Children talent type mining method and device based on XR equipment

Abstract

The invention provides a child talent type mining method and device based on XR equipment. The method comprises the steps that task content of a first task is displayed to a target user through the XR equipment; pushing the first task to the target object to obtain a first task execution result of the target object; acquiring iris image data and behavior data of the target object when the target object uses the XR equipment; according to the iris image data and the behavior data, acquiring the emotion stability of the target object for the first task; according to the first task execution result, obtaining the task completion degree of the target object for the first task; and obtaining a talent evaluation result of the target user for the first task according to the emotion stability and the task completion degree. According to the method, the problems that the time consumption is long, the cost is high, the subjectivity is high and the interests and potentials of children cannot be fully explored when the talent of the children is explored in the related technology are solved.

Description

Technical Field

[0001] This application relates to the field of information processing technology, and in particular to a method and apparatus for discovering children's talent types based on XR devices. Background Technology

[0002] Currently, with the advancement of technology, XR devices have been gradually applied in various fields such as education and entertainment. However, how to utilize these devices to discover children's talents remains a challenge. Existing technologies mainly focus on traditional testing and assessment, which are time-consuming, costly, and highly subjective, failing to fully explore children's interests and potential. Based on the actual needs in the above scenarios, this application proposes a method and apparatus for discovering children's talent types based on XR devices. Summary of the Invention

[0003] This application provides a method and apparatus for discovering children's talent types based on XR devices, in order to solve the problems that related technologies are time-consuming, costly, and highly subjective in discovering children's talents, and cannot fully explore children's interests and potential.

[0004] The first aspect of this application provides a method for discovering children's talent types based on XR devices. The method includes: displaying the task content of a first task to a target user through an XR device; pushing the first task to a target object to obtain the target object's first task execution result; obtaining iris image data and behavioral data of the target object when using the XR device, wherein the iris image data includes: first iris image data recorded when displaying the task content of the first task to the target user, and second iris image data recorded when the target object performs the first task; obtaining the target object's emotional stability for the first task based on the iris image data and behavioral data; obtaining the target object's task completion rate for the first task based on the first task execution result; and obtaining the target user's talent evaluation result for the first task based on the emotional stability and task completion rate.

[0005] This application employs the aforementioned method, using XR devices to collect and process iris image data and behavioral data of the target subject, thereby obtaining the target subject's emotional stability and task completion rate for the first task, and evaluating the target subject's performance in performing a specific task. This allows for a more objective and accurate assessment of the child's potential and developmental direction.

[0006] Optionally, the method further includes: when the emotional stability is greater than or equal to a preset emotional stability threshold and the task completion is less than a preset task completion threshold, obtaining a second task from the task library, wherein the task content of the second task is more related to the task content of the first task than a preset relevance threshold; displaying the task content of the second task to the target user and pushing the second task to the target object.

[0007] By employing the above method, during the talent evaluation of the target individual, the next task type to be explored is recommended based on their different performance levels in emotional stability and task completion. When emotional stability is high but task completion is low, a second task with a high relevance to the first task is recommended. This allows the target individual to maintain high emotional stability while further observing their performance on the second task, thus more quickly identifying a suitable talent development type.

[0008] Optionally, based on the execution result of the first task, the task completion degree of the target object for the first task is obtained, specifically including: comparing the execution result of the first task with the standard result of the first task to obtain the similarity of the first task; inputting the execution result of the first task into a preset expert model to obtain the artistic richness of the first task; and obtaining the task completion degree of the target object for the first task based on the similarity and artistic richness of the first task.

[0009] By adopting the above method, when evaluating the target object's task completion of the first task, the execution result of the first task is compared with the standard result of the first task to obtain the similarity of the first task; the execution result of the first task is input into a preset expert model to obtain the artistic richness of the first task. At the same time, the target object's ability to quickly imitate the task content is taken into account, as well as the target object's own creative ability during the execution of the task, so that the obtained task completion of the first task can more accurately and completely reflect the target object's task completion status.

[0010] Optionally, based on iris image data and behavioral data, the emotional stability of the target object in relation to the first task is obtained, specifically including: based on the first iris image data, obtaining the first emotional fluctuation state when the task content of the first task is displayed to the target user; based on the second iris image data, obtaining the second emotional fluctuation state when the target object performs the first task; based on the behavioral characteristics of the behavioral data, obtaining the behavioral fluctuation state of the target object when using the XR device; and based on the first emotional fluctuation state, the second emotional fluctuation state, and the behavioral fluctuation state, obtaining the emotional stability of the target object in relation to the first task.

[0011] By employing the above methods, iris image data and behavioral data of the target object are obtained. Based on the first emotional fluctuation state when the target user is shown the task content of the first task, the second emotional fluctuation state when the target object performs the first task, and the behavioral data throughout the task process, the emotional stability of the target object for the first task can be comprehensively evaluated. This allows for a comprehensive assessment of the target object's task completion status and effectiveness from both physiological and behavioral perspectives, thereby improving the objectivity and accuracy of the process of discovering children's talent types.

