Children's brain intelligence cognitive development ability evaluation system and method

Abstract

The application discloses a kind of children brain wisdom cognitive development ability evaluation method and system, comprising: receiving user identity information, including age, gender and education stage;Build the evaluation model covering six one-level dimensions and eight two-level dimensions, the one-level dimension includes memory, self-control, thinking force, reaction force, attention, space force, the two-level dimension includes short-term memory, working memory, inhibition control, task switching, processing speed, logical thinking, concentration, visual perception space;Randomly extract target game task set from task library, the task set covers the evaluation requirement of at least three one-level dimensions.The application proposes an evaluation method and system for children's cognitive ability, in the evaluation process of gamification, the system comprehensively covers the six cognitive fields, aiming to provide a scientific and comprehensive solution for children's cognitive development evaluation.

Description

Technical Field

[0001] This invention belongs to the field of children's cognitive development assessment, with classification number A61 B. It specifically relates to a comprehensive assessment method and system based on six core cognitive abilities (memory, attention, self-control, reaction, thinking, and spatial ability). It aims to provide an innovative, scientific, and interesting solution for the accurate assessment of children's cognitive abilities. Background Art

[0002] With increasing societal attention to early childhood education and cognitive development, scientific assessment of children's cognitive development has become a crucial research and application area. In modern society, there is growing recognition of the importance of early childhood education for children's future learning and well-being. As a core component of early childhood education, the scientific nature of its assessment methods and tools is directly linked to the effectiveness of educational interventions. Therefore, researchers and educators are seeking more precise and comprehensive assessment methods to accurately assess children's cognitive development and provide personalized educational support.

[0003] Cognitive development involves a child's perception, memory, thinking, and other abilities. The level of development of these abilities not only affects a child's academic performance in school, but also their social skills, emotional regulation, and ability to adapt to their environment. Therefore, scientifically assessing a child's cognitive development level not only helps identify potential learning disabilities or developmental delays early on, but also provides guidance for parents and educators, helping them better understand their children's needs and develop appropriate educational plans.

[0004] Currently, a variety of methods are used to assess children's cognitive development, including standardized tests, observational records, behavioral scales, and neuropsychological assessments. Each of these methods has its advantages and limitations, and researchers are working to develop more comprehensive and sensitive assessment tools that can more comprehensively reflect children's cognitive abilities. Simultaneously, with the advancement of science and technology, technologies such as artificial intelligence and big data analysis have been introduced into the field of child cognitive assessment, providing new possibilities for more accurate and personalized assessments. Patent publication number CN111820922B discloses a method for assessing computational thinking in young children. This method incorporates the key steps of a task into an activity map. Children are required to program a set of instructions on a programming board to successfully complete a specific assessment task on the activity map, with the robot's movement trajectory displayed in real time. Through programming practice, young children can fully develop and demonstrate their abilities in abstract thinking, spatial orientation, logical thinking, task decomposition, and computation. However, this model is still relatively simplistic, covering only a portion of the six core cognitive abilities and lacking comprehensiveness.

[0005] The existing technologies and products on the market currently have the following shortcomings:

[0006] (1) Single-dimensional evaluation lacks comprehensiveness

[0007] Existing assessment methods mostly focus on single-dimensional tests, such as single assessments of concentration or memory, which cannot fully reflect children's overall level in multiple key areas of cognitive development.

[0008] (2) Traditional testing methods are not interesting enough

[0009] Traditional paper-and-pencil tests or boring operational tasks are difficult to fully mobilize children's interest, resulting in the assessment results being affected by children's inattention or fatigue.

[0010] (3) Lack of intelligence and personalization

[0011] Existing assessment technologies rarely combine intelligent algorithms with big data analysis for diagnosis and screening, making it difficult to accurately identify abnormal individuals, and unable to provide personalized analysis and suggestions based on individual differences.

[0012] (4) Lack of scientific model support

[0013] Many assessment products lack a theoretical framework based on brain and cognitive development science, and the scientific nature, reliability and validity of assessment dimensions and task design are difficult to guarantee. Summary of the Invention

[0014] The present invention aims to provide a method and system for evaluating children's cognitive abilities based on six core cognitive abilities (memory, attention, self-control, reaction, thinking, and spatial ability). In the gamified evaluation process, it comprehensively covers the six cognitive areas of memory, attention, self-control, reaction, thinking, and spatial ability, providing a scientific, comprehensive, and interesting solution for children's cognitive development assessment.

[0015] The object of the present invention is achieved through the following technical solutions:

[0016] A method for assessing children's brain intelligence and cognitive development ability, comprising:

[0017] Receive user identification information, including age, gender, and education level;

[0018] Construct an assessment model covering six primary dimensions and eight secondary dimensions. The primary dimensions include memory, self-control, thinking ability, reaction ability, attention, and spatial ability. The secondary dimensions include short-term memory, working memory, inhibitory control, task switching, processing speed, logical thinking, concentration, and visual perception space.

[0019] Randomly extract a target game task set from the task library, where the task set covers at least three first-level dimensions of assessment requirements; during task execution, randomly select game tasks from the second-level dimensions for assessment, and collect behavioral data on children during the tasks, including reaction time, accuracy, and strategy selection;

[0020] Constructing a normative data table based on behavioral data of children of different age groups, wherein the normative data table includes the mean and standard deviation of each dimension;

[0021] The task score, the first-level dimension score and the total score are calculated based on the behavioral data, and the scores are converted into percentiles based on the normative data table and divided into six levels (A to F).

[0022] As a preferred method, memory includes two dimensions: short-term memory and working memory; self-control includes inhibitory control and task switching; thinking ability corresponds to logical thinking; reaction ability corresponds to processing speed; attention corresponds to concentration; spatial ability corresponds to visual perception space;

[0023] Based on the pre-trained cognitive ability assessment model, the collected data is fused with multi-dimensional features to generate ability vectors including memory index, self-control index, thinking index, reaction index, attention index, and spatial ability index;

[0024] The ability vector is compared and analyzed with the normative database, and an evaluation report containing grade symbol marks is output; the normative database is a standardized database constructed based on the behavioral data of children of different age groups, which is used to measure whether the performance of children in various cognitive dimensions is in line with the average level of their peers; the normative database contains the following contents: age segmentation, the normative database is segmented according to the age of the children, and each age group corresponds to different normative data; the mean and standard deviation of each dimension: for each first-level dimension (such as memory, self-control, etc.) and / or second-level dimension (such as short-term memory, working memory, etc.), the database stores the mean (average value) and standard deviation of the dimension in a specific age group; targeted training tasks are recommended based on the grade and score.

[0025] The normative database is a standardized database constructed based on the behavioral data of children of different age groups. It is used to measure whether the performance of children in various cognitive dimensions is consistent with the average level of their peers. Specifically, the normative database contains the following contents: Age segmentation: The normative database is segmented according to the age of the children, and each age group corresponds to different normative data. The mean and standard deviation of each dimension: For each primary dimension (such as memory, self-control, etc.) and / or secondary dimension (such as short-term memory, working memory, etc.), the database stores the mean (average value) and standard deviation of the dimension in a specific age group.

[0026] As a preferred method, the training process of the pre-trained cognitive ability assessment model includes: collecting historical user operation data sets, which are annotated with ability level labels; constructing a deep neural network model, in which the input layer receives the coordinate sequence of the operation trajectory, and the hidden layer contains LSTM units for extracting time series features; using a contrast loss function to optimize the model, and the loss function calculates the difference between the predicted ability level and the expert annotation.

[0027] As a preferred method, targeted training tasks are recommended based on levels and scores by a personalized recommendation engine, which is configured to: parse abnormal dimensions in the ability level tags; match intervention plans from a strategy knowledge base, which stores a list of training activities associated with each level interval; and generate an executable plan that includes gamified training task recommendations and family education guidance.

