A self-verifying method for monitoring children's attention

Abstract

A self-verification type child attention monitoring method of the present application constructs a rich plot database according to the plot needs of children and the public needs, then calculates the child attention value according to the interaction data of the child in the interactive plot, and then can combine the difficulty value and the attention threshold to calculate the attention evaluation value, and the attention evaluation value is used as the main basis for evaluating the child attention, and in the evaluation process, the self-verification mechanism is introduced, the next interactive plot with different difficulty values is dynamically selected according to the size of the attention evaluation value of the first interactive plot, and the positive and negative correlation between the change of the attention evaluation value and the change of the difficulty value of the two interactive plots is calculated, different measures are taken based on the positive and negative correlation, if it is positive correlation, a new interactive plot containing olfactory stimulation can be introduced again, and the positive and negative correlation is evaluated again for self-verification, to ensure the reliability and accuracy of the attention monitoring result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of children's attention monitoring, in particular to a self-verification type children's attention monitoring method. BACKGROUND

[0002] Under the background of education digitization and the surge in demand for children's healthy growth, children's attention monitoring technology has become a research hotspot. Traditional children's attention monitoring methods rely on single-dimensional data such as behavior observation or simple physiological signal collection, and have problems such as low monitoring accuracy, easy interference by subjective factors, and lack of dynamic adaptability. For example, recording children's distraction behavior through teacher classroom observation is prone to errors due to individual cognitive differences; using electroencephalogram signals alone cannot fully reflect children's attention state in complex interactive scenarios. Moreover, existing technologies lack a self-verification mechanism for monitoring results, making it difficult to ensure the reliability and effectiveness of the data and unable to dynamically adjust monitoring strategies and intervention measures according to real-time changes in children's attention. SUMMARY

[0003] In view of this, the present application provides a self-verification type children's attention monitoring method, which can self-verify children's attention monitoring results to ensure the accuracy of the monitoring results.

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

[0005] A self-verification type children's attention monitoring method includes the following steps:

[0006] Step S1, obtaining the plot demand of a child, obtaining a plurality of interactive plots of different types based on the plot demand and public demand, and extracting the difficulty value and attention threshold of each interactive plot;

[0007] Step S2, randomly selecting an interactive plot to push to the child for interaction, collecting interactive data during the interaction, calculating the child's attention value based on the interactive data, and calculating the attention evaluation value according to the difficulty value, the child's attention value, and the attention threshold;

[0008] Step S3, dynamically selecting the next interactive plot with different difficulty values according to the attention evaluation value, and calculating the attention evaluation value, then judging the positive or negative correlation between the change of the attention evaluation value and the change of the difficulty value of the two interactive plots before and after the change;

[0009] Step S4, when the positive or negative correlation is positive, introducing an olfactory stimulus in the next interactive plot, and judging the positive or negative correlation between the change of the attention evaluation value and the change of the difficulty value of the two interactive plots before and after the change;

[0010] Step S5, when the positive and negative correlation is negative, output the attention evaluation value calculated when the child interacts in the subsequent interactive plot.

[0011] Preferably, the specific steps of obtaining the plot demand of the child in step S1 are:

[0012] Step S11, collect the age, personality and gender of the child as basic attributes, collect the toy preference, game mode and daily video watching category of the child as behavior habits, and obtain the regional characteristics of the region where the child is located as cultural characteristics;

[0013] Step S12, construct a child interest portrait based on the basic attributes, behavior habits and cultural characteristics, and determine the plot demand of the child according to the child interest portrait.

[0014] Preferably, the specific steps of constructing a plurality of interactive plots of different types based on the plot demand and the public demand in step S1, and presetting the corresponding attention threshold value according to the difficulty value of each interactive plot are:

[0015] Step S13, preset a plot database, the plot database contains a plurality of interactive plots of different types, and each interactive plot is marked with a difficulty value and an attention threshold value;

[0016] Step S14, find the interactive plot of the same type as the plot demand from the plot database according to the plot demand, and output the interactive plot, and randomly select other interactive plots of different types from the plot database according to the plot demand and output the interactive plots;

[0017] Step S15, extract the difficulty value and attention threshold value of the output interactive plot.

