Educational resource processing method, device, computer equipment and storage medium

Abstract

The present application relates to speech recognition in the field of artificial intelligence, and provides a method, device, computer equipment and storage medium for processing educational resources. The method comprises: obtaining the teaching video data sent by the camera device and the identification information of the camera device, determining the class number corresponding to the teaching video data based on the identification information of the camera device, generating teaching supervision parameters according to the teaching video data and the class number, and releasing the educational resources corresponding to the class number according to the teaching supervision parameters, wherein the teaching supervision parameters include class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student sign-in parameters. By adopting this method, offline teaching supervision is combined with one-class-one-dismissal, realizing a closed-loop supervision, and there is no need for students to manually confirm the cancellation of classes, nor for supervisors to verify on-site, thereby improving the processing efficiency of educational resources.

Description

Technical Field

[0001] The present application relates to the field of speech recognition technology of artificial intelligence, and in particular to an educational resource processing method, apparatus, computer equipment, storage medium and computer program product. Background Art

[0002] At present, various places have successively introduced policies for the supervision of educational resources of teaching institutions, mainly to take corresponding supervision measures in conjunction with various platforms to supervise the educational resources of teaching institutions. Common supervision measures include the staff of the supervision department supervising the teaching behavior of teaching institutions through on-site spot checks, and then determining the issuance of educational resources; it also includes the "one lesson, one cancellation" supervision model, which means that after the educational resources enter the supervision account of the supervision platform, the educational resources are paid in accordance with the same progress as the course. The payment of educational resources in the same progress as the course means that after the students complete each class, the students or their parents will confirm the cancellation of the class on the mobile phone. After confirmation, the educational resources corresponding to the class will be released from the supervision account to ensure that the educational resources are released in the same progress as the course, thereby protecting the rights and interests of consumers.

[0003] However, the above-mentioned supervision method has the following problems: on the one hand, the supervision of educational resources mainly relies on on-site spot checks by supervisors or manual confirmation of course cancellation by students, which is time-consuming; on the other hand, in the actual supervision process, there are often some objective factors (such as forgetting to confirm, deliberate non-confirmation, etc.) that make it impossible to release educational resources in time or impossible to release them. In this case, the release of educational resources requires subsequent negotiation and communication, and the processing efficiency is too low.

[0004] Therefore, there is a need to provide a regulatory solution that can improve the efficiency of educational resource processing. Summary of the invention

[0005] Based on this, it is necessary to provide an educational resource processing method, apparatus, computer equipment, computer-readable storage medium and computer program product that can improve the processing efficiency of educational resources in response to the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for processing educational resources. The method comprises:

[0007] Acquire the teaching video data and identification information of the camera device sent by the camera device;

[0008] Based on the identification information of the camera device, determine the class number corresponding to the teaching video data;

[0009] Generate teaching supervision parameters based on teaching video data and lesson number;

[0010] According to the teaching supervision parameters, release the educational resources corresponding to the class number;

[0011] Among them, teaching supervision parameters include class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student check-in parameters.

[0012] In one embodiment, based on the identification information of the camera device, determining the class number corresponding to the teaching video data includes:

[0013] Based on the identification information of the camera device, a course information set associated with the identification information of the camera device is searched in a preset database, where the course information set includes a plurality of class hour numbers and class hour times corresponding to each class hour number;

[0014] Get the time information of the teaching video data, traverse the class time corresponding to each class number, and find the class time where the time information is located;

[0015] The class number corresponding to the found class time is determined as the class number.

[0016] In one embodiment, generating teaching supervision parameters according to teaching video data and lesson number includes:

[0017] Perform target detection on the teaching video data and identify the number of students in the first class;

[0018] Get the number of people in the second class that is stored and corresponds to the class number;

[0019] Regularly compare the number of students in the first class and the second class, and generate class number supervision parameters based on the comparison results.

[0020] In one embodiment, the camera device includes a rear camera device deployed behind the teaching scene;

[0021] According to the teaching video data and lesson number, the teaching supervision parameters are generated including:

[0022] Extracting rear-view teaching video data collected by a rear camera device from the teaching video data;

[0023] Performing facial recognition on the rear-view teaching video data to identify the facial image data of the first lecturer;

[0024] Obtaining pre-stored facial image data of a second lecturer corresponding to the class number;

[0025] The facial image data of the first teacher and the facial image data of the second teacher are compared, and the teacher supervision parameters are generated according to the comparison results of the teacher facial images.

[0026] In one embodiment, the teaching content supervision parameters include teaching content abnormality parameters;

[0027] According to the teaching video data and lesson number, the teaching supervision parameters are generated including:

[0028] Extracting audio data from teaching video data;

[0029] Get the pre-stored out-of-syllabus keywords corresponding to the lesson number;

[0030] Identify whether there are out-of-scope keywords in the audio data;

[0031] If the number of identified out-of-syllabus keywords exceeds a preset threshold, abnormal teaching content parameters are generated.

[0032] In one embodiment, the camera device includes a front camera device deployed in front of the teaching scene;

[0033] According to the teaching video data and lesson number, the teaching supervision parameters are generated including:

[0034] Extracting front-view teaching video data collected by a front camera device from the teaching video data;

[0035] Performing facial recognition on the front-view teaching video data to obtain facial image data of the first student;

[0036] Acquire pre-stored facial image data of a second student corresponding to the lesson number;

[0037] The facial image data of the first student is compared with the facial image data of the second student, and the student sign-in parameters are updated according to the comparison result of the student facial image data.

[0038] In one embodiment, releasing the educational resource corresponding to the class number according to the teaching supervision parameter includes:

[0039] For each student, based on the student's sign-in parameters, update the status of the educational resources corresponding to the class number. The status of educational resources includes pending release and frozen status;

[0040] According to the class number supervision parameters, the teaching teacher supervision parameters and the teaching content supervision parameters, it is determined whether the teaching behavior of the teaching institution associated with the class number is abnormal;

[0041] If there is no abnormality in the teaching behavior of the teaching institution associated with the class number and the status of the educational resources corresponding to the class number is to be released, the educational resources corresponding to the class number will be released.

