Bionic dynamic training system for improving attention of children

Abstract

The invention provides a bionic dynamic training system for improving the attention of children, which comprises a dynamic interaction unit, an interaction data acquisition unit, an attention evaluation unit, a compound eye window determination unit and an interaction adjustment unit which are in data connection in sequence, and the children can interact with an interaction scene provided by the dynamic interaction unit. The interaction data collection unit collects multi-modal data in the interaction process and then sends the multi-modal data to the attention evaluation unit, the attention evaluation unit obtains an attention evaluation result, and then the compound eye window determination unit can determine key parameters of a compound eye window based on the evaluation result. And finally, the interaction adjustment unit can adjust the interaction scene according to the key parameters of the compound eye window, after a visual processing mechanism of insect compound eyes is introduced, a multi-focus dynamic interface can be generated based on the attention evaluation result of children, different information is presented through multiple focuses at the same time, and the interaction effect is improved. The attention of the children is attracted to achieve the training effect of improving the attention of the children.

Description

Technical Field

[0001] The present invention relates to the technical field of attention training, and in particular to a bionic dynamic training system for improving children's attention. Background Art

[0002] According to statistics from the World Health Organization, approximately 5%-7% of children worldwide have attention disorders to varying degrees. Traditional training methods such as cognitive behavioral therapy and static electronic games are difficult to effectively improve children's attention maintenance and anti-interference ability in complex environments due to their lack of dynamic adaptability and multimodal interaction. In existing technologies, most of them train children's attention through game interactions, which has the problem of rigid content and form, and cannot dynamically adjust the interactive scenes according to the children's attention assessment results. In recent years, the development of bionic technology has provided new ideas for attention training. The distributed visual processing mechanism of insect compound eyes that realizes parallel processing and dynamic integration of multi-perspective information through thousands of small eyes provides a biological prototype for the design of efficient multi-focus interactive interfaces. However, there is currently no relevant research on effectively combining bionic structures with data-driven dynamic adjustment strategies for children's attention training. Summary of the Invention

[0003] In view of this, the present invention proposes a bionic dynamic training system for improving children's attention, which can design a multi-focus dynamic interface based on the visual processing mechanism of insect compound eyes to enhance the effectiveness of attention training.

[0004] The technical solution of the present invention is achieved as follows:

[0005] A bionic dynamic training system for improving children's attention, comprising:

[0006] Dynamic interaction unit, used to provide dynamic interactive scenes for children to interact;

[0007] An interactive data collection unit, used to collect multimodal data during children's interaction;

[0008] An attention assessment unit, used to assess children's attention based on multimodal data and obtain assessment results;

[0009] a compound eye window determination unit, configured to determine key parameters of the compound eye window according to the evaluation result;

[0010] An interactive adjustment unit, used to adjust the interactive scene according to key parameters of the compound eye window;

[0011] The dynamic interaction unit, the interaction data acquisition unit, the attention evaluation unit, the compound eye window determination unit and the interaction adjustment unit are sequentially data-connected, and the interaction adjustment unit is data-connected to the dynamic interaction unit.

[0012] Preferably, it also includes an information acquisition unit for acquiring basic information about children, including age, gender, hobbies, dominant hand, and language ability. The information acquisition unit is respectively connected to the dynamic interaction unit, the compound eye window determination unit, and the interaction adjustment unit.

[0013] Preferably, the execution steps of the dynamic interaction unit include:

[0014] Step S11: determining the type of interaction scenario based on the child's gender and interests, and determining the difficulty of the interaction scenario based on age, to obtain an initial interaction scenario;

[0015] Step S12: determine whether there is an adjustment rendering instruction. If not, push the initial interaction scene for the child to interact. If so, render the initial interaction scene based on the adjustment rendering instruction and push it to the child for interaction.

[0016] Preferably, the execution steps of the interactive data collection unit include:

[0017] Step S21: Recording gaze point trajectory, gaze duration, scanning speed, and pupil diameter change based on a computer vision algorithm, and outputting them as visual behavior;

[0018] Step S22: collecting the response time, accuracy rate, and task completion time of the child's operation in the interactive scene, and outputting them as operation behavior.

[0019] Preferably, the execution steps of the attention evaluation unit include:

[0020] Step S31: After performing noise reduction, time synchronization, and identification anonymization on the visual behavior and the operational behavior, multimodal fusion is performed to obtain fusion features;

[0021] Step S32: Input the fusion features into the trained attention evaluation model, and the attention evaluation model processes the features to obtain the evaluation results.

