AI accompanied learning virtual pet interaction method, device and equipment

Abstract

The invention provides an AI accompanying virtual pet interaction method, device and equipment, a cloud server serves as a connection center to process a request from the equipment, and the method comprises the following steps: in response to a processing instruction for acquired external visual information, processing the external visual information based on the own specific character characteristics of a virtual pet to obtain dress-up incentives; requesting the user to experience the dress-up based on the dress-up incentive and the personality traits; after the experience is completed, the dress-up experience set is removed; and pushing the learning plan to the user according to the character traits. According to the method and the device, the pet has specific characters, so that the problem of one face of thousands of people is avoided, the deep interaction between the pet and the user can simulate the complexity and the emotional depth of a real life entity, the interestingness is increased, the external visual information between the users can be converted into effective internal requirements and consumption driving force, and the interestingness is improved. And the deep interaction capability of the electronic virtual pet and the user for social emotion is improved.

Description

Technical Field

[0001] This application relates to the field of human-computer interaction technology, and in particular to a virtual pet interaction method, device and equipment for AI-assisted learning. Background Technology

[0002] Virtual pets, as a mature form of interaction, are widely used in games, educational products, and companionship products. Virtual pets refer to non-physical pets that exist in digital form, residing on computers, mobile phones, or the internet. Virtual pets can provide people with companionship, the joy of raising pets, and satisfy their love for animals. Their unique mechanisms of "personality creation" and "emotional companionship" also make them highly valuable in the fields of mental health and digital therapy. They can serve as a non-pharmacological intervention for psychological problems such as childhood autism and adult depression, providing personalized and adaptive emotional support.

[0003] However, current AI-powered virtual pets typically come from a limited, pre-set image library, allowing users to choose rather than create. Their growth paths, such as leveling up and evolution, are also fixed and predictable, resulting in all users' pets ultimately looking identical. They lack the uniqueness and exclusivity to inspire long-term emotional investment, leading to homogenization in both the virtual pet's image and its development trajectory. Interactions between virtual pets and users take forms such as "feeding-happiness" and "petting-satisfaction." However, these interactions are mostly based on simple, mechanical trigger-response logic. Mechanical interactions cannot simulate the complexity and emotional depth of real living beings, causing users to quickly become bored after the initial novelty wears off. Therefore, deep interaction between virtual pets and users still faces technological barriers, preventing truly meaningful emotional connections. In other words, current technology shows that traditional electronic virtual pets lack the ability to engage in deep socio-emotional interaction with users.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide an AI-assisted learning virtual pet interaction method, which aims to solve the technical problem that traditional electronic virtual pets lack the ability to deeply interact with users in terms of social and emotional aspects.

[0006] Firstly, a virtual pet interaction method for AI-assisted learning is provided. This method is applied to AI-assisted virtual pet interaction devices, where multiple devices are connected to a cloud server. The cloud server acts as a connection center, processing requests from the AI-assisted virtual pet interaction devices and storing data. The AI-assisted virtual pet interaction method includes: In response to the processing instructions for the acquired external visual information, the external visual information is processed based on the unique personality traits of the local virtual pet to obtain the external visual information's incentive for its own appearance. The external visual information is acquired from other virtual pets in a preset virtual social space. If it is determined that the outfit incentive exceeds a preset incentive threshold, then the action of requesting the user to experience the outfit is triggered, wherein when requesting the user to experience the outfit, the request is made based on the personality trait. In response to the user's consent instruction to try out the outfit, a request to try out the outfit is sent to the cloud server; Upon receiving the verification message from the cloud server confirming successful verification, the system receives a dress-up experience set from the cloud server that matches the external visual information and applies the dress-up experience set to the local virtual pet. The cloud server then starts timing the dress-up experience and triggers removal when the experience time exceeds the validity period. In response to the removal command from the cloud server, the costume experience set is removed from the local virtual pet; The user's learning motivation is driven by their experience with the costume outfit, based on the personality traits mentioned above. The learning plan is generated by combining the costume incentives and the user's current historical learning task completion rate.

[0007] In one possible implementation of this application, each virtual pet corresponds to a relative competitiveness value representing the user's learning ability. After the step of pushing a pre-generated learning plan to the user based on the personality trait to intrinsically drive the user's learning motivation based on their experience with the costume outfit, the process includes: Periodically obtain the user's progress on the phased learning tasks indicated by the learning plan; The periodic progress reports are used to calculate the progress of each stage and obtain the stage completion rate. The update change is calculated by first subtracting the stage completion degree from the preset completion degree threshold, and then multiplying the result of the subtraction operation with the preset learning rate. The updated change is added to the current relative competitiveness value to obtain a new, more similar relative competitiveness value; Based on the new relative competitiveness value, the unique personality traits of the local virtual pets are updated to achieve personality growth for the local virtual pets.

[0008] In one possible implementation of this application, before the step of pushing a pre-generated learning plan to the user based on the personality trait to intrinsically drive the user's learning motivation based on the user's experience with the costume set, the method includes: Retrieve the user's previous learning records from the cloud server; Based on the completion rate of the historical learning tasks corresponding to the learning record, the difference in learning points with the dress-up experience set is calculated, wherein the difference in learning points is the difference obtained by subtracting the second learning point corresponding to the completion rate of the historical learning tasks from the first learning point corresponding to the dress-up experience set. Based on the dressing-up incentives and the learning score difference, a long-term incentive goal matching the dressing-up experience set is generated; Based on the long-term incentive goals, a corresponding learning plan is generated, wherein the learning plan includes the amount of learning to be done regularly and the types of subjects to be studied.

[0009] In one possible implementation of this application, before processing the acquired external visual information based on the unique personality traits of the local virtual pet to obtain the external visual information's motivation for its own appearance, the process includes: Upon initial launch, the user is given a personality test-style question. Based on the user's answers to questions, a standard serialization process is performed to obtain a multi-dimensional user personality vector. Based on the user personality vector, a unique pet identity tag is generated for the local virtual pet; Based on the user's personality vector, a virtual pet anthropomorphic pet personality vector is generated to achieve intrinsic anthropomorphism of the virtual pet. The pet personality vector is input into a preset parametric image generation model. Based on the parametric image generation model, a virtual pet with a personality consistent with the pet personality vector is generated. The external appearance of the virtual pet is also consistent with the pet personality vector. The virtual pet is marked with the pet identity tag. Based on the pet's personality vector, corresponding dress-up experience sets are selected from a preset resource library, and the selected dress-up experience sets are applied to the virtual pet.

[0010] In one possible implementation of this application, the pet personality vector includes introversion, curiosity, and sensitivity. The higher the introversion in the pet personality vector, the larger the pupils and the more droopy the ears in the virtual pet's appearance. The higher the curiosity in the pet personality vector, the longer the tail in the virtual pet's appearance. The higher the sensitivity in the pet personality vector, the smaller the body size in the virtual pet's appearance.

[0011] In one possible implementation of this application, the external visual information is aesthetic perception information, which uses an attractiveness value to represent the degree of aesthetics. The step of processing the acquired external visual information in response to a processing instruction, based on the unique personality traits of the local virtual pet, to obtain the external visual information's incentive for the pet's appearance, includes: In response to the processing instructions for the acquired aesthetic perception information, the average level of curiosity and sensitivity of the local virtual pet is calculated from an aesthetic perspective; Calculate the product of the average value and the attractiveness value to obtain the aesthetic perception information's motivation for one's own aesthetic attire.

