CN113127682A - Topic presentation method, system, electronic device, and computer-readable storage medium - Google Patents
Topic presentation method, system, electronic device, and computer-readable storage medium Download PDFInfo
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
- G06F16/735—Filtering based on additional data, e.g. user or group profiles
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/783—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/7844—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using original textual content or text extracted from visual content or transcript of audio data
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/7867—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
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Abstract
The invention provides a topic explanation method, a system, electronic equipment and a computer readable storage medium, wherein knowledge points are associated with explanation videos in a topic library, and then the corresponding explanation videos are pushed according to the knowledge points of the topics to be explained. Therefore, the pushed explanation videos are matched with the knowledge points of the questions to be explained, users can take wrong questions or questions which cannot be made as the questions to be explained, and the pushed explanation videos are checked to understand the relevant knowledge points until the knowledge points are completely mastered, so that examination scores are improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a question explaining method, a question explaining system, electronic equipment and a computer readable storage medium.
Background
With the continuous advance of computer technology and education informatization, computer technology has been gradually applied to various activities of daily education and teaching, for example, the computer technology is correspondingly applied in teaching evaluation scenes. The main investigation forms of the domestic existing basic education and student learning conditions are still various types of examinations or tests, and under the condition, if the students can explain the knowledge points of the questions when meeting the questions or wrong questions which cannot be done, the students can quickly master the related knowledge points, so that the examination scores are improved.
At present, intelligent electronic equipment products have a plurality of problem searching APPs, and images of problems are input into the problem searching APPs, so that the problem searching APPs search problems and problem solving processes corresponding to all the problems in the images of test papers from a problem library according to the contents of the problems. However, the existing topic search APP is only limited to the search topic and cannot push the explanation video in close contact with the knowledge point related to the topic, so that a need exists for a new technology.
Disclosure of Invention
The invention aims to provide a question explaining method, a question explaining system, electronic equipment and a computer readable storage medium to assist learning and teaching.
In order to achieve the above object, the present invention provides a topic teaching method, including:
associating the knowledge point with the explanation video in the question bank; and the number of the first and second groups,
identifying the topic to be explained, and pushing the corresponding explanation video according to the knowledge point of the topic to be explained.
Optionally, when the knowledge points are associated with the explanation videos in the question bank, one knowledge point is associated with one or at least two of the explanation videos; and/or, one or at least two knowledge points are associated with one explained video.
Optionally, the topic to be explained has one or at least two knowledge points.
Optionally, acquiring knowledge points of the topic to be explained according to the identified topic content of the topic to be explained; and/or acquiring knowledge points of the question to be explained according to the identified answering step of the question to be explained; and/or generating a problem solving process according to the identified problem content of the problem to be explained, and acquiring the knowledge point of the problem to be explained according to the problem solving process.
Optionally, an image of the subject area of the subject to be explained is obtained, and the subject content of the subject to be explained is identified by using a pre-trained first character identification model; and/or acquiring an image of the answer area of the question to be explained, and identifying the answer of the question to be explained by utilizing a pre-trained second character recognition model.
Optionally, obtaining knowledge points of the questions to be explained by using a pre-trained first classification model according to the question contents; and/or acquiring knowledge points of the questions to be explained by using a pre-trained second classification model according to the question answering step; and/or acquiring knowledge points of the questions to be explained by utilizing a pre-trained third classification model according to the question solving process.
Optionally, the first classification model, the second classification model, and the third classification model all include a text classification model and a natural language understanding model.
Optionally, the step of generating the problem solving process according to the topic content includes:
determining a problem solving model according to the problem content; and the number of the first and second groups,
and generating the problem solving process according to the problem solving model.
Before the explanation video is pushed, whether the question to be explained is a wrong question is judged according to the identified answering step of the question to be explained, and when the question to be explained is a wrong question, the explanation video of the wrong question is pushed.
Optionally, when the identified to-be-explained topics are multiple, a pushing instruction is responded to push the explanation video of the to-be-explained topics corresponding to the pushing instruction.
