Common error question image generation method based on education

Abstract

The invention discloses a common error question image generation method based on education. Relates to the technical field of image processing. The education-based common wrong question image generation method comprises the following steps: screening common wrong questions for educating students from a wrong question set according to a question selection requirement of a teacher, the common wrong questions being used for indicating wrong questions commonly selected for a plurality of students; generating a text image from the common error questions; carrying out fusion processing on the text image and an identification image used for identifying wrong questions to generate a common wrong question image; and displaying the common error question image to an expected arrangement object of the teacher.

Description

Technical Field

[0001] The present application relates to the technical field of image processing, and in particular to a method for generating images of common wrong questions based on education. Background Art

[0002] In the modern education system, wrong question management is a crucial link. It not only helps students identify and make up for the weak links in the learning process, but also assists teachers to accurately locate teaching difficulties and optimize teaching strategies. However, traditional methods are particularly clumsy and inefficient in dealing with this task. For a long time, the sorting of wrong questions has mainly relied on manual operations. Teachers need to manually screen and record the wrong questions in each homework, test or exam. This method is not only time-consuming and laborious, but also prone to omissions or misjudgments.

[0003] Although traditional wrong question management systems try to introduce technical means to simplify the process, they still face many challenges in the key image generation link. These systems often directly convert wrong questions into static images, which lacks flexibility and interactivity. Once a wrong question needs to be marked or modified, it is necessary to go back to the original file and reprocess it, which not only adds extra workload, but also may lead to waste of resources, and there are great limitations for wrong question images. At the same time, this single image generation mode is highly dependent on powerful computing power and storage space. For schools or educational institutions, the long-term operating cost is high and it is difficult to adapt to rapidly changing teaching needs.

[0004] With respect to the technical problems existing in the above-mentioned prior art of generating static wrong question images, which result in high cost, waste of resources and high limitations, no effective solution has been proposed so far. Summary of the invention

[0005] The embodiments of the present application provide a method for generating common wrong question images based on education, so as to at least solve the technical problems existing in the prior art of generating static wrong question images, resulting in high cost, waste of resources and high limitations.

[0006] According to one aspect of an embodiment of the present application, a method for generating common wrong questions images based on education is provided, comprising: screening common wrong questions for educating students from a wrong question set according to the teacher's question selection requirements, wherein the common wrong questions are used to indicate wrong questions selected by multiple students; generating text images from the common wrong questions; fusing the text images with identification images used to identify the wrong questions to generate common wrong questions images; and displaying the common wrong questions images to the teacher's desired arrangement objects.

[0007] According to another aspect of an embodiment of the present application, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the above method.

[0008] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0009] According to another aspect of the embodiments of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0010] According to another aspect of an embodiment of the present application, there is also provided an education-based common wrong question image generation device, including: a wrong question recommendation module, used to screen common wrong questions for educating students from a wrong question set according to the teacher's question selection requirements, wherein common wrong questions are used to indicate wrong questions selected by multiple students; a first image production module, used to generate a text image from the common wrong questions; a second image generation module, used to fuse the text image with an identification image used to identify the wrong questions to generate a common wrong question image; and a wrong question sending module, used to display the common wrong question image to the teacher's desired arrangement object.

[0011] According to another aspect of an embodiment of the present application, there is also provided an education-based common wrong question image generation device, including: a processor; and a memory, connected to the processor, for providing the processor with instructions for processing the following processing steps: screening common wrong questions for educating students from a wrong question set according to the teacher's question selection requirements, wherein the common wrong questions are used to indicate wrong questions selected by multiple students; generating text images from the common wrong questions; fusing the text image with an identification image used to identify the wrong questions to generate a common wrong question image; and displaying the common wrong question image to the teacher's desired arrangement object.

[0012] In the embodiments of the present application, firstly, by generating common wrong questions as text images, the comprehension barriers that may be caused by pure text information are overcome, especially when facing lower grade students or non-native learners, the graphical presentation method is easier to understand and remember. Secondly, this technical solution converts the screened common wrong questions into high-definition text images. Different from the traditional one-time static image generation, this solution adopts layered processing technology to separate the wrong question content from the annotation information, and generates basic text images and dynamic logo images respectively. The system also allows teachers to adjust the annotation content or add new annotations at any time without rebuilding the entire image, which greatly improves the immediacy and flexibility of teaching interaction, avoids waste of resources, and reduces the limitations of image generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0014] Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of the present application;

[0015] Figure 2 is a schematic diagram of an image generation system according to Embodiment 1 of the present application;

[0016] Figure 3 It is a flowchart of the method for generating images of common wrong questions based on education according to the first aspect of Example 1 of the present application;

[0017] Figure 4 This is an application program interface diagram regarding teacher topic selection requirements according to Example 1 of the present application;

[0018] Figure 5 is a schematic diagram of a common wrong question image according to Example 1 of the present application;

[0019] Fig. 6A This is an application program interface diagram about the knowledge point tree according to Example 1 of the present application;

[0020] Figure 6B This is an application program interface diagram about a job tree according to Embodiment 1 of the present application;

[0021] Figure 7 This is an application program interface diagram for variant questions according to Example 1 of the present application;

[0022] Figure 8 is a schematic diagram of a common wrong question image generating device based on education according to Embodiment 2 of the present application; and

[0023] Fig. 9 It is a schematic diagram of the common wrong question image generation device based on education as described in Example 3 of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Example 1

[0027] According to this embodiment, a method embodiment of an education-based method for generating images of common wrong questions is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] The method embodiment provided in this embodiment can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 The hardware structure block diagram of a computing device for implementing a method for generating images of common wrong questions based on education is shown. Figure 1 As shown, the computing device may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, and a transmission device for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0029] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computing device. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0030] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the common wrong question image generation method based on education in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the common wrong question image generation method based on education of the above-mentioned application is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0031] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computing device. In one example, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0032] The display may be, for example, a touch screen liquid crystal display (LCD) that may enable a user to interact with a user interface of the computing device.

