A student-teacher interaction method, apparatus, medium and electronic device

Abstract

The present disclosure provides a student and teacher interaction method, device, medium and electronic equipment. The present disclosure logically reviews the question information sent by the student terminal, obtains at least one pair of suspicious knowledge point information, that is, the information in which the understanding of the knowledge point in the question information is wrong and the concept is unclear, and then obtains the matched first answer information from the teaching asset records of the teaching assets in the asset layer through the at least one pair of suspicious knowledge point information. A large number of similar questions are answered in an automatic reply manner, the answering efficiency is improved, and the answering teacher and the student can explore deeper knowledge.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of image processing, and in particular, to a student and teacher interaction method, device, medium and electronic device. BACKGROUND

[0002] Network teaching is a teaching mode mainly for teaching by using network as a communication tool for teachers and students. Network teaching includes live teaching and recorded teaching. Live teaching is the same as traditional teaching mode, students can listen to the teacher's lecture at the same time, and there is some simple exchange between teachers and students. Recorded teaching uses the service of the Internet to store the pre-recorded courses of the teacher on the server, and students can on-demand and watch the course at any time to achieve the purpose of learning. The characteristic of recorded teaching is that teaching activities can be carried out 24 hours a day, and each student can determine the learning time, content and progress according to his own actual situation, and can download the learning content on the network at any time.

[0003] Answering questions is a means for teachers to understand the teaching effect. In traditional face-to-face teaching, one answering teacher can take care of the questions raised by dozens of students. In network answering of related technology, artificial intelligence assisted technology can help the answering teacher to take care of the questions raised by tens of thousands of students. However, there are a large number of repetitive questions, which makes the answering efficiency very low.

[0004] Therefore, the present disclosure provides a student and teacher interaction method to solve one of the above technical problems. SUMMARY

[0005] The purpose of the present disclosure is to provide a student and teacher interaction method, device, medium and electronic device, which can solve at least one of the above technical problems. The specific scheme is as follows:

[0006] According to the specific embodiment of the present disclosure, in a first aspect, the present disclosure provides a student and teacher interaction method, comprising:

[0007] receiving question information sent by a student terminal;

[0008] logically reviewing the question information to obtain at least one pair of suspicious knowledge point information;

[0009] obtaining matched first answer information from the teaching asset records of the teaching assets of the asset layer based on the at least one pair of suspicious knowledge point information;

[0010] sending the first answer information to the student terminal.

[0011] Optionally, the logically reviewing the question information to obtain at least one pair of suspicious knowledge point information comprises:

[0012] obtain a plurality of knowledge point information based on the question information;

[0013] perform correlation relationship identification on the plurality of knowledge point information by using a knowledge graph in the asset layer, to obtain the at least one pair of suspicious knowledge point information, wherein the knowledge graph is a data structure established based on correlation relationships between knowledge points of each chapter.

[0014] Optionally, the knowledge graph comprises a first knowledge tree, and the first knowledge tree comprises a root layer, a chapter layer, a section layer, a module layer and a knowledge point layer arranged from top to bottom, wherein the knowledge point layer comprises leaf nodes representing knowledge points, and each leaf node comprises a knowledge point information;

[0015] Correspondingly, the performing correlation relationship identification on the plurality of knowledge point information by using the knowledge graph in the asset layer, to obtain the at least one pair of suspicious knowledge point information, comprises:

[0016] determining a first leaf node and a second leaf node in the first knowledge tree based on any pair of knowledge point information in the plurality of knowledge point information respectively;

[0017] When the nearest ancestor nodes of the first leaf node and the second leaf node in the first knowledge tree are in the root layer or the chapter layer, determining that the any pair of knowledge point information is a pair of suspicious knowledge point information.

[0018] Optionally, the knowledge graph comprises a second knowledge tree, and the second knowledge tree comprises leaf nodes representing knowledge points, and each leaf node comprises a knowledge point information;

[0019] Correspondingly, the performing correlation relationship identification on the plurality of knowledge point information by using the knowledge graph in the asset layer, to obtain the at least one pair of suspicious knowledge point information, comprises:

[0020] determining a third leaf node and a fourth leaf node in the second knowledge tree based on any pair of knowledge point information in the plurality of knowledge point information respectively;

[0021] When a shortest node distance of the third leaf node and the fourth leaf node is greater than a preset normal distance threshold, determining that the any pair of question point information is a pair of suspicious knowledge point information.

[0022] Optionally, the method further comprises:

[0023] when no matching first answer information is obtained from a teaching asset record of a teaching asset in the asset layer based on the at least one pair of suspicious knowledge point information, sending the question information to a question answering terminal;

[0024] obtaining second answer information fed back by the question answering terminal for the question information;

[0025] integrating the at least one pair of suspicious knowledge point information and the second answer information into a teaching asset record in the teaching asset through an integration system of the asset layer, and sending the second answer information to the student terminal, wherein the teaching asset record at least includes a one-to-one mapping relationship between the at least one pair of suspicious knowledge point information and the second answer information.

[0026] Optionally, the method further comprises:

[0027] Before the at least one pair of suspicious knowledge point information and the second answer information are integrated into the teaching asset record in the teaching asset through the integration system of the asset layer, performing compliance review on the second answer information through an audit system of the asset layer, and correcting error information in the second answer information based on the teaching asset.

[0028] Optionally, the method further comprises:

[0029] Before the logical review on the question information, performing compliance review on the question information through the audit system of the asset layer, and correcting error information in the question information based on the teaching asset.