[0012] Optionally, the method further includes: comparing the first emotional fluctuation state and the second emotional fluctuation state; when the emotional fluctuation intensity of the second emotional fluctuation state is less than that of the first emotional fluctuation state, judging the changing trend of the behavioral fluctuation state; when the changing trend of the behavioral fluctuation state tends to decrease, if the task completion degree is greater than or equal to a preset task completion degree threshold, adjusting the task content of the first task; and obtaining the target object's task completion degree for the first task again based on the adjusted task content.

[0013] By employing the above method, when the intensity of the target subject's second emotional fluctuation state is less than that of the first emotional fluctuation state, and the trend of the target subject's behavioral fluctuation state tends to decrease, but the task completion rate of the first task remains at a high level, the task content of the first task is adjusted. Without changing the task type, the target subject's task completion rate for the first task is obtained again. This allows for further observation of the user's behavioral fluctuation state trend after changing the task content of the first task, revealing the target subject's level of interest in the first task. Consequently, the target subject's interests are more fully considered during the talent type evaluation process.

[0014] Optionally, based on the first iris image data, the first emotional fluctuation state when displaying the task content of the first task to the target user is obtained, specifically including: when displaying the task content of the first task to the target user, recording the changes in the iris image of the target object to obtain the first iris image data; obtaining the changes in emotional features from the first iris image data, the emotional features including one or more of iris movement features, pupil state features, and eyelid closure features; and obtaining the first emotional fluctuation state based on the changing trend of the emotional features.

[0015] By analyzing the changes in various emotion-related features in iris image data, a more accurate understanding of the target user's emotional fluctuations can be obtained.

[0016] Optionally, the method further includes: when the emotional stability is less than a preset emotional stability threshold and the task completion is less than a preset task completion threshold, obtaining a third task from the task library, wherein the correlation between the task content of the third task and the task content of the first task is less than a preset correlation threshold; displaying the task content of the third task to the target user and pushing the third task to the target object.

[0017] The second aspect of this application provides a device for discovering children's talent types based on XR equipment. The device includes: a task display unit, a task push unit, a data acquisition unit, a data processing unit, and an evaluation unit. The task display unit is used to display the task content of the first task to the target user through the XR device; The task push unit is used to push the first task to the target object in order to obtain the execution result of the first task of the target object. The data acquisition unit is used to acquire iris image data and behavioral data of the target object when using the XR device. The iris image data includes: first iris image data recorded when the task content of the first task is shown to the target user, and second iris image data recorded when the target object performs the first task. The data processing unit is used to obtain the target object's emotional stability for the first task based on iris image data and behavioral data; and to obtain the target object's task completion rate for the first task based on the execution result of the first task. The evaluation unit is used to obtain the target user's talent evaluation results for the first task based on emotional stability and task completion.

[0018] A third aspect of this application provides an electronic device, which includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform any of the methods described above.

[0019] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform any of the methods described above.

[0020] Compared with related technologies, the beneficial effects of this application are: 1. This application uses the above method to collect and process iris image data and behavioral data of the target object by using XR equipment, so as to obtain the target object's emotional stability and task completion rate for the first task, and evaluate the target object's performance in performing a specific task. This can more objectively and accurately assess the child's potential and development direction.

[0021] 2. By employing the above method, during the talent evaluation of the target individual, based on their emotional stability and task completion performance, the next task type for further exploration is recommended. When emotional stability is high but task completion is low, a second task with a high degree of relevance to the first task is recommended. This allows the target individual to maintain high emotional stability while further observing their performance on the second task. This approach enables a faster identification of suitable talent development types for the target individual.

[0022] 3. By adopting the above method, when evaluating the target object's task completion of the first task, the execution result of the first task is compared with the standard result of the first task to obtain the similarity of the first task; the execution result of the first task is input into the preset expert model to obtain the artistic richness of the first task. At the same time, the target object's ability to quickly imitate the task content is taken into account, as well as the target object's own creative ability during the execution of the task, so that the obtained task completion of the first task can more accurately and completely reflect the target object's task completion status.

[0023] 4. By using the above methods, iris image data and behavioral data of the target object are obtained. Based on the first emotional fluctuation state when the target user is shown the task content of the first task, the second emotional fluctuation state when the target object performs the first task, and the behavioral data throughout the task process, the emotional stability of the target object for the first task can be comprehensively evaluated. This can comprehensively assess the target object's state and effect in completing the task from both physiological and behavioral perspectives, thereby improving the objectivity and accuracy of the process of discovering children's talent types.