[0012] In one possible implementation of this application, the external visual information is socially perceptual information, which characterizes the degree of social attraction using rarity and ability gain. The step of processing the acquired external visual information in response to a processing instruction, based on the unique personality traits of the local virtual pet, to obtain the external visual information's incentive for the pet's appearance, includes: In response to the processing instruction of the acquired social perception information, the social perception information is processed from a social perspective based on the pet personality vector of the local virtual pet to obtain the social perception information's social dressing incentive for itself, wherein the social dressing incentive is calculated based on the pet personality vector, the rarity, and the ability gain.

[0013] In one possible implementation of this application, the dress-up experience kit includes dress-up skins and / or dress-up props.

[0014] Secondly, an AI-powered virtual pet interaction device is provided. This device is deployed within an AI-powered virtual pet interaction equipment. The AI-powered virtual pet interaction method is applied to the AI-powered virtual pet interaction equipment. Multiple AI-powered virtual pet interaction devices establish communication connections with a cloud server. The cloud server acts as a connection center, processing requests from the AI-powered virtual pet interaction devices and storing data. The AI-powered virtual pet interaction device includes: An information processing unit is used to respond to a processing instruction for the acquired external visual information, process the external visual information based on the unique personality traits of the local virtual pet, and obtain the external visual information's encouragement for its own appearance, wherein the external visual information is acquired from other virtual pets in a preset virtual social space. An interaction unit is used to trigger a request to the user to experience the outfit if it is determined that the outfit incentive exceeds a preset incentive threshold, wherein the request to the user to experience the outfit is made based on the personality trait. The sending unit is used to send a request to the cloud server in response to the user's consent instruction to agree to the request to try out the outfit; The dressing-up processing unit is used to receive a dressing-up experience set that matches the external visual information sent by the cloud server when it receives a verification message that the cloud server has verified the information, and to apply the dressing-up experience set to the local virtual pet. The cloud server starts the dressing-up experience timer and triggers removal when the experience time exceeds the experience validity period. The dress-up processing unit is also used to remove the dress-up experience set from the local virtual pet in response to the removal command from the cloud server; The interaction unit is also used to push a pre-generated learning plan to the user based on the personality trait, so as to drive the user's learning motivation based on the user's experience with the dress-up outfit. The learning plan is generated based on the combination of the dress-up incentive and the user's current historical learning task completion rate.

[0015] Thirdly, an AI-powered virtual pet interaction device is provided, which is a physical node device. The AI-powered virtual pet interaction device includes: a memory, a processor, and an AI-powered virtual pet interaction program stored in the memory and executable on the processor. The processor executes the AI-powered virtual pet interaction program to implement the steps of the AI-powered virtual pet interaction method.

[0016] This application provides a virtual pet interaction method, device, and equipment for AI-powered learning companions. Compared to the technical problem of traditional electronic virtual pets lacking deep social and emotional interaction capabilities with users, this application applies the AI-powered learning companion virtual pet interaction method to AI-powered learning companion virtual pet interaction devices. Multiple AI-powered learning companion virtual pet interaction devices establish communication connections with a cloud server. The cloud server acts as a connection center, processing requests from the AI-powered learning companion virtual pet interaction devices and storing data. The AI-powered learning companion virtual pet interaction method includes: responding to a processing instruction for acquired external visual information, processing the external visual information based on the unique personality traits of the local virtual pet to obtain a dressing-up incentive from the external visual information, wherein the external visual information is acquired from other virtual pets in a preset virtual social space; if it is determined that the dressing-up incentive exceeds a preset incentive threshold, a request is triggered to the user. The process involves requesting a dress-up experience, where the request is made based on the user's personality traits. Upon receiving a consent instruction from the user agreeing to the dress-up experience request, a dress-up experience request is sent to the cloud server. Upon receiving a verification message from the cloud server confirming successful verification, the user receives a dress-up experience set matching the external visual information from the cloud server and applies the dress-up experience set to their local virtual pet. The cloud server starts a dress-up experience timer, and removal is triggered when the experience time exceeds the validity period. In response to the removal instruction from the cloud server, the dress-up experience set is removed from the local virtual pet. Finally, based on the personality traits, a pre-generated learning plan is pushed to the user to intrinsically drive their learning motivation based on their experience with the dress-up set. This learning plan is generated based on a combination of the dress-up incentive and the user's current historical learning task completion rate. In this application, virtual pets possess unique personality traits, avoiding the problem of uniformity. Deep interaction between pets and users can simulate the complexity and emotional depth of real living beings, increasing fun. External visual information between users can be transformed into effective internal needs and consumption drivers, improving the ability of electronic virtual pets to engage in deep social and emotional interaction with users. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1This is a flowchart illustrating a virtual pet interaction method for AI-assisted learning in one embodiment of this application; Figure 2 This is a schematic diagram of the structure of an AI-assisted learning virtual pet interaction device according to one embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device according to one embodiment of this application; Figure 4 This is another structural schematic diagram of a computer device in one embodiment of this application. Detailed Implementation

[0019] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Example 1 This application provides a virtual pet interaction method for AI-assisted learning. In the first embodiment of this application's AI-assisted learning virtual pet interaction method, the AI-assisted learning virtual pet interaction method is applied to an AI-assisted learning virtual pet interaction device. Multiple AI-assisted learning virtual pet interaction devices are respectively connected to a cloud server. The cloud server acts as a connection center to process requests from the AI-assisted learning virtual pet interaction devices and store data.

[0021] Virtual pets, as a mature form of interaction, are widely used in games, educational products, and companionship products. Virtual pets refer to non-physical pets that exist in digital form, residing on computers, mobile phones, or the internet. Virtual pets can provide people with companionship, the joy of raising pets, and satisfy their love for animals. Their unique mechanisms of "personality creation" and "emotional companionship" also make them highly valuable in the fields of mental health and digital therapy. They can serve as a non-pharmacological intervention for mental health issues such as childhood autism and adult depression, providing personalized and adaptive emotional support.

[0022] However, current AI-powered virtual pets typically come from a limited, pre-set image library, allowing users to choose rather than create. Their growth paths, such as leveling up and evolution, are also fixed and predictable, resulting in all users' pets ultimately looking identical. They lack the uniqueness and exclusivity to inspire long-term emotional investment, leading to homogenization in both the virtual pet's image and its growth trajectory. Interactions between virtual pets and users take forms such as "feeding-happiness" and "petting-satisfaction." However, these interactions are mostly simple, mechanical trigger-response logics. Mechanical interactions cannot simulate the complexity and emotional depth of real living beings, causing users to quickly become bored after the initial novelty wears off. Therefore, deep interaction between virtual pets and users still faces technological barriers, preventing truly meaningful emotional connections. In other words, current technology shows that traditional electronic virtual pets lack the ability to engage in deep socio-emotional interaction with users.