Optionally, after the corresponding explanation video is pushed according to the knowledge point of the topic to be explained, the method further includes:
and acquiring similar topics of the topics to be explained, and pushing an explanation video corresponding to the knowledge points of the similar topics.
Optionally, the explanation video corresponding to the play instruction is played in response to a play instruction.
The invention also provides a topic explaining system, which comprises:
the association module is used for associating the knowledge point with the explanation video in the question bank; and the number of the first and second groups,
and the pushing module is used for identifying the topic to be explained and pushing the corresponding explanation video according to the knowledge point of the topic to be explained.
The present invention also provides an electronic device, comprising:
one or more actuators; and the number of the first and second groups,
a memory for storing one or more programs; and the number of the first and second groups,
when the one or more programs are executed by the one or more actors, causing the one or more actors to implement the topic interpretation method.
The invention also provides a computer readable storage medium, which stores a computer program, and the computer program realizes the title explaining method when being executed by a processor.
The invention also provides a computer program product comprising instructions which, when executed by a processor, implement the topic interpretation method.
In the topic explanation method, the system, the electronic equipment and the computer readable storage medium provided by the invention, the knowledge points are associated with the explanation videos in the topic library, and then the corresponding explanation videos are pushed according to the knowledge points of the topics to be explained. Therefore, the pushed explanation videos are matched with the knowledge points of the questions to be explained, users can take wrong questions or questions which cannot be made as the questions to be explained, and the pushed explanation videos are checked to understand the relevant knowledge points until the knowledge points are completely mastered, so that examination scores are improved.
Drawings
FIG. 1 is a flowchart of a topic teaching method according to an embodiment of the present invention;
fig. 2a is a first schematic diagram of pushing a corresponding explanation video according to a knowledge point of a topic to be explained according to an embodiment of the present invention;
fig. 2b is a second schematic diagram of pushing a corresponding explanation video according to a knowledge point of a topic to be explained according to the embodiment of the present invention;
fig. 2c is a third schematic diagram of pushing a corresponding explanation video according to a knowledge point of a topic to be explained according to the embodiment of the present invention;
fig. 2d is a fourth schematic diagram of pushing a corresponding explanation video according to a knowledge point of a topic to be explained according to the embodiment of the present invention;
fig. 2e is a fifth schematic diagram of pushing a corresponding explanation video according to a knowledge point of a topic to be explained according to the embodiment of the present invention;
FIG. 3 is a block diagram of a topic interpretation system according to an embodiment of the present invention;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention;
wherein the reference numerals are:
10-an association module; 20-a push module; 301-a processor; 302-a memory; 303-a communication interface; 304-communication bus.
Detailed Description
The following describes in more detail embodiments of the present invention with reference to the schematic drawings. The advantages and features of the present invention will become more apparent from the following description. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
Fig. 1 is a flowchart of a title explaining method according to an embodiment of the present invention. As shown in fig. 1, the title explaining method provided in this embodiment includes steps S100 and S200.
Step S100: and associating the knowledge points with the explanation videos in the question bank.
Specifically, the knowledge points need to be classified first, for example, in the mathematical discipline, the knowledge points may be classified into "chicken and rabbit sharing in the same cage", "trigonometric function", "one-dimensional quadratic equation", and "derivative calculation", and in the physical discipline, the knowledge points may be classified into: "first newton's law", "electric field intensity calculation", "left-right rule", etc., because the existing knowledge points are huge in quantity and difficult to exhaust, this embodiment is only a simple example, and how to classify the knowledge points is not described here in too much detail.
Further, a knowledge point is associated with the lecture video in the question bank. It should be understood that one knowledge point may be associated with one or at least two of the lecture videos, and one of the lecture videos may also be associated with one or at least two of the knowledge points. That is, the knowledge point and the explanation video may be in a one-to-many relationship, a many-to-one relationship, or even a many-to-many relationship.
Step S200: identifying the topic to be explained, and pushing the corresponding explanation video according to the knowledge point of the topic to be explained.
