Classroom teaching data processing method and system based on answer code

Abstract

The application relates to a classroom teaching data processing method and system based on an answer code. The method relates to artificial intelligence and comprises the following steps: for a classroom question in classroom teaching, a corresponding classroom teaching scene image is extracted, and an answer code image of each student for the classroom question in the classroom teaching scene image is obtained. Pixel features corresponding to the answer code image are extracted, the pixel features are decoded, the answer code number and the answers corresponding to each answer code number are obtained, the students matched with the answer code number are determined, and the answers of each student for the classroom question are obtained. The method directly identifies and decodes the classroom teaching scene image, obtains the answer code number of each student and the answers of different classroom questions, understands the answering situation of the students, realizes flexible interaction between teachers and students during classroom teaching, does not need to equip each student with corresponding electronic equipment, reduces the teaching cost on the basis of realizing flexible interaction in the classroom, and improves the universality of intelligent teaching in different teaching environments.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method and system for processing classroom teaching data based on answer codes. Background Technology

[0002] With the development of computer technology and the increasing demands for higher quality offline classroom teaching, intelligent teaching technology has emerged. The basic idea of ​​intelligent teaching technology is to introduce electronic devices, divided into teacher and student ends. Each student uses their assigned electronic device to select their answer to a question posted by the teacher and provide feedback on the device. This enables real-time interaction between teachers and students, records the interaction results, and improves teaching quality.

[0003] However, traditional smart teaching technologies require personnel to be equipped with electronic devices, resulting in high teaching costs. Furthermore, because these electronic devices interact via networks, a relatively good network environment is required, making them unsuitable for schools in areas with poor network coverage, limited coverage, or tight budgets. Therefore, traditional smart teaching technologies still suffer from high teaching costs and insufficient universality. Summary of the Invention

[0004] Therefore, it is necessary to provide a classroom teaching data processing method and system based on answer codes that can reduce classroom teaching costs and improve the universality of intelligent teaching, in order to address the above-mentioned technical problems.

[0005] A method for processing classroom teaching data based on answer codes, the method comprising:

[0006] Extract corresponding classroom teaching scene images for classroom questions;

[0007] Obtain the answer code images of each student for the classroom questions in the classroom teaching scene image;

[0008] Extract the pixel features corresponding to the answer code image, and decode the image based on the pixel features to obtain the answer code number and the answer corresponding to each answer code number;

[0009] Identify the students whose answer codes match the given answer codes, and obtain each student's answers to the classroom questions.

[0010] A method for generating an answer code database, the method comprising:

[0011] Get the randomly generated answer code;

[0012] Calculate the first distance between the answer code and each historical answer code in the pre-stored answer code database;

[0013] Determine whether the first distance is not less than a preset distance threshold;

[0014] When it is determined that the first distance is not less than the preset distance threshold, the randomly generated answer code is added to the pre-stored answer code library to obtain the updated pre-stored answer code library;

[0015] Return to the step of obtaining the randomly generated answer code until the number of answer codes in the updated pre-stored answer code library reaches a preset value.

[0016] In one embodiment, the method further includes:

[0017] When it is determined that the first distance is less than the preset distance threshold, the generated answer code is identified as a failed answer code and discarded.

[0018] Count the number of failed answer codes;

[0019] Determine whether the number of failed answer codes has reached a preset threshold.

[0020] When the number of failed answer codes is detected to reach a preset threshold, the largest first distance threshold is determined from the first distances corresponding to each failed answer code.

[0021] The first distance threshold is determined as the preset distance threshold, and the updated preset distance threshold is obtained;

[0022] Return to the step of obtaining the randomly generated answer code until the number of answer codes in the updated pre-stored answer code library reaches a preset value.

[0023] A classroom teaching data processing system based on answer codes, characterized in that the system includes paper answer codes, a camera, and a server; wherein:

[0024] The paper answer codes are used to provide students with answers to classroom questions;

[0025] The camera is used to capture video of the classroom teaching process;

[0026] The server is used to: extract corresponding classroom teaching scene images for classroom teaching questions; obtain answer code images of each student for the classroom teaching scene images; extract pixel features corresponding to the answer code images, and decode them according to the pixel features to obtain the answer code number and the answer corresponding to each answer code number; determine the student matching the answer code number, and obtain the answer of each student for the classroom question.

[0027] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0028] Extract corresponding classroom teaching scene images for classroom questions;

[0029] Obtain the answer code images of each student for the classroom questions in the classroom teaching scene image;

[0030] Extract the pixel features corresponding to the answer code image, and decode the image based on the pixel features to obtain the answer code number and the answer corresponding to each answer code number;

[0031] Identify the students whose answer codes match the given answer codes, and obtain each student's answers to the classroom questions.

[0032] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0033] Extract corresponding classroom teaching scene images for classroom questions;

[0034] Obtain the answer code images of each student for the classroom questions in the classroom teaching scene image;

[0035] Extract the pixel features corresponding to the answer code image, and decode the image based on the pixel features to obtain the answer code number and the answer corresponding to each answer code number;

[0036] Identify the students whose answer codes match the given answer codes, and obtain each student's answers to the classroom questions.

[0037] In the aforementioned classroom teaching data processing method and system based on answer codes, for classroom questions, corresponding classroom scene images are extracted, and answer code images of each student for the classroom questions are obtained from these images. By extracting the pixel features corresponding to the answer code images and decoding them, the answer code number and the corresponding answer for each answer code number can be obtained. Furthermore, students matching the answer code number can be identified, thus obtaining each student's answer to the classroom question. This method directly identifies and decodes the classroom scene images corresponding to the classroom questions to obtain the answer code number for each student and their answers to different classroom questions, revealing the students' answering performance. This enables flexible interaction between teachers and students during the classroom teaching process. Since it eliminates the need to equip each student with corresponding electronic devices or configure a network for classroom interaction, it further reduces teaching costs and enhances the universality of intelligent teaching in different teaching environments while achieving flexible classroom interaction. Attached Figure Description

[0038] Figure 1 This is an application environment diagram of a classroom teaching data processing method based on answer codes in one embodiment;

[0039] Figure 2 This is a flowchart illustrating a classroom teaching data processing method based on answer codes in one embodiment;

[0040] Figure 3 This is a schematic diagram illustrating the generation of pixel features of the answer code image in one embodiment;

[0041] Figure 4 This is a flowchart illustrating the method for generating the answer code database in one embodiment;

[0042] Figure 5 This is a flowchart illustrating the method for generating the answer code library in another embodiment;

[0043] Figure 6 This is a schematic diagram of a subjective question answer sheet template in one embodiment;

[0044] Figure 7 This is a schematic diagram of the subjective question region identification in one embodiment;

[0045] Figure 8 This is a flowchart illustrating a classroom teaching data processing method based on answer codes in another embodiment;

[0046] Figure 9 This is a structural block diagram of a classroom teaching data processing system based on answer codes in one embodiment;

[0047] Figure 10 This is a schematic diagram of the class interface of a classroom teaching data processing system based on answer codes in one embodiment;

[0048] Figure 11 This is a schematic diagram of a student information interface in a classroom teaching data processing system based on answer codes, as shown in one embodiment.

[0049] Figure 12 This is a schematic diagram of the classroom question interface of a classroom teaching data processing system based on answer codes in one embodiment;

[0050] Figure 13 This is a schematic diagram of the answer result interface of a classroom teaching data processing system based on answer codes in one embodiment;

[0051] Figure 14 This is a schematic diagram of a student score ranking interface in a classroom teaching data processing system based on answer codes, as shown in one embodiment.

[0052] Figure 15This is a schematic diagram of the student-level learning analysis interface of a classroom teaching data processing system based on answer codes in one embodiment;

[0053] Figure 16 This is a schematic diagram of the subjective question review interface of a classroom teaching data processing system based on answer codes, as shown in one embodiment.