[0033] It should be noted that, in some optional embodiments, the above Figure 1 The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing devices described above.

[0034] Figure 2is a schematic diagram of an image generation system according to this embodiment. Figure 2 As shown, the system includes: an image fusion module 110, a mark adding module 120, a wrong question collecting module 130, a wrong question recommending module 140, and a wrong question distributing module 150.

[0035] The image fusion module 110 is used to fuse the text image generated according to the common wrong questions with the identification image to generate a common wrong question image.

[0036] The mark adding module 120 is used to add marks at corresponding positions of common wrong questions to generate mark images.

[0037] The wrong question collection module 130 is used to collect the wrong questions of students.

[0038] The wrong question recommendation module 140 is used to recommend wrong questions to teachers according to the teacher's question selection requirements.

[0039] The wrong question distribution module 150 is used to distribute the common wrong question images recommended for the teacher to designated students.

[0040] It should be noted that the image fusion module 110, identification adding module 120, wrong question collecting module 130, wrong question recommending module 140 and wrong question distributing module 150 in the system can all be applicable to the hardware structure described above.

[0041] Under the above operating environment, according to the first aspect of this embodiment, a method for generating common wrong question images based on education is provided. The method comprises: Figure 2 The image generation system shown in is implemented. Figure 3 A schematic diagram showing the process of the method is shown in FIG. Figure 3 As shown, the method includes:

[0042] S302: selecting common wrong questions for educating students from the wrong question set according to the teacher's question selection requirements, wherein the common wrong questions are used to indicate wrong questions selected by multiple students;

[0043] S304: Generate text images from common wrong questions;

[0044] S306: fusing the text image with the identification image for identifying wrong questions to generate a common wrong question image; and

[0045] S308: Displaying images of common wrong questions to the teacher's desired assignment subjects.

[0046] Specifically, students answer the test paper and generate answer results. Then the teacher corrects the answer results of each test paper and generates correction results. Then the staff enters the answer results of the test paper and the corresponding correction results into the wrong question collection module 130. Furthermore, the wrong question collection module 130 determines the wrong questions in the corresponding test paper according to the correction results and generates a wrong question set.

[0047] Furthermore, the teacher enters the system for recommending wrong questions to the teacher through the application on the terminal device. The application is the front end of the system for recommending wrong questions to the teacher. The teacher selects from the preset elements corresponding to the teacher's topic selection requirements on the application interface to determine the selected teacher's topic selection requirements. The preset elements corresponding to the teacher's topic selection requirements include knowledge points, homework, specific time range, class, class stratification, students, grade number of answers and scores, question types and reasons for errors. And the teacher's topic selection requirements are one or more specific preset elements selected from the preset elements.

[0048] in Figure 4 shows the interface of the application. Figure 4 As shown, for example, the teacher selects "Grade 2 Class 02 / Excellent", "Recent Year", "Single Choice Questions" and "All Reasons for Errors" in the option boxes corresponding to the preset elements.

[0049] The teacher selection requirements are determined as follows:

[0050] (1) Specific time frame: the past year;

[0051] (2) Class: Grade 2, Class 02;

[0052] (3) Class stratification: Excellent;

[0053] (4) Question type: Multiple choice;

[0054] (5) Causes of error: All causes of error.

[0055] Furthermore, the wrong question recommendation module 140 obtains the teacher's topic selection requirements selected by the teacher on the application program interface, and selects corresponding common wrong questions from the wrong question set according to the teacher's topic selection requirements. And the common wrong questions are used to indicate wrong questions selected by multiple students. Then the wrong question recommendation module 140 sends the selected common wrong questions to the image fusion module 110. The common wrong questions are texts.

[0056] Further, after receiving the common wrong questions, the image fusion module 110 processes the common wrong questions and generates a corresponding image (i.e., a text image). After that, the identification adding module 120 generates a corresponding identification image for identifying the wrong questions, and then the image fusion module 110 fuses the text image with the identification image to generate a common wrong question image including both the wrong questions and the identification. After that, the image fusion module 110 sends the common wrong question image to the wrong question distribution module 150.

[0057] Furthermore, the teacher selects a desired assignment object on the application program interface, wherein the desired assignment object may be determined according to the class, class stratification, and students.

[0058] For example, if the teacher selects all students in Grade 2 Class 02 as the desired assignment object, the wrong question distribution module 150 obtains the desired assignment object selected on the application interface, and then sends the common wrong question images received from the wrong question recommendation module 140 to the terminal device of the desired assignment object.

[0059] Thus, the expected assignment object can answer the wrong questions in the common wrong question image through the application on the terminal device. After the expected assignment object completes the answer, the corresponding answer result is uploaded to the wrong question collection module 130. The teacher can correct the test paper according to the answer result, and upload the generated correction result to the wrong question collection module 130.

[0060] Optionally, the operation of fusing a text image with an identification image for identifying wrong questions to generate a common wrong question image includes: using the text image as a background image; generating a corresponding identification image according to the identification type, and using the identification image as a foreground image; and fusing the foreground image with the background image to generate a common wrong question image, wherein the operation of generating a corresponding identification image according to the identification type, and using the identification image as a foreground image includes: when the identification type is the level of the number of wrong questions, generating a first identification at a first identification node position according to the level of the number of wrong questions; and generating an identification image as a foreground image according to the first identification, and when the identification type is the key knowledge level, generating a second identification at a second identification node position according to the key knowledge level; and generating an identification image as a foreground image according to the second identification.

[0061] Specifically, there are two types of labels in the label image generated by the label adding module 120, including: the level of the number of wrong questions and the level of key knowledge.

[0062] So refer to Figure 5 As shown, the identification adding module 120 generates a corresponding identification (ie, a first identification) at the identification position of the level of the number of wrong answerers in the identification image (ie, a first identification node position).

