Education remote monitoring management system and method based on data analysis

Abstract

The invention discloses an education remote monitoring management system and method based on data analysis, and relates to the technical field of education monitoring management, and the method comprises the following steps: 1, recording the teaching explanation of a teacher after the remote teaching of the teacher starts, and obtaining a plurality of groups of teaching statements; 2, dividing the plurality of groups of teaching statements into a plurality of teaching monitoring sections; 3, obtaining a teaching emotion value of each teaching monitoring section, and marking the teaching monitoring section as an efficient teaching section or a low-efficient teaching section according to a comparison result of the teaching emotion value and a threshold value of the teaching emotion value; according to the method, remote teaching audio of a teacher is intelligently divided into monitoring segments in a natural language processing mode, and the teaching monitoring segments can be marked as the efficient teaching segments or the low-efficiency teaching segments according to the comparison result of the teaching emotion value and the threshold value of the teaching emotion value. And the teacher and the remote students are dynamically controlled according to the marking condition.

Description

Technical Field

[0001] The present invention relates to the technical field of education monitoring management, and more specifically, to an education remote monitoring management system and method based on data analysis. Background Art

[0002] With the rapid development of information technology, the way of education is becoming more and more diversified. At present, more and more schools will choose online distance education. Online distance education has the characteristics of sharing, openness, and autonomy. However, the current online distance education teachers cannot fully understand the learning situation of each student for each knowledge point, resulting in low teaching efficiency in some teaching points. At the same time, the Academic Affairs Office cannot understand the teaching situation of each teacher in time, and it is difficult to control the teaching quality. Therefore, how to conduct effective remote monitoring of education is an urgent problem to be solved. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an education remote monitoring management system and method based on data analysis.

[0004] To achieve the above object, the present invention provides the following technical solutions: An education remote monitoring management system based on data analysis, comprising a teaching recording segmentation module, a teaching sentence processing module, a teaching segment division module, and an education monitoring management module; The teaching recording segmentation module records the teacher's teaching explanation after the teacher starts the remote teaching, and obtains multiple groups of teaching sentences; The teaching statement processing module is used to divide multiple groups of teaching statements into multiple teaching monitoring segments; The teaching segment division module obtains the teaching emotion value of each teaching monitoring segment, and marks the teaching monitoring segment as an efficient teaching segment or an inefficient teaching segment according to the comparison result between the teaching emotion value and its threshold value; The education monitoring management module manages efficient teaching segments and inefficient teaching segments.

[0005] Furthermore, the teaching sentences are obtained in the following manner: the teaching audio is generated in real time, and the recordings in the teaching audio are segmented by natural language processing to obtain multiple groups of teaching sentences, which are marked as DGi, where i is the sequential number of the teaching sentences, i=1, 2, ..., N, and N is a positive integer.

[0006] Furthermore, multiple groups of teaching statements are divided into multiple teaching monitoring segments, specifically: obtaining the teaching vector value of each group of teaching statements, obtaining the course association value of adjacent numbered teaching statements, setting the course association threshold, when the course association value is greater than or equal to the course association threshold, the adjacent numbered teaching statements are classified into the same teaching monitoring segment, when the course association value is less than the course association threshold, the adjacent numbered teaching statements are divided into two teaching monitoring segments.

[0007] Furthermore, the teaching vector value of each group of teaching sentences is obtained in the following way: the word vector of each word in the teaching sentence is obtained through the Word2Vec model, and the teaching vector value is obtained by summing up the word vectors of each word in the teaching sentence.

[0008] Furthermore, the course association value of adjacent numbered teaching sentences is obtained by the following method: obtaining the teaching vector value of the adjacent numbered teaching sentences, performing cosine similarity calculation on the teaching vector value of the adjacent numbered teaching sentences, and obtaining the course association value of the adjacent numbered teaching sentences.

[0009] Furthermore, the teaching emotion value of the teaching monitoring segment is obtained in the following way: all teaching sentences in the teaching monitoring segment are obtained, and the corresponding audio of the teaching sentences is used as the input data of the teaching emotion mobilization model to obtain the teaching emotion mobilization value, all teaching emotion mobilization values ​​are summed and averaged to obtain the average teaching emotion mobilization value, and marked as HCZ, the total number of emotion mobilization changes is obtained, and marked as GFS, the average emotion richness mobilization density is obtained, and marked as WSE, and the formula is used. Get the teaching emotion value of the teaching monitoring segment.

