Draft recognition-based answer analysis method, device and equipment and storage medium

Abstract

This application discloses a method, apparatus, device, and storage medium for answer analysis based on draft recognition. The technical solution provided by this application obtains draft input content corresponding to a preset question, performs text recognition on the draft input content to obtain draft recognized text, extracts answer process information from the draft recognized text, and performs answer analysis based on the answer process information and the standard answer process information of the preset question to obtain answer analysis results. This allows for analysis of the answer process based on the user's draft input content, enabling users to improve their learning methods and fill knowledge gaps based on the answer analysis results, effectively improving their learning efficiency and learning outcomes.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, device, and storage medium for answer analysis based on draft recognition. Background Technology

[0002] With the development of computer technology, learning machines and other practice products offer more and more functions. In addition to practicing answering questions on the practice products, users can also write down their solutions in the draft writing area provided by the practice products, making it more convenient for users to answer questions.

[0003] Currently, most practice question products simply evaluate the user's submitted answer and provide a result indicating whether it is correct. For students or learners, merely knowing that the answer is wrong does not effectively help them improve their knowledge and problem-solving skills. It fails to address specific learning methods or fill knowledge gaps, resulting in poor learning outcomes. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for answer analysis based on draft recognition, to solve the technical problem in related technologies where practice products only judge the final answer submitted by the user, resulting in users being unable to improve their learning methods and fill knowledge gaps in a targeted manner, leading to poor learning effects. The method can analyze the answer process, and users can improve their learning methods and fill knowledge gaps in a targeted manner based on the answer analysis results, thereby improving learning efficiency and learning effects.

[0005] In a first aspect, embodiments of this application provide a question-answering analysis method based on draft recognition, comprising:

[0006] Obtain the draft input content corresponding to the preset question, and perform text recognition on the draft input content to obtain the draft recognized text;

[0007] Extract the answer process information from the text identified in the draft;

[0008] The answer process information is compared with the standard answer process information of the preset questions to perform answer analysis and obtain the answer analysis results.

[0009] In a second aspect, embodiments of this application provide a question-answering analysis device based on draft recognition, including a draft recognition module, an information extraction module, and a question-answering analysis module, wherein:

[0010] The draft recognition module is used to obtain the draft input content corresponding to the preset topic, and to perform text recognition on the draft input content to obtain the draft recognition text;

[0011] The information extraction module is used to extract answer process information from the draft recognition text;

[0012] The answer analysis module is used to perform answer analysis based on the answer process information and the standard answer process information of the preset questions, and obtain the answer analysis results.

[0013] In a third aspect, embodiments of this application provide a question-answering analysis device based on draft recognition, including: a memory and one or more processors;

[0014] The memory is used to store one or more programs;

[0015] When the one or more programs are executed by the one or more processors, the one or more processors implement the draft recognition-based answer analysis method as described in the first aspect.

[0016] In a fourth aspect, embodiments of this application provide a storage medium for storing computer-executable instructions, which, when executed by a computer processor, are used to perform the draft-based answer analysis method as described in the first aspect.

[0017] This application embodiment obtains the draft input content corresponding to the preset question, performs text recognition on the draft input content to obtain the draft recognition text, extracts the answer process information from the draft recognition text, and performs answer analysis based on the answer process information and the standard answer process information of the preset question to obtain the answer analysis result. The answer process can be analyzed based on the user's draft input content. The user can improve the learning method and make up for the knowledge gaps based on the answer analysis result, effectively improving the user's learning efficiency and learning effect. Attached Figure Description

[0018] Figure 1 This is a flowchart of a question analysis method based on draft recognition provided in an embodiment of this application;

[0019] Figure 2 This is a flowchart of another question analysis method based on draft recognition provided in an embodiment of this application;

[0020] Figure 3 This is a schematic diagram of a text recognition process for draft input content provided in an embodiment of this application;

[0021] Figure 4 This is a schematic diagram of the structure of a question analysis device based on draft recognition provided in an embodiment of this application;

[0022] Figure 5 This is a schematic diagram of the structure of a question analysis device based on draft recognition provided in an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but additional steps not included in the drawings may also be present. The above processes can correspond to methods, functions, procedures, subroutines, subroutines, etc.