[0024] 5. By employing the above method, when the intensity of the target subject's second emotional fluctuation state is less than that of the first emotional fluctuation state, and the trend of the target subject's behavioral fluctuation state tends to decrease, but the task completion rate of the first task remains at a high level, the task content of the first task is adjusted. Without changing the task type, the target subject's task completion rate for the first task is obtained again. This allows for further observation of the user's behavioral fluctuation state trend after changing the task content of the first task, revealing the target subject's level of interest in the first task. Consequently, the target subject's interest is more fully considered during the talent type evaluation process. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the first process of a method for discovering children's talent types based on XR devices, provided in an embodiment of this application; Figure 2 This is a schematic diagram of the second process of a method for discovering children's talent types based on XR devices provided in an embodiment of this application; Figure 3 This is a schematic diagram of the third process of a method for discovering children's talent types based on XR devices provided in an embodiment of this application; Figure 4 This is a schematic diagram of a device for discovering children's talent types based on an XR device, provided in an embodiment of this application. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0026] Reference numerals: 41. Task display unit; 42. Task push unit; 43. Data acquisition unit; 44. Data processing unit; 45. Evaluation unit; 500. Electronic device; 501. Processor; 502. Communication bus; 503. User interface; 504. Network interface; 505. Memory. Detailed Implementation

[0027] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0028] In the description of the embodiments of this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0029] In the description of the embodiments of this application, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0030] The methods in this application embodiment can be applied to fields including but not limited to: education, career planning, mental health, and entertainment.

[0031] In the field of education, this application can be used to discover students' talent types, provide personalized educational programs for different students, and improve educational effectiveness and quality. In the field of career planning, this application can be used to assess children's individual talent types and career abilities, providing individuals with more accurate career planning and development advice. In the field of mental health, this application can be used to assess individuals' emotional state and psychological condition, providing individuals with more effective psychological intervention and treatment programs. In the field of entertainment, this application can be used to assess individuals' interests and preferences in games, movies, music, etc., providing individuals with more personalized entertainment recommendation services.

[0032] This application provides a method for discovering children's talent types based on XR devices, applied in a server, to solve the problems of related technologies being time-consuming, costly, and highly subjective in discovering children's talents, failing to fully explore children's interests and potential. Figure 1 As shown, the method includes steps S101-106.

[0033] S101 uses an XR device to display the task content of the first task to the target user.

[0034] The XR devices in this application mainly include virtual reality (VR) devices, augmented reality (AR) devices, etc. These devices can provide users with an immersive experience through interactive devices such as head-mounted displays and controllers, and acquire users' physiological and behavioral data through devices such as sensors to provide a basis for subsequent evaluation. In this application, the specific type of XR device is not specifically limited.

[0035] Specifically, when the first task is to assess drawing ability, the server will first play the steps of drawing the content to be drawn and show them to the target user. At this time, the server will record the target user's iris movement video.

[0036] S102, push the first task to the target object to obtain the execution result of the first task of the target object.

[0037] Specifically, the server instructs the target user to begin drawing based on the video content, while also recording the user's iris scan. Once the target user confirms completion, the drawing is saved. Simultaneously, the iris scan footage recorded during this time period is fed into the analysis model.

[0038] S103, acquire iris image data and behavioral data of the target object when using the XR device. The iris image data includes: first iris image data recorded when the task content of the first task is shown to the target user, and second iris image data recorded when the target object performs the first task.

[0039] In this embodiment, the built-in camera and sensors of the XR device are used to capture iris state video and screen display content of the target object. Iris image data is obtained from the two recorded iris state videos, including first iris image data recorded when the task content of the first task is displayed to the target user, and second iris image data recorded when the target object performs the first task. Simultaneously, the behavioral data in this embodiment includes: recording the target object's behavioral data when operating the XR device, such as clicking, dragging, etc.

[0040] S104. Based on iris image data and behavioral data, obtain the target object's emotional stability for the first task.

[0041] In one possible implementation, the emotional stability of the target subject in relation to the first task is obtained based on iris image data and behavioral data, such as... Figure 2 As shown, the specific steps include S1041-S044: S1041, Based on the first iris image data, obtain the first emotional fluctuation state when displaying the task content of the first task to the target user.

[0042] In this embodiment, the emotional fluctuation state is used to represent the changes in the target user's emotions while viewing or performing the task content of the first task. Based on the first iris image data, this application derives the target user's emotional fluctuation curve during the period of viewing the task content of the first task, thereby obtaining the first emotional fluctuation state.

[0043] This application takes the acquisition process of the first emotional fluctuation state as an example to give the acquisition method of the first emotional fluctuation state. The acquisition process of the second emotional fluctuation state can be referred to in this embodiment, as follows.

[0044] In one possible implementation, the first emotional fluctuation state when the task content of the first task is displayed to the target user is obtained based on the first iris image data, specifically including S1041a-S1041c.

[0045] S1041a, when the task content of the first task is displayed to the target user, the changes in the iris image of the target object are recorded to obtain the first iris image data.

[0046] S1041b, Obtain changes in emotional features from the first iris image data, including one or more of iris motion features, pupil state features, and eyelid closure features.

[0047] S1041c, based on the changing trend of emotional characteristics, the first emotional fluctuation state is obtained.

