A method, device, equipment and storage medium for monitoring and predicting attention level

Abstract

The present invention discloses a method, device, equipment and storage medium for monitoring and predicting attention level. The method comprises: step 1: obtaining pupil diameter data of the current person being measured, performing preprocessing, removing outliers, and performing linear interpolation on the gaps after removing outliers to obtain preprocessed pupil diameter data; step 2: defining an adjustment ability index according to the preprocessed pupil diameter data, and calculating the adjustment ability index to obtain a time series of the adjustment ability index; step 3: extracting a concentration index according to the time series of the adjustment ability index, and establishing a concentration prediction model, wherein there are two concentration modes, one is a concentration mode, and the other is a distraction mode; step 4: predicting and displaying the concentration state according to the established prediction model, and further providing reminders.

Description

Technical Field

[0001] The present invention relates to the field of attention level monitoring technology, and in particular to an attention level monitoring and prediction method, device, equipment and storage medium. Background Art

[0002] The human body has adaptive behaviors to adapt to a diverse and ever-changing world. There are usually two modes that alternate with each other. One mode focuses on the current task, which is called the exploitation mode, and the other mode does not focus on the current task, which is called the non-focus mode or exploration mode (reference: Gary Aston-Jones and Jonathan D.Cohen, 2005 ， Annu. Rev. Neurosci.).

[0003] From a physiological point of view, the focused mode can obtain rewards from known sources, while the non-focused mode can explore new potential rewards. The benefit of switching between these two modes is that people will not focus on the current task and ignore more opportunities. However, for more important tasks (such as driving, flying, air traffic control, online learning, etc.), the non-focused mode is obviously not conducive to the current task. On the other hand, the switching between the two modes is regular, and it is difficult to completely avoid the non-focused mode. Therefore, it is necessary to make corresponding predictions for the two modes in order to adjust the human-machine ergonomics strategy in time to avoid adverse consequences.

[0004] At present, there are several approaches to monitoring attention levels:

[0005] First, task performance evaluation attention:

[0006] For example, CN202110390953.3 discloses an attention monitoring method, which plays audio prompt information, and the audio prompt information is used to monitor the attention of at least one participant of a target task, wherein the target task uses audio content as a carrier; the interaction status of the target participant in response to the audio prompt information is collected to determine the attention monitoring result of the target participant in the process of completing the target task based on the interaction status; this application can improve the accuracy of attention monitoring results.

[0007] The second is the behavior matching method, which detects whether the attention is focused by matching the previously recorded inattention images:

[0008] For example, CN202210236388.X attention monitoring method and desk lamp obtains a user image taken by a camera on the desk lamp, and the user image is used to characterize the user's posture and facial features; based on the user's personal information, target typical features (including standard posture features and standard facial features used to characterize the user's inattention) that match the user's personal information are obtained from a typical feature library, and compared to determine whether the user's attention is focused.

[0009] The third is the physiological parameter method:

[0010] For example, CN202111543318.0, a flight crew fatigue and attention monitoring device, uses an eye scanner to detect the pilot's status. CN202211678741.6, an intelligent adaptive teaching model, uses an eye focus tracking algorithm based on iris shape and position features to obtain the student's concentration level.

[0011] However, the above methods all monitor attention through external behaviors or external direct parameters. We know that changes in behavior are affected by the brain's attention regulation mechanism and present complex diversity. The general behavior index actually reflects the behavior itself, not the level of attention. In layman's terms, the behavior of staring at a book can be understood as being very focused (high attention), but in fact, the brain may have drifted and no one knows what to think. Summary of the invention

[0012] In view of the above-mentioned problems, the present invention aims to provide a method for monitoring and predicting attention levels to solve the above problems.

[0013] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0014] A method for monitoring and predicting attention level, characterized in that it comprises the following steps:

[0015] Step 1: Data acquisition, synchronously acquiring the pupil diameter data and ambient light brightness data of the current person being measured;

[0016] Step 2: Data preprocessing: preprocess the pupil diameter data, remove outliers, and perform linear interpolation on the gaps after removing outliers to obtain preprocessed pupil diameter data;

[0017] Step 3: Index calculation: define the accommodation ability index according to the preprocessed pupil diameter data. The higher the accommodation ability index is, the higher the corresponding concentration is. The accommodation ability index is calculated to obtain the time series of the accommodation ability index. The ambient light index is calculated to obtain the time series of the ambient light index.