[0018] Preferably, the specific steps of step S2 are:

[0019] Step S21, randomly output an interactive plot with a medium difficulty value from the obtained plurality of interactive plots, and display it on the intelligent terminal for the child to interact;

[0020] Step S22, directly collect the in-game operation frequency, task completion speed and error rate of the child during the interaction process through the intelligent terminal as behavior data;

[0021] Step S23, collect heart rate variability and skin electrical reaction data through wearable devices as physiological data, and collect eye movement frequency and frown frequency through a camera as visual data;

[0022] Step S24, standardize and weight the behavior data, physiological data and visual data, and sum them to obtain the child attention value, and calculate the attention evaluation value according to the difficulty value, the child attention value and the attention threshold value.

[0023] Preferably, the expression of the attention evaluation value is:

[0024]

[0025] wherein a is the difficulty value, A is the child attention value, and B is the attention threshold value.

[0026] Preferably, the specific steps of the step S3 are:

[0027] Step S31, when the attention evaluation value is greater than 100%, the difficulty value of the next selected interactive plot is greater than the difficulty value of the previous interactive plot, and when the attention evaluation value is less than 100%, the difficulty value of the next selected interactive plot is less than the difficulty value of the previous interactive plot;

[0028] Step S32, after calculating the attention evaluation value of the next interactive plot, the attention evaluation value and the difficulty value of the next interactive plot are respectively subtracted from the attention evaluation value and the difficulty value of the previous interactive plot;

[0029] Step S33, when the positive and negative signs of the difference value of the attention evaluation value and the difference value of the difficulty value are the same, the output is positive correlation, otherwise the output is negative correlation.

[0030] Preferably, the specific steps of the step S4 include:

[0031] Step S41, when the positive and negative correlation is positive correlation, and the difficulty value of the next interactive plot is less than the difficulty value of the previous interactive plot, the next interactive plot is obtained, the specific plot content of the interactive plot is extracted, and a plurality of trigger nodes are set according to the specific plot content;

[0032] Step S42, the child attention value before the trigger node when the child interacts with the interactive plot is calculated, and when the child attention value is less than the attention threshold value of the interactive plot, the enhanced odor is released at the trigger node;

[0033] Step S43, the child attention values before all trigger nodes are averaged, and the positive and negative correlation relationship between the changes of the attention evaluation values and the changes of the difficulty values of the two interactive plots before and after is judged again, if it is still positive correlation, the step S4 is repeated based on the set number of repetitions until the positive and negative correlation relationship is negative correlation or the number of repetitions is ended.

[0034] Preferably, the specific steps of the step S4 further include:

[0035] Step S44, when the positive and negative correlation is positive correlation, and the difficulty value of the next interactive plot is greater than the difficulty value of the previous interactive plot, the next interactive plot is obtained;

[0036] Step S45, when the child interacts with the interactive plot, the interference type smell is randomly released, the attention evaluation value of the child in the interactive plot is calculated, and the positive and negative correlation between the change of the attention evaluation value of the two interactive plots before and after and the change of the difficulty value is judged again.

[0037] Step S46, if the positive and negative correlation is still positive, the current attention evaluation value is output, otherwise go to step S5.

[0038] Preferably, the enhanced smell includes mint, citrus, rosemary, cedar, lavender and the smell related to the interactive plot, and the interference type smell includes camphor, high concentration perfume, incense, high sweet and greasy smell and the smell not matching the interactive plot.

[0039] Compared with the prior art, the beneficial effects of the present application are:

[0040] ① Based on the different plot requirements of children, the database of interactive plots is constructed, when selecting the interactive plot for the child interaction, the plot requirements of the child can be met, the attention of the child can be improved, and the public requirements are also introduced for evaluating the attention of the child to the plot not interested in the child, so as to comprehensively evaluate the attention of the child;

[0041] ② When evaluating the attention of the child, the attention value of the child is calculated according to the interactive data of the child, and then the attention evaluation value can be calculated based on the difficulty value of the interactive plot and the attention threshold combined with the attention value of the child, so as to be used as the basis for evaluating the attention of the child, and the attention of the child can be accurately evaluated through the quantitative data;

[0042] ③ After obtaining the attention evaluation value of the first interactive plot, the next interactive plot can be selected, the positive and negative correlation between the change of the attention evaluation value of the two interactive plots before and after and the change of the difficulty value is based on the positive and negative correlation, the subsequent interactive plot is adjusted based on the positive and negative correlation, and the olfactory stimulation is introduced for self-verification, so as to ensure the reliability and accuracy of the attention monitoring result. BRIEF DESCRIPTION OF DRAWINGS

[0043] 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.

