An AI-question-and-answer-based data processing method and system thereof

Abstract

This application is applicable to the technical field of intelligent education, and provides a data processing method and system based on AI Q&A. The method includes first responding to a user's question instruction to obtain the wrong option information, question information, and historical answering information of the target user, then quickly generating correct answer information based on a preset knowledge database and the wrong option information, generating auxiliary knowledge information according to the question information and historical answering information, and finally effectively generating a reply result information according to the correct answer information and the auxiliary knowledge information. This application can conduct intelligent and multi-dimensional in-depth analysis of the user's learning situation, accurately locate their knowledge mastery level and weak links, dynamically generate knowledge points highly matching the user's current learning situation, provide users with more strongly relevant and high-value learning content, and improve the teaching effect.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent education, and more specifically, to a data processing method and system based on AI question-answering. Background Art

[0002] Compared with traditional paper-based examination papers, educational software allows students to study anytime and anywhere without having to carry heavy books or go to tutoring classes, thus improving the efficiency of study time utilization, which has led to a wider application of educational software.

[0003] At present, when students encounter wrong questions, they usually just simply look up the specific wrong questions. Since the current educational software cannot conduct in-depth analysis based on the students' specific circumstances, there is a problem of poor teaching effect, which needs further improvement. Summary of the invention

[0004] Based on this, the embodiment of the present application provides a data processing method and system based on AI question and answer to solve the problem of poor teaching effect in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a data processing method based on AI question and answer, the method comprising:

[0006] In response to a user's questioning instruction, obtaining the target user's wrong option information, questioning information, and historical answer information;

[0007] Generate correct answer information based on a preset knowledge database and the wrong option information;

[0008] Generate auxiliary knowledge information according to the question information and the historical answer information;

[0009] Reply result information is generated based on the correct answer information and the auxiliary knowledge information.

[0010] Compared with the prior art, the beneficial effect is as follows: the data processing method based on AI question and answer provided in the embodiment of the present application, the terminal device can first respond to the user's question instruction, obtain the target user's wrong option information, question information and historical answer information, and then quickly generate correct answer information based on the preset knowledge database and wrong option information, and then generate auxiliary knowledge information based on the question information and historical answer information, and finally effectively generate reply result information based on the correct answer information and the auxiliary knowledge information, thereby realizing in-depth analysis based on the students' specific learning situation, providing students with more knowledge points that are strongly related to wrong questions, effectively improving the teaching effect, and to a certain extent solving the current problem of poor teaching effect.

[0011] Second aspect, embodiments of the present application provide a data processing system based on AI Q&A, and the system includes:

[0012] Question information acquisition module: configured to acquire error option information, question information, and historical answering information of a target user in response to a user question instruction;

[0013] Correct answer information generation module: configured to generate correct answer information based on a preset knowledge database and the error option information;

[0014] Auxiliary knowledge information generation module: configured to generate auxiliary knowledge information according to the question information and the historical answering information;

[0015] Reply result information generation module: configured to generate reply result information according to the correct answer information and the auxiliary knowledge information.

[0016] Third aspect, embodiments of the present application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the method in the first aspect as described above are implemented.

[0017] Fourth aspect, embodiments of the present application provide a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method in the first aspect as described above are implemented.

[0018] It can be understood that the beneficial effects of the second aspect to the fourth aspect as described above can refer to the relevant descriptions in the first aspect, and will not be elaborated herein. Description of the Drawings

[0019] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art.

[0020] Figure 1 is a schematic flowchart of a data processing method provided by an embodiment of the present application;

[0021] Figure 2 is a schematic flowchart of step S300 in the data processing method provided by an embodiment of the present application;

[0022] Figure 3 is a schematic flowchart after step S300 in the data processing method provided by an embodiment of the present application;

[0023] Figure 4 is a schematic flowchart after step S307 in the data processing method provided by an embodiment of the present application;

[0024] Figure 5 Schematic diagram of the process after step S400 in the data processing method provided by an embodiment of the application;

[0025] Figure 6 Block diagram of the modules of the data processing system provided by an embodiment of the application;

[0026] Figure 7 Schematic diagram of the terminal device provided by an embodiment of the application. Detailed implementation manners

[0027] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0028] In the description of the specification and the appended claims of the present application, the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0029] The reference to "an embodiment" or "some embodiments" etc. described in the specification of the present application means that specific features, structures, or characteristics described in connection with the embodiment are included in one or more embodiments of the present application. Thus, the statements "in an embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0030] In order to illustrate the technical solutions described in the present application, the following will be described through specific embodiments.