[0054] Figure 17 This is a schematic diagram of the learning situation analysis interface for the question dimension of a classroom teaching data processing system based on answer codes in one embodiment;

[0055] Figure 18 This is a schematic diagram of the comprehensive learning analysis interface of a classroom teaching data processing system based on answer codes in one embodiment;

[0056] Figure 19 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] The classroom teaching data processing method based on answer codes provided in this application involves artificial intelligence (AI) technology. AI is the theory, methods, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. In other words, AI is a comprehensive technology within computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can react in a way similar to human intelligence. AI studies the design principles and implementation methods of various intelligent machines, enabling them to have perception, reasoning, and decision-making functions. AI technology is a comprehensive discipline involving a wide range of fields, encompassing both hardware and software technologies. Basic AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operating / interactive systems, and mechatronics. AI software technologies mainly include computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0059] Computer vision (CV), a branch of artificial intelligence software technology, is a science that studies how to enable machines to "see." More specifically, it refers to machine vision, which uses cameras and computers to replace human eyes in recognizing and measuring targets, and then performs image processing to create images more suitable for human observation or transmission to instruments for detection. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, 3D object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping (SLAM), and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0060] With the research and advancement of artificial intelligence (AI) technology, AI is being studied and applied in various fields, such as smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, autonomous driving, drones, robots, smart healthcare, smart customer service, and smart classrooms. It is believed that with the development of technology, AI will be applied in more fields and play an increasingly important role.

[0061] The classroom teaching data processing method based on answer codes provided in this application can be applied to, for example... Figure 1 In the application environment shown, the classroom camera 102 captures video of students answering questions during class and communicates with the teacher's server 104 via a network. For each classroom question, the server 104 extracts the corresponding classroom scene image from the video image captured by the camera, obtains the answer code image of each student in the classroom scene image, extracts the pixel features corresponding to the answer code image, decodes the pixel features to obtain the answer code number and the answer corresponding to each answer code number, and identifies the student matching the answer code number, thus obtaining each student's answer to the classroom question. The camera 102 can be a different model of camera that establishes a communication connection with the teacher's server 104, and the server 104 can be a standalone server or a server cluster consisting of multiple servers.

[0062] In one embodiment, such as Figure 2 As shown, a method for processing classroom teaching data based on answer codes is provided, which can be applied to... Figure 1 Taking the teacher's server as an example, the explanation includes the following steps:

[0063] Step S202: Extract the corresponding classroom teaching scene images for the classroom teaching questions.

[0064] When the teacher's server detects the teacher's action to publish a classroom question, it retrieves the submitted question, which can be edited by the teacher or selected from a pre-stored question bank. After retrieving the submitted question, the server further retrieves the time allotted for answering it during the teacher's classroom instruction. The answering time for different questions can be set or changed by the teacher in real time.

[0065] Specifically, by acquiring the answering time for different classroom questions during the classroom teaching process and extracting classroom teaching scene images within the corresponding answering time, classroom teaching scene images for the corresponding classroom questions are obtained.

[0066] Furthermore, cameras installed in the classroom capture video of each student's answers during the teaching process. By extracting classroom teaching scene images for each answering time from the video images captured by the cameras, classroom teaching scene images corresponding to different classroom questions are obtained.

[0067] Step S204: Obtain the answer code images of each student for classroom questions in the classroom teaching scene image.

[0068] Specifically, by performing image processing and contour detection on classroom teaching scene images, the initial image corresponding to the rectangular contour is extracted from the classroom teaching scene images, and then the initial image is transformed by perspective transformation and mapped to the answer code image of each student corresponding to the square contour.

[0069] The image processing for classroom teaching scene images includes local gamma correction, grayscale processing, and denoising. Contour detection is then performed on the processed images to remove non-convex polygons and non-rectangular contours. Different contour detection algorithms can be used to perform contour detection on the processed classroom teaching scene images, such as the Canny operator, Sobel operator, or Laplacian operator.

[0070] Furthermore, after image processing and contour detection of the classroom teaching scene image, the initial image corresponding to the rectangular contour is extracted from the classroom teaching scene image. Then, perspective transformation is performed on the initial image, mapping it to the answer code image of each student corresponding to the square contour. The answer code image can be an Aruco code image. By binarizing the perspective-transformed answer code image (i.e., the Aruco code image), noise or lighting conditions can be avoided from affecting the Aruco code recognition process. Specifically, Ostu's method can be used for binarization of the Aruco code image.

[0071] In one embodiment, image processing of classroom teaching scene images includes:

[0072] The inverse color of the classroom teaching scene image is calculated to obtain the corresponding inverse color calculation result. Based on the inverse color calculation result, the corresponding gamma value of each pixel value of the classroom teaching scene image is calculated. Based on the obtained gamma value, a local gamma transformation is performed on the classroom teaching scene image to obtain the transformed classroom teaching scene image. The transformed classroom teaching scene image is then converted to grayscale, and the grayscale classroom teaching scene image is then denoised to obtain the denoised air-conditioned teaching scene image.

[0073] The gamma value for each pixel value of the classroom teaching result can be obtained using the following formula (1):

[0074] (1)

[0075] Here, mask(i, j) represents the inversion of the classroom teaching scene image, obtaining the corresponding inversion calculation result. This represents the gamma value calculated for each pixel. The mask is obtained by first inverting the colors of the original image, then applying a Gaussian blur with a certain radius. If the mask value is greater than 128, it indicates that the pixel is a dark pixel and its surrounding pixels are also dark. The value needs to be less than 0 in order to brighten it. A mask value less than 128 indicates that the pixel is a bright pixel and the surrounding pixels are also bright. When the mask value is 128, no change occurs. At the same time, the further the mask value is from 128, the greater the amount of correction.

[0076] Furthermore, the classroom teaching scene image is subjected to local gamma transformation using the following formula (2) to obtain the transformed classroom teaching scene image:

[0077] (2)

[0078] in, This represents the transformed classroom teaching scene image. This represents the gamma value obtained for each pixel value. This represents the original input image, i.e., the original classroom teaching scene image. The standard formula for traditional gamma correction is expressed by introducing a... An algorithm that adapts to changes in local image information, specifically by introducing the gamma value obtained from each pixel, is used to perform local gamma transformation on classroom teaching scene images to obtain the transformed classroom teaching scene images.

[0079] Step S206: Extract the pixel features corresponding to the answer code image, and decode the image based on the pixel features to obtain the answer code number and the answer corresponding to each answer code number.

[0080] Specifically, the answer code image is recognized, the corresponding pixel features are extracted, and the pixel features are verified and decoded to generate a decoded answer code image. Then, based on a pre-stored answer code library, the decoded answer code image is recognized, the answer code number corresponding to the answer code image is matched, and the answer corresponding to each answer code number is obtained.

[0081] In this process, different students' answer code images correspond to different pixel features. By performing parity check decoding on the extracted answer code image pixel features, the decoded answer code image is obtained. Then, the decoded answer code image is identified according to the pre-stored answer code library, the answer code number corresponding to the answer code image is matched from the pre-stored answer code library, and the answer corresponding to the answer code number is obtained.

[0082] In one embodiment, recognizing the answer code image and extracting the pixel features corresponding to the answer code image includes:

[0083] The answer code image is divided into regions to obtain a preset number of cells; the pixel value of each cell is obtained; and pixel features corresponding to the answer code image are generated based on the pixel value of each cell.

[0084] Specifically, the answer code image is divided into horizontal and vertical rows and columns according to the preset area size to obtain a preset number of cells, and the pixel value of each cell is obtained. The pixel value of each cell can be 0 or 1, and the color of the corresponding cell is black or white. Based on the obtained pixel value of each cell, the pixel matrix of the answer code image is obtained, that is, the pixel feature corresponding to the answer code image.