[0044] Figure 1 The flow chart of a self-verification type child attention monitoring method of the present application;

[0045] Figure 2Flow chart of step S1 of a self-verification type children attention monitoring method of the present application;

[0046] Figure 3 Flow chart of step S2 of a self-verification type children attention monitoring method of the present application;

[0047] Figure 4 Flow chart of step S3 of a self-verification type children attention monitoring method of the present application;

[0048] Figure 5 Flow chart of step S4 of a self-verification type children attention monitoring method of the present application. DETAILED DESCRIPTION

[0049] In order to better understand the technical content of the present application, a specific embodiment is provided below, and the present application is further described in combination with the accompanying drawings.

[0050] Reference Figures 1 to 5 , the present application provides a self-verification type children attention monitoring method, comprising the following steps:

[0051] Step S1, obtaining the plot demand of children, obtaining a plurality of interactive plots of different types based on the plot demand and the public demand, and extracting the difficulty value and the attention threshold value of each interactive plot;

[0052] Step S2, randomly selecting an interactive plot to push to children for interaction, collecting interactive data in the interactive process, calculating the children attention value based on the interactive data, and calculating the attention evaluation value according to the difficulty value, the children attention value and the attention threshold value;

[0053] Step S3, dynamically selecting the next interactive plot with different difficulty values according to the attention evaluation value, and calculating the attention evaluation value, and judging the positive and negative correlation relationship between the change of the attention evaluation value and the change of the difficulty value of the two interactive plots before and after;

[0054] Step S4, when the positive and negative correlation relationship is positive, introducing olfactory stimulation in the next interactive plot, and judging the positive and negative correlation relationship between the change of the attention evaluation value and the change of the difficulty value of the two interactive plots before and after;

[0055] Step S5, when the positive and negative correlation relationship is negative, outputting the attention evaluation value calculated when the children interact in the subsequent interactive plot.

[0056] The application adopts the mode of interactive attention monitoring, that is, the interactive data of the child is collected during the child plays the game to evaluate the attention of the child, and before the attention of the child is monitored, a plot database is constructed, and the plot database includes plots of interest and plots of no interest to the child. After the plot demand of the child is obtained, the interactive plot related to the plot demand can be found based on the plot demand. For example, when the child likes the plot of space type, the interactive plot of space environment or theme can be found. In order to enrich the data amount of the plot database, the interactive plot of the public demand of no interest to the child is introduced to enrich the plot, and whether the child can concentrate on the plot of no interest can be verified. The selected interactive plot is obtained based on the current child interactive attention training software. Each interactive plot has a preset difficulty value and an attention threshold value. The greater the difficulty value, the higher the understanding ability of the child is required. When the child interacts, the child will be distracted because the difficulty is too high. The attention threshold value is the minimum value. If the child attention value calculated for the child is lower than the attention threshold value, it is evaluated as distraction, otherwise as concentration. By quantifying the child attention value, the attention of the child can be accurately evaluated.