[0023] like Figure 1 The AI-assisted learning virtual pet interaction method includes steps S110-S160: S110. In response to the processing instruction for the acquired external visual information, the external visual information is processed based on the unique personality traits of the local virtual pet to obtain the external visual information's encouragement for its own appearance, wherein the external visual information is acquired from other virtual pets in a preset virtual social space. In this embodiment, each AI-powered learning companion virtual pet interaction device corresponds to a unique virtual pet. Unlike existing technologies where users typically choose from a limited, pre-set image library, the virtual pets in this embodiment are not simply selected but are creatively generated. These virtual pets resemble the user's personality, closely approximating their real-life character traits. Users of the AI-powered learning companion virtual pet interaction device are accompanied by a similar virtual pet while using the device for learning. This unique virtual pet makes it easier for users to resonate with their learning experience and further stimulates their intrinsic motivation to learn. Each local virtual pet possesses its own unique personality traits.

[0024] Since each AI-powered learning companion's virtual pet interaction device corresponds to a unique virtual pet, simulated social interaction can be conducted based on this unique virtual pet. For example, in this embodiment, the user corresponding to the AI-powered learning companion's virtual pet interaction device is Xiaoming. Xiaoming enters the Weekend Square, a virtual social space, with his local virtual pet A. Other users holding AI-powered learning companion virtual pet interaction devices can also enter the Weekend Square with their own virtual pets. For instance, Xiaoming's classmate, Xiaohong, also enters with her virtual pet. Each virtual pet is configured with a corresponding state machine, which monitors the virtual pet's actual state. For example, if virtual pet A's state machine detects the appearance of another virtual pet B in its field of vision, it means that virtual pet A has seen another virtual pet B.

[0025] In this embodiment, the AI ​​companion learning virtual pet interaction device acquires external visual information from pet B, which is obtained from pet B in the preset virtual social space.

[0026] The AI-powered virtual pet interaction device responds to the processing instructions of the acquired external visual information.

[0027] Because each local virtual pet possesses its own unique personality traits, virtual pet A will respond to external visual information in accordance with these traits. For example, virtual pet A's personality traits are highly introverted, moderately curious, and moderately sensitive. When A sees virtual pet B wearing a sparkly astronaut outfit, the external visual information motivates A's appearance, triggering an internal state transition. Pet A then makes a request to the user. This request demonstrates a deep interaction between the virtual pet and the user. Because pet A is highly introverted and moderately curious, it says to Xiaoming, "Xiaoming, look at that astronaut outfit! It seems to glow! Can we try it on?"

[0028] The AI-powered virtual pet interaction device responds to commands that process acquired external visual information. Based on the unique personality traits of the virtual pet, it processes the external visual information to generate a dressing-up incentive. This dressing-up incentive reflects the motivating effect of external social stimuli on the virtual pet in social situations. This incentive indirectly reflects the interaction between the user and the virtual pet. Since the user brings pet A to the virtual social space, the virtual pet A's desire to be dressed up is a feedback to the user's action.

[0029] As mentioned above, the virtual pet in this embodiment is not simply selected, but rather created. This virtual pet's personality is similar to the user's, closely resembling the user's real personality traits. Each local virtual pet possesses its own unique personality characteristics. Therefore, before processing the external visual information based on the local virtual pet's unique personality traits to obtain the external visual information's influence on the user's appearance, a virtual pet needs to be generated first. For a more realistic interaction, the generated virtual pet's image should conform to the pet's own unique personality traits.

[0030] Upon initial user activation, this module acquires user preference data through guided interaction and encodes it into a personality gene seed vector. Based on this personality gene seed, the module uses a deterministic generation algorithm to create a virtual pet with a unique identifier and an initial multidimensional personality state vector, establishing a permanent and irreversible binding relationship with the user account. The multidimensional personality state vector defines the virtual pet's basic behavioral tendencies in subsequent interactions.

[0031] S110, in response to the processing instruction for the acquired external visual information, processes the external visual information based on the unique personality traits of the local virtual pet, and before obtaining the external visual information's incentive for its own appearance, includes steps A1-A6: Step A1: Upon initial startup, conduct a personality test-style questioning with the user; As an example, when 8-year-old Xiaoming first turned on his AI-powered learning companion desktop pet device, a mysterious egg gently shaking appeared on the screen. The pet asked, "Before becoming your companion, I'd like to know you a little more. What do you think is the best thing to do on a rainy day?" Options included A. Reading at home and B. Going out to splash in puddles. Multiple questions like these were actually part of a personality test for Xiaoming. After hearing the questions, Xiaoming operated the AI-powered learning companion desktop pet device, inputting his answers. The device then uploaded the answers to a pre-set cloud server. The cloud server analyzed the answers and obtained the personality test results.

[0032] Step A2: Based on the user's answer to the question, perform standard serialization processing to obtain a multi-dimensional user personality vector; As an example, Xiaoming chose option A. Based on this, five similar multiple-choice questions and answers will be generated. The final result will be five similar multiple-choice questions and answers. Xiaoming's answer sequence is [A, B, A, C, A]. Xiaoming's answer sequence is then processed using standard serialization, converting it into a multi-dimensional vector G_seed = [1, 0, 0, 1, 1, 0]. Here, the multi-dimensional vector G_seed represents the user's personality vector, signifying Xiaoming's personality traits.

[0033] Step A3: Based on the user personality vector, generate a unique pet identity tag for the local virtual pet; Similar to a user's unique name, a virtual pet also uses a pet identity tag as its unique identifier. For example, by taking a user's personality vector and the current unique world time as input, a unique ID value is calculated and output, and this unique ID value is used as the pet's identity tag.

[0034] Step A4: Based on the user personality vector, generate a humanized pet personality vector for the virtual pet to achieve internal anthropomorphism of the virtual pet; As an example, the user personality vector G_seed = [1, 0, 0, 1, 1, 0] is input into a pre-trained neural network Model_Mapping. Model_Mapping processes the input user personality vector, mapping G_seed to a three-dimensional personality vector [Introversion, Curiosity, Sensitivity], resulting in a pet personality vector Personality_Vector = [Introversion, Curiosity, Sensitivity]. Based on this pet personality vector, a virtual pet is personified. Here, Introversion represents introversion, Curiosity represents curiosity, and Sensitivity represents sensitivity.

[0035] As an example, Xiaoming's virtual pet A has a personality vector of [0.85, 0.7, 0.4]. The introversion level of 0.85 in the pet personality vector can be interpreted as virtual pet A being highly introverted, the curiosity level of 0.7 in the pet personality vector can be interpreted as virtual pet A being curious, and the sensitivity level of 0.4 in the pet personality vector can be interpreted as virtual pet A being sensitive.

[0036] Step A5: Input the pet personality vector into a preset parametric image generation model, and generate a virtual pet with a personality consistent with the pet personality vector based on the parametric image generation model. The virtual pet's external appearance is also consistent with the pet personality vector, and the virtual pet is marked with the pet identity tag. The pet personality vector [0.85, 0.7, 0.4] is input into a preset parametric image generation model, which uses a basic 3D pet model, such as a cat with a fixed image. The influence of the pet personality vector is added to this basic 3D pet model to generate a virtual pet with a personality consistent with the pet's personality vector. The external appearance also matches the pet's personality vector, and the virtual pet is labeled with the corresponding pet identity tag.

[0037] The pet's personality vector includes introversion, curiosity, and sensitivity. A higher introversion level results in larger pupils and more droopy ears in the virtual pet's appearance. A higher curiosity level results in a longer or more agile tail, and a higher sensitivity level results in a smaller body size. This ultimately generates a unique pet.

