Data processing system, electronic equipment and storage medium

Abstract

The invention discloses a data processing system, electronic equipment and a storage medium. A processor is configured in the data processing system, and the processor is configured to execute the following steps: obtaining interaction data generated by a target object in an interaction process of a preset interaction scene, the interaction data comprising multiple types of interaction information; determining index data of the preset influence indexes based on corresponding relations between the preset influence indexes and the interaction information; calling a preset classification model, wherein the classification model represents a mapping relationship between the classification index and a preset influence index; and determining index data of a classification index of the target object based on the classification model and the index data of the preset influence index, wherein the index data of the classification index represents a classification result of the target object for the hyperactivity scene. According to the scheme, the interaction data in the interaction scene is processed through the data processing system, the classification result of the target object for the hyperactivity scene is determined in combination with the classification model, and the accuracy of classification of the target object is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data processing system, electronic equipment and storage medium. Background Art

[0002] ADHD, also known as "attention deficit hyperactivity disorder" or "minor brain dysfunction syndrome", is a relatively common abnormal behavior disorder in children in clinical practice. It is characterized by obvious difficulty in concentration, short attention span, hyperactivity and impulsive behavior. Children with ADHD have a higher risk of substance abuse and may be accompanied by other mental illnesses. It is difficult to determine whether a child has ADHD in clinical diagnosis, because being lively and active is a child's nature. It is not easy to distinguish between "children's ADHD" and "children's hyperactivity" in a short period of time. Long-term human observation and behavioral analysis are required to record children's limb movement data.

[0003] The authoritative and effective method for diagnosing ADHD is a professional psychiatric assessment. It requires the presence of clinical experts and offline diagnosis in combination with scale questionnaires. When using scale questionnaires, each subject will receive a targeted hyperactive behavior questionnaire, which will be statistically analyzed to determine the degree of hyperactive behavior of the subject. The existing hyperactive behavior screening is not conducive to practical use, so the questionnaire format is difficult to be fully promoted. In addition, filling out the questionnaire will be more subjective and likely to be intentionally wrong, which will affect the accuracy and objectivity of the data obtained. There is a problem of poor accuracy in the classification of ADHD, so the hyperactive behavior screening method needs to be improved urgently. Summary of the invention

[0004] The present invention provides a data processing system, an electronic device and a storage medium to solve the problem of poor accuracy of classification results for ADHD and the problem of poor applicability of classification methods for ADHD.

[0005] According to one aspect of the present invention, a data processing system is provided, wherein a processor is configured in the data processing system, and the processor is configured to perform the following steps:

[0006] Acquire interaction data generated by the target object during the interaction process of the preset interaction scenario, the interaction data including multiple types of interaction information;

[0007] Determine the indicator data of the preset impact indicator based on the corresponding relationship between the preset impact indicator and the interaction information;

[0008] Calling a preset classification model, the classification model represents the mapping relationship between the classification index and the preset impact index;

[0009] Based on the classification model and the indicator data of the preset influencing indicator, the indicator data of the classification indicator of the target object is determined, and the indicator data of the classification indicator represents the classification result of the target object for the ADHD scenario.

[0010] Optionally, the preset impact indicators include one or more of the following: attention maintenance indicator, target capture indicator and impulse indicator.

[0011] Optionally, the correspondence between the preset impact indicators and the interactive information includes a type correspondence and a numerical mapping relationship between the preset impact indicators and the interactive information; the processor is also used to: for each preset impact indicator, determine the interactive information of the type corresponding to the preset impact indicator based on the type correspondence; determine the indicator data of the preset impact indicator based on the numerical mapping relationship and the interactive information of the type corresponding to the preset impact indicator.

[0012] Optionally, there is at least one preset interaction scene; the processor is also used to: display an interaction page corresponding to each preset interaction scene, and obtain interaction information generated in the preset interaction scene in response to an interaction operation on the interaction page.

[0013] Optionally, the processor is also used to: display a first interactive page corresponding to a first interactive scene, the first interactive scene including virtual objects, objects to be captured and obstacles; in response to interactive operations on the first interactive page, control the virtual object to move in the first interactive scene to capture objects to be captured and / or avoid obstacles; during the movement of the virtual object, obtain first interactive information; wherein the first interactive information includes one or more of the capture amount, the total number of first interactive operations, the number of collisions with obstacles and the moving distance.

[0014] Optionally, the processor is also used to: display a second interaction page corresponding to the second interaction scenario, displaying visual indication information and / or voice indication information in the second interaction page; responding to the interaction operation on the second interaction page to respond to the visual indication information and / or voice indication information, and obtaining second interaction information; wherein the second interaction information includes the number of response completions, the number of error responses, the number of response timeouts, the response duration, and the total number of second interaction operations.

[0015] Optionally, the processor is also used to: display a third interaction page corresponding to the third interaction scenario, and display positive incentive tasks and negative incentive tasks in the third interaction page; respond to interactive operations on the third interaction page to execute positive incentive tasks and negative incentive tasks, and obtain third interaction information; the third interaction information includes the number of successes and failures of positive incentive tasks, and the number of successes and failures of negative incentive tasks.

