Cognitive health management system and method based on cognitive age

Abstract

The invention discloses a cognitive health management system and method based on cognitive age. The system comprises a cognition evaluation unit used for carrying out cognition evaluation on a user; the data collection unit is used for collecting cognitive score data sets of subjects of different age groups in advance; the norm construction unit is used for constructing norm distribution corresponding to each age group; the age analysis unit is used for analyzing the cognitive score data to calculate a distance value between the cognitive score data and an average value of norm distribution of each age group; the data processing unit is used for carrying out normalization processing on all the distance values so as to output a probability value that the user belongs to each age group; the output unit is used for outputting the age group with the highest probability as the cognitive age of the user; and the feedback unit is used for feeding back personalized cognitive health maintenance and improvement suggestions to the user. The system has the advantages of being economical and practical, easy and convenient to operate, high in user acceptability, high in universality, wide in applicability, scientific, reliable and the like.

Description

Technical Field

[0001] This invention relates to a cognitive health management system based on cognitive age, and also to a corresponding cognitive health management method, belonging to the field of health management technology. Background Technology

[0002] Cognitive health management systems, by assessing individual cognitive levels and providing personalized interventions and health management, are of great significance in delaying cognitive neurodegeneration in the elderly and promoting cognitive development in children. They can detect at-risk individuals early and, through continuous monitoring and personalized interventions, improve the quality of life for the elderly and the learning abilities of children.

[0003] Existing cognitive health management systems have certain limitations. For example, some can only assess a user's cognitive level through scale tests and activities, generating written reports, but cannot directly calculate cognitive age. Cognitive age is an important indicator that directly reflects the difference between a user's cognitive function and their actual age, and is crucial for users to understand the assessment results.

[0004] In addition, there is a cognitive age prediction system based on MRI brain imaging data. This system uses MRI data to analyze the gray matter volume of multiple cognitive regions of the brain, which can scientifically and accurately predict cognitive age. However, this method relies on MRI equipment and specialized image processing technology, which is costly, complex to operate, and requires highly skilled technicians. Moreover, the MRI scanning process may cause discomfort and psychological stress to users, making it unsuitable for large-scale widespread application. Summary of the Invention

[0005] The primary technical problem to be solved by this invention is to provide a cognitive health management system based on cognitive age.

[0006] Another technical problem to be solved by the present invention is to provide a cognitive health management method based on cognitive age.

[0007] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0008] According to a first aspect of the present invention, a cognitive health management system based on cognitive age is provided, comprising:

[0009] The cognitive assessment unit has multiple preset cognitive training tasks to assess the user's cognition based on these tasks and to obtain the user's cognitive score data.

[0010] The data collection unit acquires cognitive score datasets of subjects in different age groups based on the preset multiple cognitive training tasks; wherein each cognitive score dataset consists of cognitive score data of multiple normal subjects in that age group;

[0011] The norm construction unit is connected to the data collection unit to construct different norm distributions based on cognitive score datasets for different age groups.

[0012] An age analysis unit is connected to the cognitive assessment unit to receive the user's cognitive score data; and the age analysis unit is also connected to the norm construction unit to calculate the distance between the user's cognitive score data and the average value of the norm distribution for each age group.

[0013] The data processing unit is connected to the age analysis unit to normalize all distance values ​​using the softmax function, thereby outputting the probability value of the user belonging to each age group.

[0014] An output unit, connected to the data processing unit, uses the age group with the highest output probability as the user's cognitive age.

[0015] A feedback unit, connected to the output unit, provides personalized suggestions for maintaining and improving cognitive health to the user based on the user's cognitive age.

[0016] Ideally, the norm distribution for each age group is constructed in the following way:

[0017] For any given age group, calculate the average M using the cognitive score data of all normal subjects within that age group. age and standard deviation SD age ;

[0018] Assuming that the cognitive scores of all normal subjects in this age group follow a normal distribution, based on the mean M age and standard deviation SD age Construct a norm distribution corresponding to this age group;

[0019] in,

[0020] N represents the number of subjects in each age group; i represents the subject number, i∈(1,N); X i This represents the cognitive assessment score of the i-th subject.