[0018] Step 4: Index correction: using an adaptive filter to remove ambient light interference according to the time series of the ambient light index, thereby obtaining a time series of the corrected adjustment capability index;

[0019] Step 5: Classification of attention mode: classify the current mode according to the time series of the adjustment ability index to determine whether the current person being tested is in the attention mode or the non-attention mode;

[0020] Step 6: Prediction modeling: construct a focus mode prediction model and a non-focus mode prediction model based on the time series of the corrected regulatory ability index;

[0021] Step 7: Output: Use the focus mode prediction model or the non-focus mode prediction model to predict the concentration of the current person being tested, and output the current concentration state to a display screen or network background for analysis by the person being tested or a manager. Output the predicted concentration state to a display screen or network background for analysis by the person being tested or a manager.

[0022] An attention monitoring and prediction device, characterized in that it includes a data acquisition module, a data preprocessing module, an index calculation module, an index correction module, an attention pattern classification module, a prediction modeling module and an output module;

[0023] A data acquisition module is used to synchronously acquire pupil diameter data and ambient light brightness data of the current person being measured;

[0024] A data preprocessing module is used to preprocess the pupil diameter data, remove outliers, and perform linear interpolation on the gaps after the outliers are removed to obtain preprocessed pupil diameter data;

[0025] An index calculation module is used to define an accommodation ability index according to the preprocessed pupil diameter data, wherein a higher accommodation ability index corresponds to a higher concentration, and calculate the accommodation ability index to obtain a time series of the accommodation ability index, and calculate the ambient light index to obtain a time series of the ambient light index;

[0026] An index correction module, used to remove ambient light interference using an adaptive filter according to the time series of the ambient light index, thereby obtaining a time series of the corrected adjustment capability index;

[0027] An attention mode classification module is used to classify the current mode according to the time series of the adjustment ability index to determine whether the current person being tested is in the attention mode or the non-attention mode;

[0028] A prediction modeling module, used for constructing a focus mode prediction model and a non-focus mode prediction model according to the time series of the corrected regulatory ability index;

[0029] The output module is used to predict the concentration of the current person being tested by using the focus mode prediction model or the non-focus mode prediction model, and output the current concentration state to a display screen or a network background for analysis by the person being tested or a manager, and output the predicted concentration state to a display screen or a network background for analysis by the person being tested or a manager.

[0030] An electronic device, comprising:

[0031] at least one processor;

[0032] and a memory communicatively coupled to the at least one processor;

[0033] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in claim 1.

[0034] A computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method according to claim 1.

[0035] The beneficial effects of the present invention are:

[0036] First, the present invention calculates an accommodative ability index based on pupil diameter. This index is different from ordinary indicators that correspond to the behavior itself (which is deceptive). The accommodative ability index quantifies the ability behind the behavior, and this ability cannot be deceived.

[0037] Second, the present invention can continuously monitor the attention level by adjusting the ability to quantify, and has good periodic characteristics verified by experiments, and can be predicted through modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the present invention;

[0039] Figure 2 It is the cognitive task moment-regulatory ability index curve for online courses;

[0040] Figure 3 (a)-(c) are experimental instructions and flow charts for verifying the regulatory ability in the focused mode and the non-focused mode;

[0041] Figure 4 (a)-(b) are the pupil diameter data before and after interpolation, respectively;

[0042] Figure 5 (a)-(b) is the pupil diameter and pupil accommodation index; where, Figure 5 (a) is the pupil diameter accommodation index of 12 subjects; Figure 5(b) is the pupil diameter accommodation ability index of the subjects in both modes;

[0043] Figure 6 (a)-(b) are the results of subject 1 in the functional test

[0044] Figure 7 (a)-(b) are the results of subject 2 in the functional test

[0045] Figure 8 (a)-(b) are the results of subject 3 in the functional test. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0047] like Figure 1 As shown, a method for monitoring and predicting concentration includes the following steps:

[0048] Step 1: Data acquisition, synchronously acquiring the pupil diameter data and ambient light brightness data of the current person being measured;

[0049] Step 2: Data preprocessing: preprocess the pupil diameter data, remove outliers, and perform linear interpolation on the gaps after removing outliers to obtain preprocessed pupil diameter data;

[0050] Step 3: Index calculation: define the accommodation ability index according to the preprocessed pupil diameter data, calculate the accommodation ability index to obtain the time series of the accommodation ability index, calculate the ambient light index, and obtain the time series of the ambient light index;

[0051] Step 4: Index correction: the adjustment capability index and ambient light index are obtained in parallel, and an adaptive filter is used to remove ambient light interference to obtain a time series of the corrected adjustment capability index;

[0052] Step 5: Classification of attention mode: classify the current mode according to the time series of the adjustment ability index to determine whether the current person being tested is in the attention mode or the non-attention mode;

[0053] Step 6: Prediction modeling: construct a focus mode prediction model and a non-focus mode prediction model based on the time series of the corrected regulatory ability index;

[0054] Step 7: Output: Use the focus mode prediction model or the non-focus mode prediction model to predict the concentration of the current person being tested, and output the current concentration state to a display screen or network background for analysis by the person being tested or a manager. Output the predicted concentration state to a display screen or network background for analysis by the person being tested or a manager.

[0055] Example

[0056] In order to further verify the prediction effect of the method proposed in the present invention, the following experiments were carried out respectively.

[0057] 1. Double effect experiment in pupil diameter accommodation index

[0058] Through 16 college students completing the cognitive tasks of the online course, the pupil diameters of the 16 college students were collected and calculated, and the adjustment ability curve was calculated, such as Figure 2 As shown. Figure 2 It can be seen that the regulatory ability curve shows two obvious effects: an overall decline and volatility. The overall downward trend can be explained by a decline in regulatory ability due to fatigue; but how to explain the volatility? According to prior art records, when people do cognitive tasks, their brains usually switch between a focused mode (exploitation) and a non-focused mode (exploration). The volatility of the regulatory ability curve we observed is very likely caused by the switching of the two modes. The non-focused mode requires less cognitive effort, so how should the brain's work performance and regulatory ability change? Should they decline at the same time? In order to verify this, we further conducted experimental verification to explore whether the focused mode and the non-focused mode correspond to peaks or troughs, respectively.

[0059] 2. Verify whether the focus mode or the non-focus mode represents a high adjustment ability index

[0060] (1) Experimental Materials

[0061]

[0062]

[0063] The Qixin aSee Pro eye tracker mainly used in this experiment to measure and record pupil data is a high-precision, multi-functional eye tracking instrument. It uses a unique multi-point correction algorithm to solve the error problems such as visual field distortion that exist in traditional eye trackers, and improves the accuracy and reliability of the test results. It also provides professional eye movement data analysis software that supports real-time recording, storage, playback and analysis of eye movement data, allowing users to have a more comprehensive understanding of the meaning and rules behind the eye movement data. In addition, the Qixin aSee Pro eye tracker also supports real-time data collection and storage, multi-channel data synchronous collection, and multiple eye movement task design and analysis functions, making the test results more accurate.

[0064] (2) Experimental reagents

[0065] Greentech GT5 conductive paste

[0066] (3) Subjects

[0067] This experiment recruited 19 volunteers (5 males and 14 females, average age: 21.14 years, age range: 19-27 years). All participants had normal or corrected-to-normal vision and gave written informed consent, in accordance with the requirements of the Medical Ethics Committee of the Air Force Medical University.

[0068] (4) Experimental methods

[0069] i) Experimental paradigm

[0070] The process of this experimental task is as follows Figure 3 (c) There were 60 trials in total. In each trial, the subject first saw the instruction “Please empty your mind and stay relaxed” (e.g. Figure 3 (a)). After that, a "+" sign appeared in the center of the screen, and the subject was required to look at the '+' to stabilize the subject's pupil attention position and prevent eye movement from interfering with the collection of pupil diameter. The '+' sign continued to appear for 15 seconds, during which time the subject was required to remain in a non-focused state and not engage in any mental activity. After 15 seconds, the screen appeared with the instruction "When 'T' appears, press 'Space' immediately" (e.g. Figure 3 (b)), the instructions last for 3 seconds, and then the target object 'T' will appear at any time within 1 second to 6 seconds. At this time, the subject ends the previous non-focused state and enters the focused state, always alert to the appearance of the target 'T'. After 'T' appears, the subject presses the space bar and continues to the next trial.

