Cognitive function training processing method and system

Abstract

The embodiment of the invention provides a cognitive function training processing method and system, and relates to the technical field of medical big data, and the method comprises the steps: responding to a login operation of a target user, determining a training task of the target user in a current period according to a training plan list of the target user, displaying the training task on an operation interface of the user side; in response to a trigger operation on a training control on an operation interface of the user side, executing the training task of the current period according to a task trigger sequence, and displaying a task execution result of the training task of the current period; and synchronizing the task execution result to a medical care terminal, so that the medical care terminal displays the task execution result, the detail information of the training task corresponding to the target user and the specific execution condition of each training task, and adjusts the training task of the target user in the next period according to the task execution result. According to the technical scheme provided by the embodiment of the invention, the cognitive disorder training efficiency can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical big data, and in particular to a processing method for cognitive function training and a processing system for cognitive function training. Background Art

[0003] In the prior art, intervention methods are mostly traditional paper-and-pencil methods, which are inefficient, difficult to popularize, and have poor effects. Summary of the invention

[0004] The purpose of the present disclosure is to provide a processing method and system for cognitive function training, thereby overcoming the problems of low efficiency and difficulty in popularization of manual intervention due to limitations and defects of related technologies, at least to a certain extent.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a processing method for cognitive function training is provided, which is applied to a user end, and includes: responding to a login operation of a target user, determining a training task for the target user in a current cycle according to a training plan list for the target user, and displaying the training task on an operation interface of the user end; the training plan list is obtained by setting a medical end, and is allocated to the user end by the medical end; responding to a trigger operation on a training control on an operation interface of the user end, executing the training task of the current cycle in a task triggering sequence, and displaying the task execution result of the training task of the current cycle; synchronizing the task execution result to the medical end, so that the medical end displays the task execution result, detailed information of the training task corresponding to the target user, and the specific execution status of each training task, and adjusts the training task of the target user in the next cycle according to the task execution result.

[0007] In an exemplary embodiment of the present disclosure, the training task of the target user in the current cycle is determined according to the training plan list of the target user, including: if the current cycle does not have a previous cycle adjacent to it, the training task of the current cycle is determined according to the user information of the target user and the training plan list; if the current cycle has a previous cycle adjacent to it, the training task of the current cycle is determined according to the exercise execution status of the target user in the previous cycle and the training plan list.

[0008] In an exemplary embodiment of the present disclosure, the exercise execution status includes the evaluation score of the exercise and the execution status of the exercise; the training task of the current cycle is determined according to the exercise execution status of the target user in the previous cycle and the training plan list, including: determining the evaluation score of the exercise whose execution status is executed in the previous cycle, and determining the reinforcement exercise according to the evaluation score; determining multiple exercises whose execution status is not executed in the previous cycle according to the training plan list, and determining new exercises according to the historical execution times of the multiple unexecuted exercises; determining the training tasks corresponding to the reinforcement exercises and the training tasks corresponding to the new exercises as the training tasks of the current cycle.

[0009] In an exemplary embodiment of the present disclosure, determining the evaluation score of the exercises whose execution status is executed in the previous cycle includes: determining the task score of each training task based on the time score, result score and difficulty coefficient of each training task in the executed exercises; and determining the maximum value of the task scores as the evaluation score.

[0010] In an exemplary embodiment of the present disclosure, in response to a triggering operation on a training control on an operation interface of a user terminal, training tasks of the current cycle are executed in a task triggering order, including: determining at least two dimensions of the current cycle from a plurality of candidate dimensions determined according to user information of a target user in an ascending order of difficulty of the candidate dimensions; executing training tasks corresponding to at least two dimensions in an ascending order of difficulty of the at least two dimensions; executing training tasks corresponding to at least two dimensions in an ascending order of difficulty of the at least two dimensions, including: executing a plurality of training tasks corresponding to a target exercise in each dimension in an ascending order of difficulty of a plurality of exercises corresponding to each dimension; after the execution of the plurality of training tasks corresponding to the target exercise is completed, executing training tasks corresponding to other exercises in the dimension to which the target exercise belongs, and switching to training tasks corresponding to other dimensions when the training time of the dimension reaches the prescribed training time of the dimension.

[0011] In an exemplary embodiment of the present disclosure, executing multiple training tasks corresponding to the target exercise includes: executing multiple training tasks in the target exercise according to the order of task difficulty from low to high; if the current training task in the target exercise is executed successfully, switching to the next training task; if the current training task in the target exercise is executed unsuccessfully, continuing to execute the current training task, and if the number of times the current training task is executed unsuccessfully is greater than a number threshold, jumping to the next training task; if after the last training task under the target exercise is executed, the training time of the target exercise is less than the exercise time threshold, repeatedly executing the last training task until the exercise time threshold is reached.

[0012] In an exemplary embodiment of the present disclosure, the task execution results of the training tasks of the current cycle are displayed, including: if a training task is executed incorrectly, a corresponding reminder message is displayed on the operation interface, and the training task is re-executed in response to a trigger operation received by a redo control; after each training task is completed, one or more of the task execution time and result score of each training task is displayed; after all training tasks of the current cycle are completed, the training duration of the current cycle, the exercise duration of each exercise, and the exercise score of each exercise are displayed.