[0085] Furthermore, such as Figure 3 As shown, a schematic diagram of pixel feature generation for an answer code image is provided, referring to... Figure 3 It can be seen that, based on the preset area size, Figure 3The answer code image shown in figure a is divided into 8x8 columns, resulting in 64 cells. The pixel value of each cell is then obtained, as shown in figure a. Figure 3 As shown in b, there are 64 cells with only black and white colors. Extract the pixel value corresponding to each cell and color, including 1 or 0, to obtain the pixel matrix of the answer code image.

[0086] Step S208: Identify the students whose answer codes match the question numbers and obtain each student's answers to the classroom questions.

[0087] Specifically, based on the answer code number and the pre-stored correspondence between the answer code number and the student number in the preset mapping table, the student matching the answer code number is determined, and then the answer to the classroom question for each determined student is obtained based on the answer corresponding to the answer code number.

[0088] The preset mapping table stores the correspondence between answer code numbers and student numbers. In this embodiment, each grade level has a corresponding relationship between students and answer code numbers, and answer codes can be reused across different grades. For the correspondence between students and answer code numbers within different grades, a "store first, then reconfigure" approach is adopted. That is, the correspondence between students and answer code numbers for one grade is configured and stored first. When configuring the correspondence between students and answer code numbers for the next grade, the existing correspondence is cleared, and the correspondence between students and answer code numbers for the new grade is reconfigured and stored.

[0089] Furthermore, if the number of students in each grade exceeds the number of answer codes in the pre-stored answer code database, then a correspondence can be established between each class and each student's answer code number, and answer codes can be reused between different classes. The method for configuring this correspondence at the class level is similar to the method for configuring it at the grade level.

[0090] In the aforementioned classroom teaching data processing method and system based on answer codes, for classroom questions, corresponding classroom scene images are extracted, and answer code images of each student for the classroom questions are obtained from these images. By extracting the pixel features corresponding to the answer code images and decoding them, the answer code number and the corresponding answer for each answer code number can be obtained. Furthermore, students matching the answer code number can be identified, thus obtaining each student's answer to the classroom question. This method directly identifies and decodes the classroom scene images corresponding to the classroom questions to obtain the answer code number for each student and their answers to different classroom questions, revealing the students' answering performance. This enables flexible interaction between teachers and students during the classroom teaching process. Since it eliminates the need to equip each student with corresponding electronic devices or configure a network for classroom interaction, it further reduces teaching costs and enhances the universality of intelligent teaching in different teaching environments while achieving flexible classroom interaction.

[0091] In one embodiment, such as Figure 4 As shown, a method for generating an answer code database is provided, specifically including:

[0092] 1) Obtain the randomly generated answer code.

[0093] The answer code can be an Aruco code, and Aruco codes have pre-defined generation rules. Therefore, the answer code can be randomly generated according to the pre-defined generation rules corresponding to the answer code.

[0094] 2) Calculate the first distance between the answer code and each historical answer code in the pre-stored answer code database.

[0095] Specifically, the process involves obtaining each historical answer code from the pre-stored answer code library and calculating the first distance between the randomly generated answer code and each historical answer code.

[0096] 3) Determine whether the first distance is not less than the preset distance threshold.

[0097] Specifically, by obtaining a preset distance threshold for the pre-stored answer code library, and comparing the preset distance threshold with each calculated first distance, it is determined whether each first distance is not less than the corresponding preset distance threshold.

[0098] 4) When it is determined that the first distance is not less than the preset distance threshold, the randomly generated answer code is added to the pre-stored answer code library to obtain the updated pre-stored answer code library.

[0099] Specifically, when the first distance is determined to be no less than a preset distance threshold, that is, when the first distance between the randomly generated answer code and each historical answer code in the pre-stored answer code library is no less than the preset distance threshold, the randomly generated answer code is added to the pre-stored answer code library to obtain the updated pre-stored answer code library.

[0100] When the pre-stored answer code library is empty, the randomly generated answer code is directly added to the empty answer code library.

[0101] 5) Return to the step of obtaining the randomly generated answer code until the number of answer codes in the updated pre-stored answer code library reaches the preset value.

[0102] Specifically, after obtaining the updated pre-stored answer code library, the process returns to step 1), re-obtains randomly generated answer codes, and calculates the first distance between the randomly generated answer codes and each historical answer code in the updated pre-stored answer code library. It further determines whether the first distance is not less than a preset distance threshold. When the first distance is determined to be not less than the preset distance threshold, the randomly generated answer codes are added to the pre-stored answer code library, resulting in an updated pre-stored answer code library. The number of answer codes in the updated pre-stored answer code library is then obtained, until the number of answer codes in the updated pre-stored answer code library reaches a preset value. In this embodiment, the preset value can be between 1500 and 2000.

[0103] In the above-mentioned method for generating the answer code library, randomly generated answer codes are obtained, a first distance is calculated between the answer code and each historical answer code in the pre-stored answer code library, and it is determined whether the first distance is not less than a preset distance threshold. When it is determined that the first distance is not less than the preset distance threshold, the randomly generated answer code is added to the pre-stored answer code library, resulting in an updated pre-stored answer code library. This process continues until the number of answer codes in the updated pre-stored answer code library reaches a preset value, thus obtaining a complete answer code library. This method achieves the goal of obtaining a pre-stored answer code library by randomly generating a large number of answer codes, and supports multiple people answering questions simultaneously based on the large number of answer codes in the pre-stored answer code library. It ensures that each student has a unique code, meets the needs of different numbers of students in different regions for the number of answer codes in actual classroom teaching, and improves the universality of intelligent teaching.

[0104] In one embodiment, such as Figure 5 As shown, another method for generating the answer code database is provided, which specifically includes the following steps:

[0105] 1) Obtain the randomly generated answer code.

[0106] The answer code can be an Aruco code, and Aruco codes have pre-defined generation rules. Therefore, the answer code can be randomly generated according to the pre-defined generation rules corresponding to the answer code.

[0107] 2) Calculate the first distance between the answer code and each historical answer code in the pre-stored answer code database.

[0108] Specifically, the process involves obtaining each historical answer code from the pre-stored answer code library and calculating the first distance between the randomly generated answer code and each historical answer code.

[0109] 3) Determine whether the first distance is not less than the preset distance threshold.

[0110] Specifically, by obtaining a preset distance threshold for the pre-stored answer code library, and comparing the preset distance threshold with each calculated first distance, it is determined whether each first distance is not less than the corresponding preset distance threshold.

[0111] 4) When the first distance is determined to be less than the preset distance threshold, the generated answer code is identified as a failed answer code and is removed.

[0112] Specifically, when the first distance between the randomly generated answer code and each historical answer code in the pre-stored answer code library is less than a preset distance threshold, the currently generated answer code is identified as a failed answer code. Specifically, when the first distance between the randomly generated answer code and each historical answer code in the pre-stored answer code library is less than the preset distance threshold, it indicates a conflict between the currently generated answer code and historical answer codes. This can easily cause confusion in student location during use, meaning that accurate correspondence between each student and their answer code cannot be achieved. Therefore, it is necessary to remove conflicting failed answer codes and regenerate new answer codes.

[0113] 5) Count the number of failed answer codes.

[0114] 6) Determine whether the number of failed answer codes has reached the preset threshold.

[0115] Specifically, during the random generation of answer codes and the determination of failed answer codes, the number of failed answer codes is counted in real time. In this embodiment, the preset threshold number corresponding to the number of failed answer codes can be between 4900 and 5500, that is, it is determined whether the counted number of failed answer codes reaches any value between 4900 and 5500.

