A bionic dynamic training system for improving children's attention

Abstract

The application provides a kind of bionic dynamic training system for improving children's attention, including dynamic interaction unit, interactive data acquisition unit, attention evaluation unit, compound eye window determination unit and interactive adjustment unit are sequentially data connected, children can interact with the interactive scene provided by dynamic interaction unit, interactive data acquisition unit sends the multimodal data collected in the interactive process to attention evaluation unit, attention evaluation unit obtains the evaluation result of attention, and then compound eye window determination unit can determine the key parameters of compound eye window based on the evaluation result, finally, interactive adjustment unit can adjust the interactive scene according to the key parameters of compound eye window, after introducing the visual processing mechanism of insect compound eye, a multi-focal dynamic interface can be generated based on the attention evaluation result of children, different information is presented through multiple focal points at the same time, the attention of children is attracted to achieve the training effect of improving children's attention.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of attention training, in particular to a bionic dynamic training system for improving children's attention. BACKGROUND

[0002] According to statistics of the World Health Organization, about 5%-7% of children in the world have different degrees of attention disorders. Traditional training methods such as cognitive behavior therapy and static electronic games are difficult to effectively improve the attention maintenance and anti-interference ability of children in complex environments due to the lack of dynamic adaptability and multi-modal interaction. In the prior art, the attention of children is mostly trained in the form of game interaction, and there is a problem of fixed content form. The interactive scene cannot be dynamically adjusted according to the attention evaluation result of the children. In recent years, the development of biological bionics provides a new idea for attention training. The distributed visual processing mechanism of thousands of small eyes of insect compound eyes realizes parallel processing and dynamic integration of multi-view information, which provides a biological prototype for designing an efficient multi-focus interactive interface. However, there is no related research on effectively combining the biological bionic structure and the data-driven dynamic adjustment strategy into the training of children's attention. SUMMARY

[0003] In view of this, the application provides 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 improve the effectiveness of attention training.

[0004] The technical scheme of the application is as follows:

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

[0006] A dynamic interaction unit for providing a dynamic interactive scene for children to interact;

[0007] An interactive data acquisition unit for acquiring multi-modal data during the interaction of children;

[0008] An attention evaluation unit for evaluating the attention of children based on the multi-modal data and obtaining an evaluation result;

[0009] A compound eye window determination unit for determining key parameters of a compound eye window according to the evaluation result;

[0010] An interactive adjustment unit for adjusting the interactive scene according to the key parameters of the compound eye window;

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

[0012] Preferably, it further comprises an information acquisition unit for acquiring basic information of the child, including age, gender, interest, dominant hand, language ability, and the information acquisition unit is in data connection with the dynamic interaction unit, the compound eye window determination unit and the interaction adjustment unit respectively.

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

[0014] Step S11, determining the type of the interaction scene according to the gender and interest of the child, and determining the difficulty of the interaction scene based on the age, to obtain an initial interaction scene;

[0015] Step S12, judging whether there is an adjustment rendering instruction, if not, pushing the initial interaction scene for the child to interact, if yes, rendering the initial interaction scene based on the adjustment rendering instruction and pushing it to the child to interact.

[0016] Preferably, the execution steps of the interaction data acquisition unit comprise:

[0017] Step S21, recording the gaze point trajectory, gaze duration, saccade speed and pupil diameter change based on the computer vision algorithm, and outputting as visual behavior;

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

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

[0020] Step S31, after denoising, time synchronization and anonymization processing of the visual behavior and operation behavior, multi-modal fusion is performed, and fusion features are obtained;

[0021] Step S32, inputting the fusion features into the trained attention evaluation model to obtain the evaluation result processed by the attention evaluation model.

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

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

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

[0025] Step S42, determining the shape of the compound eye window based on the evaluation result, the shape of the compound eye window including square, circle, polygon and star;

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

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

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

[0029] Step S51, obtaining the interactive scene transmitted by the dynamic interaction unit, identifying the interactive scene and obtaining the key area;

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

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

[0032] Step S53, obtaining the interactive scene transmitted by the dynamic interaction unit, segmenting and scaling the interactive scene according to the number of compound eye windows, assigning the segmented and scaled interactive scene to the compound eye windows and rearranging the order of the compound eye windows;

[0033] Step S54, determining the window transparency change frequency, the window rotation swing frequency and the window background color block flicker frequency based on the evaluation result, adding the window transparency change frequency, the window rotation swing frequency and the window background color block flicker frequency to each compound eye window and sending them to the dynamic interaction unit as adjustment rendering instructions.

