A digital training method, system, computer and storage medium for targeting cognitive enhancement

Abstract

The application provides a digital training method, system, computer and storage medium for targeted improvement of cognitive ability, and belongs to the technical field of cognitive ability training. Firstly, the cognitive ability is made to form a corresponding relationship with a specific cognitive direction and a brain area; secondly, each cognitive ability in a cognitive evaluation result is evaluated and sorted according to a specific cognitive direction damage degree; thirdly, a specific one-way cognitive ability is distributed to different cognitive training modules according to a contribution weight; fourthly, each cognitive ability in the cognitive evaluation result is distributed to different cognitive training modules according to the contribution weight; fifthly, when the specific cognitive direction damage degree is ranked in the top three, a training game of the corresponding brain area is found; and sixthly, a training analysis report is generated according to the completion of the training game, and the training process is displayed on a device terminal. The technical problem that the elderly dementia patients cannot realize targeted training when performing cognitive training is solved.

Description

Technical Field

[0001] This application relates to a training method, and more particularly to a digital training method, system, computer, and storage medium for targeted improvement of cognitive abilities, belonging to the field of cognitive ability training technology. Background Technology

[0002] Statistics show that the number of people suffering from Alzheimer's disease worldwide has reached 57 million, and is projected to reach 131 million by 2050. On average, one person is diagnosed with dementia every 3.2 seconds. Clearly, the continuously rising number of patients constitutes a serious social problem in my country.

[0003] Alzheimer's disease is a slowly progressing neurodegenerative disease. From the initial appearance of memory decline and cognitive changes to a final diagnosis of Alzheimer's, it takes an average of 12-17 years. Currently, there are no effective drugs to treat this disease. Research shows that effective lifestyle interventions and training during this period can effectively delay or prevent the onset and progression of the disease. Scientific research has proven that stimulating different brain regions through various methods can alter the function of those regions. By activating deteriorating brain regions and inhibiting overexcited brain regions, prevention and treatment of various neuropsychiatric disorders can be achieved. However, how to translate this into clinical practice remains unclear. With the development of mobile internet software technology and big data mining technology, the integration of brain science into modern software and big data analysis systems has given rise to a new disease prevention and treatment method: Digital Therapeutics (DTx). Digital therapy refers to software-driven, evidence-based intervention programs used to treat, manage, or prevent diseases. Digital therapy can be used alone or in combination with drugs, medical devices, or other therapies. Digital therapy offers advantages such as high operability, no direct physical harm, and good compliance. Digital therapy is an emerging field, and therefore there is currently no standardized management system in place.

[0004] Although it is known that activating different brain regions can improve brain function and thus enhance cognitive function in Alzheimer's patients, there is currently no scientifically developed training program available for patients with memory decline. Summary of the Invention

[0005] A brief overview of the invention is given below to provide a basic understanding of certain aspects of it. It should be understood that this overview is not an exhaustive summary of the invention. It is not intended to identify key or essential parts of the invention, nor is it intended to limit the scope of the invention. Its purpose is merely to present certain concepts in a simplified form as a prelude to the more detailed description that follows.

[0006] In view of this, in order to solve the technical problems existing in the prior art that the cognitive training of Alzheimer's patients cannot achieve targeted training and cannot dynamically revise the training plan in real time according to the degree of cognitive improvement to achieve personalized training, the present invention provides a digital training method, system, computer and storage medium for targeted improvement of cognitive ability.

[0007] Option 1: This invention provides a digital training method for targeted improvement of cognitive abilities, comprising the following steps:

[0008] Step 1: Assess cognitive abilities based on cognitive test results, match specific cognitive directions with cognitive abilities, and match brain regions with specific cognitive directions to establish a correspondence between cognitive abilities and specific cognitive directions and brain regions.