[0031] Please refer to Figure 1 , Figure 1It is a schematic flowchart of a data processing method based on AI Q&A provided by an embodiment of the present application. In this embodiment, the execution subject of the data processing method is a terminal device. It can be understood that the types of terminal devices include, but are not limited to, mobile phones, tablet computers, laptop computers, Ultra-Mobile Personal Computers (UMPCs), netbooks, Personal Digital Assistants (PDAs), etc. The embodiment of the present application does not impose any restrictions on the specific types of terminal devices.

[0032] Please refer to Figure 1 , the data processing method provided by the embodiment of the present application includes but is not limited to the following steps:

[0033] In S100, in response to a user question instruction, obtain the incorrect option information, question information, and historical answering information of the target user.

[0034] Specifically, the terminal device can first, in response to a user question instruction, obtain the incorrect option information, question information, and historical answering information of the target user. Among them, the user question instruction is used to indicate that the target user has initiated a question request. Exemplarily, when the target user answers a question incorrectly on an educational software, the target user can send a user question instruction to the terminal device through the user terminal; and the target user can be a student. The incorrect option information is used to describe the incorrect option selected by the target user in the current question; the question information is used to describe the question input by the target user for the current question; the historical answering information is used to describe the questions answered by the target user in the past.

[0035] In S200, based on a preset knowledge database and the incorrect option information, generate correct answer information.

[0036] Specifically, after the terminal device obtains the question information and historical answering information, the terminal device can, based on the preset knowledge database, retrieve the question corresponding to the incorrect option information, determine the correct answer in the current question, and quickly generate correct answer information. Among them, the knowledge database prestores multiple questions, all the pre-options corresponding to each question, and the correct option corresponding to each question; the correct answer information is used to describe the correct option of the current question.

[0037] In S300, generate auxiliary knowledge information according to the question information and historical answering information.

[0038] Specifically, after the terminal device generates the correct answer information, the terminal device can, according to the question information and historical answering information, accurately generate auxiliary knowledge information. Among them, the auxiliary knowledge information is used to assist the target user in efficiently learning the knowledge points related to the current question.

[0039] Without loss of generality, the historical answering information includes multiple incorrect question information and the corresponding chapter information of each incorrect question information. Among them, the incorrect question information is used to describe the questions that the target user answered incorrectly in history; the chapter information is used to describe the corresponding chapter of the incorrectly answered question in the course.

[0040] In some possible implementation manners, to generate effective auxiliary knowledge information, please refer to Figure 2 , step S300 includes but is not limited to the following steps:

[0041] In S310, according to a preset keyword extraction algorithm and the question information, determine the target chapter information.

[0042] Specifically, the terminal device can use the preset keyword extraction algorithm to perform keyword extraction processing on the question information, and quickly determine the target chapter information according to the keywords of the question information and the current question corresponding to the question information. Among them, the keyword extraction algorithm can be the RAKE (Rapid Automatic Keyword Extraction) algorithm; the target chapter information is used to describe the corresponding chapter of the current question in the course.

[0043] In S320, for each incorrect question information in the historical answering information: judge whether the corresponding chapter information of the incorrect question information is the same as the target chapter information.

[0044] Specifically, after the terminal device determines the target chapter information, the terminal device can perform this processing for each incorrect question information in the historical answering information: judge whether the corresponding chapter information of the incorrect question information is the same as the target chapter information.

[0045] In S330, if the corresponding chapter information of the incorrect question information is the same as the target chapter information, obtain the associated knowledge information of the incorrect question information.