[0028] As a preferred method, the dynamic adjustment of the difficulty of the training task includes: when it is detected that the user's operation accuracy is higher than a first threshold for three consecutive times, the task difficulty level is increased, and the increase includes increasing the stimulus movement speed or reducing the effective response time window; when it is detected that the user's error rate exceeds a second threshold, the auxiliary prompt mechanism is activated, and the mechanism includes highlighting the target area or extending the task time limit.

[0029] As a preferred method, an age-specific norm database is constructed, which stores the average reaction time and standard deviation of users of each age group in preset tasks; the Z algorithm is used to convert the user's original score, and the calculation formula is:

[0030]

[0031] Where X is the user score, μ is the norm mean of the corresponding age group, σ is the norm standard deviation; Z is the percentile, which generates the ability level mark.

[0032] As a preferred approach, the method for constructing the strategy knowledge base involves building a comprehensive ability training framework and establishing a dynamic mapping relationship between ability types and intervention measures, including multi-level and graded training programs; the framework covers multiple dimensions such as short-term memory, working memory, inhibitory control, task switching, processing speed, logical thinking, concentration and visual perception space, and provides personalized training intensity, task complexity and auxiliary mechanisms for different levels (A to F) to effectively improve cognitive function and optimize learning effects.

[0033] The method for constructing a strategy knowledge base includes: establishing a mapping relationship between capability types and intervention measures. The mapping relationship table covers all six first-level dimensions and the level intervals of the corresponding second-level dimensions, specifically including:

[0034] Short-Term Memory: Level A, Training Intensity: Maintain 3 sessions per week, 15 minutes per session. Task Difficulty: Increase information complexity, such as increasing from 4 to 5 numbers and incorporating color and position. Support Mechanism: No additional support, but appropriately increase the time limit for tasks, such as requiring completion within 30 seconds. Level B, Training Intensity: Maintain 5 sessions per week, 15 minutes per session. Task Difficulty: Gradually increase the amount of information, such as increasing from 4 to 5 numbers. Support Mechanism: Reveal the correct answer when an error occurs to help learners understand the reason for the error. Level C, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 15 minutes. Task Difficulty: Maintain baseline difficulty, but add cues, such as increasing the duration of the memory object display to 5 seconds. Support Mechanism: Automatically replay the memory content when an error occurs to help children review forgotten information. Level D, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 20 minutes. Task Difficulty: Maintain baseline difficulty, but add cues, such as increasing the duration of the memory object display to 5 seconds. Auxiliary mechanism: Automatically replay the memory content when an error occurs to help children review the forgotten information. Level E, training intensity: Reduce the training frequency to 5 times a week, and keep the single time duration at 15 minutes. Task difficulty: Reduce the baseline difficulty and reduce the amount of information, for example, from 5 numbers to 3 numbers. Auxiliary mechanism: Increase immediate feedback and prompts, and extend the memory time, for example, to 40 seconds. Level F, training intensity: Reduce the training frequency to 3 times a week, and increase the single time duration to 20 minutes. Task difficulty: Greatly reduce the difficulty, only require memorizing 2 numbers, and extend the memory time, for example, to 60 seconds. Auxiliary mechanism: Provide multi-sensory support, such as sound and visual assistance, to ensure understanding of task requirements.

[0035] Working Memory: Level A, Training Intensity: Maintain 3 sessions per week, 15 minutes per session. Task Difficulty: Increase complexity, such as adding a multi-step working memory task, such as sorting numbers according to a rule. Support Mechanism: No additional support, but appropriately increase the time limit for tasks, such as requiring them to complete the task within 30 seconds. Level B, Training Intensity: Maintain 5 sessions per week, 15 minutes per session. Task Difficulty: Gradually increase the amount of information, such as from 3 to 4 numbers. Support Mechanism: Display the correct answer when an error occurs to help learners understand the reason for the error. Level C, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 15 minutes. Task Difficulty: Maintain baseline difficulty, but add cues, such as a task-switch signal. Support Mechanism: Automatically replay task instructions when an error occurs to help children understand the task requirements. Level D, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 20 minutes. Task Difficulty: Maintain baseline difficulty, but add cues, such as a task-switch signal. Support Mechanism: Automatically replay task instructions when an error occurs to help children understand the task requirements. Level E, training intensity: Reduce the training frequency to 5 times a week, and maintain the single session duration at 15 minutes. Task difficulty: Reduce the baseline difficulty and reduce the amount of information, for example, from 4 numbers to 2 numbers. Auxiliary mechanism: Increase immediate feedback and prompts, and extend the task time, for example, to 40 seconds. Level F, training intensity: Reduce the training frequency to 3 times a week, and increase the single session duration to 20 minutes. Task difficulty: Greatly reduce the difficulty, and only require the completion of a simple working memory task, such as memorizing 1 number. Auxiliary mechanism: Provide multi-sensory support, such as sound and visual assistance, to ensure understanding of task requirements.

[0036] Inhibitory Control: Level A, Training Intensity: Maintain 3 sessions per week, 15 minutes per session. Task Difficulty: Increase complexity, such as by increasing the number of distractors. Support Mechanism: No additional support, but appropriately increase the time limit for the task, such as requiring the task to be completed within 30 seconds. Level B, Training Intensity: Maintain 5 sessions per week, 15 minutes per session. Task Difficulty: Gradually increase the amount of information, such as from 3 distractors to 4 distractors. Support Mechanism: Display the correct answer when an error occurs to help learners understand the reason for the error. Level C, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 15 minutes. Task Difficulty: Maintain baseline difficulty, but add prompts, such as highlighting the correct answer. Support Mechanism: Automatically extend the response time window when an error occurs to help children complete the task. Level D, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 20 minutes. Task Difficulty: Maintain baseline difficulty, but add prompts, such as highlighting the correct answer. Support Mechanism: Automatically extend the response time window when an error occurs to help children complete the task. Level E, training intensity: Reduce the training frequency to 5 times a week, and keep the single session duration at 15 minutes. Task difficulty: Reduce the baseline difficulty and reduce the amount of information, for example, from 4 distractors to 2 distractors. Auxiliary mechanism: Increase immediate feedback and prompts, and extend the task time, for example, to 40 seconds. Level F, training intensity: Reduce the training frequency to 3 times a week, and increase the single session duration to 20 minutes. Task difficulty: Greatly reduce the difficulty, and only require the completion of a simple inhibitory control task, such as choosing the correct option. Auxiliary mechanism: Provide multi-sensory support, such as sound and visual aids, to ensure understanding of task requirements.

[0037] Task Switching: Level A, Training Intensity: Maintain 3 sessions per week, 15 minutes per session. Task Difficulty: Increase complexity, such as adding multitasking tasks. Support Mechanism: No additional support, but appropriately increase the time limit for tasks, such as requiring them to complete within 30 seconds. Level B, Training Intensity: Maintain 5 sessions per week, 15 minutes per session. Task Difficulty: Gradually increase the amount of information, such as increasing from 2 tasks to 3 tasks. Support Mechanism: Display the correct answer when an error occurs to help learners understand the reason for the error. Level C, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 15 minutes. Task Difficulty: Maintain baseline difficulty, but add prompts, such as a task-switching signal. Support Mechanism: Automatically replay task instructions when an error occurs to help children understand the task requirements. Level D, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 20 minutes. Task Difficulty: Maintain baseline difficulty, but add prompts, such as a task-switching signal. Support Mechanism: Automatically replay task instructions when an error occurs to help children understand the task requirements. Level E, training intensity: Reduce the training frequency to 5 times a week, and maintain the single duration at 15 minutes. Task difficulty: Reduce the baseline difficulty and reduce the amount of information, for example, from 3 tasks to 2 tasks. Auxiliary mechanism: Increase immediate feedback and prompts, and extend the task time, for example, to 40 seconds. Level F, training intensity: Reduce the training frequency to 3 times a week, and increase the single duration to 20 minutes. Task difficulty: Greatly reduce the difficulty, and only require the completion of a simple task switching task, such as switching a single task. Auxiliary mechanism: Provide multi-sensory support, such as sound and visual assistance, to ensure understanding of task requirements.