[0016] Optionally, the processor is also used to: for each preset impact indicator, determine an indicator grade based on the indicator data of the preset impact indicator; the indicator grade represents the capability level classification of the target object corresponding to the preset impact indicator; and display the indicator data of the classification indicator, the indicator data of the preset impact indicator and one or more of the indicator grades corresponding to the preset impact indicator on a display page.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the following steps: obtaining interaction data generated by a target object during an interaction process of a preset interaction scenario, the interaction data including multiple types of interaction information; determining indicator data of preset influence indicators based on the corresponding relationship between preset influence indicators and the interaction information; calling a preset classification model, the classification model characterizing the mapping relationship between the classification indicator and the preset influence indicator; determining indicator data of the classification indicator of the target object based on the classification model and the indicator data of the preset influence indicator, the indicator data of the classification indicator characterizing the classification result of the target object for the ADHD scenario.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being used to cause a processor to execute the following steps: obtaining interaction data generated by a target object during an interaction process in a preset interaction scenario, the interaction data including multiple types of interaction information; determining indicator data of preset influence indicators based on corresponding relationships between preset influence indicators and the interaction information; calling a preset classification model, the classification model representing a mapping relationship between the classification indicator and the preset influence indicator; determining indicator data of the classification indicator of the target object based on the classification model and the indicator data of the preset influence indicator, the indicator data of the classification indicator representing a classification result of the target object for the ADHD scenario.

[0022] The technical solution of the embodiment of the present invention is to execute the following steps through a processor configured in a data processing system: obtaining interaction data generated by a target object during an interaction process of a preset interaction scenario, the interaction data including multiple types of interaction information; determining indicator data of preset influence indicators based on the corresponding relationship between preset influence indicators and the interaction information; calling a preset classification model, the classification model characterizing the mapping relationship between the classification indicator and the preset influence indicator; determining indicator data of the classification indicator of the target object based on the classification model and the indicator data of the preset influence indicator, the indicator data of the classification indicator characterizing the classification result of the target object for the ADHD scenario. This solution processes the interaction data generated by the target object performing an interaction operation in a preset interaction scenario, determines the indicator data of the preset influencing indicator, and determines the indicator data of the classification indicator of the target object in combination with the classification model and the indicator data of the preset influencing indicator, thereby obtaining the classification result of the target object for the ADHD scenario, and realizes the determination of the classification result of the target object for the ADHD scenario according to the data generated by the target object performing an interaction operation in the preset interaction scenario, avoiding the problem of poor accuracy of the classification result caused by the subjective factors of the target object, solving the problems of poor accuracy and poor applicability of the classification result for ADHD, and improving the accuracy of the classification result for ADHD of the target object. Through this solution, the target object only needs to perform an interaction operation in the preset interaction scenario to obtain the corresponding interaction data, and the data is used to determine the classification result of the target object for the ADHD scenario, thereby improving the applicability of the classification processing of the target object for ADHD.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 is a structural diagram of a data processing system provided by Embodiment 1 of the present invention;

[0026] Figure 2 is a flow chart of a method for determining a classification result applicable to an embodiment of the present invention;

[0027] Figure 3 It is a structural schematic diagram of an electronic device for implementing the method for determining a classification result according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] Figure 1 is a schematic diagram of the structure of a data processing system provided by Embodiment 1 of the present invention, such as Figure 1 As shown, the data processing system 100 is configured with a processor 101, and the processor 101 is configured to execute the following Figure 2 The flowchart of determining the classification result is shown in FIG. 1 , and this embodiment is applicable to the case of determining the classification result of ADHD. Figure 2 As shown, the processor 101 is configured to determine the classification result method, specifically performing the following steps:

[0032] S210: Acquire interaction data generated by a target object during an interaction process in a preset interaction scenario, where the interaction data includes multiple types of interaction information.

[0033] Among them, the target object can be specifically understood as an object to be tested for ADHD. The target object can be a person of any age who needs to be tested for ADHD. Since the onset of ADHD is mostly in childhood and adolescence, the target object includes, for example, children and adolescents. The preset interaction scene can be specifically understood as an interaction scene set for detecting whether the target object is an ADHD patient. The preset interaction scene can be a scene provided by a processor or an interactive device. For example, the preset interaction scene can be a game scene. By setting a game scene and obtaining interaction data during the interaction between the target object and the game scene, the participation enthusiasm of the target object can be improved and the rejection emotion of the target object can be reduced. The preset interactive scenarios are game interactive scenarios obtained by designing and developing games according to one or more corresponding principles of the CPT (Continuous Performance Test) paradigm, the Simon paradigm, and the Go / No-go paradigm. The CPT paradigm focuses on selective attention and sustained attention in complex environments; the Simon paradigm focuses on attention, hyperactivity, and conflict handling under spatial conflict; the GO / NO-GO paradigm focuses on impulse control and reaction inhibition under focus control. The preset interactive scenarios include but are not limited to the interactive scenarios corresponding to the CPT paradigm, the interactive scenarios corresponding to the Simon paradigm, and the interactive scenarios corresponding to the GO / NO-GO paradigm. The interactive data can be specifically understood as the interactive data generated when the target object performs the interactive operations in the corresponding interactive scenarios in the preset interactive scenarios.

[0034] The preset interaction scenario includes at least one interaction scenario, and accordingly, the interaction data includes multiple types of interaction information generated by the target object during the interaction process of at least one interaction scenario. The interaction data includes but is not limited to the capture amount, the total number of interaction operations, the number of collisions with obstacles, the moving distance, the number of completed responses, the number of wrong responses, the number of response timeouts, the response duration, the number of successes and failures of positive incentive tasks, and the number of successes and failures of negative incentive tasks.