[0046] Specifically, if the corresponding chapter information of the incorrect question information is the same as the target chapter information, it indicates that the current question is related to the questions that the target user answered incorrectly in history. Therefore, the terminal device can obtain the associated knowledge information of the incorrect question information. Among them, the associated knowledge information is used to describe the knowledge points related to the incorrect question information.

[0047] In S340, determine the associated knowledge information as the auxiliary knowledge information.

[0048] Specifically, after the terminal device obtains the associated knowledge information, the terminal device can quickly determine the associated knowledge information as the auxiliary knowledge information, which is beneficial for the target user to know more knowledge points related to the current question and is beneficial to improving the teaching effect.

[0049] In some possible implementation manners, to further improve the teaching effect, please refer to Figure 3 , after step S300, the method further includes but is not limited to the following steps:

[0050] In S301, in response to a depth analysis instruction, obtain multiple thinking keyword information of the target user.

[0051] Without loss of generality, the historical answering information further includes correct option information corresponding to each wrong question information, where the correct option information is used to describe the correct option corresponding to the wrong question information.

[0052] Specifically, the terminal device can obtain multiple thinking keyword information of the target user in response to a depth analysis instruction, where the depth analysis instruction is used to describe that the user has initiated a depth analysis request; the thinking keyword information is used to describe the keywords that the target user thinks when processing the current question, and the thinking keyword information can be manually input by the target user..

[0053] In S302, based on a preset question database, obtain multiple involved keyword information of the correct option information.

[0054] Specifically, after the terminal device obtains the thinking keyword information, the terminal device can obtain multiple involved keyword information of the correct option information based on a preset question database, where the involved keyword information is used to describe the keywords corresponding to the correct option information.

[0055] In S303, based on a preset synonym database, obtain first synonym information corresponding to each thinking keyword information, and obtain second synonym information corresponding to each involved keyword information.

[0056] Specifically, after the terminal device obtains multiple involved keyword information, the terminal device can obtain first synonym information corresponding to each thinking keyword information based on a preset synonym database, and obtain second synonym information corresponding to each involved keyword information, where the synonym database is used to store multiple words and their synonyms; the first synonym information is used to describe the synonym of the thinking keyword information, that is, a word that is similar in meaning to the thinking keyword information, and the second synonym information is used to describe the synonym of the involved keyword information, that is, a word that is similar in meaning to the involved keyword information.

[0057] In S304, generate first coincidence degree information according to the thinking keyword information and the involved keyword information.

[0058] Specifically, after the terminal device obtains the first synonym information and the second synonym information, the terminal device can determine the overlapping words among multiple thinking keyword information and multiple involved keyword information and determine the number of the overlapping words, so as to effectively determine and generate the first overlap degree information, where the overlapping words are used to describe the thinking keyword information that is the same as the involved keyword information.

[0059] Exemplarily, the terminal device can first determine the first quantity of the overlapping words between the multiple thinking keyword information and the multiple involved keyword information, then determine the total word quantity according to the sum of the quantities of the multiple thinking keyword information and the multiple involved keyword information, denoted as the second quantity, and then determine the first overlap degree information with the quotient of dividing the first quantity by the second quantity.

[0060] In S305, the second overlap degree information is generated according to the first synonym information and the second synonym information.

[0061] Specifically, after the terminal device generates the first overlap degree information, the terminal device can generate the second overlap degree information according to the first synonym information and the second synonym information, where the specific calculation process can refer to the relevant content in the above step S304, so it will not be elaborated.

[0062] In S306, it is judged whether the first overlap degree information is less than the preset overlap degree threshold information, and whether the second overlap degree information is less than the overlap degree threshold information.

[0063] Specifically, after the terminal device generates the second overlap degree information, the terminal device can judge whether the first overlap degree information is less than the preset overlap degree threshold information, and at the same time judge whether the second overlap degree information is less than the overlap degree threshold information

[0064] In S307, if the first overlap degree information is less than the overlap degree threshold information and the second overlap degree information is less than the overlap degree threshold information, then the thinking direction serious error information is generated, otherwise the thinking direction general error information is generated.