[0038] Processing Speed: Level A, Training Intensity: Maintain 3 sessions per week, 15 minutes per session. Task Difficulty: Increase complexity, such as by reducing stimulus presentation time. Support Mechanism: No additional support, but appropriately increase task time limits, such as requiring tasks to be completed within 30 seconds. Level B, Training Intensity: Maintain 5 sessions per week, 15 minutes per session. Task Difficulty: Gradually increase the amount of information, such as from 3 tasks to 4 tasks. Support Mechanism: Display the correct answer when an error occurs to help learners understand the reason for the error. Level C, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 15 minutes. Task Difficulty: Maintain baseline difficulty, but add prompts, such as displaying task-related graphics. Support Mechanism: Automatically extend task presentation time when an error occurs to help children complete the task. Level D, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 20 minutes. Task Difficulty: Maintain baseline difficulty, but keep task presentation time unchanged. Support Mechanism: Automatically extend task presentation time when an error occurs to help children complete the task. Level E, training intensity: Reduce the training frequency to 5 times a week, and maintain the single duration at 15 minutes. Task difficulty: Reduce the baseline difficulty and reduce the amount of information, for example, from 4 tasks to 2 tasks. Auxiliary mechanism: Increase immediate feedback and prompts, and appropriately extend the task presentation time. Level F, training intensity: Reduce the training frequency to 3 times a week, and increase the single duration to 20 minutes. Task difficulty: Greatly reduce the difficulty, and only require the completion of a simple processing speed task, such as identifying a single stimulus. Auxiliary mechanism: Provide multi-sensory support, such as sound and visual assistance, to ensure understanding of task requirements.

[0039] Logical Thinking: Level A, Training Intensity: Maintain 3 sessions per week, 15 minutes per session. Task Difficulty: Increase complexity, such as adding multi-step logical reasoning tasks. Support Mechanism: No additional support, but appropriately increase the time limit for tasks, such as requiring them to be completed within 30 seconds. Level B, Training Intensity: Maintain 5 sessions per week, 15 minutes per session. Task Difficulty: Gradually increase the amount of information, such as increasing from 2 conditions to 3 conditions. Support Mechanism: Display the correct answer when an error occurs to help learners understand the reasoning. Level C, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 15 minutes. Task Difficulty: Maintain baseline difficulty, but add prompts, such as displaying logical relationships. Support Mechanism: Automatically replay the logical reasoning process when an error occurs to help children understand the reasoning steps. Level D, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 20 minutes. Task Difficulty: Maintain baseline difficulty, but add prompts, such as displaying logical relationships. Support Mechanism: Automatically replay the logical reasoning process when an error occurs to help children understand the reasoning steps. Level E, training intensity: Reduce the training frequency to 5 times a week, and maintain the single duration at 15 minutes. Task difficulty: Reduce the baseline difficulty and reduce the amount of information, for example, from 3 conditions to 2 conditions. Auxiliary mechanism: Increase immediate feedback and prompts, and extend the task time, for example, increase it to 40 seconds. Level F, training intensity: Reduce the training frequency to 3 times a week, and increase the single duration to 20 minutes. Task difficulty: Greatly reduce the difficulty, and only require the completion of a simple logical reasoning task, such as identifying cause and effect. Auxiliary mechanism: Provide multi-sensory support, such as sound and visual assistance, to ensure understanding of task requirements.

[0040] Concentration: Level A, Training Intensity: Maintain 3 times per week, 15 minutes per session. Task Difficulty: Increase complexity, such as by increasing the number of distractors. Support Mechanism: No additional support, but appropriately increase the time limit for the task, such as requiring the task to be completed within 30 seconds. Level B, Training Intensity: Maintain 5 times per week, 15 minutes per session. Task Difficulty: Gradually increase the amount of information, such as from 3 distractors to 4 distractors. Support Mechanism: Display the correct answer when an error occurs to help learners understand the reason for the error. Level C, Training Intensity: Increase training frequency to 7 times per week, with each session lasting 15 minutes. Task Difficulty: Maintain baseline difficulty, but add prompts, such as highlighting the target stimulus. Support Mechanism: Automatically reduce the number of distractors when an error occurs to help children better focus. Level D, Training Intensity: Increase training frequency to 7 times per week, with each session lasting 20 minutes. Task Difficulty: Maintain baseline difficulty, but increase the complexity of distractors. Support Mechanism: Automatically extend the target stimulus presentation time when an error occurs to help children focus on the target. Level E, training intensity: Reduce the training frequency to 5 times a week, and maintain the single duration at 15 minutes. Task difficulty: Reduce the baseline difficulty and reduce the number and complexity of interference sources. Auxiliary mechanism: Increase immediate feedback and prompts, and extend the task time, for example, to 40 seconds. Level F, training intensity: Reduce the training frequency to 3 times a week, and increase the single duration to 20 minutes. Task difficulty: Greatly reduce the difficulty, and only require the completion of a simple concentration task, such as identifying a single target stimulus. Auxiliary mechanism: Provide multi-sensory support, such as sound and visual assistance, to ensure understanding of task requirements.

[0041] Visual Perceptual Space: Level A, Training Intensity: Maintain 3 sessions per week, 15 minutes per session. Task Difficulty: Increase complexity, such as adding a spatial rotation task. Support Mechanism: No additional support, but appropriately increase the time limit for tasks, such as requiring them to complete the task within 30 seconds. Level B, Training Intensity: Maintain 5 sessions per week, 15 minutes per session. Task Difficulty: Gradually increase the amount of information, such as from simple to complex shapes. Support Mechanism: Display the correct answer when an error occurs to help learners understand the reason for the error. Level C, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 15 minutes. Task Difficulty: Maintain baseline difficulty, but add prompts, such as showing the shape before rotation. Support Mechanism: Automatically replay the rotation process when an error occurs to help children understand spatial relationships. Level D, Training Intensity: Increase training frequency to 7 sessions per week, with each session lasting 20 minutes. Task Difficulty: Maintain baseline difficulty, but add prompts, such as showing the shape before rotation. Support Mechanism: Automatically replay the rotation process when an error occurs to help children understand spatial relationships. Level E, Training Intensity: Reduce training frequency to five times per week, maintaining a single session duration of 15 minutes. Task Difficulty: Reduce baseline difficulty and reduce information load, for example, from complex shapes to simple ones. Support Mechanisms: Increase immediate feedback and prompts, and extend task duration, for example, to 40 seconds. Level F, Training Intensity: Reduce training frequency to three times per week, increasing single session duration to 20 minutes. Task Difficulty: Significantly reduce difficulty, requiring only a simple visual-perceptual-spatial task, such as identifying basic shapes. Support Mechanisms: Provide multisensory support, such as audio and visual aids, to ensure task understanding. The strategy knowledge base construction approach provides personalized learning strategies to promote learners' cognitive development by designing graded training programs tailored to different cognitive abilities. This design utilizes a systematic, graduated approach, gradually increasing training intensity and task difficulty from low to high levels, and providing support mechanisms where necessary. As learners progress, the training program dynamically adjusts to ensure each learner receives challenge and support at an appropriate level of difficulty. It covers multiple cognitive dimensions to ensure comprehensive improvement, and supports learners who encounter difficulties through support mechanisms. The strategy knowledge base provides a structured and dynamic solution for personalized learning and has the potential to play an effective role in cognitive training.