[0071] The experimental program was written using E-prime software. The focus state instructions and target 'T' were in red font, and other instructions were in black font. The background was white to reduce the impact of light changes on pupil diameter. The light was kept constant throughout the experiment, and the experimental environment was kept quiet.

[0072] ii) Data collection

[0073] The experiment was conducted in an environment with appropriate brightness and stable light. Appropriate brightness is conducive to the collection of pupil data in cognitive tasks. The subject's head was fixed with a bracket throughout the experiment to avoid the reduction of data efficiency caused by head movement. The data was recorded using the Qixin Yiwei aSee Pro eye tracker, including information such as the position of the gaze point and the pupil diameter. The data collection and experimental process were recorded in the supporting aSeeStudio software, and a three-point calibration of both eyes was performed before each experiment. At the same time, the Borycon EEG device was used to record the EEG data of the subjects during the experiment.

[0074] iii) Data processing

[0075] The eye tracker sampling frequency is set to 100Hz. The actual sampling rate is lower than the set value, but it does not affect the processing of pupil data. The screen recording video and original pupil data in the experiment can be obtained through the aSeeStudio software.

[0076] S1: Experimental time points

[0077] The time points where the events occurred in the experiment were marked to facilitate the subsequent data segmentation. There were 60 trials in the experiment, and there were two time points in each trial, namely the start time of the non-focused state and the start time of the focused state, so a total of 120 time points needed to be marked. The traditional method is to watch the screen video recorded in the experiment and manually mark the time points. However, this method is labor-intensive and takes a lot of time and effort. To this end, Matlab software is used to write an automated processing program, and the video frames that need to be marked are selected as key frames. The program will automatically find video frames similar to the key frames, thereby automatically marking all time points and saving them.

[0078] S2: Data Segmentation

[0079] In the above process, the time points of the start of the non-focus state and the focus state have been obtained, and the two are connected to form 120 time periods. The original pupil diameter data is segmented according to the corresponding time points to generate 60 non-focus mode time periods and 60 focus mode time periods.

[0080] S3: Data Cleansing

[0081] During blinking, invalid data detected by the eye tracker will be recorded as -1. When -1 appears too many times in a data segment, subsequent calculations will be invalid. Therefore, when the number of -1 in a data segment accounts for more than 50% of the total data, this segment of data will be discarded.

[0082] S4: Data interpolation

[0083] After data cleaning, there are still invalid data in the data segment (such as Figure 4 (a)), use the two data points before and after the -1 point to perform linear interpolation to replace the -1 point (such as Figure 4 (b)).

[0084] (5) Pupil data processing results

[0085] Among the 19 subjects who completed the above experiment, the data of 5 subjects had a large amount of abnormal data and low data efficiency. Therefore, the data of these 5 subjects were discarded, and the data of the remaining 14 subjects were processed and analyzed. Figure 5(a) shows the results of pupil diameter accommodation index of 12 subjects. In each figure, the blue column represents the average value of pupil diameter accommodation index of the subjects in the non-focused state, the red column represents the average value of pupil diameter accommodation index of the subjects in the focused state, and the blue error bar above represents the corresponding standard error. Figure 5 (b) shows the pupil diameter adjustment ability index of the subjects in the two modes. It can be seen that the pupil diameter adjustment ability index of the subjects in the focused state (i.e., tense) is higher than the entropy in the non-focused state (i.e., relax) regardless of the individual or the whole.

[0086] In addition, a paired t-test was performed on the pupil diameter accommodation ability index of all subjects in the two modes, and the statistical test result was P=8.4452e-33 (P<0.05), which was significantly different.