[0010] Furthermore, the average emotion-rich mobilization density is obtained in the following way: a teaching emotion mobilization threshold is set; when the teaching emotion mobilization value is greater than or equal to the teaching emotion mobilization threshold, the teaching emotion mobilization value is marked as an emotion-rich mobilization value; when the teaching emotion mobilization value is less than the teaching emotion mobilization threshold, no corresponding processing is performed; all emotion-rich mobilization values ​​are sorted according to the numbering order of the teaching sentences; the difference between the numbers of two adjacent teaching sentences after sorting is calculated to obtain the emotion-rich mobilization density; all emotion-rich mobilization densities are summed and the average is taken to obtain the average emotion-rich mobilization density.

[0011] Furthermore, the total number of mobilization changes is obtained in the following way: all teaching emotion mobilization values ​​are sorted according to the numbering order of the teaching sentences, the difference between two adjacent teaching emotion mobilization values ​​after sorting is calculated and the absolute value is taken to obtain the emotion mobilization change value, and the emotion mobilization change threshold is set. When the emotion mobilization change value is greater than or equal to the emotion mobilization change threshold, the number of emotion mobilization changes in the teaching monitoring segment is increased by one, and all the emotion mobilization change numbers in the teaching monitoring segment are summed up to obtain the total number of emotion mobilization changes. When the emotion mobilization change value is less than the emotion mobilization change threshold, no corresponding processing is performed.

[0012] Furthermore, efficient and inefficient teaching segments are managed, specifically: When the teaching monitoring segment is marked as an inefficient teaching segment, a warning message of the inefficient teaching segment is sent to the teacher, and an inefficient warning record is generated synchronously. The inefficient warning record includes the warning time. All inefficient warning records of the teacher before the current system time are obtained, and all inefficient warning records are sorted in the order of the warning time. The time difference between the two adjacent warning times after sorting is calculated to obtain the warning interval. All warning intervals are summed and averaged to obtain the average warning interval, which is marked as YED. The total number of inefficient warning records is obtained and marked as SZK. The formula is used Get the teacher's education evaluation value QWA, where c1 is the average warning interval coefficient, c2 is the total number coefficient of inefficient warning records, set the education evaluation threshold, and when the education evaluation value is greater than or equal to the education evaluation threshold, mark the teacher as an inefficient education teacher, and upload the information of the inefficient education teacher to the school's teaching system; When the teaching monitoring segment is marked as an efficient teaching segment, the teaching monitoring start time and the teaching monitoring end time of the efficient teaching segment are obtained, and the lecture video of each remote lecturer is intercepted according to the teaching monitoring start time and the teaching monitoring end time, and the effective teaching value of each remote lecturer is obtained according to the lecture video, and an effective teaching threshold is set. When the effective teaching value is less than the effective teaching threshold, the remote lecturer is marked as an invalid lecturer, and the invalid lecturers are sorted from small to large according to the effective teaching value value. After sorting, an education attention table of the efficient teaching segment is generated, and the teacher can conduct key teaching of the content of the efficient teaching segment to the invalid lecturers according to the education attention table; The effective teaching value of the remote listening students is obtained by the following method: converting the intercepted listening video into a video frame image, further processing the video frame image, extracting the eye image data of the remote listening students in the video frame image, obtaining the listening concentration value of the eye image data, sorting the video frame images in chronological order, obtaining the listening concentration value of each video frame image, setting the listening concentration threshold, when the listening concentration value is greater than or equal to the listening concentration threshold, marking the video frame image as a valid teaching image, obtaining the total number of valid teaching images and marking it as PLK, when the listening concentration value is less than the listening concentration threshold, marking the video frame image as an invalid teaching image, sorting all invalid teaching images in chronological order, calculating the time difference between the time of two adjacent invalid teaching images after sorting, obtaining the invalid teaching interval, summing up all invalid teaching intervals and taking the average, obtaining the average invalid teaching interval, and marking it as MBZ, using the formula The effective teaching value HVL of the remote class students is obtained, where b1 is the total number coefficient of effective teaching images, and b2 is the average invalid teaching interval coefficient; The class concentration value of the eye image data is obtained by the following method: the eye image data is used as input data of the eye analysis model to obtain the class concentration value of the eye image data; Get the first-ranked teaching sentence in the efficient teaching segment, and mark the time of the first word in the first-ranked teaching sentence as the start time of teaching monitoring; get the last-ranked teaching sentence in the efficient teaching segment, and mark the time of the last word in the last-ranked teaching sentence as the start time of teaching monitoring.