[0116] 7) When the number of failed answer codes reaches the preset threshold, determine the largest first distance threshold from the first distances corresponding to each failed answer code.

[0117] Specifically, when the number of failed answer codes reaches a preset threshold, that is, when the number of failed answer codes reaches any value between 4900 and 5500, such as when the number of failed answer codes reaches 5000, then the first distance between all failed answer codes and historical answer codes is further obtained, and the largest first distance threshold is determined from the first distances corresponding to all failed answer codes.

[0118] 8) The first distance threshold is determined as the preset distance threshold, and the updated preset distance threshold is obtained.

[0119] Specifically, the updated preset distance threshold is obtained by assigning the determined maximum first distance threshold to the initial preset distance threshold.

[0120] 9) Return to step 1) until the number of answer codes in the updated pre-stored answer code library reaches the preset value.

[0121] Specifically, by re-acquiring the randomly generated answer code and calculating the first distance between the answer code and each historical answer code in the pre-stored answer code library, it is determined whether the first distance is not less than the updated preset distance threshold.

[0122] Furthermore, when it is determined that the first distance is not less than the updated preset distance threshold, the randomly generated answer code is added to the pre-stored answer code library to obtain the updated pre-stored answer code library, until the number of answer codes in the updated pre-stored answer code library reaches the preset value.

[0123] 10) When the number of failed answer codes detected does not reach the preset threshold, return to step 1).

[0124] Specifically, when the number of failed answer codes is less than the preset threshold, the process returns to the step of obtaining a randomly generated answer code.

[0125] 11) When it is determined that the first distance is not less than the preset distance threshold, the randomly generated answer code is added to the pre-stored answer code library to obtain the updated pre-stored answer code library.

[0126] Specifically, when the first distance is determined to be no less than a preset distance threshold, that is, when the first distance between the randomly generated answer code and each historical answer code in the pre-stored answer code library is no less than the preset distance threshold, the randomly generated answer code is added to the pre-stored answer code library to obtain the updated pre-stored answer code library.

[0127] 12) Return to step 1) until the number of answer codes in the updated pre-stored answer code library reaches the preset value.

[0128] Specifically, after obtaining the updated pre-stored answer code library, return to step 1), re-obtain the randomly generated answer code, and calculate the first distance between the randomly generated answer code and each historical answer code in the updated pre-stored answer code library. Further determine whether the first distance is not less than a preset distance threshold. When it is determined that the first distance is not less than the preset distance threshold, add the randomly generated answer code to the pre-stored answer code library to obtain the updated pre-stored answer code library, and obtain the number of answer codes in the updated pre-stored answer code library until the number of answer codes in the updated pre-stored answer code library reaches a preset value.

[0129] In the above method for generating the answer code library, a randomly generated answer code is obtained, and a first distance is calculated between the answer code and each historical answer code in the pre-stored answer code library. When the first distance is determined to be less than a preset distance threshold, the generated answer code is identified as a failed answer code and removed. By counting the number of failed answer codes, and when the number of failed answer codes reaches a preset threshold, the largest first distance threshold is determined from the first distances corresponding to each failed answer code. Then, by setting the first distance threshold as the preset distance threshold, an updated preset distance threshold is obtained. The first distance is re-determined based on the updated preset distance threshold. When the first distance is determined to be not less than the preset distance threshold, the randomly generated answer code is added to the pre-stored answer code library, resulting in an updated pre-stored answer code library. This process continues until the number of answer codes in the updated pre-stored answer code library reaches a preset value, thus obtaining a complete answer code library. It achieves the goal of obtaining a pre-stored answer code library by randomly generating a large number of answer codes, and supports multiple people to answer questions at the same time based on the large number of answer codes in the pre-stored answer code library, so that each student has a code, which meets the needs of different numbers of students in different regions in the actual classroom teaching process and improves the universality of intelligent teaching.

[0130] In one embodiment, the classroom questions include objective questions. The steps for obtaining the answers corresponding to each answer code number specifically include:

[0131] When the classroom question is detected to be an objective question, obtain the rotation angle of the answer code image corresponding to the answer code number;

[0132] Based on the preset correspondence between the answer options and the rotation angle, the target answer option corresponding to the rotation angle is determined, and the answer corresponding to the answer code number is obtained.

[0133] When teachers post classroom questions, they also set the corresponding question types, which include objective questions and subjective questions. When the server on the teacher's side receives the classroom questions submitted by the teacher, it further obtains the question type of the current classroom question.

[0134] Specifically, when the current classroom question is detected to be an objective question, the rotation angle of the answer code image corresponding to the answer code number is obtained, and the preset correspondence between the answer options and the rotation angle is obtained. The answer code image is divided into four options (A, B, C, and D) in 90-degree rotation ranges, and the correspondence between the rotation angle and the answer options is stored in advance.

[0135] Furthermore, based on the preset correspondence between answer options and rotation angles, for example, option A corresponds to the initial pose, option B corresponds to a 90-degree rotation, option C corresponds to a 180-degree rotation, and option D corresponds to a 270-degree rotation. Other correspondences are also possible, such as option D corresponding to the initial pose, option C corresponding to a 90-degree rotation, option B corresponding to a 180-degree rotation, and option A corresponding to a 270-degree rotation. That is, it is not limited to the preset correspondence between answer options and rotation angles listed above. The rotation direction also needs to be preset, which can be clockwise or counterclockwise. Based on the rotation angle, the target answer option corresponding to the rotation angle can be determined, thus obtaining the answer corresponding to the answer code number.

[0136] In one embodiment, the classroom questions also include subjective questions. The steps for obtaining the answers corresponding to each answer code number specifically include:

[0137] When a subjective question is detected in class, the coordinates of the first vertex of the answer code image in the current answer sheet are extracted.

[0138] The affine transformation matrix is ​​calculated based on the coordinates of the second vertex and the first vertex of the answer code image in the pre-stored answer sheet template.

[0139] Based on the affine transformation matrix, the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet are determined by mapping calculation according to the coordinates of the third vertex of the subjective question answer area in the pre-stored answer sheet template.

[0140] Based on the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet, extract the answer for the corresponding subjective question answer area.

[0141] Specifically, when the current classroom question is detected to be a subjective question, the coordinates of the first vertex of the answer code image in the current answer sheet are extracted, and the coordinates of the second vertex of the answer code image in the pre-stored answer sheet template are obtained. Then, an affine transformation matrix is ​​calculated based on the first and second vertex coordinates. Next, the coordinates of the third vertex of the subjective question answer area in the pre-stored answer sheet template are obtained, and a mapping calculation is performed based on the affine transformation matrix and the third vertex coordinates to calculate the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet. Finally, based on the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet, the answer for the corresponding subjective question answer area is extracted.

[0142] In one embodiment, such as Figure 6 As shown, a template for answering subjective questions is provided. Figure 6 It can be seen that the left side of the subjective question answer sheet template is the answer code area, while the right side is the subjective question answer area. The coordinates of the second vertex of the answer code image of the subjective question answer sheet template and the coordinates of the third vertex of the subjective question answer area are obtained.

[0143] Among them, such as Figure 7 As shown, a schematic diagram for subjective question region recognition is provided, with reference to... Figure 7 It can be seen that by obtaining the coordinates of the first vertex of the answer code image on the left side of the current answer sheet, and combining them with the coordinates of the first vertex of the answer code image on the current answer sheet, and as shown in the example... Figure 6 The coordinates of the second vertex of the answer code image in the subjective question answer sheet template shown can be used to calculate the affine transformation matrix. Then, based on the affine transformation matrix and the coordinates of the third vertex, a mapping calculation can be performed to calculate the coordinates of the fourth vertex of the subjective question answer area on the right side of the current answer sheet, further extracting the answers from the subjective question answer area of ​​the current answer sheet.

