CN110688464B - Man-machine conversation method and system - Google Patents
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Abstract
The application discloses a man-machine conversation method, which is applied to a man-machine conversation system and comprises the following steps: acquiring a plurality of historical problems of a user; determining a degree of matching between a plurality of sub-topics in the human-computer dialog system and the plurality of historical questions; determining the recommendation degrees of the plurality of sub-topics according to the matching degrees, wherein the higher the matching degree is, the lower the corresponding recommendation degree is; and when the user active question is not received within the preset time, recommending the question to the user according to a plurality of sub-topics with the front recommendation degrees. The known problems of the user are determined according to the historical problems of the user, and a proper guiding topic is found by combining the topics possessed by the system, so that the guiding topic can expand the known ability domain of the user, enhance the knowledge of the user to the system and be accurately answered by the system.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a man-machine conversation method and system.
Background
During a chat using a dialog system or product, the user often does not know how to ask questions, what to ask. When the system is unable to answer the user's question correctly, the user needs to think about other questions and system conversations. If the system cannot answer the user's question correctly for many times, the user will stop exploring the capability boundaries of the system, affecting the user experience, and also affecting the ability of the system to collect user questions and achieve active learning.
Disclosure of Invention
The embodiment of the present application provides a human-machine interaction method and system, which are used for solving at least one of the above technical problems.
In a first aspect, an embodiment of the present application provides a human-computer conversation method, which is applied to a human-computer conversation system, and the method includes:
acquiring a plurality of historical problems of a user;
determining matching degrees between a plurality of sub-topics in the man-machine conversation system and the plurality of historical questions;
determining the recommendation degrees of the plurality of sub-topics according to the matching degrees, wherein the higher the matching degree is, the lower the corresponding recommendation degree is;
and when the user active question is not received within the preset time, recommending the question to the user according to a plurality of sub-topics with the front recommendation degrees.
In a second aspect, an embodiment of the present application provides a human-machine interaction system, including:
the historical problem acquisition module is used for acquiring a plurality of historical problems of the user;
the matching degree determining module is used for determining the matching degree between a plurality of subtopics in the man-machine conversation system and the plurality of historical problems;
the recommendation degree determining module is used for determining the recommendation degrees of the plurality of sub-topics according to the matching degrees, wherein the higher the matching degree is, the lower the corresponding recommendation degree is;
and the question recommending module is used for recommending questions to the user according to a plurality of sub-topics with the forward recommending degree when the active questions of the user are not received within the preset time.
In a third aspect, embodiments of the present application provide a storage medium, where one or more programs including execution instructions are stored, where the execution instructions can be read and executed by an electronic device (including but not limited to a computer, a server, or a network device, etc.) to perform any one of the human-machine conversation methods described above in the present application.
In a fourth aspect, an electronic device is provided, comprising: the system comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute any one of the man-machine conversation methods described above.
In a fifth aspect, the present application further provides a computer program product, where the computer program product includes a computer program stored on a storage medium, and the computer program includes program instructions, when the program instructions are executed by a computer, the computer is caused to execute any one of the above man-machine conversation methods.
The beneficial effects of the embodiment of the application are that: the known problems of the user are determined according to the historical problems of the user, and a proper guiding topic is found by combining the topics possessed by the system, so that the guiding topic can expand the known ability domain of the user, enhance the knowledge of the user to the system and be accurately answered by the system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow diagram of one embodiment of a human-machine dialog method of the present application;
FIG. 2 is a flow diagram of another embodiment of a human-machine dialog method of the present application;
FIG. 3 is a schematic diagram of a skill set architecture of the human machine dialog system of the present application;
FIG. 4 is a functional block diagram of an embodiment of a human-machine dialog system of the present application;
fig. 5 is a schematic structural diagram of an embodiment of an electronic device of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
As shown in fig. 1, an embodiment of the present application provides a human-computer conversation method applied to a human-computer conversation system, where the method includes:
s10, acquiring a plurality of historical problems of a user;
s20, determining the matching degree between a plurality of sub-topics in the man-machine conversation system and the plurality of historical problems;
s30, determining the recommendation degrees of the plurality of sub-topics according to the matching degrees, wherein the higher the matching degree is, the lower the corresponding recommendation degree is;
and S40, recommending the questions to the user according to a plurality of sub-topics with the forward recommendation degree when the active questions of the user are not received within the preset time. The preset time can be adjusted differently according to different scenes, and the value can be dynamically adjusted. For example, in a story machine scenario, the value between presets may be 3s.