[0013] In an exemplary embodiment of the present disclosure, the task execution results, detailed information of the training tasks corresponding to the target users, and the specific execution status of each training task are displayed, including: in response to a first query operation on a patient data control, the task execution results of the training tasks corresponding to each target user are displayed; the task execution results include one or more of the training duration, achievement status, and training images of each cycle; in response to a second query operation on a data detail download control, the detailed information of the training tasks corresponding to each cycle of each target user is displayed; the detailed information includes one or more of the training task name, the dimension corresponding to the training task, the training task identifier, the start time, the end time, the training duration, the training score, and the training result; in response to a third query operation on an exercise data control, the name of each training task, the dimension corresponding to the training task, the training task identifier, the number of trainees, the number of people who passed, the task pass rate, the average pass time, the average training score, and the task score are displayed.

[0014] In an exemplary embodiment of the present disclosure, the training task of the target user in the next cycle is adjusted according to the task execution results, including: if the practice duration of any exercise in the task execution results is greater than the practice time threshold or the practice score is less than the score threshold, the difficulty level of the exercise in the next cycle is reduced; if the task execution time of the target training task in the task execution results is greater than the task execution time threshold or the result score is less than the score threshold, the difficulty level of the target training task in the next cycle is reduced.

[0015] According to one aspect of the present disclosure, a processing system for cognitive function training is provided, including: a user end, used to display the training tasks of the target user in the current cycle determined according to the training plan list generated by the target user, execute the training tasks of the current cycle in accordance with the task triggering sequence in response to the triggering operation of the training control, and display the task execution results of the training tasks of the current cycle; the training plan list is obtained by setting up the medical end, and is distributed to the user end by the medical end; the medical end is used to display the task execution results, detailed information of the training tasks corresponding to the target user and the specific execution status of each training task, and adjust the training tasks of the target user in the next cycle according to the task execution results.

[0016] In the processing method and processing system of cognitive function training provided in the embodiments of the present disclosure, on the one hand, a training plan list of the target user can be automatically provided, and then the training task of the target user in the current cycle can be determined, so that the target user can independently complete the training task of the current cycle, which improves the training efficiency, and can achieve multi-dimensional training and improve the training effect. On the other hand, the target user can complete the training task of the current cycle at any time, without being restricted by paper and pen training, which improves convenience, can increase the scope of application, and improves universality. On the other hand, the training task of the next cycle can be adjusted according to the task execution results, which improves the matching of the training task with the target user, and then improves the accuracy of the training task.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0019] Figure 1 A schematic diagram schematically illustrates a processing method for cognitive function training in an embodiment of the present disclosure.

[0020] Figure 2 A schematic diagram schematically illustrates the training tasks of the current cycle in an embodiment of the present disclosure.

[0021] Figure 3 A schematic diagram schematically illustrates a task overview interface in an embodiment of the present disclosure.

[0022] Figure 4A-4B A schematic diagram schematically illustrates a demonstration interface of an embodiment of the present disclosure.

[0023] Figure 5 A schematic diagram schematically illustrates an execution interface of a training task in an embodiment of the present disclosure.

[0024] Figure 6 A schematic diagram schematically illustrates the task execution time and result score of a training task in an embodiment of the present disclosure.

[0025] Figure 7 A schematic diagram schematically illustrates the task execution results of the current cycle in an embodiment of the present disclosure.

[0026] Figure 8A schematic diagram schematically illustrates the skip function in an embodiment of the present disclosure.

[0027] Fig. 9 A schematic diagram schematically illustrates the exit function in an embodiment of the present disclosure.

[0028] Fig.10 A schematic diagram schematically illustrates a patient management page on a medical terminal in an embodiment of the present disclosure.

[0029] Fig.11 A schematic diagram of adding patient data in an embodiment of the present disclosure is schematically shown.

[0030] Fig.12 A schematic diagram schematically illustrates a password checking method according to an embodiment of the present disclosure.

[0031] Fig.13 The following is a schematic diagram showing how to modify patient information according to an embodiment of the present disclosure.

[0032] Fig.14 A schematic diagram schematically illustrates a task execution result according to an embodiment of the present disclosure.

[0033] Fig.15 A schematic diagram schematically illustrates detailed information of a training task displayed in an embodiment of the present disclosure.

[0034] Fig.16 A schematic diagram showing the specific execution status of each training task according to an embodiment of the present disclosure is schematically shown.

[0035] Fig.17 A block diagram of a processing system for cognitive function training in an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0036] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0037] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0038] A method for processing cognitive function training is provided in an embodiment of the present disclosure. A list of training plans for target users can be displayed through a processing system for cognitive function training deployed on a user end or a medical end, and training tasks for the current cycle can be executed on the user end to obtain task execution results, which are then displayed on the medical end. Through the processing system for cognitive function training deployed on the user end and the medical end, cognitive impairment rehabilitation training can be implemented for the target user.

[0039] Next, refer to Figure 1 The figure shows a detailed description of the processing method of cognitive function training in the embodiment of the present disclosure.

[0040] In step S110, in response to the target user's login operation, the target user's training tasks in the current period are determined according to the target user's training plan list, and the training tasks are displayed on the operation interface of the user end.

[0041] In the disclosed embodiment, the target user may be a patient with cognitive impairment. It should be noted that the user information of the target user is obtained through authorization. A processing system for cognitive function training may be deployed or installed on the user side. The processing system for cognitive function training may be a separate application, a public account, a mini program, or a separate web page, which is not specifically limited here.

[0042] The processing system of cognitive function training mainly focuses on basic cognitive impairment in the elderly, including basic perceptual processing, basic language processing, psychomotor speed, working memory, reasoning and problem solving.