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

[0035] Compared with the prior art, the beneficial effects of the present application are:

[0036] The dynamic interaction unit provides a dynamic interaction scene for children, the children can interact through the interaction scene, and the multi-modal data in the interaction process can be collected by the interaction data collection unit, the collected multi-modal data is transmitted to the attention evaluation unit for attention evaluation, based on the result of the attention evaluation, it can be determined whether the child is in a distraction state or a concentration state, and then according to the different evaluation results, the visual processing mechanism of the compound eye of insects is introduced, the key parameters of the compound eye window are set, and 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-focal dynamic interface, and when the children interact based on the multi-focal dynamic interface, the selectivity, persistence and anti-interference ability of attention can be effectively improved, and compared with the traditional static and single training system, a dynamic training scene conforming to the cognitive development law is provided for children. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only preferred embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

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

[0039] Figure 2 An execution step diagram of a dynamic interaction unit of a bionic dynamic training system for improving children's attention according to the present application;

[0040] Figure 3 An execution step diagram of an interaction data collection unit of a bionic dynamic training system for improving children's attention according to the present application;

[0041] Figure 4 An execution step diagram of an attention evaluation unit of a bionic dynamic training system for improving children's attention according to the present application;

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

[0043] Figure 6 An execution step diagram of an interaction adjustment unit of a bionic dynamic training system for improving children's attention according to the present application;

[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 application, a specific embodiment is provided below, and the present application is further illustrated in conjunction with the accompanying drawings.

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

[0047] The dynamic interaction unit 1 is used to provide a dynamic interaction scene for children to interact;

[0048] The interaction data acquisition unit 2 is used to collect multi-modal data of children during the interaction process;

[0049] The attention evaluation unit 3 is used to evaluate the attention of children based on the multi-modal data and obtain the evaluation results;

[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] The interaction adjustment unit 5 is 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 connected in data, and the interaction adjustment unit 5 is connected with the dynamic interaction unit 1 in data.

[0053] The application is a bionic dynamic training system for improving children's attention, which adopts the way of interactive games to train children's attention, wherein the dynamic interactive unit 1 can provide a dynamic interactive scene, parents can display the interactive scene through a smart terminal and provide it to children for interaction, for example, after finding the key numbers or clues in the interactive scene, they can combine them to perform addition and subtraction operations to open the next level, through the interaction of children in the interactive scene, it can be judged whether the children are focused, while the children are interacting, the interactive data acquisition unit 2 can collect multi-modal data, and the multi-modal data can be transmitted to the attention evaluation unit 3, the attention evaluation unit 3 can process the multi-modal data based on a preset algorithm or model and obtain an evaluation result, the evaluation result is in the form of percentage, which can show the degree of concentration or distraction of children, for example, more than 50% is focused, the higher the evaluation result, the higher the degree of concentration of children, through the attention evaluation unit 3, the degree of concentration of children in each interactive scene can be obtained, so that when the children are distracted, timely intervention can be made, and when the children are more focused, the difficulty of training can be increased to realize the improvement of attention.

[0054] After obtaining the attention evaluation result of the children, based on the different evaluation results, the application introduces a biological bionic technology, refers to the function of the compound eye of insects, determines the key parameters of the compound eye window through the compound eye window determination unit 4 based on the evaluation result, and then the interactive adjustment unit 5 can adjust the interactive scene according to the key parameters of the compound eye window, so that the interactive scene becomes a multi-focal dynamic interface, children can obtain information in different compound eye windows, through the introduction of the visual processing mechanism of the compound eye of insects, more difficult interaction or more targeted interaction can be carried out, the attention of children can be improved, and compared with the traditional static and single training system, a dynamic training scene conforming to the cognitive development law is provided for children.

[0055] Preferably, it further comprises an information acquisition unit 6 for acquiring children's basic information, wherein the children's basic information includes age, gender, interest, handedness, and language ability, and the information acquisition unit 6 is data-connected with the dynamic interactive unit 1, the compound eye window determination unit 4 and the interactive adjustment unit 5 respectively.