[0048] In this embodiment, changes in emotional characteristics are obtained using first iris image data. This embodiment extracts multiple features related to emotional state from the acquired iris image, including one or more of iris motion features, pupil state features, and eyelid closure features. Iris motion features specifically include changes in the color, shape, and texture of the iris; pupil state features include pupil size; and eyelid closure features include the degree of eyelid closure.

[0049] In this embodiment, emotional features are used to represent iris image features closely related to emotional changes, including iris motion features, pupil state features, and eyelid closure features. In iris motion features, the color of the iris can reflect the emotional state of the target object. For example, when the target object feels happy or excited, the iris color may lighten, indicating increased emotional fluctuation. In pupil state features, the size of the pupil can reflect the emotional state of the target object. For example, when the target object feels curious or interested, the pupil may dilate, indicating increased emotional fluctuation; while when the target object feels afraid or nervous, the pupil may constrict, indicating increased emotional fluctuation. In eyelid closure features, the degree of eyelid closure can also reflect the emotional state of the target object. For example, when the target object feels sleepy or relaxed, the eyelids may close more tightly, indicating decreased emotional fluctuation; while when the target object feels interested, the eyelids may open wide, indicating increased emotional fluctuation.

[0050] By quantitatively analyzing the changes in the aforementioned emotional characteristics, an emotional fluctuation curve for the target user can be constructed, thereby obtaining the first emotional fluctuation state. In this embodiment, the emotional fluctuation curve is used to represent the change of the quantitative value of emotional fluctuation over time.

[0051] S1042, Based on the second iris image data, obtain the second emotional fluctuation state when the target object performs the first task.

[0052] In this embodiment, since the target user's experience is completely different during the task display and task execution processes, the iris image data needs to be collected and analyzed separately in two parts. The specific process for obtaining the second emotional fluctuation state can refer to the above embodiments, and will not be elaborated upon here.

[0053] S1043, Based on the behavioral characteristics of the behavioral data, obtain the behavioral fluctuation state of the target object when using the XR device.

[0054] In this embodiment, the behavioral fluctuation state of the target object when using the XR device is obtained based on the behavioral characteristics of the behavioral data. This can be achieved by analyzing the behavioral data of the target object. For example, the frequency, speed, and force of clicking and dragging the device when showing the task content of the first task to the target object can be analyzed, along with the changes in the behavioral characteristics of the target object during the execution of the first task, thereby obtaining the behavioral fluctuation state of the target object when using the XR device.

[0055] In this embodiment, the behavioral fluctuation state can only be obtained after the target object has completed the task content of the first task. The behavioral fluctuation state is used to represent the intensity of the emotional fluctuation of the target object's behavior before and after the target object is shown the task content of the first task and before and after the target object performs the task content of the first task.

[0056] For example, in this embodiment of the application, when the target object is shown the task content of the first task, the behavioral data records that the target object performs an operation with a frequency of S1, and the display duration of the task content of the first task is t1; when the target object performs the task content of the first task, the behavioral data records that the target object performs an operation with a frequency of S2, and the execution duration of the task content of the first task is t2. The emotional fluctuation intensity q3 of the behavior is obtained.

[0057] q3=S2t1 / S1t2 S1044, Based on the first emotional fluctuation state, the second emotional fluctuation state, and the behavioral fluctuation state, obtain the target object's emotional stability for the first task.

[0058] In this embodiment of the application, a method for calculating the emotional stability of a first task is provided, as follows: Wherein, Q represents the emotional stability of the first task, q1 represents the intensity of the emotional fluctuation in the first emotional fluctuation state during the task presentation, q2 represents the intensity of the emotional fluctuation in the second emotional fluctuation state during the task execution, and q3 represents the intensity of the emotional fluctuation in the behavioral fluctuation state. A represents the first weight parameter, and B represents the second weight parameter. The specific values ​​of the first weight parameter and the second weight parameter can be flexibly set according to the specific implementation situation, and this application embodiment does not impose specific limitations.

[0059] In the above formula, adjust the parameters In this context, e is a natural constant value. The adjustment parameter can also be set to other function values, including q3. The purpose is to make the emotional stability of the first task positively correlated with the intensity of emotional fluctuations in the behavioral fluctuation state.

[0060] In this embodiment, the intensity of emotional fluctuation is the maximum difference between the quantified values ​​of the corresponding emotional fluctuation over a duration. For example, the intensity of emotional fluctuation in the first emotional fluctuation state during a task display is the difference between the maximum and minimum values ​​of the quantified emotional fluctuation during that period.

[0061] In one possible implementation, such as Figure 3 As shown, the method also includes steps S1045-1048.

[0062] S1045, compare the first emotional fluctuation state and the second emotional fluctuation state.

[0063] S1046, When the intensity of the emotional fluctuation in the second emotional fluctuation state is less than the intensity of the emotional fluctuation in the first emotional fluctuation state, determine the trend of change in the behavioral fluctuation state.