[0030] According to the specific embodiment of the present disclosure, a second aspect of the present disclosure provides a student-teacher interaction device, comprising:

[0031] A receiving unit is configured to receive question information sent by a student terminal;

[0032] An obtaining unit is configured to perform logical review on the question information and obtain at least one pair of suspicious knowledge point information;

[0033] A matching unit is configured to obtain matched first answer information from teaching assets of an asset layer based on the at least one pair of suspicious knowledge point information;

[0034] A sending unit is configured to send the first answer information to the student terminal.

[0035] Optionally, the obtaining unit comprises:

[0036] A first obtaining subunit is configured to obtain a plurality of knowledge point information based on the question information;

[0037] A second obtaining subunit is configured to perform correlation relationship identification on the plurality of knowledge point information by using a knowledge graph in the asset layer, and obtain the at least one pair of suspicious knowledge point information, wherein the knowledge graph is a data structure established based on correlation relationships between knowledge points of each chapter of a course.

[0038] Optionally, the knowledge graph comprises a first knowledge tree, and the first knowledge tree comprises a root layer, a chapter layer, a section layer, a module layer and a knowledge point layer arranged from top to bottom, wherein the knowledge point layer comprises leaf nodes representing knowledge points, and each leaf node comprises a knowledge point information;

[0039] Correspondingly, the utilizing the knowledge graph in the asset layer to identify the correlation relationship of the plurality of knowledge point information to obtain the at least one pair of suspicious knowledge point information comprises:

[0040] a first determining subunit configured to determine a first leaf node and a second leaf node in the first knowledge tree based on any pair of knowledge point information in the plurality of knowledge point information, respectively;

[0041] a second determining subunit configured to determine the any pair of knowledge point information as a pair of suspicious knowledge point information when the closest ancestor nodes of the first leaf node and the second leaf node in the first knowledge tree are at the root layer or the chapter layer.

[0042] Optionally, the knowledge graph comprises a second knowledge tree, and the second knowledge tree comprises leaf nodes representing knowledge points, and each leaf node comprises a knowledge point information;

[0043] Correspondingly, the utilizing the knowledge graph in the asset layer to identify the correlation relationship of the plurality of knowledge point information to obtain the at least one pair of suspicious knowledge point information comprises:

[0044] a third determining subunit configured to determine a third leaf node and a fourth leaf node in the second knowledge tree based on any pair of knowledge point information in the plurality of knowledge point information, respectively;

[0045] a fourth determining subunit configured to determine the any pair of knowledge point information as a pair of suspicious knowledge point information when the shortest node distance of the third leaf node and the fourth leaf node is greater than a preset normal distance threshold.

[0046] Optionally, the apparatus further comprises an integration unit, and the integration unit comprises:

[0047] a sending subunit configured to send the question information to a question answering terminal when no matching first answer information is obtained from a teaching asset record of a teaching asset in the asset layer based on the at least one pair of suspicious knowledge point information;

[0048] an obtaining subunit configured to obtain second answer information fed back by the question answering terminal for the question information;

[0049] an integrator unit, configured to integrate the at least one pair of suspicious knowledge point information and the second answer information into a teaching asset record in the teaching asset through an integration system of the asset layer, and send the second answer information to the student terminal, wherein the teaching asset record at least includes a one-to-one mapping relationship between the at least one pair of suspicious knowledge point information and the second answer information.

[0050] Optionally, the apparatus further comprises a first review unit, the first review unit comprising:

[0051] a first review sub-unit, configured to, before the at least one pair of suspicious knowledge point information and the second answer information are integrated into the teaching asset record in the teaching asset through the integration system of the asset layer, perform compliance review on the second answer information through an audit system of the asset layer, and correct error information in the second answer information based on the teaching asset.

[0052] Optionally, the apparatus further comprises a second review unit, the second review unit comprising:

[0053] a second review sub-unit, configured to, before the question information is logically reviewed, perform compliance review on the question information through the audit system of the asset layer, and correct error information in the question information based on the teaching asset.

[0054] According to the specific embodiments of the present disclosure, in a third aspect, the present disclosure provides a computer readable storage medium having stored thereon a computer program, the program being executed by a processor to implement the student-teacher interaction method according to any one of the above.

[0055] According to the specific embodiments of the present disclosure, in a fourth aspect, the present disclosure provides an electronic device, comprising: one or more processors; a storage device configured to store one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the student-teacher interaction method according to any one of the above.

[0056] The above scheme of the present disclosure has at least the following beneficial effects compared with the prior art:

[0057] The present disclosure provides a student-teacher interaction method, device, medium and electronic equipment. The present disclosure performs logical review on question information sent by a student terminal, obtains at least one pair of suspicious knowledge point information, that is, information in which the understanding of knowledge points in the question information is wrong and the concept is unclear, and then obtains matched first answer information from the teaching asset records of the teaching assets of the asset layer through the at least one pair of suspicious knowledge point information. A large number of repetitive similar questions are answered in an automatic reply manner, the answering efficiency is improved, and the teacher and the student can explore deeper knowledge. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 A flowchart of a student-teacher interaction method according to an embodiment of the present disclosure is shown;

[0059] Figure 2 A structural schematic diagram of a meta-universe interactive teaching framework according to an embodiment of the present disclosure is shown;

[0060] Figure 3 A structural schematic diagram of an application layer according to an embodiment of the present disclosure is shown;

[0061] Figure 4 A structural schematic diagram of an asset layer according to an embodiment of the present disclosure is shown;

[0062] Figure 5 A schematic diagram of a first knowledge tree according to an embodiment of the present disclosure is shown;

[0063] Figure 6 A schematic diagram of another first knowledge tree according to an embodiment of the present disclosure is shown

[0064] Figure 7 A unit block diagram of a student-teacher interaction device according to an embodiment of the present disclosure is shown;

[0065] Figure 8 A structural schematic diagram of an electronic equipment connection structure provided according to an embodiment of the present disclosure is shown;

[0066] REFERENCE SIGNS

[0067] 1 - support layer, 2 - application layer, 3 - service layer, 4 - asset layer, 5 - artificial intelligence layer;

[0068] 21 - teaching module, 22 - interactive terminal, 23 - sensing device;

[0069] 41 - integration system, 42 - review system, 43 - atlas system. DETAILED DESCRIPTION

[0070] In order to make the objectives, technical solutions and advantages of the present disclosure clearer, the following further describes the present disclosure in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, and not all of them. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present disclosure.