[0013] Furthermore, a method for remote monitoring and management of education based on data analysis includes the following steps: Step 1: After the teacher starts teaching remotely, record the teacher's lectures and obtain multiple groups of lecture sentences; Step 2: Divide multiple groups of teaching sentences into multiple teaching monitoring segments; Step 3: Obtain the teaching emotion value of each teaching monitoring segment, and mark the teaching monitoring segment as an efficient teaching segment or an inefficient teaching segment according to the comparison result between the teaching emotion value and its threshold; Step 4: Manage efficient and inefficient teaching segments.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The method of the present invention intelligently divides the teacher's remote teaching audio into monitoring segments by natural language processing, and can mark the teaching monitoring segment as an efficient teaching segment or an inefficient teaching segment according to the comparison result between the teaching emotion value and its threshold value, and dynamically manage the teacher and the remote students according to the marking situation; 2. Setting up the lecture recording segmentation module and the lecture sentence processing module can segment and recombine the teacher's remote lecture audio through natural language processing, and intelligently divide the teacher's remote lecture audio into monitoring segments, so as to facilitate the subsequent targeted intelligent analysis of different monitoring segments. Setting up the lecture segment division module and the education monitoring management module can allow the teacher to fully understand the listening situation of each remote student on different knowledge points, and also allow the Academic Affairs Office to fully understand the teacher's teaching situation, thereby improving the supervision quality and efficiency of remote teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flowchart of a method for remote monitoring and management of education based on data analysis; Figure 2 A flowchart for the process divided into the teaching and monitoring sections; Figure 3 A flowchart for managing efficient and inefficient teaching segments. DETAILED DESCRIPTION Example 1

[0016] Reference Figure 1 , an educational remote monitoring management method based on data analysis, comprising the following steps: Step 1: After the teacher starts teaching remotely, record the teacher's lectures and obtain multiple groups of lecture sentences; Step 2: Divide multiple groups of teaching sentences into multiple teaching monitoring segments; Step 3: Obtain the teaching emotion value of each teaching monitoring segment, and mark the teaching monitoring segment as an efficient teaching segment or an inefficient teaching segment according to the comparison result between the teaching emotion value and its threshold; Step 4: Manage efficient and inefficient teaching segments.

[0017] The above method can intelligently divide the teacher's remote teaching audio into monitoring segments through natural language processing, and can mark the teaching monitoring segments as efficient teaching segments or inefficient teaching segments based on the comparison results of the teaching emotion value and its threshold, and dynamically manage the teacher and the remote students according to the marking situation. Example 2

[0018] Reference Figures 2 to 3 , an educational remote monitoring and management system based on data analysis, including a teaching recording segmentation module, a teaching sentence processing module, a teaching segment division module, and an educational monitoring and management module.

[0019] The teaching recording segmentation module records the teacher's lectures after the teacher starts remote teaching, generates teaching audio in real time, and segments the recordings in the teaching audio through natural language processing to obtain multiple groups of teaching sentences, which are marked as DGi, where i is the sequential number of the teaching sentences, i=1, 2, ..., N, and N is a positive integer.

[0020] The teaching statement processing module obtains the teaching vector value of each group of teaching statements and obtains the course association value of the teaching statements with adjacent numbers.

[0021] Example: In the lecture audio, "Good afternoon, students. Before class today, the teacher wants to show you some pictures. After you look at the pictures, the teacher wants to ask you what you see in them." Natural language processing will be used to segment the lecture audio into two groups of lecture sentences: "Good afternoon, students. Before class today, the teacher wants to show you some pictures," and "After you look at the pictures, the teacher wants to ask you what you see in them." Mark "Good afternoon, students. Before class today, the teacher wants to show you some pictures" as DG1, and mark "After you look at the pictures, the teacher wants to ask you what you see in them" as DG2.

