Personnel cognitive ability evaluation method, edge computing device and medium

Abstract

The embodiment of the invention provides a personnel cognitive ability evaluation method, an edge computing device and a medium, in the personnel cognitive ability evaluation method, after the edge computing device obtains configuration parameters of a delineation experiment, according to the configuration parameters of target stimulation and interference stimulation and the layout configuration parameters, the configuration parameters of the delineation experiment are calculated according to the configuration parameters of the target stimulation and interference stimulation, and the layout configuration parameters; displaying a display interface of the scratching experiment, responding to the operation of the user, obtaining the working efficiency, the average reaction time and the accuracy of the scratching experiment of the user according to the interaction control configuration parameters and the task control configuration parameters, and then determining the scratching experiment of the user according to the working efficiency, the average reaction time and the accuracy. According to the method, the cognitive competence of the user is evaluated, and the configuration parameters of the demarcation experiment are adjusted, so that the cognitive competence of the user can be evaluated through the demarcation experiment, the configuration parameters of the demarcation experiment are dynamically adjusted, and the training effect and the user experience of the demarcation experiment can be improved.

Description

Technical Field

[0001] This application relates to the fields of electronic digital data processing, bioinformatics, human factors intelligence, and human factors and ergonomics, and particularly to a method for assessing human cognitive abilities, an edge computing device, and a medium. Background Technology

[0002] In daily life, users tend to pay attention to things they care about first, and then process them. Attention is indispensable in people's lives, but there are individual differences in attention. The cancellation experiment is an effective means of measuring users' attention.

[0003] In the cross-out experiment, users are asked to accurately cross out a perceived object within a short period of time according to certain requirements. Therefore, users need to concentrate and accurately and quickly identify and find the specific object among many similar objects and cross it out.

[0004] However, existing technologies for conducting elimination experiments primarily focus on attention and visual search abilities, lacking targeted designs for memory training. Furthermore, the training content and difficulty are usually fixed and cannot be dynamically adjusted according to the user's skill level, resulting in limited training effectiveness. Summary of the Invention

[0005] This application provides a method for assessing human cognitive ability, an edge computing device, and a medium to evaluate user cognitive ability through a cancellation experiment, dynamically adjust the configuration parameters of the cancellation experiment, and thereby improve the training effect and user experience of the cancellation experiment.

[0006] In a first aspect, embodiments of this application provide a method for assessing a person's cognitive ability, comprising: acquiring configuration parameters for a cancellation experiment; wherein the configuration parameters include configuration parameters for the target stimulus and interference stimulus, layout configuration parameters, interaction control configuration parameters, and task control configuration parameters in the cancellation experiment; displaying a display interface for the cancellation experiment based on the configuration parameters for the target stimulus and interference stimulus, and the layout configuration parameters; wherein the display interface for the cancellation experiment includes a grid image of the cancellation experiment, and images of the target stimulus and the interference stimulus; in response to a user's operation, acquiring the user's work efficiency, average reaction time, and accuracy in this cancellation experiment based on the interaction control configuration parameters and the task control configuration parameters; and assessing the user's cognitive ability and adjusting the configuration parameters of the cancellation experiment based on the work efficiency, average reaction time, and accuracy.

[0007] The above method can assess users' cognitive abilities through cancellation experiments, dynamically adjust the configuration parameters of cancellation experiments, and thus improve the training effect and user experience of cancellation experiments.

[0008] In one possible implementation, the display interface for the cancellation experiment, based on the configuration parameters of the target stimulus and the interference stimulus, and the layout configuration parameters, can be as follows: The type and color of the target stimulus and the interference stimulus are determined according to the configuration parameters; the grid dimension, stimulus size, and target ratio of the cancellation experiment are determined according to the layout configuration parameters; wherein the type of the target stimulus and the interference stimulus includes text, icons, and / or images; images of the target stimulus and the interference stimulus are generated according to the type and color of the target stimulus and the interference stimulus, and the stimulus size; and a grid image of the cancellation experiment is generated according to the grid dimension; the display interface for the cancellation experiment is then displayed; wherein the display interface for the cancellation experiment includes the grid image and the images of the target stimulus and the interference stimulus.

[0009] The above implementation method can display the interface of the cancellation experiment based on the configuration parameters of the cancellation experiment.

[0010] In one possible implementation, the interactive control configuration parameters include a statistical indicator switch; the task control configuration parameters include the termination method of the cancellation experiment, which includes fixed workload and fixed experiment time; thus, based on the interactive control configuration parameters and the task control configuration parameters, the user's work efficiency, average reaction time, and accuracy in this cancellation experiment can be obtained as follows: when the statistical indicator switch is on, record the number of target stimuli selected by the user, the number of interfering stimuli selected, the number of target stimuli missed, the total reaction time, and the number of trials attempted by the user; according to The accuracy of the user's current elimination experiment is determined by the number of target stimuli selected, the number of interfering stimuli selected, and the number of target stimuli missed. The average reaction time of the user's current elimination experiment is determined by the sum of the reaction times and the number of trials. When the experiment ends with a fixed workload, the efficiency of the user's current elimination experiment is determined by the accuracy and the sum of the reaction times. When the experiment ends with a fixed time, the efficiency of the user's current elimination experiment is determined by the total number of stimuli and the accuracy. The total number of stimuli includes the total number of grids in the grid image.

[0011] The above implementation method can obtain the user's work efficiency, average reaction time and accuracy in this cancellation experiment based on the interactive control configuration parameters and task control configuration parameters of the cancellation experiment.

[0012] In one possible implementation, the aforementioned interactive control configuration parameters also include a feedback time; in response to the user's operation, the edge computing device can also provide feedback to the user based on the aforementioned feedback time to provide feedback on the operation result; wherein, the user's operation includes the user selecting a target stimulus or a distracting stimulus; the aforementioned feedback includes visual feedback, audio feedback, and / or text feedback.

[0013] The above implementation method can provide feedback on the operation results to the user based on the feedback time.

[0014] In one possible implementation, the interactive control configuration parameters also include a confirmation button for ending the experiment; before obtaining the user's work efficiency, average reaction time, and accuracy of this cancellation experiment based on the interactive control configuration parameters and the task control configuration parameters, the edge computing device can also determine that the user has completed the cancellation experiment in response to the operation of the confirmation button for ending the experiment; or, based on the ending method of the cancellation experiment in the task control configuration parameters, it can determine that the user has completed the cancellation experiment.

[0015] In one possible implementation, adjusting the configuration parameters of the cancellation experiment based on the aforementioned work efficiency, average reaction time, and accuracy can be achieved by: determining the respective ranges of the aforementioned work efficiency, average reaction time, and accuracy; obtaining the corresponding adjustment strategies for the aforementioned work efficiency, average reaction time, and accuracy based on the respective ranges of the aforementioned work efficiency, average reaction time, and accuracy; and adjusting the configuration parameters of the cancellation experiment based on the corresponding adjustment strategies for the aforementioned work efficiency, average reaction time, and accuracy.