[0057] When the child interacts with the interactive plot, the interaction data in the interaction process is collected, the attention value of the child is calculated based on the interaction data, the attention evaluation value is calculated according to the attention value of the child, the difficulty value and the attention threshold value, the attention evaluation value is used to evaluate whether the child is focused in the interaction process, and in order to ensure the accuracy of the attention monitoring result, the self-verification function is set, the attention evaluation value is calculated after the first interactive plot, it is judged whether the child belongs to the focused state or the distracted state, then the next interactive plot is selected for pushing, and the difficulty value of the next interactive plot is different from that of the first interactive plot, the purpose is to determine whether the evaluation of the child's attention is accurate through the difference of the interactive plot, and after selecting the next interactive plot with different difficulty values, the attention evaluation value is calculated again, and compared with the attention evaluation value calculated before, and the change of the difficulty value is combined to judge whether the change of the attention evaluation value is positively correlated with the change of the difficulty value, wherein the positive correlation includes that when the difficulty value increases, the calculated attention evaluation value also increases, and when the difficulty value decreases, the calculated attention evaluation value also decreases, and the negative correlation means that the change direction of the difficulty value of the two interactive plots before and after is opposite to the change direction of the attention evaluation value, for example, when the difficulty decreases, the attention evaluation value increases, or when the difficulty increases, the attention evaluation value decreases, according to the normal child interaction, when the difficulty decreases, the attention evaluation value should increase, and vice versa, therefore, when the positive and negative correlation is negative, it means that the attention monitoring is reasonable, therefore, the attention evaluation value calculated in the next interactive plot can be directly output, and if the positive and negative correlation is positive, for example, when the difficulty decreases, the attention evaluation value decreases, which means that the attention monitoring may be abnormal, or the child's attention is not enough, therefore, non-traditional olfactory stimulation can be introduced in the subsequent interactive plot to stimulate the child's attention, and the positive and negative correlation is judged again, through dynamically selecting the interactive plot with different difficulty values and introducing the olfactory stimulation, the attention monitoring can be reasonably verified, and reliable and accurate attention monitoring results can be obtained, so that the parents can understand the concentration of the child, and the training or learning plan of the child can be changed.

[0058] Preferably, the specific steps in step S1 are:

[0059] Step S11, collect the age, personality and gender of the child as basic attributes, collect the toy preference, game mode and daily video watching type of the child as behavior habits, and obtain the regional characteristics of the region where the child is located as cultural characteristics;

[0060] Step S12, constructing a child interest portrait based on the basic attributes, behavior habits and cultural characteristics, and determining the plot demand of the child according to the child interest portrait.

[0061] Step S13, a preset scenario database, wherein a plurality of different interactive scenarios are included, and each interactive scenario is marked with a difficulty value and an attention threshold value;

[0062] Step S14, according to the scenario requirement, an interactive scenario of the same type as the scenario requirement is found from the scenario database and output, and other interactive scenarios of different types from the scenario database are randomly selected and output;

[0063] Step S15, the difficulty value and the attention threshold value of the output interactive scenario are extracted.

[0064] In order to improve the attention of children, the interests of children need to be understood, and the age difference, personality difference and gender difference of different children will affect the specific preferences, for example, boys may prefer engineering vehicles, universe, monsters and the like, and girls may prefer animals, stories and the like, in addition, the preferences of children can also be judged according to their daily behaviors, such as the types of videos they usually watch, the preferred toys and game modes, in addition, the regional characteristics of the area where the children are located can also be obtained as cultural characteristics, and then the children interest portrait can be constructed based on the basic attributes, behavior habits and cultural characteristics, the scenario requirement can be determined according to the children interest portrait, a plurality of interactive scenarios can be obtained based on the children attention training software on the market, and the interactive scenarios are combined into a scenario database, wherein the interactive scenarios are divided into consistent and inconsistent parts, the consistent interactive scenario is found from the scenario database, and a plurality of inconsistent interactive scenarios are randomly selected and output as the selection of children training, and the difficulty value and the attention threshold value included in the interactive scenario are also output for subsequent extraction and calculation.

[0065] Preferably, the specific steps of step S2 are as follows:

[0066] Step S21, from the obtained plurality of interactive scenarios, an interactive scenario with a medium difficulty value is randomly output and displayed on the intelligent terminal for the children to interact;

[0067] Step S22, the game operation frequency, task completion speed and error rate of the children during the interaction are directly collected by the intelligent terminal as behavior data;

[0068] Step S23, the heart rate variability and skin electric reaction data are collected by the wearable device as physiological data, and the eye movement frequency and frown frequency are tracked by the camera as visual data;

[0069] Step S24, the behavior data, physiological data and visual data are standardized and weighted and summed to obtain a child attention value, and an attention evaluation value is calculated according to the difficulty value, the child attention value and the attention threshold value, and the expression of the attention evaluation value is:

[0070]

[0071] Wherein, α is the difficulty value, A is the child attention value, and B is the attention threshold value.

