Learning content generation method and device based on virtual reality, equipment, medium and product

Abstract

The invention provides a learning content generation method and device based on virtual reality, equipment, a storage medium and a program product, and can be applied to the technical field of artificial intelligence. The learning content generation method based on virtual reality comprises the following steps: generating learning content according to first user information; generating a virtual scene for learning based on the learning content and a real scene where the user is located; second user information is obtained in real time in the user learning process; and according to the second user information, performing real-time adjustment on the learning content and / or the virtual scene.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and more specifically to a method, device, equipment, medium and program product for generating learning content based on virtual reality. Background Art

[0002] With the development of Internet technology, online learning is becoming more and more popular. Phenomenon learning refers to distance learning through the Internet platform. Users can learn through electronic devices at any time according to their own schedule. There are no restrictions on time and place, which provides users with a more flexible and convenient learning method, allowing learners to acquire knowledge through the Internet anytime and anywhere.

[0003] However, existing online learning content is usually pre-generated, and users can only choose corresponding courses according to their preferences. During the user's learning process, the specific learning content in the course cannot be modified. The flexibility of the course content is low and cannot meet the user's personalized needs. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a method, apparatus, device, medium and program product for generating learning content based on virtual reality to improve the flexibility of learning content.

[0005] According to the first aspect of the present disclosure, a method for generating learning content based on virtual reality is provided, including: generating learning content based on first user information; generating a virtual scene for learning based on the learning content and the real scene in which the user is located; acquiring second user information in real time during the user's learning process; and adjusting the learning content and the virtual scene in real time based on the second user information.

[0006] According to an embodiment of the present disclosure, learning content is generated based on the first user information, including: building a user portrait based on the user's basic information and behavior information; finding similar users based on the user portrait and obtaining the behavior information of the similar users; and generating the user's learning content based on the user portrait and the learning preferences of similar users.

[0007] According to an embodiment of the present disclosure, a virtual scene for learning is generated based on learning content and a real scene in which a user is located, including: acquiring object information of real objects in the real scene; generating virtual objects corresponding to the real objects in the virtual scene based on the object information; wherein the position or state of the virtual object will change accordingly with the change of the position or state of the real object.

[0008] According to an embodiment of the present disclosure, the second user information includes physiological information of the user, and the second user information is acquired in real time by the virtual reality device.

[0009] According to an embodiment of the present disclosure, the learning content and the virtual scene are adjusted in real time according to the second user information, including: passing the second user information into a pre-trained evaluation model, determining the user's current state based on the pre-trained evaluation model; and adjusting the learning content and / or the virtual scene in real time based on the user's current state.

[0010] According to an embodiment of the present disclosure, the current state of the user includes at least the user's thinking activity; based on the user's current state, the learning content and / or virtual scene are adjusted in real time, including: adjusting the difficulty of the learning content accordingly based on the user's thinking activity; wherein, the user's thinking activity is positively correlated with the difficulty of the learning content, and when the user's thinking activity is higher than a preset threshold, the learning content is adjusted to a more difficult task or problem.

[0011] According to an embodiment of the present disclosure, the user's current state includes at least the user's attention level; based on the user's current state, the learning content and / or the virtual scene are adjusted in real time, and also includes: when the user's attention is less than a preset threshold, active elements are added to the current virtual scene; wherein the active elements include any one of color elements, dynamic elements, sound effect elements, and interactive elements.

[0012] According to an embodiment of the present disclosure, the method further includes: sending a request to obtain first user information and a request to obtain second user information to the user; if the user agrees to the request to obtain first user information and the request to obtain second user information, obtaining the first user information and the second user information.

[0013] The second aspect of the present disclosure provides a learning content generation device based on virtual reality, including: a first generation module, used to generate learning content based on first user information; a second generation module, used to generate a virtual scene for learning based on the learning content and the real scene of the user; an acquisition module, used to acquire second user information in real time during the user's learning process; and an adjustment module, used to adjust the learning content and the virtual scene in real time according to the second user information.