[0016] The above implementation method can adjust the configuration parameters of the cancellation experiment to ensure that the training content of the cancellation experiment always matches the user's ability level, thereby improving the training effect and user experience of the cancellation experiment.

[0017] In one possible implementation, after obtaining the user's efficiency, average reaction time, and accuracy in the current swipe-off experiment, the edge computing device can also obtain the user's historical experiment results. These historical results include the efficiency, average reaction time, and accuracy of swipe-off experiments conducted before the current experiment. The results of the current swipe-off experiment are compared with the historical results. Based on the comparison, training progress is evaluated, and an evaluation report is generated and saved. Based on the evaluation report, a trend chart and a progress report are generated. The trend chart displays the changing trends of the user's accuracy, average reaction time, and efficiency in each swipe-off experiment. The progress report includes a cognitive progress index and a training stage division. The cognitive progress index is determined based on the accuracy, average reaction time, and efficiency, and the training stage division includes an initial stage, a stable stage, an improvement stage, and a saturation stage.

[0018] The above implementation method supports comparison of results from multiple tasks. Based on the comparison results, after evaluating the training progress, an evaluation report is generated and saved. Then, the edge computing device can analyze the generated evaluation report and, in combination with historical evaluation reports, generate trend charts and progress reports.

[0019] In one possible implementation, the cognitive ability of the user can be assessed based on the aforementioned work efficiency, average reaction time, and accuracy as follows: determine the user's click frequency based on the number of target stimuli selected, the number of interfering stimuli selected, and the sum of the aforementioned reaction times; construct a feature vector based on the aforementioned work efficiency, average reaction time, accuracy, and click frequency; standardize the aforementioned feature vector; construct a data matrix using the standardized vector; and perform clustering based on the aforementioned data matrix to obtain the user's cognitive ability.

[0020] The above implementation method can be used to assess users' cognitive abilities.

[0021] Secondly, embodiments of this application also provide an edge computing device, including: one or more processors; a memory; multiple applications; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the edge computing device, cause the edge computing device to perform the method provided in the first aspect.

[0022] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the method provided in the first aspect.

[0023] Fourthly, embodiments of this application provide a computer program that, when executed by a computer, performs the method provided in the first aspect.

[0024] In one possible design, the program in the fourth aspect can be stored wholly or partially on a storage medium packaged with the processor, or it can be stored wholly or partially on a memory not packaged with the processor. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the structure of an edge computing device provided in one embodiment of this application; Figure 2 A flowchart illustrating a method for assessing human cognitive abilities provided in one embodiment of this application; Figure 3 This is a schematic diagram of a parameter configuration interface provided in one embodiment of this application; Figure 4 A schematic diagram of the display interface for a cancellation experiment provided in one embodiment of this application; Figure 5 A schematic diagram of the structure of a human cognitive ability assessment device provided in one embodiment of this application; Figure 6 A schematic diagram of the structure of a device for assessing human cognitive abilities provided in another embodiment of this application. Detailed Implementation

[0026] The terminology used in the implementation section of this application is for the purpose of explaining specific embodiments of this application only, and is not intended to limit this application.

[0027] Existing technologies for conducting elimination experiments primarily focus on attention and visual search abilities, lacking targeted designs for memory training. Furthermore, the training content and difficulty are usually fixed, unable to be dynamically adjusted according to user skill levels, resulting in limited training effectiveness. Additionally, existing elimination experiments are mechanical in their training process, lacking immediate feedback and incentive mechanisms, making it difficult to attract sustained user participation and hindering in-depth analysis of user training data to provide personalized training suggestions.

[0028] Based on the above problems, this application provides a method for assessing human cognitive ability, which can evaluate users' cognitive ability through a cancellation experiment and dynamically adjust the configuration parameters of the cancellation experiment to improve the training effect and user experience of the cancellation experiment.

[0029] The method for assessing human cognitive abilities provided in this application can be applied to edge computing devices, which can be personal computers (PCs), smartphones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptops, or ultra-mobile personal computers (UMPCs), etc. This application does not impose any restrictions on the specific type of edge computing device.

[0030] For example, Figure 1 This is a schematic diagram of the structure of an edge computing device provided in one embodiment of this application, as shown below. Figure 1 As shown, the edge computing device 100 may include one or more processors 110; a memory 120; multiple applications; and one or more computer programs, wherein the one or more computer programs are stored in the memory 120, and the one or more computer programs include instructions that, when executed by the edge computing device 100, may cause the edge computing device 100 to perform the human cognitive ability assessment method provided in the embodiments of this application.

[0031] In addition, the aforementioned edge computing device 100 may also include a communication interface 120. The processor 110, the communication interface 120, and the memory 120 can communicate with each other via internal connection paths to transmit control and / or data signals.

[0032] The processor 110 and memory 120 can be combined into a single processing device, but more commonly they are independent components. The processor 110 is used to execute the program code stored in the memory 120. In specific implementations, the memory 120 can be integrated into the processor 110, or it can be independent of the processor 110.

[0033] In addition, to further enhance the functionality of the edge computing device 100, the edge computing device 100 may also include one or more of an input unit 160 and a display unit 170.

[0034] Optionally, the edge computing device 100 may further include a power supply 150 for providing power to various devices or circuits in the edge computing device 100.

[0035] It should be understood that Figure 1The processor 110 in the edge computing device 100 shown can be a system-on-a-chip (SoC). The processor 110 may include a central processing unit (CPU) and may further include other types of processors, such as a graphics processing unit (GPU).

[0036] The following is combined Figure 1 This application introduces a method for assessing human cognitive abilities provided in its embodiments.

[0037] Figure 2 This is a flowchart of a method for assessing human cognitive ability provided in one embodiment of this application. The method for assessing human cognitive ability provided in this embodiment can be applied to an edge computing device 100.

[0038] like Figure 2 As shown, the methods for assessing the cognitive abilities of the aforementioned personnel may include: Step 201: Obtain the configuration parameters for the cancellation experiment.

[0039] In practice, the edge computing device 100 can display... Figure 3 The interface shown allows staff responsible for conducting swipe-off experiments with users to... Figure 3 The interface shown configures the parameters for the cancellation experiment. After the configuration is completed, the edge computing device 100 can obtain the configuration parameters for the cancellation experiment. Figure 3 This is a schematic diagram of a parameter configuration interface provided in one embodiment of this application. Of course, in some examples, the staff member responsible for conducting the cancellation experiment for the user can also be the user himself, that is, the user can configure the configuration parameters of the cancellation experiment himself.