[0009] Step 2: Assess and rank each cognitive ability in the cognitive assessment results and the degree of impairment in a specific cognitive area;

[0010] Step 3: Assign specific unidirectional cognitive abilities and each training game to different cognitive training modules according to their contribution weight;

[0011] Step 4: Assign the contribution weight of each cognitive ability in the cognitive assessment results to different cognitive training modules. Based on the degree of damage in the specific cognitive direction described in Step 2, find the training game corresponding to the brain region in the top three specific cognitive directions.

[0012] Step 5: Based on the completion status of the training game, generate a training analysis report and display the training process on the device terminal;

[0013] Step 6: Store the analysis report for each training session, which can be used to retrieve and generate a comprehensive analysis report on the correlation between multiple training sessions and the improvement in cognitive ability in real time.

[0014] Preferably, the specific method for assessing the degree of impairment of each cognitive ability and specific cognitive direction in step 2 is as follows: the sum of the actual scores of one or more individual cognitive ability tests (X1+X2+…Xn) is divided by the sum of the full scores of individual abilities (N1+N2+…Nn) to calculate the percentage of the score for that item, i.e., the remaining cognitive ability Y = (X1+X2+…Xn) / (N1+N2+…Nn); the degree of impairment (f) for that item is obtained by subtracting this percentage from 100.

[0015] The specific method for ranking the degree of impairment of each cognitive ability and specific cognitive direction in step 2 is to rank the degree of impairment of each individual specific cognitive direction according to the degree of impairment value, and define them as F1, F2, F3...Fn in order from severe to mild.

[0016] Preferably, the specific method for allocating specific unidirectional cognitive abilities to different cognitive training modules according to contribution weights in step 3 includes the following steps:

[0017] Step 31: Name the games in the training library as A, B, C, D, E, F…;

[0018] Step 32: For each game, evaluate the brain regions activated by detecting the strength of the T2-weighted signal on functional magnetic resonance imaging (fMRI). The T2-weighted signal strength, from strongest to weakest, is defined as A1, A2, A3…An; B1, B2, B3…Bn; C1, C2, C3…Cn.

[0019] Step 33: Standardize the sum of the signal strengths of the top three games for each game to 1, and calculate the weights of X1, X2, and X3 respectively: x1 = X1 / (X1+X2+X3); x2 = X2 / (X1+X2+X3); x3 = X3 / (X1+X2+X3); x1+x2+x3 = 1;

[0020] Step 34: Assign these weights x1, x2, and x3 to the brain regions corresponding to the X1, X2, and X3 signals, thereby activating the contribution value of the brain regions;

[0021] Step 35: By matching brain regions with corresponding cognitive function directions, this contribution value is allocated to different cognitive directions to calculate the contribution weight of each game's corresponding cognitive function; the cognitive function direction corresponding to x1 is used as the primary direction and assigned to the corresponding cognitive training module.

[0022] Preferably, the specific method for assigning contribution weights to each cognitive ability in the cognitive assessment results and finding the corresponding brain region training game in step 4 is as follows:

[0023] The individual cognitive directions corresponding to the damage levels F1, F2 and F3 determined in the second step are put into the cognitive training game database to fish out the corresponding training games in the cognitive training module.

[0024] For the cognitive training direction corresponding to F1, the target cognitive direction corresponding to F1 is fished out within its corresponding cognitive training module, and its contribution value is x. (max) The corresponding game;

[0025] For the target cognition training direction corresponding to F2, fish out the cognitive directions that simultaneously contain training directions corresponding to F1 or F3 and contribute x to F2 within its corresponding target cognition training module. (max) The corresponding game;

[0026] For the cognitive training direction corresponding to F3, fish out cognitive directions that simultaneously contain training for F1 or F2 and contribute x to F3 within its corresponding cognitive training module. (max) The corresponding game.

[0027] Preferably, the specific display method of the terminal device in step 5 is text, pictures, voice, or any combination of text, pictures, and voice.

[0028] Preferably, the number of training games can be dynamically increased or decreased, with no limit on the number.