[0042] In a second aspect, the present application also provides an educational resource processing device. The device comprises:

[0043] A data acquisition module, used to acquire the teaching video data sent by the camera device and the identification information of the camera device;

[0044] A lesson number determination module, used to determine the lesson number corresponding to the teaching video data based on the identification information of the camera device;

[0045] A teaching supervision parameter generation module is used to generate teaching supervision parameters according to teaching video data and lesson number;

[0046] The educational resource release module is used to release the educational resources corresponding to the class number according to the teaching supervision parameters;

[0047] Among them, teaching supervision parameters include class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student check-in parameters.

[0048] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0049] Acquire the teaching video data and identification information of the camera device sent by the camera device;

[0050] Based on the identification information of the camera device, determine the class number corresponding to the teaching video data;

[0051] Generate teaching supervision parameters based on teaching video data and lesson number;

[0052] According to the teaching supervision parameters, release the educational resources corresponding to the class number;

[0053] Among them, teaching supervision parameters include class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student check-in parameters.

[0054] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0055] Acquire the teaching video data and identification information of the camera device sent by the camera device;

[0056] Based on the identification information of the camera device, determine the class number corresponding to the teaching video data;

[0057] Generate teaching supervision parameters based on teaching video data and lesson number;

[0058] According to the teaching supervision parameters, release the educational resources corresponding to the class number;

[0059] Among them, teaching supervision parameters include class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student check-in parameters.

[0060] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0061] Acquire the teaching video data and identification information of the camera device sent by the camera device;

[0062] Based on the identification information of the camera device, determine the class number corresponding to the teaching video data;

[0063] Generate teaching supervision parameters based on teaching video data and lesson number;

[0064] According to the teaching supervision parameters, release the educational resources corresponding to the class number;

[0065] Among them, teaching supervision parameters include class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student check-in parameters.

[0066] The above-mentioned educational resource processing method, device, computer equipment, storage medium and computer program product obtain the teaching video data sent by the camera device and the identification information of the camera device, then, based on the identification information of the camera device, determine the class number corresponding to the obtained teaching video data, and then generate teaching supervision parameters from multiple supervision dimensions based on the teaching video data and the class number, including class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student sign-in parameters, and then release the educational resources corresponding to the class number based on the class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student sign-in parameters, so as to achieve automatic supervision of educational resources. In the above process, the real offline teaching data can be obtained through the camera device, and the student sign-in parameters can be generated through the teaching video data, so that the students can automatically sign in for offline classes without manually signing in and canceling the class on the mobile phone. The class number supervision parameters, teaching teacher supervision parameters and teaching content supervision parameters are generated through the teaching video data, so that the teaching behavior of the teaching institution can be supervised from multiple angles to see if there are any abnormalities, and the teaching institution can be automatically and objectively and reasonably supervised. The entire plan combines offline teaching supervision with the one-class-one-dismissal model, providing regulatory departments with offline supervision solutions. It also conducts multi-dimensional and reasonable supervision of teaching institutions through class number supervision parameters, teaching teacher supervision parameters and teaching content supervision parameters, realizing a closed loop of online and offline supervision. The supervision process enables students to automatically sign in after offline classes, without the need for students to manually confirm class cancellation or for supervisors to go to the site for verification. This omits tedious operations, saves manpower and time, and comprehensively improves the processing efficiency of educational resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1An application environment diagram of an educational resource processing method in an embodiment;

[0068] Figure 2 A schematic diagram of a flow chart of an educational resource processing method in one embodiment;

[0069] Figure 3 A schematic diagram of a flow chart of a step of determining a lesson number in an embodiment;

[0070] Figure 4 A schematic diagram of a flow chart of steps for generating teacher supervision parameters in one embodiment;

[0071] Figure 5 A flowchart of the steps of generating abnormal parameters of teaching content in one embodiment;

[0072] Figure 6 A flow chart of a student sign-in parameter update step in one embodiment;

[0073] Figure 7 A schematic diagram of a flow chart of a step of releasing educational resources corresponding to a class hour number in one embodiment;

[0074] Figure 8 is a detailed flowchart of an educational resource processing method in an embodiment;

[0075] Fig. 9 is a structural block diagram of an educational resource processing device in one embodiment;

[0076] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0078] It should be noted that the user information and data, as well as the device information involved in the technical solution of this application, are all information and data authorized by the user or fully authorized by all parties, and the acquisition, storage, use, and processing of the data are in compliance with the relevant provisions of national laws and regulations.

[0079] The educational resource processing method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the camera device 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Specifically, the camera device 102 can regularly send the teaching video data and its own device identification information in each teaching scene during the class to the server 104, and the server 104 obtains the teaching video data and the device identification information of the camera device, and determines the class number corresponding to the teaching video data based on the identification information of the camera device, and then generates teaching supervision parameters from multiple supervision dimensions based on the teaching video data and the class number, including class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student sign-in parameters, and then releases the educational resources corresponding to the class number according to the class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student sign-in parameters, so as to achieve automatic supervision of educational resources. Among them, the camera device 102 can be, but not limited to, a camera, a video camera, an intelligent camera device and other devices with camera functions. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0080] In one embodiment, Figure 2 As shown, a method for processing educational resources is provided, which is applied to Figure 1 The server in the example is used to illustrate the following steps:

[0081] Step 100, obtaining the teaching video data sent by the camera device and the identification information of the camera device.

[0082] In this embodiment, the camera device may be an intelligent camera device, the identification information of the camera device may be the device number, and the teaching video data may be the teaching video data recorded by the camera device during the class. In the specific implementation, the camera device is configured to regularly transmit the teaching video data to the server during the class time period, and the transmission period may be determined according to the specific situation, and the default is 30 minutes. Specifically, the intelligent camera device has basic functions: face recognition, temperature measurement, number detection, and video monitoring. Each intelligent camera device has a dedicated device number and supports networking. Based on the basic functions of the above-mentioned offline intelligent camera device, the following offline supervision scenarios can be implemented: face recognition of students for class sign-in confirmation, face recognition of teachers to ensure that the teacher and the teacher report information are consistent, and the students or teachers in class are measured for temperature. People with abnormal body temperature can be found in time according to the measured body temperature, the number of people is detected to detect whether the class personnel have changed significantly, and the video monitoring is used to record the teaching content to supervise the teaching content.

