A 5G+digital twin smart education platform management method

Abstract

The present invention discloses a 5G+ digital twin smart education platform management method, which relates to the technical field of teaching quality assessment, and includes the following steps: transmitting video data through 5G+ data transmission, and constructing a classroom digital twin model based on the video data; accessing a classroom behavior recognition monitoring model to record the behavior data of students in the classroom; setting test questions for this course and recording the students' answers; analyzing the behavior data and answers of students in this course to evaluate the teaching quality of this course; the present invention is used to solve the problem that the existing teaching quality assessment technology still has the inability to adjust the teaching level of teachers and students' listening behavior in the course in real time and adopts student evaluation for analysis, resulting in serious deviations in the teaching quality assessment results.

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 encompasses multiple aspects, including the evaluation of course teaching quality. Existing teaching quality evaluation technologies typically evaluate courses after they are completed, making it impossible to adjust the teacher's teaching level and student listening behavior in real time. Furthermore, existing teaching quality evaluation technologies typically require students to evaluate teachers or courses. However, students typically give high scores to teachers or courses out of respect for their teachers. Therefore, analyzing student evaluations of teachers or courses will lead to significant deviations in teaching quality evaluation results. For example, patent application publication number CN116823028A discloses a "teaching quality evaluation system and method." This solution evaluates teaching quality using student evaluation parameters, resulting in an overly subjective evaluation result. Existing teaching quality evaluation technologies also face the problem of being unable to adjust the teacher's teaching level and student listening behavior in real time, and using student evaluations for analysis, leading to significant deviations in teaching quality evaluation results. Summary of the Invention

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent. It does this 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. The classroom behavior recognition and monitoring model is then connected to the classroom digital twin model, and the classroom behavior recognition and monitoring model is used to monitor the classroom digital twin model and record the behavior data of students in the classroom. The teacher sets test questions in the smart education platform, and then asks students to answer them through the smart education platform, and records the students' answers. The listening level of students in this course is analyzed based on the students' behavior data and the corresponding behavior time period in this course. At the same time, the balance function of the listening level and the answer situation is analyzed through historical classroom monitoring and examinations. Finally, based on the balance function, the listening level and the answer situation are combined to analyze and evaluate the teaching quality of this course, so as to solve the problem that the existing teaching quality assessment technology still has the inability to make real-time adjustments to the teaching level of teachers and students' listening behavior in the course, and to use student evaluation for analysis, resulting in serious deviations in the teaching quality assessment results.

[0005] To achieve the above objectives, in a first aspect, the present application provides a 5G+ digital twin smart education platform management method, comprising the following steps:

[0006] Install cameras in the classroom, transmit video data via 5G+ data transmission, and build a classroom digital twin model based on the video data;

[0007] Connect to the classroom behavior recognition and monitoring model, monitor the classroom digital twin model through the classroom behavior recognition and monitoring model, and record the behavior data of students in the classroom;

[0008] Set test questions for this class, let students answer them through the smart education platform, and record their answers;

[0009] Analyze students' behavioral data and responses in this course to evaluate the teaching quality of this course.

[0010] Furthermore, cameras are installed in the classroom, video data is transmitted via 5G+ data transmission, and a classroom digital twin model is constructed based on the video data, including the following sub-steps:

[0011] Install high-definition cameras in classrooms to ensure that they can capture the faces of all students;

[0012] Mark the images captured by high-definition cameras as video data and transmit the video data in real time through 5G+ data transmission technology;

[0013] A classroom digital twin model is constructed based on video data, and the student's behavior and actions in the classroom digital twin model correspond to the video data in real time.