[0071] The terms used in the embodiments of the present disclosure are merely for the purpose of describing particular embodiments and are not intended to limit the present disclosure. The singular forms "a", "an" and "the" used in the embodiments of the present disclosure and the appended claims are intended to include plural forms, unless the context clearly indicates otherwise. "Plural" generally includes at least two.

[0072] It should be understood that the term "and / or" used herein only describes an association relationship of associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects.

[0073] It should be understood that although the terms first, second, third, etc. can be used in the embodiments of the present disclosure, these descriptions should not be limited to these terms. These terms are only used to distinguish the description. For example, without departing from the scope of the embodiments of the present disclosure, the first can also be referred to as the second, and similarly, the second can also be referred to as the first.

[0074] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "upon" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if it is determined" or "if (a stated condition or event) is detected" can be interpreted to mean "when it is determined" or "in response to determining" or "when (a stated condition or event) is detected" or "in response to detecting (a stated condition or event)".

[0075] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, so that a product or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such product or device. Without more limitations, the element defined by the sentence "including a" does not exclude the presence of other identical elements in the product or device including the element.

[0076] It should be particularly noted that the symbols and / or numbers present in the specification, if not marked in the description, are not drawing marks.

[0077] Optional embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0078] Embodiment 1

[0079] The embodiments provided by the present disclosure are embodiments of a student-teacher interaction method.

[0080] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Figure 1 The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0081] Step S101, receiving question information sent by a student terminal.

[0082] The method described in the embodiments of the present disclosure is applied to the service layer 3 of the meta-universe interactive teaching framework.

[0083] As shown in the figure, the meta-universe interactive teaching framework includes a support layer 1, an application layer 2, a service layer 3, an asset layer 4, and an artificial intelligence layer 5. Figure 2 The support layer 1 is configured to integrate multiple cloud platforms to provide platform as a service for teaching activities.

[0084]

[0085] ​Platform-as-a-Service (PAAS) is a business model that provides application service running and development environment as a service. The system described in the present application is developed and run based on PAAS. The support layer 11 integrates multiple cloud platforms in PAAS, for example, supports the artificial intelligence layer 55 by using Baidu cloud, supports the asset layer 44 by using Ali cloud; supports the service layer 33 by using Google cloud, runs the application program of the application layer 22 on the Digital Ocean cloud; or, uses AWS EC2 instance and load balancing while using Azure SQL for the asset layer 44, uses Amazon Cognito cloud for the listening system, tutoring system, self-learning system and class management system, all of which are used for a single application; or, runs the application program of the application layer 22 on the Digital Ocean cloud, but is completely replicated and backed up on the AWS cloud; places the development and test environment on one cloud and places the running environment on another cloud; the embodiments of the present disclosure are not limited thereto. That is, different cloud platforms are used to complete different system functions. Integrating multiple cloud platforms can build a highly scalable and reliable application environment. It is beneficial for disaster recovery, reduces cloud service costs, improves system response speed, and provides the best match for each part of business operation, for example, some databases need cold storage and are not accessed at irregular intervals, while hot data can be accessed 10 times a day and must be stored in frequently accessed storage, such as AWS S3 standard storage. By using appropriate cloud services for data storage, the best results can be achieved. Integrating multiple cloud platforms enables the system to provide services to users in different geographical locations around the world.

[0086] As shown in Figure 3 The application layer 2 includes a plurality of teaching modules 21 for conducting teaching activities, a plurality of interactive terminals 22 and a plurality of sensing devices 23, each interactive terminal 22 is associated with at least one sensing device 23, the application layer 2 is configured to: send a group building request to establish an interactive group for the interactive terminals 22 participating in the teaching activities, provide each interactive terminal 22 in the interactive group with a teaching module 21 related to the teaching activities, and display interactive information collected by each interactive terminal 22 in the teaching module 21, wherein the interactive information includes sensing information collected by the sensing device 23.

[0087] The teaching module 21 can provide users with functions related to teaching activities.

[0088] The interactive terminal 22 includes a student terminal and a question-answering terminal. The interactive group includes a network question-answering classroom. All student terminals joining the network question-answering classroom can send question information. The network question-answering classroom can be limited to an intranet, a metropolitan area network, or the Internet, and the present embodiment does not limit the network question-answering classroom.

[0089] The student terminal and the question-answering terminal can include, but are not limited to, a smart phone, a desktop computer, a tablet computer, a notebook computer, a digital assistant, a smart wearable device, and the like.

[0090] The student terminal refers to a terminal used by a user joining the network question-answering classroom as a student.

[0091] The question-answering terminal refers to a terminal used by a user joining the network question-answering classroom as a question-answering teacher.

[0092] The sensing device 23 refers to a device capable of collecting sensing information. For example, a camera for collecting video information, a sound pickup for collecting audio information.

[0093] The service layer 3 is configured to establish an interactive relationship related to the interactive group for each interactive terminal 22 in any interactive group based on the group building request, and collect and distribute the interactive information to each interactive terminal 22 and the asset layer 4 based on the interactive relationship.

[0094] The asset layer 4 is configured to integrate the accepted interactive information into a teaching asset, wherein the teaching asset is used to support the teaching activity of the teaching module 21.