[0040] Specifically, the configuration parameters mentioned above may include the configuration parameters of the target stimulus and interference stimulus, the layout configuration parameters, the interaction control configuration parameters, and the task control configuration parameters in the above cancellation experiment.

[0041] The above configuration parameters are explained in detail below.

[0042] 1. Configuration parameters for target and interference stimuli: including the type and color of the target and interference stimuli; since the configuration methods for the target and interference stimuli are the same, the configuration parameters for both will be explained together.

[0043] (1) Type: See Figure 3The types of target stimuli and interference stimuli can include text (e.g., any characters such as letters or numbers), icons (graphics given in the database), or pictures (e.g., images of real objects). Users can select the appropriate stimulus type according to their actual needs and can adjust the type accordingly.

[0044] For example: 1> The editable states of text can include: font size, color, font, whether italic, whether bold and / or whether underlined, etc.; the text can be ABCD..., 01234..., Chinese characters or special characters (@#!$%^), etc., as long as it can be entered, it can be used as a text stimulus; 2> The modifiable states of an icon can include: size, shape, and / or color, etc.; if the icons in the icon library are insufficient to support the erase experiment, other icons can be uploaded by uploading images. 3> The modifiable states of an image can include: size, etc.; in practice, a maximum of 20 images can be uploaded, each image cannot exceed 4MB, and any type of image can be uploaded, such as: .jpeg, .png, .gif, or .svg, etc.

[0045] (2) See also Figure 3 The color can be set in three ways, and users can choose any one of them when setting the color: 1. Constant Colors: The current color wheel contains the following constant colors: red, blue, green, orange, purple, cyan, yellow, black, white, and gray. During configuration, you can choose any single color from these or define a custom color from the colorimeter as the stimulus color. For example, if red is selected, then the stimulus color will be red. The stimulus here includes the target stimulus and / or interfering stimuli.

[0046] 2> Range Color: Select two colors (which can be the same or different) from constant colors or a color generator. Then, a color value will be randomly generated within the color value range of the two colors and assigned to the stimulus. For example, when red and blue are selected, a color value will be randomly generated within the color value range of red and blue and assigned to the stimulus.

[0047] 3> Arbitrary Colors: You can select up to 10 constant colors or 10 custom colors. The stimulus color will be randomly selected from these 10 colors and assigned a value.

[0048] 2. Layout configuration parameters: These can include grid dimensions, stimulus size, and target proportion.

[0049] (1) Grid dimension: The grid dimension can be configured in three ways: constant, range, and arbitrary number of grids. The default value is a constant of 10, which means that the stimulus image is presented in a 10×10 two-dimensional grid, with a total of 100 grids.

[0050] For example: 1> Constant case: The grid dimension can be set to a constant number. Then the size of the grid dimension is an n×n grid. For example, when the grid dimension is set to a constant 10, the number of grids is 10×10.

[0051] 2> Scope: See Figure 3 You can set two numbers, and the size of the grid dimension will be any number between the minimum and the maximum value. For example, if you set the range to 10 and 12, the grid dimension will be any number between 10, 11 and 12.

[0052] 3> Arbitrary Case: Randomly input 1 to 10 values, and the edge computing device 100 can randomly generate grid dimensions based on the input values. For example: Suppose we randomly input three arbitrary values: 10, 15, and 20, then the edge computing device 100 will randomly select one of these three numbers as the grid dimension, which may result in a grid of 10×10, 15×15, or 20×20.

[0053] (2) Stimulus size: can be configured in three ways: constant, range, or arbitrary. The default is constant, with a size of 50px, to ensure visual clarity and operational precision.

[0054] For example: 1> Constant case: The stimulus size can be set to a constant value, then the stimulus size is the set constant value. For example, if the constant is set to 50, then the stimulus size is 50px.

[0055] 2> Range: Two numbers can be set, and the stimulus size will be any number between the minimum and maximum values. For example, if two values ​​are set, 50 and 52, the stimulus size will be any number between 50, 51 and 52.

[0056] 3> Any case: Randomly input 1 to 10 values, and the edge computing device 100 can randomly generate stimulus sizes based on the input values. For example, assuming the input values ​​are 50, 65, and 70, the edge computing device 100 will randomly select one of these three numbers as the stimulus size, which may result in a stimulus size of 50px, 65px, or 70px.

[0057] (3) Target ratio: Used to control the proportion of the target stimulus among all stimuli, with a default value of 10%.

[0058] Target stimulus count = Target proportion × Total stimulus count, where the total stimulus count is the grid count.

[0059] The target ratio can be configured in three ways: constant, range, or arbitrary.

[0060] 1> Constant Case: The target proportion can be set to a constant value, then the probability of the target stimulus appearing is the set constant value. For example, when the target proportion is 20% and the total number of stimuli is 100: Number of target stimuli = 20% × 100 = 20.

[0061] The size of the grid dimension is any number between the minimum and maximum values. 2> Range: If two values ​​are set, the target ratio will be any number between the minimum and maximum values. For example, if two values ​​are set, 10 and 12, the target ratio will be any one of the three numbers: 10%, 11%, and 12%.

[0062] 3> Arbitrary Cases: Randomly input 1 to 10 values, and the edge computing device 100 can randomly generate a target ratio based on the input values, and then generate target stimuli based on the target ratio. For example, if three arbitrary values ​​are input, namely 10, 15, and 20, the edge computing device 100 will randomly select one of these three values ​​as the target ratio.

[0063] 3. Interactive control configuration parameters: including confirmation button, feedback time, and statistical indicator switch.

[0064] (1) Confirm button The confirmation key for task completion can be customized (e.g., Enter or Space). Once the confirmation key is pressed, the experiment ends.

[0065] (2) Feedback time: Feedback time refers to the duration of feedback provided by the edge computing device 100 after the user completes an operation (e.g., selects a target stimulus). This feedback can be visual feedback (e.g., color change), audio feedback, and / or text feedback (e.g., "correct" or "incorrect").

[0066] For specific implementation details, please refer to [link / reference]. Figure 3The feedback time can be set by an on / off switch. When the feedback time switch is set to ON, in response to the user's operation, the edge computing device 100 can provide feedback to the user according to the feedback time to show the operation result. The user's operation includes selecting a target stimulus or interfering stimulus. The feedback can include visual feedback, audio feedback, and / or text feedback. When the feedback time switch is set to OFF, no feedback is displayed after the user completes the operation.

[0067] (3) Statistical indicator switch: When set to ON, the edge computing device 100 records and displays the user's work efficiency, average response time and / or accuracy in this cancellation experiment.

[0068] 4. Task control configuration parameters: including the termination method of the cancellation experiment, which includes the following two methods: 1> Fixed workload: Users need to complete a preset number of cancellation tasks. The preset number of cancellation tasks can be set by the number of trials and the time limit per trial.