[0014] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned virtual reality-based learning content generation method.

[0015] The fourth aspect of the present disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned virtual reality-based learning content generation method.

[0016] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above-mentioned virtual reality-based learning content generation method when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0018] Figure 1 The application scenario diagram schematically shows the method, device, equipment, medium and program product for generating learning content based on virtual reality according to the embodiment of the present disclosure;

[0019] Figure 2 A flowchart of a method for generating learning content based on virtual reality according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 3 A flowchart of generating learning content according to first user information according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 4 A flowchart of generating a virtual scene for learning based on learning content and a user's real scene according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 5 A flowchart schematically shows real-time adjustment of learning content and virtual scenes according to second user information according to an embodiment of the present disclosure;

[0023] Figure 6 A block diagram schematically shows a structure of a learning content generation device based on virtual reality according to an embodiment of the present disclosure; and

[0024] Figure 7 A block diagram of an electronic device suitable for implementing a method for generating learning content based on virtual reality according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0025] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0026] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0027] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0028] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0029] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0030] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present invention provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0031] An embodiment of the present disclosure provides a method for generating learning content based on virtual reality, including: generating learning content based on first user information; generating a virtual scene for learning based on the learning content and the real scene in which the user is located; acquiring second user information in real time during the user's learning process; and adjusting the part of the learning content that the user has not learned and the virtual scene in real time based on the second user information.

[0032] Figure 1 The application scenario diagram of the virtual reality-based learning content generation method, device, equipment, medium and program product according to the embodiments of the present disclosure is schematically shown.

[0033] like Figure 1 As shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0034] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).

[0035] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0036] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0037] It should be noted that the virtual reality-based learning content generation method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the virtual reality-based learning content generation device provided in the embodiment of the present disclosure can generally be set in the server 105. The virtual reality-based learning content generation method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Correspondingly, the virtual reality-based learning content generation device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0038] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0039] The following will be based on Figure 1 The scene described by Figure 2~Figure 5 The virtual reality-based learning content generation method of the disclosed embodiment is described in detail.

[0040] Figure 2 The flowchart of the method for generating learning content based on virtual reality according to an embodiment of the present disclosure is schematically shown.

[0041] like Figure 2 As shown, the generation of learning content based on virtual reality in this embodiment includes operations S210 to S240.

[0042] In operation S210, learning content is generated according to first user information.

[0043] In some embodiments, the first user information may include, for example, basic information and behavior information of the user. The basic information of the user may include, for example, the user's age, gender, occupation, etc. The behavior information of the user may include, for example, historical learning behavior, browsing history, search history, purchase history, click behavior, etc., wherein the historical learning information may further include the learning courses and learning time previously attended by the user.

[0044] By analyzing users' basic information and behavioral information, we can understand their learning needs and habits, provide users with learning content that suits their learning style and learning goals, personalize learning content, improve the matching degree between learning content and users, and effectively improve user satisfaction.

[0045] In operation S220, a virtual scene for learning is generated based on the learning content and the real scene where the user is located.

[0046] In some embodiments, after the user learning content has been generated, the learning content and the real scene in which the user is located are combined to generate a virtual scene that matches the learning content.

[0047] For example, when the learning content is language learning, virtual scenes can be used to simulate real language environments, such as virtual foreign city scenes, business meeting scenes, daily communication scenes, etc. Users can also communicate with virtual characters in virtual scenes, thereby achieving the purpose of language learning and improving users' language listening and speaking abilities.

[0048] In operation S230, second user information is acquired in real time during the user learning process.

[0049] In some embodiments, the second user information includes the user's physiological information, such as heart rate information, skin electricity information, eye movement information, etc. The second user information is acquired in real time by the virtual reality device worn by the user, and can reflect the physiological changes in the user's perception, possibility, cognition, etc.