[0035] It should be noted that the data generated during the interaction process of the preset interaction scene is complex and has many types. It is necessary to perform statistical analysis on the data generated during the interaction process to determine the various types of information that can reflect the statistically significant differences between the ADHD subjects and the non-ADHD subjects, and use the selected various types of information as the interaction data. Specifically, by obtaining the experimental group data and the control group data, wherein the experimental group data specifically represents the sample data generated by the ADHD subjects performing the interaction operation in the preset interaction scene, and the control group data specifically represents the sample data generated by the non-ADHD subjects performing the interaction operation in the preset interaction scene, statistical analysis is performed on the experimental group data and the control group data, thereby selecting various types of data that can reflect the statistically significant differences between the experimental group data and the control group data, and using the selected various types of data as the interaction data that the processor 101 needs to obtain and process; wherein the statistical analysis method includes but is not limited to the Kolmogorov-Smirnov normality test, variance homogeneity analysis method, T test, Welch ANOVA test and Mann-Whitney U test, in a specific embodiment, the corresponding types of data in the experimental group data and the control group data can be tested and processed by the Kolmogorov-Smirnov normality test and variance homogeneity analysis method, and multiple types of data that meet the normality test and variance homogeneity test can be screened out. For the multiple types of data that meet the normality test and variance homogeneity test, further tests are performed, and multiple types of data that can characterize the statistically significant differences between the ADHD object and the non-ADHD object are screened out according to the test results, wherein the method used for further testing includes but is not limited to T test, Welch ANOVA test and Mann-Whitney U test. The sample data corresponding to the experimental group data and the control group data are analyzed and processed by the statistical analysis method, so as to screen out multiple types of information that can reflect the statistically significant differences between the ADHD object and the non-ADHD object, avoid the subsequent processing of all interactive data, reduce the amount of data processing, and reduce the interference of useless information, which is helpful to improve the accuracy and efficiency of the classification results of the classification indicators of the subsequent determination of the target object.

[0036] Specifically, when the test is started on the target object, the preset interaction scene is displayed on the visual interface, the target object operates according to the interaction operation set in the preset interaction scene, and the processor 101 collects the interaction data generated in the interaction process to obtain multiple types of interaction information. Optionally, the data processing system 100 may include a display component, which is connected to the processor. The display component may be a display or a display screen, which is used to display the preset interaction scene, and the processor obtains the interaction data formed by the execution operation input by the target object through the display component. The display component may also be an interaction device such as a mobile phone, a tablet computer, etc. The processor is connected to the interaction device in communication to obtain the interaction data transmitted by the interaction device.

[0037] S220: Determine indicator data of preset impact indicators based on corresponding relationships between preset impact indicators and interaction information.

[0038] Among them, the interaction data includes multiple types of interaction information. There are various relationships between the multiple types of interaction information. The multiple types of interaction information need to be processed through exploratory factor analysis methods, which aims to reduce the dimensions of the multiple types of interaction information, identify and explain the potential structure in the multiple types of interaction information, and simplify the complex multiple types of interaction information into multiple key dimensions. For each key dimension, a corresponding influence indicator is set to characterize the ability and characteristics of the target object in the corresponding key dimension. Optionally, the preset influence indicators include but are not limited to attention maintenance indicators, target capture indicators and impulse indicators. It should be noted that the contribution or influence of different types of interaction information in different dimensions is different, and the influence of different types of interaction information on each preset influence indicator is characterized by the load coefficient; the load coefficient of each type of interaction information on different preset influence indicators can be determined in advance by processing sample data such as experimental group data and control group data, and then the correspondence between the preset influence indicator and the corresponding interaction information is determined according to the load coefficient of each type of interaction information on different preset influence indicators, which is used to determine the indicator data of the corresponding preset influence indicator. For example, for any preset influence indicator, a correspondence can be established with each interaction information and the corresponding load coefficient, and the correspondence can be stored in a preset storage space, and then the load coefficient corresponding to each interaction information corresponding to the preset influence indicator can be determined through the correspondence, and the corresponding interaction information is weighted according to the load coefficient corresponding to each interaction information, so as to obtain the indicator data of the preset influence indicator. The load coefficient matrix corresponding to each preset influence indicator can also be determined according to the load coefficient of each type of interaction information on different preset influence indicators. The load coefficient matrix is ​​used as the correspondence between the preset influence indicator and the interaction information, and the indicator data of the preset influence indicator can be determined by multiplying each type of interaction information with the load coefficient matrix.

[0039] Specifically, when the interaction data of the target object is acquired, the corresponding relationship between the preset influence indicators and the interaction information is acquired from the preset storage space. Optionally, the preset influence indicators include one or more of the following: attention maintenance indicator, target capture indicator and impulse indicator. For any preset influence indicator, if the acquired corresponding relationship is each interaction information corresponding to the preset influence indicator and the load coefficient corresponding to each interaction information, the corresponding interaction information can be weighted by each load coefficient to obtain the indicator data of the preset influence indicator. If the acquired corresponding relationship is each interaction information corresponding to the preset influence indicator and the corresponding load coefficient matrix, each type of interaction information is converted into corresponding matrix data, and multiplied with the corresponding load coefficient matrix to obtain the indicator data of the preset influence indicator.

[0040] S230: calling a preset classification model, where the classification model represents a mapping relationship between classification indicators and preset impact indicators.

[0041] Among them, the classification model can be specifically understood as a mathematical model for determining the classification index of the target object, which specifically represents the mapping relationship between the classification index and the preset influence index. The binary logistic regression analysis method is used to fit the index data corresponding to the attention maintenance index, target capture index and impulse index of multiple sample data to obtain the mapping relationship between the classification index and the preset influence index. The mapping relationship between the classification index and the preset influence index is as follows:

[0042]

[0043] Wherein, p represents the classification index of the target object, which is used to characterize the probability that the target object is an ADHD object, C is a constant, a represents the regression coefficient corresponding to the attention maintenance index, b represents the regression coefficient corresponding to the target capture index, and d represents the regression coefficient corresponding to the impulsiveness index. In a specific embodiment, a goodness of fit test is performed on the mapping relationship between the obtained classification index and the preset influence index, which can be specifically tested by the Hosmer-Lemeshow goodness of fit test method. Through the test result, it can be determined that the goodness of fit of the mapping relationship between the classification index and the preset influence index meets the corresponding goodness of fit requirement.