[0095] The teaching asset of the present embodiment refers to a collection of knowledge information (including teaching asset records) generated based on a teaching activity. The interactive information generated by the teaching activity is systematically integrated by the asset layer 4 to form a teaching asset, which is helpful for self-directed learning of a user.

[0096] The artificial intelligence layer 5 is configured to provide intelligent services to the application layer 2, the service layer 3, or the asset layer 4 based on the teaching asset.

[0097] The question information includes help information for a student using a student terminal to seek answers to questions that are difficult to answer by himself / herself in learning.

[0098] The present embodiment divides the question information into two categories: the first category of question information is a wrong understanding of a knowledge point, a confused concept, and the first category of question information often has a large number of questions in question-answering, which makes the question-answering efficiency very low; the second category of question information is a further exploration of knowledge, and the second category of question information has a small number of questions in question-answering, but requires a teacher to carefully answer.

[0099] Step S102, logically reviewing the question information to obtain at least one pair of suspicious knowledge point information.

[0100] Knowledge point information is a relatively independent minimum unit in teaching content. For example, the formula F=ma of Newton's second law, where the knowledge points are: F represents the resultant external force, m represents the mass of the object, and a represents the acceleration; the formula E=mv of kinetic energy 2 , where the knowledge points are: E represents kinetic energy, m represents the mass of the object, and v represents the speed of the object.

[0101] The pair of suspicious knowledge point information includes two knowledge point information. The pair of suspicious knowledge point information is knowledge point information that is not logically related, for example, kinetic energy and m in Newton's second law are related, but kinetic energy and F and kinetic energy and a are not related. Therefore, kinetic energy and a and kinetic energy and F are a pair of suspicious knowledge point information, respectively.

[0102] When there is a pair of suspicious knowledge points in the question information that are not related, it indicates that the student who asked the question has an incorrect understanding of the two suspicious knowledge points, that is, the question information belongs to the first type of question information.

[0103] In some embodiments, the step of logically reviewing the question information to obtain at least one pair of suspicious knowledge point information includes the following steps:

[0104] Step S102-1, obtaining a plurality of knowledge point information based on the question information.

[0105] Step S102-2, using the knowledge graph in the asset layer 4 to identify the association relationship of the plurality of knowledge point information to obtain the at least one pair of suspicious knowledge point information.

[0106] As shown in Figure 4 , the asset layer 4 of the meta-universe interactive teaching framework further includes a graph system 43, which is configured to: based on the knowledge point type, logically sort all teaching asset records in the teaching assets to generate a knowledge graph.

[0107] Knowledge graph, known as knowledge domain visualization or knowledge field mapping map in the library and information field, is a series of various different figures showing the development process and structural relationship of knowledge. It can display knowledge resources and their mutual relations by using visualization technology to describe knowledge resources and their carriers. Knowledge graph combines the theories and methods of mathematics, graphics, information visualization technology, information science and other disciplines with methods such as citation analysis and co-occurrence analysis, and uses visualized graph to show the core structure, development history, frontier field and overall knowledge architecture of a discipline, so as to achieve the purpose of multi-disciplinary integration. It can provide practical and valuable reference for teaching.

[0108] In the embodiment, the knowledge graph is a data structure established based on the association relationship between knowledge points of each chapter of a course.

[0109] In some embodiments, the knowledge graph includes a first knowledge tree, and the first knowledge tree includes a root layer, a chapter layer, a section layer, a module layer and a knowledge point layer arranged from top to bottom. Each chapter node of the chapter layer represents a chapter in the course; each section node of the section layer represents a section in a chapter; each module node of the module layer represents a knowledge module in a section; and the knowledge point layer includes leaf nodes representing knowledge points, and each leaf node includes knowledge point information in a knowledge module.

[0110] For example, as shown in the first knowledge tree of Figure 5 The root node A1 is in the root layer, the chapter nodes B1, B2, …, Bn are in the chapter layer, the section nodes C1, C2, …, Cm are in the section layer, the module nodes D1, D2, …, Di are in the module layer, and the leaf nodes E1, E2, E3, E4, E5, E6, …, Ej are in the knowledge point layer. The section nodes C1 and C2 are child nodes of the chapter node B1, and the section node Cm is a child node of the chapter node Bn. The module nodes D1 and D2 are child nodes of the section node C1, and the module node Di is a child node of the section node Cm. The leaf nodes E1, E2 and E3 are child nodes of the module node D1, the leaf nodes E4, E5 and E6 are child nodes of the module node D2, and the leaf node Ej is a child node of the module node Di.

[0111] Correspondingly, the step of identifying the association relationship of the plurality of knowledge point information by using the knowledge graph in the asset layer 4 to obtain the at least one pair of suspicious knowledge point information includes the following steps:

[0112] Step S102-2a-1, determining a first leaf node and a second leaf node in the first knowledge tree based on any pair of knowledge point information in the plurality of knowledge point information.

[0113] For example, continuing the example of Newton's second law, as shown in the first knowledge tree of Figure 6As shown, Chapter 3 node B3 represents "Chapter 3 Newton's Laws of Motion," Section 3 node C3 under Chapter 3 node B3 represents "Section 3 The Second Law," Module 3 node D3 under Section 3 node C3 represents the formula for the Second Law F = ma, Module 3 node D3 eleventh leaf node E11 represents the net force F, Module 3 node D3 twelfth leaf node E12 represents the mass m of the object, and Module 3 node D3 thirteenth leaf node E13 represents the acceleration a of the object; Chapter 6 node B6 represents "Chapter 6 Kinetic Energy," Section 6 node C6 under Chapter 6 node B6 represents "Section 6 Definition of Kinetic Energy," and Module 6 node D6 under Section 6 node C6 represents the formula for kinetic energy E = mv0. 2 In module D6, the 64th leaf node E64 represents the kinetic energy E, the 65th leaf node E65 represents the mass m of the object, and the 66th leaf node E66 represents the velocity of the object. When the problem information includes not only the knowledge points F, m, and a of Newton's second law, but also the knowledge point information kinetic energy E, then the combinations of leaf nodes corresponding to any pair of knowledge points include: the first combination F and m, the second combination F and a, the third combination F and E, the fourth combination m and a, the fifth combination m and E, and the sixth combination a and E.