[0065] Specifically, if the first overlap degree information is less than the overlap degree threshold information and at the same time the second overlap degree information is less than the overlap degree threshold information, it indicates that the thinking direction of the target user seriously deviates from the thinking direction for selecting the correct answer. Therefore, the terminal device can generate the thinking direction serious error information, where the thinking direction serious error information is used to describe that the thinking direction of the target user seriously deviates from the thinking direction of correctly answering the question. Otherwise, the terminal device can generate the thinking direction general error information, where the thinking direction general error information is used to describe that the thinking direction of the target user generally deviates from the thinking direction of correctly answering the question.

[0066] In some possible implementation manners, in order to facilitate the target user to know their own learning situation, please refer toFigure 4 If the terminal device generates a serious error message for the thinking direction, after step S300, the method further includes but is not limited to the following steps:

[0067] In S308, each thinking keyword information and each involved keyword information are compared in sequence to determine the differential keyword set information.

[0068] Specifically, the terminal device can compare each thinking keyword information and each involved keyword information in sequence, and quickly determine the differential keyword set information from multiple thinking keyword information and multiple involved keyword information. The differential keyword set information includes multiple differential keywords, and the differential keywords are used to describe the involved keyword information different from the thinking keyword information.

[0069] In S309, based on the synonym database, multiple first-order synonym information corresponding to each differential keyword is obtained, and multiple second-order synonym information corresponding to each first-order synonym information is obtained.

[0070] Specifically, after the terminal device determines the differential keyword set information, the terminal device can obtain multiple first-order synonym information corresponding to each differential keyword based on the synonym database, and at the same time obtain multiple second-order synonym information corresponding to each first-order synonym information. The first-order synonym information is used to describe the synonyms of the differential keywords, and the second-order synonym information is used to describe the synonyms of the first-order synonym information.

[0071] In S3091, based on the multiple first-order synonym information and the multiple second-order synonym information, the core keyword information is determined.

[0072] Specifically, after the terminal device obtains the first-order synonym information and the second-order synonym information, the terminal device can first determine the first-order synonym information that is not the same as any of the thinking keyword information, and then process these first-order synonym information to determine that the first-order synonym information whose corresponding all second-order synonym information is not the same as any of the thinking keyword information is the core keyword information, effectively determining the core keyword information, so as to realize determining the keywords most lacking in the thinking direction of the target user. After the target user knows the core keyword information, it is beneficial to align his thinking direction with the thinking direction of the correct answer. The multiple first-order synonym information corresponding to the core keyword information is not the same as any of the thinking keyword information, and the multiple second-order synonym information corresponding to the core keyword information is not the same as any of the thinking keyword information, that is, each second-order synonym information corresponding to the multiple first-order synonym information of the core keyword information is not the same as any of the thinking keyword information.

[0073] In S3092, send the differential keyword set information and the core keyword information to the user terminal of the target user.

[0074] Specifically, after the terminal device determines the core keyword information, the terminal device can send the differential keyword set information and the core keyword information to the user terminal of the target user, so that the target user can directly know the places where it is different from the thinking direction of answering the questions correctly and the keywords that are most lacking in the thinking direction.

[0075] In S400, generate response result information according to the correct answer information and the auxiliary knowledge information.

[0076] Specifically, the terminal device can generate response result information according to the correct answer information and the auxiliary knowledge information, where the response result information is used to describe the analysis result obtained by deeply analyzing the learning situation of the target user.

[0077] In some possible implementation manners, in order to facilitate tracing the learning situation of the target user, please refer to Figure 5 , after step S400, the method further includes but is not limited to the following steps:

[0078] In S401, generate learning profile information based on the question information and the response result information.

[0079] Specifically, the terminal device can generate learning profile information based on the question information and the response result information, where the learning profile information is used to describe the personal profile regarding the learning situation of the target user.

[0080] In S402, send the learning profile information to the cloud server.

[0081] Specifically, after the terminal device generates the learning profile information, the terminal device can send the learning profile information to the cloud server.