[0043] First, the target user can perform a login operation through the login page to log in to the account corresponding to the target user in the processing system of cognitive function training. After successful login, the training plan list of the target user can be displayed on the operation interface of the user end. The training plan list here refers to the overall training plan for the target user, which may include one or more of the training cycle, the training tasks of each training cycle, and the training progress. The training plan list of the target user can be configured according to the user age of the target user, the physical assessment of the target user, and the cognitive impairment assessment level. Of course, the training plan list may not be displayed, and it is determined according to actual needs. It should be noted that the training plan list of the target user can be generated by the medical end and sent to the user end. The training plan list generated by the medical end can be configured and maintained by professionals with corresponding professional knowledge according to the situation of the target user, or it can be based on the various user information of the target user, based on the large model obtained through professional knowledge training. Learning and generating, etc., or it can also be generated in a combination of multiple ways, which is not limited by this disclosure. Here, the training plan list generated by a large model is used as an example for explanation.

[0044] In some embodiments, when there is no training plan list for the target user, the user information of the target user can be processed by the large model to generate a corresponding training plan list. The user information may include one or more of the user age, the physical assessment of the target user, and the cognitive impairment assessment level. Exemplarily, the cognitive impairment assessment level can be input into the large model, and the preliminary training plan is determined according to the cognitive impairment assessment level of the target user. The preliminary training plan can be further evaluated to determine the difficulty of the preliminary training plan. When the difficulty is much higher than the acceptance of the target user, the preliminary training plan can be revised based on the physical assessment of the target user and the user age, and the training tasks and training time in the preliminary training plan are replaced with the training tasks and training time that match the physical assessment of the target user and the user age, so as to revise the preliminary training plan to generate a training plan list that matches the target user. When making corrections, the difficulty coefficient of the training task can be determined according to the difficulty coefficient of the physical assessment of the target user and the difficulty coefficient of the user age, and the training tasks in the preliminary training tasks are further replaced with training tasks that match the difficulty coefficient. The large model here can be a convolutional neural network model for predicting a training plan. In addition, the user age, physical assessment condition of the target user, and cognitive impairment assessment level can also be directly input into the big model, and the user age, physical assessment condition of the target user, and cognitive impairment assessment level can be fitted into a feature vector through the big model, and then a training plan list can be predicted to determine the training cycle for the target user and the training tasks for each training cycle.

[0045] When there is a training plan list for the target user, the user information may include one or more of the user's age, the target user's physical assessment, the cognitive impairment assessment level, and the historical task execution results. The cognitive impairment assessment level may be input into the large model, and the preliminary training plan may be determined based on the target user's cognitive impairment assessment level. The preliminary training plan may be further evaluated to determine the difficulty of the preliminary training plan. When the difficulty is much higher than the target user's acceptance level, the preliminary training plan may be revised based on the target user's physical assessment, the user's age, and the historical task execution results, and the training tasks in the preliminary training plan may be replaced with training tasks that match the target user's physical assessment, the user's age, and the historical task execution results, and the training time may be revised, thereby revising the preliminary training plan to generate a training plan list that matches the target user.

[0046] For example, the training cycle for the target user and the training tasks for each training cycle can be determined based on the cognitive impairment assessment level of the target user. Specifically, it may include the length of the training cycle, the daily training duration, the training start time and the training end time, the dimensions and the training tasks for each dimension. The training progress can be determined according to the actual training situation. Furthermore, a difficulty coefficient can be determined based on the age of the target user and the physical assessment of the target user, and the training tasks in the preliminary training plan can be replaced with training tasks corresponding to the difficulty coefficient, thereby generating a training plan list.

[0047] Specifically, for the preliminary training plan, the degree of each cognitive impairment dimension of the target user can be determined according to the cognitive impairment assessment level of the target user, and the proportion of the training tasks corresponding to each cognitive impairment dimension can be further determined according to the degree of each cognitive impairment dimension. The degree of impairment is positively correlated with the proportion.

[0048] It should be noted that the training plan list can be fixed after it is generated, or it can be updated after a period of time according to the progression of the target user's condition, which is not specifically limited here. It can be updated according to one or more of the user's age, the target user's physical assessment, the cognitive impairment assessment level, and the historical task execution results.

[0049] In the disclosed embodiment, after determining the training plan list of the target user, the training tasks of the target user in the current cycle can be generated. The cycle can be daily, every two days, or weekly, etc., and is described here by taking daily as an example. The current cycle refers to the current day. The training tasks of the current cycle refer to the training tasks of the current day. The training tasks of the target user in the current cycle can be generated by the medical end and displayed on the user end, or can be directly generated by the user end, and is described here by taking the generation by the user end as an example.

[0050] The processing system for cognitive function training may include multiple training tasks and be conducted in a fully self-service manner. Each training task will include multiple parts such as task introduction, guided practice, formal training, and result feedback.

[0051] The clinical manifestations of cognitive dysfunction are heterogeneous, and there may be cognitive impairment in one or more areas including but not limited to verbal learning, working memory, executive function, and emotion recognition. Patients with different cognitive dysfunctions may have different areas and degrees of cognitive impairment. Based on this, in the embodiments of the present disclosure, comprehensive training is conducted in terms of attention, memory, search ability, execution, accuracy, reaction speed, calculation ability, hand-eye coordination ability, logical thinking ability, etc. through various types of training tasks.