[0044] Exemplarily, the indicator data corresponding to the attention maintenance index, the target capture index and the impulse index corresponding to the multiple sample data are processed by the binary logistic regression analysis method to obtain a binary logistic regression model likelihood ratio test result table, as shown in Table 1:

[0045] Table 1 Likelihood ratio test results of binary logistic regression model

[0046]

[0047] Preferably, according to the above table, C takes a value of 24.877, the regression coefficient a corresponding to the attention maintenance index takes a value of -0.355, the regression coefficient b corresponding to the target capture index takes a value of -0.245, and the regression coefficient c corresponding to the impulse index takes a value of 0.439. According to Table 1, the regression coefficient c corresponding to the impulse index takes a value of 0.439, showing a significance level of 0.01 (z=2.755, p=0.006<0.01), indicating that there is a significant positive correlation between the impulse index and the change in classification results. The odds ratio (OR value) is 1.551, which means that every time the impulse index increases by one unit, the possibility of a change in the classification result increases by 1.551 times the original. This result shows that the improvement of the impulse index score significantly increases the probability of the target object being identified as having a tendency to ADHD. The regression coefficient of the attention maintenance index was -0.355, which also showed a significant level of 0.01 (z = -2.848, p = 0.004 < 0.01), indicating that there was a significant negative correlation between the attention maintenance index and the change in classification results. The odds ratio (OR value) was 0.701, which means that every time the attention maintenance index increased by one unit, the possibility of changing the classification result decreased by 0.701 times. This result shows that higher attention maintenance index scores reduce the risk of children being classified as ADHD. The regression coefficient of the goal capture index was -0.245, which showed a significant level of 0.01 (z = -3.061, p = 0.002 < 0.01), indicating that there was a significant negative correlation between the goal capture index and the change in classification results. The odds ratio (OR value) was 0.782, which means that every time the goal capture index increased by one unit, the possibility of changing the classification result decreased by 0.782 times. This result further emphasizes the association between higher goal capture index scores and lower ADHD tendencies. It is understandable that the impulse index has a significant positive impact on the classification results, while the attention maintenance index and target capture index show a significant negative impact. Through the above analysis, it can be clearly seen that the indicator data of the preset influencing indicators provide an important quantitative basis for determining the classification results of the target objects.

[0048] Specifically, after determining the indicator data of the preset influencing indicator, the preset classification model can be retrieved from the preset storage space.

[0049] S240: Determine the indicator data of the classification indicator of the target object based on the classification model and the indicator data of the preset influencing indicator, wherein the indicator data of the classification indicator represents the classification result of the target object for the ADHD scenario.

[0050] Specifically, the indicator data of the preset influencing indicator is substituted into the classification model and solved to determine the indicator data of the classification indicator of the target object, and the classification result of the target object for the ADHD scenario is obtained according to the indicator data of the classification indicator.

[0051] On the basis of the above embodiment, at least one interaction scene is preset; the processor 101 is also used to: display the interaction page corresponding to each preset interaction scene, and obtain the interaction information generated in the preset interaction scene in response to the interaction operation on the interaction page.

[0052] In this embodiment, the preset interaction scene is a game interaction scene obtained by designing and developing games according to one or more corresponding principles of the CPT (Continuous Performance Test) paradigm, the Simon paradigm, and the Go / No-go paradigm. The CPT paradigm focuses on selective attention and sustained attention in a complex environment; the Simon paradigm focuses on attention, hyperactivity, and conflict handling under spatial conflict; the GO / NO-GO paradigm focuses on impulse control and reaction inhibition under focus control. The preset interaction scene includes but is not limited to the interaction scene corresponding to the CPT paradigm, the interaction scene corresponding to the Simon paradigm, and the interaction scene corresponding to the GO / NO-GO paradigm. Therefore, at least one interaction scene can be selected as the preset interaction scene for tests of different dimensions.

[0053] Specifically, at least one preset interaction scene can be selected in the visualization page. For each preset interaction scene, the processor 101 displays the interaction page corresponding to the corresponding preset interaction scene. In the interaction page, the target object performs an interaction operation according to the prompt information given in the interaction page. The processor 101 responds to the interaction operation performed by the target object in the interaction page, and obtains the interaction information generated in the process of performing the interaction operation in the preset scene. It should be noted that in this embodiment, the interaction information generated in the preset interaction scene specifically refers to the interaction information that is relevant to determining the classification result of the target object. The interaction information list can be obtained from the preset storage space, and the matching interaction information can be extracted from the interaction information generated in the process of performing the interaction operation in the preset interaction scene according to the interaction information set in the interaction information list. The multi-type interaction information that the processor 101 needs to obtain can also be stored in the preset storage space in other forms, which is not limited here.

[0054] Based on the above embodiment, the processor 101 is also used to: display a first interactive page corresponding to a first interactive scene, the first interactive scene including virtual objects, objects to be captured and obstacles; in response to interactive operations on the first interactive page, control the virtual object to move in the first interactive scene to capture objects to be captured and / or avoid obstacles; during the movement of the virtual object, obtain first interactive information; wherein the first interactive information includes one or more of the capture amount, the total number of first interactive operations, the number of collisions with obstacles and the moving distance.