[0144] The steps described above for obtaining the answers corresponding to each answer code number have different methods for obtaining answers for objective and subjective questions, and can record the answers for both objective and subjective questions separately, rather than being limited to recording and analyzing the answers for objective questions only. This meets the actual classroom teaching needs of different regions and further enhances the universality of intelligent teaching.

[0145] In one embodiment, a classroom teaching data processing method based on answer codes is provided, which further includes:

[0146] Obtain the learning situation analysis results, which include any one or more of the following analysis results: classroom learning situation analysis of each student, answer analysis of each classroom question, and comprehensive learning situation analysis of each student in and after class;

[0147] Among them, the analysis of students' classroom learning is obtained by analyzing the accuracy rate of students' answers to each classroom question and / or the speed at which students answer each classroom question;

[0148] The analysis of classroom question responses is obtained by analyzing the accuracy rate and / or scores of different students for each classroom question. The comprehensive learning analysis is obtained by analyzing the accuracy rate and / or answering speed of different students for each classroom question, as well as the accuracy rate and / or answering time of each student for homework. Specifically, the learning analysis results for different students include individual student classroom learning analysis, analysis of classroom question responses, and comprehensive learning analysis of each student's classroom and homework performance. The accuracy rate for each student's answers to the teacher-assigned classroom questions is obtained by evaluating the correctness of their answers. Furthermore, based on the confirmation that students' answers to the classroom questions are correct, the answering speed of different students for each classroom question can be further obtained. Therefore, the classroom learning analysis for each student can be obtained by analyzing the student's accuracy rate and / or answering speed for each classroom question.

[0149] Similarly, by evaluating the correctness of each student's answers to the classroom questions assigned by the teacher, the accuracy rate and corresponding scores for each student can be obtained, revealing the different students' responses to each question. Furthermore, by analyzing the accuracy rate and / or scores of different students for each classroom question, an analysis of the responses to each classroom question can be obtained. Further, to ensure a long-term understanding of students' learning progress, teachers can assign homework. By combining the accuracy rate and / or answering speed of different students for each classroom question with the accuracy rate and / or answering time of each student for the homework, a comprehensive analysis of students' learning progress can be obtained. Based on this comprehensive analysis of students' learning progress, further insights into different students' learning in classroom teaching can be gained. And based on the answers to homework, a linked analysis and statistical analysis of students' learning progress before and after class can be achieved, going beyond the analysis of a single classroom lesson and providing a more comprehensive understanding of each student's long-term learning progress.

[0150] The above-mentioned classroom teaching data processing method based on answer codes analyzes and statistically analyzes the learning situation of different students from multiple aspects, such as the analysis of each student's classroom learning situation, the analysis of each student's answers to classroom questions, and the comprehensive analysis of each student's learning situation in and after class. This allows for a comprehensive understanding of each student's long-term learning situation and progress, enabling more targeted teaching for different students and improving teaching effectiveness.

[0151] In one embodiment, such as Figure 8 As shown, a method for processing classroom teaching data based on answer codes is provided, which specifically includes the following steps:

[0152] 1) Obtain the time available to answer classroom questions during the course of classroom teaching.

[0153] 2) Extract classroom teaching scene images during the answering time from the video images captured by the classroom camera.

[0154] 3) Perform image processing and contour detection on the classroom teaching scene images, and extract the initial image corresponding to the rectangular contour from the classroom teaching scene images.

[0155] 4) Perform perspective transformation on the initial image to map it into the answer code image of each student corresponding to the square outline.

[0156] 5) Divide the answer code image into regions to obtain a preset number of cells.

[0157] 6) Obtain the pixel values ​​of each cell, and generate pixel features corresponding to the answer code image based on the pixel values ​​of each cell.

[0158] 7) Verify and decode the pixel features to generate the decoded answer code image.

[0159] 8) Based on the pre-stored answer code library, identify the decoded answer code image and match the answer code number corresponding to the answer code image.

[0160] 9) Obtain the question type of the detected classroom questions.

[0161] 10) When the classroom question is detected to be an objective question, obtain the rotation angle of the answer code image corresponding to the answer code number.

[0162] 11) Based on the preset correspondence between the answer options and the rotation angle, determine the target answer option corresponding to the rotation angle, and obtain the answer corresponding to the answer code number.

[0163] 12) When a subjective question is detected in class, extract the coordinates of the first vertex of the answer code image in the current answer sheet.

[0164] 13) Calculate the affine transformation matrix based on the coordinates of the second vertex and the first vertex of the answer code image in the pre-stored answer sheet template.

[0165] 14) Based on the affine transformation matrix, perform mapping calculations according to the coordinates of the third vertex of the subjective question answer area in the pre-stored answer sheet template, and determine the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet.

[0166] 15) Based on the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet, extract the answer for the corresponding subjective question answer area.

[0167] 16) Identify the students whose answer codes match the question numbers and obtain each student's answers to the classroom questions.

[0168] 17) Analyze the students' accuracy rate on each classroom question and / or the students' speed of answering each classroom question to obtain an analysis of the students' classroom learning situation.

[0169] 18) Analyze the accuracy and / or scores of different students on each classroom question to obtain an analysis of their responses to the classroom questions.

[0170] 19) Analyze the accuracy and / or speed of different students in answering each classroom question, as well as the accuracy and / or time of each student in answering the homework, to obtain a comprehensive analysis of student learning.

[0171] The aforementioned classroom teaching data processing method based on answer codes achieves the creation of a pre-stored answer code library by randomly generating a large number of answer codes. This library supports multiple students answering questions simultaneously, ensuring each student has a unique code. This meets the varying needs of different student numbers in different regions during actual classroom teaching. Furthermore, different answer acquisition methods are provided for objective and subjective questions, allowing for separate recording and analysis of answers for both, rather than being limited to objective questions. This enables flexible interaction between teachers and students during the classroom. Subsequent analysis, including classroom learning analysis, answer analysis of classroom questions, and comprehensive learning analysis of students both in and out of class, provides a comprehensive understanding of each student's long-term learning progress and facilitates more targeted teaching. Since it eliminates the need for individual electronic devices or dedicated network configurations for teacher-student interaction, it further reduces teaching costs and enhances the universality of intelligent teaching in different teaching environments, while still enabling flexible and targeted instruction.

[0172] The classroom teaching data processing method based on answer codes disclosed in this application can store data such as classroom teaching scene images, answer code images, answer code numbers, and corresponding answers on a blockchain.

[0173] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.

[0174] In one embodiment, such as Figure 9 As shown, a classroom teaching data processing system based on answer codes is provided. This system can be implemented using software modules, hardware modules, or a combination of both as part of a computer device. The system includes paper answer codes 902, a camera 904, and a server 906; wherein:

[0175] The paper answer code 902 is used to provide students with answers to classroom questions.

[0176] The camera 904 is used to capture video of the classroom teaching process.

[0177] The server 906 is used to: extract corresponding classroom teaching scene images for classroom teaching questions; obtain answer code images of each student for the classroom teaching scene images; extract pixel features corresponding to the answer code images, and decode them according to the pixel features to obtain answer code numbers and answers corresponding to each answer code number; determine the students who match the answer code numbers, and obtain the answers of each student for the classroom questions.

[0178] Teachers can access this information through a teacher's terminal, such as a shared classroom computer equipped with a classroom teaching data processing system based on answer codes. Figure 10 In the class interface of the classroom teaching data processing system based on answer codes shown, you can select the class you teach, for example, from... Figure 10 Choose one of the three classes shown, Class 1 of Grade 3 and Class 2 of Grade 3, and begin the current classroom teaching.