According to the method and the device, the known problems of the user are determined according to the historical problems of the user, and the proper guide topic is found by combining the topics possessed by the system, so that the guide topic can expand the known ability domain of the user, the user's understanding of the system is enhanced, and the user can accurately answer the guide topic by the system.
In some embodiments, each of the plurality of sub-topics corresponds to a plurality of knowledge points; determining a degree of match between a plurality of subtopics in the human-computer dialog system and the plurality of historical questions comprises:
performing the following steps for each of the sub-topics to determine a degree of match between the each sub-topic and the plurality of historical questions:
determining a plurality of similarity values between each of the plurality of historical questions and a plurality of knowledge points of a current sub-topic to determine whether the current historical question hits the current sub-topic based on the plurality of similarity values;
determining the number of matching problems according to the number of the history problems hitting the current sub-topic;
determining the matching degree corresponding to the current sub-theme according to the matching problem number; illustratively, the ratio between the number of matching questions and the number of the plurality of historical questions is determined as the matching degree of the current sub-topic.
In some embodiments, determining whether the current historical question hits the current sub-topic based on the plurality of similarity values comprises: judging whether the average value of the similarity values is larger than a set threshold value or not; if so, judging that the current historical problem hits the current sub-topic; if not, determining that the current historical problem misses the current sub-topic.
In the embodiment of the application, the average value of the similarity values is compared with the set threshold value, so that the situation that the matching degree is high when the individual knowledge point is similar to the current historical problem can be avoided, and the low recommendation degree is obtained. Because other knowledge points in the current sub-topic may have little similarity to (i.e., very weak correlation to) the current historical problem, it is obviously not appropriate to directly score the sub-topic with a low recommendation score in this case.
In some embodiments, determining a plurality of similarity values between each of the plurality of historical questions and a plurality of knowledge points of the current sub-topic comprises:
selecting two knowledge points with the minimum similarity value between every two knowledge points in the plurality of knowledge points of the current subtopic as a first knowledge point and a second knowledge point;
determining a first similarity value and a second similarity value between each of the plurality of historical problems and the first knowledge point and the second knowledge point respectively;
and when the average value of the first similarity value and the second similarity value is larger than a set threshold value, judging that the current historical problem hits the current sub-topic.
The first knowledge point and the second knowledge point selected in the embodiment of the application are two extreme conditions of a plurality of knowledge points in the current sub-topic, so that whether the current sub-topic is hit or not can be determined only by calculating and comparing the similarity value between the current historical problem and the two knowledge points, and the required calculation amount is greatly reduced.
In some embodiments, when an average of the first similarity value and the second similarity value is not greater than a set threshold, further determining a similarity value between each of the plurality of historical questions and remaining knowledge points of the plurality of knowledge points to completely determine a plurality of similarity values corresponding to each of the plurality of knowledge points.
The common practice in the prior art is to randomly push some questions as guidance questions according to system capability boundaries (the range of questions that the system can answer), and does not consider the system capability domain that the user has explored.
As shown in fig. 2, which is a schematic flowchart of an embodiment of the man-machine conversation method provided in the present application, the method selects an appropriate topic to guide a user to ask a question of a system based on historical conversation data of the user and capability boundaries of the system itself (i.e., based on the conversation history of the user and the capability boundaries of the system). Illustratively, the man-machine conversation method includes the steps of:
step 1, modeling a system capacity domain: and modeling the skills and knowledge of system configuration, and constructing a system capability domain model.
Step 2, modeling the known ability domain of the user: and mining the known capability domain of the user from the historical conversation record of the user, and constructing a model of the known capability domain of the user.
Step 3, guiding topic selection: and finding a proper guiding topic according to the user known ability domain model and the system ability domain model, wherein the guiding topic can expand the user known ability domain, enhance the user's understanding of the system, and can be accurately answered by the system. The method and the system have the following advantages: firstly, conversation efficiency is improved; second, user satisfaction is improved.
Further, when the user asks a guided topic, the system automatically updates the user's known capabilities domain, and if the user already knows that the system can resolve such topic, the probability that such later topic will appear in the guided question is reduced. Therefore, the interactive experience of the user and the system is improved, the number of rounds and the viscosity of interaction between the user and the system are increased, the problem guiding method and the problem guiding system for autonomous learning are further realized, the conversation efficiency is improved, and the communication target of the user is efficiently achieved.
Illustratively, for step 1, each skill Si will have several topics, including sub-topics, including several knowledge points, according to the system configured skills < S1, S2. Fig. 3 is a schematic diagram of a skill structure of the man-machine interaction system of the present application, and system capability domain modeling is performed based on the schematic diagram.