[0014] Furthermore, the classroom behavior recognition and monitoring model is connected to monitor the classroom digital twin model through the classroom behavior recognition and monitoring model, and the behavior data of students in the classroom is recorded, including the following sub-steps:

[0015] The classroom behavior recognition and monitoring model can identify students' behaviors in class, including raising hands, looking up, looking down, writing, and sleeping;

[0016] The corresponding proportions of hand raising, head raising, head lowering, writing and sleeping identified in the classroom behavior recognition and monitoring model are named as hand raising proportion, head raising proportion, head lowering proportion, writing proportion and sleeping proportion respectively to obtain behavior data;

[0017] 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 records the behavioral data 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 and the behavioral time period is obtained. At the same time, the next timing starts until the end of the class.

[0018] Furthermore, setting test questions for this class and asking students to answer them through the smart education platform and recording students' answers include the following sub-steps:

[0019] Teachers set test questions in the smart education platform;

[0020] Let students answer questions through the smart education platform and record their answers.

[0021] Furthermore, the teacher sets test questions in the smart education platform, including the following sub-steps:

[0022] When the teacher is preparing for the next class, he / she will name the next class as the class to be taught;

[0023] The teacher sets corresponding questions in the smart education platform based on the knowledge to be explained in class, and names them as test questions.

[0024] Furthermore, allowing students to answer questions through the smart education platform and recording their answers includes the following sub-steps:

[0025] The teacher must ensure that the first test time is left for students to answer before the get out of class ends;

[0026] During the first test time before get out of class ends, students answer the test questions on the smart education platform using their learning tablets;

[0027] The student's answers are recorded, including the number of correct answers, the number of incorrect answers, and the number of blank answers.

[0028] Furthermore, analyzing the students' behavioral data and responses in this class and evaluating the teaching quality of this class includes the following sub-steps:

[0029] Analyze students' listening level in this course based on their behavioral data and corresponding behavioral time periods;

[0030] A balance function between listening level and answering situation through historical classroom monitoring and examination analysis;

[0031] Based on the balance function, the teaching quality of this course is analyzed and evaluated in combination with the listening level and answering situation.

[0032] Furthermore, analyzing the listening level of students in this course based on the students' behavioral data and the corresponding behavioral time periods in this course includes the following sub-steps:

[0033] Sleeping is classified as distraction, while raising hands, looking up, and writing are classified as listening behaviors. The head-up ratio and head-down ratio in the behavior data are obtained. If the head-down ratio is smaller than the head-up ratio, the head-down is classified as distraction. Otherwise, the head-down is classified as listening. The determination of whether the head-down is distraction or listening behavior changes in real time based on the head-up ratio and head-down ratio in different behavior time periods.

[0034] The behavior time periods are numbered from early to late, using the symbol T n Indicates that, where n is a positive integer and n is the serial number of T, for T n The behavioral data in the data set 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 ;

[0035] For any T n , calculate the sum of P(n,m) belonging to the listening behavior, named the listening sum, represented by symbol a, calculate the sum of P(n,m) belonging to the distraction behavior, named the distraction sum, represented by symbol b, calculate a / (a+b) to get T n The proportion of lectures, represented by the symbol A n Indicates that T is obtained by calculating b / (a+b) n The distraction ratio, represented by the symbol B n express;

[0036] Calculate T n The duration of the corresponding behavior time period is marked as duration, and is represented by the symbol U n express;

[0037] By formula Calculate the listening level of this course, where R is the listening level and max(n) is the maximum value of n.

[0038] Furthermore, the balance function of listening level and answering situation through historical classroom monitoring and test analysis includes the following sub-steps:

[0039] 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 piece of historical analysis data;

[0040] 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;

[0041] For any S i ,Analyze historical monitoring through classroom behavior recognition monitoring model,analyze S i The listening level of each course in the course 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 class;

[0042] 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 Corresponding E i ;

[0043] S i The corresponding history 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 and name it the classroom balance relationship diagram. i and F i S i Enter the classroom balance relationship diagram;

[0044] Perform regression analysis on the classroom balance relationship diagram and select the regression function with the smallest standard deviation as the balance function.