[0050] Virtual reality devices may include, for example, head-mounted displays, hand controllers, stereo headphones, etc. The head-mounted display is used to present the virtual world to the user, and usually includes a display screen, lenses, and sensors, which allow the user to see the virtual environment and can be used to collect the user's eye movement information. The hand controller is used to allow the user to perform interactive operations, such as grabbing objects, clicking on the operation interface, etc. Biosensors can be installed on the hand controller to collect the user's heart rate information, skin electrical information and other physiological information.

[0051] In operation S240, the learning content and the virtual scene are adjusted in real time according to the second user information.

[0052] In some embodiments, the second user information can effectively reflect the physiological changes of the user during the learning process. For example, when the user is interested in a certain content, the heart rate usually increases, and when the user feels bored or tired, the heart rate usually decreases. Therefore, the user's current learning status can be judged based on the second user information, and the learning content and virtual scenes can be adjusted in real time based on the user's current learning status, so that the user can always maintain a relatively active learning state, thereby ensuring better learning results.

[0053] In the embodiment of the present disclosure, before obtaining the user's information, it is necessary to obtain the user's consent or authorization.

[0054] For example, before executing operation S210 and operation S230, a request to obtain the first user information and a request to obtain the second user information may be sent to the user. If the user agrees to the request to obtain the first user information and the request to obtain the second user information, the first user information and the second user information are obtained, and the above operations S210 and the above operations S230 are executed.

[0055] The virtual reality-based learning content generation method provided by the present disclosure, on the one hand, combines learning courses with virtual reality, so that users can obtain a more immersive learning experience; on the other hand, the second user information is obtained in real time during the user's learning process, and the learning content and virtual scenes are adjusted in real time based on the second user information, thereby improving the flexibility of the learning method, so that the learning content and virtual scenes can be adjusted in real time as the user's learning status changes, thereby improving the user's learning experience and learning effect.

[0056] Figure 3 A flowchart of generating learning content according to first user information according to an embodiment of the present disclosure is schematically shown.

[0057] like Figure 3 As shown, the generation of learning content according to the first user information in this embodiment includes operations S310 to S330.

[0058] In operation S310, a user portrait is constructed based on the user's basic information and behavior information.

[0059] In some embodiments, basic user information may include, for example, age, gender, geographic location, occupation, educational background, financial status, etc., which may reflect the user's background and characteristics. User behavior information may include the user's historical learning behavior, browsing history, search history, purchase history, click behavior, etc., which may reflect the user's preferences, interests, and potential needs to a certain extent.

[0060] Clean and integrate the collected user information, remove duplicate, erroneous or incomplete data, extract target features from the cleaned user information and analyze the target features, and build user portraits based on the analysis results. User portraits are used to describe the typical characteristics, preferences and needs of users.

[0061] In operation S320, similar users are found according to the user portraits and learning preferences of the similar users are obtained.

[0062] In some embodiments, in some embodiments, a recommendation system or similarity calculation method can be used to match other users who have similar characteristics and needs as the user. These similar users may have similar interests, learning habits or professional backgrounds as the current user, and thus can be used as reference objects to generate personalized learning content.

[0063] Obtaining the learning preferences of these users and incorporating the learning preferences of these similar users into one of the reference factors for generating learning content will help improve the accuracy of generating personalized learning content for the current users.

[0064] In a specific implementation process, for example, the similarity between users may be calculated based on methods such as Euclidean distance and cosine similarity to obtain similar users of the current user.

[0065] In operation S330 , learning content for the user is generated based on the user portrait and learning preferences of similar users.

[0066] In some embodiments, the learning content for the user is jointly generated based on the two dimensions of user portrait and similar user learning preferences, which can effectively improve the matching degree between the learning content and the user and realize personalized customization of the learning content.

[0067] Figure 4 The flowchart of generating a virtual scene for learning based on learning content and user's real scene according to an embodiment of the present disclosure is schematically shown.

[0068] like Figure 4 As shown, the generation of a virtual scene for learning based on learning content and a user's real scene in this embodiment includes operations S410 to S430.

[0069] In operation S410 , a virtual scene to be generated is determined based on the learning content.