[0064] S1047, When the trend of change in the behavior fluctuation state tends to decrease, if the task completion degree is greater than or equal to the preset task completion degree threshold, adjust the task content of the first task.

[0065] S1048, Based on the adjusted task content, obtain the target object's task completion rate for the first task again.

[0066] In this embodiment, when the intensity of the second emotional fluctuation state is less than that of the first emotional fluctuation state, it indicates that the target user's interest in the task content of the first task is decreasing. At this time, the trend of the behavioral fluctuation state is judged. When the trend of the behavioral fluctuation state tends to decrease, it can be verified again that the target user's interest in the task content of the first task is weakening. If the target user's task completion rate is greater than or equal to the preset task completion rate threshold, it indicates that the target user has a high ability to complete the first task. The target user's emotional fluctuation state and behavioral fluctuation state can be obtained again by adjusting the task content of the first task, so that the target user can further obtain the task completion rate of the first task when the interest rate is high.

[0067] In this embodiment of the application, step S1047 further includes: if the task completion degree is less than a preset task completion degree threshold, the first task is replaced with other tasks in the task library that process the first task.

[0068] In this embodiment of the application, in step S1048, for the adjusted task content, since it still belongs to the first task, it is not necessary to display the adjusted task content again. Instead, the target user can directly enter the task execution step. For the adjusted task content, the target user's behavior fluctuation state at this time is calculated as the ratio of the operation frequency when displaying the task content before the adjustment of the first task to the operation frequency when executing the task content after the adjustment of the first task.

[0069] In this embodiment of the application, for the task content of the first task, the intensity q3 of the emotional fluctuation of the behavior is determined according to the following formula.

[0070] q3=S2t1 / S1t2 Here, q3 reflects the ratio of S2 / t2 to S1 / t1. When q3 is less than 1, the trend of change in the behavioral fluctuation state tends to decrease; when q3 is greater than 1, the trend of change in the behavioral fluctuation state tends to increase.

[0071] In this embodiment of the application, for the task content after the first task is adjusted, the intensity q3 of the emotional fluctuation of the behavior is determined according to the following formula.

[0072] q3=S3t1 / S1t3 Specifically, when the target object executes the adjusted task content of the first task, there are operations with a frequency of S3, and the execution time of the adjusted task content of the first task is t3. Similarly, when q3 is less than 1, the trend of behavioral fluctuation tends to decrease; when q3 is greater than 1, the trend of behavioral fluctuation tends to increase.

[0073] In this embodiment of the application, the task content of the first task can be adjusted by randomly selecting from the task content database of the first task, or by obtaining manually selected task content.

[0074] S105, Based on the execution result of the first task, obtain the target object's task completion rate for the first task.

[0075] In one possible implementation, the task completion degree of the target object for the first task is obtained based on the execution result of the first task, specifically including S1051-S1053.

[0076] S1051, compare the execution result of the first task with the standard result of the first task to obtain the similarity of the first task.

[0077] In this embodiment, when the first task is drawing ability, the standard result of the first task is a standard task image. The similarity of the first task can be obtained by performing similarity analysis between the drawing image completed by the target user and the standard task image. Specifically, feature vectors of the drawing image and the standard image are extracted using a convolutional neural network model. This convolutional neural network model can be any classification model. The output of the penultimate layer of the convolutional neural network model is extracted as the feature vector. Then, the cosine distance between the two feature vectors is calculated to obtain the first task similarity value. In this embodiment, image similarity analysis can also be performed in other ways, which will not be elaborated upon here.

[0078] When the first task is musical ability, the standard result of the first task is the standard task audio. The similarity of the first task can be obtained by comparing the audio data of the target user's performance with the standard task audio and performing similarity analysis.

[0079] S1052, input the execution result of the first task into the preset expert model to obtain the artistic richness of the first task.

[0080] In this embodiment, an aesthetic score is given by a preset expert model, which scores the aesthetic components of the completed work. During the implementation of this function, different talent tags are set for different task types. For example, when the first task is painting ability, the talent tags are various painting styles, such as realistic, impressionistic, Fauvist, abstract, and comic styles. By inputting the execution result of the first task into the preset expert model, the painting style type and intensity of the target user's completed painting image are identified, thereby obtaining the artistic richness of the first task.

[0081] Specifically, the process begins by extracting features such as color, shape, and texture from completed artwork images. This can be achieved using computer vision techniques, such as image processing and computer vision libraries (like OpenCV). Then, deep learning techniques are used to classify the extracted features. Convolutional Neural Networks (CNNs) or other deep learning models can be used for feature classification. Training the model requires a large number of labeled datasets, such as images with known artistic styles. Finally, the artistic style of the image is identified based on the classification results. The classification result is one or more specific artistic styles and their corresponding strengths, thus yielding the artistic richness of the first task. When the first task is musical ability, the talent label is various music genres, including classical, pop, rock, jazz, and folk music.