[0069] Among them, the number of trials: Figure 3 Setting it to 1 indicates that each cancellation experiment includes one complete cancellation task; Single trial time limit: Figure 3 The time limit is set to 60 seconds, meaning that users need to complete a cancellation task within 60 seconds.

[0070] 2> Fixed Experiment Time: The cancellation experiment is conducted within a set time.

[0071] Figure 3 The configuration parameters displayed can be configured according to the actual experimental needs (difficulty level) during implementation, thus providing better experimental methods for different users and improving user experience.

[0072] Step 202: Based on the configuration parameters of the target stimulus and interference stimulus, and the layout configuration parameters, display the display interface of the cancellation experiment.

[0073] The display interface of the aforementioned cancellation experiment includes the grid image of the cancellation experiment, as well as the images of the target stimulus and the interference stimulus.

[0074] Specifically, based on the configuration parameters of the target stimulus and the interference stimulus, as well as the layout configuration parameters, the display interface for the cancellation experiment can be as follows: the type and color of the target stimulus and the interference stimulus are determined according to the configuration parameters of the target stimulus and the interference stimulus; and the grid dimension, stimulus size of the target stimulus and the interference stimulus, and target ratio of the cancellation experiment are determined according to the layout configuration parameters; wherein, the type of the target stimulus and the interference stimulus can be text, icon, and / or image.

[0075] Then, the edge computing device 100 can generate images of the target stimulus and the interference stimulus based on the type and color of the target stimulus and the interference stimulus, as well as the stimulus magnitude of the target stimulus and the interference stimulus; and generate a grid image of the cancellation experiment based on the grid dimension of the cancellation experiment.

[0076] Subsequently, the edge computing device 100 can display the display interface of the above-mentioned cancellation experiment; wherein the display interface of the above-mentioned cancellation experiment includes the above-mentioned grid image, as well as the images of the above-mentioned target stimulus and the above-mentioned interference stimulus.

[0077] Figure 4 This is a schematic diagram of the display interface for a cancellation experiment provided in one embodiment of this application. Figure 4 It can be seen that the display interface of the above-mentioned erasure experiment includes a grid image, and Figure 4 In the experiment, the target stimulus is represented by a circular icon, as shown in Figure 41. Additionally, among the circular icons shown in Figure 41, three have squares. The circular icons with squares indicate the target stimulus that has been selected by the user during the cancellation experiment. Figure 4 In the diagram, the type of interfering stimulus is represented by a square icon, as shown in Figure 42.

[0078] Step 203: In response to the user's operation, based on the above-mentioned interaction control configuration parameters and task control configuration parameters, obtain the user's work efficiency, average reaction time and accuracy of this cancellation experiment.

[0079] As described above, the interactive control configuration parameters may include a statistical indicator switch, and the task control configuration parameters may include the termination method for the cancellation experiment, which may include fixed workload and fixed experiment time. Thus, based on the interactive control configuration parameters and the task control configuration parameters, the user's work efficiency, average reaction time, and accuracy in this cancellation experiment can be obtained as follows: When the above statistical indicator switch is on, record the number of target stimuli selected by the user, the number of interference stimuli selected, the number of target stimuli missed, the total reaction time, and the number of trials performed by the user. The total reaction time is the total time the user spends on the cancellation experiment.

[0080] Then, the edge computing device 100 can determine the accuracy of the user's current cancellation experiment based on the number of target stimuli selected by the user, the number of interfering stimuli selected, and the number of target stimuli missed; and determine the average reaction time of the user's current cancellation experiment based on the total reaction time and the number of trials; and when the fixed workload is the ending method, determine the work efficiency of the user's current cancellation experiment based on the accuracy and the total reaction time; and when the fixed experiment time is the ending method, determine the work efficiency of the user's current cancellation experiment based on the total number of stimuli and the accuracy; wherein the total number of stimuli can be the total number of grids in the grid image.

[0081] Specifically, accuracy, average reaction time, and work efficiency can be determined using the following formulas: Accuracy = Number of selected target stimuli / (Number of selected target stimuli + Number of selected interfering stimuli + Number of missed target stimuli); Average reaction time = Total reaction time / Number of trials; The calculation methods for work efficiency vary depending on the termination method, and include the following two approaches: With a fixed workload as the end point: Work efficiency = (100 × accuracy) / total reaction time; Ending the experiment at a fixed time: Work efficiency = Total number of stimuli × Accuracy.

[0082] In some examples, before step 204, the edge computing device 100 may first determine that the user has completed the above-mentioned cancellation experiment. Specifically, in response to the operation of the confirmation button for ending the experiment, the edge computing device 100 may determine that the user has completed the above-mentioned cancellation experiment; or, according to the ending method of the above-mentioned cancellation experiment in the above-mentioned task control configuration parameters, the edge computing device 100 may determine that the user has completed the above-mentioned cancellation experiment.

[0083] In other words, in practical implementation, the user selects the target stimulus through mouse clicks or touch operations. The edge computing device 100 automatically records whether the user's selected stimulus is correct and starts a timer. If the user completes the experiment before the time expires, the user can end the cancellation experiment early by pressing the confirmation button; or, according to the termination method of the cancellation experiment in the task control configuration parameters mentioned above, the edge computing device 100 can determine that the user has completed the cancellation experiment. After determining that the user has completed the cancellation experiment, the edge computing device 100 will count the number of target stimuli selected, the number of interfering stimuli selected, the total reaction time, the total number of stimuli, and the number of target stimuli missed during the user's cancellation experiment. Then, it can obtain the accuracy, average reaction time, and work efficiency of the user's cancellation experiment according to the formulas introduced above.

[0084] Step 204: Based on the above work efficiency, average reaction time and accuracy, assess the user's cognitive ability and adjust the configuration parameters of the above cancellation experiment.

[0085] Specifically, adjusting the configuration parameters of the cancellation experiment based on the aforementioned work efficiency, average response time, and accuracy can be achieved as follows: The edge computing device 100 determines the respective ranges of the work efficiency, average response time, and accuracy; then, based on these ranges, it obtains the corresponding adjustment strategies for each of the work efficiency, average response time, and accuracy. The edge computing device 100 can then adjust the configuration parameters of the cancellation experiment according to these adjustment strategies. For example, adjusting the configuration parameters of the cancellation experiment could include increasing the target ratio or interference density, shortening the time limit, or increasing the number of trials.

[0086] The following section introduces the adjustment strategies for the aforementioned work efficiency, average response time, and accuracy.

[0087] 1. The adjustment strategies corresponding to accuracy can be shown in Table 1.

[0088] Table 1 2. The adjustment strategies for the average reaction time are shown in Table 2.

[0089] Table 2 3. The adjustment strategies for work efficiency are shown in Table 3.