[0022] Preferably, the training steps of the attention assessment model are: constructing an attention assessment model based on a deep learning algorithm, obtaining a large amount of children's attention training data, dividing the children's attention training data into a training set and a test set, training the attention assessment model based on the training set, and testing it with the test set after the training is completed, and stopping the training when the test accuracy meets the requirements.

[0023] Preferably, the execution steps of the compound eye window determination unit include:

[0024] Step S41: receiving the child's age and language ability transmitted by the information acquisition unit, determining a lower limit of the number of compound eye windows based on the child's age and language ability, determining an additional number of compound eye windows based on the evaluation result, and summing the lower limit and the additional number to obtain a total number of compound eye windows;

[0025] Step S42: determining a compound eye window shape based on the evaluation result, wherein the compound eye window shape includes square, circle, polygon and star;

[0026] Step S43: obtaining the size of the available screen area for displaying the interactive scene, obtaining the maximum display area of each compound eye window based on the size of the available screen area, the total number of compound eye windows, and the shape of the compound eye windows, and adjusting the maximum display area based on the evaluation result to obtain the actual display area of each compound eye window;

[0027] Step S44: output the total number of compound eye windows, the shape of the compound eye windows, and the actual display area as key parameters of the compound eye windows.

[0028] Preferably, when the assessment result shows that the child is in a distracted state, the execution steps of the interaction adjustment unit include:

[0029] Step S51: Acquire the interactive scene transmitted by the dynamic interactive unit, identify the interactive scene, and obtain the key area;

[0030] Step S52: Allocate the key area to the corresponding compound eye window and send it to the dynamic interaction unit as an adjustment rendering instruction.

[0031] Preferably, when the assessment result shows that the child is in a focused state, the execution steps of the interaction adjustment unit include:

[0032] Step S53: Acquire the interactive scene transmitted by the dynamic interactive unit, divide and scale the interactive scene according to the number of compound eye windows, assign the divided and scaled interactive scenes to the compound eye windows, and disrupt the order of the compound eye windows;

[0033] Step S54: Determine the window transparency change frequency, the window rotation and swing frequency, and the window background color block flashing frequency based on the evaluation results, add the window transparency change frequency, the window rotation and swing frequency, and the window background color block flashing frequency to each compound eye window, and send them to the dynamic interaction unit as adjustment rendering instructions.

[0034] Preferably, in step S53, when the order of the compound eye windows is disrupted, more compound eye windows containing key information are displayed on the side of the dominant hand.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The dynamic interaction unit provides children with dynamic interaction scenes, through which children can interact, and the multimodal data in the interaction process can be collected by the interaction data collection unit. The collected multimodal data will be transmitted to the attention evaluation unit for attention evaluation. Based on the results of the attention evaluation, it can be determined whether the child is in a distracted state or a focused state. Then, according to the different evaluation results, the visual processing mechanism of the insect compound eye is introduced, and the key parameters of the compound eye window are set. Finally, the interaction adjustment unit can adjust the interaction scene according to the key parameters of the compound eye window, so that the interaction scene forms a multi-focus dynamic interface. When children interact based on the multi-focus dynamic interface, they can effectively improve the selectivity, continuity and anti-interference ability of their attention. Compared with the traditional static and single training system, it provides children with a dynamic training scene that conforms to the laws of cognitive development. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 This is a schematic diagram of a bionic dynamic training system for improving children's attention according to the present invention;

[0039] Figure 2 A diagram showing the execution steps of a dynamic interaction unit of a bionic dynamic training system for improving children's attention according to the present invention;

[0040] Figure 3 This is a diagram of the execution steps of an interactive data acquisition unit of a bionic dynamic training system for improving children's attention according to the present invention;

[0041] Figure 4 A diagram illustrating the execution steps of an attention evaluation unit of a bionic dynamic training system for improving children's attention according to the present invention;

[0042] Figure 5 This is a diagram of the execution steps of a compound eye window determination unit of a bionic dynamic training system for improving children's attention according to the present invention;

[0043] Figure 6 A diagram illustrating the execution steps of an interactive adjustment unit of a bionic dynamic training system for improving children's attention according to the present invention;

[0044] In the figure, 1. Dynamic interaction unit; 2. Interaction data acquisition unit; 3. Attention evaluation unit; 4. Compound eye window determination unit; 5. Interaction adjustment unit; 6. Information acquisition unit. DETAILED DESCRIPTION