[0042] As a preferred method, the collected data includes the spatiotemporal characteristics of the touch operation trajectory, the task completion progress and the number of interruptions, and the frequency statistics of false triggering of interference items. The multi-dimensional feature fusion steps specifically include:

[0043] Perform wavelet transform on spatiotemporal features to extract frequency domain features of operation trajectories;

[0044] The task completion progress is quantified as the time efficiency coefficient, and the calculation formula is:

[0045]

[0046] Where T 基准 is the normative average completion time, T 实际 The actual time consumed by users; weighted fusion of multi-source features to generate a comprehensive ability score.

[0047] The fusion of collected data and multi-dimensional features is a system for comprehensively assessing user capabilities. By collecting data and extracting features from various dimensions, it generates a score reflecting the user's overall ability. The specific steps and methods are as follows: Data Collection: Spatiotemporal Features of Touch Operation Trajectory: This records the path and timing of all user operations while touching the screen. This includes gestures, sliding speed, acceleration, and more. This can reflect the user's operational proficiency, coordination, and reaction speed. Task Completion Progress and Interruptions: This monitors the user's progress in completing specific tasks, including the difference between the time required and the expected time, and the number of interruptions during the task. This can assess the user's task efficiency and focus. Frequency Statistics of False Triggers of Distractors: This records the frequency with which users trigger options or functions unrelated to the current task. This can reflect the user's focus and accuracy in complex task environments. Feature Extraction and Processing: Wavelet Transform of Spatiotemporal Features: This converts the collected spatiotemporal feature data from the time domain to the frequency domain. Wavelet transforms effectively capture local features in the signal, helping to analyze the rhythm and patterns of user operations. For example, the frequency characteristics of different gestures such as quick swipes, taps, and long presses in user operations can be identified. Quantification of task completion progress: The task completion progress is converted into a time efficiency coefficient. This coefficient can quantify the efficiency with which users complete tasks. A larger value indicates faster task completion and higher efficiency. Multidimensional feature fusion: Using an attention mechanism, weights are dynamically assigned to data in each dimension based on the importance of different features. The attention mechanism can learn which features are more critical for evaluating user capabilities, thereby giving them higher weights during the fusion process. This step can help the system better focus on important information and reduce interference from irrelevant information.

[0048] Generate a comprehensive ability score: By weightedly integrating the processed multi-dimensional data, a comprehensive score is generated. This score can fully reflect the user's comprehensive ability in terms of operational proficiency, task efficiency, and concentration, providing a comprehensive assessment result.

[0049] Through the above steps, the collected data and multi-dimensional feature fusion methods can be effectively combined to form a comprehensive and intelligent user capability assessment system. This not only helps understand and evaluate users' operational capabilities, but also provides users with targeted improvement suggestions, improving user experience and operational efficiency.

[0050] As a preferred embodiment, it also includes a data security module configured as follows:

[0051] Desensitize user identity information and generate an anonymous unique identifier;

[0052] Use homomorphic encryption technology to encrypt and transmit operation trajectory data;

[0053] Establish a two-way authentication channel between the local storage device and the cloud server.

[0054] A children's brain intelligence and cognitive development ability assessment system adopts the above-mentioned children's brain intelligence and cognitive development ability assessment method; comprising:

[0055] User interface module, used to display game tasks and feedback reports, supporting touch interaction;

[0056] A task evaluation module, configured to execute game tasks and record behavioral data, wherein the tasks are randomly selected according to the secondary dimension;

[0057] Data analysis module, used to calculate percentiles and ranks based on the norm data table and generate multidimensional scores;

[0058] Report generation module, used to integrate radar charts, grade marks and suggestions to generate visual reports;

[0059] The background management module is used to store user data, configure system parameters and manage the training task library.

[0060] The present invention has at least the following beneficial effects: it proposes a method and system for assessing children's cognitive abilities. The system focuses on six core cognitive abilities: memory, attention, self-control, reaction, thinking, and spatial awareness. Through a gamified assessment process, the system comprehensively covers these six cognitive areas, aiming to provide a scientific, comprehensive, and engaging solution for assessing children's cognitive development.

[0061] The present invention provides personalized basic data for subsequent evaluation by receiving user identity information, including age, gender and education stage. The model design can comprehensively and meticulously evaluate the level of children's brain and cognitive development. During the task execution process, game tasks are randomly selected from the secondary dimensions for evaluation to collect children's behavioral data in the tasks, including reaction time, accuracy and strategy selection. This random sampling and task design method not only ensures the comprehensiveness of the evaluation, but also increases the fun and interactivity of the evaluation, allowing children to participate more actively and proactively in the evaluation process.

[0062] A normative data table was constructed based on the behavioral data of children of different age groups. This normative data table contains the mean and standard deviation of each dimension, providing a scientific basis for subsequent score conversion and grading. Task scores, primary dimension scores, and total scores were calculated based on the collected behavioral data. Scores were then converted to percentiles based on the normative data table, resulting in six grading levels. This scoring and grading method can intuitively and accurately reflect children's cognitive development level, providing a strong reference for parents and educators. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] To reveal the technical details of the embodiments of the present invention, the following is a brief introduction to the drawings involved in the embodiments. It should be emphasized that these drawings only illustrate several embodiments of the present invention and should not be considered as defining the scope of the invention. Those skilled in the art can deduce other relevant drawings based on these drawings without engaging in creative work.

[0064] Figure 1 This is a schematic diagram of the evaluation structure;

[0065] Figure 2 To construct normative data representation intention;

[0066] Figure 3 This is an example diagram of task content in the embodiment;

[0067] Figure 4 It is a heptagonal radar chart. DETAILED DESCRIPTION

[0068] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0069] In the following, embodiments of the present disclosure are described in detail with the aid of accompanying drawings. However, please be aware that the present disclosure is not limited to the specific forms shown herein. Rather, it should be understood to encompass various variations, equivalents, and / or alternatives to the embodiments of the present disclosure. In describing the drawings, the same reference numerals will be used to indicate similar components.

[0070] In the various embodiments of the present disclosure, expressions such as "first," "second," "the first," or "the second" are intended to modify different components, rather than to indicate order and / or importance, and do not limit the corresponding components. For example, a first user device and a second user device each represent different user devices, although they both fall within the scope of user devices. Similarly, a first component can be named a second component, and a second component can be named a first component, which does not change their essential attributes within the scope of the present disclosure.

[0071] In this disclosure, terms are used to illustrate specific embodiments and do not constitute limitations of this disclosure. In this context, the use of the singular also encompasses the plural, unless the text clearly indicates otherwise. In the process of explanation, it should be understood that terms such as "including" or "having" are intended to indicate the presence of a feature, quantity, step, operation, structural component, part, or combination thereof, and do not preclude the possibility or addition of one or more other features, quantities, steps, operations, structural components, parts, or combinations thereof.

[0072] It should be understood that while the following description provides extensive specific details intended to facilitate a comprehensive understanding of the example embodiments, those skilled in the art will appreciate that the example embodiments can be implemented without these specific details. For example, systems may be presented in block diagram form to avoid excessive detail that would obscure the clarity of the examples. In other cases, unnecessary details regarding well-known processes, structures, and techniques may be omitted to maintain clarity of the examples.

[0073] A method for assessing children's brain intelligence and cognitive development ability, comprising:

[0074] Receive user identification information, including age, gender, and education level;

[0075] Construct an assessment model covering six primary dimensions and eight secondary dimensions. The primary dimensions include memory, self-control, thinking ability, reaction ability, attention, and spatial ability. The secondary dimensions include short-term memory, working memory, inhibitory control, task switching, processing speed, logical thinking, concentration, and visual perception space.