[0024] Existing practice problems typically only judge the final answer submitted by the user and indicate whether it is correct. If the answer is incorrect, it only provides a simple error message without analyzing the reasons for the mistake. However, for students or learners, merely knowing that the answer is wrong does not effectively help them improve their knowledge and problem-solving skills. They cannot understand their problems in problem-solving approaches or calculation processes from this simple feedback, making it impossible to specifically improve their learning methods and fill knowledge gaps, resulting in poor learning efficiency and effectiveness. Therefore, this application provides a draft recognition-based answer analysis method to address the technical problem of existing practice problems that only judge the final answer submitted by the user, leading to a lack of targeted improvement in learning methods and knowledge gaps, resulting in poor learning outcomes.

[0025] Figure 1 A flowchart of a draft recognition-based answer analysis method provided in this application embodiment is given. The draft recognition-based answer analysis method provided in this application embodiment can be executed by a draft recognition-based answer analysis device. The draft recognition-based answer analysis device can be implemented by hardware and / or software and integrated into a draft recognition-based answer analysis device (e.g., a learning tablet).

[0026] The following description uses a draft-based answer analysis device as an example to illustrate the draft-based answer analysis method. (Reference) Figure 1 The answer analysis method based on draft recognition includes:

[0027] S110: Obtain the draft input content corresponding to the preset question, and perform text recognition on the draft input content to obtain the draft recognition text.

[0028] In one embodiment, the question analysis device provided in this application is configured with a question practice product (e.g., question practice software). When the question practice software is running, a corresponding question practice interface can be displayed on the screen of the question analysis device. The question practice interface displays one or more questions. Users can enter answers to the questions on the question practice interface, and write drafts to input draft content (e.g., write down the problem-solving ideas or process).

[0029] Optionally, the draft input can be entered in the preset answer area or draft area on the practice interface. For example, during the answering process, the user can write the calculation process in the area corresponding to the question, or in the preset draft area. The answer analysis device will collect the input content generated by the user's written calculation process and record it as draft input content.

[0030] For example, the draft input content corresponding to a preset question is obtained, where the preset question can be the question that needs to be analyzed in the current question. Optionally, the corresponding question can be determined according to the input position of the draft input content. For example, if the input position of the draft input content is the answer area of ​​the question, or the draft area corresponding to the question, the correspondence between the draft input content and the question can be determined. Alternatively, it can be determined according to the distance between the draft input content and each question on the practice interface. For example, the question and draft input content that are closest to the center position of the draft input content and the center position of the question content on the practice interface are taken as having a corresponding relationship.

[0031] In one embodiment, after obtaining the draft input content corresponding to the preset topic, text recognition can be performed on the draft input content, and the draft recognition text corresponding to the draft input content can be obtained based on the text recognition result.

[0032] S120: Extract answer process information from the draft text.

[0033] For example, after obtaining the draft recognition text corresponding to the draft input content, answer process information is extracted from the draft recognition text. This answer process information can be used to reflect the user's answering process for preset questions, such as the user's calculation process, derivation process, etc.

[0034] Optionally, extracting answer process information from the draft text can be based on preset answer process extraction rules or through a trained answer process extraction model.

[0035] S130: Analyze the answer process information and the standard answer process information of the preset questions to obtain the answer analysis results.

[0036] For example, standard answer process information for preset questions is obtained. This standard answer process information can be used to reflect the standard (correct) answer process for solving the preset questions, such as standard calculation processes, derivation processes, etc. Optionally, the standard answer process information for the questions can be stored locally on the answer analysis device or stored on a server (e.g., a database).

[0037] Furthermore, the answer process information is analyzed based on the standard answer process information of the preset questions to obtain the answer analysis results. These results can be used to reflect whether the answer process information matches or is consistent with the standard answer process, to indicate the locations where the answer process information does not match or is inconsistent with the standard answer process, and to reflect the reasons for such mismatches or inconsistencies.