[0083] In actual applications, after the teaching institution completes the deployment of the smart camera equipment, it completes the settlement on the supervision platform and obtains its exclusive institution number. Subsequently, the information is entered, and the classroom information and the number of the camera equipment deployed in the classroom are entered on the platform. The background associates the equipment number of the smart camera with the classroom number and the institution number, so that the subject corresponding to the smart camera equipment can be quickly identified. Next, enter the course class information (including the classroom number, course name, class time, number of class hours), student information (including student name, student high-definition photo) and teaching teacher information including (teacher name, teacher high-definition photo), etc. The platform will generate a class number for each class, a student number for each student, and a teacher number for each teacher, and then bind the classroom information with the class information, student information and teacher information to establish the association between the classroom and the class, and between the student and the teacher. In this way, each institution number in the supervision platform corresponds to multiple classroom numbers, each classroom number corresponds to multiple class numbers, and each class number corresponds to a student number and a teacher number. Through the above-mentioned information input and information association operations, the equipment numbers of the video equipment are linked to the teaching institution information (including classrooms, courses, students, teachers, and class hours), that is, the corresponding teaching institution information, classroom information, course information, student information, teacher information, and class hour information can be found through the equipment number of the video equipment.

[0084] Step 200, based on the identification information of the camera device, determine the lesson number corresponding to the teaching video data.

[0085] As described in the above embodiment, since the device number of the camera device has established an association with the class information, the class number corresponding to the teaching video data can be found according to the device number of the camera device, that is, the class video of which the teaching video data is recorded can be identified. In specific implementation, the class number to which the teaching video data belongs can be determined according to the time information of the teaching video data, that is, the specific class video data of which the teaching video data is recorded can be determined.

[0086] Step 300, generating teaching supervision parameters according to the teaching video data and the class number, the teaching supervision parameters including class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student sign-in parameters.

[0087] After determining the class number of the teaching video data, the teaching video data may be processed by face recognition, voice recognition, etc., and then combined with the class number and the student information, teacher information, and course information associated with the class number, to generate teaching supervision parameters of multiple dimensions. Specifically, the teaching supervision parameters include class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters, and student sign-in parameters. In other embodiments, the teaching supervision parameters may also include body temperature supervision parameters. As described in the above embodiments, the intelligent camera device has functions such as face recognition, headcount, temperature measurement and video recording. In another embodiment, the teaching video data may carry the number of students in the class, student facial image data, teacher facial image data, and temperature data of students and teachers, etc., which are identified by the intelligent camera device. Then, the corresponding number of students in the class, high-definition photos of students, and high-definition photos of teachers can be obtained according to the class number. Then, the number of students in the class, high-definition photos of students, and high-definition photos of teachers are combined with the number of students in the class, facial image data of students, facial image data of teachers, temperature data of students and teachers, and the video data captured by the intelligent camera device to generate class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters, student sign-in parameters, and temperature supervision parameters, etc. The specific parameters can be determined according to the actual situation and are not limited here.

[0088] Step 400, releasing the educational resources corresponding to the class number according to the teaching supervision parameters.

[0089] In actual application, the educational resources corresponding to each class number of each student are released according to the supervision mode of one class and one elimination. Therefore, it can be understood that before supervision is implemented according to the teaching supervision parameters, the educational resources corresponding to the class number of each student who has not signed in for class should be in a "frozen" state. In specific implementation, after obtaining multi-dimensional teaching supervision parameters including class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student sign-in parameters, it can be determined whether the student has completed the course study of the class number according to the above teaching supervision parameters, and whether the teaching behavior of the teaching institution meets the preset educational resource supervision requirements. The educational resource supervision requirements are mainly used to supervise whether there are abnormalities in the teaching behavior of the teaching institution. If it is determined that the teaching supervision parameters meet the preset educational resource supervision requirements, that is, there is no abnormality in the teaching behavior, the educational resources corresponding to the class number can be released. Specifically, the release process of educational resources can be based on the sign-in supervision parameters of each student, that is, if the sign-in supervision parameters indicate that the course of the student's class number has been signed in, then the corresponding resource release message is sent to the resource supervisor, so that the resource supervisor can distribute the educational resources corresponding to the student's class number to the corresponding teaching institution. It can be understood that the above method is only illustrated by an example using a piece of teaching video data. In actual application, this method can process multiple pieces of teaching video data at the same time, and each piece of teaching video data is processed in the above manner to release the educational resources corresponding to the class number to which each teaching video data belongs.

[0090] In the above-mentioned educational resource processing method, the teaching video data sent by the camera device and the identification information of the camera device are obtained, and then, based on the identification information of the camera device, the class number corresponding to the obtained teaching video data is determined, and then, based on the teaching video data and the class number, the teaching supervision parameters are generated from multiple supervision dimensions, including the class number supervision parameters, the teaching teacher supervision parameters, the teaching content supervision parameters and the student sign-in parameters, and then, according to the class number supervision parameters, the teaching teacher supervision parameters, the teaching content supervision parameters and the student sign-in parameters, the educational resources corresponding to the class number are released to achieve automatic supervision of educational resources. In the above process, the real offline teaching data can be obtained through the camera device, and the student sign-in parameters are generated through the teaching video data, which can realize the automatic sign-in of students for offline classes, without the need to manually sign in and cancel the class confirmation on the mobile phone. The class number supervision parameters, the teaching teacher supervision parameters and the teaching content supervision parameters are generated through the teaching video data, which can supervise whether there are abnormalities in the teaching behavior of the teaching institution from multiple angles, and automatically and objectively and reasonably supervise the teaching institution. The entire plan combines offline teaching supervision with the one-class-one-dismissal model, providing regulatory authorities with an offline supervision solution. The supervision process also enables students to automatically sign in after offline classes, without the need for students to manually confirm their cancellation of classes, or for supervisors to verify on-site. The plan also uses class number supervision parameters, teaching teacher supervision parameters, and teaching content supervision parameters to conduct reasonable multi-dimensional supervision of teaching institutions, achieving a closed loop of online and offline supervision, which comprehensively improves the processing efficiency of educational resources.

