A personalized intelligent analysis method and system for homework duration

Abstract

The present invention provides a personalized intelligent analysis method and system for homework duration, characterized by including: a data collection unit, a data preprocessing unit, and an artificial intelligence training platform; the data collection unit is used to obtain user information; the data preprocessing unit is used to preprocess the obtained user information to obtain the average duration of a range of days for current various types of homework; the artificial intelligence training platform is used to output a personalized ideal homework duration according to the average duration of various types of homework after preprocessing. The solution provided by the present invention has the following beneficial effects: a personalized intelligent analysis method and system for key homework duration, which recommends a personalized homework duration mode in the most ideal physical and mental health state to the user, helps the user optimize the time distribution, and through visual display, helps the user select the optimization route of the most suitable homework duration mode for himself / herself, thereby helping the user to gradually optimize the homework distribution in a personalized manner and improve the physical and mental health state.

Description

Technical Field

[0001] This application relates to the field of medical devices, and particularly to a method and system for intelligent analysis of personalized operation duration. Background Art

[0002] The modern life rhythm is getting faster and faster, and life is getting busier and busier. In terms of time arrangement, some people are prone to sacrificing sleep and trying to squeeze as much time as possible for work or entertainment, which has a great impact on physical and mental health; some people spend a lot of time on various studies / work, but the efficiency may not be high because they do not sort out their own state and studies / work in a timely manner every day and summarize and reflect on their goals and methodologies; some people have a strong sense of urgency about time and always feel that there are endless things to do, and it is difficult to calm down inside. In such an anxious and impatient state, they will not be able to grasp the priorities of things, resulting in a more unreasonable time arrangement.

[0003] Therefore, it is very important to arrange various operations such as personal life, study, and work in an orderly manner. The various life activities carried out by a person every 24 hours are called operations. A reasonable arrangement of operation time is not only to make the most of time and create results to the greatest extent, but more importantly, it should be based on ensuring and improving physical and mental health. Based on the human body system model, this invention analyzes the daily operation duration of users and recommends a more ideal personalized key operation duration mode for them, helping users gradually optimize the key operation duration mode and improve the physical and mental health state.

[0004] The following are two examples in the context of the prior art:

[0005] Example 1: Chinese Patent "CN201810150885.1 Time Management Method and System" discloses a time management method and system, which relates to the field of intelligent terminal technology. The method includes: obtaining the average time consumed by the unit task of the application and the daily active time period of the application through the behavior data of the user using the application; obtaining the idle duration and idle time of the user; recommending relevant applications according to the idle duration and idle time; the relevant applications include: applications whose idle duration is close to the average time consumed by the unit task of the application and whose idle time is within the daily active time period of the application. Compared with the prior art, the time management method provided by this application matches the application with an average time consumed by the unit task close to the idle duration of the user; according to the idle time, matches the application whose daily active time period coincides with the idle time; recommends the applications that meet the above two matching conditions to the user, and the user can reasonably utilize fragmented time through the recommended applications.

[0006] Example 2: Chinese Patent "CN201610575104.4 Method and User Equipment for Managing Entertainment Time" discloses a method and user equipment for managing entertainment time. The management method includes: a first user equipment obtains the entertainment time of a first user, and when the entertainment time is greater than or equal to an entertainment time threshold, sends a first message to a second user equipment; after receiving the first message, the second user equipment updates the entertainment time threshold and / or sets the usage permission of the first user according to the input operation of the second user, and sends a second message to the first user equipment, where the second message includes the updated information of the entertainment time threshold and / or the usage permission of the first user; after receiving the second message, the first user equipment updates the entertainment time threshold according to the updated information, and disables the application according to the usage permission. The present invention solves the problem that the prior art cannot effectively manage the entertainment time of students.

[0007] Defects in the background art: Due to the diverse and complex types of activities / assignments of modern people, and the different personality traits and physical and mental health levels of each person, it is necessary to plan the time arrangement in a focused and personalized manner by combining the specific situation of the individual and considering the impact of the combination of activities, so as to achieve efficient output and improvement of physical and mental health as much as possible.

[0008] However, the current related time management technologies do not conduct personalized system design and intelligent management of time on the premise of ensuring and improving the overall physical and mental health. For example, Chinese Patent "CN201810150885.1 Method and System for Managing Time" mainly considers the utilization of the user's idle time, which is a mechanical recommendation. It does not consider the activity arrangements that have an important impact on the user's physical and mental health, nor does it consider the combined impact between activities. Although this can reduce the waste of time, it cannot ensure the utilization efficiency of time and the improvement of physical and mental health. Chinese Patent "CN201610575104.4 Method and User Equipment for Managing Entertainment Time" can only prevent the use of intelligent devices and only considers the management of part of the user's time, which is simple and one-sided, and cannot guarantee the reasonable utilization of time.

[0009] Therefore, it is an urgent problem to be solved to improve the user's physical and mental health and efficiency level through personalized and intelligent recommendation of the key operation duration mode. Summary of the Invention

[0010] The main purpose of the present invention is to overcome the defects of the above prior art, and provides a personalized operation duration intelligent analysis method and system to solve the problem that the prior art cannot guarantee the reasonable utilization of time.