[0022] The teaching vector value of each group of teaching sentences is obtained in the following way: the word vector of each word in the teaching sentence is obtained through the Word2Vec model, and the word vector value of each word in the teaching sentence is obtained by summing up the word vectors of each word in the teaching sentence. The word vector of each word is obtained by training the Word2Vec model with a large amount of teaching sentence data.

[0023] The course association value of the adjacent numbered teaching sentences is obtained by the following method: obtaining the teaching vector value of the adjacent numbered teaching sentences, performing cosine similarity calculation on the teaching vector value of the adjacent numbered teaching sentences, and obtaining the course association value of the adjacent numbered teaching sentences.

[0024] Set the course association threshold. The course association threshold is a system preset value and can be modified according to actual needs.

[0025] When the course association value is greater than or equal to the course association threshold, the teaching statements with adjacent numbers are classified as the same teaching monitoring segment.

[0026] When the course association value is less than the course association threshold, the teaching statements with adjacent numbers are divided into two teaching monitoring segments.

[0027] By setting up a teaching recording segmentation module and a teaching sentence processing module, the teacher's remote teaching audio can be segmented and recombined through natural language processing, and the teacher's remote teaching audio can be intelligently divided into monitoring segments, which is convenient for subsequent targeted intelligent analysis of different monitoring segments.

[0028] The teaching segment division module obtains the teaching emotion value of each teaching monitoring segment and sets the teaching emotion threshold. The teaching emotion threshold is a system preset value and can be modified according to actual needs. When the teaching emotion value is greater than or equal to the teaching emotion threshold, the teaching monitoring segment is marked as an efficient teaching segment.

[0029] When the teaching emotion value is less than the teaching emotion threshold, the teaching monitoring segment is marked as an inefficient teaching segment.

[0030] When the teaching monitoring segment is marked as an inefficient teaching segment, the education monitoring management module sends a warning message of the inefficient teaching segment to the teacher, and simultaneously generates an inefficient warning record, which includes the warning time. All inefficient warning records of the teacher before the current time of the system are obtained, and all inefficient warning records are sorted in the order of the warning time. The time difference between the two adjacent warning times after sorting is calculated to obtain the warning interval, and all warning intervals are summed and averaged to obtain the average warning interval, which is marked as YED. The total number of inefficient warning records is obtained and marked as SZK. The formula is used Get the teacher's education evaluation QWA, where c1 is the average warning interval coefficient, c2 is the total number coefficient of inefficient warning records, the value of c1 is 0.57, and the value of c2 is 0.62. Set the education evaluation threshold. The education evaluation threshold is a system preset value and can be modified according to actual needs. When the education evaluation value is greater than or equal to the education evaluation threshold, the teacher is marked as an inefficient education teacher, and the information of the inefficient education teacher is uploaded to the school's academic affairs system. When the education evaluation value is less than the education evaluation threshold, no corresponding processing is performed.

[0031] When the teaching monitoring segment is marked as an efficient teaching segment, the teaching monitoring start time and the teaching monitoring end time of the efficient teaching segment are obtained, and the lecture video of each remote listening student is intercepted according to the teaching monitoring start time and the teaching monitoring end time. The effective teaching value of each remote listening student is obtained according to the lecture video, and the effective teaching threshold is set. The effective teaching threshold is a system preset value and can be modified according to actual needs. When the effective teaching value is greater than or equal to the effective teaching threshold, no corresponding processing is performed. When the effective teaching value is less than the effective teaching threshold, the remote listening student is marked as an invalid listening student, and the invalid listening students are sorted from small to large according to the effective teaching value value. After sorting, an education attention table for the efficient teaching segment is generated, and the teacher can focus on teaching the content of the efficient teaching segment to the invalid listening students according to the education attention table.

[0032] The effective teaching value of the remote listening students is obtained by the following method: converting the intercepted listening video into a video frame image, further processing the video frame image, extracting the eye image data of the remote listening students in the video frame image, obtaining the listening concentration value of the eye image data, sorting the video frame images in chronological order, obtaining the listening concentration value of each video frame image, setting the listening concentration threshold, when the listening concentration value is greater than or equal to the listening concentration threshold, marking the video frame image as a valid teaching image, obtaining the total number of valid teaching images and marking it as PLK, when the listening concentration value is less than the listening concentration threshold, marking the video frame image as an invalid teaching image, sorting all invalid teaching images in chronological order, calculating the time difference between the time of two adjacent invalid teaching images after sorting, obtaining the invalid teaching interval, summing up all invalid teaching intervals and taking the average, obtaining the average invalid teaching interval, and marking it as MBZ, using the formula The effective teaching value HVL of the remote students is obtained, where b1 is the total number coefficient of effective teaching images, b2 is the average invalid teaching interval coefficient, the value of b1 is 0.76, and the value of b2 is 0.43.