[0114] Step S102-2a-2: When the nearest ancestor of the first leaf node and the second leaf node in the first knowledge tree is at the root level or chapter level, determine that any pair of knowledge point information is a pair of suspicious knowledge point information.

[0115] For example, continuing the above example, the nearest ancestor node of the first combination F and m in the first knowledge tree is the third module node D3, which is at the module level. Therefore, the first combination is not a pair of suspicious knowledge points. The nearest ancestor node of the second combination F and a in the first knowledge tree is the third module node D3, which is at the module level. Therefore, the second combination is not a pair of suspicious knowledge points. The nearest ancestor node of the third combination F and E in the first knowledge tree is the root node A1, which is at the root level. Therefore, the third combination is a pair of suspicious knowledge points. The nearest ancestor node of the fourth combination m and a in the first knowledge tree is the third module node D3, which is at the module level. Therefore, the fourth combination is not a pair of suspicious knowledge points. The nearest ancestor node of the fifth combination m and E in the first knowledge tree is the sixth module node D6, which is at the module level. Therefore, the fifth combination is not a pair of suspicious knowledge points. The nearest ancestor node of the sixth combination a and E in the first knowledge tree is the root node A1, which is at the root level. Therefore, the sixth combination is a pair of suspicious knowledge points.

[0116] Since each chapter in the first knowledge tree is relatively independent in the knowledge classification, the embodiment utilizes the logical relationship between the leaf nodes corresponding to a pair of knowledge point information and the nearest ancestor nodes in the first knowledge tree. When the nearest ancestor nodes of the leaf nodes corresponding to a pair of knowledge point information in the first knowledge tree are at the root layer or the chapter layer, it indicates that the leaf nodes corresponding to the pair of knowledge point information are logically irrelevant, that is, a pair of logically irrelevant knowledge point information is a pair of suspicious knowledge point information, and the problem information sent by the student terminal belongs to the first type of problem information.

[0117] The embodiment identifies whether the knowledge point information corresponding to a pair of leaf nodes is a pair of suspicious knowledge point information through the logical relationship between the nearest ancestor nodes of the pair of leaf nodes in the knowledge tree. Compared with finding suspicious knowledge point information in problem information through complex semantic analysis or finding suspicious knowledge point information in problem information through a trained neural network model, the embodiment greatly reduces the complexity of finding suspicious knowledge point information in problem information, improves the data processing speed, shortens the response time of answering questions, and further improves the efficiency of answering questions.

[0118] In other embodiments, the knowledge graph includes a second knowledge tree, and the second knowledge tree includes leaf nodes representing knowledge points, and each leaf node includes a knowledge point information.

[0119] The second knowledge tree in the embodiment includes but is not limited to the data structure of the first knowledge tree, for example, the chapter layer is cancelled, and each node of the root layer and the section layer is connected, thereby reducing the number of layers of the data structure and improving the efficiency of data retrieval.

[0120] Correspondingly, the step of utilizing the knowledge graph in the asset layer 4 to identify the correlation relationship of the plurality of knowledge point information and obtaining the at least one pair of suspicious knowledge point information includes the following steps:

[0121] In step S102-2b-1, the third leaf node and the fourth leaf node in the second knowledge tree are determined based on any pair of knowledge point information in the plurality of knowledge point information.

[0122] The method for determining the third leaf node and the fourth leaf node in the second knowledge tree in the embodiment is similar to the method for determining the first leaf node and the second leaf node in the first knowledge tree described above, and will not be described here. Please refer to the examples above.

[0123] In step S102-2b-2, when the shortest node distance between the third leaf node and the fourth leaf node is greater than a preset normal distance threshold, the pair of problem point information is determined as a pair of suspicious knowledge point information.

[0124] In the second knowledge tree, the shortest distance between any two leaf nodes refers to the distance of the shortest path from one leaf node to the other. The shortest distance between two leaf nodes is equal to the number of nodes in the path minus one. For example, ... Figure 6 As shown, the shortest path from the eleventh leaf node E11 to the twelfth leaf node E12 corresponding to the first combination F and m passes through the third module node D3, meaning the shortest path includes three nodes E11, D3, and E12. Therefore, the shortest node distance between a pair of leaf nodes E11 and E12 is 2. The shortest path from the thirteenth leaf node E13 to the sixty-fourth leaf node E64 corresponding to the sixth combination a and E passes through nine nodes: E13, D3, C3, B3, A1, B6, C6, D6, and E64. Therefore, the shortest node distance between a pair of leaf nodes E13 and E64 is 8. If the preset normal distance threshold is 2, then the shortest node distance between the leaf nodes E11 and E12 corresponding to the first combination F and m is equal to the preset normal distance threshold, indicating that the first combination F and m have a normal logical relationship. However, the shortest node distance between the leaf nodes E13 and E64 corresponding to the sixth combination a and E is greater than the preset normal distance threshold, indicating that the logical relationship between the sixth combination a and E is abnormal, and they are a pair of suspicious knowledge points.

[0125] In this specific embodiment, the shortest node distance between the leaf nodes corresponding to a pair of knowledge points is used to determine the logical relationship between the pair of knowledge points. When the shortest node distance between the leaf nodes corresponding to a pair of knowledge points is greater than a preset normal distance threshold, it is determined that the logical relationship between the pair of knowledge points is abnormal and is a pair of suspicious knowledge points. In other words, the problem information sent by the student terminal belongs to the first type of problem information.