[0055] Among them, the first interactive page corresponding to the first interactive scene can be specifically understood as the interactive page corresponding to the CPT paradigm, which is used to test the target object's ability to maintain attention and control the ability to select actions in a complex environment. The objects set in the interactive scene include but are not limited to virtual objects, objects to be captured and obstacles. In the interactive scene, the virtual object represents the target object. Exemplarily, in the interactive scene, the virtual object can be replaced by the image of a kitten; the object to be captured is an object that requires the virtual object to perform a grabbing operation. The object to be captured specifically includes objects within the cognitive range of the target object, including but not limited to apples and letters. Exemplarily, if the object to be captured is an apple, the grabbing operation can be performed directly. If the object to be captured is a letter, the target object is required to grab it according to the prompt information on the interactive page, rather than performing a grabbing operation on all the letters displayed on the interactive page, to assist in testing the target object's ability to maintain attention; if the object to be captured is an obstacle, the target object is required to perform an operation to avoid the obstacle. Exemplarily, a box is set as an obstacle in the interactive scene. When the virtual object in the interactive scene encounters an obstacle, the target object needs to perform an avoidance operation. It should be noted that the objects to be captured and the obstacles can be suspended in the air or set on the moving road surface. The capture amount represents the number of times or scores that the target object successfully performs the capture operation. Exemplarily, a counting tag can be set on the first interactive page. Each time a capture is successful, a plus-one process is automatically performed on the counting tag to record the number of successful capture operations or the corresponding scores in real time. The total number of first interactive operations represents the total number of interactive operations performed by the target object in the first interactive page. The moving distance represents the distance moved by the target object during the game time period set in the first interactive page. Corresponding tags can be set in the first interactive page for one or more of the capture amount, the total number of first interactive operations, the number of collisions with obstacles, and the moving distance, and the real-time data of the corresponding interactive information can be displayed in the corresponding tags. The first interactive information generated in the first interactive page can be acquired and stored in real time by the processor 101.

[0056] Specifically, the processor 101 is also used to display the first interactive page corresponding to the first interactive scene. The target object can perform the interactive operation set in the interactive page through a touch screen or an external device to control the virtual object in the first interactive page to move, wherein the interactive operation includes a moving operation, a capturing operation, and an avoiding operation. In the process of controlling the movement of the virtual object, an object to be captured and / or an obstacle will appear in the first interactive page. When encountering an object to be captured, the virtual object is controlled to perform a capturing operation on the object to be captured. Exemplarily, the target object can be set to control the virtual object to perform a click operation at the location of the object to be captured to complete the capturing operation of the object to be captured. It should be noted that if the object to be captured is a letter, the target object needs to be captured according to the letter displayed on the screen, that is, if the letter appearing on the first interactive page is different from the letter in the displayed letter combination during the movement, the capturing operation is not required. In the process of the target object controlling the virtual object to move in the first interactive page, the processor 101 obtains the first interactive information generated by performing the interactive operation, and the first interactive information includes but is not limited to the capture amount, the total number of first interactive operations, the number of collisions with obstacles, and the moving distance.

[0057] In this embodiment, by setting interactive operations in the first interactive page, the virtual object is controlled to respond to stimuli such as objects to be captured and obstacles that continuously appear in the first interactive page, thereby evaluating the target object's concentration ability and reaction speed.

[0058] Based on the above embodiment, the processor 101 is also used to: display a second interaction page corresponding to the second interaction scene, displaying visual indication information and / or voice indication information in the second interaction page; responding to the interaction operation on the second interaction page to respond to the visual indication information and / or voice indication information, and obtaining second interaction information; wherein the second interaction information includes the number of response completions, the number of error responses, the number of response timeouts, the response duration, and the total number of second interaction operations.

[0059] Among them, the second interactive page corresponding to the second interactive scene can be specifically understood as an interactive page designed and developed according to the Simon paradigm, which is used to simulate the attention dilemma faced by the target subject with ADHD in learning and life, and evaluate the target subject's attention maintenance ability and impulsiveness / hyperactivity in an attention-distracted environment. The visual indication information specifically represents the information displayed on the second interactive page in the form of text or graphics, which is used to guide the target object to control the virtual object to move and perform interactive operations. The voice prompt information specifically represents the information that guides the target object to control the virtual object to move and perform interactive operations in the form of voice. The second interactive information represents the interactive information generated by performing interactive operations on the interactive page corresponding to the second interactive scene, and the second interactive information includes but is not limited to the number of completed responses, the number of wrong responses, the number of response timeouts, the response duration, and the total number of second interactive operations. The number of completed responses represents the number of responses of the target object to the interactive operations performed in the second interactive page. The number of wrong responses represents the number of times the target object fails to perform interactive operations in accordance with the visual indication information and / or voice indication information in the interactive page in the second interactive page. The number of response timeouts represents the number of times a single interactive operation exceeds the preset operation duration. The response duration represents the time between the moment when the target object controls the virtual object to perform the interactive operation and the prompt moment of the corresponding interactive operation prompt information, which is used to determine whether the target object responds to the visual indication information and / or the voice indication information in a timely manner. The total number of second interactive operations represents the total number of interactive operations performed by the target object in the second interactive page. The second interactive information generated in the second interactive page can be acquired and stored in real time by the processor 101.

[0060] Specifically, the processor 101 is also used to display the second interactive page corresponding to the second interactive scene. The target object can perform the interactive operation set in the interactive page through a touch screen or an external device, and control the virtual object in the second interactive page to move according to the visual indication information and / or the voice indication information. Exemplarily, the virtual object in the second interactive page is controlled to move, prompting the target object to perform a jump operation, and the target object can slide up through the touch screen operation to control the virtual object to perform a jump operation; prompting the target object to perform a slide operation, and the target object can slide down through the touch screen operation to control the virtual object to perform a slide operation; prompting the target object to perform a left move operation, and the target object can slide to the left through the touch screen operation to control the virtual object to perform a left move operation; prompting the target object to perform a right move operation, and the target object can slide to the right through the touch screen operation to control the virtual object to perform a right move operation. The target object needs to perform the corresponding interactive operation according to the guidance of the periodic visual indication information and / or the voice indication information. The second interactive information generated in the second interactive page is obtained in real time by the processor 101.