[0076] Randomly extract a target game task set from the task library, where the task set covers at least three first-level dimensions of assessment requirements; during task execution, randomly select game tasks from the second-level dimensions for assessment, and collect behavioral data on children during the tasks, including reaction time, accuracy, and strategy selection;

[0077] Constructing a normative data table based on behavioral data of children of different age groups, wherein the normative data table includes the mean and standard deviation of each dimension;

[0078] The task score, first-level dimension score and total score are calculated based on the behavioral data, and the scores are converted into percentiles based on the normative data table, divided into six levels (A to F), and a visual report containing a radar chart, level markings and personalized suggestions is generated. The beneficial effects of this evaluation method are mainly reflected in the following aspects: First, through the gamified evaluation method, the participation and interest of children are improved; second, through a comprehensive evaluation model and detailed dimensional division, the level of children's brain and cognitive development can be accurately and comprehensively evaluated; third, by constructing a normative data table and performing score conversion and level division, parents and educators are provided with intuitive and scientific evaluation results and reference basis. These beneficial effects have jointly promoted the improvement of children's brain and cognitive development capabilities and the scientific development of education.

[0079] In one embodiment:

[0080] Memory includes two dimensions: short-term memory and working memory; self-control includes inhibitory control and task switching; thinking ability corresponds to logical thinking; reaction ability corresponds to processing speed; attention corresponds to concentration; spatial ability corresponds to visual perception space;

[0081] Based on the pre-trained cognitive ability assessment model, the collected data is fused with multi-dimensional features to generate ability vectors including memory index, self-control index, thinking index, reaction index, attention index, and spatial ability index;

[0082] The ability vector is compared and analyzed with the normative database, and an evaluation report containing grade symbol marks is output; the normative database is a standardized database constructed based on the behavioral data of children of different age groups, which is used to measure whether the performance of children in various cognitive dimensions is in line with the average level of their peers; the normative database contains the following contents: age segmentation, the normative database is segmented according to the age of the children, and each age group corresponds to different normative data; the mean and standard deviation of each dimension: for each first-level dimension (such as memory, self-control, etc.) and second-level dimension (such as short-term memory, working memory, etc.), the database stores the mean (average value) and standard deviation of the dimension in a specific age group; targeted training tasks are recommended based on the grades and scores, and the difficulty of the training tasks is dynamically adjusted and designed based on the psychological experimental paradigm.

[0083] Ability Vector: This system uses behavioral data (such as reaction time, accuracy, and strategy selection) combined with pre-trained cognitive ability assessment models to generate ability vectors containing memory, self-control, and thinking indices. Each index represents a child's performance on a specific dimension. Norm Database: Based on the child's age, the mean (μ) and standard deviation (σ) data for the corresponding age group are queried from the norm database.

[0084] The normative database is a standardized database constructed based on the behavioral data of children of different age groups. It is used to measure whether the performance of children in various cognitive dimensions is in line with the average level of their peers. Specifically, the normative database contains the following contents: Age segmentation: The normative database is segmented according to the age of the children, and each age group corresponds to different normative data. The mean and standard deviation of each dimension: For each primary dimension (such as memory, self-control, etc.) and secondary dimension (such as short-term memory, working memory, etc.), the database stores the mean (average value) and standard deviation of the dimension in a specific age group.

[0085] Behavioral data indicators: The behavioral data in the database includes reaction time (i.e., the time it takes to complete a task), accuracy (the accuracy of task completion), strategy selection (the type of strategy used in the task), etc. This structure provides a standardized reference for children of different ages to assess their cognitive abilities in specific dimensions.

[0086] In one embodiment, the training process of the pre-trained cognitive ability assessment model includes: collecting a historical user operation data set, wherein the data set is annotated with an ability level label assessed by an expert; constructing a deep neural network model, wherein the input layer receives a coordinate sequence of the operation trajectory, and the hidden layer contains an LSTM unit for extracting time series features; and optimizing the model using a contrast loss function, wherein the loss function calculates the difference between the predicted ability level and the expert labeling.

[0087] Specifically, during the data collection phase, historical user operation data is collected: through applications or experimental equipment, the user's operation trajectory when completing a specific task is recorded. This operation data includes touch coordinate sequences (such as the location and timestamp of gestures, slides, and clicks) and task completion status (such as completion time, number of interruptions, etc.). Data storage: The collected operation data is stored in an appropriate format (such as CSV, JSON) to facilitate subsequent processing. Data annotation: Expert evaluation annotation: Invite domain experts (such as psychologists or education experts) to evaluate user operation data and assign each user a capability level label. These labels can be categorical (such as A, B, C, etc.). Annotation consistency check: Ensure the consistency of evaluations by different experts. The annotation quality can be verified by statistical indicators of evaluation consistency between experts (such as Cohen's Kappa coefficient). Data preprocessing: Data cleaning: Remove noise data, outliers, or missing values ​​to ensure data quality. For example, check whether the coordinates in the operation trajectory exceed the screen range or whether the task completion time is within a reasonable range. Data normalization / standardization: Normalize the coordinates of the operation trajectory and other data to ensure that data of different scales are comparable in the model. Data splitting: Split the dataset into training, validation, and test sets in proportion, usually in a ratio of 70%, 15%, and 15%.

[0088] Model design: Input layer: Design the input layer of the model to receive the coordinate sequence of the operation trajectory. For example, the input can be a sequence of two-dimensional coordinates (x, y) over time, with a shape of (time steps, 2). Hidden layer: Use LSTM (Long Short-Term Memory) units to process time series features, because LSTM can effectively capture long-term dependencies in sequence data. The hidden layer can include multiple LSTM layers, or be combined with other types of neural network layers (such as fully connected layers). Output layer: Design the output layer and select the appropriate activation function according to the task type. For example, for classification tasks, the Softmax activation function can be used to output the probability distribution of each ability level; for regression tasks, the linear activation function can be used to output the predicted ability score.

[0089] Model training: Define the loss function: Use a contrastive loss function, such as cross-entropy loss (classification) or mean squared error (MSE) (regression), to measure the difference between the predicted value and the true label. Select an optimizer: Select an appropriate optimization algorithm, such as the Adam optimizer, to adjust the model parameters to minimize the loss function. Training process: Input training data into the model, perform forward propagation, and calculate the predicted output. Calculate the loss between the predicted output and the true label. Based on the loss value, perform backpropagation and adjust the model parameters. Repeat the above steps until the model performs as expected on the validation set or the set number of training times is reached.

[0090] Model evaluation: Validation set evaluation: During training, regularly use the validation set to evaluate model performance, such as accuracy, F1 score, and mean squared error, to monitor overfitting. Test set evaluation: After training, use an independent test set for a final evaluation of the model to obtain performance metrics on unseen data.

[0091] Model Optimization and Parameter Adjustment: Hyperparameter Tuning: Adjust the model's hyperparameters (such as the number of LSTM layers, number of units, learning rate, batch size, etc.) through methods such as grid search, random search, or Bayesian optimization to achieve optimal performance. Model Architecture Adjustment: Adjust the model's structure based on evaluation results and performance bottlenecks, such as increasing or decreasing the number of layers, changing the akt ivas i function type, etc. Model Deployment: Saving the trained model: Save the trained model parameters so that they can be loaded and used in actual applications. Integration into Applications: Integrate the trained model into relevant application systems for real-time user cognitive ability assessment.

[0092] Through the above steps, a pre-trained cognitive ability assessment model can be implemented. This model can predict a user's cognitive ability level based on their operation trajectory, providing objective user ability assessment in the education field. In one embodiment, a personalized recommendation engine is used to recommend targeted training tasks based on the level and score. The personalized recommendation engine is configured to: parse abnormal dimensions in the ability level mark; match intervention plans from a policy knowledge base that stores a list of training activities associated with each level interval; and generate an executable plan that includes gamified training task recommendations and family education guidance.