[0072] When the child performs the attention monitoring training, the difficulty value of the first selected interactive plot is moderate, so as to avoid the child's mental fluctuations caused by too large difficulty from affecting the attention monitoring. When the child interacts in the interactive plot, the interactive data in the interactive process can be collected, including the behavior data, physiological data and visual data. For the behavior data, if the operation frequency in the game is too high, the task completion speed is slow, the error rate is high and the like, it can be considered that the child's attention is poor. Similarly, the wearable device can collect the heart rate variability and skin galvanic response. If the low frequency part of the heart rate variability increases or the skin galvanic response increases, it can be considered that the child's attention decreases. The camera can collect the eye movement frequency and frown frequency. If the eye movement frequency is too high and the frown frequency is too high, it can be considered that the child's attention is low. After the above collected data is standardized, the child attention value can be obtained by using the weighted sum method, and then the attention evaluation value can be calculated based on the attention evaluation value expression formula. The attention evaluation value adopts 100% mode. Greater than or equal to 100% indicates that the child is focused, and less than 100% indicates that the child is distracted.

[0073] Preferably, the specific steps of step S3 are:

[0074] Step S31, when the attention evaluation value is greater than 100%, the difficulty value of the next selected interactive plot is greater than that of the previous interactive plot, and when the attention evaluation value is less than 100%, the difficulty value of the next selected interactive plot is less than that of the previous interactive plot;

[0075] Step S32, after the attention evaluation value of the next interactive plot is calculated, the attention evaluation value and the difficulty value of the next interactive plot are respectively subtracted from the attention evaluation value and the difficulty value of the previous interactive plot;

[0076] Step S33, when the difference between the attention evaluation values and the difference between the difficulty values have the same positive and negative signs, the output is positive correlation, otherwise the output is negative correlation.

[0077] When the attention evaluation value calculated based on the child in the first interactive plot is judged as focused, in the next interactive plot, an interactive plot with a greater difficulty value can be selected, and when it is judged as distracted, an interactive plot with a lower difficulty value can be pushed, the purpose being to verify whether the attention monitoring result is accurate through the difference in the judgment of the child's attention in the two interactive plots, so that after the attention evaluation value of the next interactive plot is calculated, the positive and negative correlation between the change of the attention evaluation value and the change of the difficulty value of the two interactive plots can be judged, different verification measures are taken based on different correlation relationships.

[0078] Preferably, the specific steps of the step S4 include:

[0079] Step S41, when the positive and negative correlation is positive correlation, and the difficulty value of the next interactive plot is less than that of the previous interactive plot, the next interactive plot is obtained, the specific plot content of the interactive plot is extracted, and a plurality of trigger nodes are set according to the specific plot content;

[0080] Step S42, calculating the child's attention value before the trigger node when the child interacts with the interactive plot, and releasing the enhanced odor at the trigger node when the child's attention value is less than the attention threshold of the interactive plot;

[0081] Step S43, averaging the child's attention value before all trigger nodes, and judging again the positive and negative correlation between the change of the attention evaluation value and the change of the difficulty value of the two interactive plots, if it is still positive correlation, repeating the step S4 based on the set number of repetitions until the positive and negative correlation is negative correlation or the number of repetitions is over.