[0063] As for the level of the number of wrong questions, the level of the number of wrong questions includes: level 1, level 2 and level 3. For example, if the level of the number of wrong questions is level 1, it means that the number of people who got the question wrong in the whole grade is less than or equal to one-third of the total number of people in the whole grade, and the corresponding mark is one star; if the level of the number of wrong questions is level 2, it means that the number of people who got the question wrong in the whole grade is greater than one-third and less than two-thirds of the total number of people in the whole grade, and the corresponding mark is two stars; if the level of the number of wrong questions is level 3, it means that the number of people who got the question wrong in the whole grade is greater than or equal to two-thirds of the total number of people in the whole grade, and the corresponding mark is three stars.

[0064] For the key knowledge level, a wrong question may involve multiple knowledge points, so the identification adding module 120 adds a key knowledge identification (i.e., a second identification) at the position corresponding to each knowledge point in the question in the identification image (i.e., the second identification node position) according to the importance of each knowledge point, such as adding a background.

[0065] Thus, the image fusion module 110 uses the identification image including the identification of the key knowledge level and the identification of the number of wrong questions as the foreground image and the text image as the background image. Then, the image fusion module 110 fuses the foreground image and the background image to generate a common wrong question image.

[0066] As described in the background technology, although the traditional wrong question management system attempts to introduce technical means to simplify the process, it still faces many challenges in the key image generation link. These systems often directly convert wrong questions into static images, which lacks flexibility and interactivity. Once a wrong question needs to be highlighted or modified, it is necessary to go back to the original file and reprocess it, which not only adds extra workload, but also may lead to waste of resources, and there are great limitations for wrong question images. At the same time, this single image generation mode is highly dependent on powerful computing power and storage space. For schools or educational institutions, the long-term operating cost is high and it is difficult to adapt to rapidly changing teaching needs.

[0067] In view of the technical problems described above, through the technical solutions of the embodiments of the present application, firstly, by generating common wrong questions as text images, the understanding barriers that may be caused by pure text information are overcome, especially when facing lower grade students or non-native learners, the graphical presentation method is easier to understand and remember. Secondly, this technical solution converts the screened common wrong questions into high-definition text images. Different from the traditional one-time static image generation, this solution adopts layered processing technology to separate the wrong question content from the annotation information, and generates basic text images and dynamic logo images respectively. The system also allows teachers to adjust the annotation content or add new annotations at any time without rebuilding the entire image, which greatly improves the immediacy and flexibility of teaching interaction, avoids waste of resources, and reduces the limitations of image generation.

[0068] Optionally, when the identification type is the key knowledge level, the operation of generating a second identification at the second identification node position according to the key knowledge level includes: identifying the text information of common wrong questions and determining the key knowledge information in the text information; determining the position information of the key knowledge information in the text image; determining the second identification node position corresponding to the position information in the identification image according to the position information in the text image; and generating a second identification at the second identification node position according to the key knowledge level.

[0069] Specifically, before generating an identifier corresponding to the key knowledge level, the identifier adding module 120 will also identify the text information (i.e., the question text) of each common wrong question received, and determine the importance of each knowledge point reflected in the question of the common wrong question and the corresponding text (i.e., the key knowledge information) according to the importance of each knowledge point recorded in the database.

[0070] Furthermore, the identification adding module 120 marks the position information of each key knowledge information in the text image, thereby determining the second identification node position corresponding to the position information on the identification image based on the position information, and thereby generating a corresponding identification of the key knowledge level corresponding to the knowledge point at the position at the second identification node position (i.e., the second identification).

[0071] Therefore, this technical solution can distinguish each key piece of knowledge information by marking the position of the key knowledge information in the question, thereby attracting students' attention and attention.

[0072] Optionally, the operation of generating a second identifier at a second identifier node position according to the key knowledge level includes: generating a second identifier of a corresponding color for the key knowledge information according to the key knowledge level.

[0073] Specifically, in the question, different key knowledge information may correspond to different key knowledge levels, so the identification adding module 120 is pre-set with the colors of identifications of different key knowledge levels. For example, the key knowledge levels include: low level, medium level and high level. The colors of the identifications of low level, medium level and high level are different. Therefore, the technical solution can quickly lock the key knowledge information by the color of the identification.

[0074] Optionally, the method also includes: receiving answer results and correction results generated by scanning the test paper by a scanning device; determining the first wrong question in the test paper according to the correction results; and generating a wrong question set according to the first wrong question and the corresponding answer results.

[0075] Specifically, taking a paper test paper as an example, after the student answers and the teacher corrects the test paper, the scanning device scans the test paper to identify each test question in the test paper and the corresponding answer result and correction result. Afterwards, the scanning device uploads the answer result and correction result of the test question to the wrong question collection module 130. The test paper also includes the student's identification code, so the scanning device can scan the student's identification code, thereby associating the answer result and correction result of the test question with the student. The scanning device can be a scanner.

[0076] Furthermore, the wrong question collection module 130 determines the right or wrong result and score of the question according to the question type and the correction result, and selects the wrong question (ie, the first wrong question) from all the questions in the test paper according to the right or wrong result and the score.

[0077] More specifically, for multiple-choice questions, the teacher's grading results only include the overall score of the question type, and there is no right or wrong result for each sub-question. Therefore, the wrong question collection module 130 matches the student's answer results for the multiple-choice questions with the correct answer. If they are consistent, full marks are given, if they are completely inconsistent, zero marks are given, and if they are partially consistent, points are assigned proportionally, thereby determining the score of each multiple-choice question. For fill-in-the-blank or short-answer questions, the wrong question collection module 130 identifies the right and wrong marks and scores marked by the teacher on the test paper. Finally, the grading results and scores of each sub-question in a fill-in-the-blank or short-answer question are summarized and statistically analyzed to obtain the score of the big question.