[0052] In some embodiments, the training task of the current cycle can be determined based on the training plan list according to the type of the current cycle. The type of the current cycle can be used to indicate whether there is a previous cycle adjacent to the current cycle. If there is no previous cycle adjacent to the current cycle, the training task of the current cycle is determined based on the user information of the target user and the training plan list; if there is a previous cycle adjacent to the current cycle, the training task of the current cycle is determined based on the exercise execution status of the target user in the previous cycle and the training plan list.

[0053] Exemplarily, when the current cycle is the first day, there is no previous cycle adjacent to the current cycle. In this case, the training tasks of the current cycle in the training plan list can be directly used as the training tasks of the current cycle. In addition, a reference training task of the current cycle can be automatically generated based on the user information of the target user, and the reference training task can be further matched with the training task of the current cycle in the training plan list. If the two are successfully matched, the training task of the current cycle in the training plan list can be used as the training task of the current cycle. If the two are not successfully matched, the correlation between the reference training task and the training task in the training plan list and the user information of the target user can be determined, and the one with the greatest correlation can be determined as the training task of the current cycle.

[0054] When the current cycle is not the first day, the current cycle has an adjacent previous cycle. Based on this, the training tasks of the current cycle can be determined according to the target user's exercise execution status and the training plan list in the previous cycle. The target user's exercise execution status in the previous cycle is used to indicate the target user's training performance in each exercise in the previous cycle. The exercise execution status may include the evaluation score of the exercise and the execution status of the exercise. Exercise refers to the type of training included in each dimension. Each dimension may include multiple exercises, and each exercise may include multiple training tasks. Dimensions may include, for example, attention, hand-eye coordination, execution, creativity, cognitive flexibility, memory, and the like. Exercises may include, for example, double counting, single-finger exercises, sequential connection, and the like. Training tasks may include, for example, counting blocks, searching for coins, bird migration, and the like.

[0055] Exemplarily, when determining the training task of the current cycle according to the exercise execution of the target user in the previous cycle, the training task can be determined according to the reinforcement exercise and the exercise, which can specifically include the following steps: determining the evaluation score of the exercise whose execution status is executed in the previous cycle, and determining the reinforcement exercise according to the evaluation score; from the multiple exercises whose execution status is not executed in the previous cycle, determining the candidate exercise according to the historical execution times of the multiple exercises that are not executed, and determining the candidate exercise that matches the training plan list as the new exercise; determining the training task corresponding to the reinforcement exercise and the training task corresponding to the new exercise as the training task of the current cycle. In some embodiments, the training of the current cycle consists of reinforcement exercises and new exercises, and the reinforcement exercises are performed first, and then the new exercises are performed. Among them, the number of reinforcement exercises can be greater than the new exercises, or less than the new exercises, which is not specifically limited here. Alternatively, in the first few cycles, the number of reinforcement learning can be greater than the new exercises; in the next few cycles, the number of reinforcement learning can be less than the new exercises. For example, if the training process for the target user is one month, the number of exercises in each cycle (every day) is 4. The number of reinforcement learning on the first day is 0, and the number of new exercises is 4. The number of reinforcement exercises for days 2-10 is 3, and the number of new exercises is 1. The number of reinforcement exercises for days 11-20 is 2, and the number of new exercises is 2. The number of reinforcement exercises for days 21-30 is 1, and the number of new exercises is 3.

[0056] In each cycle, the number of exercises can be determined according to actual needs, for example, it can be 3, 4 or 5, etc., and 3 is used as an example here. When determining the intensive exercises, you can select 2 poorly performed exercises from the exercises executed in the previous cycle for training. Specifically, you can calculate the evaluation score of each exercise that has been executed, and then arrange the evaluation scores in order from small to large, and determine the top N as intensive exercises. The value of N is the same as the number of intensive exercises, for example, it can be 1 or 2, which is determined according to actual needs.

[0057] Specifically, the task score of each training task in the executed exercises can be determined according to the time score, result score and difficulty coefficient of each training task in the executed exercises; the maximum value in the task score is determined as the evaluation score of the executed exercises. Exemplarily, the time score, result score and difficulty coefficient can be multiplied to obtain the task score of each training task in the executed exercises, for example, it can be expressed as task score = time score * result score * difficulty coefficient. Among them, the time score refers to the normalized value (1-10) of the task standard time to the timeout time. The result score refers to the normalized value (1-10) of the task score, and the result score can be obtained based on the number of correct answers and the number of incorrect answers in each training task. The difficulty coefficient refers to the normalized value (1-3) of the task difficulty.

[0058] In addition, the average of the task scores of each training task in the executed exercises can also be used as the evaluation score of the executed exercises. For intensive exercises, the lower the evaluation score of the exercises in the previous cycle, the greater the probability of being selected as the intensive exercises in the current cycle.

[0059] When determining a new exercise, it can be selected from the exercises whose execution status is not executed in the previous cycle. The new exercise can be determined based on the execution status of the previous cycle, the historical execution times, and the dimension to which the reinforcement exercise belongs. Exemplarily, the other exercises in the training plan list except the executed exercises can be determined as multiple exercises that were not executed in the previous cycle, and based on the historical execution times of the multiple exercises, the historical execution times are determined in ascending order as candidate new exercises. In addition, the exercises arranged in the top M positions that belong to different dimensions from the reinforcement exercises in the candidate new exercises can be used as new exercises. The value of M is the same as the number of new exercises, which is determined specifically according to actual needs. For new exercises, the lower the historical execution times of the exercises that were not executed in the previous cycle, the greater the probability of being selected as the new exercise of the current cycle. It should be noted that reinforcement learning has different dimensions from new exercises.