[0082] In this embodiment, talent tags can also be created by teachers or artists who score selected children's works, and then used to train a neural network to obtain a preset expert model. The training process of the neural network in this embodiment can be found in related technologies; this embodiment only provides a brief description.

[0083] S1053, based on the similarity to the first task and the artistic richness, obtain the task completion degree of the target object for the first task.

[0084] In this embodiment, the task completion degree of the first task is obtained by adding the first task similarity and artistic richness after assigning weight ratios to them respectively. In this embodiment, the specific values ​​of the weight ratio of the first task similarity and the weight ratio of artistic richness are not specifically limited. In practical applications, they can be specifically set according to the needs of the developers.

[0085] S106: Based on emotional stability and task completion, obtain the target user's talent evaluation results for the first task.

[0086] In this embodiment, multiple pre-defined evaluation thresholds are used to assess the target user's talent for the first task. It is important to note that, since the target user may be a child, negative evaluation results should be avoided to prevent low evaluations from causing psychological distress. Evaluation results are presented to the target user using ratings such as medium, relatively high, high, and very high.

[0087] In one possible implementation, the method further includes steps S201-S202.

[0088] S201, when the emotional stability is greater than or equal to the preset emotional stability threshold and the task completion is less than the preset task completion threshold, a second task is obtained from the task library. The correlation between the task content of the second task and the task content of the first task is greater than the preset correlation threshold.

[0089] S202, Show the target user the task content of the second task and push the second task to the target object.

[0090] In one possible implementation, the method further includes S203-S204.

[0091] S203, when the emotional stability is less than the preset emotional stability threshold and the task completion is less than the preset task completion threshold, a third task is obtained from the task library. The correlation between the task content of the third task and the task content of the first task is less than the preset correlation threshold.

[0092] S204, Show the third task content to the target user and push the third task to the target object.

[0093] Steps S201-204 provide a method for adjusting the task type for the target user. During the talent evaluation of the target user, based on their different performances in emotional stability and task completion, the next task type to be explored is recommended to the target user.

[0094] In S201-S202, when emotional stability is high but task completion is low, a second task with a high relevance to the first task is recommended. This allows the target individual to maintain high emotional stability while further observing their performance on the second task. This enables a faster identification of suitable talent development types for the target individual.

[0095] In steps S203-S204, when the emotional stability is less than a preset emotional stability threshold and the task completion rate is less than a preset task completion rate threshold, a third task is retrieved from the task library, the task content of the third task is displayed to the target user, and the third task is pushed to the target user again, and steps S101-106 are executed again. For example, if the first task is an art-related task, the third task can be set as a sports-related task.

[0096] In this embodiment of the application, the preset correlation threshold in step S201 and the preset correlation threshold in step S203 can be set to two different values ​​or the same value, depending on the actual situation. This embodiment of the application does not make specific limitations on this.

[0097] In this embodiment of the application, the third task may also be other tasks that have a low degree of correlation with the task content of the first task, such as a literature task.

[0098] In this embodiment of the application, task types are exemplarily categorized, including art tasks, sports tasks, literature tasks, science tasks, and other task categories. The correlation between the content of each pre-set task type can be obtained and compared with a preset correlation threshold to determine the third task.

[0099] Specifically, the correlation between the content of each pre-set task type is obtained, including: a correlation of 0.2 between art tasks and sports tasks, 0.7 between art tasks and literature tasks, and 0.4 between art tasks and science tasks. When the preset correlation threshold is 0.5, literature tasks with a correlation greater than 0.5 are filtered out. From the remaining task types (science tasks and sports tasks), further task types are selected, including two methods: randomly selecting task types or prioritizing the task type with the lowest correlation. When prioritizing the task type with the lowest correlation, the third task is set as a sports task.

[0100] The beneficial effects that can be achieved by adopting the above embodiments include one or more of the following: 1. This application uses the above method to collect and process iris image data and behavioral data of the target object by using XR equipment, so as to obtain the target object's emotional stability and task completion rate for the first task, and evaluate the target object's performance in performing a specific task. This can more objectively and accurately assess the child's potential and development direction.

[0101] 2. By employing the above method, during the talent evaluation of the target individual, based on their emotional stability and task completion performance, the next task type for further exploration is recommended. When emotional stability is high but task completion is low, a second task with a high relevance to the first task is recommended. This allows the target individual to maintain high emotional stability while further observing their performance on the second task. This approach enables a faster identification of suitable talent development types for the target individual.

[0102] 3. By adopting the above method, when evaluating the target object's task completion of the first task, the execution result of the first task is compared with the standard result of the first task to obtain the similarity of the first task; the execution result of the first task is input into the preset expert model to obtain the artistic richness of the first task. At the same time, the target object's ability to quickly imitate the task content is taken into account, as well as the target object's own creative ability during the execution of the task, so that the task completion of the target object can be evaluated more accurately and completely.