[0029] Option 2: A digital training system for targeted improvement of cognitive abilities, which is a system for implementing the digital training method for targeted improvement of cognitive abilities in Option 1. The system includes a cognitive ability assessment database and assessment device, a cognitive training game database and training device, a training effect report generation device, a cognitive assessment report generation device, an AI analysis device for cognitive training schemes based on cognitive assessment ability, and a dynamic cognitive training scheme push device.

[0030] The cognitive ability assessment database and assessment device are used to store multiple neuropsychological assessment scales;

[0031] The cognitive training game database and training device are used to store multiple cognitive training modules and cognitive training games;

[0032] The training effect report generation device is used to generate a training effect report;

[0033] The cognitive assessment report generation device is used to generate cognitive assessment reports;

[0034] The AI ​​analysis device for the cognitive training scheme based on cognitive assessment ability direction is used to weight and calculate the weight of the degree of impairment in each cognitive direction in the cognitive assessment results and the contribution value of the cognitive training game to the improvement of the cognitive direction, so as to achieve a targeted cognitive training effect based on the cognitive impairment direction.

[0035] The dynamic cognitive training program push device is used to display the cognitive assessment and training process and training results on the terminal device, and is used for real-time dynamic push, storage and retrieval based on the cognitive assessment results.

[0036] Specifically, the cognitive ability assessment database and assessment device are also used to classify neuropsychological assessment scales into multiple functions based on different ways of cognitive function performance; at the same time, each function is associated with a different brain region, so that the test score results can reflect the state of cognitive function and brain region function simultaneously.

[0037] Preferably, the cognitive training module includes a memory module, a judgment module, an abstract thinking module, an attention module, a calculation module, a visuospatial ability module, an executive ability module, a language ability module, a naming cognition module, and an orientation module.

[0038] Option 3: A computer, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the digital training method for targeted improvement of cognitive abilities described in Option 1.

[0039] Option 4: A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the digital training method for targeted improvement of cognitive abilities described in Option 1.

[0040] The beneficial effects of this invention are as follows: This invention provides a method and system for dynamic targeted training of cognitive function based on cognitive direction impairment. It automatically combines multiple games from multiple modules according to the contribution of training games to improving cognitive direction, calculating a training content combination that matches the direction and degree of cognitive impairment. Based on assessment scores, the severity is determined, training difficulty and time are calculated, and comprehensive training content and plans are provided. This achieves a cyclical training process: pre-training assessment of cognitive function – push of cognitive training plan – post-training evaluation of cognitive function – push of updated cognitive training plan – re-evaluation after updated cognitive function training. The training process and results are dynamically stored and retrieved on PCs, tablets, mobile phones, and other terminal devices, enabling dynamic management of targeted cognitive ability training. This invention enables real-time dynamic training, evaluation, and management of individual cognitive abilities anytime, anywhere. The training process is time-saving and labor-saving, combining scientific rigor and engaging elements to achieve dynamic, targeted cognitive function training. It solves the problem that Alzheimer's patients cannot achieve targeted training during cognitive training and cannot dynamically revise training plans in real-time based on the degree of cognitive improvement to achieve personalized training. Attached Figure Description

[0041] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0042] Figure 1 This is a flowchart of the method of the present invention;

[0043] Figure 2 This is a diagram of the cognitive training module and game database structure of the present invention;

[0044] Figure 3This is a schematic diagram illustrating the principle of evaluating activated brain regions during game implementation based on fMRI, as per the present invention.

[0045] Figure 4 This is a schematic diagram of the cognitive training game affiliation training module of the present invention. Detailed Implementation

[0046] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0047] Example 1, Reference Figure 1-4 This embodiment describes a digital training method for targeted improvement of cognitive abilities, comprising the following steps:

[0048] Step 1: Assess cognitive abilities based on cognitive test results, match specific cognitive directions with cognitive abilities, and match brain regions with specific cognitive directions to establish a correspondence between cognitive abilities and specific cognitive directions and brain regions.