Specifically, firstly, an image acquisition device can be adopted to photograph or pick up the questions to be explained, so as to acquire the images of the questions to be explained, and thus identify the questions to be explained. The image acquisition device may include a camera, an imaging module, an image processing module, and the like, and may further include a communication module for receiving or downloading an image, and the image acquisition device may be provided independently, or may be included in a user terminal such as a smart phone, a tablet computer, and the like.
Next, a region identification model may be used to determine a question region in which the question content in the image of the question to be explained is located and/or an answer region in which the answer step is located. The region identification model can be a neural network model, the input of the region identification model is the image of the question to be explained, and the output is the image of the question region or the answer region.
Specifically, a single image of the topic to be explained may be provided, so that the region identification model can be directly used to identify the topic region of the topic to be explained. Further, an image of the test paper containing the questions to be explained (an image of the entire test paper or a partial image of the entire test paper) may be provided, and then the question regions where the questions on the test paper are located are identified by using the region identification model. After the topic areas are identified, each topic area is cut into a single image or not cut actually, and each topic area is separated into a single image for processing during processing, and the images are sequenced according to the topic position information. Further, identifying the subject content of the subject to be explained by utilizing a pre-trained first character identification model according to the image of the subject area; and/or identifying the answering step (the answering step made by the user) of the question to be explained by utilizing a pre-trained second character recognition model according to the image of the answering area.
It should be understood that, when the step of identifying the answer of the question to be explained is performed, the answer of the question to be explained (the answer made by the user) is also identified; of course, the user may not answer the question, so the step of identifying the answer of the question to be explained can be omitted; accordingly, the step of determining the answer area in which the answer step is located may be omitted.
Further, when the user does not answer the question, a question solving process can be generated according to the question content of the question to be explained. In particular, the problem solving process may comprise a problem solving step or a description of steps comprising a problem solving step and at least part of a problem solving step. The problem solving step represents a specific process of deriving from the topic content to a problem solving answer, and the step descriptions may include a description of the reason why such a problem solving step was employed, an explanation of mathematical tools employed in the problem solving step, and so forth. The problem solving step is usually indispensable, and the step description may be given according to the complexity of the problem solving step, for example, for a very simple problem solving step, no relevant step description may be generated.
The problem solving process can be expressed by words or can also be represented by graphs (for example, a function graph involved in the subject content and the like).
Specifically, the step of generating the problem solving process according to the topic content may include:
and determining the topic type of the topic to be explained according to the topic content, and then determining a problem solving model according to the topic type. The problem types can include calculation problems, application problems, filling-in-the-blank problems, selection problems, operation problems and the like, and the problem solving model can particularly include a calculation model for the calculation problems, a natural language processing model and/or a vector model for the application problems and the like.
Then, the problem solving process is generated according to the problem solving model. For example, when the topic type is a computational topic, the problem solving model actually obtains a corresponding rule from a preset rule base according to the formal features (such as the number, the highest power, the position, the computation symbols, and the like of the topic content) of the topic content, and generates the problem solving process according to the corresponding rule. For example: the problem solving process of the problem to be explained is solving a binary equation of once, a factorization method, a matching method, an element changing method, a discriminant method, a Vida theorem or a coefficient waiting method and the like, and each step in the problem solving process can be identified as a corresponding problem solving method, namely the form characteristic of the content of the problem. It should be understood that the problem solving process is generated according to the problem solving model, and simultaneously, the problem solving answers are also generated. In some embodiments, the problem solving process is only displayed when triggered by the user, in view of the teaching/learning effect. For example, after the user views the answers to the questions, the user may think about the questions themselves, and when the user needs to view the questions solving process, the user may trigger (for example, by operating a specific operation device or a specific area in a display screen of a display device) the question solving process to be displayed.
It should be understood that, since the question answering step is performed by the user and may not be correct, in this embodiment, when the difference between the question answering step and the question solving process is determined to be large through the question solving model, the knowledge points obtained according to the question answering step may be discarded, and the knowledge points obtained according to the question solving process are used as the knowledge points of the question to be explained.
