CN114896513A - Learning content recommendation method, device, equipment and storage medium - Google Patents
Learning content recommendation method, device, equipment and storage medium Download PDFInfo
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Abstract
The present disclosure relates to a learning content recommendation method, apparatus, device, and storage medium, the method comprising: displaying a three-dimensional scene, wherein the three-dimensional scene comprises at least one three-dimensional model; responding to the triggering operation of any three-dimensional model in the three-dimensional scene, and acquiring model information corresponding to the three-dimensional model; acquiring target learning content matched with the model information; recommending the target learning content to the user. According to the embodiment of the method and the device, the three-dimensional model is selected through triggering operation of the three-dimensional model in the three-dimensional scene, the learning content needing to be recommended to the student is determined based on the model information of the three-dimensional model, and the recommendation of the learning content in the three-dimensional scene is achieved, so that the student can obtain more comprehensive information, and the curiosity of the student is met.
Description
Technical Field
The present disclosure relates to the field of computer processing technologies, and in particular, to a learning content recommendation method, apparatus, device, and storage medium.
Background
With the continuous development of virtual reality technology and/or augmented reality, a three-dimensional scene is constructed through a three-dimensional model, so that a user can feel a plurality of scenes more intuitively to a great extent, and the user experience is improved.
The three-dimensional scene is applied to the teaching field and the three-dimensional full real classroom. In a three-dimensional full-real classroom, students take lessons in a 3D scene in the form of virtual characters and follow virtual teachers. In a three-dimensional full-real classroom, a three-dimensional scene can be constructed according to teaching material contents, images and the like, so that students can learn personally on the scene, and the learning interest of the students is improved.
In a three-dimensional truthful classroom, students can freely walk in a 3D scene by virtual roles, and can not timely acquire more comprehensive information when meeting interested contents, so that the curiosity of the students is met.
Disclosure of Invention
In order to solve the above technical problem, embodiments of the present disclosure provide a learning content recommendation method, apparatus, device, and storage medium, which determine learning content to be recommended to a student based on model information in a three-dimensional scene, and implement recommendation of the learning content in the three-dimensional scene, so that the student obtains more comprehensive information and satisfies curiosity of the student.
In a first aspect, an embodiment of the present disclosure provides a learning content recommendation method, including:
displaying a three-dimensional scene, wherein the three-dimensional scene comprises at least one three-dimensional model;
responding to the triggering operation of any three-dimensional model in the three-dimensional scene, and acquiring model information corresponding to the three-dimensional model;
acquiring target learning content matched with the model information;
recommending the target learning content to the user.
In a second aspect, an embodiment of the present disclosure provides a learning content recommendation apparatus, including:
the system comprises a scene page display module, a scene page display module and a scene processing module, wherein the scene page display module is used for displaying a three-dimensional scene, and the three-dimensional scene comprises at least one three-dimensional model;
the model information acquisition module is used for responding to the triggering operation of any three-dimensional model in the three-dimensional scene and acquiring the model information corresponding to the three-dimensional model;
the learning content acquisition module is used for acquiring target learning content matched with the model information;
and the learning content recommending module is used for recommending the target learning content to the user.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the learning content recommendation method of any one of the first aspects described above.
In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the learning content recommendation method according to any one of the first aspects described above.
The embodiment of the disclosure provides a learning content recommendation method, a learning content recommendation device, learning content recommendation equipment and a storage medium, wherein the method comprises the following steps: displaying a three-dimensional scene, wherein the three-dimensional scene comprises at least one three-dimensional model; responding to the triggering operation of any three-dimensional model in the three-dimensional scene, and acquiring model information corresponding to the three-dimensional model; acquiring target learning content matched with the model information; recommending the target learning content to the user. According to the embodiment of the method and the device, the three-dimensional model is selected through triggering operation of the three-dimensional model in the three-dimensional scene, the learning content needing to be recommended to the student is determined based on the model information of the three-dimensional model, and recommendation of the learning content in the three-dimensional scene is achieved, so that the student can obtain more comprehensive information, and curiosity of the student is met.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
Fig. 1 is a schematic flowchart of a learning content recommendation method in an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a learning content recommendation method in an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of a learning content recommendation method in an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a learning content recommendation device in an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device in an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are illustrated in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and the embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The following describes in detail a method for adjusting data sequence according to an embodiment of the present application with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a learning content recommendation method in an embodiment of the present disclosure, where the embodiment is applicable to a case of learning content recommendation based on a three-dimensional scene, and the method may be executed by a learning content recommendation device, where the learning content recommendation device may be implemented in a software and/or hardware manner, and the learning content recommendation device may be configured in an electronic device.