[0049] Specifically, the cognitive assessment is the MMSE test scale; that is, the Brief Mental State Scale.

[0050] Specifically, the cognitive dimensions assessed by the MMSE test scale include orientation, memory, attention and calculation, language ability, and overall executive function.

[0051] Specifically, the severity is determined based on the assessment score, the training difficulty and time are calculated, and a comprehensive training plan is given in combination with the training content.

[0052] Step 2: Assess and rank each cognitive ability in the cognitive assessment results and the degree of impairment in a specific cognitive area;

[0053] Specifically, the assessment method for the degree of impairment in a specific cognitive direction is as follows: The sum of the actual scores of one or more individual cognitive ability tests (X1+X2+…Xn) is divided by the sum of the full scores of each individual ability (N1+N2+…Nn) to calculate the percentage score for that item, i.e., the residual cognitive ability Y = (X1+X2+…Xn) / (N1+N2+…Nn). This percentage is then subtracted from 100 to obtain the degree of impairment (f) for that item. Each specific cognitive direction is ranked according to its degree of impairment, from most severe to least severe, and defined as F1, F2, F3…Fn. Cognitive directions with impairment levels of F1, F2, and F3 are selected as the directions requiring training. The calculation formula is: f = (1-(X1+X2+…Xn) / (N1+N2+…Nn).

[0054] Specifically, the scoring method for the degree of impairment in a specific cognitive area is as follows: the scores of the three tests are added together, and if the sum is less than 70% of the total score (this standard will be adjusted at any time based on big data statistical results), it is judged as a decline in this function (but this standard is revised after a large number of sample analyses).

[0055] For example: Orientation: Total score of 30 points in 3 tests, actual score of 25 points, representing 83% of the total score; Memory: Total score of 18 points in 3 tests, actual score of 6 points, representing 33.3% of the total score; Attention and Calculation: Total score of 15 points in 3 tests, actual score of 5 points, representing 46.6% of the total score; Language Ability: Total score of 12 points in 3 tests, actual score of 9 points, representing 75% of the total score; Comprehensive Executive Ability: Total score of 15 points in 3 tests, actual score of 9 points, representing 60% of the total score. The assessment results for cognitive impairment, from most severe to least severe, are: Memory (33.3%), Calculation (46.6%), and Executive Ability (60%).

[0056] Step 3: Assign specific unidirectional cognitive abilities and each training game to different cognitive training modules based on their contribution weight; this includes the following steps:

[0057] Step 31: Name the games in the training library as A, B, C, D, E, F…;

[0058] Step 32: For each game, evaluate the brain regions activated by detecting the strength of the T2-weighted signal on functional magnetic resonance imaging (fMRI). The T2-weighted signal strength, from strongest to weakest, is defined as A1, A2, A3…An; B1, B2, B3…Bn; C1, C2, C3…Cn.

[0059] Step 33: Standardize the sum of the signal strengths of the top three games for each game to 1, and calculate the weights of X1, X2, and X3 respectively: x1 = X1 / (X1+X2+X3); x2 = X2 / (X1+X2+X3); x3 = X3 / (X1+X2+X3); x1+x2+x3 = 1;

[0060] Step 34: Assign these weights x1, x2, and x3 to the brain regions corresponding to the X1, X2, and X3 signals, thereby activating the contribution value of the brain regions;

[0061] Step 35: By matching brain regions with corresponding cognitive function directions, this contribution value is allocated to different cognitive directions to calculate the contribution weight of each game's corresponding cognitive function; the cognitive function direction corresponding to x1 is used as the primary direction and assigned to the corresponding cognitive training module.

[0062] Specifically, the cognitive training module includes a memory module, a judgment module, an abstract thinking module, an attention module, a calculation module, a visuospatial ability module, an executive ability module, a language ability module, a naming cognition module, and an orientation module.