[0078] Furthermore, the wrong question collection module 130 records the first wrong question of each student and the answer result into a wrong question collection, wherein the wrong question collection includes not only the first wrong question recorded this time, but also the wrong questions recorded before.

[0079] Therefore, the present technical solution uses a scanner to scan the test paper and identify the student's answer results and the teacher's correction results. In the prior art, the wrong questions are intelligently collected and sorted by using devices such as smart paper and pen and high-speed scanners, but high-speed scanners can only collect one homework at a time, which is inefficient. The scanner in the present technical solution can quickly collect a large number of wrong homework in a few seconds, which improves efficiency.

[0080] Optionally, the operation of screening common wrong questions for educating students from a wrong question set according to the teacher's topic selection requirements includes: associating the first wrong question in the wrong question set with a preset element corresponding to the teacher's topic selection requirements through a preset big data model, wherein the teacher's topic selection requirements include at least one of knowledge points, homework, specific time range, class, class stratification, students, number of respondents and scores, question types, and causes of errors; obtaining the teacher's topic selection requirements selected by the teacher, determining a second wrong question corresponding to the teacher's topic selection requirements according to the preset elements corresponding to the teacher's topic selection requirements; and recommending the second wrong question to the teacher as a common wrong question.

[0081] Specifically, the wrong question recommendation module 140 receives the first wrong question sent by the wrong question collection module 130, and then inputs the first wrong question into the preset big data model. Then, the wrong question recommendation module 140 classifies each first wrong question according to each preset element through the big data model, thereby associating each preset element with the first wrong question.

[0082] For example, for question types (i.e., preset elements), the wrong question recommendation module 140 determines whether the question type of each first wrong question is a multiple-choice question type, a judgment question type, a fill-in-the-blank question type, or a short-answer question type through a big data model, thereby associating the first wrong question with the corresponding question type; for students (i.e., preset elements), the wrong question recommendation module 140 determines the student ID of each first wrong question through a big data model, and associates the student ID with the corresponding wrong question; for knowledge points (i.e., preset elements), the wrong question recommendation module 140 determines the knowledge points of each first wrong question through a big data model, such as "the definition of opposite numbers", "solving a linear equation of two variables", or "finding the absolute value of a number", etc. It should be noted that for the knowledge points of preset elements, a wrong question can be associated with multiple knowledge points.

[0083] Therefore, according to the above method, the wrong question recommendation module 140 associates other preset elements with each first wrong question, which will not be repeated here.

[0084] Furthermore, the teacher selects from preset elements corresponding to the teacher's topic selection requirements on the application program interface, thereby determining the teacher's topic selection requirements.

[0085] Furthermore, the wrong question recommendation module 140 obtains the teacher's topic selection requirements selected by the teacher on the application program interface, and selects corresponding wrong questions (i.e., the second wrong questions) from all wrong questions in the wrong question set according to the teacher's topic selection requirements. For example, the teacher's topic selection requirements may be: the teacher selects "Grade 2 Class 02 / Excellent", "Recent Year", "Single Choice Questions" and "All Causes of Errors" as the teacher's topic selection requirements in the option box corresponding to the preset elements.

[0086] Furthermore, the wrong question recommendation module 140 sends the screened second wrong question as a common wrong question to the image fusion module 110. After receiving the common wrong question, the image fusion module 110 processes the common wrong question and generates a corresponding image (i.e., a text image). After that, the identification adding module 120 generates a corresponding identification image for identifying the wrong question, and then the image fusion module 110 fuses the text image with the identification image to generate a common wrong question image including both the wrong question and the identification. After that, the image fusion module 110 sends the common wrong question image to the wrong question distribution module 150.

[0087] Therefore, this technical solution associates each wrong question with a preset element, so that after the teacher selects the teacher's topic selection requirements, the teacher can quickly filter out the wrong question set according to the teacher's topic selection requirements, accurately provide the teacher with test questions that meet the teacher's requirements, and improve the efficiency of topic selection.

[0088] Optionally, in the case where the preset element is a knowledge point, the operation of associating the first wrong question in the wrong question set with the preset element corresponding to the teacher's question selection requirement through the preset big data model includes: associating the first wrong question in the wrong question set with the knowledge point through the big data model to generate a knowledge point tree, wherein each knowledge point node on the knowledge point tree is associated with the first wrong question.

[0089] Specifically, Fig. 6A The application interface of the knowledge point tree is shown. Fig. 6A As shown, the wrong question recommendation module 140 inputs the first wrong question into the big data model, and associates the first wrong question with the knowledge point of the preset element through the big data model, thereby generating a knowledge point tree with the knowledge point as the node. The knowledge point node at least includes: "the definition of opposite number", "solving a linear equation of two variables" and "finding the absolute value of a number".

[0090] Therefore, the wrong question recommendation module 140 lists each knowledge point node on the application interface. When the teacher selects a corresponding knowledge point node, the wrong question recommendation module 140 determines the wrong question associated with the knowledge point node (ie, the second wrong question) from the wrong question set.

[0091] Therefore, this technical solution uses a knowledge point tree to display each knowledge point node and the wrong questions associated with it, so that teachers can intuitively select and determine knowledge points, quickly select teacher topic selection requirements, and improve topic selection efficiency.

[0092] Optionally, in the case where the preset element is a homework, the operation of associating the first wrong question in the wrong question set with the preset element corresponding to the teacher's question selection requirement through the preset big data model includes: associating the first wrong question in the wrong question set with the homework through the big data model to generate a homework tree, wherein each homework node on the homework tree is associated with the first wrong question.

[0093] Specifically, Figure 6B The application interface showing the job tree. Figure 6B As shown, the wrong question recommendation module 140 inputs the first wrong question into the big data model, and associates the first wrong question with the homework of the preset element through the big data model, thereby generating a homework tree with the homework as the node. The homework nodes at least include: "May 17 homework 1", "May 17 homework 2" and "Second unit test".