[0082] In a possible implementation manner, after step S400, the terminal device can also evaluate and review the response result information from multiple dimensions. For example, assign a weight of 0.4 to the accuracy of the response result information, assign a weight of 0.2 to the integrity of the response result information, assign a weight of 0.2 to the relevance of the response result information, assign a weight of 0.1 to the logic of the response result information, assign a weight of 0.1 to the timeliness of the response result information, and then multiply the evaluation scores of each dimension by the corresponding weights and add them up to obtain the overall evaluation score.

[0083] In a possible implementation, after step S400, the terminal device can also collect the user's evaluation of the reply result information, quantify the user feedback data. For example, mark satisfaction as 1 and dissatisfaction as 0, and then incorporate the user feedback score into the calculation of the overall evaluation score. For example, appropriately reduce the weights of other aspects, and then assign a weight of 0.2 to the user feedback score, and then comprehensively calculate the final evaluation result with the scores of other dimensions.

[0084] The implementation principle of the data processing method based on AI Q&A in the embodiments of the present application is as follows: The terminal device can first respond to the user's question instruction, obtain the wrong option information, question information, and historical answering information of the target user, and then quickly generate the correct answer information based on the preset knowledge database and the wrong option information, and then generate auxiliary knowledge information according to the question information and historical answering information. Finally, according to the correct answer information and the auxiliary knowledge information, effectively generate the reply result information, so as to realize in-depth analysis in combination with the specific learning situation of the student, provide more strongly relevant knowledge points for the student appropriately, and effectively improve the teaching effect.

[0085] It should be noted that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0086] The embodiments of the present application also provide a data processing system based on AI Q&A. For the convenience of description, only the parts related to the present application are shown, as Figure 6 shown. The system 60 includes:

[0087] The question information acquisition module 61 acquires the wrong option information, question information, and historical answering information of the target user in response to the user's question instruction;

[0088] The correct answer information generation module 62 generates the correct answer information based on the preset knowledge database and the wrong option information;

[0089] The auxiliary knowledge information generation module 63 generates the auxiliary knowledge information according to the question information and historical answering information;

[0090] The reply result information generation module 64 generates the reply result information according to the correct answer information and the auxiliary knowledge information.

[0091] Optionally, the historical answering information includes multiple wrong question information and the corresponding chapter information of each wrong question information; the above-mentioned auxiliary knowledge information generation module 63 includes:

[0092] The target chapter information determination sub-module: used to determine the target chapter information according to the preset keyword extraction algorithm and the question information;

[0093] Chapter information judgment sub-module: used to judge, for each wrong question information in the historical answer information, whether the chapter information corresponding to the wrong question information is the same as the target chapter information;

[0094] Associated knowledge information acquisition sub-module: used to acquire the associated knowledge information of the wrong question information if the chapter information corresponding to the wrong question information is the same as the target chapter information;

[0095] Auxiliary knowledge information determination sub-module: used to determine the associated knowledge information as auxiliary knowledge information.

[0096] Optionally, the historical answer information further includes the correct option information corresponding to each wrong question information; the system 60 further includes:

[0097] Thinking keyword information acquisition module: used to acquire multiple thinking keyword information of the target user in response to a deep analysis instruction;

[0098] Involved keyword information acquisition module: used to acquire multiple involved keyword information of the correct option information based on a preset question database;

[0099] Synonym information acquisition module: used to acquire the first synonym information corresponding to each thinking keyword information and acquire the second synonym information corresponding to each involved keyword information based on a preset synonym database;

[0100] First coincidence degree information generation module: used to generate first coincidence degree information according to the thinking keyword information and the involved keyword information;

[0101] Second coincidence degree information generation module: used to generate second coincidence degree information according to the first synonym information and the second synonym information;

[0102] Coincidence degree information judgment module: used to judge whether the first coincidence degree information is less than a preset coincidence degree threshold information and whether the second coincidence degree information is less than the coincidence degree threshold information;

[0103] Thinking direction seriously wrong information generation module: used to generate thinking direction seriously wrong information if the first coincidence degree information is less than the coincidence degree threshold information and the second coincidence degree information is less than the coincidence degree threshold information, otherwise generate thinking direction conventional wrong information.