[0060] After the intensive exercises and the new exercises are determined, the training tasks corresponding to the intensive exercises and the training tasks corresponding to the new exercises can be determined as the training tasks of the current cycle. Figure 2 When displaying the training tasks of the current cycle, the task description of each training task can also be displayed.

[0061] In step S120, in response to the triggering operation of the training control on the operation interface of the user end, the training tasks of the current cycle are executed according to the task triggering sequence, and the task execution results of the training tasks of the current cycle are displayed.

[0062] In the embodiment of the present disclosure, when displaying the training task of the current cycle on the operation interface of the user end, a training control may also be displayed, and the training control is used to trigger the execution of the training task. If a trigger operation acting on the training control is detected, the execution of the training task of the current cycle may be triggered. The training task of the current cycle may include at least two dimensions. At least two dimensions may be determined based on the candidate dimensions corresponding to the target user, for example, at least two dimensions of the current cycle may be determined in order from low to high according to the difficulty of the candidate dimensions. The candidate dimensions may be determined based on the user information of the target user, and the candidate dimensions corresponding to different target users may also be different. After determining at least two dimensions, the task triggering order may be determined to execute the training tasks of at least two dimensions. The task triggering order may, for example, be an order from low to high according to the difficulty of at least two dimensions, that is, the training tasks of the dimensions with lower difficulty are first executed continuously, and when the training time of the dimension with lower difficulty reaches the prescribed time corresponding to the dimension, the training tasks of the dimension with higher difficulty are then executed.

[0063] Each dimension may include multiple exercises, and each exercise may include multiple training tasks. The training time for each cycle may be 30 minutes, and each cycle of training may include 3 exercises. The training time threshold for each exercise may be 10 minutes, that is, each exercise is trained continuously for 10 minutes before the next exercise is trained.

[0064] In the process of executing the training tasks corresponding to at least two dimensions in the order of difficulty from low to high, for each dimension, multiple training tasks corresponding to the same type of exercises can be executed in sequence according to the order of difficulty of the multiple exercises of the dimension from low to high. After the execution of multiple training tasks corresponding to the same type of exercises is completed, the training tasks corresponding to other types of exercises under the dimension are executed, and when the training time of the dimension reaches the specified time of the dimension, the training tasks corresponding to the other dimensions are switched. In some embodiments, for each dimension, multiple training tasks corresponding to the same type of exercise can be executed first, and after the execution of all training tasks under the exercise is completed, the training tasks corresponding to other exercises under the dimension can be switched to continue to be executed, until the training time of the dimension reaches the specified time preset for the dimension, the training of the dimension is stopped, and the training tasks corresponding to other dimensions are switched to continue to be executed.

[0065] Exemplarily, in the process of executing each exercise under each dimension, since each exercise may include multiple training tasks, multiple training tasks in the target exercise can be executed according to the order of the difficulty of the training tasks from low to high. The target exercise can be the exercise with the lowest difficulty. If the current training task in the target exercise is executed successfully, switch to the next training task of the target exercise. If the current training task in the target exercise is not executed successfully, continue to execute the current training task, and if the number of times the current training task is not executed successfully is greater than the number threshold, jump to the next training task in the target exercise. By analogy, if the training time of the target exercise is less than the practice time threshold of the target exercise after the last training task under the target exercise is executed, repeat the last training task under the target exercise until the training time of the target exercise reaches the preset practice time threshold. Among them, each training task also corresponds to a task execution time threshold. When the task execution time of each training task is greater than the task execution time threshold, it can be considered that the training task has not passed.

[0066] When the training time of the target exercise is greater than the training time threshold of the target exercise, you can switch to executing the training tasks corresponding to other exercises under the dimension to which the target exercise belongs. Each dimension also includes a pre-set prescribed training time, which is used to indicate the time required for training in each dimension. When the training time of the dimension reaches the prescribed training time of the dimension, you can switch to other dimensions and complete the training tasks corresponding to each exercise in the other dimensions in turn until the training tasks of the current cycle are completed.

[0067] It should be noted that if the target user has always failed to perform the training task of a certain exercise, the exercise corresponding to the training task can be skipped. In order to avoid the problem of poor training effect caused by skipping a certain exercise, a candidate exercise can be selected for supplementary training based on the similarity between the skipped exercise and other unperformed exercises.

[0068] After clicking the training control presented on the user side, you can enter the task overview interface of the training tasks of the current cycle recommended by the cognitive function training processing system. Figure 3 As shown in the figure, the task overview interface can display the training progress and historical training status, such as cumulative targets achieved, cumulative training time, and cumulative scores, etc.

[0069] After entering each training task, you will first be presented with a demonstration of the training task. Specifically, you can click Figure 4A To view the controls shown above, jump to Figure 4B The demonstration interface shown. On the demonstration interface, the specific operation method of each training task can be shown in the form of a short video.

[0070] If a trigger operation on the training control on the demonstration interface is detected, the formal training stage can be entered. In the formal training stage, a variety of different training tasks can be included, and the operation method of each training task can be different. After entering the formal training stage, the first training task can be displayed first. The first training task is the lowest difficulty training task corresponding to the lowest difficulty dimension. The execution interface of the training task can be as follows Figure 5 As shown in . Figure 5 As shown in , the operation area can be displayed, and the training time can be displayed. At the same time, the skip control, the redo control, and the exit control can also be displayed. If the training task is executed incorrectly, the corresponding reminder information will be displayed on the operation interface. The reminder information can be displayed in text or played by voice. In order to facilitate user identification, the reminder information can also be displayed distinctively, such as highlighting, bolding, or enlarging, flashing, etc. The reminder information can be information corresponding to the error type of the execution error. For example, when the error type is an operation error, the reminder information can be "You can't connect all the cakes by doing this." When a trigger operation acting on the redo control is detected, the training task can be re-executed. The trigger operation can be, for example, a click operation or a press operation acting on the redo control, etc., which is not specifically limited here.