[0021] Preferably, the distance between the user's cognitive score data and the mean value of the normative distribution for each age group is calculated, specifically including:

[0022] The cognitive score data of the users are respectively substituted into the norm distribution corresponding to each age group;

[0023] The distance D between the user's cognitive score data and the mean of the norm for each age group is calculated using the cumulative distribution function |0.5-Φ(X)| of the normal distribution. age D age =|0.5-Φ(X)|;

[0024] The cumulative distribution function |0.5-Φ(X)| is used to calculate the probability that the random variable X is less than or equal to the input value Z. For the norm distribution of any age group, the formula for the cumulative distribution function is as follows:

[0025]

[0026] erf represents the error function, defined as:

[0027] Where Z represents the input value of the cumulative distribution function; y represents the input value of the error function; and t represents the integration variable, used to define the integration range of the error function, representing the variable in each small interval during the integration process.

[0028] A preferred approach is to use the softmax function to normalize all distance values, specifically including:

[0029] For any age group, the distance value D age The probability value P of the user belonging to this age group is calculated using the following formula.

[0030] Where M represents the total number of age groups; k represents the label of the age group, k∈(1,M); and e represents the exponential function with the natural number e as the base.

[0031] Preferably, the cognitive assessment unit includes:

[0032] The perception test module includes a time selection task corresponding to time perception, a paper-folding task corresponding to spatial perception, a random motion point task corresponding to motion perception, and a symbol search task corresponding to processing speed.

[0033] The attention test module includes pre-set tasks corresponding to alert attention, click / non-click tasks corresponding to focused attention, visual search tasks corresponding to selective attention, and cancellation tasks corresponding to sustained attention.

[0034] The memory test module includes a forward digit span task corresponding to verbal working memory and a backward digit span task corresponding to memory span.

[0035] The suppression control module is pre-set with side suppression tasks corresponding to interference control and digital comparison tasks corresponding to conflict suppression.

[0036] Ideally, the number of normal subjects in each age group is the same.

[0037] Preferably, the cognitive health management system is an APP installed on an electronic device or an accessible website set in a browser.

[0038] Preferably, the subjects of different age groups include normal children aged 6 to 12 years, with each year as one age group.

[0039] According to a second aspect of the present invention, a cognitive health management method based on cognitive age is provided, comprising the following steps:

[0040] Cognitive score datasets of subjects in different age groups are obtained based on a variety of preset cognitive training tasks; wherein each cognitive score dataset consists of cognitive score data of multiple normal subjects in that age group;

[0041] Based on cognitive score datasets for different age groups, different norm distributions were constructed.

[0042] The cognitive assessment of the user is carried out based on a variety of preset cognitive training tasks, and the cognitive score data of the user is obtained.

[0043] Calculate the distance between the user's cognitive score data and the mean value of the norm distribution for each age group;

[0044] The softmax function is used to normalize all distance values, thereby outputting the probability value of the user belonging to each age group;

[0045] The age group with the highest output probability is taken as the user's cognitive age;

[0046] Based on the user's cognitive age, provide the user with personalized suggestions for maintaining and improving cognitive health.

[0047] Preferably, the cognitive health management method further includes:

[0048] At preset intervals, a cognitive assessment is conducted on the user, and the user's corresponding cognitive age is obtained.

[0049] After a cognitive management cycle, the user is shown a curve showing the change in their cognitive age during that cycle.

[0050] Compared with the prior art, the present invention has the following technical effects:

[0051] (1) Economical and easy to operate

[0052] Low cost: Compared to methods that rely on MRI data, methods that calculate cognitive age based on behavioral data are less expensive and do not require expensive equipment or specialized operations.

[0053] Easy to operate: The data collection and analysis process is relatively simple, and ordinary users can easily understand and participate.

[0054] (2) Time efficiency

[0055] Rapid results: Compared to methods that require lengthy MRI scans and data processing, computational methods based on behavioral data can obtain cognitive age assessment results more quickly.

[0056] (3) High user acceptance

[0057] Non-invasive: The process of collecting behavioral data is user-friendly, requiring no scanning with medical equipment, thus reducing discomfort and anxiety.

[0058] Privacy protection: Compared to methods involving brain imaging data, behavioral data is easier to manage and control in terms of privacy protection, thus enhancing users' sense of privacy and security.

[0059] (4) Popularity and applicability

[0060] Potential for large-scale application: Due to its low cost, ease of operation and high efficiency, the method of calculating cognitive age based on behavioral data is suitable for large-scale application and can cover a wider range of users.