[0090] Table 3 Specifically, based on the aforementioned work efficiency, average reaction time, and accuracy, assessing a user's cognitive ability can be achieved by: determining the user's click frequency based on the number of target stimuli selected, the number of distracting stimuli selected, and the sum of the aforementioned reaction times. Then, based on the aforementioned work efficiency, average reaction time, accuracy, and click frequency, a feature vector is constructed. This feature vector is then standardized, and a data matrix is ​​constructed using the standardized vectors. Finally, clustering is performed based on the aforementioned data matrix to obtain the user's cognitive ability.

[0091] In this embodiment, the edge computing device 100 can construct a user's cognitive ability profile based on statistical data. Based on the user's cognitive ability, collected data, and historical trend analysis, it can generate more precise adjustment strategies for the user, which can be used for personalized training and scientific research analysis. Furthermore, the edge computing device 100 can also incorporate electrodermal activity (EDA) to record changes in electrical conductance during target recognition or mis-scribing, identify emotional interference or impulsive reactions, and optimize the feedback mechanism.

[0092] The process of assessing a user's cognitive abilities is explained below.

[0093] 1. Data Collection Collect user behavior data during the swipe-off experiment, including: a. Average reaction time (reflects processing speed); b. Precision (reflecting accuracy and impulsiveness); c. Click frequency (reflects the rhythm of operation); d. Work efficiency (reflecting fatigue level).

[0094] The click frequency can be calculated using the following formula: Click frequency = (number of target stimuli selected + number of interfering stimuli selected) / total reaction time.

[0095] 2. Derivative Feature Construction Based on the above behavioral data, more cognitively meaningful feature vectors are constructed, as shown in Table 4.

[0096] Table 4 3. Standardized Procedures Standardization is used to eliminate the differences in the dimensions of different feature vectors, so that clustering algorithms can treat each dimension fairly.

[0097] Recommended method: Z-score standardization The goal of Z-score standardization is to transform each feature into a distribution with a mean of 0 and a standard deviation of 1, so that data of different scales can participate in clustering or modeling fairly. The formula for Z-score standardization is shown in equation (1).

[0098] Z = X − μ / σ (1) In equation (1): Z is the standardized feature vector, X is the original feature vector, μ is the mean of the feature vector, and σ is the standard deviation of the feature vector.

[0099] The standardized implementation steps may include: Step 1), Prepare the data matrix: Assume there is a two-dimensional array, where each row represents a user and each column represents a feature vector (e.g., attention stability or impulsivity).

[0100] Step 2), extract the data for each feature vector by column: extract each column (i.e. each feature vector) separately to calculate the mean and standard deviation of that feature vector.

[0101] Step 3), calculate the mean and standard deviation of each eigenvector: For each column of data, calculate its mean (μ) and standard deviation (σ). These two values ​​are used for subsequent standardization.

[0102] Step 4), standardize each data point: For a certain feature value X of each user, use equation (1) to standardize it.

[0103] Step 5) Construct a new standardized data matrix: Recombine all the standardized vectors into a new two-dimensional array as input for subsequent clustering or modeling.

[0104] Step 6) Optionally, outlier handling: If the values ​​of some normalized vectors are far from 0 (e.g., more than ±3), it may be considered whether there is an abnormal user or data error.

[0105] 4. The clustering results are shown in Table 5.

[0106] Table 5 In addition, the edge computing device 100 can also construct a radar chart based on the above-mentioned work efficiency, average response time and accuracy; wherein the radar chart is used to show the user's performance in the dimensions of attention, reaction speed and / or task execution.

[0107] (1) Attention dimension Source indicator: Accuracy of task cancellation; Characteristics: High value: indicates that the user can focus on the target stably and that the influence of interference factors is small; Low value: May indicate distraction and difficulty in target recognition.

[0108] (2) Reaction rate dimension Source metrics: average reaction time, task completion time; Characteristics: High value (fast): Fast information processing speed and high task proficiency; Low value (slow): This may be due to conservative strategy, high cognitive load, or unfamiliarity with the task.

[0109] (3) Task execution capability dimension Source indicators: Work efficiency performance characteristics: High value: The task is completed with high quality and the execution process is smooth; Low value: May indicate unfamiliarity with operation, misunderstanding of task, or cognitive fatigue.

[0110] The shape of the radar chart and the corresponding cognitive type can be shown in Table 6.

[0111] Table 6 In some examples, after step 203, the edge computing device 100 can also acquire the user's historical experimental results. These historical experimental results may include the user's efficiency, average reaction time, and accuracy in cancellation experiments conducted before the current cancellation experiment. Then, the edge computing device 100 can compare the results of the current cancellation experiment with the historical experimental results. Based on the comparison results, it evaluates the training progress, generates and saves an evaluation report, and then generates a trend chart and a progress report based on the evaluation report. The trend chart displays the changing trends of the user's accuracy, average reaction time, and efficiency in each cancellation experiment. The progress report includes a cognitive progress index and a training stage division. The cognitive progress index is determined based on the accuracy, average reaction time, and efficiency, and the training stage division includes an initial stage, a stable stage, an improvement stage, and a saturation stage.

[0112] In other words, the method for assessing human cognitive ability provided in this application supports comparison of results from multiple tasks. Based on the comparison results, after evaluating the training progress, an assessment report is generated and saved. Then, the edge computing device 100 can analyze the generated assessment report and, in conjunction with historical assessment reports, generate trend charts and progress reports.

[0113] The trend chart here can be a line chart, used to show the changing trends in accuracy, reaction time, and work efficiency for each task.

[0114] 1. Accuracy trends can include: an upward trend, large fluctuations, and consistently below the threshold.

[0115] An upward trend indicates that users have improved target recognition capabilities and are more focused. Significant fluctuations may indicate unstable task strategies or cognitive fatigue. If the score remains consistently below the threshold (e.g., 80%), it is recommended to reduce the difficulty of the task or adjust the type of stimulus.

[0116] 2. The average reaction time trend can include: gradually shortening, fluctuating, and consistently longer.

[0117] Gradually shortening the time indicates an increase in information processing speed and task proficiency. Fluctuations in performance may be due to external interference or fluctuations in task difficulty. A persistently long duration suggests either a national crisis or excessive cognitive load on the user.

[0118] 3. Trends in work efficiency can include: efficiency improvement, efficiency decline, and fluctuation.

[0119] Improved efficiency – This indicates that users gradually master task strategies during training, their concentration increases, and it is usually accompanied by a shorter reaction time and / or a lower error rate, which can be regarded as an important signal of training effectiveness; Decreased efficiency may be caused by cognitive fatigue, excessively difficult tasks, or distraction. If the decline continues, the edge computing device 100 will suggest adjusting task parameters or arranging a break. Fluctuation trend – indicates that the user's state is unstable and may be affected by external environment or psychological factors.