[0038] Step A6: Based on the pet personality vector, select the corresponding dress-up experience set from the preset resource library, and apply the selected dress-up experience set to the virtual pet.

[0039] To enrich the design layers of the virtual pets, based on key personality tags such as "introverted" and "curious," a unique and visible subset of resources is selected from a vast resource library. This visible resource subset is the dress-up experience set. The dress-up experience set includes dress-up skins and / or dress-up props, such as a study-themed skin series or a magnifying glass series prop. The selected dress-up experience set is then applied to the virtual pet.

[0040] Among them, the virtual pet A can obtain external visual information from the outside world in many ways. For example, by anthropomorphizing the pet, it can be compared to the psychology of humans who want to possess beautiful things when they see them, and external visual information can be divided into the first type of aesthetic perception information. In addition, it can be compared to the social needs of wearing accessories in human social situations, and external visual information can be divided into the second type of social perception information.

[0041] The first method involves the external visual information being aesthetic perception information, where an attractiveness value represents the degree of aesthetic appeal. In response to the processing instruction for the acquired external visual information, step S110 processes the external visual information based on the unique personality traits of the local virtual pet to obtain the external visual information's incentive for its own appearance, including steps S1101-S1102: S1101, In response to the processing instruction for the acquired aesthetic perception information, calculate the average value of the local virtual pet's curiosity and sensitivity from an aesthetic perspective; Among them, aesthetic perception information is represented by the attraction value Attraction Desire_Valu, which indicates the degree of aesthetics. The higher the attraction value, the higher the degree of aesthetics.

[0042] As an example, the state machine of virtual pet A detects a skin with an attraction value of 0.9 within its field of vision. Since this skin has a high attraction value and is one that virtual pet A does not currently own, its aesthetic incentive for virtual pet A is calculated. If the calculated aesthetic incentive exceeds a threshold, it means that virtual pet A strongly desires to own it.

[0043] Before calculating aesthetic enhancements, the pet's personality should be considered. First, the average levels of curiosity and sensitivity for the local virtual pet should be calculated from an aesthetic perspective. With a curiosity level of 0.7 and a sensitivity level of 0.4, the average of these two levels should be calculated.

[0044] S1102. Calculate the product of the average value and the attractiveness value to obtain the aesthetic perception information's motivation for one's own aesthetic attire.

[0045] Calculate the product of the average value and the attraction value of 0.9. The formula for calculating the aesthetic perception information's incentive to improve one's appearance, Desire_Value, is: Desire_Value = (Curiosity + Sensitivity) / 2 * AttractionDesire_Value, where Curiosity and Sensitivity represent the degree of curiosity and sensitivity, respectively, and AttractionDesire_Value is the attraction value.

[0046] The aesthetic incentive for pet A with an attraction value of 0.9 is calculated as (0.7 + 0.4) / 2 * 0.9 = 0.495.

[0047] In some embodiments, an incentive threshold is preset, which represents the threshold of external information stimulation to the pet. If the aesthetic outfit incentive exceeds the incentive threshold, it means that pet A also wants the same aesthetic outfit, such as skin.

[0048] As an example, the incentive threshold for initiating a try-on request is set to 0.45. When the aesthetic outfit incentive of 0.495 exceeds the incentive threshold of 0.45 for initiating a try-on request, pet A triggers an internal state transition and says to Xiaoming, "Xiaoming, look at that astronaut's suit, it seems to glow! Can we try it on?"

[0049] After Xiaoming agrees, the local device sends a try-on request to the cloud server. After the cloud server verifies the request, it temporarily applies the interstellar astronaut skin to Pet A's model and starts a 24-hour countdown. In addition, the interstellar astronaut skin has a "trial" watermark.

[0050] The aforementioned aesthetic dressing incentives are calculated based on the pet's curiosity and sensitivity levels within its personality vector. In some embodiments, the calculation method is not limited to this. Aesthetic dressing incentives can also be calculated based on the sensitivity levels within the pet's personality vector.

[0051] For example, virtual pet A sees pet B using a skin in the social square. Pet B is wearing the "Interstellar Astronaut" skin and using the "Super Rocket Boots" item. The skin's attractiveness value is 0.9. The product of virtual pet A's sensitivity value (0.4) and pet B's skin's attractiveness value (0.9) is calculated, resulting in an aesthetic dressing incentive of 0.9 * 0.4 = 0.36. If this exceeds the preset incentive threshold for initiating a try-on request, pet A further turns to Xiaoming, and its animation displays a slightly shy yet longing attitude, saying, "Xiaoming, look at that astronaut suit, it seems to glow! Can we try it on?" The slightly shy yet longing attitude displayed in the animation is determined by its pet personality vector.

[0052] The second type involves external visual information that is socially perceptual information. This socially perceptual information represents the degree of social attraction using rarity and ability gain. S110 responds to the processing instruction for the acquired external visual information by processing the external visual information based on the unique personality traits of the local virtual pet, obtaining the external visual information's incentive for its own appearance, including:

[0053] In response to the processing instruction of the acquired social perception information, the social perception information is processed from a social perspective based on the pet personality vector of the local virtual pet to obtain the social perception information's social dressing incentive for itself, wherein the social dressing incentive is calculated based on the pet personality vector, the rarity, and the ability gain.

[0054] As an example, Pet A's behavior decision engine also analyzes the "Super Rocket Boots" item. It calculates the item's social appearance incentive. The "Super Rocket Boots" item has a rarity of 0.95 and an ability boost ΔPower of 0.5. The social appearance incentive SP is calculated as: SP = (Rarity + Ability Boost) * (Curiosity + Item's Preset Competitiveness). This gives Pet A a social appearance incentive of 1.45 for the "Super Rocket Boots." This value of 1.45 far exceeds the preset social appearance threshold of 1.0. Therefore, the output decision engine immediately triggers a higher-priority "expressing strong desire" behavior. Pet A's animation becomes eager, and its eyes light up. The degree to which Pet A's animation exceeds the social appearance threshold is determined by how much the social appearance incentive exceeds it. The greater the exceedance, the more eager the expression. Pet A says to Xiaoming, "Wow! Look at those rocket boots! If we had one too, we could complete the 'Interstellar Express' mission faster than Xiaohong! Let's go see how to get it!"

[0055] S120. If it is determined that the dressing-up incentive exceeds a preset incentive threshold, then the behavior of requesting the user to experience the dressing-up is triggered, wherein when requesting the user to experience the dressing-up, the request is made based on the personality trait. Existing social features mostly remain at a superficial level, such as visiting others' profiles, liking posts, and leaderboards, resulting in a shallow social value chain. Social observation among users struggles to translate into effective intrinsic needs and consumption drivers. For example, when users see rare items or skins owned by others, beyond envy, there's a lack of a systematic mechanism to guide users to acquire them through their own efforts, creating a break in the closed loop of social incentives.

[0056] If the outfit incentive exceeds a preset incentive threshold, a request to the user to try on the outfit is triggered. For example, if the aesthetic outfit incentive of 0.495 exceeds the incentive threshold of 0.45 for initiating a try-on request, the request to the user to try on the outfit is triggered. Pet A triggers an internal state transition and says to Xiaoming, "Xiaoming, look at that astronaut suit, it seems to glow! Can we try it on?" Or perhaps the Super Rocket Boots item provides a social outfit incentive of 1.45 for Pet A. This value of 1.45 far exceeds the preset social outfit threshold of 1.0. Pet A says to Xiaoming, "Wow! Look at those rocket boots! If we had one too, we could complete the interstellar delivery mission faster than Xiaohong! Let's go see how to get them!"