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

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

[0058] Step S11, determine the type of the interaction scene according to the gender and interest of the child, and determine the difficulty of the interaction scene based on the age, and obtain an initial interaction scene;

[0059] Step S12, judge whether there is an adjustment rendering instruction, if not, push the initial interaction scene for the child to interact, if there is, 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 type of interaction scene, for example, boys who like space scenes can be pushed to interact with space type scenes, and according to the different ages of children, the difficulty of the interaction scene will be determined, based on which a variety of initial interaction scenes can be extracted, and then the dynamic interaction unit 1 will judge whether there is an adjustment rendering instruction transmitted by the interaction adjustment unit 5, generally in the initial interaction, there will be no adjustment rendering instruction, at this time the initial interaction scene can be directly pushed out for the child to interact, and after the child completes the first interaction, the interaction scene will be adjusted based on the attention assessment result of the child, at this time the interaction adjustment unit 5 will generate an adjustment rendering instruction, and the dynamic interaction unit 1 receives the adjustment rendering instruction, which can adjust the interaction scene to further strengthen the attention training of the child.

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

[0062] Step S21, record the gaze point trajectory, gaze duration, saccade speed and pupil diameter change based on computer vision algorithm, and output as visual behavior;

[0063] Step S22, collect the response time, accuracy and task completion time of the child's operation in the interaction scene, and output as operation behavior.

[0064] During the interaction of the child, the visual behavior can be collected through the camera, which can judge whether the child is focused on a certain position and the time length, and the operation behavior can be collected through the detection algorithm built in the interaction scene, for example, the response time of each operation, if the response time is too long, it means that the child is not very focused, which will also lengthen the task completion time, in addition, the low accuracy also means that the child is not very focused.

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

[0066] Step S31, after denoising, time synchronization and identification anonymization processing of visual behavior and operation behavior, multi-modal fusion is performed, and fusion features are obtained;

[0067] In step S32, the fusion features are input into the trained attention evaluation model, and an evaluation result is obtained by processing of the attention evaluation model. The training of the attention evaluation model comprises the following steps: constructing an attention evaluation model based on a deep learning algorithm, obtaining a large amount of child attention training data, dividing the child attention training data into a training set and a test set, training the attention evaluation model based on the training set, and testing the attention evaluation model after the training is completed, and stopping the training when the test accuracy reaches a requirement.

[0068] After obtaining the multi-modal data, the method of deep learning is used to evaluate the attention of the child. The visual behavior and the operation behavior are preprocessed to ensure the reliability and authenticity of the data, and then multi-modal fusion is performed to obtain fusion features. The fusion features can be directly input into the trained attention evaluation model, and the evaluation result in percentage is output by the attention evaluation model. The attention evaluation model is trained and tested based on a large amount of historical child attention training data. The child attention training data is divided into a training set and a test set according to a ratio of 7:3. The training set is used for training. When the training reaches a certain stage, the test effect of the attention evaluation model is evaluated by the test set. If the test accuracy reaches a preset threshold, the training is stopped, and the training of the attention evaluation model is completed.

[0069] Preferably, the execution step of the compound eye window determination unit 4 comprises:

[0070] In step S41, the age and language ability of the child transmitted by the information receiving unit 6 are received. The lower limit of the number of compound eye windows is determined based on the age and language ability of the child. The increase in the number of compound eye windows is determined according to the evaluation result. The total number of compound eye windows is obtained by summing the lower limit and the increase in the number.

[0071] In step S42, the shape of the compound eye window is determined based on the evaluation result. The shape of the compound eye window comprises a square, a circle, a polygon, and a star.

[0072] In step S43, the size of the available area of the screen for displaying the interactive scene is obtained. The maximum display area of each compound eye window is obtained based on the size of the available area of the screen, the total number of compound eye windows, and the shape of the compound eye window. The actual display area of each compound eye window is obtained by adjusting the maximum display area based on the evaluation result.

[0073] In step S44, the total number of compound eye windows, the shape of the compound eye window, and the actual display area are output as the key parameters of the compound eye window.