[0179] And when teachers from Figure 10 In the class interface of the classroom teaching data processing system based on answer codes shown, after selecting the corresponding class, such as Class 1 of Grade 3, you will be redirected to... Figure 11 The image shows the student information interface of a classroom teaching data processing system based on answer codes. (Refer to...) Figure 11 As can be seen, the student information interface can display the student information of all students in the selected third grade class, including student names and student seats.

[0180] Specifically, when server 904, connected to the classroom's shared computer, detects a teacher's action to publish a classroom question, it retrieves the submitted question and displays it on the classroom's large screen or via a projector connected to the shared computer. The question can be edited by the teacher or selected from a pre-stored question bank.

[0181] Furthermore, after server 904 receives the classroom questions submitted by the teacher, it further obtains the response time for these questions triggered during the teacher's classroom teaching process. The response time for different classroom questions can be set or changed by the teacher in real time. Camera 902, installed in the classroom, captures video of each student's responses during the teaching process. From the video images captured by camera 902, classroom scene images for each response time can be extracted, thus obtaining classroom scene images corresponding to different classroom questions.

[0182] In one embodiment, camera 904 and server 906 can be located on the same local area network (LAN) without requiring network reconfiguration for data communication between them. Since camera 904 and server 906 are not network-restricted, camera 904 can scan paper answer codes 902 even in a "no-network environment," meaning it can collect student answer data even without an internet connection and send it to server 906 on the same LAN, thus displaying real-time student responses to different classroom questions. Furthermore, the data collected by camera 904 and stored locally can be synchronized to the cloud and integrated with other student data, such as homework responses, for further analysis.

[0183] In one embodiment, after obtaining classroom teaching scene images corresponding to different classroom questions, the server 904 is further configured to: perform image processing and contour detection on each classroom teaching scene image, extract the initial image corresponding to the rectangular contour from the classroom teaching scene image, and then perform perspective transformation on the initial image to map it into the answer code image of each student corresponding to the square contour.

[0184] Specifically, server 904 identifies the answer code image, extracts the corresponding pixel features, verifies and decodes the pixel features, and generates a decoded answer code image. Then, based on a pre-stored answer code library, it identifies the decoded answer code image, matches the answer code number corresponding to the answer code image, and obtains the answer corresponding to each answer code number.

[0185] Among them, the camera 902 installed in the classroom collects video of each student's answer during the classroom teaching process. That is, during the corresponding answer time, students hold up paper answer codes and face the camera so that the camera can collect video of their answers.

[0186] Accordingly, within the allotted time for each classroom question, whether a student has answered a question is determined by whether they have scanned a QR code, i.e., whether the scan was successful or not. This is based on the reference... Figure 12The classroom question interface of the classroom teaching data processing system based on answer codes, as shown, displays the specific content of the questions posted by the teacher and the answer status of each student. Specifically, if a student's answer code has been scanned within the designated answering time for the corresponding question, that student's status is "answered." If a student's answer code was not scanned successfully (i.e., the student did not raise their answer code to answer), that student's status is "not answered." Figure 12 The left side of the classroom question interface shows detailed information about each student's answer status.

[0187] For example, refer to Figure 12 It is known that the classroom question posted by the teacher is "In winter, the baby is afraid of the cold and refuses to take off his hat even when he is indoors. But he sees someone who obediently takes off his hat. Who is that person?" The corresponding options include four options: "Impossible", "No", "Yes", and "Maybe not". Students submit their answers by holding up their answer codes and facing the camera.

[0188] Furthermore, after completing the current lesson's questions, the user will be redirected to a page such as... Figure 13 The answer result interface of the classroom teaching data processing system based on answer codes shown is for reference. Figure 13 It can be seen that, for the current classroom question, the number of times each option was selected for that question can be obtained, and the sum of the number of times each option was selected equals the number of students in the current classroom.

[0189] In one embodiment, the student matching the answer code number is determined based on the answer code number and the correspondence between the answer code number and the student number stored in the preset mapping table. Then, the answers to the classroom questions for each determined student are obtained based on the answers corresponding to the answer code number.

[0190] The preset mapping table stores the correspondence between answer code numbers and student numbers. In this embodiment, each grade level has a corresponding relationship between students and answer code numbers, and answer codes can be reused across different grades. For the correspondence between students and answer code numbers within different grades, a "store first, then reconfigure" approach is adopted. That is, the correspondence between students and answer code numbers for one grade is configured and stored first. When configuring the correspondence between students and answer code numbers for the next grade, the existing correspondence is cleared, and the correspondence between students and answer code numbers for the new grade is reconfigured and stored.

[0191] Furthermore, if the number of students in each grade exceeds the number of answer codes in the pre-stored answer code database, then a correspondence can be established between each class and each student's answer code number, and answer codes can be reused between different classes. The method for configuring this correspondence at the class level is similar to the method for configuring it at the grade level.

[0192] In the aforementioned classroom teaching data processing system based on answer codes, the system extracts corresponding classroom scene images for classroom questions and obtains the answer code images of each student within these images. By extracting the pixel features of the answer code images and decoding them, the system obtains the answer code number and the corresponding answer. This further identifies students whose answer codes match, thus revealing each student's answer to the classroom question. This method directly identifies and decodes the classroom scene images corresponding to the classroom questions to obtain the answer code number for each student and their answers to different questions, providing insight into student performance. It enables flexible interaction between teachers and students during the classroom teaching process. Since it eliminates the need for electronic devices for each student and network configuration for teacher-student interaction, it further reduces teaching costs and enhances the universality of intelligent teaching in different teaching environments.

[0193] In one embodiment, after all classroom questions in the current class have been answered, server 904 is further configured to: analyze the students' accuracy rate and / or the students' speed of answering each classroom question to obtain a student learning situation analysis; analyze the accuracy rate and / or score of different students for each classroom question to obtain a classroom question answering analysis; and analyze the accuracy rate and / or speed of different students for each classroom question, as well as the accuracy rate and / or time of each student for homework assignments.

[0194] Specifically, server 904 can assess the correctness of each student's answers to the classroom questions assigned by the teacher, thus obtaining the accuracy rate for each student on each question. Furthermore, based on the accuracy of students' answers, it can further obtain the response speed of different students for each classroom question. Then, based on the accuracy rate and response speed of different students for each classroom question, a comprehensive result can be obtained, such as... Figure 14 The student score ranking interface shown is from the classroom teaching data processing system based on answer codes. Figure 14 As can be seen from the student score ranking interface, the preset number of students can be obtained, such as the score ranking of the top 5 students.

[0195] Among them, such as Figure 15As shown, a student-level learning analysis interface is provided for a classroom teaching data processing system based on answer codes. (Refer to...) Figure 15 As can be seen, in the student-level learning analysis interface, the student's classroom learning analysis can be obtained based on the accuracy rate of each student on each classroom question and the corresponding score. This analysis can include the number of participants, the average accuracy rate of classroom questions, the accuracy rate of each student on each classroom question, each student's final score, and unanswered questions.

[0196] Furthermore, bar charts or tables can be used to illustrate each student's accuracy rate on each lesson question, each student's final score, and the number of unanswered questions, etc. Figure 15 The student-level learning analysis interface is shown below. In one embodiment, when a teacher publishes classroom questions, they simultaneously set the corresponding question types, including objective and subjective questions. When the teacher's server 904 receives the submitted classroom questions, it further determines the question type. Specifically, when the question type is detected to be an objective question, the server obtains the rotation angle of the answer code image corresponding to the answer code number, and acquires the preset correspondence between the answer options and the rotation angle. Based on this preset correspondence, the target answer option corresponding to the rotation angle can be determined, thus obtaining the answer corresponding to the answer code number.