[0091] like Figure 3 As shown, in one embodiment, step 200 includes:

[0092] Step 220: Based on the identification information of the camera device, a course information set associated with the identification information of the camera device is searched in a preset database. The course information set includes multiple class numbers and class times corresponding to each class number.

[0093] Step 240, obtain the time information of the teaching video data, traverse the class time corresponding to each class number, and find out the class time where the time information is located.

[0094] Step 260, determining the class number corresponding to the found class time as the class number.

[0095] Specifically, generally speaking, the course information will be arranged in advance, that is, presented in the form of a timetable, such as a mathematics class in classroom numbered 001 from 9:00 a.m. to 10:00 a.m. on August X, 202X, and the class number of the mathematics class is 0003. And the duration of each class is fixed, such as 45 minutes or 1 hour, and the teaching video data recorded by the camera device also carries the shooting time information, and as described in the above embodiment, the device number of the camera device has established an association with the class information. Therefore, in a specific implementation, determining the class number corresponding to the teaching video data can be: first, based on the device number of the camera device, search in the preset database to find the classroom number associated with the device number of the camera device, and then find the associated course information set from the classroom number. The course information set includes multiple class numbers and the class time corresponding to each class number. Then, extract the time information in the teaching video data, that is, the shooting time, traverse the class time corresponding to each class number, filter out the class numbers with class time on the shooting day, and then compare the class time with the shooting time one by one to determine whether the shooting time falls within the current class time. If so, the class number corresponding to the current class time is determined as the class number of the teaching video data. For example, the shooting time of the teaching video data is 9:30 am on January 1, 2022. According to the device number of the camera device, the class number and the class time corresponding to each class number on January 1, 2022 are found. The class number is 0002: the class time is 9 am-9:45 am, and the class number is 0003: the class time is 10 am-10:45 am. Then, according to the shooting time of 9:30 am, finally, the class number is locked to 0002. In this embodiment, by establishing an association between the device number of the camera device and the class number, it is convenient to quickly locate the class number corresponding to the teaching video data when the teaching video data and the device number of the camera device are obtained, so as to realize the "one-to-one" accurate identification of a section of teaching video data and the class number.

[0096] In one embodiment, the class number supervision parameter includes a class number abnormality parameter.

[0097] Step 300 includes: Step 320, performing target detection on the teaching video data, identifying the number of people in the first class, obtaining the pre-stored number of people in the second class corresponding to the class number, regularly comparing the number of people in the first class and the number of people in the second class, and generating class number supervision parameters based on the class number comparison results.

[0098] The class number supervision parameters include normal class number parameters and abnormal class number parameters. In specific implementation, intelligent cameras may be installed in front and behind each classroom. The process of generating class number supervision parameters may be: target detection such as character detection is performed on the teaching video data, and the number of recognized characters is counted to obtain the number of people in the first class. Then, the pre-stored second class number corresponding to the class number is obtained, and then the first class number and the second class number are compared regularly for many times, such as once every 10 minutes, and the comparison results of each class number are recorded. If the first class number and the second class number are inconsistent and far apart, such as a difference of 10, the current comparison result is judged as an abnormal comparison result and recorded. If after multiple comparisons, more than 2 or more abnormal comparison results appear, the abnormal class number parameter is generated, otherwise, the normal class number parameter is generated. It can be understood that if a student asks for leave, the student can apply for leave on the platform before class. After the leave application is approved, the platform will automatically update the class number corresponding to the class number. In specific implementation, the teaching video data shot by the intelligent camera installed in front of the classroom can be extracted, face detection can be performed on the teaching video data, the number of recognized characters can be counted, and the number of people in the first class can be obtained.

[0099] In another embodiment, the number of students in the first class can also be obtained by extracting the number of students in the first class carried by the teaching video data. Since the intelligent camera device has a number detection function, the camera device can detect the number of students in the teaching scene by itself to obtain the number of students in the first class, and send the number of students in the first class to the server. The above two situations can be determined according to the actual situation and are not limited here. In this embodiment, by comparing the number of students in the first class and the number of students in the second class multiple times and regularly, it is possible to avoid the situation where students leave the classroom in the middle of the class due to something and make a misjudgment, thereby improving the accuracy of the class number supervision parameters.

[0100] like Figure 4 As shown, in one embodiment, the camera device includes a rear camera device deployed behind the teaching scene;

[0101] Step 300 includes:

[0102] Step 342, extracting the rear-view teaching video data captured by the rear camera device from the teaching video data.

[0103] Step 344, performing facial recognition on the rear-view teaching video data to identify the facial image data of the first teacher.

[0104] Step 346, obtaining the pre-stored facial image data of the second teacher corresponding to the class number.

[0105] Step 348, compare the facial image data of the first teacher and the facial image data of the second teacher, and generate teacher supervision parameters according to the comparison result of the teacher facial images.

[0106] The teacher supervision parameters include abnormal teacher information parameters and normal teacher information parameters. As described in the above embodiment, intelligent camera devices can be installed in front and behind each classroom, and the intelligent camera device installed in front of the classroom (referred to as the front camera device) can capture the facial information of the students, and the intelligent camera device installed in the back of the classroom (referred to as the rear camera device) can capture the facial information of the teacher. The rear-view teaching video data refers to the teaching video data shot from the perspective of the teacher's back by the rear camera device. In practical applications, the front camera device and the rear camera device of each classroom will transmit teaching video data during class. Therefore, in this embodiment, the teaching video data shot by the rear camera device can be extracted according to the device number of the rear camera device, and then the rear-view teaching video data is subjected to facial recognition to identify the first teacher's facial image data. Specifically, the frame image in the teaching video data can be intercepted, and the frame image can be an image containing only the facial image data of the person standing in front of the classroom, and the frame image is subjected to facial recognition to identify the first teacher's facial image data. Then, obtain the pre-stored high-definition photo of the teacher on record corresponding to the class number, that is, the facial image data of the second teacher, and perform image matching on the facial image data of the first teacher and the facial image data of the second teacher. If the matching between the two fails, it is determined that the facial image data of the first teacher and the facial image data of the second teacher are inconsistent, and the abnormal parameters of the teacher information are generated. If the matching between the two is successful, it is determined that the facial image data of the first teacher and the facial image data of the second teacher are the same teacher, and the normal parameters of the teacher information are generated. Furthermore, only information related to the abnormal parameters of the teacher can be recorded. In this embodiment, by installing intelligent camera equipment at the front and back of the classroom respectively, the facial image data of the students and teachers can be accurately distinguished, and the facial image data of the teacher can be quickly identified, thereby improving the efficiency of generating the supervision parameters of the teacher.