[0063] Step 4: Assign the contribution weight of each cognitive ability in the cognitive assessment results to different cognitive training modules. Based on the degree of damage in the specific cognitive direction described in Step 2, find the training game corresponding to the brain region in the top three specific cognitive directions.

[0064] The individual cognitive directions corresponding to the damage levels F1, F2 and F3 determined in the second step are put into the cognitive training game database to fish out the corresponding training games in the cognitive training module.

[0065] For the cognitive training direction corresponding to F1, the target cognitive direction corresponding to F1 is fished out within its corresponding cognitive training module, and its contribution value is x. (max) The corresponding game;

[0066] For the target cognition training direction corresponding to F2, fish out the cognitive directions that simultaneously contain training directions corresponding to F1 or F3 and contribute x to F2 within its corresponding target cognition training module. (max) The corresponding game;

[0067] For the cognitive training direction corresponding to F3, fish out cognitive directions that simultaneously contain training for F1 or F2 and contribute x to F3 within its corresponding cognitive training module. (max) The corresponding game.

[0068] Specifically, based on the contribution of training games to improving cognitive directions, multiple games in multiple modules are automatically combined to calculate a combination of training content that matches the degree of cognitive impairment.

[0069] Specifically, the number of training games can be dynamically increased or decreased, with no limit on the number.

[0070] Specifically, among the selected training games, two combinations of three games each were chosen, with each game in each combination containing at least two training elements related to cognitive impairment.

[0071] Based on the scale test results, the basic task quantity to be completed in game training is specified in sequence. The scale test results are divided into severe, moderate, and mild, MCI.

[0072] Complete 1-2 levels of the game with heavy difficulty; complete 1-3 levels with moderate difficulty; complete 1-4 levels with light difficulty; and complete 1-4 levels with MCI. Each effective training session lasts 30 minutes, twice a day, with at least a 4-hour interval between sessions. The game combinations for each of the two daily training sessions are randomly assigned, but the same two game combinations must be completed each day. A questionnaire test is conducted after 30 days of training. New game combinations are then assigned based on the questionnaire test results.

[0073] Step 5: Based on the completion status of the training game, generate a training analysis report and display the training process on the device terminal;

[0074] Specifically, the terminal device in step 5 can display text, images, voice, or any combination of text, images, and voice.

[0075] Specifically, the terminal device is a PC, tablet, or mobile phone; the testing process and results are implemented through software on PCs, tablets, mobile phones, and all other mobile devices.

[0076] Step 6: Store the analysis report for each training session, which can be used to retrieve and generate a comprehensive analysis report on the correlation between multiple training sessions and the improvement in cognitive ability in real time.

[0077] Example 2, Reference Figure 1-4 This embodiment describes a digital training system for targeted improvement of cognitive abilities, which is a system for implementing the digital training method for targeted improvement of cognitive abilities in Scheme 1. The system includes a cognitive ability assessment database and assessment device, a cognitive training game database and training device, a training effect report generation device, a cognitive assessment report generation device, an AI analysis device for cognitive training schemes based on cognitive assessment ability, and a dynamic cognitive training scheme push device.

[0078] The cognitive ability assessment database and assessment device are used to store multiple neuropsychological assessment scales;

[0079] The cognitive training game database and training device are used to store multiple cognitive training modules and cognitive training games;

[0080] The training effect report generation device is used to generate a training effect report;

[0081] The cognitive assessment report generation device is used to generate cognitive assessment reports;

[0082] The AI ​​analysis device for the cognitive training scheme based on cognitive assessment ability direction is used to weight and calculate the weight of the degree of impairment in each cognitive direction in the cognitive assessment results and the contribution value of the cognitive training game to the improvement of the cognitive direction, so as to achieve a targeted cognitive training effect based on the cognitive impairment direction.

[0083] The dynamic cognitive training program push device is used to display the cognitive assessment and training process and training results on the terminal device, and is used for real-time dynamic push, storage and retrieval based on the cognitive assessment results.