[0057] When requesting a user to experience the outfit, the request is made based on the user's personality traits. For example, the slightly shy yet longing posture shown in the animation is determined by the pet's personality vector.

[0058] S130. In response to the user's consent instruction to the request to experience the outfit, send an outfit experience request to the cloud server; Virtual pet A requests the user's consent to try out the outfit. Once the user agrees, they can issue a consent command on the device, which then sends the outfit request, such as a try-on request, to the cloud server.

[0059] S140. Upon receiving the verification message from the cloud server confirming successful verification, the system receives a dress-up experience set from the cloud server that matches the external visual information and applies the dress-up experience set to the local virtual pet. The cloud server starts timing the dress-up experience and triggers removal when the experience time exceeds the time limit. The system awaits verification from the cloud server. Upon successful verification, the cloud server sends a verification message to the AI-powered learning companion device and distributes a dress-up experience set that matches the external visual information. For example, if the external visual information corresponds to the "Interstellar Astronaut" skin, the dress-up experience set received by the AI-powered learning companion device from the cloud server will be the "Interstellar Astronaut" skin. The "Interstellar Astronaut" skin is temporarily applied to the local virtual pet A.

[0060] The cloud server starts timing the dress-up experience. When the experience time exceeds the 24-hour validity period, it will be removed.

[0061] S150, In response to the removal command from the cloud server, the costume experience set is removed from the local virtual pet; In response to the removal command from the cloud server, the costume experience set is removed from the local virtual pet, and pet A returns to its original state.

[0062] Pet A's state machine triggers a "retrograde incentive" event. Responding based on its personality vector, Pet A makes a slightly frustrated animation and says, "I'm back to normal, but if we complete all the learning challenges this week, we'll have enough points to redeem half of the 'Interstellar Astronaut' skin! Let's do our best!"

[0063] S160. Based on the personality traits, a pre-generated learning plan is pushed to the user to drive the user's learning motivation based on the user's experience with the costume set. The learning plan is generated based on the combination of the costume incentive and the user's current historical learning task completion rate.

[0064] The system automatically creates a long-term motivational goal associated with the skin in the task list, successfully internalizing external social stimuli into the user's learning motivation. Based on the stated personality traits, a pre-generated learning plan is pushed to the user, intrinsically driving their learning motivation based on their experience with the costume set. The learning plan is generated based on a combination of the costume incentive and the user's current historical learning task completion rate.

[0065] Before step S160, steps B1-B4 are included: B1. Obtain the user's previous learning records from the cloud server; The cloud server stores usage records for each AI-powered learning companion device, including learning records of users completing learning tasks using the AI-powered learning companion device.

[0066] The AI-powered learning companion desk pet device in this embodiment obtains the user's previous learning records from the cloud server.

[0067] B2. Based on the completion rate of the historical learning task corresponding to the learning record, calculate the learning score difference with the dress-up experience set, wherein the learning score difference is the difference obtained by subtracting the second learning score corresponding to the completion rate of the historical learning task from the first learning score corresponding to the dress-up experience set. Based on a user's previous learning records, the user's historical completion status is evaluated, and then represented by the historical task completion rate. Since the virtual pet currently requires a costume experience set, the degree to which the user has previously completed learning needs improvement, or this could be used as an opportunity to stimulate the user's intrinsic motivation to learn. Correspondingly, the difference between the user's previous learning completion rate and the learning completion rate corresponding to the costume experience set is calculated, and this difference can be represented in the form of learning points. For example, if a user frequently gave up halfway through completing learning tasks, resulting in a low completion rate in their learning record, they would have earned few points. However, the costume experience set requires a significant number of learning points. Therefore, based on the difference in points, the user is encouraged to learn. The learning points required for the costume experience set are designated as the first learning point, and the learning points corresponding to the user's previous historical learning task completion rate are designated as the second learning point. The difference between the first and second learning points is used to motivate the user to learn.

[0068] B3. Based on the dressing-up incentive and the learning score difference, generate a long-term incentive goal that matches the dressing-up experience set; Based on the outfit incentives and the learning score difference, a long-term incentive goal matching the outfit experience set is generated. The system automatically creates a long-term incentive goal associated with that skin in the task list.

[0069] B4. Based on the long-term incentive goal, generate the corresponding learning plan, wherein the learning plan includes the amount of learning to be done regularly and the types of subjects to be studied.

[0070] For example, the virtual pet says to the user, "If we complete all the learning challenges this week, we'll have enough points to redeem half of the 'Interstellar Astronaut' skin! Let's do our best!" This interaction directly highlights the long-term goal of "obtaining the Super Rocket Boots" in the user's task system and automatically filters out all relevant tasks that can obtain the item or its fragments, presenting them to the user.

[0071] Monitor the user's subsequent learning progress, record the user's learning progress on this device, and upload it to the cloud server.

[0072] This is used to slowly and continuously adjust certain dimension values ​​in the multidimensional personality state vector based on long-term interaction data between the user and the virtual pet, such as interaction frequency and task completion status, so that the virtual pet's personality evolves over time.

[0073] Each virtual pet corresponds to a relative competitiveness value representing the user's learning ability. Step S160, after pushing a pre-generated learning plan to the user based on the personality trait to intrinsically drive the user's learning motivation through their experience with the costume set, includes steps C1-C5: C1. Periodically obtain the user's progress on the phased learning tasks indicated by the learning plan; The system periodically retrieves the user's progress on the learning tasks assigned in the learning plan. Over the next month, inspired by his pet A, Xiaoming completed several challenging "Interstellar" series missions.

[0074] C2. Settle the periodically obtained phase completion status to obtain the phase completion rate; C3. Based on the stage completion degree and the preset completion degree threshold, first perform a subtraction operation, and then multiply the subtraction result with the preset learning rate to calculate the update change. C4. Add the updated change to the current relative competitiveness value to obtain a new relative competitiveness value that is more similar; C5. Based on the new relative competitiveness value, update the unique personality traits of the local virtual pet to achieve personality growth for the local virtual pet.

[0075] A settlement is performed every weekend, detecting the task completion rate relative to the competitiveness value. If the task completion rate is extremely high, the operation is initiated. The competitiveness value is P.competitiveness.

[0076] The evolutionary algorithm is P.competitiveness_new = P.competitiveness_old + α * (SuccessRate - β), where α is the learning rate, β is the expected success rate baseline (e.g., 0.5), P.competitiveness_new is the updated relative competitiveness value, P.competitiveness_old is the current relative competitiveness value, and SuccessRate is the task completion rate.

[0077] After several iterations, Pet A's relative competitiveness score slowly increased from 0.3 to 0.35. This increase in relative competitiveness indicates that Pet A will exhibit a stronger desire when facing similar social competition scenarios in the future. The threshold for triggering its needs will relatively decrease, reflecting the growth of its "personality."