[0107] like Figure 5 As shown, in one embodiment, the teaching content supervision parameters include teaching content abnormality parameters;

[0108] Step 300 includes:

[0109] Step 360, extracting the audio data from the teaching video data.

[0110] Step 362, obtain the pre-stored out-of-syllabus keywords corresponding to the lesson number.

[0111] Step 364, identify whether there are out-of-syllabus keywords in the audio data.

[0112] Step 366: If the number of identified out-of-syllabus keywords exceeds a preset threshold, abnormal parameters of the teaching content are generated.

[0113] The teaching content supervision parameters include normal teaching content parameters and abnormal teaching content parameters. Out-of-syllabus keywords refer to keywords that exceed the content scope of teaching or examinations specified in the syllabus of each stage of study. In actual applications, operators can set out-of-syllabus keywords for the class number of each subject (Chinese, mathematics, English, history, politics, chemistry, physics, etc.) in each stage of study (kindergarten, elementary school grades 1-6, junior high school grades 7-9, high school stage) in advance. For example, the out-of-syllabus keywords for first-grade elementary school mathematics are "function", "Pythagorean theorem", etc. Specifically, they can be set according to the keywords of the content scope of teaching or examinations specified in the syllabus of each stage of study. In specific implementation, the process of generating teaching content supervision parameters can be: using an audio data extraction method to extract the audio data of the teaching video data, then obtaining the pre-stored out-of-syllabus keywords corresponding to the class number, using a voice keyword recognition method to identify whether there are out-of-syllabus keywords in the audio data, and statistically identifying the number of out-of-syllabus keywords. If the number of out-of-syllabus keywords exceeds a preset number threshold, such as 5, it is determined that the current teaching content is abnormal, and abnormal teaching content parameters are generated and recorded. Otherwise, normal teaching content parameters are generated and no records are made. In other embodiments, the recording data of the smart device can be directly obtained, and voice keyword recognition can be performed on the recording data.

[0114] In another embodiment, the generation of teaching content supervision parameters can also be done by the supervisor checking the teaching video data. If the supervisor finds that there is an abnormality in the teaching content, such as seeing that the teaching content is the textbook content of the second grade Chinese language, but the course information entered into the system is the fifth grade Chinese language of elementary school, the supervisor will manually add a record of the abnormal teaching content on the platform.

[0115] In this embodiment, by pre-setting the out-of-syllabus keywords for each lesson number, and then extracting the audio data from the teaching video data, combining the voice keyword recognition method, the out-of-syllabus keywords in the audio data are identified to achieve automatic supervision of the teaching content.

[0116] like Figure 6 As shown, in one embodiment, the camera device includes a front camera device deployed in front of the teaching scene, and step 300 includes:

[0117] Step 380, extracting the front-view teaching video data collected by the front camera device from the teaching video data;

[0118] Step 382, ​​performing facial recognition on the teaching video data to obtain facial image data of the first student;

[0119] Step 384, obtaining the pre-stored facial image data of the second student corresponding to the class number;

[0120] Step 386, compare the first student's facial image data with the second student's facial image data, and update the student's sign-in parameters according to the comparison result of the student's facial image data.

[0121] The student sign-in parameter is a parameter that indicates whether the student has signed in. In this embodiment, the student sign-in parameter defaults to "not signed in". In practical applications, the class sign-in confirmation based on the teaching video data can be: according to the device number of the front camera device, the front-view teaching video data collected by the front camera device is extracted, and then the front-view teaching video data is subjected to facial recognition to obtain the facial image of the first student, and then the pre-stored high-definition photo of the student corresponding to the class number, that is, the facial image data of the second student, is obtained, and the first student image data and the second student facial image data are compared. For the students who have successfully compared, the student sign-in parameter of the class number is updated from "not signed in" to "signed in". For the students who have failed to compare successfully, the sign-in failure message is fed back to the student's account for confirmation. If it is a platform misjudgment, the relevant class attendance certificate document can be submitted to the platform, and the platform further reviews the class attendance certificate document to update the student sign-in parameter. In other embodiments, the update of the student sign-in parameters may also be to obtain the facial image data of each student sent by the intelligent camera device, compare the obtained student facial image data with the pre-stored second student facial image data corresponding to the class number, and then update the student sign-in parameters for the student who successfully matched. In this embodiment, by extracting the front-view teaching video data collected by the front camera device, and then performing facial recognition on the front-view teaching video data, the student's automatic class sign-in confirmation is realized, and the student does not need to manually sign in for the class, which is both intelligent and convenient, and improves the efficiency of sign-in confirmation.

[0122] In one embodiment, the method further includes: acquiring facial image data and body temperature data of the students and the teacher sent by the camera device, comparing the body temperature data with a preset body temperature threshold, and generating a body temperature monitoring parameter based on the body temperature comparison result.

[0123] The temperature monitoring parameters include normal temperature parameters and abnormal temperature parameters. In specific implementation, the facial image data and temperature data of the students and teachers are one-to-one corresponding, the temperature threshold can be set to 37.3 degrees Celsius, and the acquired temperature data are compared with 37.3 degrees Celsius one by one. If the temperature data of the students and teachers is less than 37.3 degrees Celsius, normal temperature parameters are generated. If the temperature data of the students and teachers is greater than or equal to 37.3 degrees Celsius, abnormal temperature data is generated, and the facial image data of the students or teachers with abnormal body temperature is compared with the pre-stored facial image data of the students or teachers, so as to obtain the corresponding student number or teacher number of the students or teachers with abnormal body temperature. In this embodiment, by measuring the temperature of the students or teachers in the classroom and performing abnormal temperature detection, the students or teachers with abnormal body temperature can be found in time to ensure the physical and mental health of the students or teachers.