[0061] Exemplarily, the second interactive page corresponding to the second interactive scene displays a foggy environment and a periodic task. If the second interactive page is displayed as a foggy environment, indicating that the virtual object is in a scene without vision, the target object needs to control the virtual object to maintain the current interactive operation. If the second interactive page is displayed as a periodic task, the target object needs to control the virtual object to perform the periodic task, wherein the periodic task can be prompted by displaying visual indication information and / or voice indication information, and the target object controls the virtual object to perform the corresponding interactive operation according to the visual indication information and / or voice indication information. In the process of the target object performing the interactive operation in the second interactive page, the second interactive information is obtained in real time by the processor 101, and the second interactive information includes but is not limited to the number of completed responses, the number of error responses, the number of response timeouts, the response duration, and the total number of second interactive operations.

[0062] Based on the above embodiment, the processor 101 is also used to: display a third interaction page corresponding to the third interaction scenario, and display positive incentive tasks and negative incentive tasks in the third interaction page; respond to interactive operations on the third interaction page to execute positive incentive tasks and negative incentive tasks, and obtain third interaction information; the third interaction information includes the number of successes and failures of positive incentive tasks, and the number of successes and failures of negative incentive tasks.

[0063] Among them, the third interactive page corresponding to the third interactive scenario can be specifically understood as an interactive page designed and developed according to the GO / NO-GO paradigm, which is used to evaluate the impulse inhibition ability and attention maintenance ability of the target object. The positive incentive task represents the task of reminding the target object to control the virtual object to perform interactive operations, and the negative incentive task represents the task of inhibiting the target object from controlling the virtual object to perform interactive operations. For example, if an upward or downward arrow is displayed on the interactive page, the target object is reminded to perform the corresponding interactive operation according to the arrow prompt information. The interactive operation represented by the upward arrow is sliding up, and the interactive operation represented by the downward arrow is sliding down; if the left or right arrow is displayed on the interactive page, it represents that the target object cannot control the virtual object to perform any operation. It should be noted that positive incentive tasks and negative incentive tasks can also be displayed through other display information, and the arrow is just a display information representing positive incentive tasks and negative incentive tasks.

[0064] Specifically, the processor 101 is also used to display a third interaction page corresponding to the third interaction scenario. The target object can execute the positive incentive tasks and negative incentive tasks displayed on the third interaction page through a touch screen or an external device; the target object performs corresponding interaction operations according to the positive incentive tasks and negative incentive tasks displayed on the third interaction page. If it is a positive incentive task, the target object controls the virtual object to perform the corresponding interaction operation and obtains third interaction information, and the third interaction information includes the number of successes and failures of the positive incentive task; if it is a negative incentive task, the target object controls the virtual object not to perform any interaction operations, and obtains third interaction information, and the third interaction information includes the number of successes and failures of the negative incentive task.

[0065] On the basis of the above embodiments, the correspondence between the preset impact indicators and the interactive information includes a type correspondence and a numerical mapping relationship between the preset impact indicators and the interactive information; the processor 101 is also used to: for each preset impact indicator, determine the interactive information of the type corresponding to the preset impact indicator based on the type correspondence; determine the indicator data of the preset impact indicator based on the numerical mapping relationship and the interactive information of the type corresponding to the preset impact indicator.

[0066] It should be noted that different preset influence indicators show different positive loads on some types of interaction information and different negative loads on other types of interaction information. The experimental group data and the control group data can be statistically analyzed in advance to determine the type correspondence between each preset influence indicator and the interaction information, as well as the numerical mapping relationship, wherein the numerical mapping relationship represents the relationship between each type of interaction data and the corresponding load coefficient. The type correspondence and numerical mapping relationship between the preset influence indicator and the interaction information can be stored in the preset storage space in advance. For example, for the attention maintenance indicator, the corresponding types of interaction information include 12 categories, namely z1, z2, ..., z 12 ,By statistically analyzing 12 types of interactive information, the load coefficient corresponding to each type of interactive information is determined. The expression of the corresponding relationship between the type of attention maintenance index and the interactive information and the numerical mapping relationship is as follows:

[0067] Attention maintenance index =

[0068] 0.149×z1+0.141×z2+0.229×z3-0.173×z4-0.286×z5+0.376×z6-0.133×z7;

[0069] +0.274×z8+0.336×z9-0.035×z 10 -0.276×z 11 +0.017×z 12

[0070] The constants in the above formula are the load coefficients of the corresponding types of mutual information. The expressions of the type correspondence and value mapping between the target capture index and the mutual information and the type correspondence and value mapping between the impulse index and the mutual information can be determined in the above manner.

[0071] Specifically, the processor 101 is also used to obtain, for each preset impact indicator, the type correspondence and numerical mapping relationship between the preset impact indicator and the interaction information from the preset storage space, thereby determining the type of interaction information corresponding to the preset impact indicator according to the type correspondence between the preset impact indicator and the interaction information, and then determining the load coefficient corresponding to each type of interaction information according to the numerical mapping relationship, and performing weighted summation processing on the corresponding type of interaction information according to the load coefficient corresponding to each type of interaction information, thereby obtaining the indicator data of the corresponding preset impact indicator.