[0045] In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0046] See also Figures 1 to 6 The present invention provides a bionic dynamic training system for improving children's attention, comprising:

[0047] Dynamic interaction unit 1, used to provide dynamic interaction scenes for children to interact;

[0048] Interaction data collection unit 2, used to collect multimodal data during the children's interaction;

[0049] an attention assessment unit 3, configured to assess the child's attention based on multimodal data and obtain an assessment result;

[0050] The compound eye window determination unit 4 is used to determine the key parameters of the compound eye window according to the evaluation results;

[0051] Interaction adjustment unit 5, used to adjust the interaction scene according to the key parameters of the compound eye window;

[0052] The dynamic interaction unit 1 , the interaction data acquisition unit 2 , the attention evaluation unit 3 , the compound eye window determination unit 4 and the interaction adjustment unit 5 are sequentially data-connected, and the interaction adjustment unit 5 is data-connected to the dynamic interaction unit 1 .

[0053] The present invention provides a bionic dynamic training system for improving children's attention. It uses interactive games to train children's attention. A dynamic interaction unit 1 can provide dynamic interactive scenes. Parents can display the interactive scenes through smart terminals and provide them to children for interaction. For example, after finding key numbers or clues in the interactive scenes, they can combine them to perform operations such as addition and subtraction to unlock the next level. The child's focus can be determined based on their interaction in the interactive scenes. During the child's interaction, an interaction data collection unit 2 can collect multimodal data, which can be transmitted to an attention evaluation unit 3. The attention evaluation unit 3 can process the multimodal data based on a preset algorithm or model and obtain an evaluation result. The evaluation result is in the form of a percentage, which can show the child's concentration or distraction level. For example, more than 50% is concentration, and the higher the evaluation result, the higher the child's concentration level. The attention evaluation unit 3 can obtain the child's concentration level in each interactive scene, so that timely intervention can be made when the child is distracted, and the training difficulty can be increased when the child is more focused, thereby achieving attention improvement training.

[0054] After obtaining the children's attention assessment results, based on the differences in the assessment results, the present invention introduces biomimetic technology, refers to the functions of insect compound eyes, and determines the key parameters of the compound eye window based on the assessment results through the compound eye window determination unit 4. Then the interaction adjustment unit 5 can adjust the interaction scene according to the key parameters of the compound eye window, so that the interaction scene becomes a multi-focus dynamic interface. Children can obtain information in different compound eye windows. By introducing the visual processing mechanism of insect compound eyes, more difficult interactions or more targeted interactions can be carried out, thereby improving children's attention. Compared with traditional static and single training systems, it provides children with a dynamic training scene that conforms to the laws of cognitive development.

[0055] Preferably, it also includes an information acquisition unit 6 for acquiring basic information of the child, which includes age, gender, interests and hobbies, dominant hand, and language ability. The information acquisition unit 6 is data-connected with the dynamic interaction unit 1, the compound eye window determination unit 4 and the interaction adjustment unit 5 respectively.

[0056] In order to ensure that the dynamic interaction unit 1 can provide a suitable interaction scene for children, and at the same time ensure that the adjustment of the interaction scene by the compound eye window determination unit 4 and the interaction adjustment unit 5 will not exceed the children's cognition, an information acquisition unit 6 is set to obtain basic information of the children.

[0057] Preferably, the execution steps of the dynamic interaction unit 1 include:

[0058] Step S11: determining the type of interaction scenario based on the child's gender and interests, and determining the difficulty of the interaction scenario based on age, to obtain an initial interaction scenario;

[0059] Step S12: determine whether there is an adjustment rendering instruction. If not, push the initial interaction scene for the child to interact. If so, render the initial interaction scene based on the adjustment rendering instruction and push it to the child for interaction.

[0060] Children of different genders and interests have different needs for the types of interactive scenes. For example, for a boy who likes space scenes, a space-type interactive scene can be pushed to him. The difficulty of the interactive scene will be determined according to the age of the child. Based on this, a variety of initial interactive scenes can be extracted. The dynamic interaction unit 1 will then determine whether there is an adjustment rendering instruction transmitted by the interaction adjustment unit 5. Generally, there will be no adjustment rendering instruction during the first interaction. At this time, the initial interactive scene can be directly pushed out for the child to interact. After the child completes the first interaction, the interactive scene will be adjusted based on the child's attention evaluation results. At this time, the interactive adjustment unit 5 will generate an adjustment rendering instruction. After receiving the adjustment rendering instruction, the dynamic interaction unit 1 can adjust the interactive scene to further strengthen the child's attention training.