[0094] Therefore, the wrong question recommendation module 140 lists each homework node on the application interface. When the teacher selects the corresponding homework node, the wrong question recommendation module 140 determines the wrong question associated with the homework node (ie, the second wrong question) from the wrong question set.

[0095] Therefore, this technical solution uses the homework tree to display each homework node and the wrong questions associated with it, so that teachers can intuitively select and determine homework, quickly select teacher topic selection requirements, and improve topic selection efficiency.

[0096] Optionally, in a case where the preset element is a student, the operation of associating the first wrong question in the wrong question set with the preset element corresponding to the teacher's question selection requirement through the preset big data model includes: associating the first wrong question in the wrong question set with the student through the big data model to generate grade data, wherein the operation of generating grade data includes: calculating the accuracy of the first wrong question based on the scores of multiple questions in the first wrong question associated with the student and the full marks of the multiple questions; and calculating the grade data based on the accuracy of the first wrong question and the number of students.

[0097] Specifically, the wrong question recommendation module 140 inputs the first wrong question into the big data model, and associates the first wrong question with a student of a preset element through the big data model. For example, the wrong question recommendation module 140 determines the student ID of each first wrong question through the big data model, and associates the student ID with the wrong question corresponding to it. Further, the wrong question recommendation module 140 determines the class corresponding to each student through the big data model, and associates the class with the wrong question of each student. Further, the wrong question recommendation module 140 calculates the accuracy rate of each wrong question (i.e., the first wrong question) of each student in a certain grade.

[0098] For example, the wrong question 1 of student A includes multiple sub-questions. Then the wrong question recommendation module 140 calculates the correctness of the wrong question 1 of student A according to the correctness calculation formula. The correctness calculation formula is:

[0099] Correct rate = sum of scores of multiple questions / sum of full marks of multiple questions.

[0100] Furthermore, the wrong question recommendation module 140 calculates the correct rate of each student in the grade for wrong question 1 according to the above method. Each student in the grade is all students in the grade, including students whose answers to wrong question 1 are wrong and students whose answers to wrong question 1 are correct.

[0101] Furthermore, the wrong question recommendation module 140 calculates the grade data according to the correct rate of wrong question 1 of each student and the number of students in the grade using the grade data calculation formula. The grade data is used to indicate the average of the first wrong question in the grade. The grade data calculation formula is:

[0102] Grade data = the sum of each student’s correct rate / number of students in the grade.

[0103] The number of students in the grade is used to indicate the number of students in that grade who answered question 1.

[0104] Furthermore, the wrong question recommendation module 140 calculates the grade data corresponding to other first wrong questions in the wrong question set according to the above method for calculating the grade data corresponding to wrong question 1. No further details will be given here.

[0105] In addition to grade data, the wrong question recommendation module 140 will also use the big data model to count the number of students who have made mistakes and the list of names for each wrong question in real time based on the class and students required by the teacher for the question selection.

[0106] In addition, the class stratification includes excellent, good and weak, and the wrong question recommendation module 140 can determine the class stratification corresponding to the student according to the interval of the average score of each student in multiple examinations of a certain subject.

[0107] For example, if a student's average score in a certain subject is below 60 points, then the class stratification of the student is weak; if the student's average score in a certain subject is between 60 and 80 points, then the class stratification of the student is good; if the student's average score in a certain subject is above 80 points, then the class stratification of the student is excellent.

[0108] In addition, the wrong question recommendation module 140 also associates various preset elements through the big data model. For example, students are associated with corresponding classes, classes are associated with corresponding knowledge points, etc., so that the wrong question recommendation module 140 associates knowledge points, homework, specific time range, class, class stratification, students, the number of answers and scores of grades, question types, and wrong reasons in sequence through the big data model.

[0109] Therefore, when a teacher selects a predetermined element as a teacher topic selection requirement in the application program interface, other predetermined elements associated with the teacher topic selection requirement can be automatically displayed.

[0110] For example, when the teacher selects "Grade 2 Class 02" in the application interface, the homework tree automatically displays the homework associated with Grade 2 Class 02.

[0111] Therefore, this technical solution associates the student ID with the wrong questions, so that the grade data can be quickly counted and the efficiency of calculating statistical data can be improved.

[0112] Optionally, the operation of determining the second wrong question corresponding to the teacher's topic selection requirement includes: when there are multiple teacher's topic selection requirements, determining the second wrong question based on the intersection of the first wrong questions corresponding to the teacher's topic selection requirements; and when the first wrong question in the intersection is zero, obtaining a variant question corresponding to the teacher's topic selection requirement as the second wrong question.

[0113] Specifically, when the teacher selects multiple teacher selection requirements in the application program interface, the wrong question recommendation module 140 determines the corresponding wrong questions (i.e., the first wrong questions) according to each teacher selection requirement. Then, the wrong question recommendation module 140 determines the intersection of the first wrong questions corresponding to each teacher selection requirement, i.e., the same first wrong questions, and determines the wrong questions in the intersection as the second wrong questions.

[0114] Further, refer to Figure 7 As shown, when the number of wrong questions in the intersection of the first wrong questions corresponding to the teacher's topic selection requirements is zero, the wrong question recommendation module 140 obtains the variant questions corresponding to the teacher's topic selection requirements from the wrong question collection module 130 as the second wrong questions.

[0115] Therefore, this technical solution recommends variant questions as the second wrong questions (i.e., common wrong questions) to teachers, so that when there are no wrong questions that meet the teacher's requirements for topic selection, other test questions can be flexibly recommended to teachers to meet the teacher's requirements. In addition, students can also draw inferences from the variant questions and master the knowledge of the corresponding knowledge points.

[0116] Optionally, the method further includes: recommending personalized wrong questions corresponding to the students from a wrong question collection to the teacher according to the teacher's topic selection requirements; and sending personalized wrong questions to the corresponding students according to the teacher's expected assignment objects.