[0082] Specifically, when the positive-negative relationship is positive correlation, and the difficulty value of the next interactive plot is less than the difficulty value of the previous interactive plot, it indicates that the attention evaluation value of the child is reduced synchronously in the case of reducing the difficulty value, and the possible reason is that the attention of the child changes or the attention monitoring mechanism fails. In order to verify the accuracy of the attention monitoring result, after the next interactive plot is selected, the specific plot content of the interactive plot is extracted, and a plurality of trigger nodes in the plot are obtained. Based on the trigger nodes, the interactive plot is divided into stages, and then when the child interacts, the attention value of the child before each trigger node is calculated and compared with the attention threshold value of the current interactive plot. When the attention value of the child is less than the attention threshold value, it is judged that the child is distracted, and at this time, the enhanced smell such as mint, citrus, rosemary, cedar, lavender and the smell related to the interactive plot can be released at the trigger node to improve the concentration of the child. After the whole interactive plot is completed, the attention value of the child in each plot segment is averaged to calculate the attention evaluation value, and the positive-negative relationship between the previous and next interactive plots is judged again. If it is still positive correlation, step S4 can be repeated based on the set number of repetitions until the positive-negative relationship is negative correlation or the number of repetitions is completed. If it is still positive correlation when the number of repetitions is completed, it can be basically determined that the child monitoring function is abnormal, and the related parameters need to be adjusted.

[0083] Preferably, the specific steps of step S4 further comprise:

[0084] Step S44, when the positive-negative relationship is positive correlation, and the difficulty value of the next interactive plot is greater than the difficulty value of the previous interactive plot, the next interactive plot is obtained.

[0085] Step S45, when the child interacts with the interactive plot, the interference type smell is randomly released, the attention evaluation value of the child in the interactive plot is calculated, and the positive-negative relationship between the changes of the attention evaluation values of the previous and next interactive plots and the changes of the difficulty values is judged again.

[0086] Step S46, if the positive-negative relationship is still positive correlation, the current attention evaluation value is output, otherwise go to step S5.

[0087] When the positive and negative correlation is positive correlation, and the difficulty value of the next interactive plot is greater than the difficulty value of the previous interactive plot, it indicates that the attention evaluation value of the child increases with the increase of the difficulty, and the child has high concentration. At this time, the interference type smell can be released when interacting with the next interactive plot, such as camphor, high concentration perfume, incense, high sweet and greasy smell, and smell not matching the interactive plot, so as to distract the child, then calculate the attention evaluation value in this interactive plot, and calculate the positive and negative correlation between the change of the attention evaluation value of the two interactive plots and the change of the difficulty value. If it is still positive correlation, it means that the interference type smell does not work on the child, and the child is more focused. At this time, the current attention evaluation value can be output as the basis for evaluating the child's attention.

[0088] If the positive and negative correlation is negative correlation, it means that the interference type smell has an effect on the child's attention. At this time, an interactive plot can be randomly pushed, and the attention evaluation value calculated when the interactive plot is interacted can be output.

[0089] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A self-verifying method of monitoring a child's attention, characterized by, The method comprises the following steps: Step S1, obtaining the plot demand of the child, obtaining a plurality of interactive plots of different types based on the plot demand and the public demand, and extracting the difficulty value and the attention threshold value of each interactive plot; Step S2, randomly selecting an interactive plot to push to the child for interaction, collecting interaction data in the interaction process, calculating the child attention value based on the interaction data, and calculating the attention evaluation value according to the difficulty value, the child attention value and the attention threshold value; Step S3, dynamically selecting the next interactive plot with different difficulty values according to the attention evaluation value, and calculating the attention evaluation value, and judging the positive and negative correlation between the change of the attention evaluation value and the change of the difficulty value of the two interactive plots before and after the calculation; Step S4, when the positive and negative correlation is positive, introducing olfactory stimulation in the next interactive plot, and judging the positive and negative correlation between the change of the attention evaluation value and the change of the difficulty value of the two interactive plots before and after the calculation; when the positive and negative correlation is negative, the attention evaluation value calculated when the child interacts in the subsequent interactive plot is output.

2. The self-authenticating child attention monitoring method of claim 1, wherein, The specific steps of obtaining the plot demand of the child in step S1 are: Step S11, collecting the age, personality and gender of the child as basic attributes, collecting the toy preference, game mode and daily video watching type of the child as behavior habits, and obtaining the regional characteristics of the area where the child is located as cultural characteristics; Step S12, constructing a child interest portrait based on the basic attributes, behavior habits and cultural characteristics, and determining the plot demand of the child according to the child interest portrait.