[0061] Wide applicability: Behavioral data reflects users' daily behavioral patterns and cognitive abilities, thus it is applicable to people of different ages and cognitive states, and has strong universality.

[0062] (5) Scientificity and reliability

[0063] Behavioral data analysis: Although it is an indirect measurement method, it can still provide relatively accurate and reliable cognitive age assessment results through scientific data processing and analysis techniques.

[0064] Supporting research: Existing studies have shown a certain correlation between behavioral data and cognitive function, supporting the scientific validity and feasibility of calculating cognitive age based on behavioral data. Attached Figure Description

[0065] Figure 1 A schematic diagram of a cognitive health management system based on cognitive age provided in the first embodiment of the present invention;

[0066] Figure 2 The flowchart illustrates a cognitive health management method based on cognitive age, as provided in the second embodiment of the present invention. Detailed Implementation

[0067] The technical content of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0068] This invention provides a cognitive health management system based on cognitive age, comprising two parts: cognitive assessment and cognitive age analysis. Cognitive assessment is used to evaluate and test various cognitive abilities. Through a series of assessment tools and methods, the system can acquire user performance data across different cognitive dimensions. This data includes, but is not limited to, perception, attention, memory, and executive function. Cognitive age analysis calculates the user's cognitive age by analyzing the data obtained during cognitive assessment. Cognitive age is derived by comparing the user's cognitive score data with the performance of a standard population. This module uses specific algorithms to process and analyze the data, ultimately generating a cognitive age report for the user. The entire cognitive health management system aims to help users understand their own cognitive health status and, through monitoring and managing cognitive age, provide personalized suggestions for cognitive health maintenance and improvement.

[0069] First Embodiment

[0070] like Figure 1 As shown, the first embodiment of the present invention provides a cognitive health management system based on cognitive age, including a cognitive assessment unit 1, a data collection unit 2, a norm construction unit 3, an age analysis unit 4, a data processing unit 5, an output unit 6, and a feedback unit 7. The cognitive assessment unit 1 is used to conduct cognitive assessments on users, while the data collection unit 2, norm construction unit 3, age analysis unit 4, data processing unit 5, output unit 6, and feedback unit 7 are used together to perform cognitive analysis on users. The cognitive assessment and cognitive analysis processes are described in detail below:

[0071] I. Cognitive Assessment

[0072] In this embodiment of the invention, the cognitive assessment unit 1 has multiple preset cognitive training tasks to assess the user's cognition based on these tasks and obtain the user's cognitive score data. Specifically, the configuration of the multiple cognitive assessment tasks is as follows:

[0073] 1. Perception and Sensory Testing Module

[0074] This is used to measure children's time perception, spatial perception, motion perception, and processing speed. Specific items include:

[0075] (1) Time selection task

[0076] The time selection task required participants to look at the screen. When a bomb icon appeared on the screen, participants began to count silently in seconds. After the bomb icon changed to an explosion icon, participants selected the option closest to their count from the given choices.

[0077] (2) Origami Task

[0078] Participants were asked to imagine a square piece of paper, fold it according to their imagination, then imagine making a hole in it, and finally unfold it back into a square. After unfolding, participants were asked to select how many holes would appear on the paper and their locations.

[0079] (3) Random motion point task

[0080] The task involves displaying multiple moving points on the screen, some of which move in a single direction, either left or right. Participants are instructed to observe the trajectories of these points, identify those moving in a single direction (left or right), and click the corresponding arrow button.

[0081] (4) Symbol search task

[0082] The screen is divided into two parts: the first part contains two shapes, and the second part contains five shapes. Participants are asked to determine whether any of the five shapes in the second part contain one of the two shapes from the first part. If yes, they click the "with text" button; otherwise, they click the "no text" button.

[0083] 2. Attention Test Module

[0084] This measurement is used to assess children's alertness, focused attention, selective attention, and sustained attention. Specific items include:

[0085] (1) Alertness tasks

[0086] Participants were asked to look at the numbers on the screen and, as soon as the numbers started to increase, to click the "stop" button as quickly as possible to stop the increase.

[0087] (2) Click / Don't click the task

[0088] Two different stimuli are presented consecutively on the screen. Participants click on the stimulus that meets the requirements of the prompt. For example, participants click anywhere on the screen when they see a tiger, but do not click when they see a giraffe.