[0033] The lecture concentration value of the eye image data is obtained in the following manner: the eye image data is used as input data of an eye analysis model to obtain the lecture concentration value of the eye image data.

[0034] The eye analysis model is obtained in the following way: obtain multiple eye image data, the eye image data is collected by remote students during the actual remote teaching process, construct an eye image data set through multiple eye image data, assign different listening concentration values ​​to different eye image data, divide the eye image data set into a training set and a validation set, and obtain the eye analysis model through neural network iterative training. The larger the value of the listening concentration value, the more focused the eyes of the remote students are.

[0035] The teaching emotion value of the teaching monitoring segment is obtained in the following way: obtain all the teaching sentences in the teaching monitoring segment, use the corresponding audio of the teaching sentences as the input data of the teaching emotion mobilization model, obtain the teaching emotion mobilization value, sum up all the teaching emotion mobilization values ​​and take the average value to obtain the average teaching emotion mobilization value, and mark it as HCZ, obtain the total number of emotion mobilization changes, and mark it as GFS, obtain the average emotion richness mobilization density, and mark it as WSE, and use the formula The teaching emotion values ​​of the teaching monitoring segment are obtained, the value of a1 is 0.53, the value of a2 is 0.39, and the value of a3 is 1.02.

[0036] The total number of mobilization changes is obtained in the following way: sort all teaching emotion mobilization values ​​according to the numbering order of the teaching sentences, calculate the difference between the two adjacent teaching emotion mobilization values ​​after sorting and take the absolute value to obtain the emotion mobilization change value, set the emotion mobilization change threshold, the emotion mobilization change threshold is a system preset value, which can be modified according to actual needs. When the emotion mobilization change value is greater than or equal to the emotion mobilization change threshold, the number of emotion mobilization changes in the teaching monitoring segment is increased by one, and all the emotion mobilization change times in the teaching monitoring segment are summed up to obtain the total number of emotion mobilization changes. When the emotion mobilization change value is less than the emotion mobilization change threshold, no corresponding processing is performed.

[0037] The average emotion-rich mobilization density is obtained in the following way: setting the teaching emotion mobilization threshold, which is a system preset value and can be modified according to actual needs; when the teaching emotion mobilization value is greater than or equal to the teaching emotion mobilization threshold, the teaching emotion mobilization value is marked as an emotion-rich mobilization value; when the teaching emotion mobilization value is less than the teaching emotion mobilization threshold, no corresponding processing is performed; all emotion-rich mobilization values ​​are sorted in the order of the numbering of the teaching sentences; the difference between the numbers of the two adjacent teaching sentences after sorting is calculated to obtain the emotion-rich mobilization density; all emotion-rich mobilization densities are summed and averaged to obtain the average emotion-rich mobilization density.

[0038] The teaching emotion mobilization model is obtained in the following way: multiple groups of teaching sentence audios are obtained, and the teaching sentence audios are all audios captured from the teacher's previous remote teaching. The teaching sentence audios are used as training data for the convolutional neural network model, and the training data is assigned a teaching emotion mobilization value. The training data is iteratively trained through training sets and validation sets to obtain the teaching emotion mobilization model. The larger the teaching emotion mobilization value, the richer the teacher's emotions are in the teaching sentences.

[0039] Get the first-ranked teaching sentence in the efficient teaching segment, and mark the time of the first word in the first-ranked teaching sentence as the start time of teaching monitoring; get the last-ranked teaching sentence in the efficient teaching segment, and mark the time of the last word in the last-ranked teaching sentence as the start time of teaching monitoring.

[0040] The video of the lecture can be obtained through the camera of the remote student by pointing the camera at the remote student.