[0197] When the current question type is detected as a subjective question, server 904 extracts the coordinates of the first vertex of the answer code image in the current answer sheet and obtains the coordinates of the second vertex of the answer code image in the pre-stored answer sheet template. Then, it calculates the affine transformation matrix based on the first and second vertex coordinates. Next, it obtains the coordinates of the third vertex of the subjective question answer area in the pre-stored answer sheet template and performs a mapping calculation based on the affine transformation matrix and the third vertex coordinates to calculate the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet. Finally, based on the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet, the answer for the corresponding subjective question answer area is extracted.

[0198] When it is detected that the current classroom question is a subjective question, it can be handled through methods such as... Figure 16 The interface shown is for reviewing subjective questions in a classroom teaching data processing system based on answer codes, which retrieves the student's answers to the corresponding subjective questions. (Refer to...) Figure 16 As can be seen, the subjective question review interface displays the names of all students. When the mouse hovers over a student's name, the corresponding student's subjective question answers will be displayed on the subjective question review interface for teachers to score and comment on.

[0199] In one embodiment, the analysis of responses to classroom questions can be achieved by evaluating the correctness of each student's answers to the various classroom questions assigned by the teacher, thus obtaining the accuracy rate and corresponding score for each student on each question, and consequently, the response analysis for each student on each classroom question. In other words, by analyzing the accuracy rate and / or score of different students on each classroom question, the response analysis for each classroom question can be obtained.

[0200] Among them, such as Figure 17 As shown, a question-based learning analysis interface is provided for a classroom teaching data processing system based on answer codes. (Refer to...) Figure 17 As can be seen, in the learning analysis interface at the question level, the analysis of students' answers to each classroom question can be obtained based on their accuracy rate and corresponding scores. This analysis can include the number of participants, the average accuracy rate of classroom questions, the accuracy rate of each student for each classroom question, as well as the question types, scores, and average grades for different questions.

[0201] Furthermore, bar charts or tables can be used to analyze the accuracy rate, question type, score, and average grade of different classroom questions, etc. Figure 17 The interface for analyzing student learning across different question dimensions is shown. In one embodiment, to ensure a long-term understanding of students' learning progress, teachers can also assign homework. Server 904 can then obtain a comprehensive analysis of student learning by combining the accuracy and / or speed of different students on each classroom question, as well as the accuracy and / or time taken to complete each student's homework assignments. Based on this comprehensive analysis, further insights can be gained into the learning progress of different students in classroom teaching. Furthermore, by analyzing and statistically processing student learning both in and out of class based on homework responses, the analysis can be extended beyond a single classroom lesson, providing a more comprehensive understanding of each student's long-term learning progress.

[0202] Specifically, such as Figure 18 As shown, a comprehensive learning analysis interface for a classroom teaching data processing system based on answer codes is provided, referring to... Figure 18 As can be seen from the comprehensive learning analysis interface, the student's answers to all classroom questions and homework assignments can be obtained based on the student's name. This includes the accuracy and / or answering speed of different students for each classroom question, as well as the accuracy and / or answering time of each student for homework assignments, and the teacher corresponding to each classroom.

[0203] Furthermore, the responses to all in-class questions and homework assignments for each student can be displayed in a table format, such as... Figure 18 The comprehensive learning analysis interface shown is presented.

[0204] In addition, after the current class is completed, the teacher can also select outstanding students to receive awards based on their learning progress during the current class.

[0205] The aforementioned classroom teaching data processing system based on answer codes analyzes and statistically analyzes the learning situation of different students from multiple perspectives, including classroom learning analysis, answer analysis of classroom questions, and comprehensive learning analysis of students in and out of class. This allows for a comprehensive understanding of each student's long-term learning situation and progress, enabling more targeted teaching for different students and improving teaching effectiveness.

[0206] In one embodiment, a classroom teaching data processing system based on answer codes is provided, wherein the server is further used for:

[0207] Obtain the randomly generated answer code; calculate the first distance between the answer code and each historical answer code in the pre-stored answer code library; determine whether the first distance is not less than a preset distance threshold; when it is determined that the first distance is not less than the preset distance threshold, add the randomly generated answer code to the pre-stored answer code library to obtain the updated pre-stored answer code library; return to the step of obtaining the randomly generated answer code until the number of answer codes in the updated pre-stored answer code library reaches a preset value.

[0208] In the aforementioned classroom teaching data processing system based on answer codes, randomly generated answer codes are obtained. The system calculates the first distance between the randomly generated answer code and each historical answer code in the pre-stored answer code database, and determines whether this first distance is not less than a preset distance threshold. When the first distance is determined to be not less than the preset distance threshold, the randomly generated answer code is added to the pre-stored answer code database, resulting in an updated database. This process continues until the number of answer codes in the updated database reaches a preset value, thus obtaining a complete database. This system achieves the goal of obtaining a pre-stored answer code database by randomly generating a large number of answer codes, and supports multiple students answering questions simultaneously based on the large number of answer codes in the database. It ensures that each student has a unique answer code, meeting the needs of different student numbers in different regions during actual classroom teaching, and improving the universality of intelligent teaching.

[0209] In one embodiment, a classroom teaching data processing system based on answer codes is provided, wherein the server is further used for:

[0210] When the first distance is determined to be less than the preset distance threshold, the generated answer code is identified as a failed answer code and discarded; the number of failed answer codes is counted; it is determined whether the number of failed answer codes has reached the preset number threshold; when the number of failed answer codes is detected to have reached the preset number threshold, the largest first distance threshold is determined from the first distances corresponding to each failed answer code; the first distance threshold is set as the preset distance threshold, and the updated preset distance threshold is obtained; the process returns to the step of obtaining randomly generated answer codes until the number of answer codes in the updated pre-stored answer code library reaches the preset value.

[0211] In the aforementioned classroom teaching data processing system based on answer codes, when the first distance is determined to be less than a preset distance threshold, the generated answer code is identified as a failed answer code and discarded. By counting the number of failed answer codes, and when the number of failed answer codes reaches a preset threshold, the largest first distance threshold is determined from the first distances corresponding to each failed answer code. Then, by setting the first distance threshold as the preset distance threshold, an updated preset distance threshold is obtained. The first distance is then re-determined based on the updated preset distance threshold. When the first distance is determined to be not less than the preset distance threshold, the randomly generated answer code is added to the pre-stored answer code library, resulting in an updated pre-stored answer code library. This process continues until the number of answer codes in the updated pre-stored answer code library reaches a preset value, thus obtaining a complete answer code library. It achieves the goal of obtaining a pre-stored answer code library by randomly generating a large number of answer codes, and supports multiple people to answer questions at the same time based on the large number of answer codes in the pre-stored answer code library, so that each student has a code, which meets the needs of different numbers of students in different regions in the actual classroom teaching process and improves the universality of intelligent teaching.

[0212] Specific limitations regarding the answer code-based classroom teaching data processing system can be found in the above description of the limitations of the answer code-based classroom teaching data processing method, and will not be repeated here. Each module in the above-mentioned answer code-based classroom teaching data processing system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0213] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 19As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores classroom teaching scene images, answer code images, answer code numbers, and corresponding answers. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a classroom teaching data processing method based on answer codes.

[0214] Those skilled in the art will understand that Figure 19 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0215] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0216] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0217] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the steps in the above method embodiments.