[0120] The progress report here includes a cognitive progress index and training phase divisions. 1) Cognitive Progress Index: Edge computing devices can calculate the “Cognitive Progress Index” based on historical data. The Cognitive Progress Index is a comprehensive indicator used to quantify the training effect and aims to assess the degree of improvement in a user’s cognitive ability in multiple tasks. The Cognitive Progress Index can integrate data from multiple key dimensions (e.g., accuracy, average reaction time, work efficiency) into a single value, which is convenient for tracking training progress and individual differences, as shown in Equation (2).

[0121] Cognitive Progress Index = (ΔAccuracy + ΔWork Efficiency) / ΔAverage Reaction Time (2) In equation (2), Δaccuracy is the improvement in accuracy between the current task and the initial task; Δefficiency is the improvement in the number of correct cancellations per unit time; and Δreaction time is the change in average reaction time (which is usually desired to be shortened).

[0122] Equation (2) means that, under the premise of shortening the average reaction time, the greater the improvement in accuracy and work efficiency, the more significant the cognitive progress.

[0123] 2) Training phase division (four phases), as shown in Table 7.

[0124] Table 7 In this embodiment, the results and reports of the cancellation experiment can be exported in portable document format (PDF) or comma-separated values ​​(CSV) format for easy use in scientific research or teaching.

[0125] In the aforementioned method for assessing cognitive abilities, after the edge computing device 100 acquires the configuration parameters for the cancellation experiment, it displays the cancellation experiment interface based on the configuration parameters for the target stimulus and interference stimulus, as well as the layout configuration parameters. Responding to user operations, and based on the interaction control configuration parameters and task control configuration parameters, the edge computing device 100 can acquire the user's work efficiency, average reaction time, and accuracy in this cancellation experiment. Then, based on the work efficiency, average reaction time, and accuracy, it assesses the user's cognitive abilities and adjusts the cancellation experiment's configuration parameters. This allows for the assessment of the user's cognitive abilities through the cancellation experiment, dynamic adjustment of the cancellation experiment's configuration parameters, and ultimately, improvement of the cancellation experiment's training effect and user experience.

[0126] The cognitive ability assessment method provided in this application, through various task types and dynamically adjusted cancellation experiment configuration parameters, ensures that training content always matches the user's ability level. It can also generate suitable training tasks based on the needs of different users, improving the relevance and effectiveness of training. The method utilizes artificial intelligence algorithms to ensure the scientific validity and fairness of training task generation and difficulty adjustment, providing more precise adjustment strategies for different users, enhancing user experience, and automating the training process, significantly reducing human intervention.

[0127] The cognitive ability assessment method provided in this application can be used to improve users' memory, attention and information processing abilities, and can be widely applied in education, medical rehabilitation and / or vocational training.

[0128] 1. Application scenarios in the field of medical rehabilitation: 1) Cognitive assessment after stroke Objective: To assess whether the patient has unilateral spatial neglect (especially common after right brain injury).

[0129] Case study tools: 1> Apple Deletion Test: Patients need to cross out a whole apple (target stimulus), and the interference stimulus is a notched apple. This test can distinguish between "egocentric neglect" and "object-centric neglect". 2> Bells test: Patients are asked to cross out the bell icon from the distracting graphic to assess spatial attention distribution.

[0130] Significance for rehabilitation: It helps doctors determine the type of neglect disorder and develop targeted rehabilitation training programs.

[0131] 2) Childhood Attention Deficit Assessment Objective: To assess whether a child has attention deficit or impulse control problems.

[0132] Case study tools: 1> Teddy Bear Removal Test: Applicable to children aged 3–8 years, requiring them to cross out all teddy bear patterns (target stimulus), with other toy patterns as interfering stimuli.

[0133] Rehabilitation significance: Used for screening attention-deficit hyperactivity disorder (ADHD) and assisting in the development of behavioral intervention plans.

[0134] 2. Application scenarios in the field of vocational training: 1) Call center employee attention training Objective: To improve employees' sustained attention and error control capabilities in high-intensity, multi-tasking environments.

[0135] Application method: Employees complete the cancellation task within a limited time, and the accuracy rate and reaction time are recorded.

[0136] Feedback mechanism: Adjust the task pace or arrange attention recovery training based on work efficiency and accuracy.

[0137] 2) Screening for Security Inspector and Quality Inspector Positions Objective: To evaluate visual search strategies and target recognition capabilities.

[0138] Application: Simulating complex image environments, requiring rapid identification and erasure of specific symbols.

[0139] Application of the results: Used to screen candidates with high accuracy and resistance to interference.

[0140] 3. Application scenarios in the education industry: 1) Cognitive training courses for junior and senior high school students Objective: To improve students' sustained attention and task execution ability. Application method: Conduct regular elimination training and observe changes in attention stability and reaction speed.

[0141] Feedback mechanism: Adjust training frequency or difficulty based on performance trends.

[0142] 2) Research on Teacher Classroom Management Objective: To study the impact of different teaching environments on students' attention.

[0143] Application method: Conduct elimination experiments under different classroom settings, such as traditional lectures vs. project-based learning.

[0144] Research indicators: attention concentration index, accuracy, and fatigue sensitivity.

[0145] It is understood that some or all of the steps or operations in the above embodiments are merely examples, and other operations or variations thereof can be performed in the embodiments of this application. Furthermore, the steps may be performed in different orders as presented in the above embodiments, and it is not necessary to perform all the operations in the above embodiments.

[0146] It is understood that, in order to achieve the above-mentioned functions, edge computing devices include hardware and / or software modules that perform the respective functions. Based on the algorithm steps of the examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application in conjunction with the embodiments, but such implementation should not be considered beyond the scope of this application.

[0147] This embodiment can divide the edge computing device into functional modules according to the above method embodiment. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0148] Figure 5 This is a schematic diagram of the structure of a cognitive ability assessment device provided in one embodiment of the present application. The cognitive ability assessment device in this embodiment can be set in an edge computing device.

[0149] When dividing each function into modules according to its corresponding function. Figure 5 A schematic diagram of a possible composition of the cognitive ability assessment device 500 involved in the above embodiments is shown.

[0150] like Figure 5 As shown, the cognitive ability assessment device 500 may include: an acquisition module 501, a display module 502, an assessment module 503, and an adjustment module 504; The acquisition module 501 is used to acquire the configuration parameters of the cancellation experiment; wherein the configuration parameters include the configuration parameters of the target stimulus and interference stimulus, the layout configuration parameters, the interaction control configuration parameters, and the task control configuration parameters in the cancellation experiment. Display module 502 is used to display the display interface of the cancellation experiment according to the configuration parameters of the target stimulus and the interference stimulus, and the layout configuration parameters; wherein the display interface of the cancellation experiment includes the grid image of the cancellation experiment, and the images of the target stimulus and the interference stimulus. The acquisition module 501 is also used to respond to the user's operation and, based on the above-mentioned interaction control configuration parameters and the above-mentioned task control configuration parameters, acquire the user's work efficiency, average reaction time and accuracy of this cancellation experiment. The assessment module 503 is used to assess the user's cognitive ability based on the above-mentioned work efficiency, average reaction time, and accuracy. The adjustment module 504 is used to adjust the configuration parameters of the above-mentioned cancellation experiment based on the above-mentioned work efficiency, average reaction time and accuracy.