[0078] Through a personality gene creation and permanent binding mechanism, the absolute uniqueness and irreplaceability of each virtual pet are ensured, elevating the user experience from simply choosing a pet to creating a companion, greatly deepening the emotional connection. By quantifying personality into a calculable and evolvable multi-dimensional personality vector, and using this to drive the pet's autonomous behavioral decisions, virtual pets are given life. Their interactions are no longer mechanical program responses, but rather genuine interactions full of personalization and unpredictability. A social incentive mechanism based on internal state and external perception transforms vague envy into quantifiable social potential and concrete action commands, constructing a deep incentive loop of perception-decision-request-conversion, significantly enhancing long-term user stickiness.

[0079] This application provides a virtual pet interaction method, device, and equipment for AI-powered learning companions. Compared to the technical problem of traditional electronic virtual pets lacking deep social and emotional interaction capabilities with users, this application applies the AI-powered learning companion virtual pet interaction method to AI-powered learning companion virtual pet interaction devices. Multiple AI-powered learning companion virtual pet interaction devices establish communication connections with a cloud server. The cloud server acts as a connection center, processing requests from the AI-powered learning companion virtual pet interaction devices and storing data. The AI-powered learning companion virtual pet interaction method includes: responding to a processing instruction for acquired external visual information, processing the external visual information based on the unique personality traits of the local virtual pet to obtain a dressing-up incentive from the external visual information, wherein the external visual information is acquired from other virtual pets in a preset virtual social space; if it is determined that the dressing-up incentive exceeds a preset incentive threshold, a request is triggered to the user. The process involves requesting a dress-up experience, where the request is made based on the user's personality traits. Upon receiving a consent instruction from the user agreeing to the dress-up experience request, a dress-up experience request is sent to the cloud server. Upon receiving a verification message from the cloud server confirming successful verification, the user receives a dress-up experience set matching the external visual information from the cloud server and applies the dress-up experience set to their local virtual pet. The cloud server starts a dress-up experience timer, and removal is triggered when the experience time exceeds the validity period. In response to the removal instruction from the cloud server, the dress-up experience set is removed from the local virtual pet. Finally, based on the personality traits, a pre-generated learning plan is pushed to the user to intrinsically drive their learning motivation based on their experience with the dress-up set. This learning plan is generated based on a combination of the dress-up incentive and the user's current historical learning task completion rate. In this application, virtual pets possess unique personality traits, avoiding the problem of uniformity. Deep interaction between pets and users can simulate the complexity and emotional depth of real living beings, increasing fun. External visual information between users can be transformed into effective internal needs and consumption drivers, improving the ability of electronic virtual pets to engage in deep social and emotional interaction with users.

[0080] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0081] In one embodiment, such as Figure 2This invention provides an AI-powered virtual pet interaction device for learning, which is deployed within an AI-powered virtual pet interaction platform. The AI-powered virtual pet interaction method is applied to these devices. Multiple AI-powered virtual pet interaction devices establish communication connections with a cloud server, which acts as a connection center, processing requests from the devices and storing data. Each AI-powered virtual pet interaction device corresponds one-to-one with the AI-powered virtual pet interaction method described in the previous embodiment. Figure 2 As shown, the AI-powered virtual pet interaction device includes an information processing unit 101, an interaction unit 102, a sending unit 103, and a dressing-up processing unit 104. Detailed descriptions of each functional module are as follows: Information processing unit 101 is used to respond to the processing instruction of the acquired external visual information, process the external visual information based on the unique personality traits of the local virtual pet, and obtain the external visual information's encouragement for its own appearance, wherein the external visual information is acquired from other virtual pets in a preset virtual social space. The interaction unit 102 is used to trigger the behavior of requesting the user to experience the outfit if it is determined that the outfit incentive exceeds a preset incentive threshold, wherein when requesting the user to experience the outfit, the request is made based on the personality trait. Sending unit 103 is used to send an outfit experience request to the cloud server in response to the user's consent instruction to the outfit experience request; The dress-up processing unit 104 is used to receive a dress-up experience set that matches the external visual information sent by the cloud server when it receives a verification message that the cloud server has verified the dress-up, and apply the dress-up experience set to the local virtual pet. The cloud server starts the dress-up experience timer and triggers removal when the experience time exceeds the experience validity period. The dress-up processing unit 104 is also used to remove the dress-up experience set from the local virtual pet in response to the removal command from the cloud server; The interaction unit 102 is also used to push a pre-generated learning plan to the user based on the personality trait, so as to drive the user's learning motivation based on the user's experience with the dress-up outfit. The learning plan is generated based on the combination of the dress-up incentive and the user's current historical learning task completion rate.

[0082] In one possible implementation of this application, the AI-assisted learning virtual pet interaction device further includes a personality evolution unit 105. Each virtual pet corresponds to a relative competitiveness value representing the user's learning ability. After the personality trait is used to push a pre-generated learning plan to the user to intrinsically drive the user's learning motivation based on the user's experience with the dress-up outfit, the personality evolution unit 105 is specifically used for: Periodically obtain the user's progress on the phased learning tasks indicated by the learning plan; The periodic progress reports are used to calculate the progress of each stage and obtain the stage completion rate. The update change is calculated by first subtracting the stage completion degree from the preset completion degree threshold, and then multiplying the result of the subtraction operation with the preset learning rate. The updated change is added to the current relative competitiveness value to obtain a new, more similar relative competitiveness value; Based on the new relative competitiveness value, the unique personality traits of the local virtual pets are updated to achieve personality growth for the local virtual pets.

[0083] In one possible implementation of this application, the AI-assisted learning virtual pet interaction device further includes a learning plan generation unit 106. Before pushing a pre-generated learning plan to the user based on the personality traits to intrinsically drive the user's learning motivation based on the user's experience with the costume set, the learning plan generation unit 106 is specifically used for: Retrieve the user's previous learning records from the cloud server; Based on the completion rate of the historical learning tasks corresponding to the learning record, the difference in learning points with the dress-up experience set is calculated, wherein the difference in learning points is the difference obtained by subtracting the second learning point corresponding to the completion rate of the historical learning tasks from the first learning point corresponding to the dress-up experience set. Based on the dressing-up incentives and the learning score difference, a long-term incentive goal matching the dressing-up experience set is generated; Based on the long-term incentive goals, a corresponding learning plan is generated, wherein the learning plan includes the amount of learning to be done regularly and the types of subjects to be studied.

[0084] In one possible implementation of this application, the AI-assisted learning virtual pet interaction device further includes a virtual pet generation unit 107. Before processing the acquired external visual information based on the unique personality traits of the local virtual pet, in response to processing instructions, and before obtaining the external visual information's motivation for the virtual pet's appearance, the virtual pet generation unit 107 is specifically used for: Upon initial launch, the user is given a personality test-style question. Based on the user's answers to questions, a standard serialization process is performed to obtain a multi-dimensional user personality vector. Based on the user personality vector, a unique pet identity tag is generated for the local virtual pet; Based on the user's personality vector, a virtual pet anthropomorphic pet personality vector is generated to achieve intrinsic anthropomorphism of the virtual pet. The pet personality vector is input into a preset parametric image generation model. Based on the parametric image generation model, a virtual pet with a personality consistent with the pet personality vector is generated. The external appearance of the virtual pet is also consistent with the pet personality vector. The virtual pet is marked with the pet identity tag. Based on the pet's personality vector, corresponding dress-up experience sets are selected from a preset resource library, and the selected dress-up experience sets are applied to the virtual pet.