[0124] like Figure 7 As shown, in one embodiment, step 400 includes:

[0125] Step 420, for each student, based on the student's sign-in parameters, update the status of the educational resource corresponding to the class number, the status of the educational resource includes a pending release status and a frozen status.

[0126] Step 422, if the status of the educational resource corresponding to the class number is to be released, determine whether there is any abnormality in the teaching behavior of the teaching institution associated with the class number based on the class number supervision parameters, the teaching teacher supervision parameters and the teaching content supervision parameters.

[0127] Step 424: If there is no abnormality in the teaching behavior of the teaching institution associated with the class number and the status of the educational resources corresponding to the class number is to be released, the educational resources corresponding to the class number are released.

[0128] The status of the educational resources for each class number of each student is "frozen" by default. In specific implementation, the release process of the educational resources of the class number can be: for each student, determine whether the student has signed in for the class number based on the student's sign-in parameters. If it is determined that the student has signed in, then the status of the educational resources corresponding to the student's class number is updated from "frozen" to "pending release". If it is determined that the student has not signed in successfully, then the status of the educational resources corresponding to the student's class number continues to remain "frozen". After updating the status of the educational resources corresponding to the class number, judge whether there is any abnormality in the teaching behavior of the teaching institution associated with the class number based on the class number supervision parameters, the teaching teacher supervision parameters and the teaching content supervision parameters. Specifically, the process of whether the teaching behavior is abnormal can be: if there is a record of abnormal number of students in the class, an early warning message is issued, if there are more than 2 records of abnormal parameters of the teaching teacher information, it is determined that the teaching behavior of the teaching institution associated with the class number is abnormal, if there are 2 records of abnormal parameters of the teaching content, it is determined that the teaching behavior of the teaching institution associated with the class number is abnormal, if there are abnormal temperature parameters, it is determined that the teaching behavior of the teaching institution associated with the class number is abnormal, and for the above situation, it can be that at least one situation occurs, then the teaching behavior of the teaching institution associated with the class number is determined to be abnormal. It can be set according to the specific requirements of the regulatory department, and it is not limited here. If the teaching behavior of the teaching institution associated with the class number is not abnormal and the status of the educational resources corresponding to the class number is to be released, a resource release message is sent to the regulatory account, so that the regulatory account releases the educational resources corresponding to the class number to the account of the teaching institution, otherwise, the educational resource release failure message is pushed to enable the teaching institution to make timely rectifications and standardize its teaching behavior. After completing the rectification, the teaching institution can submit the corresponding rectification certificate to the platform to review whether the certificate meets the preset rectification requirements and whether the information is true and valid. After the rectification certificate is reviewed and approved, the education resource unfreezing message is sent to the supervision account to release the education resources corresponding to the class number to the account of the teaching institution. In this embodiment, based on the teaching supervision parameters, the effect of offline supervision of the teaching institution is achieved in an online manner from multiple dimensions, and the automatic release of the education resources corresponding to the class number is realized, thereby improving the processing efficiency of education resources.

[0129] In order to make a clearer explanation of the educational resource processing method provided by this application, Figure 8 and one A specific embodiment is described below, and the specific embodiment may include the following steps:

[0130] Step 1: Obtain the teaching video data sent by the camera device and the identification information of the camera device.

[0131] Step 2: Based on the identification information of the camera device, find out the course information set associated with the identification information of the camera device in the preset database, the course information set includes multiple class numbers and the class time corresponding to each class number, obtain the time information of the teaching video data, traverse the class time corresponding to each class number, find out the class time where the time information is located, and determine the class number corresponding to the found class time as the class number.

[0132] Step 3: Perform target detection on the teaching video data, identify the number of people in the first class, obtain the pre-stored number of people in the second class corresponding to the class number, and regularly compare the number of people in the first class and the second class. If the error between the number of people in the first class and the number of people in the second class exceeds the preset threshold for more than or equal to 2 times, then generate abnormal parameters for the number of people in the class; otherwise, generate normal parameters for the number of people in the class.

[0133] Step 4: Extract the rear-view teaching video data collected by the rear camera equipment from the teaching video data, perform facial recognition on the rear-view teaching video data, identify the facial image data of the first teacher, obtain the pre-stored facial image data of the second teacher corresponding to the class number, and perform image matching on the facial image data of the first teacher and the facial image data of the second teacher. If the matching between the two fails, it is determined that the facial image data of the first teacher and the facial image data of the second teacher are inconsistent, and abnormal teacher information parameters are generated. If the matching between the two succeeds, it is determined that the facial image data of the first teacher and the facial image data of the second teacher are the same teacher, and normal teacher information parameters are generated.

[0134] Step 5: Extract the audio data of the teaching video data, obtain the pre-stored out-of-syllabus keywords corresponding to the lesson number, identify whether there are out-of-syllabus keywords in the audio data, and identify whether there are out-of-syllabus keywords in the audio data. If the number of out-of-syllabus keywords exceeds the preset threshold, such as 5, it is determined that there is an abnormality in the current teaching content, and abnormal teaching content parameters are generated and recorded. Otherwise, normal teaching content parameters are generated.

[0135] Step 6: Extract the front-view teaching video data collected by the front camera device from the teaching video data, perform facial recognition on the teaching video data, identify the facial image data of the first student, obtain the pre-stored facial image data of the second student corresponding to the class number, compare the first student image data and the second student facial image data, and for students who have successfully matched, update the student sign-in parameter of the class number from "not signed in" to "signed in", and for students who have failed to match successfully, feedback a sign-in failure message to the student's account.