[0072] In some embodiments, the attention maintenance index may have a corresponding relationship with the average reaction time, the no-view reaction time, the number of timeouts, the number of successes of the positive incentive task, and the number of successes of the negative incentive task, and a positive load is expressed in the average reaction time, the no-view reaction time, and the number of timeouts, and a negative load is expressed in the number of successes of the positive incentive task and the number of successes of the negative incentive task. The target capture index and the impulse index have a corresponding relationship with the capture amount, the moving distance, and the number of collisions with obstacles, wherein a positive load is expressed in the capture amount and the moving distance, and a negative load is expressed in the number of collisions with obstacles. The impulse index has a corresponding relationship with the number of successes of the positive incentive task, the number of successes of the negative incentive task, the number of failures of the positive incentive task, the number of failures of the negative incentive task, and the total number of second interactive operations, wherein a positive load is expressed in the number of failures of the positive incentive task, the number of failures of the negative incentive task, and the total number of second interactive operations, and a negative load is expressed in the number of successes of the positive incentive task and the number of successes of the negative incentive task.

[0073] On the basis of the above embodiment, the processor 101 is also used to: for each preset impact indicator, determine the indicator grade based on the indicator data of the preset impact indicator; the indicator grade represents the capability level classification of the target object corresponding to the preset impact indicator; and display the indicator data of the classification indicator, the indicator data of the preset impact indicator and one or more of the indicator grades corresponding to the preset impact indicator on the display page.

[0074] It should be noted that the higher the indicator data of the preset influencing indicator, the stronger the ability of the target object in the dimension of the corresponding preset influencing indicator, that is, the corresponding indicator classification can be determined by the indicator data of the preset influencing indicator, and the ability level classification corresponding to the preset influencing indicator of the target object can be characterized by the indicator classification. A corresponding evaluation standard can be set for each preset influencing indicator, and the evaluation standard corresponding to each preset influencing indicator includes multiple ability level classifications and indicator data thresholds corresponding to each ability level classification. Exemplarily, for the attention maintenance indicator, the corresponding ability level classification includes weak, weak and extremely weak levels, the indicator data threshold corresponding to the weak level can be set to the first threshold, the indicator data threshold corresponding to the weak level can be set to the second threshold, and the indicator data threshold corresponding to the extremely weak level can be set to the third threshold, the first threshold is greater than the second threshold, and the second threshold is greater than the third threshold. It should be noted that for the target capture indicator and the impulse indicator, this can be the ability level classification of the corresponding indicator and the indicator data threshold corresponding to the ability level classification according to the actual situation.

[0075] Specifically, for each preset impact indicator, the processor 101 retrieves the corresponding evaluation standard, matches the indicator data corresponding to the preset impact indicator with the corresponding estimation standard, thereby determining the capability indicator classification to which the indicator data of the preset impact indicator belongs, so as to obtain the indicator classification corresponding to the preset impact indicator. A data display area is set in the display page, and the indicator data of the classification indicator, the indicator data of the preset impact indicator, and one or more of the indicator classifications corresponding to the preset impact indicator are displayed in the data display area, so that the target object and its corresponding user can clearly understand the classification reason and classification basis of the classification result corresponding to the target object, and can also clearly understand the strength of the target object's ability displayed in the dimensions of different preset impact indicators, which has strong explanatory power.

[0076] The technical solution of this embodiment is to execute the following steps through a processor configured in a data processing system: obtain interaction data generated by a target object during an interaction process of a preset interaction scenario, the interaction data including multiple types of interaction information; determine indicator data of preset influence indicators based on the corresponding relationship between preset influence indicators and the interaction information; call a preset classification model, the classification model represents the mapping relationship between the classification indicator and the preset influence indicator; determine indicator data of the classification indicator of the target object based on the classification model and the indicator data of the preset influence indicator, the indicator data of the classification indicator represents the classification result of the target object for the ADHD scenario. This solution processes the interaction data generated by the target object performing an interaction operation in a preset interaction scenario, determines the indicator data of the preset influencing indicator, and determines the indicator data of the classification indicator of the target object in combination with the classification model and the indicator data of the preset influencing indicator, thereby obtaining the classification result of the target object for the ADHD scenario, and realizes the determination of the classification result of the target object for the ADHD scenario according to the data generated by the target object performing an interaction operation in the preset interaction scenario, avoiding the problem of poor accuracy of the classification result caused by the subjective factors of the target object, solving the problems of poor accuracy and poor applicability of the classification result for ADHD, and improving the accuracy of the classification result for ADHD of the target object. Through this solution, the target object only needs to perform an interaction operation in the preset interaction scenario to obtain the corresponding interaction data, and the data is used to determine the classification result of the target object for the ADHD scenario, thereby improving the applicability of the classification processing of the target object for ADHD.

[0077] Embodiment 2

[0078] Figure 3 1 is a schematic diagram of the structure of an electronic device provided in Embodiment 2 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0079] like Figure 3As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0080] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0081] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 performs the following steps: obtaining the interaction data generated by the target object during the interaction process of the preset interaction scene, the interaction data including multiple types of interaction information; determining the index data of the preset influence index based on the corresponding relationship between the preset influence index and the interaction information; calling a preset classification model, the classification model characterizing the mapping relationship between the classification index and the preset influence index; determining the index data of the classification index of the target object based on the classification model and the index data of the preset influence index, the index data of the classification index characterizing the classification result of the target object for the ADHD scene.