[0104] Optionally, the system 60 further includes:

[0105] Difference keyword set information determination module: used to compare each thinking keyword information and each involved keyword information in turn to determine the difference keyword set information, where the difference keyword set information includes multiple difference keywords, and the difference keywords are used to describe the involved keyword information different from the thinking keyword information;

[0106] First-order near-synonym information acquisition module: used to obtain multiple first-order near-synonym information corresponding to each difference keyword based on the near-synonym database, and obtain multiple second-order near-synonym information corresponding to each first-order near-synonym information;

[0107] Core keyword information determination module: used to determine the core keyword information based on multiple first-order near-synonym information and multiple second-order near-synonym information, where none of the multiple first-order near-synonym information corresponding to the core keyword information is the same as any thinking keyword information, and none of the multiple second-order near-synonym information corresponding to the core keyword information is the same as any thinking keyword information;

[0108] Difference keyword set information sending module: used to send the difference keyword set information and the core keyword information to the user terminal of the target user.

[0109] Optionally, the system 60 further includes:

[0110] Learning file information generation module: used to generate learning file information based on the question information and the reply result information;

[0111] Learning file information sending module: used to send the learning file information to the cloud server.

[0112] It should be noted that the information interaction, execution process, etc. between the above modules, due to being based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, please refer to the method embodiment part specifically, and will not be elaborated here.

[0113] The embodiment of the present application also provides a terminal device, as Figure 7 shown, the terminal device 70 of this embodiment includes: a processor 71, a memory 72, and a computer program 73 stored in the memory 72 and operable on the processor 71. When the processor 71 executes the computer program 73, it implements the steps in the above method embodiment of data processing, such as Figure 1 the steps S100 to S400 shown; or, when the processor 71 executes the computer program 73, it implements the functions of each module in the above device, such as Figure 6 the functions of module 61 to module 64 shown.

[0114] The terminal device 70 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device 70 includes but is not limited to a processor 71 and a memory 72. Those skilled in the art can understand that Figure 7 These are only examples of the terminal device 70 and do not constitute a limitation on the terminal device 70. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, the terminal device 70 may also include an input / output device, a network access device, a bus, etc.

[0115] Among them, the processor 71 can be a Central Processing Unit (CPU), or it can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.; the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0116] The memory 72 can be an internal storage unit of the terminal device 70, such as the hard disk or memory of the terminal device 70. The memory 72 can also be an external storage device of the terminal device 70, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the terminal device 70; further, the memory 72 can also include both the internal storage unit and the external storage device of the terminal device 70. The memory 72 can also store a computer program 73 and other programs and data required by the terminal device 70. The memory 72 can also be used to temporarily store data that has been output or will be output.

[0117] An embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps of the above-mentioned method embodiments. The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form, etc.; the computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0118] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereby. Therefore, all equivalent changes made according to the method, principle, and structure of the present application should be covered within the protection scope of the present application.

Claims

1. A data processing method based on AI question and answer, characterized in that: The method comprises: In response to a user's questioning instruction, obtaining the target user's wrong option information, questioning information, and historical answer information, wherein the historical answer information includes a plurality of wrong question information and chapter information corresponding to each of the wrong question information, and the historical answer information also includes the correct option information corresponding to each of the wrong question information; Generate correct answer information based on a preset knowledge database and the wrong option information; Generate auxiliary knowledge information according to the question information and the historical answer information; Generate response result information according to the correct answer information and the auxiliary knowledge information; Wherein, after generating the auxiliary knowledge information according to the question information and the historical answer information, the method further comprises: In response to the deep analysis instruction, obtaining multiple thinking keyword information of the target user; Based on the preset question database, multiple keyword information related to the correct option information is obtained; Based on a preset synonym database, first synonym information corresponding to each of the thinking keyword information is obtained, and second synonym information corresponding to each of the related keyword information is obtained; Generate first coincidence information according to the thought keyword information and the related keyword information; generating second coincidence degree information according to the first synonym information and the second synonym information; Determine whether the first coincidence information is less than a preset coincidence threshold information, and whether the second coincidence information is less than the coincidence threshold information; If the first overlap information is less than the overlap threshold information, and the second overlap information is less than the overlap threshold information, a serious thinking direction error message is generated, otherwise a normal thinking direction error message is generated.