[0136] Step 7: Obtain the facial image data and body temperature data of the students and teachers sent by the camera device, and compare the acquired body temperature data with 37.3 degrees Celsius one by one. If the body temperature data of the students and teachers is less than 37.3 degrees Celsius, generate normal body temperature parameters; if the body temperature data of the students and teachers is greater than or equal to 37.3 degrees Celsius, generate abnormal body temperature data, and compare the facial image data of the students or teachers with abnormal body temperature with the pre-stored facial image data of the students or teachers to obtain the corresponding student number or teacher number of the students or teachers with abnormal body temperature.

[0137] Step 8: For each student, according to the student sign-in parameters, update the status of the educational resources corresponding to the class number. The status of educational resources includes pending release and frozen status. If the status of the educational resources corresponding to the class number is pending release, determine whether the teaching behavior of the teaching institution associated with the class number is abnormal according to the class number supervision parameters, the teaching teacher supervision parameters and the teaching content supervision parameters. If the teaching behavior of the teaching institution associated with the class number is not abnormal and the status of the educational resources corresponding to the class number is pending release, then release the educational resources corresponding to the class number. Otherwise, the status of the educational resources corresponding to the class number of the student continues to remain "frozen", and push the message of educational resource release failure, so that the teaching institution can make timely rectification and standardize its teaching behavior. After completing the rectification, the teaching institution can submit the corresponding rectification certificate document to the platform to review whether the certificate document meets the preset rectification requirements and whether the information is true and valid. After the rectification certificate document is reviewed and approved, the educational resource unfreezing message is sent to the supervision account to release the educational resources corresponding to the class number to the teaching institution's account.

[0138] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0139] Based on the same inventive concept, the embodiment of the present application also provides an educational resource processing device for implementing the educational resource processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more educational resource processing device embodiments provided below can refer to the limitations of the educational resource processing method above, and will not be repeated here.

[0140] In one embodiment, Fig. 9 As shown, an educational resource processing device is provided, including: a data acquisition module 910, a class number determination module 920, a teaching supervision parameter generation module 930 and an educational resource release module 940, wherein:

[0141] The data acquisition module 910 is used to acquire the teaching video data sent by the camera device and the identification information of the camera device.

[0142] The lesson number determination module 920 is used to determine the lesson number corresponding to the teaching video data based on the identification information of the camera device.

[0143] The teaching supervision parameter generation module 930 is used to generate teaching supervision parameters according to the teaching video data and the class number, wherein the teaching supervision parameters include class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student sign-in parameters.

[0144] The educational resource releasing module 940 is used to release the educational resources corresponding to the class number according to the teaching supervision parameters.

[0145] In one embodiment, the class number determination module 920 is also used to find out a course information set associated with the identification information of the camera device in a preset database based on the identification information of the camera device, the course information set includes multiple class numbers and the class time corresponding to each class number, obtain the time information of the teaching video data, traverse the class time corresponding to each class number, find out the class time where the time information is located, and determine the class number corresponding to the found class time as the class number.

[0146] In one embodiment, the teaching supervision parameter generation module 930 is also used to perform target detection on the teaching video data, identify the number of students in the first class, obtain the pre-stored number of students in the second class corresponding to the class number, regularly compare the number of students in the first class and the number of students in the second class, and generate class number supervision parameters based on the class number comparison results.

[0147] In one embodiment, the camera device includes a rear camera device deployed behind the teaching scene;

[0148] The teaching supervision parameter generation module 930 is also used to extract the rear-view teaching video data collected by the rear camera equipment in the teaching video data, perform facial recognition on the rear-view teaching video data, identify the facial image data of the first teaching teacher, obtain the pre-stored facial image data of the second teaching teacher corresponding to the class number, compare the facial image data of the first teaching teacher with the facial image data of the second teaching teacher, and generate the teaching teacher supervision parameters according to the comparison results of the teaching teacher facial images.

[0149] In one embodiment, the teaching content supervision parameters include teaching content abnormality parameters;

[0150] The teaching supervision parameter generation module 930 is also used to extract the audio data of the teaching video data, obtain the pre-stored out-of-syllabus keywords corresponding to the class number, and identify whether there are out-of-syllabus keywords in the audio data. If the number of identified out-of-syllabus keywords exceeds the preset number threshold, abnormal teaching content parameters are generated.

[0151] In one embodiment, the camera device includes a front camera device deployed in front of the teaching scene;

[0152] The teaching supervision parameter generation module 930 is also used to extract the front-view teaching video data collected by the front camera device in the teaching video data, perform facial recognition on the front-view teaching video data, identify the facial image data of the first student, obtain the pre-stored facial image data of the second student corresponding to the class number, compare the first student image data and the second student facial image data, and update the student sign-in parameters according to the comparison results of the student facial image data.

[0153] In one embodiment, the educational resource release module 940 is also used to update the status of the educational resources corresponding to the class number for each student according to the student check-in parameters, the status of the educational resources includes a pending release status and a frozen status, and to determine whether there is any abnormality in the teaching behavior of the teaching institution associated with the class number according to the class number supervision parameters, the teaching teacher supervision parameters and the teaching content supervision parameters. If there is no abnormality in the teaching behavior of the teaching institution associated with the class number and the status of the educational resources corresponding to the class number is a pending release status, the educational resources corresponding to the class number are released.

[0154] Each module in the educational resource processing device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0155] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.10As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as identification information of the camera device, teaching video data, and teaching supervision parameters. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for processing educational resources is implemented.

[0156] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0157] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned educational resource processing method when executing the computer program.

[0158] In one embodiment, the processor also implements the steps in the above-mentioned educational resource processing method when executing the computer program.

[0159] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned educational resource processing method are implemented.

[0160] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps in the educational resource processing method when executed by a processor.

[0161] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0162] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0163] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for processing educational resources, characterized in that: The method comprises: Acquire teaching video data sent by a camera device and identification information of the camera device; Based on the identification information of the camera device, determining the class number corresponding to the teaching video data; Generate teaching supervision parameters according to the teaching video data and the class number, wherein the teaching supervision parameters include class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student sign-in parameters; For each student, according to the student's sign-in parameters, the state of the educational resource corresponding to the class number is updated, and the state of the educational resource includes a pending release state and a frozen state; According to the class number supervision parameter, the teaching teacher supervision parameter and the teaching content supervision parameter, judging whether the teaching behavior of the teaching institution associated with the class number is abnormal; If there is no abnormality in the teaching behavior of the teaching institution associated with the class number and the status of the educational resources corresponding to the class number is to be released, the educational resources corresponding to the class number are released.