For example: the electronic device may be a mobile terminal, a fixed terminal, or a portable terminal, such as a mobile handset, a station, a unit, a device, a multimedia computer, a multimedia tablet, an internet node, a communicator, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a Personal Communication Systems (PCS) device, a personal navigation device, a Personal Digital Assistant (PDA), an audio/video player, a digital camera/camcorder, a positioning device, a television receiver, a radio broadcast receiver, an electronic book device, a gaming device, or any combination thereof, including accessories and peripherals of these devices, or any combination thereof.
The following steps are repeated: the electronic device may be a server, where the server may be an entity server or a cloud server, and the server may be one server or a server cluster.
As shown in fig. 1, the learning content recommendation method provided by the embodiment of the present disclosure mainly includes steps S101 to S104.
S101, displaying a three-dimensional scene, wherein the three-dimensional scene comprises at least one three-dimensional model.
The three-dimensional scene is information such as various material forms and spatial relations of the real world, which is actually simulated by using a virtualization technology. Three-dimensional scenes generally include two view modes, planar scenes and spherical scenes. The plane scene means that the earth sphere is unfolded into a plane, the whole earth is simulated, and the scene display is carried out in a form similar to a plane; the spherical scene is a three-dimensional scene in which a scene of the earth surface is simulated and displayed by a sphere. In the embodiment of the present disclosure, the display manner of the three-dimensional scene is not limited, the three-dimensional scene may be displayed on an interface of the electronic device, or a virtual three-dimensional scene may be displayed in a real scene by using devices such as a virtual reality VR, an augmented reality AR, and a mixed reality MR.
In an embodiment of the present disclosure, the three-dimensional scene may be a three-dimensional classroom scene. The three-dimensional classroom scene related in the embodiment of the disclosure comprises at least one virtual object and a plurality of three-dimensional models. The virtual object may be a virtual character controlled by the user through the user terminal, and the virtual character may execute a corresponding action according to a control instruction of each user terminal. For example: in a three-dimensional classroom scene, a student takes a class in the three-dimensional scene in a virtual role form and follows a virtual teacher, and the student freely walks in the three-dimensional scene in a virtual role form in a post-class time period.
In one embodiment of the present disclosure, the three-dimensional model may be a prop or a Non-Player Character (NPC) preset in a three-dimensional classroom by a scene developer, for example: virtual lectures, virtual blackboards, virtual tables, and the like.
In one embodiment of the present disclosure, the three-dimensional scene may be changed according to a knowledge point that a teacher gives lessons, that is, the knowledge point is constructed and displayed in the form of a three-dimensional scene. The three-dimensional scene can also be constructed and displayed according to the learning content of the students. For example: when the learning content is ancient poem 'little lotus just reveal sharp corner, early the dragonfly sets up the top', can construct the ancient poem three-dimensional scene of "tender and lovely little lotus just reveal the sharp corner from the surface of water, early the little dragonfly of a skin of transferring sets up its top", the three-dimensional model that mainly includes in this three-dimensional scene is: lotus leaf, water surface, dragonfly, lotus, etc. Therefore, the students have a feeling of being personally on the scene in the learning process, and the learning interest of the students is improved.
S102, responding to the triggering operation of a user on any three-dimensional model in the three-dimensional scene, and obtaining model information corresponding to the three-dimensional model.
In the embodiment of the disclosure, in a post-class time period, a student freely walks in a three-dimensional scene in a virtual role mode, and can perform triggering operation on any one three-dimensional model in the three-dimensional scene. The triggering operation may be a click operation of the three-dimensional model by the student. The click operation may be responded by an input device built in the electronic device, for example: the clicking operation may touch the touchable display screen, may press a physical key or lever of the electronic device, or the like. The click operation may be responded by an input device externally connected to the electronic device, for example: the input device may be a mouse, a keyboard, etc. Further, when the electronic device is any one of VR, AR, and MR, the input device may also be an image capturing device, the image capturing device captures a gesture posture of the student, and when the gesture posture is a gesture corresponding to a click operation, a click operation on the three-dimensional model is responded to, where the gesture corresponding to the click operation may be set according to an actual setting, for example: the gesture corresponding to the clicking operation may be a pinch of a thumb tip and an index finger tip.