[0151] Figure 6 This is a schematic diagram of the structure of a device for assessing human cognitive abilities provided in another embodiment of this application. Figure 5 Compared to the cognitive ability assessment device shown, the difference is that the display module 502 may include: a determination submodule 5021, a generation submodule 5022, and an interface display submodule 5023; The determination submodule 5021 is used to determine the type and color of the target stimulus and the interference stimulus according to the configuration parameters of the target stimulus and the interference stimulus; and to determine the grid dimension, stimulus size of the target stimulus and the interference stimulus, and target ratio of the cancellation experiment according to the layout configuration parameters; wherein the type of the target stimulus and the interference stimulus includes text, icon and / or image; The generation submodule 5022 is used to generate images of the target stimulus and the interference stimulus based on the type and color of the target stimulus and the interference stimulus, as well as the stimulus magnitude of the target stimulus and the interference stimulus; and to generate a grid image of the cancellation experiment based on the grid dimension of the cancellation experiment. The interface display submodule 5023 is used to display the display interface of the above-mentioned cancellation experiment; wherein, the display interface of the above-mentioned cancellation experiment includes the above-mentioned grid image, as well as the images of the target stimulus and the interference stimulus.

[0152] In some examples of this embodiment, the above-mentioned interactive control configuration parameters may include a statistical indicator switch; the above-mentioned task control configuration parameters include the termination method of the above-mentioned cancellation experiment, and the termination method includes fixed workload and fixed experiment time.

[0153] The acquisition module 501 is specifically used to record, when the above statistical indicator switch is in the on state, the number of target stimuli selected by the user, the number of interference stimuli selected, the number of target stimuli missed, the total reaction time, and the number of trials by the user; to determine the accuracy of the user's current cancellation experiment based on the number of target stimuli selected, the number of interference stimuli selected, and the number of target stimuli missed; to determine the average reaction time of the user's current cancellation experiment based on the total reaction time and the number of trials; and to determine the work efficiency of the user's current cancellation experiment based on the accuracy and the total reaction time when the fixed workload is the ending method; and to determine the work efficiency of the user's current cancellation experiment based on the total number of stimuli and the accuracy when the fixed experiment time is the ending method; wherein, the total number of stimuli includes the total number of grids in the grid image.

[0154] In other examples, the above-mentioned interactive control configuration parameters also include feedback time; the above-mentioned personnel cognitive ability assessment device also includes: feedback module 505; The feedback module 505 is used to respond to the user's operation and provide feedback to the user according to the aforementioned feedback time, so as to provide feedback on the operation result to the user; wherein, the aforementioned user operation may include the user's operation of selecting a target stimulus or a distracting stimulus; the aforementioned feedback includes visual feedback, audio feedback and / or text feedback.

[0155] The above-mentioned interactive control configuration parameters may also include a confirmation button to end the experiment, and the above-mentioned personnel cognitive ability assessment device also includes: a completion confirmation module 506; The completion module 506 is used to determine that the user has completed the cancellation experiment in response to the operation of the confirmation button for ending the experiment before the acquisition module 501 acquires the user's work efficiency, average reaction time and accuracy of this cancellation experiment; or, based on the cancellation experiment end method in the task control configuration parameters, determine that the user has completed the cancellation experiment.

[0156] The adjustment module 504 is specifically used to determine the range of the above-mentioned work efficiency, average reaction time and accuracy, obtain the adjustment strategy corresponding to the above-mentioned work efficiency, average reaction time and accuracy according to the range of the above-mentioned work efficiency, average reaction time and accuracy, and adjust the configuration parameters of the above-mentioned cancellation experiment according to the adjustment strategy corresponding to the above-mentioned work efficiency, average reaction time and accuracy.

[0157] In some other examples, the above interactive control configuration parameters may also include: comparison module 507 and report generation module 508; The acquisition module 501 is also used to acquire the user's historical experimental results after acquiring the user's work efficiency, average reaction time and accuracy in the current cancellation experiment; the historical experimental results include the user's work efficiency, average reaction time and accuracy in cancellation experiments conducted before the current cancellation experiment; The comparison module 507 is used to compare the results of this cancellation experiment with the results of historical experiments; The report generation module 508 is used to evaluate training progress based on comparison results, generate and save an evaluation report, and generate a trend chart and a progress report based on the evaluation report. The trend chart is used to show the changing trends of the user's accuracy, average reaction time and work efficiency in each swipe experiment. The progress report includes a cognitive progress index and a training stage division. The cognitive progress index is determined based on accuracy, average reaction time and work efficiency. The training stage division includes an initial stage, a stable stage, an improvement stage and a saturation stage.

[0158] In some other examples, the evaluation module 503 is specifically used to determine the user's click frequency based on the number of target stimuli selected by the user, the number of interference stimuli selected, and the sum of the above reaction times; construct a feature vector based on the above work efficiency, average reaction time, accuracy, and click frequency; standardize the above feature vector; construct a data matrix using the standardized vector; and perform clustering based on the above data matrix to obtain the user's cognitive ability.

[0159] This application Figures 2-4 All relevant content of each step involved in the method embodiment shown can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0160] The cognitive ability assessment device 500 provided in this embodiment is used to perform the present application. Figures 2-4 The method for assessing human cognitive abilities provided in the illustrated embodiment can achieve the same effect as the method described above.

[0161] It should be understood that the cognitive ability assessment device 500 can correspond to... Figure 1 The edge computing device 100 shown. The function of the acquisition module 801 can be derived from... Figure 1 The processor 110 and communication interface 120 in the edge computing device 100 shown are implemented; the functions of the evaluation module 503 and adjustment module 504, as well as the completion determination module 506, comparison module 507 and report generation module 508, can be implemented by... Figure 1 The edge computing device 100 shown implements the functions of the processor 110; the display module 502 and the feedback module 505 can be implemented by... Figure 1 The processor 110 and display unit 170 in the edge computing device 100 shown are implemented.

[0162] When using integrated units, the cognitive ability assessment device 500 may include a processing module, a storage module, and a communication module.

[0163] The processing module can be used to control and manage the actions of the cognitive ability assessment device 500, for example, it can support the cognitive ability assessment device 500 in executing the steps performed by the aforementioned module. The storage module can be used to support the cognitive ability assessment device 500 in storing program code and data. The communication module can be used to support communication between the cognitive ability assessment device 500 and other devices.