[0093] In one embodiment, the dynamic adjustment of the difficulty of the training task includes: when it is detected that the user's operation accuracy is higher than a first threshold for three consecutive times, the task difficulty level is increased, and the increase includes increasing the stimulus movement speed or reducing the effective response time window; when it is detected that the user's error rate exceeds a second threshold, the auxiliary prompt mechanism is activated, and the mechanism includes highlighting the target area or extending the task time limit.

[0094] In one embodiment, an age-specific norm database is constructed, which stores the average reaction time and standard deviation of users of each age group in a preset task; the Z algorithm is used to convert the user's original score, and the calculation formula is:

[0095]

[0096] Where X is the user score, μ is the normative mean of the corresponding age group, and σ is the normative standard deviation; Z is the percentile (generally needs to be multiplied by 100), which generates the ability level mark.

[0097] In one embodiment, the method for constructing the strategy knowledge base involves building a comprehensive ability training framework and establishing a dynamic mapping relationship between ability types and intervention measures, including multi-level and graded training programs; the framework covers multiple dimensions such as short-term memory, working memory, inhibitory control, task switching, processing speed, logical thinking, concentration and visual perceptual space, and provides personalized training intensity, task complexity and auxiliary mechanisms for different levels (A to F) to effectively improve cognitive function and optimize learning effects.

[0098] In one embodiment, the collected data includes the spatiotemporal features of the touch operation trajectory, the task completion progress and the number of interruptions, and the frequency statistics of false triggering of interference items. The multi-dimensional feature fusion step specifically includes:

[0099] Perform wavelet transform on spatiotemporal features to extract frequency domain features of operation trajectories;

[0100] The task completion progress is quantified as the time efficiency coefficient, and the calculation formula is:

[0101]

[0102] where T 基准 is the normal mode average completion time, T 实际 is the user's actual time consumption; a attention mechanism is adopted to weight and fuse multi-source features to generate a comprehensive ability score.

[0103] Data collection and multi-dimensional feature fusion method is a system for comprehensive evaluation of user ability, through different dimensional data collection and feature extraction, a score reflecting the user's comprehensive ability is generated. The following are the specific steps and methods: Data collection: spatiotemporal features of touch operation trajectory: record all the operation paths and time information of the user when touching the screen. This includes the user's gestures, sliding speed, acceleration, etc. This can reflect the user's operation proficiency, coordination ability and reaction speed. Task completion progress and interruption frequency: monitor the user's progress in completing a specific task, including the difference between the time required to complete the task and the expected time, and the number of interruptions during the task. This can evaluate the user's task processing efficiency and concentration. Frequency statistics of interference item mis-triggering: record the frequency of the user triggering options or functions unrelated to the current task during operation. This can reflect the user's concentration and accuracy in a complex task environment. Feature extraction and processing: wavelet transform of spatiotemporal features: convert the collected spatiotemporal feature data from the time domain to the frequency domain. Wavelet transform can effectively capture local features in the signal, helping to analyze the rhythm and pattern of user operation. For example, it can identify the frequency characteristics of different gestures such as fast sliding, tapping, and long pressing in user operation. Quantification of task completion progress: convert the task completion progress into a time efficiency coefficient. This coefficient can quantify the user's efficiency in completing the task, and the larger the value, the faster the task is completed and the higher the efficiency. Multi-dimensional feature fusion: use attention mechanism to dynamically allocate weights to each dimension of data according to the importance of different features. The attention mechanism can learn which features are more critical to evaluating user ability, and thus give higher weights in the fusion process. This step can help the system focus on important information and reduce the interference of irrelevant information.

[0104] Generate comprehensive ability score: generate a comprehensive score by weighting and fusing the processed multi-dimensional data. This score can comprehensively reflect the user's comprehensive ability in operation proficiency, task efficiency, concentration, etc., providing a comprehensive evaluation result.

[0105] Through the above steps, the collected data and multi-dimensional feature fusion method can be effectively combined to form a comprehensive and intelligent user ability evaluation system. This not only helps to understand and evaluate the user's operation ability, but also provides targeted improvement suggestions for the user, improving user experience and operation efficiency.

[0106] In one embodiment, a data security processing method is further included, configured to:

[0107] The user identity information is desensitized to generate an anonymized unique identifier;

[0108] The operation track data is encrypted and transmitted by using a homomorphic encryption technology;

[0109] A bidirectional verification channel is established between the local storage device and the cloud server.

[0110] A children's brain intelligence cognitive development ability evaluation system adopts the above-mentioned children's brain intelligence cognitive development ability evaluation method; comprising:

[0111] A user interface module is used to display game tasks and feedback reports and support touch interaction;

[0112] A task evaluation module is used to execute game tasks and record behavior data, and the tasks are randomly called according to the two-level dimensions;

[0113] A data analysis module is used to calculate the percentile and grade according to the norm data table and generate multi-dimensional scores;

[0114] A report generation module is used to integrate radar charts, grade markers and suggestions to generate visual reports;

[0115] A background management module is used to store user data, configure system parameters and manage training task libraries. In one embodiment, the construction of the norm data table includes: collecting the reaction time and accuracy of children of different ages in the specified game tasks; calculating the mean and standard deviation of each two-level dimension according to the age layer; associating the mean and standard deviation with the one-level dimension to generate an age-dimension control table.

[0116] The grade division specifically includes:

[0117] A grade corresponds to a percentile of ≥76, marked as ↑↑↑↑; B grade corresponds to 51-75, marked as ↑; C grade corresponds to 26-50, marked as —; D grade corresponds to 11-25, marked as ↓; E grade corresponds to 2-10, marked as ↓↓; F grade corresponds to ≤1, marked as ↓↓↓.

[0118] The design of the game task is based on a psychological experiment paradigm, including:

[0119] The inhibition control task is based on the Flanker task or Go / No-Go paradigm, the short-term memory task is based on the design of spatial memory reproduction, and the logical thinking task is realized through path planning and problem solving;

[0120] The task adopts a cartoon interface, touch screen interaction and dynamic difficulty adjustment, wherein the dynamic difficulty adjustment comprises increasing task complexity or adjusting operation speed in real time according to child performance.

[0121] The generation of the visualization report comprises generating a heptagon radar chart based on the first-level dimension scores, marking each dimension level mark, outputting a development level and a national level comparison description according to the total score, and providing personalized training suggestions for low-score dimensions, the suggestions comprising specific activity strategies and training task recommendations.

[0122] The children's brain intelligence cognitive development ability evaluation system, the task evaluation module comprises a dynamic difficulty adjustment unit, and the specific implementation manner is as follows: in the logical thinking task, the number of obstacles is increased or decreased according to the path planning accuracy of the child; in the processing speed task, the stimulus presentation interval time is shortened or lengthened according to the reaction time; in the inhibition control task, the interference stimulus density is increased or decreased based on the number of consecutive correct times.

[0123] The data analysis module further comprises an abnormality detection unit, which is configured to: when the reaction time of the child in the task exceeds 3 times the standard deviation of the norm data table, trigger a data review mechanism; and when the number of unfinished tasks exceeds a threshold value, automatically adjust the task difficulty or reassign the evaluation task combination.

[0124] The report generation module generates personalized suggestions by the following steps: extracting dimensions with total scores lower than a preset threshold as key improvement items; associating game tasks of corresponding dimensions in a training task library to generate a recommended task list; and providing a family activity plan and an education strategy in combination with the age of the child and norm data.

[0125] The background management module supports: customizing an evaluation task combination, configuring the number of tasks according to the weight of a first-level dimension; exporting age stratification statistical results of a norm data table; and batch generating a group screening report, marking a list of abnormal children in each dimension and an intervention priority.