In the embodiment of the present disclosure, the model information corresponding to the three-dimensional model may be mainly understood as description information of the three-dimensional model. For example: the lotus leaf, the water surface, the dragonfly, the lotus flower and the like in the ancient poem three-dimensional scene are model information corresponding to the three-dimensional model. In the embodiment of the present disclosure, the three-dimensional models in the three-dimensional scene only exist in the form of model images, and the model images corresponding to the three-dimensional models are not saved in the three-dimensional scene. Therefore, in the embodiment of the present disclosure, after the triggering operation on the three-dimensional model is responded, the model information corresponding to the three-dimensional model needs to be acquired.
In one embodiment of the present disclosure, the correspondence between the three-dimensional model and the model information may be established in advance. Specifically, after responding to the trigger operation of the three-dimensional model, a model ID corresponding to the three-dimensional model is acquired, the model ID is searched in a pre-established correspondence based on the model ID, and the searched model information corresponding to the model ID is determined as the model information corresponding to the three-dimensional model.
In one embodiment of the disclosure, an input control is displayed in response to a trigger operation on a three-dimensional model, input information is acquired in response to an input operation on the input control, and the input information is used as model information corresponding to the three-dimensional model.
In one embodiment of the disclosure, in response to a trigger operation on a three-dimensional model, an image of the three-dimensional model is identified through an image identification model, and the identification result is used as model information corresponding to the three-dimensional model.
In one embodiment of the present disclosure, in response to a trigger operation on any one three-dimensional model in the three-dimensional scene, obtaining model information corresponding to the three-dimensional model includes: responding to the triggering operation of any three-dimensional model in the three-dimensional scene, and acquiring a model identifier corresponding to the three-dimensional model; inquiring in a preset model information base based on the model identification to obtain an inquiry result; and determining model information corresponding to the three-dimensional model based on the query result.
In the embodiment of the present disclosure, the three-dimensional model and the model identifier are in a one-to-one correspondence relationship, and the model identifier refers to an identifier used for uniquely characterizing the three-dimensional model, for example: the model identification may be a model ID.
In one embodiment of the present disclosure, an association relationship between the three-dimensional model and the model identifier is established in advance. Responding to the trigger operation of the three-dimensional model, and determining a model identifier corresponding to the three-dimensional model based on the incidence relation and the three-dimensional model corresponding to the trigger operation.
In this disclosure, the preset model information base includes a model identifier and model information corresponding to the model identifier, where one model information may include one or more words describing the three-dimensional model. For example: the model information corresponding to the model ID 2 is autumn and playground.
In the embodiment of the disclosure, the model identifier is used for querying in a preset model information base to obtain a query result, and the model information corresponding to the three-dimensional model is determined according to the query result.
In one embodiment of the present disclosure, determining, based on the query result, model information corresponding to the three-dimensional model includes: in response to the fact that the query result is determined to be query failure, photographing the three-dimensional model to obtain a model image corresponding to the three-dimensional model; carrying out image recognition on the model image to obtain key information of the model image;
in an embodiment of the present disclosure, if the query result is that the query is successful, it indicates that the model information corresponding to the model ID is stored in the preset model information base in advance, and the query result may carry the model information as the model information corresponding to the three-dimensional model corresponding to the trigger operation. The preset model information base can be established in advance by professional personnel.
In an embodiment of the present disclosure, if the query result is a query failure, it indicates that the preset model information base does not store the model information corresponding to the model ID, and at this time, a photographing function of the virtual camera is called to photograph the three-dimensional model, or a screen capture function of the electronic device is called to perform screen capture processing on the three-dimensional model, so as to obtain a model image corresponding to the three-dimensional model. For example: when the three-dimensional model is a lotus three-dimensional model, a lotus image is obtained.