Further, acquiring knowledge points of the question to be explained according to the identified question content of the question to be explained; and/or acquiring knowledge points of the question to be explained according to the identified answering step of the question to be explained; and/or generating a problem solving process according to the identified problem content of the problem to be explained, and acquiring the knowledge point of the problem to be explained according to the problem solving process. That is to say, the knowledge point of the question to be explained can be obtained through one or at least two of the question content, the question answering step and the question solving process of the question to be explained, so that the obtained knowledge point of the question to be explained is more comprehensive and accurate, and the follow-up pushing of the explained video is facilitated.
It should be understood that, since there are many ways to obtain the knowledge points of the subject to be explained, each way may be the same or different, so that the subject to be explained may actually have one or at least two knowledge points.
In the embodiment, a pre-trained first classification model is used for acquiring knowledge points of the questions to be explained according to the question contents; and/or acquiring knowledge points of the questions to be explained by using a pre-trained second classification model according to the question answering step; and/or acquiring knowledge points of the questions to be explained by utilizing a pre-trained third classification model according to the question solving process. In addition, the first classification model, the second classification model and the third classification model include a text classification model and a natural language understanding model, and since the text classification model is classified according to character contents, but the same knowledge point may have different expression modes, for example, the knowledge point of "chicken rabbit is in cage" is not necessarily defined by "chicken rabbit", and after the text classification model is combined with the natural language understanding model, even if the expression modes are different, the text classification model and the natural language understanding model can be accurately classified into the same knowledge point.
It is to be understood that the region recognition model, the first character recognition model, the second character recognition model, the third character recognition model, the first classification model, the second classification model, and the third classification model in the present embodiment may be any known neural network model. The neural network model may be pre-trained by any known method using a large number of training samples, according to the specific required inputs and outputs.
Further, the corresponding explanation video is pushed according to the knowledge points of the topic to be explained, and as the topic to be explained can have one or at least two knowledge points, the pushed explanation video can also be one or at least two.
In some embodiments, the instructional video is triggered to play only when it is triggered for educational/learning effect. For example, the user may think about the procedure of solving the problem by himself, and when the user needs to view the explanation video, a play instruction is sent (for example, by operating a specific operation device or a specific area in a display screen of a display device) to play the explanation video.
Fig. 2a is a first schematic view of an explanation video pushed according to the knowledge point of the topic to be explained provided in this embodiment. As shown in fig. 2a, firstly, the image obtaining device is adopted to take a picture of the topic to be explained, and the region identification model is utilized to determine the topic region where the topic content in the image of the topic to be explained is located. Then, a first character recognition model is adopted to recognize the title content as' how many square meters a school has a circular flower bed with a diameter of 8cm and the area of the flower bed? And (3) repairing a path with the width of 2cm around the flower bed, wherein the area of the path is the square meter ". And then acquiring an explanation video corresponding to the knowledge point of the topic to be explained as 'the same rabbit cage' by using the first classification model according to the topic content, and pushing the explanation video corresponding to the knowledge point of 'the same rabbit cage'. And the user sends the playing instruction by clicking 'explanation', and the explained video is played.
Fig. 2b is a second schematic view of an explanation video pushed according to the knowledge point of the topic to be explained provided in this embodiment. As shown in fig. 2b, the difference from fig. 2a is that after the knowledge point of the topic to be explained is "chicken rabbit is in cage", 5 explanation videos are pushed because the knowledge point of "chicken rabbit is in cage" is associated with 5 explanation videos. And the user sends the playing instruction by clicking the video 1, the video 2, the video 3, the video 4 or the video 5, and the explanation video corresponding to the playing instruction is played.