[0038] Optionally, after obtaining the answer analysis results, these results can be displayed to the user, for example, through pop-ups or text prompts. Users can use the analysis results to identify the location and cause of errors during the answering process. This helps users gain a deeper understanding of the reasons for their mistakes, going beyond simply knowing whether the answer is right or wrong. It allows users to improve their learning methods in a targeted manner, enhance their knowledge mastery, and increase their learning efficiency. It also reduces the time spent on aimless practice questions, allowing users to focus their energy on areas where their problem-solving abilities are weak, thus improving their problem-solving skills more quickly. Furthermore, for teachers, the answer analysis results and draft input content collected by the practice product can help them understand the knowledge weaknesses of their students, optimize teaching content and methods, and improve teaching quality.

[0039] In one possible embodiment, the draft-based answer analysis method provided in this application, after performing answer analysis based on the answer process information and the standard answer process information of the preset questions to obtain the answer analysis results, may further include: generating learning suggestion information based on the answer analysis results and displaying the learning suggestion information.

[0040] For example, after obtaining the answer analysis results, learning suggestion information can be generated based on these results. This learning suggestion information can be targeted learning recommendations based on the answer analysis results, such as recommending learning materials for relevant knowledge points or practice questions of similar question types. Optionally, the answer analysis results can be sent to a trained learning suggestion model or a large language model. The learning suggestion model or the large language model can then analyze and process the answer analysis results to obtain learning suggestion information tailored to them. This application provides users with more valuable feedback and learning suggestions by generating learning suggestion information based on answer analysis results, helping users improve their learning efficiency and effectiveness.

[0041] The above describes a process that involves obtaining draft input content corresponding to a preset question, performing text recognition on the draft input content to obtain draft recognition text, extracting answer process information from the draft recognition text, and performing answer analysis based on the answer process information and the standard answer process information of the preset question to obtain answer analysis results. The answer process can be analyzed based on the user's draft input content, and the user can improve their learning methods and fill knowledge gaps based on the answer analysis results, effectively improving the user's learning efficiency and learning effect.

[0042] Based on the above embodiments, Figure 2 A flowchart of another draft-based answer analysis method provided in this application is given. This draft-based answer analysis method is a concretization of the above-described draft-based answer analysis method. (Reference) Figure 2 The answer analysis method based on draft recognition includes:

[0043] S210: Obtain the draft input content corresponding to the preset question, and perform text recognition on the draft input content to obtain the draft recognition text.

[0044] In one possible embodiment, the draft recognition-based answer analysis method provided in this application performs text recognition on the draft input content to obtain draft recognized text. This can be achieved through a combination of one or more of the following methods: optical character recognition (OCR), handwriting recognition, and neural network recognition (e.g., using a deep learning-based image recognition model to recognize text in the draft input content). This application improves the text recognition accuracy and effectively enhances the answer analysis accuracy by performing text recognition on the draft input content through a combination of one or more of these methods.

[0045] In one possible embodiment, such as Figure 3 The provided flowchart illustrates a text recognition process for draft input content. The draft-based question analysis method provided in this application performs text recognition on draft input content to obtain draft-recognized text, including:

[0046] S211: Perform text recognition on the draft input to obtain candidate recognition text.

[0047] S212: Obtain the draft recognition text by identifying key information from the candidate recognition text.

[0048] For example, text recognition of draft input content can be performed through one or more combinations of optical character recognition, handwriting recognition, and neural network recognition to obtain the recognized text corresponding to the draft input content, and the recognized text obtained can be used as candidate recognized text.

[0049] Furthermore, key information is identified from the candidate recognition text to obtain key information within the candidate recognition text, and a draft recognition text is generated based on the identified key information. Optionally, the key information provided in this application may include one or more combinations of numbers, operators, and formulas.

[0050] This application obtains candidate recognition text by performing text recognition on the draft input content, and extracts key information from the candidate recognition text to generate draft recognition text. This reduces the impact of non-key content in the draft input content on the answer analysis, while retaining text content such as numbers, operators, and formulas that have a greater impact on the answer analysis. This allows for a more accurate analysis of the user's answer process and thought process, thereby improving the accuracy of the answer analysis.