Illustratively, for step 2, a user historical problem set U < q1, q 2.. Qn > is collected, the semantic similarity between qi and knowledge points in skills configured by the system is compared, when the similarity is greater than a certain threshold, the subject of qi can be determined, and the corresponding skill can also be determined. Set U is traversed and each qi is mapped into each skill. Then, normalization is performed to obtain the probability that each skill is recommended. The similarity can be obtained by calculating a certain threshold value through a semantic matching algorithm, for example, sentences can be represented as sentence vectors through a deep learning model, and then the cosine distance between the two sentences is calculated. The specific value of the threshold may be determined according to the scenario processed by the system, and may be set to 0.8 in general, which is not limited in this application.
Taking a certain U < q1, q 2.. Qn > as an example, C1 problems in U fall to the skill S1; there are C2 questions in U that fall into skill S2; there are C3 questions in U that fall into skill S3; there are C4 questions in U that fall into skill S4;
Θ i =1-Ci/SUM (Ci). Similarly, the recommendation degree of the subject Ti and the sub-subject STi in a certain skill Si can be calculated, which can be seen in the following table:
| skill of skill | Themes | Sub-themes | Number of matching problems | Degree of recommendability |
| S1 | T1 | ST1 | C1 | Θ1 |
| S2 | T1 | ST2 | C2 | Θ2 |
| S3 | T1 | ST2 | C3 | Θ3 |
| S4 | T1 | ST3 | C4 | Θ4 |
For example, the human-machine conversation method and system of the present application can be applied to any kind of IOT intelligent devices, including, but not limited to, story machines, smart speakers, and the like. The skills S1, S2, S3, S4 may be chatting, encyclopedia. The topic T1 may be a topic of mood in a chat skill or a historical topic in an encyclopedia skill.
Illustratively, for step 3, when the user enters the conversation for the first time, according to the recommendability Θ i of each skill, a certain topic is selected first, then according to the recommendability of the topic Ti, the recommendability of the sub-topic, the topic and the sub-topic, a certain knowledge point of the selected sub-topic is recommended as a guide topic to the user.
The recommendation to be solved by the application is to guide the user to explore the boundaries of the dialog system and recommend the similar problems. If the user has asked some kind of question, such as "one hundred thousand why" for a period of time, then for the next period of time the system will think that the user already knows that the dialog system can answer the question "one hundred thousand why". Then the question recommendation for the class "why ten million" decays. If the user does not ask the 'one hundred thousand why' category questions over time, it may be that the user has forgotten that the dialog system can answer the 'one hundred thousand why' category questions, and this time the recommendation of the 'one hundred thousand why' category questions is strengthened.
In addition, it can be considered that different sessions are calculated once, and it is ensured that a knowledge point of a sub-topic cannot be found in the same session.
And finally, the recommendability of the skills, the subjects and the subtopics is updated regularly.
Guided speech generation
After selecting the subtopic and knowledge points, generating the final guide language according to the phony template
For example:
< hello, i is >
< I know much about S1, S2, S3 >
< you can ask me this way { reverse asking form of knowledge points } >)
It should be noted that for simplicity of description, the above-mentioned method embodiments are described as a series of acts, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application. In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
As shown in fig. 4, an embodiment of the present application further provides a human-machine dialog system 400, which includes:
a historical question acquisition module 410, configured to acquire a plurality of historical questions of a user;
a matching degree determination module 420, configured to determine matching degrees between a plurality of sub-topics in the human-computer interaction system and the plurality of historical questions;
a recommendation degree determining module 430, configured to determine recommendation degrees of the multiple sub-topics according to the matching degrees, where the higher the matching degree is, the lower the corresponding recommendation degree is;
and the question recommending module 440 is used for recommending questions to the user according to the plurality of sub-topics with the top recommendation degree when the active questions of the user are not received within the preset time.
According to the method and the device, the known problems of the user are determined according to the historical problems of the user, and the proper guide topics are found by combining with the topics possessed by the system, so that the guide topics can expand the known ability domain of the user, the user can be more informed of the system, and the system can accurately answer the guide topics.
In some embodiments, each of the plurality of sub-topics corresponds to a plurality of knowledge points; the matching degree determination module is configured to perform the steps of:
performing the following steps for each of the sub-topics to determine a degree of match between the each sub-topic and the plurality of historical questions:
determining a plurality of similarity values between each of the plurality of historical questions and a plurality of knowledge points of a current sub-topic to determine whether the current historical question hits the current sub-topic based on the plurality of similarity values;
determining the number of matching problems according to the number of the history problems hitting the current sub-topic;
and determining the matching degree corresponding to the current sub-theme according to the matching problem number.