[0164] The processing module can be a processor or controller, which can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination of computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module can be a memory. The communication module can specifically be a device that interacts with other edge computing devices, such as radio frequency circuitry, a Bluetooth chip, and / or a Wi-Fi chip.

[0165] In one embodiment, when the processing module is a processor and the storage module is a memory, the personnel cognitive ability assessment device 500 involved in this embodiment can be a device with... Figure 1 The device with the structure shown.

[0166] This application also provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to execute this application. Figures 2-4 The method provided in the illustrated embodiment.

[0167] This application also provides a computer program product, which includes a computer program that, when run on a computer, causes the computer to execute this application. Figures 2-4 The method provided in the illustrated embodiment.

[0168] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0169] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0170] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0171] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method of evaluating cognitive ability of a person, characterized by, The method comprises: obtaining configuration parameters of a cancellation experiment; wherein the configuration parameters comprise configuration parameters of target stimuli and interference stimuli in the cancellation experiment, layout configuration parameters, interaction control configuration parameters, and task control configuration parameters; displaying a display interface of the cancellation experiment according to the configuration parameters of the target stimuli and the interference stimuli and the layout configuration parameters; wherein the display interface of the cancellation experiment comprises a grid image of the cancellation experiment and images of the target stimuli and the interference stimuli; obtaining working efficiency, average reaction time, and accuracy of the user in the current cancellation experiment according to the interaction control configuration parameters and the task control configuration parameters in response to an operation of the user; evaluating cognitive ability of the user and adjusting the configuration parameters of the cancellation experiment according to the working efficiency, the average reaction time, and the accuracy.

2. The method of claim 1, wherein, The displaying of the display interface of the cancellation experiment according to the configuration parameters of the target stimuli and the interference stimuli and the layout configuration parameters comprises: determining types and colors of the target stimuli and the interference stimuli according to the configuration parameters of the target stimuli and the interference stimuli; and determining grid dimensions, stimulus sizes of the target stimuli and the interference stimuli, and target proportions according to the layout configuration parameters; wherein the types of the target stimuli and the interference stimuli comprise text, icons, and / or pictures; generating images of the target stimuli and the interference stimuli according to the types and colors of the target stimuli and the interference stimuli and the stimulus sizes of the target stimuli and the interference stimuli; and generating a grid image of the cancellation experiment according to the grid dimensions of the cancellation experiment; displaying the display interface of the cancellation experiment; wherein the display interface of the cancellation experiment comprises the grid image and the images of the target stimuli and the interference stimuli.

3. The method of claim 1, wherein, The interaction control configuration parameters comprise a statistical index switch; the task control configuration parameters comprise an end mode of the cancellation experiment, and the end mode comprises a fixed workload and a fixed experiment time; The obtaining of the working efficiency, the average reaction time, and the accuracy of the user in the current cancellation experiment according to the interaction control configuration parameters and the task control configuration parameters comprises: when the statistical index switch is in an on state, recording the number of the target stimuli selected by the user, the number of the interference stimuli selected by the user, the number of the target stimuli missed by the user, the total reaction time, and the number of attempts of the user; and when the statistical index switch is in an off state, recording the number of the target stimuli selected by the user, the number of the interference stimuli selected by the user, and the number of attempts of the user. determine the accuracy of the user in the current cancellation experiment according to the number of times the user selects the target stimulus, the number of times the user selects the interference stimulus, and the number of times the user misses the target stimulus; determine the average reaction time of the user in the current cancellation experiment according to the total reaction time and the number of test times of the user; when the fixed workload is the end mode, determine the work efficiency of the user in the current cancellation experiment according to the accuracy and the total reaction time; when the fixed experiment time is the end mode, determine the work efficiency of the user in the current cancellation experiment according to the total number of stimuli and the accuracy; wherein the total number of stimuli includes the total number of grids in the grid image.

4. The method of claim 3, wherein, The interaction control configuration parameter further includes a feedback time; The method further includes: In response to the operation of the user, feedback is given to the user according to the feedback time to feed back the operation result to the user; wherein the operation of the user includes the operation of the user selecting the target stimulus or the interference stimulus; the feedback includes visual feedback, sound feedback and / or text feedback.

5. The method of claim 4, wherein, The interaction control configuration parameter further includes a confirmation button for ending the experiment; Before the work efficiency, the average reaction time and the accuracy of the user in the current cancellation experiment are obtained according to the interaction control configuration parameter and the task control configuration parameter, the method further includes: In response to the confirmation button for ending the experiment being operated, it is determined that the user completes the cancellation experiment; or, According to the end mode of the cancellation experiment in the task control configuration parameter, it is determined that the user completes the cancellation experiment.

6. The method of claim 1, wherein, The adjustment of the configuration parameter of the cancellation experiment according to the work efficiency, the average reaction time and the accuracy includes: determining the range interval to which the work efficiency, the average reaction time and the accuracy respectively belong; According to the range interval to which the work efficiency, the average reaction time and the accuracy respectively belong, the adjustment strategy corresponding to the work efficiency, the average reaction time and the accuracy respectively is obtained; According to the adjustment strategy corresponding to the work efficiency, the average reaction time and the accuracy respectively, the configuration parameter of the cancellation experiment is adjusted.

7. The method of claim 1, wherein, After the work efficiency, the average reaction time and the accuracy of the user in the current cancellation experiment are obtained, the method further includes: obtaining the historical experiment result of the user; the historical experiment result includes the work efficiency, the average reaction time and the accuracy of the cancellation experiment performed by the user before the current cancellation experiment; comparing the result of the current cancellation experiment with the historical experiment result; According to the comparison result, the training progress is evaluated, an evaluation report is generated and saved; According to the evaluation report, a trend chart and a progress report are generated; wherein the trend chart is used to show the change trend of the accuracy, the average reaction time and the work efficiency of the user in each cancellation experiment; the progress report includes a cognitive progress index and a training stage division, the cognitive progress index is determined according to the accuracy, the average reaction time and the work efficiency, and the training stage division includes an initial stage, a stable stage, an improvement stage and a saturation stage.

8. The method of claim 3, wherein, The evaluating the cognitive ability of the user according to the work efficiency, the average reaction time and the accuracy comprises: determining a click frequency of the user according to the number of times that the user selects the target stimulus, the number of times that the user selects the interference stimulus and the total reaction time; constructing a feature vector according to the work efficiency, the average reaction time, the accuracy and the click frequency; normalizing the feature vector; constructing a data matrix by using the normalized vector; performing clustering according to the data matrix to obtain the cognitive ability of the user.

9. An edge computing device, characterized by comprise: one or more processors; a memory; a plurality of application programs; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs comprise instructions that, when executed by the edge computing device, cause the edge computing device to perform the method of any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and when the computer program runs on a computer, the computer program causes the computer to perform the method of any one of claims 1-8.

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