[0070] In some embodiments, after determining the learning content, the type of virtual scene to be generated is determined based on the learning content. For example, when the learning content is business English, the corresponding virtual scene may be a company meeting room. When the learning content is financial security, the corresponding virtual scene may be a financial transaction platform, an investment platform, etc.

[0071] In operation S420, object information of a real object in a real scene is acquired.

[0072] In some embodiments, the real scene may include any scene in reality, and the object information of the real object refers to any information related to the real object in the real scene, for example, it may include the attribute information and position information of the real object. Among them, the attribute information includes the color, size, shape, etc. of the real object, and the position information may include the coordinate information of the real object in the current real scene or the relative position relationship between the real object and other real objects.

[0073] In operation S430, a virtual object corresponding to the real object is generated in the virtual scene based on the object information.

[0074] In some embodiments, after obtaining the object information of the real object, the virtual scene is generated based on the object information, wherein the virtual scene is refined according to the object information during the generation process, so that the virtual scene and the real scene are integrated with high quality. By obtaining the object information of the real object, a virtual object corresponding to the real object is generated in the virtual scene, realizing the combination of virtual and real, and increasing the user's immersive experience.

[0075] Take the virtual scene of a company's conference room as an example. The real scene includes a table and several stools. At this time, the table and stools are real objects. The size and shape of the table and stools are their attribute information, and the relative position relationship between the table and stools is their position information. At this time, the virtual scene can be created with the table and stools as the base point, where the table corresponds to the table in the company's conference room in the virtual scene, and the stool corresponds to the stool in the company's conference room in the virtual scene (the table and stool in the virtual scene are both virtual objects). And based on the position of the table and stool, other virtual scenes can be developed to improve the integration of virtual scenes and real scenes.

[0076] The position or state of the virtual object will change accordingly with the change of the position or state of the real object.

[0077] For example, in a virtual scene of a company meeting, a virtual water cup is placed on the conference room table. The virtual water cup corresponds to a real water cup in the real scene. When the user in the real scene drinks water from the real water cup, the water level in the real water cup drops. At this time, the water level in the virtual water cup in the virtual scene will also drop accordingly, so that the state of the virtual object is synchronized with the state of the real object, effectively improving the fusion effect of the virtual scene and the real scene.

[0078] Figure 5 A flowchart of real-time adjustment of learning content and virtual scenes according to second user information according to an embodiment of the present disclosure is schematically shown.

[0079] like Figure 5 As shown, the real-time adjustment of the learning content and the virtual scene according to the second user information in this embodiment includes operations S510 to S520.

[0080] In operation S510, the second user information is passed into the pre-trained evaluation model, and the current state of the user is determined based on the pre-trained evaluation model.

[0081] In operation S520, the learning content and / or the virtual scene are adjusted in real time based on the current state of the user.

[0082] In some embodiments, the user's physiological information can reflect the user's learning status to a certain extent. For example, when the user is interested in a certain content, the heart rate usually increases, and when the user feels bored or tired, the heart rate usually decreases. Alternatively, when the user is interested in a certain content, the eye movement reaction is usually concentrated on the key parts of the content, and when the user feels bored or tired, the eye movement reaction will be scattered or slow. Therefore, the user's current learning status can be evaluated through the user's physiological information, so as to adjust the learning content and / or virtual scene in real time according to the user's learning status.

[0083] The second user information collected in real time is passed into the pre-trained evaluation model, which extracts the target features from the second user information and evaluates the user status based on the target features to obtain the user's current status. The learning content and / or virtual scene are adjusted in real time according to the user's current status, so that the learning content can be more in line with the user's actual needs and ability level, helping users to better understand and absorb knowledge and improve learning effects.

[0084] The evaluation model may include, for example, a decision tree model, a random forest model, a convolutional neural network model, etc.

[0085] During the specific implementation process, the user's current state can reflect the user's attention level and thinking activity at the current moment.