[0061] Preferably, the execution steps of the interactive data collection unit 2 include:

[0062] Step S21: Recording gaze point trajectory, gaze duration, scanning speed, and pupil diameter change based on a computer vision algorithm, and outputting them as visual behavior;

[0063] Step S22: collecting the response time, accuracy rate, and task completion time of the child's operation in the interactive scene, and outputting them as operation behavior.

[0064] During the interaction process, children's visual behavior can be collected through the camera. Visual behavior can determine whether the child is focused on a certain position and for how long. At the same time, the built-in detection algorithm of the interactive scene can collect operational behavior, such as the response time of each operation. If the response time is too long, it means that the child is not very focused, which will result in a longer task completion time. In addition, a low accuracy rate also indicates that the child is not very focused.

[0065] Preferably, the execution steps of the attention evaluation unit 3 include:

[0066] Step S31: After performing noise reduction, time synchronization, and identification anonymization on the visual behavior and the operational behavior, multimodal fusion is performed to obtain fusion features;

[0067] Step S32: input the fusion features into the trained attention assessment model, and the attention assessment model processes the results to obtain the assessment results. The training steps of the attention assessment model are: constructing an attention assessment model based on a deep learning algorithm, obtaining a large amount of children's attention training data, dividing the children's attention training data into a training set and a test set, training the attention assessment model based on the training set, and testing it with the test set after the training is completed, and stopping the training when the test accuracy meets the requirements.

[0068] After obtaining multimodal data, the present invention adopts a deep learning method to evaluate children's attention, pre-processes visual behavior and operational behavior to ensure the reliability and authenticity of the data, and then performs multimodal fusion to obtain fusion features, which can be directly input into the trained attention evaluation model. The attention evaluation model outputs an evaluation result in a percentage system, wherein the attention evaluation model is trained and tested by a large amount of historical children's attention training data, wherein the children's attention training data is divided into a training set and a test set in a ratio of 7:3. The training set is used for training. When the training reaches a certain stage, the test set is used to evaluate the test effect of the attention evaluation model. If the test accuracy reaches a preset threshold, the training is stopped to complete the training of the attention evaluation model.

[0069] Preferably, the execution steps of the compound eye window determination unit 4 include:

[0070] Step S41: receiving the child's age and language ability transmitted by the information acquisition unit 6, determining a lower limit of the number of compound eye windows based on the child's age and language ability, determining an additional number of compound eye windows according to the evaluation result, and summing the lower limit and the additional number to obtain a total number of compound eye windows;

[0071] Step S42: determining a compound eye window shape based on the evaluation result, wherein the compound eye window shape includes square, circle, polygon and star;

[0072] Step S43: obtaining the size of the available screen area for displaying the interactive scene, obtaining the maximum display area of each compound eye window based on the size of the available screen area, the total number of compound eye windows, and the shape of the compound eye windows, and adjusting the maximum display area based on the evaluation result to obtain the actual display area of each compound eye window;

[0073] Step S44: output the total number of compound eye windows, the shape of the compound eye windows, and the actual display area as key parameters of the compound eye windows.

[0074] For children of different ages and cognitive abilities, if the number of compound eye windows set is too large, it will burden the children and prevent them from obtaining information from multiple compound eye windows. If the number is too small, the training effect cannot be achieved. Therefore, the compound eye window determination unit 4 will first set a lower limit and an upper limit for the number of compound eye windows according to the child's age and language ability, and then determine the number of compound eye windows to be increased based on the evaluation results. The evaluation results are in percentage, and 10% is used as an interval to determine the number of increases. At the same time, 50% is used as a reference point. For every increase or decrease of 10%, the number of increases is increased by one. Finally, the total number of compound eye windows can be obtained based on the increase number and the lower limit. If the total number of compound eye windows is greater than the upper limit, the total number of compound eye windows is the upper limit.