[0085] In one possible implementation of this application, when the virtual pet generation unit 107 inputs the pet personality vector into a preset parametric image generation model and generates a virtual pet with a personality consistent with the pet personality vector based on the parametric image generation model, the pet personality vector includes introversion, curiosity, and sensitivity. The higher the introversion in the pet personality vector, the larger the pupils and the more droopy the ears in the virtual pet's appearance. The higher the curiosity in the pet personality vector, the longer the tail in the virtual pet's appearance. The higher the sensitivity in the pet personality vector, the smaller the body size in the virtual pet's appearance.

[0086] In one possible implementation of this application, the external visual information is aesthetic perception information, which uses an attractiveness value to represent the degree of aesthetics. The information processing unit 101, in response to a processing instruction on the acquired external visual information, processes the external visual information based on the unique personality traits of the local virtual pet to obtain the external visual information's incentive for its own appearance. Specifically, this is used for: In response to the processing instructions for the acquired aesthetic perception information, the average level of curiosity and sensitivity of the local virtual pet is calculated from an aesthetic perspective; Calculate the product of the average value and the attractiveness value to obtain the aesthetic perception information's motivation for one's own aesthetic attire.

[0087] In one possible implementation of this application, the external visual information is socially perceptual information, which characterizes the degree of social attraction using rarity and ability gain. The information processing unit 101, in response to processing instructions on the acquired external visual information, processes the external visual information based on the unique personality traits of the local virtual pet to obtain the external visual information's incentive for its own appearance, including: In response to the processing instruction of the acquired social perception information, the social perception information is processed from a social perspective based on the pet personality vector of the local virtual pet to obtain the social perception information's social dressing incentive for itself, wherein the social dressing incentive is calculated based on the pet personality vector, the rarity, and the ability gain.

[0088] In one possible implementation of this application, when the dressing-up processing unit 104 receives a dressing-up experience set that matches the external visual information sent by the cloud server upon receiving a verification message indicating that the cloud server has verified the information, and applies the dressing-up experience set to the local virtual pet, the dressing-up experience set includes dressing-up skins and / or dressing-up props.

[0089] This application provides a virtual pet interaction method, device, and equipment for AI-powered learning companions. Compared to the technical problem of traditional electronic virtual pets lacking deep social and emotional interaction capabilities with users, this application applies the AI-powered learning companion virtual pet interaction method to AI-powered learning companion virtual pet interaction devices. Multiple AI-powered learning companion virtual pet interaction devices establish communication connections with a cloud server. The cloud server acts as a connection center, processing requests from the AI-powered learning companion virtual pet interaction devices and storing data. The AI-powered learning companion virtual pet interaction method includes: responding to a processing instruction for acquired external visual information, processing the external visual information based on the unique personality traits of the local virtual pet to obtain a dressing-up incentive from the external visual information, wherein the external visual information is acquired from other virtual pets in a preset virtual social space; if it is determined that the dressing-up incentive exceeds a preset incentive threshold, a request is triggered to the user. The process involves requesting a dress-up experience, where the request is made based on the user's personality traits. Upon receiving a consent instruction from the user agreeing to the dress-up experience request, a dress-up experience request is sent to the cloud server. Upon receiving a verification message from the cloud server confirming successful verification, the user receives a dress-up experience set matching the external visual information from the cloud server and applies the dress-up experience set to their local virtual pet. The cloud server starts a dress-up experience timer, and removal is triggered when the experience time exceeds the validity period. In response to the removal instruction from the cloud server, the dress-up experience set is removed from the local virtual pet. Finally, based on the personality traits, a pre-generated learning plan is pushed to the user to intrinsically drive their learning motivation based on their experience with the dress-up set. This learning plan is generated based on a combination of the dress-up incentive and the user's current historical learning task completion rate. In this application, virtual pets possess unique personality traits, avoiding the problem of uniformity. Deep interaction between pets and users can simulate the complexity and emotional depth of real living beings, increasing fun. External visual information between users can be transformed into effective internal needs and consumption drivers, improving the ability of electronic virtual pets to engage in deep social and emotional interaction with users.

[0090] Specific limitations regarding the AI-powered virtual pet interaction device for learning companions can be found in the above section on the limitations of the AI-powered virtual pet interaction method, and will not be repeated here. Each module in the aforementioned AI-powered virtual pet interaction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0091] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a device bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores operating devices, computer programs, and the database. The internal memory provides an environment for the operation of the operating devices and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a server-side method for AI-assisted virtual pet interaction.

[0092] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a device bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores operating devices and computer programs. The internal memory provides an environment for the operation of the operating devices and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements client-side functions or steps of an AI-assisted virtual pet interaction method.

[0093] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: In response to the processing instructions for the acquired external visual information, the external visual information is processed based on the unique personality traits of the local virtual pet to obtain the external visual information's incentive for its own appearance. The external visual information is acquired from other virtual pets in a preset virtual social space. If it is determined that the outfit incentive exceeds a preset incentive threshold, then the action of requesting the user to experience the outfit is triggered, wherein when requesting the user to experience the outfit, the request is made based on the personality trait. In response to the user's consent instruction to try out the outfit, a request to try out the outfit is sent to the cloud server; Upon receiving the verification message from the cloud server confirming successful verification, the system receives a dress-up experience set from the cloud server that matches the external visual information and applies the dress-up experience set to the local virtual pet. The cloud server then starts timing the dress-up experience and triggers removal when the experience time exceeds the validity period. In response to the removal command from the cloud server, the costume experience set is removed from the local virtual pet; The user's learning motivation is driven by their experience with the costume outfit, based on the personality traits mentioned above. The learning plan is generated by combining the costume incentives and the user's current historical learning task completion rate.

[0094] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: In response to the processing instructions for the acquired external visual information, the external visual information is processed based on the unique personality traits of the local virtual pet to obtain the external visual information's incentive for its own appearance. The external visual information is acquired from other virtual pets in a preset virtual social space. If it is determined that the outfit incentive exceeds a preset incentive threshold, then the action of requesting the user to experience the outfit is triggered, wherein when requesting the user to experience the outfit, the request is made based on the personality trait. In response to the user's consent instruction to try out the outfit, a request to try out the outfit is sent to the cloud server; Upon receiving the verification message from the cloud server confirming successful verification, the system receives a dress-up experience set from the cloud server that matches the external visual information and applies the dress-up experience set to the local virtual pet. The cloud server then starts timing the dress-up experience and triggers removal when the experience time exceeds the validity period. In response to the removal command from the cloud server, the costume experience set is removed from the local virtual pet; The user's learning motivation is driven by their experience with the costume outfit, based on the personality traits mentioned above. The learning plan is generated by combining the costume incentives and the user's current historical learning task completion rate.