Fig. 2c is a third schematic view of pushing a corresponding explanation video according to the knowledge point of the topic to be explained provided in this embodiment. As shown in fig. 2c, firstly, the image obtaining device is adopted to take a picture of the question to be explained, and the region identification model is utilized to determine the question region where the question content in the image of the question to be explained is located and the answer region where the answer step is located. Then, the title content is identified as '4 apples in a shop with 55 kg for each apple frame and 135 kg for selling' by using a first character recognition model. How many kilograms of apples there are in store? How many kilograms are left now? "; and recognizing the answer step by adopting a second character recognition model, wherein the answer step is that 4X55 is 220 (kilogram), 220 + 135 is 95 (kilogram), and answering: 95 kg "remaining; generating answer questions according to the question contents of the questions to be explained, wherein the answer questions are generated by the method comprising the following steps: now 85 kg remains "(the problem solving process is now hidden from view). And then acquiring knowledge points of the question to be explained according to the question content, the question answering step and/or the question solving process, and then pushing an explanation video corresponding to the knowledge points.
Further, when an image of a test paper containing the to-be-explained subject is provided, a plurality of the to-be-explained subjects can be identified. To can find corresponding explanation video treat the explanation topic, can not go the corresponding explanation video of propelling movement earlier voluntarily, but wait to carry out the propelling movement and show after the user sends the propelling movement instruction (for example send the propelling movement instruction through clicking specific question of waiting to explain) explanation video, thereby avoid because treat the explanation topic and the video quantity of explaining leads to showing not problem too much.
Further, in this embodiment, after the corresponding explanation video is pushed according to the knowledge point of the topic to be explained, a similar topic of the topic to be explained is also obtained, and the explanation video corresponding to the knowledge point of the similar topic is pushed.
Similar questions of the questions to be explained can be searched in the question bank according to the question contents of the questions to be explained. The method comprises the following specific steps:
searching in the question bank, searching for a characteristic vector matched with the characteristic vector of the question content of the question to be explained, and determining all questions corresponding to the characteristic vectors similar to the characteristic vector of the question content of the question to be explained in the question bank as similar questions of the question to be explained.
In this embodiment, the shortest editing distance between the feature vector of the topic content of the topic to be explained and the feature vector of the topic stem of the topic in the topic library is used as the condition for judging the similar topic, for example, when the shortest editing distance between the feature vector of the topic stem of the topic in the topic library and the feature vector of the topic stem of the topic to be explained is less than a set value, it can be determined that the topic belongs to the similar topic of the topic to be explained, that is, all the topics with the shortest editing distance less than the set value are the similar topics of the topic to be explained.
It should be understood that, for the to-be-explained topic not containing the picture, the feature vector of the text part of the topic content of the to-be-explained topic is directly used as the feature vector of the topic content of the to-be-explained topic, and for the to-be-explained topic containing the picture of the topic content, the feature vector of the picture of the topic content of the to-be-explained topic and the feature vector of the text part of the topic content can be spliced to serve as the feature vector of the topic content of the to-be-explained topic, and the description is not explained one by one here.
As an optional embodiment, a similar question of the question to be explained can be searched out in the question bank by utilizing the question solving process and/or the question answering process of the question to be explained.
Specifically, the question solving process/question answering process is reversely input into the question solving model, and a plurality of characteristics of the question solving process/question answering process are obtained. And then searching in the question bank, and determining the question similar to at least one characteristic of the question solving process as the similar question of the question to be explained.
Similar questions of the questions to be explained can be searched in the question bank according to the question solving process/question answering process. The method comprises the following specific steps:
next, the content of the problem solving process is identified by using the pre-trained first neural network model, and the content of the problem solving process is, for example, the text content and/or the picture content of the problem solving process.
And reversely inputting the content of the question solving process/question answering process into the question solving model to obtain a plurality of characteristics of the question solving process/question answering process. The input of the solution model is the content of the solution process/answer process, and the output is the corresponding characteristic of the solution process/answer process.
And then searching in the question bank, and determining the question similar to at least one characteristic of the question solving process/question answering process as the similar question of the question to be explained.
It should be understood that the question content of the question to be explained and the question solving process/answering process of the question to be explained can be used alone for searching out the similar question of the question to be explained in the question bank, and in some cases, can also be used in combination for searching out the similar question of the question to be explained in the question bank, so as to improve the accuracy and the comprehensiveness of the search.