[0075] In addition to the quantity, the present invention can also evaluate the shape of the compound eye window, which is also determined based on the evaluation results. The corresponding difficulty of square, circle, polygon and star represents continuously increasing. The more difficult it is for children to obtain key information from compound eye windows of different shapes. Finally, the actual display area of the compound eye window needs to be determined. First, the size of the available area for evaluating the interactive scene is obtained. Then, based on the total number of compound eye windows, the shape of each compound eye window, and the size of the available area, the maximum display area of each compound eye window can be obtained. Then, the maximum display area is adjusted according to the evaluation results. 50% is also used as the benchmark. When the evaluation result is greater than 50%, the larger the value, the smaller the actual display area of the compound eye window will be. When the evaluation result is less than 50%, the smaller the value, the larger the actual display area of the compound eye window will be, so as to adapt to children with different evaluation results. Finally, the total number of compound eye windows, the shape of the compound eye window and the actual display area are output as key parameters of the compound eye window for the interactive adjustment unit 5 to set the adjustment strategy.

[0076] Preferably, when the assessment result shows that the child is in a distracted state, the execution steps of the interaction adjustment unit 5 include:

[0077] Step S51: Acquire the interactive scene transmitted by the dynamic interactive unit 1, identify the interactive scene, and obtain the key area;

[0078] Step S52: Allocate the key area to the corresponding compound eye window and send it to the dynamic interaction unit 1 as an adjustment rendering instruction.

[0079] When the evaluation result is less than 50%, it means that the child is in a distracted state. In order to improve the child's concentration, a simple interactive scene needs to be set. The interactive adjustment unit 5 will first obtain the interactive scene and extract the key areas from the interactive scene. For example, in the interactive scene of mathematical operations, the areas where the elements of the calculation formula that are separated into various positions of the interactive scene are located are extracted, and then the corresponding compound eye windows are allocated, while other areas are hidden. When this strategy is sent to the dynamic interactive unit 1 as an adjustment rendering instruction, the dynamic interactive unit 1 can display a multi-focus dynamic interactive interface. Children can easily obtain key information from the compound eye window and interact. By reducing the difficulty, children can adapt to the interactive scene, thereby improving their attention.

[0080] Preferably, when the evaluation result shows that the child is in a focused state, the execution steps of the interaction adjustment unit 5 include:

[0081] Step S53: Acquire the interactive scene transmitted by the dynamic interaction unit 1, segment and scale the interactive scene according to the number of compound eye windows, assign the segmented and scaled interactive scene to the compound eye windows, and disrupt the order of the compound eye windows. When disrupting the order of the compound eye windows, more compound eye windows containing key information are displayed on the dominant hand side;

[0082] Step S54: Determine the window transparency change frequency, the window rotation and swing frequency, and the window background color block flashing frequency based on the evaluation results, add the window transparency change frequency, the window rotation and swing frequency, and the window background color block flashing frequency to each compound eye window, and send them to the dynamic interaction unit 1 as an adjustment rendering instruction.

[0083] When the evaluation result is greater than 50%, it indicates that the child is in a state of concentration. At this time, the interaction adjustment unit 5 will first obtain the interaction scene transmitted by the dynamic interaction unit 1, and then divide and scale the interaction scene. The number of divisions is consistent with the number of compound eye windows. Then, the divided sub-scenes are respectively assigned to the compound eye windows, and the order of the compound eye windows is disrupted. The original overall interaction scene is divided into multiple scenes, and the order is disrupted. This can increase the difficulty of children's interaction. In order to improve the effect of attention training, interference items can also be added to the compound eye window, where the interference items include adjusting the transparency of the compound eye window, rotating and swinging the window, and The background color block of the window is flashed. When children obtain information through the compound eye window, interference items can interfere, increasing the difficulty of interaction, so as to achieve higher concentration training for children in a focused state. In addition, taking into account the differences in children's handedness, for example, the sensitivity of the left area of left-handed children will be higher, so the compound eye window containing key information can be displayed more in the left area to improve children's ability to obtain key information. Finally, the interaction adjustment unit 5 can send the above strategy as an adjustment rendering instruction to the dynamic interaction unit 1, and the dynamic interaction unit 1 can adjust the interaction scene to achieve improved attention training.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A bionic dynamic training system for improving children's attention, characterized in that: include: Dynamic interaction unit, used to provide dynamic interactive scenes for children to interact; An interactive data collection unit, used to collect multimodal data during children's interaction; An attention assessment unit, used to assess children's attention based on multimodal data and obtain assessment results; a compound eye window determination unit, configured to determine key parameters of the compound eye window according to the evaluation result; An interactive adjustment unit, used to adjust the interactive scene according to key parameters of the compound eye window; The dynamic interaction unit, the interaction data acquisition unit, the attention evaluation unit, the compound eye window determination unit and the interaction adjustment unit are sequentially data-connected, and the interaction adjustment unit is data-connected to the dynamic interaction unit.