[0011] This application includes a personalized intelligent analysis system for homework duration, which includes: a data collection unit, a data preprocessing unit, and an artificial intelligence training platform; the data collection unit is used to obtain user information; the data preprocessing unit is used to preprocess the obtained user information to obtain the average duration of a range of days for current various types of homework; the artificial intelligence training platform is used to output personalized ideal homework durations according to the average durations of various types of homework after preprocessing.

[0012] Preferably, the user information obtained by the data collection unit includes gender, age, residential area, occupation, and past medical history.

[0013] Preferably, the user information obtained by the data collection unit includes the duration of various types of homework of the user every day, and various types of homework include but are not limited to sleep, three quiet activities, reading classics, work, and summary.

[0014] Preferably, the artificial intelligence training platform is used to output a personalized growth matrix and a growth curve index matrix according to the average durations of various types of homework after preprocessing.

[0015] Preferably, it further includes a data processing unit for obtaining the ideal homework duration of the growth mode; specifically including: calculating the distance between the current homework duration of the user and the ideal homework duration to evaluate the gap between the current and ideal homework durations; calculating the adjustment duration of each homework under different personalized growth modes; calculating the personalized recommended duration of each homework under different growth modes; predicting the growth curves of different growth modes, calculating the distance between the personalized recommended duration and the ideal homework duration of each homework under different growth modes, and the distance between each homework and the ideal homework duration changes over time. When it is 0, this growth mode reaches the ideal homework duration.

[0016] Preferably, it further includes a visual output unit for displaying one or more of the following content: the adjustment duration of each homework, the current and ideal states, the homework distribution recommended by different growth modes, the overall distance between different growth modes and the ideal homework duration changing over time, and the time required to reach the ideal homework duration.

[0017] It also includes a method for intelligent analysis of personalized homework duration, which includes: obtaining user information; preprocessing the obtained user information to obtain the average duration of a range of days for current various types of homework; outputting personalized ideal homework durations through the artificial intelligence training platform according to the average durations of various types of homework after preprocessing.

[0018] Preferably, the obtained user information includes gender, age, residential area, occupation, and past medical history.

[0019] Preferably, the obtained user information includes the duration of various types of daily tasks of the user. The various types of tasks include, but are not limited to, sleep, three quiet activities, reading classics, work, and summary.

[0020] Preferably, based on the average duration of each type of task after preprocessing, a personalized growth matrix and a growth curve index matrix are output.

[0021] Preferably, obtain the ideal task duration of the growth mode; specifically include: evaluate the gap between the current task duration and the ideal task duration by calculating the distance between the current task duration of the user and the ideal task duration; calculate the adjustment duration of each task under different personalized growth modes; calculate the personalized recommended duration of each task under different growth modes; predict the growth curves of different growth modes, calculate the distance between the personalized recommended duration and the ideal task duration of each task under different growth modes, and the distance between each task and the ideal task duration changes over time. When it is 0, the growth mode reaches the ideal task duration.

[0022] Preferably, the visual display includes one or more of the following: the adjustment duration of each task, the current and ideal states, the task distribution recommended by different growth modes, the overall distance between different growth modes and the ideal task duration over time, and the time required to reach the ideal task duration.

[0023] The beneficial effects of the solution of the present invention are as follows:

[0024] In view of the diverse and complex situations of activities / tasks and the mutual promotion or inhibition effects among the combinations of tasks, based on the human body system model, this application classifies daily tasks into key tasks and non-key tasks. The key tasks are further divided into sleep, three quiet activities, reading classics, learning / work, and summary. Other tasks and tasks without records are classified as non-key tasks, so as to achieve focused time management.

[0025] In view of the current lack of personalization in time management, the present invention constructs an intelligent analysis method and system for personalized key task duration, recommends the personalized task duration mode in the most ideal physical and mental health state to the user, and helps the user optimize the time distribution. And through visual display, it helps the user select the optimization route of the most suitable task duration mode for themselves. Thus, it helps the user to gradually optimize the task distribution in a personalized manner and improve the physical and mental health state. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] This application will be further described by way of exemplary embodiments, which will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0027] Figure 1Schematic diagram of a personalized intelligent analysis method and system for homework duration;

[0028] Figure 2 Schematic diagram of the current homework distribution;

[0029] Figure 3 Schematic diagram of the homework distribution in the ideal physical and mental health state;

[0030] Figure 4 Schematic diagram of the adjustment of the duration of each homework;

[0031] Figure 5 Schematic diagram of the homework distribution in growth mode 1;

[0032] Figure 6 Schematic diagram of the homework distribution in growth mode 2;

[0033] Figure 7 Schematic diagram of the homework distribution in growth mode 3;

[0034] Figure 8 Schematic diagram of the growth curves of the three growth modes. Detailed implementation manners

[0035] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application, and the technical features of each embodiment can be combined with each other to form the actual solutions for achieving the invention purpose. For those of ordinary skill in the art, without creative efforts, the present application can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structures or operations.

