evaluate the teaching quality of this course.

2. According to a 5G+digital twin smart education platform management method according to claim 1, it is characterized in that: Installing cameras in the classroom, transmitting video data through 5G+ data transmission, and building a classroom digital twin model based on video data includes the following sub-steps: Install high-definition cameras in classrooms to ensure that the cameras can capture the faces of all students; Mark the images captured by the high-definition camera as video data, and transmit the video data in real time through 5G+ data transmission technology; A classroom digital twin model is constructed based on video data, and the behavior actions of students in the classroom digital twin model correspond to the video data in real time.

3. According to a 5G+digital twin smart education platform management method according to claim 2, it is characterized in that: Accessing the classroom behavior recognition monitoring model, monitoring the classroom digital twin model through the classroom behavior recognition monitoring model, and recording the behavior data of students in the classroom includes the following sub-steps: The classroom behavior recognition monitoring model can identify students' behaviors in class, including raising hands, looking up, lowering heads, writing, and sleeping; The proportions of hand raising, head raising, head lowering, writing and sleeping identified in the classroom behavior recognition monitoring model are named as hand raising proportion, head raising proportion, head lowering proportion, writing proportion and sleeping proportion respectively to obtain behavior data; The behavioral data are all in percentage format, and the behavioral time period is additionally recorded. When the class starts, the built-in timer starts timing, and the behavioral data is recorded once per second. If the behavioral data is the same as the behavioral data of the previous second, the timing continues. If the behavioral data is different from the behavioral data of the previous second, the timing is stopped to obtain the behavioral time period, and the next timing starts at the same time until the end of the class.

4. According to a 5G+digital twin smart education platform management method according to claim 3, it is characterized in that: Set test questions for this course and let students answer them through the smart education platform. Recording students' answers includes the following sub-steps: The teacher sets the test questions in the smart education platform; Let students answer questions through the smart education platform and record their answers.

5. According to a 5G+digital twin smart education platform management method according to claim 4, it is characterized in that: The teacher sets the test questions in the smart education platform, which includes the following sub-steps: When the teacher is preparing for the next class, he / she names the next class as the class to be taught; The teacher sets corresponding questions in the smart education platform according to the knowledge to be explained in class, and names them as test questions.

6. A 5G+digital twin smart education platform management method according to claim 5, characterized in that: Allowing students to answer questions through the smart education platform and recording their answers includes the following sub-steps: The teacher must ensure that the first test time is left for students to answer before the get out of class ends; During the first test time before get out of class ends, students answer test questions on the smart education platform through learning tablets; The students' answers are recorded, including the number of correct answers, the number of incorrect answers, and the number of blank answers.

7. A 5G+digital twin smart education platform management method according to claim 6, characterized in that: Analyzing the behavior data and answers of students in this course and evaluating the teaching quality of this course includes the following sub-steps: Analyze the listening level of students in this course based on their behavior data and corresponding behavior time periods; A balance function between listening level and answering situation through historical classroom monitoring and examination analysis; Based on the balance function, the teaching quality of this course is analyzed and evaluated in combination with the listening level and answering situation.

8. A 5G+digital twin smart education platform management method according to claim 7, characterized in that: Analyzing the listening level of students in this course based on their behavior data and corresponding behavior time periods includes the following sub-steps: Sleeping is classified as distracting behavior, while raising hands, raising heads, and writing are classified as listening behaviors. The head-raising ratio and head-lowering ratio in the behavior data are obtained. If the head-lowering ratio is less than the head-raising ratio, the head-lowering is classified as distracting behavior, otherwise, the head-lowering is classified as listening behavior. The judgment of whether the head-lowering is classified as distracting behavior or listening behavior is changed in real time according to the head-raising ratio and head-lowering ratio in different behavior time periods; The behavior time periods are numbered from early to late, using the symbol T n Indicates, where n is a positive integer and n is the serial number of T, for T n The behavior data in the data are numbered and marked as P(n,m) in the order of hand raising ratio, head raising ratio, head lowering ratio, writing ratio and sleeping ratio, where m is a positive integer and (n,m) is the sequence number of P, 1≤m≤5, and P(n,m) represents T n The mth behavior data in; For any T n , calculate the sum of P(n,m) belonging to the listening behavior, named the listening sum, represented by the symbol a, calculate the sum of P(n,m) belonging to the distraction behavior, named the distraction sum, represented by the symbol b, calculate a / (a+b) to get T n The proportion of lectures, represented by the symbol A n Indicates that, calculate b / (a+b) to get T n The distraction ratio is represented by the symbol B n express; Calculate T n The duration of the corresponding behavior time period is marked as the duration, represented by the symbol U n express; By formula Calculate the listening level of this course, where R is the listening level and max(n) is the maximum value of n.

9. A 5G+digital twin smart education platform management method according to claim 8, characterized in that: The balance function of listening level and answering situation through historical classroom monitoring and examination analysis includes the following sub-steps: For any subject, obtain historical classroom monitoring and test scores, named as historical monitoring and historical scores respectively, the historical scores are the pass rate of students in the exam of the subject, the historical monitoring is the classroom monitoring between two adjacent historical scores, and the historical monitoring corresponds to the second historical score between the two adjacent historical scores, forming a historical analysis data; The historical analysis data is numbered by the symbol S i Represents, where i is a positive integer and i is the sequence number of S; For any S i ,Analyze historical monitoring through classroom behavior recognition monitoring model, analyze S i The listening level of each course in S is numbered and represented by the symbol H(i,j), where j is a positive integer and (i,j) is the serial number of H, H(i,j) represents S i The level of listening in the jth course; For any value of i, calculate the average value of H(i,j), marked as E i , for each S i Analyze and get S i The corresponding E i ; S i The corresponding historical grade is marked as F i , with E i is the X axis, F i Establish a plane rectangular coordinate system for the Y axis, named the classroom balance relationship diagram, according to E i and F i S i Enter the classroom balance relationship diagram; Perform regression analysis on the classroom balance relationship diagram and select the regression function with the smallest standard deviation as the balance function.

10. A 5G+digital twin smart education platform management method according to claim 9, characterized in that: Based on the weighing function, the teaching quality of this course is analyzed and evaluated in combination with the listening level and answering situation, including the following sub-steps: Get the answer status, mark the correct number, wrong number and blank number as N1, N2 and N3 respectively, calculate N1 / (N1+N2+N3), mark it as the answer correctness rate, and represent it by the symbol Z; Obtain the listening level, substitute the listening level into the balance function, and solve for the balance threshold; Compare the correct answer rate with the balance threshold. If the correct answer rate is less than the balance threshold, a low-quality signal is output; otherwise, a normal-quality signal is output. If the output quality signal is low, it indicates that the teaching quality of the teacher of this course is low.

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