[0086] Based on the user's current status, the learning content and / or virtual scene are adjusted in real time, including:

[0087] The difficulty of the learning content is adjusted accordingly based on the user's thinking activity. Among them, the user's thinking activity is positively correlated with the difficulty of the learning content. When the user's thinking activity is higher than the preset threshold, the learning content is adjusted to a more difficult task or problem. Correspondingly, when the learning content is adjusted in real time, the virtual scene will also be adjusted accordingly according to the adjusted learning content.

[0088] And, based on the user's attention level, the virtual scene is appropriately adjusted so that the user's attention can be maintained at a high level, thereby ensuring the learning effect.

[0089] When the user's attention is less than the preset threshold, active elements are added to the current virtual scene to improve the user's attention, so that the user can refocus the scattered attention on the learning content and improve the learning effect.

[0090] Among them, active elements include any one of color elements, dynamic elements, sound effect elements, and interactive elements. Color elements are bright colors with high saturation or elements with high contrast; dynamic elements include moving objects, dynamic backgrounds, etc.; sound effect elements may include adding background music and sound effects to the current virtual scene; interactive elements may include buttons, triggers, and other elements used to increase user participation.

[0091] Furthermore, the learning content generation method provided by the embodiment of the present disclosure can not only adjust the learning content and / or virtual scene in real time based on the second user information, but also adjust the learning content in real time based on the user's staged learning results in the current learning process. For example, after the user completes a stage of learning, the user's learning results in that stage are determined based on the user's interactive feedback in the current stage, and the subsequent learning content is adjusted based on the learning results. Among them, the learning stage can be divided according to the length of learning, or by content type. The learning results can reflect the user's mastery of different knowledge points in the learning stage. When the user's mastery of a certain knowledge point is not ideal, content related to the knowledge point can be added to the subsequent learning content to help the user master the knowledge point as soon as possible.

[0092] The learning content generation method provided by the embodiments of the present disclosure adjusts the learning content and / or virtual scenes in real time based on the user's current learning status. While ensuring the personalization of the learning content, different learning content and / or virtual scenes are set based on the user's different learning status, thereby effectively improving the user's learning efficiency and obtaining better learning results.

[0093] Based on the above-mentioned virtual reality-based learning content generation method, the present disclosure also provides a virtual reality-based learning content generation device. Figure 6 The device is described in detail.

[0094] Figure 6 The structural block diagram of a virtual reality-based learning content generation device according to an embodiment of the present disclosure is schematically shown.

[0095] like Figure 6 As shown, the virtual reality-based learning content generation device 600 of this embodiment includes a first generation module 610 , a second generation module 620 , an acquisition module 630 and an adjustment module 640 .

[0096] The first generating module 610 is used to generate learning content according to the first user information. In one embodiment, the first generating module 610 can be used to perform the operation S210 described above, which will not be described in detail here.

[0097] The second generation module 620 is used to generate a virtual scene for learning based on the learning content and the real scene that the user is in. In one embodiment, the second generation module 620 can be used to perform the operation S220 described above, which will not be repeated here.

[0098] The acquisition module 630 is used to acquire the second user information in real time during the user learning process. In one embodiment, the acquisition module 630 can be used to perform the operation S230 described above, which will not be described in detail here.

[0099] The adjustment module 640 is used to adjust the learning content and the virtual scene in real time according to the second user information. In one embodiment, the adjustment module 640 can be used to perform the operation S240 described above, which will not be described in detail here.

[0100] According to an embodiment of the present disclosure, any multiple modules of the first generation module 610, the second generation module 620, the acquisition module 630, and the adjustment module 640 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first generation module 610, the second generation module 620, the acquisition module 630, and the adjustment module 640 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the first generating module 610 , the second generating module 620 , the acquiring module 630 , and the adjusting module 640 may be at least partially implemented as a computer program module, and when the computer program module is executed, a corresponding function may be performed.

[0101] Figure 7 A block diagram of an electronic device suitable for implementing a method for generating learning content based on virtual reality according to an embodiment of the present disclosure is schematically shown.