[0041] Setting up the teaching segment division module and the education monitoring management module can allow teachers to fully understand each remote student's listening situation on different knowledge points, and also allow the Academic Affairs Office to fully understand the teacher's teaching situation, thereby improving the supervision quality and efficiency of remote teaching.

[0042] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0043] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0044] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0045] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0046] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0047] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0048] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. 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 and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0049] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An educational remote monitoring and management system based on data analysis, characterized in that: It includes lecture recording segmentation module, lecture sentence processing module, lecture segment division module and education monitoring management module; The teaching recording segmentation module records the teacher's teaching explanation after the teacher starts the remote teaching, and obtains multiple groups of teaching sentences; The teaching statement processing module is used to divide multiple groups of teaching statements into multiple teaching monitoring segments; The teaching segment division module obtains the teaching emotion value of each teaching monitoring segment, and marks the teaching monitoring segment as an efficient teaching segment or an inefficient teaching segment according to the comparison result between the teaching emotion value and its threshold value; The education monitoring management module manages efficient teaching segments and inefficient teaching segments.

2. According to the data analysis-based education remote monitoring management system of claim 1, it is characterized in that: The teaching sentences are obtained in the following way: the teaching audio is generated in real time, and the recordings in the teaching audio are segmented by natural language processing to obtain multiple groups of teaching sentences, which are marked as DGi, where i is the sequential number of the teaching sentence, i=1, 2, ..., N, and N is a positive integer.

3. The educational remote monitoring and management system based on data analysis according to claim 1 is characterized in that: Divide multiple groups of teaching statements into multiple teaching monitoring segments, specifically: obtain the teaching vector value of each group of teaching statements, obtain the course association value of adjacent numbered teaching statements, set the course association threshold, when the course association value is greater than or equal to the course association threshold, classify the adjacent numbered teaching statements into the same teaching monitoring segment, when the course association value is less than the course association threshold, divide the adjacent numbered teaching statements into two teaching monitoring segments.

4. The educational remote monitoring and management system based on data analysis according to claim 3 is characterized in that: The teaching vector value of each group of teaching sentences is obtained in the following way: the word vector of each word in the teaching sentence is obtained through the Word2Vec model, and the teaching vector value is obtained by summing up the word vectors of each word in the teaching sentence.

5. The educational remote monitoring and management system based on data analysis according to claim 3 is characterized in that: The course association value of the adjacent numbered teaching sentences is obtained by the following method: obtaining the teaching vector value of the adjacent numbered teaching sentences, performing cosine similarity calculation on the teaching vector value of the adjacent numbered teaching sentences, and obtaining the course association value of the adjacent numbered teaching sentences.

6. The educational remote monitoring and management system based on data analysis according to claim 1 is characterized in that: The teaching emotion value of the teaching monitoring segment is obtained in the following way: obtain all the teaching sentences in the teaching monitoring segment, use the corresponding audio of the teaching sentences as the input data of the teaching emotion mobilization model, obtain the teaching emotion mobilization value, sum up all the teaching emotion mobilization values ​​and take the average value to obtain the average teaching emotion mobilization value, and mark it as HCZ, obtain the total number of emotion mobilization changes, and mark it as GFS, obtain the average emotion richness mobilization density, and mark it as WSE, and use the formula Get the teaching emotion value of the teaching monitoring segment.

7. The educational remote monitoring and management system based on data analysis according to claim 6 is characterized in that: The average emotion-rich mobilization density is obtained in the following way: a teaching emotion mobilization threshold is set; when the teaching emotion mobilization value is greater than or equal to the teaching emotion mobilization threshold, the teaching emotion mobilization value is marked as an emotion-rich mobilization value; when the teaching emotion mobilization value is less than the teaching emotion mobilization threshold, no corresponding processing is performed; all emotion-rich mobilization values ​​are sorted in the order of the numbering of the teaching sentences; the difference between the numbers of the two adjacent teaching sentences after sorting is calculated to obtain the emotion-rich mobilization density; all emotion-rich mobilization densities are summed and averaged to obtain the average emotion-rich mobilization density.