[0071] After each training task is completed, the task execution time and result score of each training task can be displayed in the form of a floating window. In addition, the number of correct answers and wrong answers can also be displayed. Figure 6 as shown in .

[0072] After completing the training task of the current cycle, refer to Figure 7 As shown in , the training duration of the current cycle, the exercise duration of each exercise in the current cycle, and the exercise score of each exercise can be displayed. In addition, the achievement mark can also be displayed.

[0073] During each training task, if you click the skip control, the following will be displayed: Figure 8 If you click the exit control, you can see the following Fig. 9 Exit sign shown.

[0074] When displaying the task execution results of the training tasks of the current cycle, the predicted training tasks of the next cycle can also be determined based on the task execution results of the current cycle and the user information of the target user. For example, a machine learning model can be trained based on the training tasks of the sample cycle, the task execution results of the training tasks of the sample cycle, the historical user information, and the historical training tasks of the next cycle of the sample cycle, and then the task execution results of the current cycle and the user information of the target user are input into the machine learning model for convolution processing and full connection processing, and the type of predicted training tasks of the next cycle is output. And the predicted training tasks can be sent to the medical end so that the medical end can review the predicted training tasks of the next cycle and display the review results on the user end.

[0075] It should be noted that for the user side, the font and icon size can be increased according to the age of the target user, and at the same time, a voice playback function can be provided. When the processing system based on cognitive function training realizes user self-training, it can provide navigation operations for each step, provide animation and visual feedback to guide user operations, provide clear operation instructions and error prompts, provide obvious prompt information and button status, provide auxiliary functions, increase voice input or text reading functions, improve accessibility; provide help documents and video guidance to help elderly users operate quickly.

[0076] In step S130, the task execution results are synchronized to the medical end so that the medical end displays the task execution results, detailed information of the training tasks corresponding to the target user, and the specific execution status of each training task, and adjusts the training tasks of the target user in the next cycle according to the task execution results.

[0077] In the disclosed embodiment, the user end can synchronize the task execution results of the current cycle to the medical end. The medical end refers to the client held by the doctor. The processing system for cognitive function training can still be deployed or installed on the medical end. The processing system for cognitive function training can be a separate application, a public account, a mini program, or a separate web page, which is not specifically limited here.

[0078] The medical terminal can display the user information of all users within the doctor's authority. Based on this, the medical terminal can display the patient management page to view all patients on the page, that is, to view the user information of all users. Fig.10 As shown in , the patient management page may include a functional control area, including a patient management control and a data statistics control, wherein the data statistics control includes a patient data control, an exercise data control, and a data detail download control. The patient management page also includes a search box, which can respond to a search operation on the search box and display a user's patient data. It may also include a patient addition control to add patient data. Adding patient data can be as follows: Fig.11 shown.

[0079] The patient management page can display the patient number, name, age, mobile phone number, condition, minimum daily training time, maximum daily training time, training frequency, training cycle, etc. The patient management page can also include a patient information operation area, such as viewing passwords and modifying information. Viewing passwords can be done as follows: Fig.12 As shown, on the patient management page, click View Password and you will see the login account, which is the mobile phone number, and the password randomly generated by the system in the pop-up box.

[0080] To modify patient information, Fig.13 As shown in the figure, on the far right of the patient list, click Modify Information in the operation bar and save the changes after the pop-up page is modified.

[0081] The medical end can adjust the training task of the target user in the next cycle according to the received task execution result. Exemplarily, the task execution result may include the execution result of the training task and the execution result of the exercise. If the exercise time of any exercise in the task execution result is greater than the exercise time threshold or the exercise score is less than the score threshold, reduce the difficulty level of the exercise in the next cycle. If the task execution time of the target training task in the task execution result is greater than the task execution time threshold or the result score is less than the score threshold, reduce the difficulty level of the target training task in the next cycle. When reducing the difficulty level, it can be based on the first difference between the exercise time of any exercise and the exercise time threshold, and the second difference between the exercise score and the score threshold. Specifically, the weight of the exercise time and the weight of the exercise score can be determined, and the weight is further weighted and summed with the first difference and the second difference to obtain the sum result. The sum result is further matched with the difficulty level to adjust to the appropriate difficulty level. The way to adjust the difficulty level of the training task is the same as the way to adjust the difficulty level of the exercise, which will not be repeated here. By adjusting the difficulty level, real-time adjustment of the task can be achieved, and accuracy is improved.

[0082] On the medical side, if the first query operation acting on the patient data control is detected, the task execution results of the training task corresponding to each target user can be displayed; the task execution results include one or more of the patient number, patient name, training time of each cycle, standard achievement, and training images. For specific task execution results, please refer to Fig.14 as shown in .

[0083] If a second query operation acting on the data detail download control is detected, the detailed information of the training task corresponding to each target user in each cycle is displayed. The detailed information includes one or more of the training task name, the dimension corresponding to the training task, the training task identifier, the start time, the end time, the training duration, the training score, and the training result. Among them, the detailed information within different statistical time periods can be displayed. The statistical time period can be daily, weekly, or all. For example, the detailed information of each day, weekly, or all can be displayed. The detailed information of the training task can be specifically referred to. Fig.15 as shown in .