[0045] Furthermore, based on the weighing function, the teaching quality of the course is analyzed and evaluated in combination with the listening level and answering situation, including the following sub-steps:

[0046] Obtain the answer status, mark the correct number, incorrect number and blank number as N1, N2 and N3 respectively, calculate N1 / (N1+N2+N3), mark it as the answer accuracy rate, and represent it with the symbol Z;

[0047] Obtain the listening level, substitute the listening level into the balance function, and solve for the balance threshold;

[0048] 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.

[0049] If the output quality signal is low, it is marked that the teaching quality of the teacher of this course is low.

[0050] Beneficial effects of the present invention: The present invention installs a camera in the classroom, transmits video data through 5G+ data transmission, and constructs a classroom digital twin model based on the video data. Then, the classroom behavior recognition and monitoring model is connected to the classroom digital twin model, and the classroom behavior recognition and monitoring model is used to monitor the classroom digital twin model and 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. Then, the students are monitored by the classroom behavior recognition and monitoring model, which can prevent the classroom behavior recognition and monitoring model from collecting students' personal information, thereby improving the security of students' privacy in data collection and ensuring the objectivity of the collected data.

[0051] The present invention sets test questions in the smart education platform by the teacher, and then asks students to answer them through the smart education platform, records the students' answers, and then analyzes the students' listening level in the course based on the students' behavioral data and the corresponding behavioral time period in the course. 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 teaching quality of the 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 for class, 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 students' mastery of the knowledge points. The teaching quality of the course is analyzed and evaluated based on 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, the teaching quality evaluation results can be used to determine whether the poor teaching quality is a problem of teacher preparation or classroom management, thereby improving the accuracy and effectiveness of teaching quality evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of the steps of the method of the present invention;

[0053] Figure 2 A flow chart of the steps for calculating the lecture level of the present invention;

[0054] Figure 3 It is the classroom balance relationship diagram of the present invention;

[0055] Figure 4 Schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0057] Example 1, please refer to Figure 1 As shown, this application provides a 5G+ digital twin smart education platform management method, including the following steps:

[0058] Step S1: Install a camera in the classroom, transmit video data via 5G+ data transmission, and build a classroom digital twin model based on the video data. Step S1 includes the following sub-steps:

[0059] Step S101: Install a high-definition camera in the classroom to ensure that the camera can capture the faces of all students.

[0060] Step S102: Mark the image captured by the high-definition camera as video data, and transmit the video data in real time using 5G+ data transmission technology;

[0061] Step S103: constructing a classroom digital twin model based on the video data, where the student's actions in the classroom digital twin model correspond to the video data in real time;

[0062] In specific implementation, in daily life, high-definition cameras have been installed in the 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. Then, the existing digital twin technology is used to build a classroom digital twin model. The classroom digital twin model converts the students in the video data into virtual characters and replicates the students' movements, which can protect students' privacy to a certain extent.

[0063] Step S2: 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; Step S2 includes the following sub-steps:

[0064] Step S201: The classroom behavior recognition monitoring model can recognize students' behaviors in class, including raising hands, looking up, looking down, writing, and sleeping;

[0065] Step S202: The corresponding proportions of hand-raising, head-raising, head-lowering, writing, and sleeping identified in the classroom behavior recognition and monitoring model are named as hand-raising ratio, head-raising ratio, head-lowering ratio, writing ratio, and sleeping ratio, respectively, to obtain behavior data;

[0066] 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, the behavior time period is obtained, and the next timing is started at the same time until the class ends;

[0067] In the specific implementation, raising the head means that the student is looking at the blackboard, lowering the head means that the student is looking at the desktop without holding a pen in his hand, and actions such as raising the hand, raising the head, lowering the head, 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, when the class starts, the timing is started, and the hand-raising ratio, raising the head ratio, lowering the head 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, raising the head ratio, lowering the head 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.