[0103] 4. By using the above methods, iris image data and behavioral data of the target object are obtained. Based on the first emotional fluctuation state when the target user is shown the task content of the first task, the second emotional fluctuation state when the target object performs the first task, and the behavioral data throughout the task process, the emotional stability of the target object for the first task can be comprehensively evaluated. This can comprehensively assess the target object's state and effect in completing the task from both physiological and behavioral perspectives, thereby improving the objectivity and accuracy of the process of discovering children's talent types.

[0104] 5. By employing the above method, when the intensity of the target subject's second emotional fluctuation state is less than that of the first emotional fluctuation state, and the trend of the target subject's behavioral fluctuation state tends to decrease, but the task completion rate of the first task remains at a high level, the task content of the first task is adjusted. Without changing the task type, the target subject's task completion rate for the first task is obtained again. This allows for further observation of the user's behavioral fluctuation state trend after changing the task content of the first task, revealing the target subject's level of interest in the first task. Consequently, the target subject's interest is more fully considered during the talent type evaluation process.

[0105] This application provides a second aspect of the application, which offers a device for discovering children's talent types based on XR equipment, such as... Figure 4 As shown, the device includes: a task display unit 41, a task push unit 42, a data acquisition unit 43, a data processing unit 44, and an evaluation unit 45.

[0106] The task display unit 41 is used to display the task content of the first task to the target user through the XR device.

[0107] The task push unit 42 is used to push the first task to the target object in order to obtain the first task execution result of the target object.

[0108] The data acquisition unit 43 is used to acquire iris image data and behavioral data of the target object when using the XR device. The iris image data includes: first iris image data recorded when the task content of the first task is shown to the target user, and second iris image data recorded when the target object performs the first task.

[0109] The data processing unit 44 is used to obtain the target object's emotional stability for the first task based on iris image data and behavioral data; and to obtain the target object's task completion rate for the first task based on the execution result of the first task.

[0110] Evaluation unit 45 is used to obtain the target user's talent evaluation results for the first task based on emotional stability and task completion.

[0111] In one possible implementation, the device further includes a first content adjustment unit.

[0112] The first content adjustment unit is used to retrieve a second task from the task library when the emotional stability is greater than or equal to a preset emotional stability threshold and the task completion is less than a preset task completion threshold. The task content of the second task is more related to the task content of the first task than a preset relevance threshold. The second task content is then displayed to the target user, and the second task is pushed to the target object.

[0113] In one possible implementation, the device further includes a second content adjustment unit.

[0114] The second content adjustment unit is used to retrieve a third task from the task library when the emotional stability is less than a preset emotional stability threshold and the task completion is less than a preset task completion threshold. The task content of the third task is less than a preset correlation threshold with the task content of the first task. The third task content is then displayed to the target user, and the third task is pushed to the target object.

[0115] In one possible implementation, the data processing unit 44 is specifically used to: compare the execution result of the first task with the standard result of the first task to obtain the similarity of the first task; input the execution result of the first task into a preset expert model to obtain the artistic richness of the first task; and obtain the task completion degree of the target object for the first task based on the similarity of the first task and the artistic richness.

[0116] In one possible implementation, the data processing unit 44 is further specifically configured to: obtain a first emotional fluctuation state when displaying the task content of the first task to the target user based on the first iris image data; obtain a second emotional fluctuation state when the target object performs the first task based on the second iris image data; obtain the behavioral fluctuation state of the target object when using the XR device based on the behavioral characteristics of the behavioral data; and obtain the emotional stability of the target object for the first task based on the first emotional fluctuation state, the second emotional fluctuation state, and the behavioral fluctuation state.

[0117] In one possible implementation, the device further includes a third content adjustment unit.

[0118] The third content adjustment unit is used to compare the first emotional fluctuation state and the second emotional fluctuation state; when the emotional fluctuation intensity of the second emotional fluctuation state is less than that of the first emotional fluctuation state, the changing trend of the behavioral fluctuation state is judged; when the changing trend of the behavioral fluctuation state tends to decrease, if the task completion degree is greater than or equal to the preset task completion degree threshold, the task content of the first task is adjusted; based on the adjusted task content, the task completion degree of the target object for the first task is obtained again.

[0119] In one possible implementation, the data processing unit 44 is further specifically used to: record the changes in the iris image of the target object when the task content of the first task is displayed to the target user, and obtain the first iris image data; obtain the changes in emotional features from the first iris image data, the emotional features including one or more of iris movement features, pupil state features and eyelid closure features; and obtain the first emotional fluctuation state based on the changing trend of the emotional features.

[0120] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0121] Please see Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 500 may include: at least one processor 501, at least one network interface 504, user interface 503, memory 505, and at least one communication bus 502.

[0122] The communication bus 502 is used to enable communication between these components.

[0123] The user interface 503 may include a display screen and a camera. Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0124] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0125] The processor 501 may include one or more processing cores. The processor 501 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 505, and by calling data stored in memory 505. Optionally, the processor 501 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 501 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 501 and may be implemented as a separate chip.