[0126] In one embodiment:

[0127] (1) Evaluation model construction

[0128] A. Evaluation structure

[0129] The evaluation task covers eight secondary dimensions (short-term memory, working memory, inhibition control, task switching, processing speed, logical thinking, concentration, and visual perception space) belonging to six first-level dimensions (memory, self-control, thinking, reaction, attention, and spatial force). Each evaluation randomly selects game tasks from the specified secondary dimensions to ensure diversity and comprehensiveness of the evaluation. The evaluation structure is shown in detail in Figure 1 .

[0130] B. Norm data establishment

[0131] The key indicators (such as reaction time, accuracy) of children of different ages in the task are collected, the average value and standard deviation of each dimension are calculated, and the norm data table is constructed (see Figure 2 ).

[0132] C. Grading method

[0133] a. Score calculation

[0134] Task score: After completing each game task, the score is recorded according to the performance, and the default score of the unfinished task is 50 points.

[0135] Primary dimension score: average all task scores in the same primary dimension, rounding to the nearest integer. Total score: average the scores of all primary dimensions, rounding to the nearest integer.

[0136] b. Percentile calculation and conversion

[0137] Based on the norm table, the task score is converted into percentile, representing the relative position of the child in the norm group.

[0138] c. Specific percentile grade division

[0139] According to the percentile, it is divided into six grades:

[0140] A (excellent): percentile ≥ 76;

[0141] B (safe-good): percentile 51 to 75;

[0142] C (safe-basic normal): percentile 26 to 50; D (deviation): percentile 11 to 25;

[0143] E (crisis-poor): percentile 2 to 10;

[0144] F (crisis-lag): percentile ≤ 1.

[0145] d. Visual markers

[0146] In order to intuitively present the grade of each ability, the grade in the report uses a marker symbol:

[0147] A: ↑↑↑↑

[0148] B: ↑

[0149] C: —

[0150] D: ↓

[0151] E: ↓↓

[0152] F: ↓↓↓

[0153] (2) Scientific basis for game task design

[0154] Game task design is based on psychological experiment paradigms and cognitive development theories, combined with the scientific assessment needs of six core cognitive abilities, to ensure the scientificity and interest of the task. Specifically:

[0155] A. Scientific design principles

[0156] The task involves following psychological experiment paradigms or theoretical frameworks. For example:

[0157] a. Inhibition control: "Wandering" is based on the Flanker task, requiring children to ignore interference information and focus on the judgment of target stimuli; "Star lighting" integrates Go / No-Go paradigm, to exercise children's rapid response and self-inhibition ability to specific targets.

[0158] b. Short-term memory: "Ice memory" is based on the design of spatial memory tasks, requiring children to remember the location of specific objects and reproduce them; "Smart rabbit" simulates a life scene, by remembering the fruits ordered by guests, to strengthen the application of short-term memory.

[0159] c. Logical thinking: "Swamp adventure" and "Breakthrough" respectively exercise children's reasoning and spatial planning ability through route planning and problem solving.

[0160] d. Processing speed: "Nutrition balance" and "Ice and snow world" design complex stimuli to assess children's ability to quickly identify and respond to visual information. Task content examples are shown in Figure 3 .

[0161] B. Interest and interaction

[0162] a. Cartoon design

[0163] The task attracts children's attention and stimulates children's interest through bright colors and dynamic effects.

[0164] b. Simple touch screen operation

[0165] The task allows children to easily complete the task and experience interactive fun through sliding, clicking, dragging and other interactive methods.

[0166] c. Dynamic difficulty adjustment

[0167] The difficulty of the task is adjusted in real time according to the performance of the children, such as the number of accessories and the speed of the conveyor belt in "Rainbow cake" gradually increasing with performance, to ensure that the task adapts to different ability levels.

[0168] The feedback mechanism of the task ensures that children receive timely encouragement, such as showing the correct route after completing "Final destination" to reinforce memory.

[0169] 3. Application scenarios

[0170] (1) Group screening: This system can be used to screen the cognitive abilities of large groups of students and identify students who need additional support or intervention.

[0171] (2) Early intervention and personalized education: It can help identify early cognitive development problems, provide children with personalized educational intervention plans, and promote their all-round development.

[0172] (3) Family education support: Provide parents with scientific cognitive development reports to help them understand their children’s strengths and weaknesses and optimize family education strategies.

[0173] (4) Extracurricular activities and gamified learning: Through fun game tasks in the system, children are encouraged to improve their cognitive abilities in an informal learning environment, combining daily learning with extracurricular activities.

[0174] (5) Career development and selection: It can be used to assess the cognitive abilities of specific groups and assist in talent selection and career planning.

[0175] The implementation of this invention includes the specific implementation steps of system architecture design, data processing flow, task execution and scoring mechanism. By analyzing task data through artificial intelligence algorithms, the system can automatically generate personalized assessment reports and provide capacity improvement suggestions. The following is a specific implementation of the system:

[0176] 1. System architecture and module design

[0177] The system's hardware platform includes smart mobile devices (such as tablets and smartphones) and PCs, and is required to support touch operations, high processing power, stable network connections, and graphics and sound output.

[0178] The system architecture can be divided into the following modules:

[0179] (1) User interface module: used to display game tasks, feedback and reports.

[0180] (2) Task evaluation module: responsible for executing various game tasks and recording children's performance.

[0181] (3) Data processing and analysis module: collects various data in the task, performs data analysis and calculates scores. (4) Report generation module: generates personalized assessment reports based on the analysis results, and provides detailed capability analysis and development suggestions.

[0182] (5) Backend management module: used to manage and view user data, perform data storage and system configuration.

[0183] 2. Evaluation Process and Methods

[0184] This evaluation system has designed a standardized evaluation process, which is as follows:

[0185] (1) Input information

[0186] Collect children's basic information, including name, gender, age, grade, etc.

[0187] (2) Assessment tasks

[0188] The tasks involve six core cognitive areas, each of which contains multiple sub-tasks, which can effectively evaluate children's performance in various cognitive dimensions.

[0189] (3) Data collection and analysis

[0190] The system automatically records key data such as task completion time and accuracy. Based on the accuracy, speed, strategy selection and other dimensions of task completion, it uses big data models to provide standardized scoring.

[0191] (4) Scoring mechanism

[0192] The scores in each field are calculated using standardization and normalized to a percentage.

[0193] (5) Report generation

[0194] Based on the assessment results, the system will automatically generate a cognitive ability assessment report, including the overall score, analysis of each field, ability radar chart, and personalized development recommendations based on the assessment results.

[0195] (6) Output report example

[0196] During the report generation process, the system will provide visual feedback based on the child's assessment results and provide parents or educators with specific suggestions, including:

[0197] A. Basic Information

[0198] The report begins by listing the child's name, sex, age, grade, when the test was taken, and how long the test took.

[0199] B. Evaluation results display

[0200] a. Overall score and development level: For example, "In this assessment, the overall score for cognitive development was 67 points (out of 100 points), and the development level was B (↑), which is a good performance and is at a relatively high level nationwide."

[0201] b. Radar chart: Heptagonal radar chart (see Figure 4 ) intuitively displays the scores in six core areas and the balance of overall cognitive ability.

[0202] c. Performance by area: Detailed description of scores and ability characteristics in each area. Examples are as follows:

[0203] Self-control score: 75 points (out of 100 points), development level is B, good performance, and at a relatively high level nationwide.

[0204] Characteristics: Ability to focus on the current task without being distracted by irrelevant factors. Ability to achieve goals quickly and accurately in complex environments. Strong emotional and mental control, demonstrating good behavioral self-control and the ability to cope with stress.

[0205] Parents' advice: Maintain good self-management and give children more room to develop independently.