[0082] In some embodiments, the execution steps described above may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more of the steps described above may be executed. Alternatively, in other embodiments, the processor 11 may be configured to perform the following steps in any other appropriate manner (e.g., by means of firmware): obtaining the interaction data generated by the target object during the interaction process of the preset interaction scene, the interaction data including multiple types of interaction information; determining the index data of the preset influence index based on the corresponding relationship between the preset influence index and the interaction information; calling a preset classification model, the classification model characterizing the mapping relationship between the classification index and the preset influence index; determining the index data of the classification index of the target object based on the classification model and the index data of the preset influence index, the index data of the classification index characterizing the classification result of the target object for the ADHD scene.

[0083] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0084] The computer programs for implementing the execution steps described above may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0085] Embodiment 3

[0086] Embodiment 3 of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute the following steps:

[0087] Acquire the interaction data generated by the target object during the interaction process of the preset interaction scenario, the interaction data including multiple types of interaction information; determine the indicator data of the preset influence indicator based on the corresponding relationship between the preset influence indicator and the interaction information; call the preset classification model, the classification model represents the mapping relationship between the classification indicator and the preset influence indicator; determine the indicator data of the classification indicator of the target object based on the classification model and the indicator data of the preset influence indicator, the indicator data of the classification indicator represents the classification result of the target object for the ADHD scenario.

[0088] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0089] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0090] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0091] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0092] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0093] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data processing system, characterized in that: The data processing system is provided with a processor, and the processor is configured to perform the following steps: Acquire interaction data generated by a target object during an interaction process of a preset interaction scenario, wherein the interaction data includes multiple types of interaction information; Determining indicator data of the preset impact indicators based on the corresponding relationships between the preset impact indicators and the interaction information; Calling a preset classification model, wherein the classification model represents a mapping relationship between a classification indicator and the preset impact indicator; Based on the classification model and the indicator data of the preset influencing indicator, indicator data of the classification indicator of the target object is determined, and the indicator data of the classification indicator represents the classification result of the target object for the ADHD scenario.

2. The data processing system according to claim 1, characterized in that The preset influence indicators include one or more of the following: attention maintenance indicator, target capture indicator and impulse indicator.

3. The data processing system according to claim 2, characterized in that: The correspondence between the preset impact indicators and the interaction information includes a type correspondence and a value mapping between the preset impact indicators and the interaction information; The processor is also used to: for each of the preset impact indicators, determine the interactive information of the type corresponding to the preset impact indicator based on the type correspondence relationship; and determine the indicator data of the preset impact indicator based on the numerical mapping relationship and the interactive information of the type corresponding to the preset impact indicator.

4. The data processing system according to claim 1, characterized in that: The preset interaction scenario is at least one; The processor is further configured to: display an interaction page corresponding to each of the preset interaction scenarios, and obtain interaction information generated in the preset interaction scenario in response to an interaction operation on the interaction page.

5. The data processing system according to claim 4, characterized in that: The processor is further configured to: display a first interactive page corresponding to a first interactive scene, wherein the first interactive scene includes a virtual object, an object to be captured, and an obstacle; In response to the interactive operation of the first interactive page, controlling the virtual object to move in the first interactive scene to capture the object to be captured and / or avoid the obstacle; During the movement of the virtual object, obtaining first interaction information; The first interaction information includes one or more of the capture quantity, the total number of first interaction operations, the number of collisions with the obstacle, and the moving distance.

6. The data processing system according to claim 4, characterized in that: The processor is further configured to: Displaying a second interaction page corresponding to the second interaction scenario, wherein the second interaction page displays visual indication information and / or voice indication information; In response to the interactive operation on the second interactive page, respond to the visual indication information and / or the voice indication information, and obtain second interactive information; The second interaction information includes the number of response completions, the number of error responses, the number of response timeouts, the response duration, and the total number of second interaction operations.

7. The data processing system according to claim 4, characterized in that: The processor is further configured to: Displaying a third interaction page corresponding to the third interaction scenario, wherein the positive incentive task and the negative incentive task are displayed on the third interaction page; In response to the interactive operation on the third interactive page, the positive incentive task and the negative incentive task are executed, and third interactive information is obtained; the third interactive information includes the number of successes and failures of the positive incentive task, and the number of successes and failures of the negative incentive task.

8. The data processing system according to claim 1, characterized in that: The processor is further configured to: for each of the preset impact indicators, determine an indicator classification based on the indicator data of the preset impact indicator; the indicator classification represents the capability level classification of the target object corresponding to the preset impact indicator; The indicator data of the classification indicator, the indicator data of the preset impact indicator, and one or more indicator grades corresponding to the preset impact indicator are displayed on the display page.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the following steps: Acquire interaction data generated by a target object during an interaction process of a preset interaction scenario, wherein the interaction data includes multiple types of interaction information; determine indicator data of the preset influence indicators based on corresponding relationships between preset influence indicators and the interaction information; call a preset classification model, wherein the classification model represents a mapping relationship between classification indicators and the preset influence indicators; determine indicator data of the classification indicators of the target object based on the classification model and the indicator data of the preset influence indicators, wherein the indicator data of the classification indicators represents a classification result of the target object for the ADHD scenario.

10. A computer-readable storage medium, characterized in that: The computer readable storage medium stores computer instructions, and the computer instructions are used to cause the processor to execute the following steps: Acquire interaction data generated by a target object during an interaction process of a preset interaction scenario, wherein the interaction data includes multiple types of interaction information; determine indicator data of the preset influence indicators based on corresponding relationships between preset influence indicators and the interaction information; call a preset classification model, wherein the classification model represents a mapping relationship between the classification indicators and the preset influence indicators; Based on the classification model and the indicator data of the preset influencing indicator, indicator data of the classification indicator of the target object is determined, and the indicator data of the classification indicator represents the classification result of the target object for the ADHD scenario.

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