[0117] Specifically, when the teacher needs to screen the corresponding wrong questions that meet the learning progress of individual students, the corresponding teacher selection requirements can be selected in the application interface, so that the wrong question recommendation module 140 determines the individual wrong questions corresponding to the student according to the teacher's selection requirements and recommends them to the teacher. After that, the wrong question recommendation module 140 sends the individual wrong question to the image fusion module 110. After receiving the individual wrong question, the image fusion module 110 processes the individual wrong question and generates a corresponding image (i.e., a text image). After that, the identification adding module 120 generates a corresponding identification image for identifying the wrong question, and then the image fusion module 110 fuses the text image with the identification image to generate an individual wrong question image that includes both the wrong question and the identification. After that, the image fusion module 110 sends the individual wrong question image to the wrong question distribution module 150.

[0118] Furthermore, the teacher selects the desired assignment target as the student in the application interface, and the wrong question distribution module 150 determines the desired assignment target. Then, the wrong question distribution module 150 sends the received personalized wrong question image sent by the wrong question recommendation module 140 to the desired assignment target, that is, the terminal device of the student.

[0119] Therefore, in addition to common wrong questions, this technical solution also recommends individual wrong questions suitable for individual students to teachers, so that suitable wrong questions can be arranged according to the actual situation of individual students. Therefore, this technical solution can recommend wrong questions to teachers in many aspects to meet the needs of teachers.

[0120] In addition, according to a second aspect of this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the above method.

[0121] In addition, according to a third aspect of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0122] In addition, according to a fourth aspect of this embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0123] Therefore, according to this embodiment, firstly, by generating common wrong questions as text images, the comprehension barriers that may be caused by pure text information are overcome, especially when facing lower grade students or non-native learners, the graphical presentation method is easier to understand and remember. Secondly, this technical solution converts the screened common wrong questions into high-definition text images. Different from the traditional one-time static image generation, this solution adopts layered processing technology to separate the wrong question content from the annotation information, and generate basic text images and dynamic logo images respectively. The system also allows teachers to adjust the annotation content or add new annotations at any time without rebuilding the entire image, which greatly improves the immediacy and flexibility of teaching interaction, avoids waste of resources, and reduces the limitations of image generation.

[0124] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0125] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0126] Example 2

[0127] Figure 8 The apparatus 800 for generating common wrong question images based on education according to this embodiment is shown, and the apparatus 800 corresponds to the method described in the first aspect of embodiment 1. Figure 8 As shown, the device 800 includes: a wrong question recommendation module 810, which is used to screen common wrong questions for educating students from a wrong question set according to the teacher's question selection requirements, wherein the common wrong questions are used to indicate wrong questions selected by multiple students; a first image generation module 820, which is used to generate a text image from the common wrong questions; a second image generation module 830, which is used to fuse the text image with an identification image used to identify the wrong questions to generate a common wrong question image; and a wrong question sending module 840, which is used to display the common wrong question image to the teacher's desired arrangement object, wherein the second image generation module 830 includes: a background image generation submodule, which is used to use the text image as a background image; a foreground image generation submodule, which is used to generate a corresponding identification image according to the identification type, and The identification image is used as the foreground image; and a wrong-question image generation submodule is used to fuse the foreground image with the background image to generate the common wrong-question image, wherein the foreground image generation submodule includes: a first identification generation unit, which is used to generate a first identification at a first identification node position according to the level of the number of wrong-question answers when the identification type is the level of the number of wrong-question answers; and a first image generation unit, which is used to generate the identification image as the foreground image according to the first identification, and a second identification generation unit, which is used to generate a second identification at a second identification node position according to the level of the key knowledge when the identification type is the level of key knowledge; and a second image generation unit, which is used to generate the identification image as the foreground image according to the second identification.

[0128] Optionally, the second identifier generating unit includes: identifying the text information of the common wrong questions and determining the key knowledge information in the text information; determining the position information of the key knowledge information in the text image; determining the second identifier node position corresponding to the position information in the identifier image according to the position information in the text image; and generating the second identifier at the second identifier node position according to the key knowledge level.

[0129] Optionally, the operation of generating the second identifier at the second identifier node position according to the key knowledge level includes: generating a second identifier of a corresponding color for the key knowledge information according to the key knowledge level.

[0130] Optionally, the device 800 also includes: a first generating module, used to receive the answer results and correction results generated by the scanning device on the test paper; a first determining module, used to determine the first wrong question in the test paper according to the correction results; and a second generating module, used to generate a wrong question set according to the first wrong question and the corresponding answer results.

[0131] Optionally, the wrong question recommendation module 810 includes: an element association submodule, which is used to associate the first wrong question in the wrong question set with a preset element corresponding to the teacher's topic selection requirement through a preset big data model, wherein the teacher's topic selection requirement includes at least one of the knowledge points, homework, specific time range, class, class stratification, students, number of respondents and scores in the grade, question type and cause of error; a second determination submodule, which is used to obtain the teacher's topic selection requirement selected by the teacher, and determine the second wrong question corresponding to the teacher's topic selection requirement according to the preset elements corresponding to the teacher's topic selection requirement; and a recommendation submodule, which is used to recommend the second wrong question to the teacher as a common wrong question.

[0132] Optionally, when the preset element is a knowledge point, the element association submodule includes: a first generation unit, used to associate the first wrong question in the wrong question set with the knowledge point through a big data model, and generate a knowledge point tree, wherein each knowledge point node on the knowledge point tree is associated with the first wrong question.

[0133] Optionally, when the preset element is a homework, the element association submodule includes: a second generation unit, used to associate the first wrong question in the wrong question set with the homework through a big data model, and generate a homework tree, wherein each homework node on the homework tree is associated with the first wrong question.