[0206] C. Personalized development suggestions

[0207] If the score in a certain area is low, parents will be prompted about what activities or strategies can be used to help children improve their abilities in that area, and relevant learning resources or training activities will be recommended based on the child's performance to promote the overall improvement of the child's cognitive ability.

[0208] Although the preferred embodiments of the present invention have been described in detail, it is still possible for those skilled in the art to make further adjustments and improvements to these embodiments after understanding their basic innovative concepts. Therefore, the appended claims are intended to cover these preferred embodiments, as well as all changes and modifications within the scope of the present invention. The foregoing is only an example of the preferred embodiments of the present invention and does not constitute a limitation thereto. It should be clear that any modification, equivalent substitution or improvement made under the guidance of the spirit and principles of the present invention should be deemed to be included in the scope of protection of the present invention.

Claims

1. A method for evaluating children's brain and cognitive development ability, characterized by: include: Receive user identification information, including age, gender, and education level; Construct an assessment model covering six primary dimensions and eight secondary dimensions. The primary dimensions include memory, self-control, thinking ability, reaction ability, attention, and spatial ability. The secondary dimensions include short-term memory, working memory, inhibitory control, task switching, processing speed, logical thinking, concentration, and visual perception space. Randomly extract a target game task set from the task library, where the task set covers at least three first-level dimensions of assessment requirements; during task execution, randomly select game tasks from the second-level dimensions for assessment, and collect behavioral data on children during the tasks, including reaction time, accuracy, and strategy selection; Constructing a normative data table based on behavioral data of children of different age groups, wherein the normative data table includes the mean and standard deviation of each dimension; Calculating the task score, the first-level dimension score, and the total score based on the behavioral data, and converting the scores into percentiles based on the norm data table, and dividing them into six levels; Memory includes two dimensions: short-term memory and working memory; self-control includes inhibitory control and task switching; thinking ability corresponds to logical thinking; reaction ability corresponds to processing speed; attention corresponds to concentration; spatial ability corresponds to visual perception space; Based on the pre-trained cognitive ability assessment model, the collected data is fused with multi-dimensional features to generate ability vectors including memory index, self-control index, thinking index, reaction index, attention index, and spatial ability index; Comparing and analyzing the ability vector with a normative database, and outputting an assessment report containing grade symbols; the normative database is a standardized database constructed based on behavioral data of children of different age groups, used to measure whether the child's performance in various cognitive dimensions meets the average level of their peers; the normative database includes the following contents: age segmentation, the normative database is segmented according to the child's age, and each age group corresponds to different normative data; the mean and standard deviation of each dimension: for each primary dimension and / or secondary dimension, the database stores the mean and standard deviation of the dimension in a specific age group; and recommending targeted training tasks based on the grade and score; The training process of the pre-trained cognitive ability assessment model includes: collecting a historical user operation dataset annotated with ability level labels; constructing a deep neural network model with an input layer receiving a coordinate sequence of operation trajectories and a hidden layer containing LSTM units for extracting temporal features; and optimizing the model using a contrastive loss function that calculates the difference between the predicted ability level and the expert annotations. The collected data includes the spatiotemporal characteristics of the touch operation trajectory, the task completion progress and the number of interruptions, and the frequency statistics of false triggering of interference items. The multi-dimensional feature fusion steps specifically include: Perform wavelet transform on spatiotemporal features to extract frequency domain features of operation trajectories; The task completion progress is quantified as the time efficiency coefficient, and the calculation formula is: Where T 基准 is the normative average completion time, T 实际 The actual time consumed by users; weighted fusion of multi-source features to generate a comprehensive ability score.

2. A method for evaluating children's brain and cognitive development ability according to claim 1, characterized in that: The personalized recommendation engine recommends targeted training tasks based on levels and scores. The personalized recommendation engine is configured to: parse abnormal dimensions in the ability level tags; match intervention plans from the strategy knowledge base, which stores a list of training activities associated with each level interval; and generate an executable plan that includes gamified training task recommendations and family education guidance.

3. A method for evaluating children's brain and cognitive development ability according to claim 1, characterized in that: The dynamic adjustment of the difficulty of the training task includes: when it is detected that the user's operation accuracy is higher than the first threshold for three consecutive times, the task difficulty level is increased, and the increase includes increasing the stimulus movement speed or reducing the effective response time window; when it is detected that the user's error rate exceeds the second threshold, the auxiliary prompt mechanism is activated, and the mechanism includes highlighting the target area or extending the task time limit.

4. A method for evaluating children's brain and cognitive development ability according to claim 1, characterized in that: An age-specific norm database is constructed, which stores the average reaction time and standard deviation of users of each age group in the preset tasks; the Z algorithm is used to convert the user's original score, and the calculation formula is: Where X is the user score, μ is the norm mean of the corresponding age group, σ is the norm standard deviation; Z is the percentile, which generates the ability level mark.

5. A method for evaluating children's brain and cognitive development ability according to claim 2, characterized in that: The method for constructing the strategy knowledge base involves building a comprehensive ability training framework and establishing a dynamic mapping relationship between ability types and intervention measures, including multi-level and graded training programs; the framework covers multiple dimensions such as short-term memory, working memory, inhibitory control, task switching, processing speed, logical thinking, concentration and visual perceptual space, and provides personalized training intensity, task complexity and auxiliary mechanisms for different levels.

6. A method for evaluating children's brain and cognitive development ability according to claim 1, characterized in that: The fusion of collected data and multi-dimensional features is a method for comprehensively evaluating user capabilities. By collecting data from different dimensions and extracting features, a score that reflects the user's overall capabilities is generated. Specifically: Data collection: The spatiotemporal characteristics of touch operation trajectories, recording all operation paths and time information when the user touches the screen; including the user's gestures, sliding speed, and acceleration, reflecting the user's operation proficiency, coordination ability, and reaction speed; Task completion progress and interruptions: Monitors the user's progress in completing specific tasks, including the difference between the time required to complete the task and the expected time, as well as the number of interruptions during the task, to assess the user's task processing efficiency and concentration; Frequency statistics of false triggering of distracting items: This records the frequency with which users trigger options or functions unrelated to the current task during operation, reflecting the user's concentration and accuracy in complex task environments; Feature extraction and processing: Perform wavelet transform on spatiotemporal features: convert the collected spatiotemporal feature data from the time domain to the frequency domain; help analyze the rhythm and pattern of user operations; Quantification of task completion progress: converting task completion progress into time efficiency coefficient; This coefficient can quantify the efficiency of users completing tasks. The larger the value, the faster the task is completed and the higher the efficiency. Multi-dimensional feature fusion: Using the attention mechanism, we dynamically assign weights to data in each dimension based on the importance of different features. Generate comprehensive ability score: Generate a comprehensive score by weighted fusion of processed multi-dimensional data; This score can comprehensively reflect the user's comprehensive ability in terms of operational proficiency, task efficiency, and concentration.

7. A method for evaluating children's brain and cognitive development ability according to claim 1, characterized in that: Desensitize user identity information and generate an anonymous unique identifier; Homomorphic encryption technology is used to encrypt and transmit operation trajectory data; Establish a two-way authentication channel between the local storage device and the cloud server.

8. A children's brain and cognitive development ability assessment system, characterized by: A method for evaluating children's brain intelligence and cognitive development ability according to any one of claims 1 to 7 is used; comprising: User interface module, used to display game tasks and feedback reports, supporting touch interaction; A task evaluation module, configured to execute game tasks and record behavioral data, wherein the tasks are randomly selected according to the secondary dimension; Data analysis module, used to calculate percentiles and ranks based on the norm data table and generate multidimensional scores; Report generation module, used to integrate radar charts, grade marks and suggestions to generate visual reports; The background management module is used to store user data, configure system parameters and manage the training task library.

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