Further, there may be one or at least two similar topics to be explained, and there may also be one or at least two explained videos pushed according to the similar topics to be explained. After the explanation videos corresponding to the knowledge points of the similar topics are pushed, the user can also play the corresponding explanation videos by sending the playing instruction.
Fig. 2d is a fourth schematic view of pushing a corresponding explanation video according to the knowledge point of the topic to be explained. As shown in fig. 2d, firstly, the image obtaining device is adopted to take a picture of the topic to be explained, and the region identification model is utilized to determine the topic region where the topic content in the image of the topic to be explained is located. Then, the first character recognition model is adopted to recognize that the title content is that the radius of a circle is enlarged to 3 times of the original radius, and the perimeter and the area are enlarged to 3 times of the original radius. Identifying 3 similar questions of the question to be explained according to the question content of the question to be explained, wherein the similar questions are 'same-type question one', 'same-type question two' and 'same-type question three'. The user can display the explanation video corresponding to the similar topic one by clicking the similar topic one.
Further, in this embodiment, before the video is explained by pushing, the method further includes determining whether the question to be explained is a wrong question according to the answer step of the question to be explained, when the question to be explained is a wrong question, actively pushing the video to be explained of the wrong question, and selecting whether to push the video to be explained according to the requirement of the user for the correct question, for example, actively pushing the video to be explained after receiving the push instruction sent by the user.
Specifically, when some questions to be explained are wrong questions, the explanation video of the wrong questions can be actively pushed without waiting for the push instruction of the user.
Fig. 2e is a fourth schematic view of pushing a corresponding explanation video according to the knowledge point of the topic to be explained. As shown in fig. 2e, the answer step of the question to be explained is identified as "26 +59-42+ 10-26 +16+ 10-42 + 10-52" by using the second character recognition model according to the image of the answer area, and the generated answer step is "26 +59-42+ 10-26 +17+ 10-43 + 10-53", and the question to be explained is judged to be a wrong question by comparing the features of the answer step and the answer step or comparing the features of the answer and the answer to solve the question, and then the explanation video of the wrong question is automatically pushed.
Corresponding to the embodiment of the title searching method, the embodiment provides a title explaining system. Fig. 3 is a block diagram of a topic interpretation system provided in this embodiment. As shown in fig. 3, the title explaining system includes:
the association module 10 is used for associating the knowledge point with the explanation video in the question bank; and the number of the first and second groups,
and the pushing module 20 is configured to identify the topic to be explained, and push the corresponding explanation video according to the knowledge point of the topic to be explained.
Further, the embodiment provides an electronic device which can be used for topic explanation. Fig. 4 is a block diagram of the electronic device provided in this embodiment, and as shown in fig. 4, the electronic device includes:
one or more processors 301;
a memory 302 for storing one or more programs;
when executed by one or more of the processors 301, the one or more programs enable the one or more processors 301 to implement the title interpretation method as set forth in the above embodiments.
In this embodiment, the processor 301 and the memory 302 are both one, the electronic device further includes a communication interface 303, and the processor 301, the memory 302, and the communication interface 303 complete mutual communication through a communication bus 304. The communication bus 304 mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 304 may be divided into an address bus, a data bus, a control bus, and the like.
The memory 302 is used as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the title interpreting method in the embodiment of the present invention. The processor 301 executes various functional applications and data processing of the electronic device by executing the software programs, instructions and modules stored in the memory 302, so as to implement the title explaining method.
The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 302 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 examples, the memory 302 may further include memory located remotely from the processor 301, which may be connected to the electronic 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 electronic device provided by the embodiment belongs to the same inventive concept as the title teaching method provided by the above embodiment, and technical details that are not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.
The present embodiment provides a storage medium on which a computer program is stored, which when executed by the processor 301 implements the title explaining method as set forth in the above embodiments.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods of the embodiments of the present invention.
In summary, in the topic explanation method, system, electronic device and computer-readable storage medium provided in this embodiment, the knowledge point is associated with the explanation video in the topic database, and then the corresponding explanation video is pushed according to the knowledge point of the topic to be explained. Therefore, the pushed explanation videos are matched with the knowledge points of the questions to be explained, users can take wrong questions or questions which cannot be made as the questions to be explained, and the pushed explanation videos are checked to understand the relevant knowledge points until the knowledge points are completely mastered, so that examination scores are improved.