2. The method according to claim 1, characterized in that The generating of auxiliary knowledge information according to the question information and the historical answer information includes: Determine target chapter information according to a preset keyword extraction algorithm and the question information; For each of the wrong question information in the historical answer information: determining whether the chapter information corresponding to the wrong question information is the same as the target chapter information; If the chapter information corresponding to the erroneous title information is the same as the target chapter information, then obtaining the associated knowledge information of the erroneous title information; The associated knowledge information is determined to be the auxiliary knowledge information.

3. The method according to claim 1, characterized in that If the serious error in thinking direction information is generated, after generating the auxiliary knowledge information according to the question information and the historical answer information, the method further includes: Compare each of the thinking keyword information and each of the related keyword information in sequence to determine difference keyword set information, wherein the difference keyword set information includes a plurality of difference keywords, and the difference keywords are used to describe the related keyword information that is different from the thinking keyword information; Based on the synonym database, obtaining a plurality of first-order synonym information corresponding to each of the difference keywords, and obtaining a plurality of second-order synonym information corresponding to each of the first-order synonym information; Determine core keyword information based on the plurality of first-order synonym information and the plurality of second-order synonym information, wherein the plurality of first-order synonym information corresponding to the core keyword information is not the same as any of the thinking keyword information, and the plurality of second-order synonym information corresponding to the core keyword information is not the same as any of the thinking keyword information; The difference keyword set information and the core keyword information are sent to a user terminal of the target user.

4. The method according to claim 1, characterized in that: After generating the reply result information according to the correct answer information and the auxiliary knowledge information, the method further includes: Generate learning profile information based on the question information and the answer result information; Send the learning profile information to the cloud server.

5. A data processing system based on AI question and answer, characterized in that: The system comprises: Question information acquisition module: used to respond to the user's question instruction and acquire the target user's wrong option information, question information and historical answer information, wherein the historical answer information includes multiple wrong question information and the corresponding chapter information of each wrong question information, and the historical answer information also includes the correct option information corresponding to each wrong question information; Correct answer information generating module: used for generating correct answer information based on a preset knowledge database and the wrong option information; Auxiliary knowledge information generation module: used to generate auxiliary knowledge information according to the question information and the historical answer information; A reply result information generating module: used for generating reply result information according to the correct answer information and the auxiliary knowledge information; Wherein, the system further comprises: Thinking keyword information acquisition module: used to obtain multiple thinking keyword information of the target user in response to the deep analysis instruction; Keyword information acquisition module: used to acquire multiple keyword information of correct option information based on a preset question database; Synonym information acquisition module: used for acquiring first synonym information corresponding to each of the thinking keyword information and second synonym information corresponding to each of the related keyword information based on a preset synonym database; A first coincidence information generating module: used for generating first coincidence information according to the thinking keyword information and the related keyword information; A second coincidence information generating module: used for generating second coincidence information according to the first synonym information and the second synonym information; Coincidence information judgment module: used to judge whether the first coincidence information is less than a preset coincidence threshold information, and whether the second coincidence information is less than the coincidence threshold information; Thinking direction serious error information generating module: used for generating thinking direction serious error information if the first overlap information is less than the overlap threshold information and the second overlap information is less than the overlap threshold information, otherwise generating thinking direction normal error information.

6. The system according to claim 5, characterized in that The auxiliary knowledge information generation module includes: Target chapter information determination submodule: used to determine the target chapter information according to a preset keyword extraction algorithm and the question information; The chapter information determination submodule is used to determine, for each of the wrong question information in the historical answer information, whether the chapter information corresponding to the wrong question information is the same as the target chapter information; Related knowledge information acquisition submodule: used for acquiring related knowledge information of the erroneous title information if the chapter information corresponding to the erroneous title information is the same as the target chapter information; Auxiliary knowledge information determination submodule: used to determine that the associated knowledge information is the auxiliary knowledge information.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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