[0095] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0096] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other storage media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0098] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An AI companion virtual pet interaction method, characterized by, The AI-powered virtual pet interaction method is applied to AI-powered virtual pet interaction devices. Multiple AI-powered virtual pet interaction devices establish communication connections with a cloud server. The cloud server acts as a connection center, processing requests from the AI-powered virtual pet interaction devices and storing data. The AI-powered virtual pet interaction method includes: In response to the processing instructions for the acquired external visual information, the external visual information is processed based on the unique personality traits of the local virtual pet to obtain the external visual information's incentive for its own appearance. The external visual information is acquired from other virtual pets in a preset virtual social space. If it is determined that the outfit incentive exceeds a preset incentive threshold, then the action of requesting the user to experience the outfit is triggered, wherein when requesting the user to experience the outfit, the request is made based on the personality trait. In response to the user's consent instruction to try out the outfit, a request to try out the outfit is sent to the cloud server; Upon receiving the verification message from the cloud server confirming successful verification, the system receives a dress-up experience set from the cloud server that matches the external visual information and applies the dress-up experience set to the local virtual pet. The cloud server then starts timing the dress-up experience and triggers removal when the experience time exceeds the validity period. In response to the removal command from the cloud server, the costume experience set is removed from the local virtual pet; The user's learning motivation is driven by their experience with the costume outfit, based on the personality traits mentioned above. The learning plan is generated by combining the costume incentives and the user's current historical learning task completion rate. 2.The AI companion virtual pet interaction method of claim 1, wherein, Each virtual pet corresponds to a relative competitiveness value representing the user's learning ability. The step of pushing a pre-generated learning plan to the user based on the personality trait, thereby intrinsically driving the user's learning motivation based on their experience with the costume set, includes: Periodically obtain the user's progress on the phased learning tasks indicated by the learning plan; The periodic progress reports are used to calculate the progress of each stage and obtain the stage completion rate. The update change is calculated by first subtracting the stage completion degree from the preset completion degree threshold, and then multiplying the result of the subtraction operation with the preset learning rate. The updated change is added to the current relative competitiveness value to obtain a new, more similar relative competitiveness value; Based on the new relative competitiveness value, the unique personality traits of the local virtual pets are updated to achieve personality growth for the local virtual pets.

3. The virtual pet interaction method for AI-assisted learning according to claim 1, characterized in that, Before the step of pushing a pre-generated learning plan to the user based on the personality trait to intrinsically drive the user's learning motivation based on the user's experience with the costume set, the following steps are included: Retrieve the user's previous learning records from the cloud server; Based on the completion rate of the historical learning tasks corresponding to the learning record, the difference in learning points with the dress-up experience set is calculated, wherein the difference in learning points is the difference obtained by subtracting the second learning point corresponding to the completion rate of the historical learning tasks from the first learning point corresponding to the dress-up experience set. Based on the dressing-up incentives and the learning score difference, a long-term incentive goal matching the dressing-up experience set is generated; Based on the long-term incentive goals, a corresponding learning plan is generated, wherein the learning plan includes the amount of learning to be done regularly and the types of subjects to be studied.

4. The virtual pet interaction method for AI-assisted learning according to claim 1, characterized in that, The step of responding to the processing instruction for the acquired external visual information, processing the external visual information based on the unique personality traits of the local virtual pet, and obtaining the external visual information's incentive for its own appearance, includes: Upon initial launch, the user is given a personality test-style question. Based on the user's answers to questions, a standard serialization process is performed to obtain a multi-dimensional user personality vector. Based on the user personality vector, a unique pet identity tag is generated for the local virtual pet; Based on the user's personality vector, a virtual pet anthropomorphic pet personality vector is generated to achieve intrinsic anthropomorphism of the virtual pet. The pet personality vector is input into a preset parametric image generation model. Based on the parametric image generation model, a virtual pet with a personality consistent with the pet personality vector is generated. The external appearance of the virtual pet is also consistent with the pet personality vector. The virtual pet is marked with the pet identity tag. Based on the pet's personality vector, corresponding dress-up experience sets are selected from a preset resource library, and the selected dress-up experience sets are applied to the virtual pet.

5. The virtual pet interaction method for AI-assisted learning according to claim 4, characterized in that, The pet personality vector includes introversion, curiosity, and sensitivity. The higher the introversion in the pet personality vector, the larger the pupils and the more droopy the ears in the virtual pet's appearance. The higher the curiosity in the pet personality vector, the longer the tail in the virtual pet's appearance. The higher the sensitivity in the pet personality vector, the smaller the size of the virtual pet's appearance.

6. The virtual pet interaction method for AI-assisted learning according to claim 5, characterized in that, The external visual information is aesthetic perception information, which uses an attractiveness value to represent the degree of aesthetics. The processing instruction for the acquired external visual information is used to process the external visual information based on the unique personality traits of the local virtual pet, resulting in an incentive from the external visual information for the pet's appearance, including: In response to the processing instructions for the acquired aesthetic perception information, the average level of curiosity and sensitivity of the local virtual pet is calculated from an aesthetic perspective; Calculate the product of the average value and the attractiveness value to obtain the aesthetic perception information's motivation for one's own aesthetic attire.

7. The virtual pet interaction method for AI-assisted learning according to claim 5, characterized in that, The external visual information is socially perceptual information, which represents the degree of social attraction using rarity and ability gain. The processing instruction for the acquired external visual information is used to process the external visual information based on the unique personality traits of the local virtual pet, resulting in the external visual information's incentive for the pet's appearance, including: In response to the processing instruction of the acquired social perception information, the social perception information is processed from a social perspective based on the pet personality vector of the local virtual pet to obtain the social perception information's social dressing incentive for itself, wherein the social dressing incentive is calculated based on the pet personality vector, the rarity, and the ability gain.

8. The virtual pet interaction method for AI-assisted learning according to claim 1, characterized in that, The costume experience kit includes costume skins and / or costume props.

9. A virtual pet interaction device for AI-assisted learning, characterized in that, The AI-powered virtual pet interaction device is deployed on the AI-powered virtual pet interaction equipment. The AI-powered virtual pet interaction method according to any one of claims 1-8 is applied to the AI-powered virtual pet interaction equipment. Multiple AI-powered virtual pet interaction devices establish communication connections with a cloud server, whereby the cloud server acts as a connection center, processing requests from the AI-powered virtual pet interaction devices and storing data. The AI-powered virtual pet interaction device comprises: An information processing unit is used to respond to a processing instruction for the acquired external visual information, process the external visual information based on the unique personality traits of the local virtual pet, and obtain the external visual information's encouragement for its own appearance, wherein the external visual information is acquired from other virtual pets in a preset virtual social space. An interaction unit is configured to trigger a request to the user to experience the outfit if it is determined that the outfit incentive exceeds a preset incentive threshold, wherein the request to the user to experience the outfit is made based on the personality trait. The sending unit is used to send a request to the cloud server in response to the user's consent instruction to agree to the request to try out the outfit; The dressing-up processing unit is used to receive a dressing-up experience set that matches the external visual information sent by the cloud server when it receives a verification message that the cloud server has verified the information, and to apply the dressing-up experience set to the local virtual pet. The cloud server starts the dressing-up experience timer and triggers removal when the experience time exceeds the experience validity period. The dress-up processing unit is also used to remove the dress-up experience set from the local virtual pet in response to the removal command from the cloud server; The interaction unit is also used to push a pre-generated learning plan to the user based on the personality trait, so as to drive the user's learning motivation based on the user's experience with the dress-up outfit. The learning plan is generated based on the combination of the dress-up incentive and the user's current historical learning task completion rate.

10. A virtual pet interaction device for AI-assisted learning, characterized in that, The system includes a memory, a processor, and an AI-powered virtual pet interaction program stored in the memory and executable on the processor. The processor executes the AI-powered virtual pet interaction program to implement the steps of the AI-powered virtual pet interaction method according to any one of claims 1 to 8.

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