[0051] In one possible embodiment, the draft recognition-based answer analysis method provided in this application can further perform text recognition on the draft input content to obtain the draft recognized text before performing text recognition on the draft input content. Accordingly, performing text recognition on the draft input content to obtain the draft recognized text can be performed on the corrected draft input content to obtain the draft recognized text.

[0052] For example, after obtaining the draft input content corresponding to the preset question, the draft input content is corrected. This correction process can be one or more combinations of skewing correction, noise reduction, perspective correction, line alignment, font style correction, handwriting style correction, and blurred handwriting correction. The correction process can be based on preset correction rules or performed using a trained correction model. After correcting the draft input content, text recognition can be performed on the corrected draft input content to obtain the recognized draft text. This application, by correcting the draft input content, makes the text content in the draft input content more regular, thereby improving the accuracy of text recognition of the draft input content.

[0053] S220: Extract answer process information from draft text.

[0054] S230: Get the answer to the preset question.

[0055] S240: When the answer to a question is inconsistent with the reference answer to the pre-set question, analyze the reasons for the error based on the information of the answer process and the standard answer process information of the pre-set question, and obtain the results of the error analysis.

[0056] For example, the system obtains the answer key and reference answer for a preset question, and corrects the answer key based on the reference answer. The answer key is considered correct when it matches the reference answer, and incorrect when they do not. Optionally, the consistency between the answer key and the reference answer can be determined according to preset rules or by a trained answer correction model. The reference answer can be stored locally on the answer analysis device or on a server (e.g., a database).

[0057] In one embodiment, when the user's answer matches the reference answer to a preset question, it can be assumed that the user has mastered the knowledge points of that preset question, and no answer analysis is performed on the draft input, thus improving the user's learning efficiency. Conversely, when the user's answer does not match the reference answer to a preset question, it can be assumed that the user has not mastered the knowledge points of that preset question, and an error analysis is performed based on the answer process information and the standard answer process information of the preset question. This application performs targeted error analysis when the user's input answer is incorrect, effectively improving the user's practice efficiency while ensuring the user's learning effect.

[0058] In one embodiment, the draft-based answer analysis method provided in this application analyzes the causes of errors in answering questions based on the answer process information and the standard answer process information of preset questions, obtaining the error analysis results. This analysis can be a combination of one or more of the following: calculation error analysis (e.g., analyzing whether there are errors in addition, subtraction, multiplication, and division), formula application error analysis (e.g., analyzing whether incorrect formulas were used or misunderstood), and problem-solving approach error analysis (e.g., analyzing whether the correct problem-solving method was not followed). Optionally, the answer error analysis can be performed according to preset error analysis rules or through a trained error analysis model. This application, through answer error analysis processing such as calculation error analysis, formula application error analysis, and problem-solving approach error analysis, accurately identifies the steps in a user's answer where errors occurred, comprehensively understands the user's problem-solving approach and calculation process, accurately identifies the reasons for the user's errors, provides accurate answer feedback and learning suggestions, and helps users improve their learning outcomes.

[0059] The above describes a process that involves obtaining draft input for a preset question, performing text recognition on the draft input to obtain draft text, extracting answer process information from the draft text, and then performing answer analysis based on this information and the standard answer process information for the preset question. This analysis allows users to analyze their answer process based on their draft input, enabling them to improve their learning methods and fill knowledge gaps, thus effectively improving their learning efficiency and effectiveness. Furthermore, when a user's answer is incorrect, targeted error analysis is performed to improve practice efficiency while ensuring learning effectiveness. This accurately identifies the points where errors occur, comprehensively understands the user's problem-solving approach and calculation process, accurately pinpoints the reasons for errors, and provides accurate feedback and learning suggestions to help users improve their learning outcomes.

[0060] Figure 4 A schematic diagram of a draft-based answer analysis device provided in an embodiment of this application is given. (Reference) Figure 4 The answer analysis device based on draft recognition includes a draft recognition module 41, an information extraction module 42, and an answer analysis module 43.

[0061] The draft recognition module 41 is used to obtain the draft input content corresponding to the preset question and perform text recognition on the draft input content to obtain the draft recognition text; the information extraction module 42 is used to extract the answer process information from the draft recognition text; and the answer analysis module 43 is used to perform answer analysis based on the answer process information and the standard answer process information of the preset question to obtain the answer analysis result.