In some embodiments, determining the matching degree corresponding to the current sub-topic according to the matching question number comprises: and determining the ratio of the number of the matching problems to the number of the plurality of historical problems as the matching degree of the current sub-topic.
In some embodiments, determining whether the current historical question hits the current sub-topic based on the plurality of similarity values comprises: judging whether the average value of the similarity values is larger than a set threshold value or not; if so, judging that the current historical problem hits the current sub-topic; if not, the current historical problem is judged to miss the current sub-topic.
In some embodiments, determining a plurality of similarity values between each of the plurality of historical questions and a plurality of knowledge points of the current sub-topic comprises:
selecting two knowledge points with the minimum similarity value between every two knowledge points in the plurality of knowledge points of the current subtopic as a first knowledge point and a second knowledge point;
determining a first similarity value and a second similarity value between each of the plurality of historical problems and the first knowledge point and the second knowledge point, respectively;
and when the average value of the first similarity value and the second similarity value is larger than a set threshold value, judging that the current historical problem hits the current sub-topic.
In some embodiments, when an average of the first similarity value and the second similarity value is not greater than a set threshold, further determining a similarity value between each of the plurality of historical questions and remaining knowledge points of the plurality of knowledge points to completely determine a plurality of similarity values corresponding to each of the plurality of knowledge points.
In some embodiments, the present application provides a non-volatile computer-readable storage medium, in which one or more programs including execution instructions are stored, where the execution instructions can be read and executed by an electronic device (including but not limited to a computer, a server, or a network device, etc.) to perform any one of the man-machine conversation methods described above in the present application.
In some embodiments, the present application further provides a computer program product, the computer program product comprising a computer program stored on a non-volatile computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform any one of the above human-computer interaction methods.
In some embodiments, the present application further provides an electronic device, which includes: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a human-machine conversation method.
In some embodiments, the present application further provides a storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement a man-machine interaction method.
The human-computer conversation system of the embodiment of the present application can be used for executing the human-computer conversation method of the embodiment of the present application, and accordingly achieves the technical effect achieved by the implementation of the human-computer conversation method of the embodiment of the present application, and details are not repeated here. In the embodiment of the present application, the relevant functional module may be implemented by a hardware processor (hardware processor).
Fig. 5 is a schematic hardware structure diagram of an electronic device for performing a man-machine interaction method according to another embodiment of the present application, and as shown in fig. 5, the electronic device includes:
one or more processors 510 and memory 520, with one processor 510 being an example in fig. 5.
The apparatus for performing the man-machine conversation method may further include: an input device 530 and an output device 540.
The processor 510, the memory 520, the input device 530, and the output device 540 may be connected by a bus or other means, and the bus connection is exemplified in fig. 5.
The memory 520 is a non-volatile computer-readable storage medium and can be used for storing non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions/modules corresponding to the man-machine interaction method in the embodiment of the present application. The processor 510 executes various functional applications of the server and data processing by operating non-volatile software programs, instructions and modules stored in the memory 520, so as to implement the man-machine conversation method of the above method embodiment.
The memory 520 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the human-machine conversation apparatus, and the like. Further, memory 520 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 520 may optionally include memory located remotely from processor 510, which may be connected to the human dialog device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 530 may receive input numeric or character information and generate signals related to user settings and function control of the human interactive device. The output device 540 may include a display device such as a display screen.
The one or more modules are stored in the memory 520 and, when executed by the one or more processors 510, perform the human-machine dialog method of any of the method embodiments described above.
The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the methods provided in the embodiments of the present application.
The electronic device of the embodiments of the present application exists in various forms, including but not limited to:
(1) A mobile communication device: such devices are characterized by mobile communications capabilities and are primarily targeted at providing voice, data communications. Such terminals include: smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
(2) Ultra mobile personal computer device: the equipment belongs to the category of personal computers, has calculation and processing functions and generally has mobile internet access characteristics. Such terminals include: PDA, MID, and UMPC devices, etc., such as ipads.
(3) A portable entertainment device: such devices can display and play multimedia content. This type of device comprises: audio and video players (e.g., iPod), handheld game consoles, electronic books, story machines, smart speakers, and smart toys and portable car navigation devices.
(4) A server: the device for providing the computing service comprises a processor, a hard disk, a memory, a system bus and the like, and the server is similar to a general computer architecture, but has higher requirements on processing capacity, stability, reliability, safety, expandability, manageability and the like because of the need of providing high-reliability service.