8. The educational remote monitoring and management system based on data analysis according to claim 6 is characterized in that: The total number of mobilization changes is obtained in the following way: sort all teaching emotion mobilization values ​​according to the numbering order of the teaching sentences, calculate the difference between the two adjacent teaching emotion mobilization values ​​after sorting and take the absolute value to obtain the emotion mobilization change value, set the emotion mobilization change threshold, when the emotion mobilization change value is greater than or equal to the emotion mobilization change threshold, increase the number of emotion mobilization changes in the teaching monitoring segment by one, sum up all the emotion mobilization change numbers in the teaching monitoring segment, and obtain the total number of emotion mobilization changes; when the emotion mobilization change value is less than the emotion mobilization change threshold, no corresponding processing is performed.

9. The educational remote monitoring and management system based on data analysis according to claim 1 is characterized in that: Manage efficient and inefficient teaching segments, specifically: When the teaching monitoring segment is marked as an inefficient teaching segment, a warning message of the inefficient teaching segment is sent to the teacher, and an inefficient warning record is generated synchronously. The inefficient warning record includes the warning time. All inefficient warning records of the teacher before the current system time are obtained, and all inefficient warning records are sorted in the order of the warning time. The time difference between the two adjacent warning times after sorting is calculated to obtain the warning interval. All warning intervals are summed and averaged to obtain the average warning interval, which is marked as YED. The total number of inefficient warning records is obtained and marked as SZK. The formula is used Get the teacher's education evaluation value QWA, where c1 is the average warning interval coefficient, c2 is the total number coefficient of inefficient warning records, set the education evaluation threshold, and when the education evaluation value is greater than or equal to the education evaluation threshold, mark the teacher as an inefficient education teacher, and upload the information of the inefficient education teacher to the school's teaching system; When the teaching monitoring segment is marked as an efficient teaching segment, the teaching monitoring start time and the teaching monitoring end time of the efficient teaching segment are obtained, and the lecture video of each remote lecturer is intercepted according to the teaching monitoring start time and the teaching monitoring end time, and the effective teaching value of each remote lecturer is obtained according to the lecture video, and an effective teaching threshold is set. When the effective teaching value is less than the effective teaching threshold, the remote lecturer is marked as an invalid lecturer, and the invalid lecturers are sorted from small to large according to the effective teaching value value. After sorting, an education attention table of the efficient teaching segment is generated, and the teacher can conduct key teaching of the content of the efficient teaching segment to the invalid lecturers according to the education attention table; The effective teaching value of the remote listening students is obtained by the following method: converting the intercepted listening video into a video frame image, further processing the video frame image, extracting the eye image data of the remote listening students in the video frame image, obtaining the listening concentration value of the eye image data, sorting the video frame images in chronological order, obtaining the listening concentration value of each video frame image, setting the listening concentration threshold, when the listening concentration value is greater than or equal to the listening concentration threshold, marking the video frame image as a valid teaching image, obtaining the total number of valid teaching images and marking it as PLK, when the listening concentration value is less than the listening concentration threshold, marking the video frame image as an invalid teaching image, sorting all invalid teaching images in chronological order, calculating the time difference between the time of two adjacent invalid teaching images after sorting, obtaining the invalid teaching interval, summing up all invalid teaching intervals and taking the average, obtaining the average invalid teaching interval, and marking it as MBZ, using the formula The effective teaching value HVL of the remote class students is obtained, where b1 is the total number coefficient of effective teaching images, and b2 is the average invalid teaching interval coefficient; The class concentration value of the eye image data is obtained by the following method: the eye image data is used as input data of the eye analysis model to obtain the class concentration value of the eye image data; Get the first-ranked teaching sentence in the efficient teaching segment, and mark the time of the first word in the first-ranked teaching sentence as the start time of teaching monitoring; get the last-ranked teaching sentence in the efficient teaching segment, and mark the time of the last word in the last-ranked teaching sentence as the start time of teaching monitoring.

10. A method for remote monitoring and management of education based on data analysis, applied to the remote monitoring and management system of education based on data analysis as claimed in claim 1, characterized in that: The steps include: Step 1: After the teacher starts teaching remotely, record the teacher's lectures and obtain multiple groups of lecture sentences; Step 2: Divide multiple groups of teaching sentences into multiple teaching monitoring segments; Step 3: Obtain the teaching emotion value of each teaching monitoring segment, and mark the teaching monitoring segment as an efficient teaching segment or an inefficient teaching segment according to the comparison result between the teaching emotion value and its threshold; Step 4: Manage efficient and inefficient teaching segments.

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