In an embodiment of the present disclosure, when the three-dimensional model is photographed, the photographing may be performed according to a current viewing angle of the virtual student as a photographing origin to obtain a model image. The three-dimensional model can be photographed, and a plurality of model images at different angles can be photographed by controlling the virtual camera to continuously move along with the current visual angle of the virtual student. Thus, a plurality of model images can be identified to acquire more accurate model information.
The key information can be understood as an image result obtained after the model image is subjected to image recognition. The key information may be a processed image result or an unprocessed image result. The processed image result may be understood as removing useless information in the image result, for example: and eliminating adjectives, adverbs and quantifiers in the image result. For example: the image result is a red lotus, the quantifier 'one' and the adjective 'red' are removed, and the noun 'lotus' is used as key information. It should be noted that any image recognition model may be used to perform image recognition on the model image. The neural network and the training method used by the image recognition model are not specifically limited in this embodiment.
In one embodiment of the present disclosure, after determining key information of a model image as model information corresponding to the three-dimensional model, the method further includes: and establishing a corresponding relation between the model identification and the model information, and storing the corresponding relation into a preset model information base so that a subsequent user can conveniently acquire the model information.
S103, acquiring target learning content matched with the model information.
In the embodiment of the present disclosure, the target learning content matched with the model information may be understood as learning resources such as knowledge points, practice problems, extension training, video explanation, image explanation, and the like related to the model information.
In one embodiment of the present disclosure, the model information is used as a keyword to search in the internet and/or a pre-constructed knowledge graph, and the searched content is used as target learning content matched with the model information.
In one embodiment of the present disclosure, acquiring target learning content matched with the model information includes: determining a first keyword based on the model information; searching based on the first keyword to obtain a first search result; determining a second keyword based on the user preference information and/or the scene information; searching in the first search result based on the second keyword to obtain a target search result; and determining the target search result as target learning content matched with the model information.
In one embodiment of the present disclosure, preference information corresponding to the user is acquired; and/or acquiring scene information corresponding to the three-dimensional scene; searching based on the model information, the preference information and/or the scene information to obtain a target search result; and determining the target search result as target learning content matched with the model information.
In the embodiment of the present disclosure, the preference information corresponding to the user may be understood as some information characterizing characteristics of the user, for example: user gender, user grade, user historical preferences, and the like. The preference information corresponding to the user is information that can be used for learning content screening after being authorized by the user.
In the embodiment of the present disclosure, the scene information may be understood as information characterizing scene content of a three-dimensional scene. For example: in the ancient poetry three-dimensional scene, the scene information can be ancient poetry, and can also be information which indicates scene contents such as a lotus pool, summer and the like.
In one embodiment of the disclosure, the model information and the preference information are used as keywords to search in the internet and/or a pre-constructed knowledge graph, and the search result is used as target learning content. For example: keywords may be "lotus" and "third grade".
In one embodiment of the disclosure, the model information and the scene information are used as keywords to search in the internet and/or a pre-constructed knowledge graph, and the search result is used as target learning content. For example: the keywords may be "lotus" and "ancient poem".
In one embodiment of the disclosure, the model information, the preference information and the scene information are used as keywords to search in the internet and/or a pre-constructed knowledge graph, and the search result is used as target learning content. For example: the keywords may be "lotus", "third grade" and "ancient poem".
In the embodiment of the present disclosure, the first keyword is determined based on the model information, the model information may be directly used as the first keyword, or a word with a similar meaning may be taken as the first keyword after the model information is subjected to word segmentation or fuzzy meaning. For example: the model information is the ' lotus ', the ' lotus ' can be directly used as a first keyword, and the ' lotus ' fuzzy word meaning ' Han ' 33807 ', ' lotus ' and the like can be obtained as the first keyword.
In one embodiment of the disclosure, a search is conducted in the internet and/or a pre-constructed knowledge graph based on a first keyword, and the search result is taken as a first search result. The first search result is obtained after the first keyword is used for searching.
In the embodiment of the disclosure, the second keyword is determined based on the preference information corresponding to the user, or the second keyword is determined according to the scene information, or the second keyword is determined based on the preference information corresponding to the user and the scene information. The second keyword may be one word or a group of multiple words, and is not specifically limited in this embodiment.
It should be noted that, a determination manner of the second keyword is similar to that of the first keyword, and specifically, reference may be made to the description in the foregoing embodiment, and this embodiment is not limited in detail again.