[0087] (6) Experimental conclusion

[0088] This experiment mainly compares the pupil diameter adjustment ability index in the non-focused mode and the focused mode, records the pupil diameter of the subjects, and performs analysis and calculation. It can be seen from the experimental results that the pupil diameter adjustment ability index is higher in the focused mode. The subjects make less cognitive effort in the non-focused mode, and when the subjects switch to the focused state, their cognitive functions are activated, and people are more likely to be in a state of concentration. This explains that the volatility observed in the pupil diameter adjustment ability index is the switching of the two working modes. Since the trough in the pupil diameter adjustment ability represents the non-focused mode, it is not conducive to driving, and the trend of the pupil diameter adjustment ability decreasing is not conducive to driving. The two are consistent, and the lower the more unfavorable it is for driving. This proves the feasibility of the present invention to make advance predictions of brain fatigue manifestations through the pupil diameter adjustment ability index curve.

[0089] 3. Experiment of the advanced prediction method proposed by the present invention

[0090] (1) Performance test content

[0091] In order to further verify the performance of the fatigue advance prediction method proposed in the present invention, this experiment was tested through three indicators and compared with the perclos fatigue detection algorithm to verify the accuracy of the prediction method of the present invention.

[0092] (2) Test environment

[0093] Software environment: Visual Studio 2022, MATLAB R2022a, Anaconda 2022

[0094] (3) Subjects

[0095] Five volunteers (3 males and 2 females, average age: 20.6 years old, age range: 19-27 years old) were recruited. All five volunteers participated in the simulated driving experiment.

[0096] (4) Experimental process

[0097] S1: Align the human eyeball with the eye tracker at a certain distance, and calibrate according to the eye tracker calibration software process to ensure that the experimental data collected is true and reliable. Adjust the camera of another computer to ensure that facial information can be collected;

[0098] S2: Use software X-plane for simulated driving;

[0099] S3: When the person being tested starts the simulated driving, the fatigue advance prediction method code of the present invention (the test software designed based on the prediction method of the present invention) is run, and the perclos fatigue detection algorithm code is run at the same time as a fatigue detection control to verify the accuracy of the prediction method;

[0100] S4: Conduct test item 1 and test item 2 respectively;

[0101] i) Test Item 1: Whether the software can successfully draw the linear prediction and AR model images after running for five minutes, and whether it can display the predicted fatigue time in the text display area;

[0102] ii) Test Item 2: Compare the time error between the intelligent prediction time of brain fatigue of the present invention and the time error between the fatigue reported by the perclos program, and determine whether it is within the acceptable 3 minutes;

[0103] S5: Test and record the results of the above test items, as shown in Table 1.

[0104] Table 1 Functional test data record table

[0105]

[0106]

[0107] In the brain fatigue intelligent prediction test experiment, it can be seen in the tests of multiple subjects that as the driving task progresses, the drawn curve forms a trend of volatility and a slow decline. After the software has been running for 5 minutes, a prediction curve appears and the predicted fatigue time is displayed. The error with the fatigue time measured by the perclos program is no more than 3 minutes.

[0108] (5) Experimental results

[0109] The gain regulation signal regulation ability index in the non-focus mode and the focus mode was compared, and the pupil data and EEG data of the subjects were recorded and analyzed and calculated. It can be seen from the experimental results that the pupil regulation ability index is higher in the focus mode. The subjects made less cognitive effort in the non-focus mode, and the functional state of the subjects as a complex system was at a lower level. When the subjects switched to the focus state, their cognitive functions were activated. This also shows that when the regulation ability index of the gain regulation signal is at a high level, people are more likely to be in a state of concentration. When it decreases, it may indicate that they are in a non-focused state such as distraction or fatigue.

[0110] Therefore, it can be concluded that this experiment proves that the fluctuation in the pupil diameter accommodation ability index is the switching of two working modes, the peak is the focus mode, and the trough is the non-focus mode.

[0111] Combined with the brain fatigue intelligent prediction test, it can be found that by simulating the fatigue condition of users caused by long-term driving, and using the PERCLOS fatigue detection algorithm as a comparative judgment in real time, the functions of the brain fatigue intelligent prediction system are correct according to three different test indicators. It successfully draws linear predictions and AR model images and displays the predicted remaining fatigue time. It also gives timely stimulation when the user has brain fatigue to improve concentration. That is, users can predict fatigue conditions in advance and avoid driving risks based on this system.