[0089] (3) Visual search task

[0090] Participants judged whether a target object appeared in the displayed material and responded quickly and accurately by pressing buttons. For example, they judged whether a green line existed among many red lines on the screen.

[0091] (4) Cancel the task

[0092] Participants are required to memorize the target images appearing in the image matrix and click on them in order from top to bottom and from left to right. Missed images cannot be retried. The test lasts for 3 minutes.

[0093] Preferably, the size of the graphic matrix is ​​15*10.

[0094] 3. Memory Test Module

[0095] Used to measure children's verbal working memory and memory span. Specific items include:

[0096] (1) Digital breadth forward and backward task

[0097] Participants were asked to memorize the numbers presented in sequence and repeat them in order.

[0098] (2) Number breadth backwards task

[0099] The participants memorized the numbers presented in sequence and then repeated them in reverse order.

[0100] 4. Suppression Control Module

[0101] Used to measure children's interference control and conflict inhibition. Specific items include:

[0102] (1) Lateral inhibition task

[0103] Participants were asked to focus on the graphic in the center of the screen, determine its orientation, and then click the corresponding directional button based on that orientation.

[0104] (2) Number comparison task

[0105] Participants were instructed to repeatedly switch selection criteria according to rules to suppress habitual thinking. For example: "Observe the values ​​and sizes of the numbers on the left and right sides, and quickly select the corresponding numbers according to the questions. When switching questions, pay attention to the changes in the questions, and complete the task quickly and well."

[0106] II. Cognitive Age Analysis

[0107] In this embodiment, firstly, the cognitive score datasets of subjects in different age groups are pre-collected by the data collection unit 2. Then, the norm construction unit 3 constructs a norm distribution corresponding to each age group. Next, the age analysis unit 4 analyzes the user's cognitive score data to calculate the distance between the user's cognitive score data and the average value of the norm distribution for each age group. Then, the data processing unit 5 normalizes all distance values ​​to output the probability value of the user belonging to each age group. Finally, the output unit 6 outputs the age group with the highest probability as the user's cognitive age. Finally, based on the user's cognitive age, the feedback unit 7 provides the user with personalized suggestions for maintaining and improving cognitive health.

[0108] The following is a detailed explanation of each unit:

[0109] 1. Data Collection Unit

[0110] In this embodiment, the data collection unit 2 pre-collected cognitive score data of normal children aged 6 to 12 years. Each year is considered an age group, with at least 100 subjects in each age group. Preferably, the number of normal subjects in each age group is the same; however, in other embodiments, the number may differ.

[0111] Furthermore, it is understood that each child needs to undergo cognitive assessments based on the aforementioned multiple cognitive training tasks to obtain their cognitive assessment scores for each task, which will serve as their cognitive score data. Therefore, once all children of the same age have completed the cognitive assessments, their combined cognitive score data constitutes the cognitive score dataset for that age group. In this embodiment, it is necessary to obtain the cognitive score dataset corresponding to each age group.

[0112] 2. Normative building blocks

[0113] In this embodiment, the norm construction unit 3 is connected to the data collection unit 2 to construct different norm distributions based on cognitive score datasets of different age groups.

[0114] Specifically, the norm distribution for each age group is constructed in the following way:

[0115] For any given age group, calculate the average M using the cognitive score data of all normal subjects within that age group. age and standard deviation SD age ;

[0116] Assuming that the cognitive scores of all normal subjects in this age group follow a normal distribution, based on the mean M age and standard deviation SD age Construct a norm distribution corresponding to this age group;

[0117] in,

[0118] N represents the number of subjects in each age group; i represents the subject number, i∈(1,N); X i This represents the cognitive assessment score of the i-th subject.

[0119] It is understandable that, due to the average value M corresponding to each cognitive score dataset... age and standard deviation SD age Since they are all different, each score dataset corresponds to a normal distribution.

[0120] 3. Age Analysis Unit

[0121] In this embodiment, the age analysis unit 4 is connected to the cognitive assessment unit 1 to receive the user's cognitive score data. Furthermore, the age analysis unit 4 is also connected to the norm construction unit 3 to calculate the distance between the user's cognitive score data and the average value of the norm distribution for each age group. The specific calculation process is as follows:

[0122] First, the users' cognitive score data are fed into the norm distribution corresponding to each age group;

[0123] Then, the distance D between the user's cognitive score data and the mean of the norm for each age group is calculated using the cumulative distribution function |0.5-Φ(X)| of the normal distribution. age D age =|0.5-Φ(X)|.