[0062] The above describes a process that involves obtaining draft input content corresponding to a preset question, performing text recognition on the draft input content to obtain draft recognition text, extracting answer process information from the draft recognition text, and performing answer analysis based on the answer process information and the standard answer process information of the preset question to obtain answer analysis results. The answer process can be analyzed based on the user's draft input content, and the user can improve their learning methods and fill knowledge gaps based on the answer analysis results, effectively improving the user's learning efficiency and learning effect.

[0063] In one possible embodiment, the draft recognition-based question analysis device further includes an answer acquisition module, which is used to acquire the answer corresponding to a preset question;

[0064] Accordingly, the answer analysis module 43 performs answer analysis based on the answer process information and the standard answer process information of the preset questions, and obtains the answer analysis results, including:

[0065] When the answer to a question is inconsistent with the reference answer to the pre-set question, the error cause analysis is performed based on the information of the answer process and the standard answer process information of the pre-set question to obtain the error cause analysis results.

[0066] In one possible embodiment, the answer analysis module 43 performs error analysis based on the answer process information and the standard answer process information of the preset questions, and obtains the error analysis results, including:

[0067] Based on the information from the answer process and the standard answer process information of the preset questions, one or more of the following combinations are used to analyze the causes of errors: calculation error analysis, formula application error analysis, and problem-solving approach error analysis.

[0068] In one possible embodiment, the draft recognition module 41 performs text recognition on the draft input content to obtain draft recognition text, including:

[0069] The draft input content is processed by one or more of the following methods: optical character recognition, handwriting recognition, and neural network recognition, to obtain the draft recognized text.

[0070] In one possible embodiment, the draft recognition module 41 performs text recognition on the draft input content to obtain draft recognition text, including:

[0071] The draft input is subjected to text recognition to obtain candidate recognition text;

[0072] The draft recognition text is obtained by identifying key information from the candidate recognition text. The key information includes one or more combinations of numbers, operators, and formulas.

[0073] In one possible embodiment, before the draft recognition module 41 performs text recognition on the draft input content to obtain the draft recognition text, it also performs correction processing on the draft input content.

[0074] Accordingly, the draft recognition module 41 performs text recognition on the draft input content to obtain draft recognition text, including: performing text recognition on the corrected draft input content to obtain draft recognition text.

[0075] In one possible embodiment, the draft-based answer analysis device further includes a learning suggestion module, which generates and displays learning suggestion information based on the answer analysis results.

[0076] It is worth noting that in the above embodiments of the answer analysis device based on draft recognition, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application.

[0077] This application also provides a draft recognition-based answer analysis device, which can integrate the draft recognition-based answer analysis apparatus provided in this application. Figure 5 This is a schematic diagram of the structure of a question analysis device based on draft recognition provided in an embodiment of this application. (Reference) Figure 5 The draft recognition-based answer analysis device includes: an input device 53, an output device 54, a memory 52, and one or more processors 51; the memory 52 is used to store one or more programs; when one or more programs are executed by one or more processors 51, the one or more processors 51 implement the draft recognition-based answer analysis method provided in the above embodiments. The input device 53, output device 54, memory 52, and processors 51 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0078] The memory 52, as a computing device readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the draft recognition-based answer analysis method provided in any embodiment of this application (e.g., the draft recognition module 41, information extraction module 42, and answer analysis module 43 in the draft recognition-based answer analysis device). The memory 52 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the device, etc. Furthermore, the memory 52 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 52 may further include memory remotely located relative to the processor 51, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0079] Input device 53 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 54 may include display devices such as a display screen.

[0080] The processor 51 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 52, thereby realizing the above-mentioned answer analysis method based on draft recognition.

[0081] The above-described draft recognition-based answer analysis device, equipment, and computer can be used to execute the draft recognition-based answer analysis method provided in any of the above embodiments, and have corresponding functions and beneficial effects.

[0082] This application embodiment also provides a storage medium for storing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to execute the draft recognition-based answer analysis method provided in the above embodiment. The draft recognition-based answer analysis method includes: obtaining draft input content corresponding to a preset question, and performing text recognition on the draft input content to obtain draft recognition text; extracting answer process information from the draft recognition text; and performing answer analysis based on the answer process information and the standard answer process information of the preset question to obtain the answer analysis result.