In one embodiment of the disclosure, the first search result is searched again based on the second keyword, and the learning content suitable for the student in the scene is screened out from the massive information.
In one embodiment of the present disclosure, as shown in fig. 2, a virtual student selects a three-dimensional model in a three-dimensional scene S201, and acquires model information corresponding to the three-dimensional model S202. S203, searching the related knowledge points in the network according to the model information. S204, acquiring learning preference information including dimension information such as gender, grade, historical preference and the like. And S205, acquiring scene information at the same time. S206, taking the personal information and the scene information of the student as input, and screening out the knowledge points suitable for the student in the scene from the mass information in the network. And S207, recommending the screened knowledge points (in the forms of texts, images, audios, videos and the like) in a personalized manner and presenting the knowledge points to students.
And S104, recommending the target learning content to the user.
In the embodiment of the present disclosure, the target learning content is displayed to the student in the form of text, image, voice, or video, so that the student obtains the personalized learning content.
The embodiment of the disclosure provides a learning content recommendation method, which includes: displaying a three-dimensional scene, wherein the three-dimensional scene comprises at least one three-dimensional model; responding to the triggering operation of a user on any one three-dimensional model in the three-dimensional scene, and acquiring model information corresponding to the three-dimensional model; acquiring target learning content matched with the model information; recommending the target learning content to the user. According to the embodiment of the method and the device, the three-dimensional model is selected through triggering operation of the three-dimensional model in the three-dimensional scene, the learning content needing to be recommended to the student is determined based on the model information of the three-dimensional model, and recommendation of the learning content in the three-dimensional scene is achieved, so that the student can obtain more comprehensive information, and curiosity of the student is met.
On the basis of the above embodiments, the learning content recommendation method is specifically optimized in the embodiments of the present disclosure, and as shown in fig. 3, the optimized learning content recommendation method in the embodiments of the present disclosure mainly includes steps S301 to S306.
S301, displaying a three-dimensional scene, wherein the three-dimensional scene comprises at least one three-dimensional model.
S302, responding to the triggering operation of a user on any one three-dimensional model in the three-dimensional scene, and obtaining model information corresponding to the three-dimensional model.
And S303, acquiring target learning content matched with the model information.
S301 to S303 in the embodiment of the present disclosure are the same as the operation steps of S101 to S103 provided in the above embodiment, and reference may be made to the description in the above embodiment specifically, and this embodiment is not limited in detail.
S304, classifying the target learning content according to a preset classification mode to obtain one or more classification categories.
In the embodiment of the present disclosure, a classification manner is preset, and the classification manner is mainly used for classifying the target learning content.
For example: the preset classification mode is classified according to the types of the learning contents: ancient poems, American languages, practice problems, news and the like, and can also be classified according to the grade, for example: first grade, fifth grade, ninth grade, etc. This may be classified by textbook version, for example: human, Shandong, Su and so on.
In one embodiment of the present disclosure, the classification manners are preset, and in response to a content classification trigger event, the target learning content is classified according to the preset classification manner. The content classification triggering event may be a content classification triggering event automatically generated after the target content is detected to be pulled from the internet and/or a knowledge base. The content classification triggering event may be a content classification triggering event generated after a triggering operation of the content classification control by the user is detected.
It should be noted that, if a content classification triggering event is not detected, the target learning content is displayed in the order of decreasing association degree with the keyword.
S305, displaying the one or more classification categories and target learning content corresponding to each classification category in a content display area of the three-dimensional scene.
In the embodiment of the disclosure, a content display area is displayed in a three-dimensional scene, and the content display area is used for displaying target learning content to a user for viewing.
The number of the target learning content corresponding to the classification category may be set according to an actual situation, for example: the predetermined number may be 3 or 5, or other values. The target learning content corresponding to the classification category can be understood as the target learning content associated with the category
In one embodiment of the present disclosure, the target learning content is displayed in a classified manner. Specifically, the target learning content is divided into 3 categories: category 1, category 2, category 3. Each category comprises target learning content corresponding to the category, and the category 1 comprises target learning content 11, target learning content 12 and target learning content 13; category 2 includes target learning content 21, target learning content 22, target learning content 23; category 3 includes target learning content 31, target learning content 32, and target learning content 33.