[0068] Step S3: Set test questions for this class, let students answer them through the smart education platform, and record the students' answers. Step S3 includes the following sub-steps:

[0069] Step S301: The teacher sets test questions in the smart education platform;

[0070] Step S301 includes the following sub-steps:

[0071] Step S3011: When the teacher is preparing for the next class, the teacher names the next class as the class to be taught;

[0072] In step S3012, the teacher sets corresponding questions in the smart education platform based on the knowledge to be explained in class, and names them as test questions;

[0073] In specific implementation, the test questions are set by the teacher in the smart education platform when preparing lessons, and will not be explained in detail in this embodiment;

[0074] Step S302: Allow students to answer questions through the smart education platform and record their answers;

[0075] Step S302 includes the following sub-steps:

[0076] Step S3021: The teacher must ensure that the first test time is left for students to answer before the get out of class ends;

[0077] Step S3022: During the first test period before get out of class ends, the student answers the test questions on the smart education platform using the learning tablet.

[0078] Step S3023, recording the student's answers, including the number of correct answers, wrong answers, and blank answers;

[0079] In specific implementation, the learning tablet is a learning tool provided by the school for students. Existing learning platforms that support the smart education platform are 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 based on 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 within 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.

[0080] Step S4: Analyze the student behavior data and responses in this class to evaluate the teaching quality of this class. Step S4 includes the following sub-steps:

[0081] See also Figure 2 As shown, step S401, analyzing the listening level of students in the current course based on the students' behavior data and the corresponding behavior time period;

[0082] Step S401 includes the following sub-steps:

[0083] Step S4011: Sleeping is classified as a distracting behavior, while raising hands, looking up, and writing are classified as listening behaviors. The head-up ratio and head-down ratio in the behavior data are obtained. If the head-down ratio is less than the head-up ratio, the head-down is classified as a distracting behavior; otherwise, the head-down is classified as listening behavior. The determination of whether the head-down is a distracting behavior or a listening behavior is changed in real time based on the head-up ratio and head-down ratio in different behavior time periods.

[0084] Step S4012: number the time periods from morning to night, using the symbol T nIndicates that, where n is a positive integer and n is the serial number of T, for T n The behavioral data in the data set 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 ;

[0085] 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 at the book, 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, distracted 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 to 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 behavioral data are recorded, that is, 236 behavioral time periods, numbered to obtain T n , 1≤n≤236;

[0086] Step S4013, for any T n , calculate the sum of P(n,m) belonging to the listening behavior, named the listening sum, represented by symbol a, calculate the sum of P(n,m) belonging to the distraction behavior, named the distraction sum, represented by symbol b, calculate a / (a+b) to get T n The proportion of lectures, represented by the symbol A n Indicates that T is obtained by calculating b / (a+b) n The distraction ratio, represented by the symbol B n express;

[0087] Step S4014, calculate T n The duration of the corresponding behavior time period is marked as duration, and is represented by the symbol U n express;

[0088] 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;

[0089] In the specific implementation, for example, in T1, P(1,1) to P(1,5) are 0%, 80%, 20%, 0% and 0% respectively, among which the proportion of looking up is 80% and the proportion of looking down is 20%. Therefore, looking down is a distracting behavior. Statistics show that a=0%+80%+0%=80%, b=20%+0%=20%, and the calculated listening ratio A1 is 80%, and the distraction ratio B1 is 20%. The behavior time period of T1 is [0s, 4s], and the calculated duration U1 is 4s. For all T n Perform the same process to obtain A n 、B n and U n , and then calculate the listening level R of this class through the formula. Due to the large amount of data, it is inconvenient to show it in detail in this embodiment. Only the final calculated listening level is given. The listening level R of this class is calculated to be 95%, which means that from the overall perspective, 95% of the students in this class are listening carefully, while 5% of the students are not listening carefully. However, the number of students who are not listening carefully is not fixed, but is the result of observation from a global perspective;

[0090] Step S402: Analyze the balance function of lecture level and answering situation through historical classroom monitoring and examination analysis;

[0091] Step S402 includes the following sub-steps:

[0092] Step S4021: For any subject, historical classroom monitoring and test scores are obtained, which are named "historical monitoring" and "historical score" respectively. The historical score is the pass rate of students in the subject exam, and the historical monitoring is the classroom monitoring between two adjacent historical scores. The historical monitoring corresponds to the second historical score between the two adjacent historical scores, forming a piece of historical analysis data.