[0112] (6) Functional testing

[0113] The function of the prediction method proposed in the present invention was verified by conducting a teaching content learning task test on three subjects.

[0114] First, subject 1 watched the video teaching content and recorded the pupil diameter ( Figure 6 a first line). According to the method of the present invention, the physiological brain fatigue index ( Figure 6 a The second row) black line, establish a prediction model, obtain the parameters of the prediction model according to the data, and draw the prediction line ( Figure 6 Red dashed line in b);

[0115] The prediction line parameters are: (Unit: Knight). The slope is -0.0012506t n The intercept is 1.2805 nits per second. The threshold of apparent brain fatigue determined by experiment is 0.2 nits. So, according to calculation, fatigue can be reached after about 14 minutes.

[0116] Secondly, subject 2 performed the same experimental steps as above, and the results were as follows Figure 7 a- Figure 7 As shown in b, the prediction line parameters are: (Unit: nat) According to calculations, fatigue can be reached after about 101 minutes.

[0117] Finally, subject 3 performed the same experimental steps as above, and the results were as follows Figure 8 a- Figure 8 b, and its prediction line parameters are: (Unit: nat) According to calculations, fatigue can be reached after about 149 minutes.

[0118] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

[0119] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for monitoring and predicting attention level, characterized in that: The following steps are involved: Step 1: Data acquisition, synchronously acquiring the pupil diameter data and ambient light brightness data of the current person being measured; Step 2: Data preprocessing: preprocess the pupil diameter data, remove outliers, and perform linear interpolation on the gaps after removing outliers to obtain preprocessed pupil diameter data; Step 3: Index calculation: define the accommodation ability index according to the preprocessed pupil diameter data. The higher the accommodation ability index is, the higher the corresponding concentration is. The accommodation ability index is calculated to obtain the time series of the accommodation ability index. The ambient light index is calculated to obtain the time series of the ambient light index. Step 4: Index correction: using an adaptive filter to remove ambient light interference based on the time series of the ambient light index, thereby obtaining a time series of the corrected adjustment capability index; Step 5: Classify the attention mode, classify the current mode according to the time series of the adjustment ability index, and determine whether the current person being tested is in the attention mode or the non-attention mode; Step 6: Prediction modeling: construct a focus mode prediction model and a non-focus mode prediction model based on the time series of the corrected regulatory ability index; Step 7: Output: Use the focus mode prediction model or the non-focus mode prediction model to predict the concentration of the current person being tested, and output the current concentration state to a display screen or network background for analysis by the person being tested or a manager. Output the predicted concentration state to a display screen or network background for analysis by the person being tested or a manager.

2. An attention monitoring and prediction device, characterized in that: It includes a data acquisition module, a data preprocessing module, an index calculation module, an index correction module, an attention pattern classification module, a prediction modeling module and an output module; A data acquisition module is used to synchronously acquire the pupil diameter data and ambient light brightness data of the current person being measured; A data preprocessing module is used to preprocess the pupil diameter data, remove outliers, and perform linear interpolation on the gaps after removing the outliers to obtain preprocessed pupil diameter data; An index calculation module is used to define an accommodation ability index according to the preprocessed pupil diameter data, wherein a higher accommodation ability index corresponds to a higher concentration, and calculate the accommodation ability index to obtain a time series of the accommodation ability index, and calculate the ambient light index to obtain a time series of the ambient light index; An index correction module, used to remove ambient light interference using an adaptive filter according to the time series of the ambient light index, thereby obtaining a time series of the corrected adjustment capability index; An attention mode classification module is used to classify the current mode according to the time series of the adjustment ability index to determine whether the current person being tested is in the attention mode or the non-attention mode; A prediction modeling module, used for constructing a focus mode prediction model and a non-focus mode prediction model according to the time series of the corrected regulatory ability index; The output module is used to predict the concentration of the current person being tested by using the focus mode prediction model or the non-focus mode prediction model, and output the current concentration state to a display screen or a network background for analysis by the person being tested or a manager, and output the predicted concentration state to a display screen or a network background for analysis by the person being tested or a manager.

3. An electronic device, characterized in that: include, at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in claim 1.

4. A computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to claim 1.

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