[0083] Storage medium – any type of memory device or storage device. The term “storage medium” is intended to include: mounting media, such as CD-ROMs, floppy disks, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disks or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. Furthermore, storage media may reside in a first computer system in which a program is executed, or may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term “storage medium” can include two or more storage media that may reside in different locations (e.g., in different computer systems connected via a network). Storage media may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0084] Of course, the computer-executable instructions stored in the storage medium provided in the embodiments of this application are not limited to the draft recognition-based answer analysis method provided above, but can also execute related operations in the draft recognition-based answer analysis method provided in any embodiment of this application.

[0085] The draft recognition-based answer analysis device, equipment, and storage medium provided in the above embodiments can execute the draft recognition-based answer analysis method provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the draft recognition-based answer analysis method provided in any embodiment of this application.

[0086] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments provided herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A question-answering analysis method based on draft recognition, characterized in that, include: Obtain the draft input content corresponding to the preset question, and perform text recognition on the draft input content to obtain the draft recognized text; Extract the answer process information from the text identified in the draft; The answer process information is compared with the standard answer process information of the preset questions to perform answer analysis and obtain the answer analysis results.

2. The answer analysis method based on draft recognition according to claim 1, characterized in that, Before performing answer analysis based on the answer process information and the standard answer process information of the preset questions to obtain the answer analysis result, the method further includes: Get the answers to the preset questions; Accordingly, the step of performing answer analysis based on the answer process information and the standard answer process information of the preset questions to obtain the answer analysis results includes: If the answer provided is inconsistent with the reference answer to the preset question, an error analysis is performed based on the answer process information and the standard answer process information of the preset question to obtain the error analysis results.

3. The answer analysis method based on draft recognition according to claim 2, characterized in that, The step of performing error analysis based on the answer process information and the standard answer process information of the preset questions to obtain error analysis results includes: Based on the answer process information and the standard answer process information of the preset questions, one or more of the following combinations are performed: calculation error analysis, formula application error analysis, and problem-solving approach error analysis to obtain the error cause analysis result.

4. The answer analysis method based on draft recognition according to claim 1, characterized in that, The step of performing text recognition on the draft input content to obtain the draft recognized text includes: The draft input content is subjected to one or more combinations of optical character recognition, handwriting recognition, and neural network recognition to obtain the draft recognition text.

5. The answer analysis method based on draft recognition according to claim 1, characterized in that, The step of performing text recognition on the draft input content to obtain the draft recognized text includes: The draft input content is subjected to text recognition to obtain candidate recognition text; The candidate recognition text is subjected to key information recognition to obtain draft recognition text, wherein the key information includes one or more combinations of numbers, operators and formulas.

6. The answer analysis method based on draft recognition according to claim 1, characterized in that, Before performing text recognition on the draft input to obtain the draft recognized text, the method further includes: The draft input content is corrected. Accordingly, the step of performing text recognition on the draft input content to obtain the draft recognized text includes: The corrected draft input content is subjected to text recognition to obtain the draft recognized text.

7. The answer analysis method based on draft recognition according to claim 1, characterized in that, After performing answer analysis based on the answer process information and the standard answer process information of the preset questions to obtain the answer analysis result, the method further includes: Learning suggestions are generated based on the answer analysis results and then displayed.

8. A question analysis device based on draft recognition, characterized in that, It includes a draft recognition module, an information extraction module, and a question analysis module, among which: The draft recognition module is used to obtain the draft input content corresponding to the preset topic, and to perform text recognition on the draft input content to obtain the draft recognition text; The information extraction module is used to extract answer process information from the draft recognition text; The answer analysis module is used to perform answer analysis based on the answer process information and the standard answer process information of the preset questions, and obtain the answer analysis results.

9. A question analysis device based on draft recognition, characterized in that, include: Memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the draft recognition-based answer analysis method as described in any one of claims 1-7.

10. A storage medium for storing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the draft-based answer analysis method as described in any one of claims 1-7.

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