[0074] For children of different ages and cognitive abilities, if the number of compound eye windows is too large, it will cause the burden of children, so that they cannot obtain information from multiple compound eye windows, and if it is too small, it cannot achieve the training effect, therefore, the compound eye window determination unit 4 will set a lower limit and an upper limit of the number of compound eye windows according to the age and language ability of the children, and then determine the increase number of compound eye windows according to the evaluation results, and the evaluation results are in percentage, and 10% is used as an interval to determine the increase number, and 50% is used as a reference point, and the increase number is increased by one every time 10% is increased or decreased, and finally the total number of compound eye windows can be obtained based on the increase number and the lower limit, and 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 number, the shape of the compound eye window can also be evaluated, and the shape of the compound eye window is also determined based on the evaluation results, in which square, circle, polygon and star represent the corresponding difficulty rising, and it is more difficult for children to obtain key information from compound eye windows of different shapes, and finally the actual display area of the compound eye window needs to be determined, first obtain the available area size for evaluation of the interactive scene, then according to the total number of compound eye windows, the shape of each compound eye window, and the available area size, the maximum display area of each compound eye window can be obtained, then the maximum display area is adjusted according to the evaluation results, and 50% is used as a reference, and when the evaluation result is greater than 50%, the larger the case, the smaller the actual display area of the compound eye window, and when the evaluation result is less than 50%, the smaller the case, the larger the actual display area of the compound eye window, so as to adapt to children with different evaluation results, and finally the total number of compound eye windows, the shape of the compound eye window and the actual display area are output as the key parameters of the compound eye window, so that the interactive adjustment unit 5 sets the adjustment strategy.

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

[0077] Step S51, obtaining the interactive scene transmitted by the dynamic interactive unit 1, identifying the interactive scene, and obtaining the key area;

[0078] Step S52, assigning the key area to the corresponding compound eye window, and sending it to the dynamic interactive unit 1 as an adjustment rendering instruction.

[0079] When the evaluation result is less than 50%, it indicates 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 area from the interactive scene, for example, in the interactive scene of mathematical operation, the area where the elements of the operation formula separated to each position of the interactive scene are extracted, then the corresponding compound eye window is allocated, and other areas are hidden, when this strategy is sent to the dynamic interaction unit 1 as an adjustment rendering instruction, the dynamic interaction unit 1 can display a multi-focus dynamic interaction interface, the child can easily obtain key information from the compound eye window and interact, through the way of reducing difficulty, the child can adapt to the interactive scene, so as to improve attention.

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

[0081] Step S53, obtaining the interactive scene transmitted by the dynamic interaction unit 1, segmenting and scaling the interactive scene according to the number of compound eye windows, allocating the segmented and scaled interactive scene to the compound eye windows, and disordering the order of the compound eye windows, when disordering the order of the compound eye windows, more compound eye windows containing key information are displayed on the side of the dominant hand;

[0082] Step S54, determining the window transparency change frequency, the window rotation swing frequency and the window background color block flicker frequency based on the evaluation result, adding the window transparency change frequency, the window rotation swing frequency and the window background color block flicker frequency to each compound eye window, and sending them to the dynamic interaction unit 1 as adjustment rendering instructions.

[0083] When the evaluation result is greater than 50%, it indicates that the child is in a state of concentration, at this time, the interactive adjustment unit 5 will first obtain the interactive scene transmitted by the dynamic interactive unit 1, then segment and scale the interactive scene, the number of segments is consistent with the number of compound eye windows, then the segmented sub-scenes are respectively distributed into the compound eye windows, and the order of the compound eye windows is disturbed, the originally integral interactive scene is segmented into multiple scenes, and after the order is disturbed, the difficulty of the child's interaction can be increased, in order to improve the effect of attention training, interference items can also be added in the compound eye windows, the interference items include adjusting the transparency of the compound eye window, rotating and swinging the window, and flashing the background color block of the window, when the child obtains information through the compound eye window, the interference items can interfere and increase the difficulty of the interaction, so as to realize the training of higher concentration for the child in a state of concentration, in addition, considering that there are differences in the habitual hand of children, for example, the sensitivity of the left side area of the left-handed child is higher, therefore, the compound eye window containing key information can be displayed more in the left side area, so as to improve the ability of the child to obtain key information, finally, the interactive adjustment unit 5 can send the above strategy as an adjustment rendering instruction to the dynamic interactive unit 1, and the dynamic interactive unit 1 can adjust the interactive scene, so as to realize the improvement training of attention.