It should be noted that, in the present specification, all the embodiments are described in a related manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the apparatus, the electronic device, and the computer-readable storage medium, since they are substantially similar to the embodiments of the method, the description is simple, and for the relevant points, reference may be made to the partial description of the embodiments of the method.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (16)
1. A topic interpretation method, comprising:
associating the knowledge point with the explanation video in the question bank; and the number of the first and second groups,
identifying the topic to be explained, and pushing the corresponding explanation video according to the knowledge point of the topic to be explained.
2. The topic explanation method according to claim 1, characterized in that when associating the knowledge point with the explanation video in the question bank, one knowledge point associates one or at least two of the explanation videos; and/or, one or at least two knowledge points are associated with one explained video.
3. The title interpreting method according to claim 1, wherein said title to be interpreted has one or at least two knowledge points.
4. The title explaining method of any one of claims 1 to 3, characterized in that knowledge points of the title to be explained are obtained according to the identified title content of the title to be explained; and/or acquiring knowledge points of the question to be explained according to the identified answering step of the question to be explained; and/or generating a problem solving process according to the identified problem content of the problem to be explained, and acquiring the knowledge point of the problem to be explained according to the problem solving process.
5. The title explaining method of claim 4, wherein an image of a title region of the title to be explained is acquired, and a first character recognition model trained in advance is used to recognize the title content of the title to be explained; and/or acquiring an image of the answer area of the question to be explained, and identifying the answer of the question to be explained by utilizing a pre-trained second character recognition model.
6. The topic explanation method according to claim 4, characterized in that knowledge points of the topic to be explained are obtained by using a first classification model trained in advance according to the topic content; and/or acquiring knowledge points of the questions to be explained by using a pre-trained second classification model according to the question answering step; and/or acquiring knowledge points of the questions to be explained by utilizing a pre-trained third classification model according to the question solving process.
7. The topic interpretation method of claim 6, wherein the first classification model, the second classification model and the third classification model each comprise a text classification model and a natural language understanding model.
8. The topic interpretation method of claim 4, wherein the step of generating the problem solving process from the topic content comprises:
determining a problem solving model according to the problem content; and the number of the first and second groups,
and generating the problem solving process according to the problem solving model.
9. The topic explanation method according to claim 4, characterized in that before the video for explanation is pushed, it is further determined whether the topic to be explained is a wrong topic according to the step of identifying the answer of the topic to be explained, and when the topic to be explained is a wrong topic, the video for explanation of the wrong topic is pushed.
10. The topic explanation method according to claim 1, characterized in that when a plurality of topics to be explained are identified, an explanation video of the topics to be explained corresponding to a push instruction is pushed in response to a push instruction.
11. The topic explanation method according to claim 1, after pushing a corresponding explanation video according to the knowledge point of the topic to be explained, further comprising:
and acquiring similar topics of the topics to be explained, and pushing an explanation video corresponding to the knowledge points of the similar topics.
12. The title explaining method of claim 1 or 11, wherein the explaining video corresponding to the playing instruction is played in response to a playing instruction.
13. A topic interpretation system, comprising:
the association module is used for associating the knowledge point with the explanation video in the question bank; and the number of the first and second groups,
and the pushing module is used for identifying the topic to be explained and pushing the corresponding explanation video according to the knowledge point of the topic to be explained.
14. An electronic device, comprising:
one or more actuators; and the number of the first and second groups,
a memory for storing one or more programs; and the number of the first and second groups,
when executed by the one or more actors, cause the one or more actors to implement a topic interpretation method as recited in any of claims 1-12.
15. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when executed by a processor, the computer program implements a topic interpretation method according to any one of claims 1 to 12.
16. A computer program product comprising instructions which, when executed by a processor, implement a topic interpretation method as claimed in any one of claims 1 to 12.
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