[0084] If the third query operation acting on the exercise data control is detected, the specific execution status of each training task in the cognitive impairment training process is displayed, and the specific execution status includes one or more of the name of the training task, the dimension corresponding to the training task, the training task identifier, the number of trainees, the number of passers, the task pass rate, the average pass time, the average training score, and the task score. Among them, you can filter according to the training time and the exercise name. The specific execution status of the training task can be as follows Fig.16 as shown in .

[0085] In the disclosed embodiment, an MVC architecture with separated front-end and back-end can be used. After the user completes the request through the interface interaction layer, it will be forwarded to the Web request distribution layer via routing. The distribution layer will forward to the corresponding Web request controller (Controller layer) according to the request type. The specific business logic will be forwarded to the data service layer according to business needs. The data returned after processing by the data service layer will be encapsulated as VO according to the request requirements and the request result will be returned, and finally the corresponding function will be presented to the user. For example, the first query operation, the second query operation and the third query operation of the medical end can be forwarded to the Web request distribution layer via routing. The distribution layer will forward to the corresponding Web request controller according to different query operations, forward the query operation to the data service layer, and then encapsulate the obtained results into VO and display them to display the task execution results of the training task corresponding to the target user, the detailed information of the training task and the specific execution status of each training task. Encapsulation into VO (ValueObject) means that the background data is directly transmitted to the front end, and the front end needs to filter and process the data before it can be displayed on the page.

[0086] During the entire interaction process, common components such as Spring Web, security, permission control, and data cache are also used. In order to protect the user's data security, encryption technology can be used to protect the user information of the target user and ensure data security during data transmission and storage. During the operation, there is an automatic data saving function. The data is automatically backed up and saved in the database server every day. After an unexpected restart, the backup data will remain in the state of the last save. The recovery process after restart includes: logging in to the database server and entering the data backup directory; selecting the data version to be restored, executing the recovery command, and restoring the backup data in time series; connecting to the database server and checking whether the backup and restore are correct.

[0087] The technical solution in the disclosed embodiment, on the one hand, can automatically provide a training plan list for the target user, and then determine the training tasks of the target user in the current cycle, so that the target user can independently complete the training tasks of the current cycle, thereby improving the training efficiency, and can achieve multi-dimensional training and improve the training effect. On the other hand, the target user can complete the training tasks of the current cycle at any time, without being restricted by paper and pen training, which improves convenience, can increase the scope of application, and improves universality. On the other hand, the training tasks of the next cycle can be adjusted according to the results of task execution, which improves the matching of the training tasks with the target users, thereby improving the training effect.

[0088] In the embodiment of the present disclosure, a processing system for cognitive function training is also provided. Fig.17 As shown in FIG. 1 , the processing system 1700 for cognitive function training includes:

[0089] The user terminal 1701 is used to display the training tasks of the target user in the current period determined according to the training plan list of the target user, execute the training tasks of the current period according to the task triggering sequence in response to the triggering operation of the training control, and display the task execution results of the training tasks of the current period; the training plan list is obtained through the setting of the medical terminal and is distributed to the user terminal by the medical terminal;

[0090] The medical terminal 1702 is used to display the task execution results, detailed information of the training tasks corresponding to the target user and the specific execution status of each training task, and adjust the training tasks of the target user in the next cycle according to the task execution results.

[0091] It should be noted that the specific details of each module in the above-mentioned cognitive function training processing system have been described in detail in the corresponding cognitive function training processing method, so they will not be repeated here.

[0092] The exemplary embodiment of the present disclosure also provides an electronic device. The electronic device may be the above-mentioned client or a server. Generally, the electronic device may include a processor and a memory, the memory is used to store executable instructions of the processor, and the processor is configured to execute the above-mentioned processing method for cognitive function training by executing the executable instructions.

[0093] The electronic device according to this embodiment of the present disclosure is described below. The electronic device is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit, at least one storage unit, a bus connecting different system components (including the storage unit and the processing unit), and a display unit.

[0094] The storage unit stores a program code, which can be executed by the processing unit, so that the processing unit performs the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit can perform the following steps: Figure 1 Follow the steps shown in .

[0095] The storage unit may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM) and / or a cache memory unit, and may further include a read-only memory unit (ROM).

[0096] The storage unit may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0097] The bus may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, a graphics acceleration interface, a processing unit, or a local bus using any of a variety of bus architectures.

[0098] The electronic device can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), and can also communicate with one or more devices that enable users to interact with the electronic device, and / or communicate with any device (such as routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. This communication can be carried out through an input / output (I / O) interface. In addition, the electronic device can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs) and / or public networks, such as the Internet) through a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0099] In an embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present disclosure can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0100] According to the program product for implementing the above method in the embodiment of the present disclosure, it can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0101] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0102] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0103] The program code contained on the readable medium can be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the disclosed operation can be written in any combination of one or more programming languages, including object-oriented programming languages-such as Java, C++, etc., and also including conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device, partially on the remote computing device, or completely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0104] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the embodiment of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0105] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0106] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for processing cognitive function training, applied to a user end, characterized in that: include: In response to a login operation of a target user, determining a training task for the target user in a current period according to a training plan list of the target user, and displaying the training task on an operation interface of the user terminal; The training plan list is obtained through the settings of the medical terminal and distributed to the user terminal by the medical terminal; In response to a triggering operation on a training control on an operation interface of the user terminal, executing the training tasks of the current cycle according to a task triggering sequence, and displaying the task execution results of the training tasks of the current cycle; The task execution results are synchronized to the medical end so that the medical end displays the task execution results, detailed information of the training tasks corresponding to the target users and the specific execution status of each training task, and adjusts the training tasks of the target users in the next cycle according to the task execution results.