[0084] The above only describes the preferred embodiments of the present application and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A bionic dynamic training system for improving attention of children, characterized in that, The application relates to a dynamic interactive system for children, comprising: a dynamic interactive unit for providing a dynamic interactive scene for children to interact with; an interactive data acquisition unit for acquiring multi-modal data of children during interaction; an attention evaluation unit for evaluating the attention of children based on the multi-modal data and obtaining an evaluation result; an eye window determination unit for determining key parameters of an eye window according to the evaluation result; an interactive adjustment unit for adjusting the interactive scene according to the key parameters of the eye window; the dynamic interactive unit, the interactive data acquisition unit, the attention evaluation unit, the eye window determination unit and the interactive adjustment unit are sequentially connected, and the interactive adjustment unit is connected with the dynamic interactive unit; an information acquisition unit is further included for acquiring basic information of children, including age, gender, interest, dominant hand and language ability, and the information acquisition unit is connected with the dynamic interactive unit, the eye window determination unit and the interactive adjustment unit respectively; the execution steps of the eye window determination unit comprise: step S41: receiving the age and language ability of children transmitted by the information acquisition unit, determining the lower limit of the number of eye windows based on the age and language ability of children, determining the increase number of eye windows according to the evaluation result, summing the lower limit and the increase number to obtain the total number of eye windows; step S42: determining the shape of the eye window based on the evaluation result, wherein the shape of the eye window comprises a square, a circle, a polygon and a star; step S43: acquiring the available area size of a screen for displaying the interactive scene, acquiring the maximum display area of each eye window based on the available area size of the screen, the total number of eye windows and the shape of the eye window, adjusting the maximum display area based on the evaluation result to obtain the actual display area of each eye window; step S44: outputting the total number of eye windows, the shape of the eye window and the actual display area as the key parameters of the eye window.

2. The bionic dynamic training system for improving children's attention according to claim 1, characterized in that, the execution steps of the dynamic interactive unit comprise: step S11: determining the type of the interactive scene according to the gender and interest of children, and determining the difficulty of the interactive scene based on the age to obtain an initial interactive scene; step S12: judging whether there is an adjustment rendering instruction, if not, pushing the initial interactive scene for children to interact with, and if yes, rendering the initial interactive scene based on the adjustment rendering instruction and pushing the rendered interactive scene for children to interact with.

3. The bionic dynamic training system for improving children's attention according to claim 1, characterized in that, the execution steps of the interactive data acquisition unit comprise: step S21: recording the gaze point trajectory, gaze duration, saccade speed and pupil diameter change based on a computer vision algorithm and outputting visual behavior; step S22: acquiring the response time, accuracy and task completion time of children during operation in the interactive scene and outputting operation behavior.

4. The bionic dynamic training system for improving children's attention according to claim 3, characterized in that, the execution steps of the attention evaluation unit comprise: step S31: after noise reduction, time synchronization and identification anonymization processing of the visual behavior and the operation behavior, performing multi-modal fusion and obtaining fusion features; step S32: inputting the fusion features into a trained attention evaluation model to obtain an evaluation result.

5. The bionic dynamic training system for improving children's attention according to claim 4, characterized in that, The training step of the attention evaluation model is: constructing an attention evaluation 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 evaluation model based on the training set, and testing the attention evaluation model based on the test set after the training is completed, and stopping the training when the test accuracy reaches the requirement.

6. The bionic dynamic training system for improving children's attention according to claim 1, characterized in that, When the evaluation result shows that the child is in a distracted state, the execution step of the interaction adjustment unit includes: Step S51, obtaining the interaction scene transmitted by the dynamic interaction unit, identifying the interaction scene, and obtaining the key area; Step S52, assigning the key area to the corresponding compound eye window and sending it to the dynamic interaction unit as an adjustment rendering instruction.

7. The bionic dynamic training system for improving children's attention according to claim 1, characterized in that, When the evaluation result shows that the child is in a focused state, the execution step of the interaction adjustment unit includes: Step S53, obtaining the interaction scene transmitted by the dynamic interaction unit, segmenting and scaling the interaction scene according to the number of compound eye windows, assigning the segmented and scaled interaction scene to the compound eye window, and rearranging the order of the compound eye window; Step S54, determining the window transparency change frequency, the window rotation swing frequency and the window background color block flicker frequency based on the evaluation result, adding the window transparency change frequency, the window rotation swing frequency and the window background color block flicker frequency to each compound eye window, and sending them to the dynamic interaction unit as adjustment rendering instructions.

8. The bionic dynamic training system for improving children's attention according to claim 7, characterized in that, When the order of the compound eye window is rearranged, the compound eye window containing the key information is displayed more on the dominant hand side.

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