[0084] Specifically, the cognitive ability assessment database and assessment device are also used to classify neuropsychological assessment scales into multiple functions based on different ways of cognitive function performance; at the same time, each function is associated with a different brain region, so that the test score results can reflect the state of cognitive function and brain region function simultaneously.

[0085] Specifically, the cognitive training module includes a memory module, a judgment module, an abstract thinking module, an attention module, a calculation module, a visuospatial ability module, an executive ability module, a language ability module, a naming cognition module, and an orientation module.

[0086] The computer device of the present invention may include a processor and a memory, such as a microcontroller containing a central processing unit. Furthermore, when the processor executes the computer program stored in the memory, it implements the steps of the aforementioned recommendation method for modifyable relationship-driven recommendation data based on CREO software.

[0087] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0088] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0089] Computer-readable storage medium embodiments

[0090] The computer-readable storage medium of the present invention can be any form of storage medium that can be read by the processor of a computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. The computer-readable storage medium stores a computer program. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-described modeling method for modifyable relation-driven modeling data based on CREO software can be implemented.

[0091] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0092] Although the invention has been described with reference to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and instructional purposes, and not for the purpose of interpreting or limiting the subject matter of the invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the invention is illustrative and not restrictive, and the scope of the invention is defined by the appended claims.

Claims

1. A digital training method for targeted cognitive enhancement, characterized in that, The cognitive ability evaluation database and evaluation device, the cognitive training game database and training device, the training effect report generation device, the cognitive evaluation report generation device, the cognitive training scheme AI analysis device based on the cognitive evaluation ability direction, and the dynamic cognitive training scheme pushing device are included. The cognitive ability evaluation database and evaluation device is used for storing a plurality of neuropsychological evaluation scales. The cognitive training game database and training device is used for storing a plurality of cognitive training modules and cognitive training games. The training effect report generation device is used for generating a training effect report. The cognitive evaluation report generation device is used for generating a cognitive evaluation report. The cognitive training scheme AI analysis device based on the cognitive evaluation ability direction is used for performing weight distribution and calculation on the weight of the damage degree of each cognitive direction in the cognitive evaluation result and the contribution value of the cognitive training game to the improvement of the cognitive direction, so as to realize the targeted cognitive training effect based on the cognitive damage direction. The dynamic cognitive training scheme pushing device is used for displaying the cognitive evaluation and training process and the training result on a terminal device, and is used for real-time dynamic pushing, storage and retrieval based on the cognitive evaluation result. Step 1, according to the cognitive evaluation result, the cognitive ability is evaluated, a specific cognitive direction is matched according to the cognitive ability, and a brain area is matched according to the specific cognitive direction, so that the cognitive ability, the specific cognitive direction and the brain area form a corresponding relationship. Step 2, each cognitive ability in the cognitive evaluation result is evaluated and sorted according to the damage degree of the specific cognitive direction, and the specific method is that the sum (X1+X2+…Xn) of the actual scores of one or more single cognitive abilities is divided by the sum (N1+N2+…Nn) of the full scores of the single abilities, to calculate the score percentage of each cognitive ability, that is, the residual cognitive ability Y=(X1+X2+…Xn) / (N1+N2+…Nn); 100 is subtracted from the percentage to obtain the damage degree value f of each cognitive ability; according to the damage degree value, each single specific cognitive direction is sorted according to the damage degree, and according to the damage degree from heavy to light, F1, F2, F3…Fn are defined in turn, the cognitive directions with damage degrees F1, F2 and F3 are selected as the directions needing training, and the calculation formula is: f =(1-(X1+X2+…Xn)) / (N1+N2+…Nn); for the scoring method of the damage degree of the specific cognitive direction, the scores of three tests are added, and