[0134] Optionally, when the preset element is a student, the element association submodule includes: a third generation unit, used to associate the first wrong question in the wrong question set with the student through a big data model, and generate grade data, wherein the operation of generating grade data includes: calculating the accuracy of the first wrong question based on the scores of multiple questions in the first wrong question associated with the student and the full marks of the multiple questions; and calculating the grade data based on the accuracy of the first wrong question and the number of students.

[0135] Optionally, the second determination submodule includes: a first determination unit, used to determine the second wrong question based on the intersection of the first wrong questions corresponding to the teacher's topic selection requirements when the teacher has multiple topic selection requirements; and a second determination unit, used to obtain a variant question corresponding to the teacher's topic selection requirements as the second wrong question when the first wrong question in the intersection is zero.

[0136] Optionally, the device 800 also includes: a personalized wrong question recommendation module, which is used to recommend personalized wrong questions corresponding to students from the wrong question set to the teacher according to the teacher's topic selection requirements; and a personalized wrong question sending module, which is used to send personalized wrong questions to corresponding students according to the teacher's expected assignment objects.

[0137] Therefore, according to this embodiment, firstly, by generating common wrong questions as text images, the comprehension barriers that may be caused by pure text information are overcome, especially when facing lower grade students or non-native learners, the graphical presentation method is easier to understand and remember. Secondly, this technical solution converts the screened common wrong questions into high-definition text images. Different from the traditional one-time static image generation, this solution adopts layered processing technology to separate the wrong question content from the annotation information, and generate basic text images and dynamic logo images respectively. The system also allows teachers to adjust the annotation content or add new annotations at any time without rebuilding the entire image, which greatly improves the immediacy and flexibility of teaching interaction, avoids waste of resources, and reduces the limitations of image generation.

[0138] Example 3

[0139] Fig. 9 The apparatus 900 for generating a common wrong question image based on education according to this embodiment is shown. The apparatus 900 corresponds to the method according to the first aspect of embodiment 1. Fig. 9As shown, the device 900 includes: a processor 910; and a memory 920, connected to the processor 910, for providing instructions for the processor 910 to process the following processing steps: screening common wrong questions for educating students from a wrong question set according to the teacher's question selection requirements, wherein the common wrong questions are used to indicate wrong questions selected by multiple students; generating text images from the common wrong questions; fusing the text image with an identification image for identifying wrong questions to generate a common wrong question image; and displaying the common wrong question image to the teacher's desired arrangement object, wherein the operation of fusing the text image with the identification image for identifying wrong questions to generate a common wrong question image includes: using the text image as a background image; according to the identification The method comprises the following steps: generating a corresponding identification image according to the identification type, and using the identification image as the foreground image; and fusing the foreground image with the background image to generate the common wrong question image, wherein the operation of generating a corresponding identification image according to the identification type, and using the identification image as the foreground image comprises: when the identification type is the level of the number of wrong questions, generating a first identification at a first identification node position according to the level of the number of wrong questions; and generating the identification image as the foreground image according to the first identification, and when the identification type is the key knowledge level, generating a second identification at a second identification node position according to the key knowledge level; and generating the identification image as the foreground image according to the second identification.

[0140] Optionally, when the identification type is the key knowledge level, the operation of generating the second identification at the second identification node position according to the key knowledge level includes: identifying the text information of the common wrong questions and determining the key knowledge information in the text information; determining the position information of the key knowledge information in the text image; determining the second identification node position corresponding to the position information in the identification image according to the position information in the text image; and generating the second identification at the second identification node position according to the key knowledge level.

[0141] Optionally, the operation of generating the second identifier at the second identifier node position according to the key knowledge level includes: generating a second identifier of a corresponding color for the key knowledge information according to the key knowledge level.

[0142] Optionally, the memory 920 is also used to provide instructions for the processor 910 to process the following processing steps: receiving the answer results and correction results generated by the scanning device on the test paper; determining the first wrong question in the test paper according to the correction results; and generating a wrong question set according to the first wrong question and the corresponding answer results.

[0143] Optionally, the operation of screening common wrong questions for educating students from a wrong question set according to the teacher's topic selection requirements includes: associating the first wrong question in the wrong question set with a preset element corresponding to the teacher's topic selection requirements through a preset big data model, wherein the teacher's topic selection requirements include at least one of knowledge points, homework, specific time range, class, class stratification, students, number of respondents and scores, question types, and causes of errors; obtaining the teacher's topic selection requirements selected by the teacher, determining a second wrong question corresponding to the teacher's topic selection requirements according to the preset elements corresponding to the teacher's topic selection requirements; and recommending the second wrong question to the teacher as a common wrong question.

[0144] Optionally, in the case where the preset element is a knowledge point, the operation of associating the first wrong question in the wrong question set with the preset element corresponding to the teacher's question selection requirement through the preset big data model includes: associating the first wrong question in the wrong question set with the knowledge point through the big data model to generate a knowledge point tree, wherein each knowledge point node on the knowledge point tree is associated with the first wrong question.

[0145] Optionally, in the case where the preset element is a homework, the operation of associating the first wrong question in the wrong question set with the preset element corresponding to the teacher's question selection requirement through the preset big data model includes: associating the first wrong question in the wrong question set with the homework through the big data model to generate a homework tree, wherein each homework node on the homework tree is associated with the first wrong question.

[0146] Optionally, in a case where the preset element is a student, the operation of associating the first wrong question in the wrong question set with the preset element corresponding to the teacher's question selection requirement through the preset big data model includes: associating the first wrong question in the wrong question set with the student through the big data model to generate grade data, wherein the operation of generating grade data includes: calculating the accuracy of the first wrong question based on the scores of multiple questions in the first wrong question associated with the student and the full marks of the multiple questions; and calculating the grade data based on the accuracy of the first wrong question and the number of students.