(5) And other electronic devices with data interaction functions.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the above technical solutions substantially or contributing to the related art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (8)
1. A man-machine conversation method is applied to a man-machine conversation system, and comprises the following steps:
acquiring a plurality of historical problems of a user;
determining matching degrees between a plurality of sub-topics in the man-machine conversation system and the plurality of historical questions;
determining the recommendation degrees of the plurality of sub-topics according to the matching degrees, wherein the higher the matching degree is, the lower the corresponding recommendation degree is;
when the user active question is not received within the preset time, recommending the question to the user according to a plurality of sub-topics with the front recommendation degrees;
each of the plurality of subtopics corresponds to a plurality of knowledge points respectively;
determining a degree of match between a plurality of subtopics in the human-computer dialog system and the plurality of historical questions comprises:
performing the following steps for each of the sub-topics to determine a degree of match between the each sub-topic and the plurality of historical questions:
determining a plurality of similarity values between each of the plurality of historical questions and a plurality of knowledge points of a current sub-topic to determine whether the current historical question hits the current sub-topic based on the plurality of similarity values;
determining the number of matching problems according to the number of the historical problems hitting the current subtopic;
determining the matching degree corresponding to the current sub-theme according to the matching problem number;
the determining a plurality of similarity values between each of the plurality of historical questions and a plurality of knowledge points of a current sub-topic to determine whether the current historical question hits the current sub-topic based on the plurality of similarity values comprises:
selecting two knowledge points with the minimum similarity value between every two knowledge points in the plurality of knowledge points of the current subtopic as a first knowledge point and a second knowledge point;
determining a first similarity value and a second similarity value between each of the plurality of historical problems and the first knowledge point and the second knowledge point, respectively;
and when the average value of the first similarity value and the second similarity value is larger than a set threshold value, judging that the current historical problem hits the current sub-topic.
2. The method of claim 1, wherein determining a degree of matching for the current sub-topic from the number of matching questions comprises: and determining the ratio of the number of the matching questions to the number of the plurality of historical questions as the matching degree of the current sub-topic.
3. The method of claim 1, wherein determining whether a current historical question hits the current sub-topic according to the plurality of similarity values comprises:
judging whether the average value of the similarity values is larger than a set threshold value or not; if so, judging that the current historical problem hits the current sub-topic; if not, determining that the current historical problem misses the current sub-topic.
4. A human-machine dialog system comprising:
the historical problem acquisition module is used for acquiring a plurality of historical problems of the user;
the matching degree determining module is used for determining the matching degree between a plurality of subtopics in the man-machine conversation system and the plurality of historical problems;
the recommendation degree determining module is used for determining recommendation degrees of the plurality of sub-topics according to the matching degree, wherein the higher the matching degree is, the lower the corresponding recommendation degree is;
the question recommending module is used for recommending questions to the user according to a plurality of sub-topics with the front recommending degrees when the active questions of the user are not received within the preset time;
each of the plurality of subtopics corresponds to a plurality of knowledge points respectively;
the matching degree determination module is configured to perform the steps of:
performing the following steps for each of the sub-topics to determine a degree of match between the each sub-topic and the plurality of historical questions:
determining a plurality of similarity values between each of the plurality of historical questions and a plurality of knowledge points of a current sub-topic to determine whether the current historical question hits the current sub-topic based on the plurality of similarity values;
determining the number of matching problems according to the number of the history problems hitting the current sub-topic;
determining the matching degree corresponding to the current sub-theme according to the matching problem number;
the determining a plurality of similarity values between each of the plurality of historical questions and a plurality of knowledge points of a current sub-topic to determine whether the current historical question hits the current sub-topic based on the plurality of similarity values comprises:
selecting two knowledge points with the minimum similarity value between every two knowledge points in the plurality of knowledge points of the current subtopic as a first knowledge point and a second knowledge point;
determining a first similarity value and a second similarity value between each of the plurality of historical problems and the first knowledge point and the second knowledge point respectively;
and when the average value of the first similarity value and the second similarity value is larger than a set threshold value, judging that the current historical problem hits the current sub-topic.
5. The system of claim 4, wherein determining a degree of match for the current sub-topic based on the number of matching questions comprises: and determining the ratio of the number of the matching questions to the number of the plurality of historical questions as the matching degree of the current sub-topic.
6. The system of claim 4, wherein determining whether a current historical question hits the current sub-topic based on the plurality of similarity values comprises:
judging whether the average value of the similarity values is larger than a set threshold value or not; if so, judging that the current historical problem hits the current sub-topic; if not, determining that the current historical problem misses the current sub-topic.
7. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method of any one of claims 1-3.
8. A storage medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 3.
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