if the score is lower than 70% of the total score, it is determined that the function is decreased, and the percentage will be adjusted in real time according to the big data statistical result; The specific method of step 2 is that each single specific cognitive direction is sorted according to the damage degree, and according to the damage degree from heavy to light, F1, F2, F3…Fn are defined in turn. Step 3, for the specific single cognitive ability, each training game is distributed to different cognitive training modules according to the contribution weight, and the method includes the following steps: Step 31, the games in the training library are named as A, B, C, D, E, F…; Step 32, each game is activated brain area evaluation by detecting the strength of T2-weighted functional magnetic resonance imaging signal; T2-weighted signal intensity from strong to weak, defined as A1, A2, A3…An; B1, B2, B3…Bn; C1, C2, C3…Cn in turn; Step 33, the sum of the signal intensity of the top three of each game is standardized to 1, and the weight of X1, X2, X3 is calculated respectively, x1=X1 / ( X1+X2+X3); x2=X2 / ( X1+X2+X3); x3=X3 / ( X1+X2+X3); x1+ x2+ x3=1; Step 34, the weight x1, x2, x3 is allocated to the brain area corresponding to X1, X2, X3 signal, that is, the contribution value of activated brain area; Step 35, by matching the brain area with the corresponding cognitive function direction, the contribution weight of each game corresponding to the cognitive function is calculated; the cognitive function direction corresponding to x1 is regarded as the main direction and is attributed to the matching cognitive training module; The cognitive training module includes memory ability module, judgment module, abstract thinking ability module, attention module, calculation ability module, visual spatial ability module, execution ability module, language ability module, naming cognitive ability module and directional force module; Step 4, each cognitive ability in the cognitive evaluation result is allocated to different cognitive training module according to the contribution weight, and the specific cognitive direction with the damage degree ranking in the top three in step 2 is found out to find the training game of the corresponding brain area, and the specific method is: According to the contribution size of the cognitive direction that can be improved by the training game, the multiple games in the multiple modules are automatically combined, and the training content combination matching the damage degree of the cognitive damage direction is calculated; The single cognitive direction corresponding to the damage degree F1, F2 and F3 determined in step 2 is put into the cognitive training game database, and the training game in the corresponding cognitive training module is fished; For the cognitive training direction corresponding to F1, the target cognitive direction corresponding to F1 is obtained in the cognitive training module corresponding to F1, and the contribution value is x (max) Corresponding game; For the target cognitive training direction corresponding to F2, in the target cognitive training module corresponding thereto, the cognitive direction corresponding to F1 or F3 is simultaneously fished, and the contribution value of F2 is x (max) Corresponding game; For the F3 corresponding cognitive training direction, in its corresponding cognitive training module to pick up at the same time contains training F1 or F2 corresponding cognitive direction and for F3 contribution value is x (max) Corresponding game; In the selected training game, two combinations containing three games are selected, and each game in each combination contains at least two training contents related to cognitive function damage; According to the scale test result, the basic task quantity required in game training is defined in turn, and the scale test result is divided into severe, moderate, mild and MCI; Severe complete game 1-2 levels; moderate complete game 1-3 levels; mild complete game 1-4 levels, MCI complete 1-4 levels; Each effective training time is 30 minutes, 2 times a day, and the interval between 2 times of training is at least 4 hours; the game combination of 2 times of training is randomly pushed every day; scale test is carried out once every 30 days of training; new game combination is pushed according to the scale test result; Step 5, generate training analysis report according to the completion of training game, and display the training process on the device terminal; Step 6, store each training analysis report for real-time retrieval and generate correlation comprehensive analysis report of multiple training and cognitive ability improvement degree.

2. The method of claim 1, wherein, The specific display mode of the device terminal in step 5 is text, picture or voice, or any combination of text, picture and voice.

3. The method of claim 2, wherein, The number of training games is dynamically increased or decreased without limitation.

4. A computer, characterized in that The computer program is stored in the memory and executed by the processor to realize the steps of the method in claim 1 or 2 or 3.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the method in claim 1 or 2 or 3.

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