And displaying the classification categories and the target learning contents corresponding to the classification categories in a content display area so as to be convenient for clearly knowing various different learning contents.
In one embodiment of the present disclosure, the method further comprises: determining the quantity of target learning contents corresponding to a first classification category of the one or more classification categories, wherein the first classification category is any one of the one or more classification categories; when the number of the target learning contents corresponding to the first classification category is not larger than a preset display number threshold value, displaying all the target learning contents corresponding to the first classification category in the content display area; and when the number of the target learning contents corresponding to the first classification category is larger than a preset display number threshold, displaying all the target learning contents corresponding to the first classification category in a content display area in a paging mode.
In the embodiment of the present disclosure, the category may be subjected to a triggering operation, and the triggering operation on the category may be a clicking operation on the category or a triggering operation on an expansion control corresponding to the category. And responding to the triggering operation of the user on the category, and displaying all target learning contents corresponding to the category in the content display area.
In one embodiment of the present disclosure, to avoid an influence on the three-dimensional scene, the content display area is a portion of the three-dimensional scene, which may not display all target learning content corresponding to one category.
The preset display number threshold may be determined according to the size of the content display area. For example: the content presentation area may present 10-item target learning content. And if the number of the target learning contents corresponding to the first classification category is less than 10, displaying all the target learning contents in the content display area, and if the number of the target learning contents corresponding to the first classification category is more than 10, displaying the target learning contents in the content display area in a paging manner.
In the embodiment of the present disclosure, in response to a trigger operation of a user on the category, target learning content subsequent to the first category is displayed in a content display area, so that the user can obtain more and richer learning content included under the category.
The embodiment of the disclosure provides a learning content recommendation method, which includes: displaying a three-dimensional scene, wherein the three-dimensional scene comprises at least one three-dimensional model; responding to the triggering operation of a user on any one three-dimensional model in the three-dimensional scene, and acquiring model information corresponding to the three-dimensional model; acquiring target learning content matched with the model information; classifying the target learning content according to a preset classification mode to obtain one or more classification categories; and displaying the one or more classification categories and target learning content corresponding to each classification category in a content display area of the three-dimensional scene. According to the method and the device for recommending the learning content, the three-dimensional model is selected through triggering operation of the three-dimensional model in the three-dimensional scene, the learning content needing to be recommended to students is determined based on the model information of the three-dimensional model, the learning content is classified and then displayed to the user, and the user is aware of the fact that the user can rapidly and conveniently acquire the needed learning content.
In an embodiment of the present disclosure, the above embodiment is further optimized, and the optimized learning content recommendation method further includes: the one or more classification categories include an image category, wherein the method further comprises: displaying the target learning content of the image category in the content display area in a first size; and displaying the target learning content in a second size in response to a triggering operation on the target learning content of the image category, wherein the first size is smaller than the second size.
In the embodiment of the present disclosure, the first size may be a size of a thumbnail, and the second size may be a size of an original of an image corresponding to the target learning content, or a size of an enlarged thumbnail.
In the embodiment of the present disclosure, if the target learning content is of an image type, that is, the target learning content is on one image, the image is displayed in a content display area together with other target learning content in the form of a thumbnail. In this way, more target learning contents can be presented in the content presentation area, so that the user views more learning contents.
In one embodiment of the present disclosure, in response to a triggering operation on the image, for example: and clicking to display the image after the image is amplified. The enlargement ratio can be determined according to the size of the content display area, that is, the image can be spread to occupy the whole content display area after enlargement. In this way, the image is displayed after being enlarged, and the user can view detailed information in the image.
In an embodiment of the present disclosure, the above embodiment is further optimized, and the optimized learning content recommendation method further includes: the one or more category categories include a video category, wherein the method further comprises: displaying a cover page picture of the target learning content of the video category in the content display area; and responding to the triggering operation of the cover picture, and playing the target learning content of the video category.
In the embodiment of the present disclosure, if the target learning content is a video type, that is, the target learning content exists in a video form, for example: an analytic video of an ancient poem. The video is displayed in a content display area together with other target learning content in the form of a cover page. In this way, more target learning contents can be presented in the content presentation area, so that the user views more learning contents.
In one embodiment of the present disclosure, in response to a triggering operation on the cover sheet, for example: and clicking to operate, receiving a playing instruction corresponding to the video, responding to the playing instruction, and playing the video in the content display area, so that the user can check detailed information in the video and improve the learning interest of students.