[0124] The cumulative distribution function |0.5-Φ(X)| is used to calculate the probability that the random variable X is less than or equal to the input value Z. For the norm distribution of any age group, the formula for the cumulative distribution function is as follows:

[0125]

[0126] erf represents the error function, defined as:

[0127] Where Z represents the input value of the cumulative distribution function; y represents the input value of the error function; and t represents the integration variable, used to define the integration range of the error function, representing the variable in each small interval during the integration process.

[0128] 4. Data Processing Unit

[0129] In this embodiment, the data processing unit 5 is connected to the age analysis unit 4 to normalize all distance values ​​using the softmax function, thereby outputting the probability value of the user belonging to each age group. Specifically, the data processing process is as follows:

[0130] For any age group, the distance value D age The probability value P of a user belonging to this age group is calculated using the following formula.

[0131] Where M represents the total number of age groups; k represents the label of the age group, k∈(1,M); and e represents the exponential function with the natural number e as the base.

[0132] 5. Output Unit

[0133] In this embodiment, the output unit 6 is connected to the data processing unit 5, and the age group with the highest output probability is taken as the user's cognitive age.

[0134] Specifically, by indexing the multiple probability values ​​P to find the probability value P with the largest value, the algorithm finds the probability value P with the largest value. final P final =argmax(P). Where argmax represents the index corresponding to the category with the highest probability, that is, the age with the highest probability is output as the final analysis result.

[0135] 6. Feedback Unit

[0136] In this embodiment, the feedback unit 7 is connected to the output unit 6 to provide personalized suggestions for maintaining and improving cognitive health based on the user's cognitive age.

[0137] It is understood that in this embodiment, through the synergistic effect of the data collection unit 2, the norm construction unit 3, the age analysis unit 4, the data processing unit 5, the output unit 6 and the feedback unit 7, the cognitive analysis of the user is realized, thereby outputting the user's corresponding cognitive age and personalized suggestions for maintaining and improving cognitive health.

[0138] In the above embodiments, preferably, the cognitive health management system is an app installed on an electronic device or an accessible website accessible through a browser. This meets the needs of continuous health management in the internet age, including family intervention and follow-up, avoiding the need for children and parents to spend excessive time and energy, and eliminating the impact of factors such as on-site conditions and the trainer's operational standards on the effectiveness of cognitive management.

[0139] Second Embodiment

[0140] like Figure 2As shown, based on the first embodiment described above, the second embodiment of the present invention provides a cognitive health management method based on cognitive age, specifically including steps S1 to S7:

[0141] S1: Conduct cognitive assessments on users based on a variety of preset cognitive training tasks and obtain users' cognitive score data.

[0142] Specifically, in this embodiment, a variety of preset cognitive training tasks can acquire user performance data across different cognitive dimensions. This data includes, but is not limited to, perception, attention, memory, and executive function. The specific task configuration is described in the first embodiment for cognitive assessment unit 1, and will not be repeated here.

[0143] S2: Based on a variety of pre-set cognitive training tasks, obtain cognitive score datasets of subjects of different age groups.

[0144] In this embodiment, before conducting cognitive analysis on users, it is necessary to collect cognitive score datasets of subjects in different age groups. Each cognitive score dataset consists of cognitive score data from multiple normal subjects in that age group.

[0145] In this embodiment, the subjects were normal children aged 6 to 12 years, with each age group consisting of one year, and at least 100 subjects in each age group. Each child was assessed through multiple pre-set cognitive training tasks to obtain their score on each task, which served as their cognitive score data. Thus, the cognitive score data of all children in the same age group collectively constituted the cognitive score dataset for that age group.

[0146] S3: Construct different norm distributions based on cognitive score datasets for different age groups.

[0147] After obtaining the cognitive score datasets of subjects of different age groups based on step S2, a norm distribution needs to be constructed for each cognitive score dataset. In this embodiment, the specific process of constructing the norm distribution can be referred to the description of norm construction unit 3 in the first embodiment, and will not be repeated here.

[0148] S4: Calculate the distance between the user's cognitive score data and the mean value of the norm distribution for each age group.