[0126] This specific embodiment determines the logical relationship between a pair of knowledge points by finding the shortest node distance between a pair of leaf nodes in the knowledge tree, thereby identifying whether the knowledge points corresponding to the pair of leaf nodes are a pair of suspicious knowledge points. Compared with searching for suspicious knowledge points in question information through complex semantic analysis or through a trained neural network model, this specific embodiment greatly reduces the complexity of searching for suspicious knowledge points in question information, improves data processing speed, shortens the question-answering response time, and thus improves question-answering efficiency.

[0127] Step S103: Based on the at least one pair of suspicious knowledge point information, obtain the matching first answer information from the teaching asset record of the teaching asset in the asset layer 4.

[0128] The teaching asset record includes at least one-to-one mapping relationship between the at least one pair of questionable knowledge point information and the first answer information.

[0129] The first answer information is answer information given by a tutoring teacher in a historical tutoring process for similar question information.

[0130] In step S104, the first answer information is sent to the student terminal.

[0131] The embodiments of the present disclosure perform logical review on question information sent by a student terminal, obtain at least one pair of suspicious knowledge point information, that is, information in which understanding of knowledge points in question information is erroneous and concepts are unclear, and then obtain matched first answer information from teaching asset records of teaching assets in asset layer 4 through the at least one pair of suspicious knowledge point information. A large number of repeated similar questions are answered in an automatic reply manner, tutoring efficiency is improved, and a tutoring teacher and a student can explore deeper knowledge.

[0132] In some embodiments, the method further includes the following steps:

[0133] In step S104a-1, when matched first answer information is not obtained from teaching asset records of teaching assets in asset layer 4 based on the at least one pair of suspicious knowledge point information, the question information is sent to a tutoring terminal.

[0134] When matched first answer information is not obtained from teaching asset records of teaching assets in asset layer 4, it is indicated that a tutoring teacher has never given answer information for the question information in a historical tutoring process, and corresponding teaching asset records are not saved in teaching assets. Therefore, the embodiments submit the question to a tutoring terminal, and the tutoring teacher answers the question information.

[0135] In step S104a-2, second answer information fed back by the tutoring terminal for the question information is obtained.

[0136] In step S104a-3, the at least one pair of suspicious knowledge point information and the second answer information are integrated into teaching asset records in the teaching assets by the integration system 41 of asset layer 4, and the second answer information is sent to the student terminal, where the teaching asset records at least include the at least one pair of suspicious knowledge point information and the second answer information.

[0137] As Figure 4 In the embodiments, the asset layer 4 further includes an integration system 41, which is configured to: classify and extract the interactive information based on each knowledge point type, obtain key information of a corresponding knowledge point type; and generate a teaching asset record of the corresponding knowledge point type based on the key information and the interactive information of each knowledge point type, where the teaching asset record includes information in at least one form.

[0138] Knowledge point type refers to the type, tag form, and summary form of knowledge points based on the smallest relatively independent unit in interactive information.

[0139] Presentation formats include: text, audio, video, image, translation, classification, and / or abstract.

[0140] After the teacher answers the question, similar questions can be answered automatically in the future, thus improving the efficiency of answering questions.

[0141] In some specific embodiments, the method further includes the following steps:

[0142] Before the integration system 41 of the asset layer 4 integrates the at least one pair of suspicious knowledge point information and the second answer information into the teaching asset record in the teaching asset, the audit system 42 of the asset layer 4 conducts a compliance review on the second answer information and corrects the erroneous information in the second answer information based on the teaching asset.

[0143] like Figure 4 As shown, in this specific embodiment, the asset layer 4 further includes an auditing system 42. The auditing system 42 is configured to: conduct a compliance review of the interactive information before the integration system 41 generates teaching asset records based on the interactive information, and correct errors (such as slips of the tongue) in the interactive information based on the teaching assets. For example, mistakenly stating "pressure" as "pressure intensity." This avoids errors appearing in the answer information and ensures the accuracy of the answer information.

[0144] In some other specific embodiments, the method further includes the following steps:

[0145] Before conducting a logical review of the problem information, the audit system 42 of the asset layer 4 conducts a compliance review of the problem information and corrects any errors in the problem information based on the teaching assets.

[0146] For example, if a question incorrectly refers to the physics term "inertia" as "inertial force," the review system (System 42) will correct the error to "inertia" after reviewing the question. This improves the efficiency of answering questions.

[0147] Example 2

[0148] This disclosure also provides apparatus embodiments that follow the above embodiments, for implementing the method steps described in the above embodiments. The interpretation of the same names is the same as that in the above embodiments, and they have the same technical effects as those in the above embodiments, so they will not be repeated here.

[0149] As Figure 7 shown, the present disclosure provides a student and teacher interaction device 700, comprising:

[0150] A receiving unit 701 is configured to receive question information sent by a student terminal;

[0151] An obtaining unit 702 is configured to logically review the question information and obtain at least one pair of suspicious knowledge point information;

[0152] A matching unit 703 is configured to obtain matched first answer information from teaching assets in an asset layer 4 based on the at least one pair of suspicious knowledge point information;

[0153] A sending unit 704 is configured to send the first answer information to the student terminal.

[0154] Optionally, the obtaining unit 702 comprises:

[0155] A first obtaining subunit is configured to obtain a plurality of knowledge point information based on the question information;

[0156] A second obtaining subunit is configured to use a knowledge graph in the asset layer 4 to identify the association relationship of the plurality of knowledge point information and obtain the at least one pair of suspicious knowledge point information, wherein the knowledge graph is a data structure established based on the association relationship between knowledge points of each chapter of a course.