[0102] like Figure 7As shown, the electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 to a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include an onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0103] In RAM 703, various programs and data required for the operation of electronic device 700 are stored. Processor 701, ROM 702 and RAM 703 are connected to each other via bus 704. Processor 701 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 702 and / or RAM 703. It should be noted that the program can also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0104] According to an embodiment of the present disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to the bus 704. The electronic device 700 may further include one or more of the following components connected to the I / O interface 705: an input portion 706 including a keyboard, a mouse, etc.; an output portion 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 708 including a hard disk, etc.; and a communication portion 709 including a network interface card such as a LAN card, a modem, etc. The communication portion 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage portion 708 as needed.

[0105] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0106] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 702 and / or RAM 703 described above and / or one or more memories other than ROM 702 and RAM 703.

[0107] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the virtual reality-based learning content generation method provided by the embodiment of the present disclosure.

[0108] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 701. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0109] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 709, and / or installed from the removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0110] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0111] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0112] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0113] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0114] The embodiments of the present disclosure are described above. However, these embodiments are only for the purpose of illustration and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for generating learning content based on virtual reality, characterized in that: The method comprises: generating learning content according to the first user information; Based on the learning content and the real scene where the user is located, generating a virtual scene for learning; Acquire the second user information in real time during the user learning process; The learning content and / or the virtual scene are adjusted in real time according to the second user information.

2. The learning content generation method according to claim 1, characterized in that: The first user information includes basic information and behavior information of the user, and the generating of learning content according to the first user information includes: Build a user profile based on the basic information and behavior information of the user; Find similar users based on the user portrait and obtain behavior information of the similar users; Based on the user portrait and the learning preferences of similar users, learning content for the user is generated.

3. The learning content generation method according to claim 1, characterized in that: The generating a virtual scene for learning based on the learning content and the real scene where the user is located includes: Determining a virtual scene to be generated based on the learning content; Acquire object information of real objects in the real scene; generating a virtual object corresponding to the real object in the virtual scene based on the object information; The position or state of the virtual object will change accordingly with the change of the position or state of the real object.

4. The learning content generation method according to claim 1, characterized in that: The second user information includes physiological information of the user, and the second user information is acquired in real time by the virtual reality device.

5. The learning content generation method according to claim 4, characterized in that: The real-time adjustment of the learning content and / or the virtual scene according to the second user information includes: The second user information is passed into a pre-trained evaluation model, and a current state of the user is determined based on the pre-trained evaluation model; Based on the user's current status, the learning content and / or virtual scene are adjusted in real time.

6. The learning content generation method according to claim 5, characterized in that: The current state of the user at least includes the user's thinking activity; The real-time adjustment of the learning content and / or virtual scene based on the current state of the user includes: Adjust the difficulty of learning content based on the user's active thinking; Among them, the user's thinking activity is positively correlated with the difficulty of the learning content. When the user's thinking activity is higher than the preset threshold, the learning content will be adjusted to more difficult tasks or problems.

7. The learning content generation method according to claim 5, characterized in that: The user's current state includes at least the user's attention level; The real-time adjustment of the learning content and / or virtual scene based on the current state of the user also includes: When the user's attention is less than a preset threshold, adding an active element to the current virtual scene; The active element includes any one of a color element, a dynamic element, a sound effect element, and an interactive element.

8. A method according to claim 1, characterized in that The method further comprises: Sending a request to obtain first user information and a request to obtain second user information to the user; In a case where the user agrees to the request to obtain the first user information and the request to obtain the second user information, the first user information and the second user information are obtained.

9. A learning content generation device based on virtual reality, characterized in that: The device comprises: A first generating module, used to generate learning content according to the first user information; A second generating module is used to generate a virtual scene for learning based on the learning content and the real scene where the user is located; An acquisition module, used to acquire second user information in real time during the user learning process; and An adjustment module is used to adjust the learning content and / or the virtual scene in real time according to the second user information.

10. An electronic device comprising: one or more processors; a storage device for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

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

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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