[0149] After constructing different norm distributions based on step S3, the user's cognitive score data obtained in step S1 needs to be input into each norm distribution to calculate the distance value D between it and the mean of the norm distribution for each age group. age .

[0150] Among them, regarding the distance value Dage The specific calculation process can be referred to the description of age analysis unit 4 in the first embodiment, and will not be repeated here.

[0151] S5: The softmax function is used to normalize all distance values, thereby outputting the probability value of the user belonging to each age group.

[0152] When different distance values ​​D are calculated based on step S4 age Then, all distance values ​​need to be normalized, and the distance value D will be normalized. age This is converted into a probability value P. The specific normalization process can be found in the description of data processing unit 5 in the first embodiment, and will not be repeated here.

[0153] S6: Output the age group with the highest probability as the user's cognitive age.

[0154] S7: Provide users with personalized suggestions for maintaining and improving their cognitive health based on their cognitive age.

[0155] In the above embodiments, preferably, the cognitive health management method further includes:

[0156] S8: Periodic feedback.

[0157] Specifically, a cognitive management cycle is one month. A cognitive assessment is conducted on users every three days, and their corresponding cognitive age is obtained. Therefore, after one cognitive management cycle (i.e., one month), a curve showing the user's cognitive age change during that cycle is displayed.

[0158] Understandably, by periodically providing users with feedback on their cognitive age changes, it can help them better understand themselves, allow them to more intuitively perceive their own changes, help them build confidence, and improve their initiative and compliance in cognitive management.

[0159] In summary, the cognitive health management system and method based on cognitive age provided by the embodiments of the present invention have the following beneficial effects:

[0160] (1) Economical and easy to operate

[0161] Low cost: Compared to methods that rely on MRI data, methods that calculate cognitive age based on behavioral data are less expensive and do not require expensive equipment or specialized operations.

[0162] Easy to operate: The data collection and analysis process is relatively simple, and ordinary users can easily understand and participate.

[0163] (2) Time efficiency

[0164] Rapid results: Compared to methods that require lengthy MRI scans and data processing, computational methods based on behavioral data can obtain cognitive age assessment results more quickly.

[0165] (3) High user acceptance

[0166] Non-invasive: The process of collecting behavioral data is user-friendly, requiring no scanning with medical equipment, thus reducing discomfort and anxiety.

[0167] Privacy protection: Compared to methods involving brain imaging data, behavioral data is easier to manage and control in terms of privacy protection, thus enhancing users' sense of privacy and security.

[0168] (4) Popularity and applicability

[0169] Potential for large-scale application: Due to its low cost, ease of operation and high efficiency, the method of calculating cognitive age based on behavioral data is suitable for large-scale application and can cover a wider range of users.

[0170] Wide applicability: Behavioral data reflects users' daily behavioral patterns and cognitive abilities, thus it is applicable to people of different ages and cognitive states, and has strong universality.

[0171] (5) Scientificity and reliability

[0172] Behavioral data analysis: Although it is an indirect measurement method, it can still provide relatively accurate and reliable cognitive age assessment results through scientific data processing and analysis techniques.

[0173] Supporting research: Existing studies have shown a certain correlation between behavioral data and cognitive function, supporting the scientific validity and feasibility of calculating cognitive age based on behavioral data.

[0174] It should be noted that the above embodiments are merely illustrative examples. The technical solutions of each embodiment can be combined, and all are within the protection scope of this invention.

[0175] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0176] The cognitive health management system and method based on cognitive age provided by this invention have been described in detail above. Any obvious modifications made by those skilled in the art without departing from the essence of this invention will constitute an infringement of the patent rights of this invention and will incur corresponding legal liability.

Claims

1. A cognitive health management system based on cognitive age, characterized in that... include: The cognitive assessment unit has multiple preset cognitive training tasks to assess the user's cognition based on these tasks and to obtain the user's cognitive score data. The data collection unit acquires cognitive score datasets of subjects in different age groups based on the preset multiple cognitive training tasks; wherein each cognitive score dataset consists of cognitive score data of multiple normal subjects in that age group; The norm construction unit is connected to the data collection unit to construct different norm distributions based on cognitive score datasets for different age groups. An age analysis unit is connected to the cognitive assessment unit to receive the user's cognitive score data; and the age analysis unit is also connected to the norm construction unit to calculate the distance between the user's cognitive score data and the average value of the norm distribution for each age group. The data processing unit is connected to the age analysis unit to normalize all distance values ​​using the softmax function, thereby outputting the probability value of the user belonging to each age group. An output unit, connected to the data processing unit, uses the age group with the highest output probability as the user's cognitive age. A feedback unit, connected to the output unit, provides personalized suggestions for maintaining and improving cognitive health to the user based on the user's cognitive age.