[0093] In specific implementation, usually, schools set up different quizzes, midterm exams, and final exams. Assuming that the school does not set quizzes 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 monitorings. At the same time, the pass rate of students in different subjects in the midterm exams and final exams is the historical score. If quizzes are included, the quizzes, 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. All historical analysis data saved by the school can be obtained, and different subjects can be analyzed independently.

[0094] 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;

[0095] In a specific implementation, the school in this embodiment sets a small test between the beginning of the school and the midterm exam, and sets a small test between the end of the midterm exam and the final exam, that is, the period from the beginning of the school to the first small test is one historical analysis data, the first small test to the midterm exam is the second historical analysis data, and so on. For example, for mathematics, the school saves historical analysis data for the three years from 2021 to 2024, two semesters per year, and four 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;

[0096] Step S4023, for any S i ,Analyze historical monitoring through classroom behavior recognition monitoring model,analyze S i The listening level of each course in the course 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 class;

[0097] 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 Corresponding E i ;

[0098] See also Figure 3 As shown, step S4025, S i The corresponding history 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 and name it the classroom balance relationship diagram. i and F i S i Enter the classroom balance relationship diagram;

[0099] 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;

[0100] In the specific implementation, each subject is analyzed separately. i All belong to the mathematics discipline, S i The analysis process of the listening level H(i,j) is the same as step S4, which will not be described in detail in this embodiment. Assuming that S1 contains 17 courses, H(1,1) to H(1,17) are calculated, and their average value is calculated to obtain E1. For each S i Analyze and get S iCorresponding E i ; Construct the classroom balance relationship diagram as follows Figure 3 As shown 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 ;

[0101] Step S403: Analyze and evaluate the teaching quality of the course based on the balance function and the listening level and answering situation;

[0102] Step S403 includes the following sub-steps:

[0103] Step S4031: Obtain the answer status, mark the correct number, incorrect number, and blank number as N1, N2, and N3 respectively, calculate N1 / (N1+N2+N3), mark it as the answer accuracy rate, and represent it with the symbol Z;

[0104] Step S4032: Obtain the listening level, substitute the listening level into the balance function, and solve to obtain the balance threshold;

[0105] Step S4033: Compare the answer accuracy rate with the balance threshold. If the answer accuracy rate is less than the balance threshold, a low-quality signal is output; otherwise, a normal-quality signal is output.

[0106] Step S4034: If the output quality signal is low, the teaching quality of the teacher of this course is marked as low;

[0107] In specific implementation, after the class ends, 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 teaching quality of the teacher of this class as low. The teacher is notified to optimize the explanation content when preparing for the next class. The listening level is 95%, and the balance threshold is 90.8%. This 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 rate 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.

[0108] Example 2, please refer to Figure 4 As shown, Figure 4The present invention provides a structural diagram of an electronic device, which 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 via the communication bus. The memory stores computer-readable instructions, and the processor can call instructions from the memory. When the computer-readable instructions are executed by the processor, the processor performs the steps in a 5G+ digital twin smart education platform management method to achieve the following functions: transmitting video data via 5G+ data transmission and constructing a classroom digital twin model based on the video data; accessing a classroom behavior recognition and monitoring model to record student behavior data in the classroom; setting test questions for the current class and recording student responses; and analyzing student behavior data and responses in the current class to evaluate the teaching quality of the current class.

[0109] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit 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, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling 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, and other media that can store program code.