[0218] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this 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, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0219] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0220] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for processing classroom teaching data based on answer codes, characterized in that, The method includes: Extract corresponding classroom teaching scene images for classroom questions; Obtain the answer code images of each student for the classroom questions in the classroom teaching scene image; The answer code image is identified, the pixel features corresponding to the answer code image are extracted, and the pixel features are verified and decoded to generate a decoded answer code image; According to the pre-stored answer code library, the decoded answer code image is identified, the answer code number corresponding to the answer code image is matched, and the answer corresponding to each answer code number is obtained; when the classroom question is an objective question, the rotation angle of the answer code image corresponding to the answer code number is obtained, and according to the preset correspondence between the answer option and the rotation angle, the target answer option corresponding to the rotation angle is determined, and the answer corresponding to the answer code number is obtained; The update method of the pre-stored answer code library includes: obtaining a randomly generated answer code, calculating a first distance between the answer code and each historical answer code in the pre-stored answer code library; when it is determined that the first distance is not less than a preset distance threshold, adding the randomly generated answer code to the pre-stored answer code library to obtain an updated pre-stored answer code library; Identify the students whose answer codes match the given answer codes, and obtain each student's answers to the classroom questions.

2. The method according to claim 1, characterized in that, The extraction of corresponding classroom teaching scene images for classroom questions includes: Obtain the time allotted for answering classroom questions during the course of classroom teaching; Extract classroom teaching scene images during the answering time.

3. The method according to claim 2, characterized in that, The step of extracting classroom teaching scene images during the answering time includes: extracting classroom teaching scene images during the answering time from video images captured by a camera in the classroom.

4. The method according to claim 1, characterized in that, The step of recognizing the answer code image and extracting the pixel features corresponding to the answer code image includes: The answer code image is divided into regions to obtain a preset number of cells; Obtain the pixel value of each cell; Based on the pixel values ​​of each cell, pixel features corresponding to the answer code image are generated.

5. The method according to claim 1, characterized in that, The classroom questions include subjective questions; obtaining the answers corresponding to each of the answer codes includes: When the classroom question is detected to be a subjective question, the coordinates of the first vertex of the answer code image in the current answer sheet are extracted; The affine transformation matrix is ​​calculated based on the coordinates of the second vertex of the answer code image in the pre-stored answer sheet template and the coordinates of the first vertex. Based on the affine transformation matrix, and according to the coordinates of the third vertex of the subjective question answering area in the pre-stored answer sheet template, a mapping calculation is performed to determine the coordinates of the fourth vertex of the subjective question answering area in the current answer sheet; Based on the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet, extract the answer for the corresponding subjective question answer area.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: obtaining learning analysis results, which include any one or more of the following analysis results: classroom learning analysis of each student, answer analysis of each classroom question, and comprehensive learning analysis of each student in and after class; The analysis of students' classroom learning is obtained by analyzing the accuracy rate of students on each classroom question and / or the speed at which students answer each classroom question. The analysis of classroom question answers is obtained by analyzing the accuracy and / or score of different students for each classroom question; the comprehensive learning analysis is obtained by analyzing the accuracy and / or answering speed of different students for each classroom question, as well as the accuracy and / or answering time of each student for homework.

7. The method according to any one of claims 1 to 5, characterized in that, The step of obtaining the answer code images of each student for the classroom questions in the classroom teaching scene image includes: Image processing and contour detection are performed on the classroom teaching scene image to extract the initial image corresponding to the rectangular contour from the classroom teaching scene image; The initial image is subjected to perspective transformation and mapped to the answer code image of each student corresponding to a square outline.

8. The method according to any one of claims 1 to 5, characterized in that, The method further includes: When it is determined that the first distance is less than the preset distance threshold, the generated answer code is identified as a failed answer code and discarded. Count the number of failed answer codes; Determine whether the number of failed answer codes has reached a preset threshold. When the number of failed answer codes is detected to reach a preset threshold, the largest first distance threshold is determined from the first distances corresponding to each failed answer code. The first distance threshold is determined as the preset distance threshold, and the updated preset distance threshold is obtained; Return to the step of obtaining the randomly generated answer code until the number of answer codes in the updated pre-stored answer code library reaches a preset value.

9. A classroom teaching data processing system based on answer codes, characterized in that, The system includes paper answer codes, a camera, and a server; wherein: The paper answer codes are used to provide students with answers to classroom questions; The camera is used to capture video of the classroom teaching process; The server is used for: extracting corresponding classroom teaching scene images for classroom questions; obtaining answer code images of each student for the classroom questions in the classroom teaching scene images; recognizing the answer code images, extracting the pixel features corresponding to the answer code images, and verifying and decoding the pixel features to generate decoded answer code images; recognizing the decoded answer code images according to a pre-stored answer code library, matching the answer code numbers corresponding to the answer code images, and obtaining the answers corresponding to each answer code number; when the classroom question is an objective question... The process involves: obtaining the rotation angle of the answer code image corresponding to the answer code number; determining the target answer option corresponding to the rotation angle based on a preset correspondence between the answer option and the rotation angle; and obtaining the answer corresponding to the answer code number. The update method of the pre-stored answer code library includes: obtaining a randomly generated answer code; calculating a first distance between the answer code and each historical answer code in the pre-stored answer code library; and when the first distance is determined to be not less than a preset distance threshold, adding the randomly generated answer code to the pre-stored answer code library to obtain an updated pre-stored answer code library. Identify the students whose answer codes match the given answer codes, and obtain each student's answers to the classroom questions.

10. The system according to claim 9, characterized in that, The server is also used for: Obtain the response time for classroom questions triggered during the classroom teaching process; extract classroom teaching scene images within the response time.

11. The system according to claim 9, characterized in that, The server is also used for: The classroom teaching scene images during the answering time are extracted from the video images captured by the classroom camera.

12. The system according to claim 9, characterized in that, The server is also used for: The answer code image is divided into regions to obtain a preset number of cells; the pixel value of each cell is obtained; and pixel features corresponding to the answer code image are generated based on the pixel value of each cell.

13. The system according to claim 9, characterized in that, The server is also used for: When the classroom question is detected to be a subjective question, the coordinates of the first vertex of the answer code image in the current answer sheet are extracted; the affine transformation matrix is ​​calculated based on the coordinates of the second vertex of the answer code image in the pre-stored answer sheet template and the coordinates of the first vertex. Based on the affine transformation matrix, the coordinates of the third vertex of the subjective question answer area in the pre-stored answer sheet template are used to perform mapping calculations to determine the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet; based on the coordinates of the fourth vertex of the subjective question answer area in the current answer sheet, the answers for the corresponding subjective question answer areas are extracted.

14. The system according to any one of claims 9-13, characterized in that, The server is also used for: The learning situation analysis results are obtained, which include any one or more of the following analysis results: classroom learning situation analysis of each student, answer analysis of each classroom question, and comprehensive learning situation analysis of each student in and after class; wherein, the classroom learning situation analysis of students is obtained by analyzing the accuracy rate and / or the speed at which students answer each classroom question; the answer analysis of classroom questions is obtained by analyzing the accuracy rate and / or score of different students on each classroom question; the comprehensive learning situation analysis is obtained by analyzing the accuracy rate and / or the speed at which different students answer each classroom question, and the accuracy rate and / or the time taken to answer each student's homework.

15. The system according to any one of claims 9-13, characterized in that, The server is also used for: Image processing and contour detection are performed on the classroom teaching scene image to extract the initial image corresponding to the rectangular contour from the classroom teaching scene image; perspective transformation is performed on the initial image to map it into the answer code image of each student corresponding to the square contour.

16. The system according to any one of claims 9-13, characterized in that, The server is also used for: When it is determined that the first distance is less than the preset distance threshold, the generated answer code is identified as a failed answer code and discarded. Count the number of failed answer codes; Determine whether the number of failed answer codes has reached a preset threshold; when the number of failed answer codes reaches the preset threshold, determine the largest first distance threshold from the first distances corresponding to each failed answer code; The first distance threshold is determined as the preset distance threshold, and the updated preset distance threshold is obtained; Return to the step of obtaining the randomly generated answer code until the number of answer codes in the updated pre-stored answer code library reaches a preset value.

17. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

19. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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