[0157] Optionally, the knowledge graph comprises a first knowledge tree, and the first knowledge tree comprises a root layer, a chapter layer, a section layer, a module layer and a knowledge point layer arranged from top to bottom, wherein the knowledge point layer comprises leaf nodes representing knowledge points, and each leaf node comprises a knowledge point information;

[0158] Correspondingly, the use of the knowledge graph in the asset layer 4 to identify the association relationship of the plurality of knowledge point information and obtain the at least one pair of suspicious knowledge point information comprises:

[0159] A first determining subunit is configured to determine a first leaf node and a second leaf node in the first knowledge tree based on any pair of knowledge point information in the plurality of knowledge point information;

[0160] A second determining subunit is configured to determine the any pair of knowledge point information as a pair of suspicious knowledge point information when the nearest ancestor nodes of the first leaf node and the second leaf node in the first knowledge tree are in the root layer or the chapter layer.

[0161] Optionally, the knowledge graph comprises a second knowledge tree, and the second knowledge tree comprises leaf nodes representing knowledge points, and each leaf node comprises a knowledge point information;

[0162] Correspondingly, the knowledge graph in the asset layer 4 is used to identify the correlation of the plurality of knowledge point information, and at least one pair of suspicious knowledge point information is obtained, which includes:

[0163] The third determination subunit is configured to determine a third leaf node and a fourth leaf node in the second knowledge tree based on any pair of knowledge point information in the plurality of knowledge point information.

[0164] The fourth determination subunit is configured to determine the pair of suspicious knowledge point information when the shortest node distance of the third leaf node and the fourth leaf node is greater than a preset normal distance threshold.

[0165] Optionally, the device further includes an integration unit, which includes:

[0166] The sending subunit is configured to send the question information to a question answering terminal when no matching first answer information is obtained from the teaching asset record of the teaching asset in the asset layer 4 based on the at least one pair of suspicious knowledge point information.

[0167] The acquisition subunit is configured to acquire second answer information fed back by the question answering terminal for the question information.

[0168] The integration subunit is configured to integrate the at least one pair of suspicious knowledge point information and the second answer information into a teaching asset record in the teaching asset through the integration system 41 of the asset layer 4, and send the second answer information to the student terminal, wherein the teaching asset record at least includes a one-to-one mapping relationship between the at least one pair of suspicious knowledge point information and the second answer information.

[0169] Optionally, the device further includes a first review unit, which includes:

[0170] The first review subunit is configured to perform compliance review on the second answer information through the audit system 42 of the asset layer 4 before the at least one pair of suspicious knowledge point information and the second answer information are integrated into the teaching asset record in the teaching asset through the integration system 41 of the asset layer 4, and correct error information in the second answer information based on the teaching asset.

[0171] Optionally, the device further includes a second review unit, which includes:

[0172] The second review subunit is configured to perform compliance review on the question information through the audit system 42 of the asset layer 4 before the logical review on the question information, and correct error information in the question information based on the teaching asset.

[0173] The embodiments of the present disclosure perform logical review on the question information sent by the student terminal, obtain at least one pair of suspicious knowledge point information, that is, information in which the understanding of the knowledge point in the question information is wrong and the concept is confused and unclear, and then obtain the matched first answer information from the teaching asset records of the teaching assets in the asset layer 4 through the at least one pair of suspicious knowledge point information. A large number of repetitive similar questions are answered in an automatic reply manner, the answering efficiency is improved, and therefore the teacher and the student can discuss more in-depth knowledge.

[0174] Embodiment 3

[0175] As Figure 8 shown, the embodiments provide an electronic device, comprising: at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions executable by the one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method steps of the above embodiments.

[0176] Embodiment 4

[0177] The embodiments of the present disclosure provide a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the method steps of the above embodiments.

[0178] Embodiment 5

[0179] Reference will now be made to Figure 8 , which shows a structural schematic diagram of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 8 The electronic device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present disclosure.

[0180] As Figure 8As shown, the electronic device can include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801 that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 802 or loaded into a random access memory (RAM) 803 from a storage device 808. Various programs and data required for operation of the electronic device are also stored in the RAM 803. The processing device 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0181] Generally, the following devices can be connected to the I / O interface 805: input devices 806 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 805 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 808 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 809. The communication devices 809 can allow the electronic device to communicate wirelessly or wired with other devices to exchange data. Although Figure 8 The electronic device is shown with various devices, but it is understood that all of the shown devices are not required to be implemented or present. More or less devices can alternatively be implemented or present.

[0182] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication devices 809, or installed from the storage devices 808, or installed from the ROM 802. When the computer program is executed by the processing device 801, the above-mentioned functions defined in the methods of embodiments of the present disclosure are performed.

[0183] Note that the computer readable medium described above can be a computer readable signal medium or a computer readable storage medium or any combination thereof. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus or device. In the disclosure, the computer readable signal medium can include a computer readable program code propagated on or through a computer readable medium, in baseband or as part of a carrier wave. The computer readable signal medium can take a variety of forms, including but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. The computer readable signal medium can be any computer readable medium that can be used to carry or store computer readable program code for use by or in connection with an instruction execution system, apparatus or device. The program code contained on the computer readable medium can be transmitted or received over any suitable medium, including but not limited to, wire, cable, fiber optic, RF (radio frequency), or any suitable combination of the foregoing.

[0184] The computer readable medium described above can be included in the electronic device described above; alternatively, the computer readable medium can exist as a separate entity in which the electronic device is enclosed.

[0185] Computer program code for carrying out operations of the disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0186] The flowcharts and block diagrams in the attached drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts and block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by a dedicated hardware-based system that carries out specified functions or operations (e.g., a combination of an analog and digital processor) or can be implemented by a combination of dedicated and general-purpose hardware (e.g., a combination of an analog and digital processor), software on general purpose processors or a combination of the two.