2. The educational resource processing method according to claim 1, characterized in that: The determining, based on the identification information of the camera device, the class number corresponding to the teaching video data comprises: Based on the identification information of the camera device, searching a preset database for a course information set associated with the identification information of the camera device, the course information set including a plurality of class hour numbers and class hour times corresponding to each class hour number; Obtain the time information of the teaching video data, traverse the class time corresponding to each class number, and find the class time where the time information is located; The class number corresponding to the found class time is determined as the class number.

3. The educational resource processing method according to claim 1, characterized in that: Generating teaching supervision parameters according to the teaching video data and the lesson number includes: Performing target detection on the teaching video data to identify the number of students in the first class; Obtain the number of people for the second class period that is pre-stored and corresponds to the class period number; Regularly compare the number of students in the first class hour and the number of students in the second class hour, and generate the number of students in the class hour supervision parameter according to the comparison result.

4. The educational resource processing method according to claim 1, characterized in that: The camera device includes a rear camera device deployed behind the teaching scene; Generating teaching supervision parameters according to the teaching video data and the lesson number includes: Extracting rear-view teaching video data collected by the rear camera device from the teaching video data; Performing facial recognition on the rear-view teaching video data to identify facial image data of the first teaching teacher; Acquire pre-stored facial image data of a second teacher corresponding to the class number; The facial image data of the first teaching teacher and the facial image data of the second teaching teacher are compared, and the teaching teacher supervision parameters are generated according to the teaching teacher facial image comparison result.

5. The educational resource processing method according to any one of claims 1 to 4, characterized in that: The teaching content supervision parameters include teaching content abnormality parameters; Generating teaching supervision parameters according to the teaching video data and the lesson number includes: Extracting audio data from the teaching video data; Obtaining pre-stored out-of-syllabus keywords corresponding to the lesson number; Identify whether the audio data contains the super-syllabus keyword; If the number of identified out-of-syllabus keywords exceeds a preset threshold, the teaching content abnormality parameter is generated.

6. The educational resource processing method according to any one of claims 1 to 4, characterized in that: The camera device includes a front camera device deployed in front of the teaching scene; Generating teaching supervision parameters according to the teaching video data and the lesson number includes: Extracting the front-view teaching video data collected by the front camera device from the teaching video data; Performing facial recognition on the front-view teaching video data to obtain facial image data of the first student; Acquire pre-stored facial image data of a second student corresponding to the lesson number; The facial image data of the first student and the facial image data of the second student are compared, and the student sign-in parameters are updated according to the comparison result of the student facial image data.

7. An educational resource processing device, characterized in that: The device comprises: A data acquisition module, used to acquire the teaching video data sent by the camera device and the identification information of the camera device; A lesson number determination module, used to determine the lesson number corresponding to the teaching video data based on the identification information of the camera device; A teaching supervision parameter generation module, used to generate teaching supervision parameters according to the teaching video data and the class number, wherein the teaching supervision parameters include class number supervision parameters, teaching teacher supervision parameters, teaching content supervision parameters and student sign-in parameters; The educational resource release module is used to update the status of the educational resources corresponding to the class number for each student according to the student check-in parameters; if the status of the educational resources corresponding to the class number is in a to-be-released state, determine whether there is any abnormality in the teaching behavior of the class number according to the class number supervision parameters, the teaching teacher supervision parameters and the teaching content supervision parameters; if there is no abnormality in the teaching behavior of the teaching institution associated with the class number and the status of the educational resources corresponding to the class number is in a to-be-released state, then release the educational resources corresponding to the class number.

8. The educational resource processing device according to claim 7, characterized in that: The class number determination module is also used to search for a course information set associated with the identification information of the camera device in a preset database based on the identification information of the camera device, the course information set including multiple class numbers and class times corresponding to each class number, obtain the current system time, traverse the class times corresponding to each class number, find out the class time where the current system time is located, and determine the class number corresponding to the found class time as the class number.

9. The educational resource processing device according to claim 7, characterized in that: The class number supervision parameters include class number abnormality parameters; The teaching supervision parameter generation module is also used to perform target detection on the teaching video data, identify the number of class members in the current teaching scene, and obtain the number of class members corresponding to the class number. If the number of class members in the current teaching scene is inconsistent with the number of class members corresponding to the class number, the class number abnormality parameter is generated.

10. The educational resource processing device according to claim 7, characterized in that: The camera device includes a rear camera device deployed behind the teaching scene, and the teaching supervision data includes the teacher supervision parameter including the teacher information abnormality parameter; The teaching supervision parameter generation module is also used to extract the rear-view teaching video data collected by the rear camera device from the teaching video data, perform facial recognition on the rear-view teaching video data, identify the facial image data of the teaching teacher, and obtain the facial image data of the teaching teacher corresponding to the class number. If the facial image data of the identified teaching teacher is inconsistent with the facial image data of the teaching teacher corresponding to the class number, the teaching teacher information abnormality parameter is generated.

11. The educational resource processing device according to any one of claims 7 to 10, characterized in that: The teaching content supervision parameters include teaching content abnormality parameters; The teaching supervision parameter generation module is also used to obtain the out-of-syllabus keywords corresponding to the class number, extract the audio data of the teaching video data, and identify whether the out-of-syllabus keywords exist in the audio data. If the number of identified out-of-syllabus keywords exceeds a preset threshold, the teaching content abnormality parameters are generated.

12. The educational resource processing device according to any one of claims 7 to 10, characterized in that: The teaching supervision parameter generation module is also used to perform facial information recognition on the teaching video data, identify the student's facial image data, obtain the initial student's facial image data corresponding to the class number, compare the identified student image data with the initial student's facial image data, and update the student's sign-in parameters based on the comparison results.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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