[0126] The memory 505 may include random access memory (RAM) or read-only memory. Optionally, the memory 505 may include a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 505 may also be at least one storage device located remotely from the aforementioned processor 501. Figure 5 As shown, the memory 505, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application for discovering children's talent types.

[0127] exist Figure 5In the electronic device 500 shown, the user interface 503 is mainly used to provide an interface for users to input data and obtain user input data; while the processor 501 can be used to call the application stored in the memory 505 for the discovery of children's talent types. When executed by one or more processors, the electronic device 500 performs one or more of the methods described in the above embodiments.

[0128] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0129] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0131] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0132] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0133] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0134] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practical application of the disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

Claims

1. A method for discovering children's talent types based on XR devices, characterized in that, The method includes: The task content of the first task is displayed to the target user through XR devices; The first task is pushed to the target object to obtain the first task execution result of the target object; Acquire iris image data and behavioral data of the target object when using an XR device. The iris image data includes: first iris image data recorded when the task content of the first task is shown to the target user, and second iris image data recorded when the target object performs the first task. Based on the iris image data and the behavioral data, the emotional stability of the target object in relation to the first task is obtained; Based on the execution result of the first task, obtain the task completion degree of the target object for the first task; Based on the emotional stability and the task completion rate, obtain the target user's talent evaluation result for the first task.

2. The method according to claim 1, characterized in that, The method further includes: When the emotional stability is greater than or equal to a preset emotional stability threshold and the task completion is less than a preset task completion threshold, a second task is obtained from the task library, and the correlation between the task content of the second task and the task content of the first task is greater than a preset correlation threshold. The task content of the second task is displayed to the target user, and the second task is pushed to the target object.

3. The method according to claim 1, characterized in that, The step of obtaining the task completion degree of the target object for the first task based on the execution result of the first task specifically includes: The similarity of the first task is obtained by comparing the execution result of the first task with the standard result of the first task. The execution result of the first task is input into a preset expert model to obtain the artistic richness of the first task; The task completion degree of the target object for the first task is obtained based on the first task similarity and the artistic richness.

4. The method according to claim 1, characterized in that, The step of obtaining the emotional stability of the target object for the first task based on the iris image data and the behavioral data specifically includes: Based on the first iris image data, obtain the first emotional fluctuation state when displaying the task content of the first task to the target user; Based on the second iris image data, obtain the second emotional fluctuation state when the target object performs the first task; Based on the behavioral characteristics of the behavioral data, the behavioral fluctuation state of the target object when using the XR device is obtained; Based on the first emotional fluctuation state, the second emotional fluctuation state, and the behavioral fluctuation state, the emotional stability of the target object in relation to the first task is obtained.

5. The method according to claim 4, characterized in that, The method further includes: Compare the first emotional fluctuation state with the second emotional fluctuation state; When the intensity of the second emotional fluctuation state is less than the intensity of the first emotional fluctuation state, the trend of the behavioral fluctuation state is determined. When the trend of the change in the behavior fluctuation state tends to decrease, if the task completion degree is greater than or equal to the preset task completion degree threshold, the task content of the first task is adjusted. Based on the adjusted task content, the task completion rate of the target object for the first task is obtained again.

6. The method according to claim 4, characterized in that, The step of obtaining the first emotional fluctuation state when displaying the task content of the first task to the target user based on the first iris image data specifically includes: When the task content of the first task is displayed to the target user, the changes in the iris image of the target object are recorded to obtain the first iris image data; Changes in emotional features are obtained from the first iris image data, wherein the emotional features include one or more of iris motion features, pupil state features, and eyelid closure features; The first emotional fluctuation state is obtained based on the changing trend of the emotional characteristics.

7. The method according to claim 1, characterized in that, The method further includes: When the emotional stability is less than a preset emotional stability threshold and the task completion is less than a preset task completion threshold, a third task is obtained from the task library. The correlation between the task content of the third task and the task content of the first task is less than a preset correlation threshold. The task content of the third task is displayed to the target user, and the third task is pushed to the target object.

8. A device for discovering children's talent types based on XR equipment, characterized in that, The device includes: a task display unit, a task push unit, a data acquisition unit, a data processing unit, and an evaluation unit; The task display unit is used to display the task content of the first task to the target user through the XR device; The task push unit is used to push the first task to the target object in order to obtain the first task execution result of the target object; The data acquisition unit is used to acquire iris image data and behavioral data of the target object when using the XR device. The iris image data includes: first iris image data recorded when the task content of the first task is shown to the target user, and second iris image data recorded when the target object performs the first task. The data processing unit is configured to obtain the emotional stability of the target object in relation to the first task based on the iris image data and the behavioral data; and to obtain the task completion rate of the target object in relation to the first task based on the execution result of the first task. The evaluation unit is used to obtain the target user's talent evaluation result for the first task based on the emotional stability and the task completion rate.

9. An electronic device, characterized in that, The device includes a processor, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

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