Fig. 4 is a schematic structural diagram of a learning content recommendation device in an embodiment of the present disclosure, where the embodiment is applicable to learning content recommendation based on a three-dimensional scene, the learning content recommendation device may be implemented in a software and/or hardware manner, and the learning content recommendation device may be configured in an electronic device.
As shown in fig. 4, the learning content recommendation apparatus 40 provided in the embodiment of the present disclosure mainly includes a scene page presentation module 41, a model information acquisition module 42, a learning content acquisition module 43, and a learning content recommendation module 44.
The scene page display module 41 is configured to display a three-dimensional scene, where the three-dimensional scene includes at least one three-dimensional model; the model information acquiring module 42 is configured to respond to a triggering operation of a user on any one three-dimensional model in the three-dimensional scene, and acquire model information corresponding to the three-dimensional model; a learning content obtaining module 43, configured to obtain target learning content matched with the model information; a learning content recommending module 44, configured to recommend the target learning content to the user.
In one embodiment of the present disclosure, the model information obtaining module 42 includes: the model identification obtaining unit is used for responding to the triggering operation of any one three-dimensional model in the three-dimensional scene to obtain a model identification corresponding to the three-dimensional model; the query result acquisition unit is used for querying in a preset model information base based on the model identification to obtain a query result; and the model information acquisition unit is used for determining model information corresponding to the three-dimensional model based on the query result.
In an embodiment of the present disclosure, the model information obtaining unit is specifically configured to, in response to determining that the query result is a query failure, take a picture of the three-dimensional model to obtain a model image corresponding to the three-dimensional model; carrying out image recognition on the model image to obtain key information of the model image; and determining the key information of the model image as the model information corresponding to the three-dimensional model.
In one embodiment of the present disclosure, the apparatus further comprises: the classification module is used for classifying the target learning content according to a preset classification mode to obtain one or more classification categories; and the learning content display module is used for displaying the one or more classification categories and the target learning content corresponding to each classification category in a content display area of the three-dimensional scene.
In an embodiment of the present disclosure, the learning content presentation module is specifically configured to determine, for a first classification category of the one or more classification categories, a target learning content number corresponding to the first classification category, where the first classification category is any classification category of the one or more classification categories; when the number of the target learning contents corresponding to the first classification category is not larger than a preset display number threshold value, displaying all the target learning contents corresponding to the first classification category in the content display area; and when the number of the target learning contents corresponding to the first classification category is larger than a preset display number threshold, displaying all the target learning contents corresponding to the first classification category in a content display area in a paging mode.
In one embodiment of the present disclosure, the one or more classification categories include an image category, and the learning content presentation module is specifically configured to present, in the content presentation area, a target learning content of the image category in a first size; and displaying the target learning content in a second size in response to a triggering operation on the target learning content of the image category, wherein the first size is smaller than the second size.
In one embodiment of the present disclosure, the one or more classification categories include a video category, and the learning content display module is specifically configured to display a cover page of target learning content of the video category in the content display area; and responding to the triggering operation of the cover picture, and playing the target learning content of the video category.
In an embodiment of the present disclosure, the learning content recommendation module 44 is specifically configured to determine a first keyword based on the model information; searching based on the first keyword to obtain a first search result; determining a second keyword based on the user preference information and/or the scene information; searching in the first search result based on the second keyword to obtain a target search result; and determining the target search result as target learning content matched with the model information.
The learning content recommendation device provided in the embodiment of the disclosure may execute the steps executed in the learning content recommendation method provided in the embodiment of the disclosure, and the steps and the beneficial effects are not repeated here.