[0147] Optionally, the operation of determining the second wrong question corresponding to the teacher's topic selection requirement includes: when there are multiple teacher's topic selection requirements, determining the second wrong question based on the intersection of the first wrong questions corresponding to the teacher's topic selection requirements; and when the first wrong question in the intersection is zero, obtaining a variant question corresponding to the teacher's topic selection requirement as the second wrong question.

[0148] Optionally, the memory 920 is also used to provide instructions for the processor 910 to process the following processing steps: recommending personalized wrong questions corresponding to students from the wrong question collection to the teacher according to the teacher's question selection requirements; and sending personalized wrong questions to the corresponding students according to the teacher's expected assignment objects.

[0149] Therefore, according to this embodiment, firstly, by generating common wrong questions as text images, the comprehension barriers that may be caused by pure text information are overcome, especially when facing lower grade students or non-native learners, the graphical presentation method is easier to understand and remember. Secondly, this technical solution converts the screened common wrong questions into high-definition text images. Different from the traditional one-time static image generation, this solution adopts layered processing technology to separate the wrong question content from the annotation information, and generate basic text images and dynamic logo images respectively. The system also allows teachers to adjust the annotation content or add new annotations at any time without rebuilding the entire image, which greatly improves the immediacy and flexibility of teaching interaction, avoids waste of resources, and reduces the limitations of image generation.

[0150] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0151] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0152] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0153] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0155] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0156] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for generating common wrong question images based on education, characterized in that: include: Screening common wrong questions for educating students from a wrong question set according to the teacher's question selection requirements, wherein the common wrong questions are used to indicate wrong questions selected by multiple students; Generating text images from the common wrong questions; The text image is fused with an identification image for identifying wrong questions to generate a common wrong question image; as well as The common wrong question image is shown to the teacher's desired assignment object, wherein The operation of fusing the text image with the identification image used to identify wrong questions to generate the common wrong question image includes: using the text image as a background image; generating a corresponding identification image according to the identification type, and using the identification image as a foreground image; and fusing the foreground image with the background image to generate the common wrong question image, wherein The operation of generating the corresponding identification image according to the identification type and using the identification image as the foreground image includes: generating a first identification at a first identification node position according to the level of the number of wrong answers when the identification type is the level of the number of wrong answers; and generating the identification image as the foreground image according to the first identification, and In the case where the identification type is the key knowledge level, a second identification is generated at a second identification node position according to the key knowledge level; and the identification image is generated as the foreground image according to the second identification.

2. The method according to claim 1, characterized in that In the case where the identification type is a key knowledge level, the operation of generating a second identification at a second identification node position according to the key knowledge level includes: Identify the text information of the common wrong questions and determine the key knowledge information in the text information; Determining the position information of the key knowledge information in the text image; Determining, according to the position information in the text image, the position of the second identification node in the identification image corresponding to the position information; and The second identifier is generated at the second identifier node position according to the key knowledge level.

3. The method according to claim 2, characterized in that The operation of generating the second identification at the second identification node position according to the key knowledge level includes: According to the key knowledge level, the second mark of the corresponding color is generated for the key knowledge information.

4. The method according to claim 1, characterized in that Also includes: Receive the answer results and correction results generated by the scanning device after scanning the test paper; Determine the first wrong question in the test paper according to the correction result; as well as The wrong question set is generated according to the first wrong question and the corresponding answer result.

5. The method according to claim 1, characterized in that The operation of selecting common wrong questions from the wrong question set for educating students according to the teacher's question selection requirements includes: Associating the first wrong question in the wrong question set with a preset element corresponding to the teacher's question selection requirement through a preset big data model, wherein the teacher's question selection requirement includes at least one of knowledge points, homework, specific time range, class, class stratification, students, number of respondents and scores of grade, question type, and error cause; Obtaining the teacher's topic selection requirement selected by the teacher, and determining a second wrong question corresponding to the teacher's topic selection requirement according to a preset element corresponding to the teacher's topic selection requirement; and The second wrong question is recommended to the teacher as the common wrong question.

6. The method according to claim 5, characterized in that In the case where the preset element is the knowledge point, the operation of associating the first wrong question in the wrong question set with the preset element corresponding to the teacher's question selection requirement through a preset big data model includes: The first wrong question in the wrong question set is associated with the knowledge point through the big data model to generate a knowledge point tree, wherein each knowledge point node on the knowledge point tree is associated with the first wrong question.

7. The method according to claim 5, characterized in that In the case where the preset element is the homework, the operation of associating the first wrong question in the wrong question set with the preset element corresponding to the teacher's question selection requirement through a preset big data model includes: The first wrong question in the wrong question set is associated with the homework through the big data model to generate a homework tree, wherein each homework node on the homework tree is associated with the first wrong question.

8. The method according to claim 5, characterized in that In the case where the preset element is the student, the operation of associating the first wrong question in the wrong question set with the preset element corresponding to the teacher's question selection requirement through a preset big data model includes: The first wrong question in the wrong question set is associated with the student through the big data model to generate grade data, wherein The operation of generating the grade data includes: Calculating the accuracy rate of the first wrong question according to the scores of multiple sub-questions in the first wrong question associated with the student and the full marks of the multiple sub-questions; and The grade data is calculated according to the correct rate of the first wrong questions and the number of the students.

9. The method according to claim 6, characterized in that The operation of determining the second wrong question corresponding to the teacher's question selection requirement includes: In the case where there are multiple teacher selection requirements, determining the second wrong question according to the intersection of the first wrong questions corresponding to the teacher selection requirements; and When the first wrong question in the intersection is zero, a variant question corresponding to the teacher's question selection requirement is obtained as the second wrong question.

10. The method according to claim 1, characterized in that Also includes: Recommending individual wrong questions corresponding to the student to the teacher from the wrong question collection according to the teacher's question selection requirements; as well as According to the teacher's desired assignment object, the individual wrong questions are sent to the corresponding students.

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