[0110] 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 behavior data and answers of students in this course to evaluate the teaching quality of this course.

[0111] Through the description of the above embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the essence of the above technical solutions or the portion that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various embodiments or certain portions of the embodiments.

[0112] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. 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.

[0113] 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 them. 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 by: The steps include: Install cameras in the classroom, transmit video data via 5G+ data transmission, and build a classroom digital twin model based on the video data; Connect to the classroom behavior recognition and monitoring model, monitor the classroom digital twin model through the classroom behavior recognition and monitoring model, and record the behavior data of students in the classroom; Set test questions for this class, let students answer them through the smart education platform, and record their answers; Analyze students' behavior data and responses in this class to evaluate the teaching quality of this class, including the following sub-steps: Analyze students' listening level in this course based on their behavioral data and corresponding behavioral time periods; The balance function of listening level and answering status analyzed through historical classroom monitoring and examinations includes the following sub-steps: 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 piece of 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 the course 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 class; 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 Corresponding E i ; S i The corresponding history 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 and name it the classroom balance relationship diagram. 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; Based on the balance 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: Obtain the answer status, mark the correct number, incorrect number and blank number as N1, N2 and N3 respectively, calculate N1 / (N1+N2+N3), mark it as the answer accuracy rate, and represent it with 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 is marked that the teaching quality of the teacher of this course is low.

2. A 5G+digital twin smart education platform management method according to claim 1, characterized in that: 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 they can capture the faces of all students; Mark the images captured by high-definition cameras 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 student's behavior and actions in the classroom digital twin model correspond to the video data in real time.

3. A 5G+digital twin smart education platform management method according to claim 2, characterized in that: Connecting to the classroom behavior recognition and monitoring model, monitoring the classroom digital twin model through the classroom behavior recognition and monitoring model, and recording the student behavior data in the classroom includes the following sub-steps: The classroom behavior recognition and monitoring model can identify students' behaviors in class, including raising hands, looking up, looking down, writing, and sleeping; The corresponding proportions of hand raising, head raising, head lowering, writing and sleeping identified in the classroom behavior recognition and 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 records the behavioral data 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 and the behavioral time period is obtained. At the same time, the next timing starts until the end of the class.

4. A 5G+digital twin smart education platform management method according to claim 3, characterized in that: Set test questions for this class and let students answer them through the smart education platform. Recording students' answers includes the following sub-steps: Teachers set test questions in the smart education platform; Let students answer questions through the smart education platform and record their answers.

5. A 5G+digital twin smart education platform management method according to claim 4, characterized in that: The teacher sets test questions in the smart education platform, which includes the following sub-steps: When the teacher is preparing for the next class, he / she will name the next class as the class to be taught; The teacher sets corresponding questions in the smart education platform based on 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 responses 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 the test questions on the smart education platform using their learning tablets; The student's 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 students' listening level in this course based on their behavioral data and corresponding behavioral time periods includes the following sub-steps: Sleeping is classified as distraction, while raising hands, looking up, and writing are classified as listening behaviors. The head-up ratio and head-down ratio in the behavior data are obtained. If the head-down ratio is smaller than the head-up ratio, the head-down is classified as distraction. Otherwise, the head-down is classified as listening. The determination of whether the head-down is distraction or listening behavior changes in real time based on the head-up ratio and head-down ratio in different behavior time periods. The behavior time periods are numbered from early to late, using the symbol T n Indicates that, where n is a positive integer and n is the serial number of T, for T n The behavioral data in the data set 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 symbol a, calculate the sum of P(n,m) belonging to the distraction behavior, named the distraction sum, represented by symbol b, calculate a / (a+b) to get T n The proportion of lectures, represented by the symbol A n Indicates that T is obtained by calculating b / (a+b) n The distraction ratio, represented by symbol B n express; Calculate T n The duration of the corresponding behavior time period is marked as duration, and is 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.

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