3. The self-authenticating child attention monitoring method of claim 1, wherein, The specific steps of constructing a plurality of interactive plots of different types based on the plot demand and the public demand in step S1 are: Step S13, presetting a plot database, wherein the plot database contains a plurality of interactive plots of different types, and each interactive plot is marked with a difficulty value and an attention threshold value; Step S14, searching for an interactive plot of the same type as the plot demand from the plot database according to the plot demand, and outputting the interactive plot, and randomly selecting other interactive plots of different types from the plot database according to the plot demand and outputting the interactive plots; Step S15, extracting the difficulty value and the attention threshold value of the output interactive plot.

4. The self-authenticating child attention monitoring method of claim 1, wherein, The specific steps of step S2 are: Step S21, randomly outputting an interactive plot with a medium difficulty value from the plurality of interactive plots obtained, and displaying the interactive plot on the intelligent terminal for the child to interact; Step S22, directly collecting the in-game operation frequency, task completion speed and error rate of the child during the interaction process as behavior data through the intelligent terminal; Step S23, collecting heart rate variability and skin electrical reaction data as physiological data through a wearable device, and tracking eye movement frequency and frown frequency as visual data through a camera; Step S24, standardizing and weighting the behavior data, physiological data and visual data, and summing to obtain the child attention value, and calculating the attention evaluation value according to the difficulty value, the child attention value and the attention threshold value.

5. The self-authenticating child attention monitoring method of claim 1 or 4, wherein, The expression of the attention evaluation value is: wherein A is the child attention value, B is the attention threshold value.

6. The self-authenticating child attention monitoring method of claim 1, wherein, The specific steps of step S3 are: Step S31, when the attention evaluation value is greater than 100%, the difficulty value of the next selected interactive plot is greater than that of the previous interactive plot, and when the attention evaluation value is less than 100%, the difficulty value of the next selected interactive plot is less than that of the previous interactive plot; Step S32, after calculating the attention evaluation value of the next interactive plot, the attention evaluation value and the difficulty value of the next interactive plot are respectively subtracted from the attention evaluation value and the difficulty value of the previous interactive plot; Step S33, when the positive and negative signs of the difference value of the attention evaluation value and the difference value of the difficulty value are the same, the output is positive correlation, otherwise the output is negative correlation.

7. The self-authenticating child attention monitoring method of claim 1, wherein, The specific steps of the step S4 include: Step S41, when the positive and negative correlation is positive correlation, and the difficulty value of the next interactive plot is less than that of the previous interactive plot, the next interactive plot is obtained, the specific plot content of the interactive plot is extracted, and a plurality of trigger nodes are set according to the specific plot content; Step S42, the attention value of the child before the trigger node when the child interacts with the interactive plot is calculated, and the enhanced odor is released at the trigger node when the attention value of the child is less than the attention threshold of the interactive plot; Step S43, the attention value of the child before all trigger nodes is averaged, and the positive and negative correlation between the change of the attention evaluation value and the change of the difficulty value of the previous and next interactive plots is judged again, if it is still positive correlation, the step S4 is repeated based on the set number of repetitions until the positive and negative correlation is negative correlation or the number of repetitions is ended.

8. The self-authenticating child attention monitoring method of claim 7, wherein, The specific steps of the step S4 also include: Step S44, when the positive and negative correlation is positive correlation, and the difficulty value of the next interactive plot is greater than that of the previous interactive plot, the next interactive plot is obtained; Step S45, when the child interacts with the interactive plot, the interference odor is randomly released, the attention evaluation value of the child in the interactive plot is calculated, and the positive and negative correlation between the change of the attention evaluation value and the change of the difficulty value of the previous and next interactive plots is judged again; Step S46, if the positive and negative correlation is still positive correlation, the current attention evaluation value is output, otherwise a piece of interactive plot is randomly pushed, and then the attention evaluation value calculated when the interactive plot is interacted is output.

9. The self-authenticating child attention monitoring method of claim 8, wherein, The enhanced odor includes mint, citrus, rosemary, cedar, lavender and odor related to the interactive plot, and the interference odor includes camphor, high-concentration perfume, incense, high sweet and greasy odor and odor not matching the interactive plot.

Images