2. The method for cognitive function training according to claim 1, characterized in that: The determining the training task of the target user in the current period according to the training plan list of the target user includes: If the current cycle does not have a previous cycle adjacent to it, determining the training task of the current cycle according to the user information of the target user and the training plan list; If the current cycle has a previous cycle adjacent to it, the training task of the current cycle is determined according to the exercise execution status of the target user in the previous cycle and the training plan list.

3. The method for cognitive function training according to claim 2, characterized in that: The exercise execution status includes the evaluation score of the exercise and the execution status of the exercise; and determining the training task of the current cycle according to the exercise execution status of the target user in the previous cycle and the training plan list includes: Determine the evaluation scores of the exercises whose execution status is "executed" in the previous cycle, and determine the reinforcement exercises according to the evaluation scores; Determine multiple exercises that are in an unexecuted state in the previous cycle according to the training plan list, and determine new exercises according to the historical execution counts of the multiple unexecuted exercises; The training task corresponding to the intensive exercise and the training task corresponding to the new exercise are determined as the training tasks of the current cycle.

4. The method for cognitive function training according to claim 3, characterized in that: Determining the evaluation score of the exercise whose execution status is executed in the previous cycle includes: Determine the task score of each training task based on the time score, result score and difficulty coefficient of each training task in the executed exercises; The maximum value among the task scores is determined as the evaluation score.

5. The method for cognitive function training according to claim 1, characterized in that: The responding to the triggering operation of the training control on the operation interface of the user terminal and executing the training task of the current cycle according to the task triggering sequence includes: Determine at least two dimensions of the current cycle from a plurality of candidate dimensions determined according to the user information of the target user according to the order of difficulty of the candidate dimensions from low to high; Execute the training tasks corresponding to the at least two dimensions in order of difficulty from low to high; The performing of the training tasks corresponding to the at least two dimensions in order of difficulty from low to high includes: Under each dimension, multiple training tasks corresponding to the target exercise are performed according to the order of difficulty of multiple exercises corresponding to each dimension from low to high; After the execution of multiple training tasks corresponding to the target exercise is completed, the training tasks corresponding to other exercises under the dimension to which the target exercise belongs are executed, and when the training time of the dimension reaches the prescribed training time of the dimension, the training tasks corresponding to other dimensions are switched.

6. The method for cognitive function training according to claim 5, characterized in that: The multiple training tasks corresponding to the execution target exercise include: Perform multiple training tasks in target practice in order of task difficulty from low to high; If the current training task in the target exercise is executed successfully, switch to the next training task; If the current training task in the target exercise fails to be executed, continue to execute the current training task, and if the number of times the current training task fails to be executed is greater than a number threshold, jump to the next training task; If the training time of the target exercise is less than the training time threshold after the last training task under the target exercise is completed, the last training task is repeatedly executed until the training time threshold is reached.

7. The method for cognitive function training according to claim 1, characterized in that: The displaying of the task execution result of the training task of the current cycle includes: If the training task is executed incorrectly, a corresponding reminder message is displayed on the operation interface, and in response to a trigger operation received by the redo control, the training task is re-executed; After each training task is completed, one or more of the task execution time and result score of each training task is displayed; After all training tasks of the current cycle are completed, the training duration of the current cycle, the training duration of each exercise, and the exercise score of each exercise are displayed.

8. The method for cognitive function training according to claim 1, characterized in that: The display of the task execution result, detailed information of the training task corresponding to the target user, and the specific execution status of each training task includes: In response to the first query operation on the patient data control, the task execution result of the training task corresponding to each target user is displayed; the task execution result includes one or more of the training duration of each cycle, the standard achievement, and the training image; In response to the second query operation on the data detail download control, detailed information of the training task corresponding to each period of each target user is displayed; the detailed information includes one or more of the training task name, the dimension corresponding to the training task, the training task identifier, the start time, the end time, the training duration, the training score, and the training result; In response to the third query operation on the practice data control, one or more of the name of each training task, the dimension corresponding to the training task, the training task identifier, the number of trainees, the number of passers-by, the task pass rate, the average pass time, the average training score, and the task score are displayed.

9. The method for cognitive function training according to claim 1, characterized in that: The adjusting the training task of the target user in the next cycle according to the task execution result includes: If the practice time of any exercise in the task execution result is greater than the practice time threshold or the practice score is less than the score threshold, reduce the difficulty level of the exercise in the next cycle; If the task execution time of the target training task in the task execution result is greater than the task execution time threshold or the result score is less than the score threshold, reduce the difficulty level of the target training task in the next cycle.

10. A processing system for cognitive function training, characterized in that: include: The user terminal is used to display the training tasks of the target user in the current cycle determined according to the training plan list of the target user, execute the training tasks of the current cycle in the task triggering order in response to the triggering operation of the training control, and display the task execution results of the training tasks of the current cycle; The training plan list is obtained through the settings of the medical terminal and distributed to the user terminal by the medical terminal; The medical end is used to display the task execution results, detailed information of the training tasks corresponding to the target user and the specific execution status of each training task, and adjust the training tasks of the target user in the next cycle according to the task execution results.

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