[0187] The units described in the embodiments of the present disclosure can be implemented by software, or by hardware. In some cases, the name of the unit does not constitute a limitation on the unit itself.

[0188] Finally, it should be noted that the various embodiments described in the specification are presented by way of example only and are not intended to limit the scope of the disclosure. In each of the embodiments, the same or similar elements are denoted by the same reference numerals, and only the differences from one embodiment to another are described.

[0189] The above embodiments are only used to illustrate the technical solutions of the present disclosure, but not limit the present disclosure; although the present disclosure is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for student-teacher interaction, characterized in that, include: Receive question information sent from student terminals; The problem information was logically reviewed to obtain at least one pair of suspicious knowledge points. Based on the at least one pair of questionable knowledge points, a matching first answer is obtained from the teaching asset records of the teaching assets at the asset layer; The first answer information is sent to the student terminal; The logical review of the question information yields at least one pair of suspicious knowledge points, including: Based on the aforementioned question information, multiple knowledge point information is obtained; The knowledge graph in the asset layer is used to identify the relationships between the multiple knowledge points to obtain at least one pair of suspicious knowledge points. The knowledge graph is a data structure built based on the relationships between knowledge points in each chapter of the course. The knowledge graph includes a first knowledge tree, which includes a root layer, chapter layer, section layer, module layer and knowledge point layer arranged from top to bottom. The knowledge point layer includes leaf nodes that represent knowledge points, and each leaf node includes a knowledge point information. The step of using the knowledge graph in the asset layer to identify the relationships between the multiple knowledge points and obtain the at least one pair of suspicious knowledge points includes: Based on any pair of knowledge point information from the plurality of knowledge point information, determine the first leaf node and the second leaf node in the first knowledge tree respectively; When the nearest ancestor of the first leaf node and the second leaf node in the first knowledge tree is at the root level or the chapter level, the pair of knowledge point information is determined to be a pair of suspicious knowledge point information. The knowledge graph includes a second knowledge tree, which includes leaf nodes representing knowledge points, and each leaf node includes a knowledge point information. Accordingly, the step of using the knowledge graph in the asset layer to identify the relationships between the multiple knowledge points and obtain the at least one pair of suspicious knowledge points includes: Based on any pair of knowledge point information from the plurality of knowledge point information, determine the third leaf node and the fourth leaf node in the second knowledge tree respectively; When the shortest distance between the third leaf node and the fourth leaf node is greater than a preset normal distance threshold, any pair of knowledge point information is determined to be a pair of suspicious knowledge point information.

2. The method according to claim 1, characterized in that, The method further includes: When no matching first answer information is obtained from the teaching asset record of the teaching asset at the asset layer based on the at least one pair of questionable knowledge point information, the question information is sent to the question-answering terminal; Obtain the second answer information fed back by the Q&A terminal in response to the question information; The asset layer integration system integrates the at least one pair of suspicious knowledge point information and the second answer information into a teaching asset record in the teaching assets, and sends the second answer information to the student terminal. The teaching asset record includes at least a one-to-one mapping relationship between the at least one pair of suspicious knowledge point information and the second answer information.

3. The method according to claim 2, characterized in that, The method further includes: Before the integration system of the asset layer integrates the at least one pair of suspicious knowledge point information and the second answer information into the teaching asset record in the teaching asset, the audit system of the asset layer conducts a compliance review on the second answer information and corrects the erroneous information in the second answer information based on the teaching asset.

4. The method according to claim 1, characterized in that, The method further includes: Before conducting a logical review of the problem information, the asset layer's auditing system conducts a compliance review of the problem information and corrects any errors in the problem information based on the teaching assets.

5. An interactive device for students and teachers, characterized in that, include: The receiving unit is used to receive question information sent by student terminals; The obtaining unit is used to perform logical review on the problem information and obtain at least one pair of suspicious knowledge point information; The logical review of the question information yields at least one pair of suspicious knowledge points, including: Based on the aforementioned question information, multiple knowledge point information is obtained; The knowledge graph in the asset layer is used to identify the relationships between the multiple knowledge points to obtain at least one pair of suspicious knowledge points. The knowledge graph is a data structure built based on the relationships between knowledge points in each chapter of the course. The knowledge graph includes a first knowledge tree, which includes a root layer, chapter layer, section layer, module layer and knowledge point layer arranged from top to bottom. The knowledge point layer includes leaf nodes that represent knowledge points, and each leaf node includes a knowledge point information. The step of using the knowledge graph in the asset layer to identify the relationships between the multiple knowledge points and obtain the at least one pair of suspicious knowledge points includes: Based on any pair of knowledge point information from the plurality of knowledge point information, determine the first leaf node and the second leaf node in the first knowledge tree respectively; When the nearest ancestor of the first leaf node and the second leaf node in the first knowledge tree is at the root level or the chapter level, the pair of knowledge point information is determined to be a pair of suspicious knowledge point information. The knowledge graph includes a second knowledge tree, which includes leaf nodes representing knowledge points, and each leaf node includes a knowledge point information. Accordingly, the step of using the knowledge graph in the asset layer to identify the relationships between the multiple knowledge points and obtain the at least one pair of suspicious knowledge points includes: Based on any pair of knowledge point information from the plurality of knowledge point information, determine the third leaf node and the fourth leaf node in the second knowledge tree respectively; When the shortest distance between the third leaf node and the fourth leaf node is greater than a preset normal distance threshold, any pair of knowledge point information is determined to be a pair of suspicious knowledge point information. A matching unit is configured to obtain matching first answer information from the teaching assets at the asset layer based on the at least one pair of questionable knowledge point information; The sending unit is used to send the first answer information to the student terminal.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 4.

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