Fig. 5 is a schematic structural diagram of an electronic device in an embodiment of the present disclosure. Referring now specifically to fig. 5, a schematic diagram of an electronic device 500 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device 500 in the disclosed embodiment may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), a wearable terminal device, etc., and a stationary terminal such as a digital TV, a desktop computer, a smart home device, etc. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, the electronic device 500 may include a processing means (e.g., a central processing unit, a graphic processor, etc.) 501, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503 to implement the learning content recommendation method of the embodiment as described in the present disclosure. In the RAM 503, various programs and data necessary for the operation of the terminal apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, Liquid Crystal Displays (LCDs), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 503 may allow the terminal device 500 to perform wireless or wired communication with other devices to exchange data. While fig. 5 illustrates a terminal apparatus 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated in the flow chart, thereby implementing the learning content recommendation method as described above. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 501.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may 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 present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the terminal device, cause the terminal device to: displaying a three-dimensional scene, wherein the three-dimensional scene comprises at least one three-dimensional model; responding to the triggering operation of any three-dimensional model in the three-dimensional scene, and acquiring model information corresponding to the three-dimensional model; acquiring target learning content matched with the model information; recommending the target learning content to the user.
Optionally, when the one or more programs are executed by the terminal device, the terminal device may further perform other steps described in the above embodiments.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may 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 case of a remote computer, the remote computer may 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 may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures 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 flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may 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 illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is 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 a machine-readable storage medium would include an electrical connection based on 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.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other combinations of features described above or equivalents thereof without departing from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (11)
1. A learning content recommendation method, comprising:
displaying a three-dimensional scene, wherein the three-dimensional scene comprises at least one three-dimensional model;
responding to the triggering operation of any three-dimensional model in the three-dimensional scene, and acquiring model information corresponding to the three-dimensional model;
acquiring target learning content matched with the model information;
recommending the target learning content to the user.
2. The method of claim 1, wherein obtaining model information corresponding to any one three-dimensional model in the three-dimensional scene in response to a triggering operation on the three-dimensional model comprises:
responding to the triggering operation of any three-dimensional model in the three-dimensional scene, and acquiring a model identifier corresponding to the three-dimensional model;
inquiring in a preset model information base based on the model identification to obtain an inquiry result;
and determining model information corresponding to the three-dimensional model based on the query result.
3. The method of claim 2, wherein determining model information corresponding to the three-dimensional model based on the query result comprises:
in response to the fact that the query result is determined to be query failure, the three-dimensional model is photographed, and a model image corresponding to the three-dimensional model is obtained;
carrying out image recognition on the model image to obtain key information of the model image;
and determining the key information of the model image as the model information corresponding to the three-dimensional model.
4. The method according to any one of claims 1-3, further comprising:
classifying the target learning content according to a preset classification mode to obtain one or more classification categories;
and displaying the one or more classification categories and target learning content corresponding to each classification category in a content display area of the three-dimensional scene.
5. The method of claim 4, further comprising:
determining the quantity of target learning contents corresponding to a first classification category of the one or more classification categories, wherein the first classification category is any one of the one or more classification categories;
when the number of the target learning contents corresponding to the first classification category is not larger than a preset display number threshold value, displaying all the target learning contents corresponding to the first classification category in the content display area;
and when the number of the target learning contents corresponding to the first classification category is larger than a preset display number threshold, displaying all the target learning contents corresponding to the first classification category in a content display area in a paging mode.
6. The method of claim 4, wherein the one or more classification categories include an image category,
wherein the method further comprises:
displaying the target learning content of the image category in the content display area in a first size;
and displaying the target learning content in a second size in response to a triggering operation on the target learning content of the image category, wherein the first size is smaller than the second size.
7. The method of claim 4, wherein the one or more classification categories comprise video categories, and wherein the method further comprises:
displaying a cover page picture of the target learning content of the video category in the content display area;
and responding to the triggering operation of the cover picture, and playing the target learning content of the video category.
8. The method of claim 1, wherein obtaining target learning content matching the model information comprises:
determining a first keyword based on the model information;
searching based on the first keyword to obtain a first search result;
determining a second keyword based on the user preference information and/or the scene information;
searching in the first search result based on the second keyword to obtain a target search result;
and determining the target search result as target learning content matched with the model information.
9. A learning content recommendation apparatus characterized by comprising:
the scene page display module is used for displaying a three-dimensional scene, wherein the three-dimensional scene comprises at least one three-dimensional model;
the model information acquisition module is used for responding to the triggering operation of any three-dimensional model in the three-dimensional scene and acquiring the model information corresponding to the three-dimensional model;
the learning content acquisition module is used for acquiring target learning content matched with the model information;
and the learning content recommending module is used for recommending the target learning content to the user.
10. An electronic device, characterized in that the electronic device comprises:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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