2. The cognitive health management system as described in claim 1, characterized in that... The norm distribution for each age group was constructed as follows: For any given age group, calculate the average M using the cognitive score data of all normal subjects within that age group. age and standard deviation SD age ; Assuming that the cognitive scores of all normal subjects in this age group follow a normal distribution, based on the mean M age and standard deviation SD age Construct a norm distribution corresponding to this age group; in, N represents the number of subjects in each age group; i represents the subject number, i∈(1,N); X i This represents the cognitive assessment score of the i-th subject.

3. The cognitive health management system as described in claim 1, characterized in that... Based on the user's cognitive score data, the distance between the score and the mean of the norm distribution for each age group is calculated, specifically including: The cognitive score data of the users are respectively substituted into the norm distribution corresponding to each age group; The distance D between the user's cognitive score data and the mean of the norm for each age group is calculated using the cumulative distribution function |0.5-Φ(X)| of the normal distribution. age D age =|0.5-Φ(X)|; The cumulative distribution function |0.5-Φ(X)| is used to calculate the probability that the random variable X is less than or equal to the input value Z. For the norm distribution of any age group, the formula for the cumulative distribution function is as follows: erf represents the error function, defined as: Where Z represents the input value of the cumulative distribution function; y represents the input value of the error function; and t represents the integration variable, used to define the integration range of the error function, representing the variable in each small interval during the integration process.

4. The cognitive health management system as described in claim 1, characterized in that... The softmax function is used to normalize all distance values, specifically including: For any age group, the distance value D age The probability value P of the user belonging to this age group is calculated using the following formula. Where M represents the total number of age groups; k represents the label of the age group, k∈(1,M); and e represents the exponential function with the natural number e as the base.

5. The cognitive health management system as described in claim 1, characterized in that... The cognitive assessment unit includes: The perception test module includes a time selection task corresponding to time perception, a paper-folding task corresponding to spatial perception, a random motion point task corresponding to motion perception, and a symbol search task corresponding to processing speed. The attention test module includes pre-set tasks corresponding to alert attention, click / non-click tasks corresponding to focused attention, visual search tasks corresponding to selective attention, and cancellation tasks corresponding to sustained attention. The memory test module includes a forward digit span task corresponding to verbal working memory and a backward digit span task corresponding to memory span. The suppression control module is pre-set with side suppression tasks corresponding to interference control and digital comparison tasks corresponding to conflict suppression.

6. The cognitive health management system as described in any one of claims 1 to 5, characterized in that: The number of normal subjects in each age group was the same.

7. The cognitive health management system as described in claim 1, characterized in that: The cognitive health management system is an app installed on an electronic device or an accessible website set up in a browser.

8. The cognitive health management system as described in claim 1, characterized in that: The subjects in the different age groups included normal children aged 6 to 12 years, with each year representing one age group.

9. A cognitive health management method based on cognitive age, characterized in that... Includes the following steps: Cognitive score datasets of subjects in different age groups are obtained based on a variety of preset cognitive training tasks; wherein each cognitive score dataset consists of cognitive score data of multiple normal subjects in that age group; Based on cognitive score datasets for different age groups, different norm distributions were constructed. The cognitive assessment of the user is carried out based on a variety of preset cognitive training tasks, and the cognitive score data of the user is obtained. Calculate the distance between the user's cognitive score data and the mean value of the norm distribution for each age group; The softmax function is used to normalize all distance values, thereby outputting the probability value of the user belonging to each age group; The age group with the highest output probability is taken as the user's cognitive age; Based on the user's cognitive age, provide the user with personalized suggestions for maintaining and improving cognitive health.

10. The cognitive health management method as described in claim 9, characterized in that... Also includes: At preset intervals, a cognitive assessment is conducted on the user, and the user's corresponding cognitive age is obtained. After a cognitive management cycle, the user is shown a curve showing the change in their cognitive age during that cycle.

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