2. A bionic dynamic training system for improving children's attention according to claim 1, characterized in that: It also includes an information acquisition unit for acquiring basic information about children, including age, gender, hobbies, dominant hand, and language ability. The information acquisition unit is respectively connected to the dynamic interaction unit, the compound eye window determination unit, and the interaction adjustment unit.

3. A bionic dynamic training system for improving children's attention according to claim 2, characterized in that: The execution steps of the dynamic interaction unit include: Step S11: determining the type of interaction scenario based on the child's gender and interests, and determining the difficulty of the interaction scenario based on age, to obtain an initial interaction scenario; Step S12: determine whether there is an adjustment rendering instruction. If not, push the initial interaction scene for the child to interact. If so, render the initial interaction scene based on the adjustment rendering instruction and push it to the child for interaction.

4. The bionic dynamic training system for improving children's attention according to claim 1, characterized in that: The execution steps of the interactive data collection unit include: Step S21: Recording gaze point trajectory, gaze duration, scanning speed, and pupil diameter change based on a computer vision algorithm, and outputting them as visual behavior; Step S22: collecting the response time, accuracy rate, and task completion time of the child's operation in the interactive scene, and outputting them as operation behavior.

5. A bionic dynamic training system for improving children's attention according to claim 4, characterized in that: The execution steps of the attention evaluation unit include: Step S31: After performing noise reduction, time synchronization, and identification anonymization on the visual behavior and the operational behavior, multimodal fusion is performed to obtain fusion features; Step S32: Input the fusion features into the trained attention evaluation model, and the attention evaluation model processes the features to obtain the evaluation results.

6. A bionic dynamic training system for improving children's attention according to claim 5, characterized in that: The training steps of the attention assessment model are as follows: constructing an attention assessment model based on a deep learning algorithm, obtaining a large amount of children's attention training data, dividing the children's attention training data into a training set and a test set, training the attention assessment model based on the training set, and testing it with the test set after the training is completed, and stopping the training when the test accuracy meets the requirements.

7. The bionic dynamic training system for improving children's attention according to claim 2, characterized in that: The execution steps of the compound eye window determination unit include: Step S41: receiving the child's age and language ability transmitted by the information acquisition unit, determining a lower limit of the number of compound eye windows based on the child's age and language ability, determining an additional number of compound eye windows based on the evaluation result, and summing the lower limit and the additional number to obtain a total number of compound eye windows; Step S42: determining a compound eye window shape based on the evaluation result, wherein the compound eye window shape includes square, circle, polygon and star; Step S43: obtaining the size of the available screen area for displaying the interactive scene, obtaining the maximum display area of each compound eye window based on the size of the available screen area, the total number of compound eye windows, and the shape of the compound eye windows, and adjusting the maximum display area based on the evaluation result to obtain the actual display area of each compound eye window; Step S44: output the total number of compound eye windows, the shape of the compound eye windows, and the actual display area as key parameters of the compound eye windows.

8. The bionic dynamic training system for improving children's attention according to claim 1, characterized in that: When the assessment result shows that the child is in a distracted state, the execution steps of the interaction adjustment unit include: Step S51: Acquire the interactive scene transmitted by the dynamic interactive unit, identify the interactive scene, and obtain the key area; Step S52: Allocate the key area to the corresponding compound eye window and send it to the dynamic interaction unit as an adjustment rendering instruction.

9. The bionic dynamic training system for improving children's attention according to claim 2, characterized in that: When the assessment result shows that the child is in a focused state, the execution steps of the interaction adjustment unit include: Step S53: Acquire the interactive scene transmitted by the dynamic interactive unit, divide and scale the interactive scene according to the number of compound eye windows, assign the divided and scaled interactive scenes to the compound eye windows, and disrupt the order of the compound eye windows; Step S54: Determine the window transparency change frequency, the window rotation and swing frequency, and the window background color block flashing frequency based on the evaluation results, add the window transparency change frequency, the window rotation and swing frequency, and the window background color block flashing frequency to each compound eye window, and send them to the dynamic interaction unit as adjustment rendering instructions.

10. The bionic dynamic training system for improving children's attention according to claim 9, characterized in that: In step S53, when the order of the compound eye windows is disrupted, more compound eye windows containing key information are displayed on the side of the dominant hand.

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