[0036] It should be understood that the "system" and "unit" used herein are a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions. And, the "system" and "unit" can be implemented by software or hardware, and can be the name of an entity or a virtual part with this function.

[0037] Flowcharts are used in the present application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the operations before or after do not necessarily need to be executed precisely in sequence. On the contrary, the steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes. The technical solutions in each embodiment can be combined with each other to achieve the purpose of the present invention.

[0038] Embodiment 1

[0039] A personalized intelligent analysis system for homework duration, as Figure 1 shown, includes: a data collection unit, a data preprocessing unit, and an artificial intelligence training platform; the data collection unit is used to obtain user information; the data preprocessing unit is used to preprocess the obtained user information to obtain the average duration of a certain range of days for current various types of homework; the artificial intelligence training platform is used to output personalized ideal homework durations according to the average durations of various types of homework after preprocessing.

[0040] In view of the current lack of personalization in time management, the present invention constructs an intelligent analysis method and system for personalized key homework duration, recommends a personalized homework duration mode in the most ideal physical and mental health state to users, and helps users optimize time distribution. And through visual display, it helps users select the optimization route of the most suitable homework duration mode for themselves. Thus, it helps users gradually optimize the homework distribution in a personalized manner and improve the physical and mental health state.

[0041] Embodiment 2

[0042] This application includes a data collection unit, a data preprocessing unit, an artificial intelligence training platform, a data processing unit, and a visual output unit.

[0043] The data collection unit collects the basic information of the user through the information entry interface of the data collection unit, such as gender, age, residential area, occupation, previous medical history, etc.

[0044] The user classifies each homework according to the homework classification guide, and lists some common activities that can be classified into various types of homework, forming a homework classification guide. The homework is divided into sleep, three quiet, reading classics, learning / work, summary, etc. For example, writing homework is classified as learning / work homework, and reading "Tao Te Ching" is classified as reading classics homework, which helps users classify the homework correctly.

[0045] The user enters the duration O of various types of homework every day through the data entry interface k，i .

[0046]

[0047] Among them, k = 1, 2…, 6, 7 respectively represent key homework such as sleep, three quiet, reading classics, work, summary, etc., as well as others and no record (non-key homework).

[0048] i = 1, 2…, n respectively represent the homework duration data entered by the user on the first day, the second day… the nth day.

[0049] #(1), #(2)… are formula numbers, not the formulas themselves.

[0050] In view of the diverse and complex situations of activities / assignments and the combination of assignments that may promote or inhibit each other, this application is based on the human body system model and divides daily assignments into key assignments and non-key assignments. Key assignments are further divided into sleep, three quietness, reading classics, study / work, and summary. Other assignments and unrecorded assignments are all classified as non-key assignments, so as to achieve focused management of time. Among them, the three quietnesses include rest, meditation, and enlightenment. Rest means putting down all thoughts and resting quietly. Meditation means thinking easily and passively. Enlightenment means feeling quietly and passively. Reading classics means reading books written by ancient sages that contain wisdom and are timeless, such as Tao Te Ching. Summarizing means sorting out and summarizing all activities in life. Other assignments refer to activities that cannot be classified as key assignments, such as leisure and entertainment. Unrecorded assignments are activities that are not recorded in the present invention in a day and are automatically calculated by this application.

[0051] Data preprocessing unit: The data preprocessing unit preprocesses the job duration data entered by the user, averages the data of the past n days selected by the user, and obtains the average duration of each type of current job.

[0052]

[0053] k=1, 2…, 6, 7 represent sleep, three quietness, reading classics, work, summary and other key tasks and no records (non-key tasks).

[0054] After data preprocessing, the average duration of each type of work is transmitted to the AI ​​training platform. The AI ​​training platform will make a preliminary sub-classification of the population based on factors such as the age, gender, physique, personality and occupation of the subjects. In particular, when the same user is evaluated multiple times, his own data set constitutes a personalized sub-class. According to different sub-classes, learning and training are carried out to output personalized ideal work duration and personalized growth matrix θ k，j And the growth curve index matrix β k，j .

[0055]

[0056]

[0057]

[0058] Among them, k=1, 2…, 6, 7 represent sleep, three quiet times, reading classics, work, summary (critical tasks), and others and no records (non-critical tasks) respectively.

[0059] j = 1, 2, 3 represent three different personalized growth models. Specifically, j = 1 represents the growth model with the relatively smallest adjustment range of the homework duration and the relatively longest time spent growing to the ideal homework duration; j = 2 represents the growth model with the relatively moderate adjustment range of the homework duration and the medium time spent growing to the ideal homework duration; j = 3 represents the growth model with the relatively largest adjustment range of the homework duration and the relatively shortest time spent growing to the ideal homework duration.

[0060] The data processing unit, in the first step, calculates the current average homework duration of the user and the ideal homework duration to obtain the distance D k , and evaluates the gap existing in the current and ideal homework durations.

[0061]

[0062] In the second step, for personalized homework duration adjustment, calculate the adjustment duration A of each homework under different personalized growth models k，j .

[0063] A k，j = D k * θ k，j #(7)

[0064] where θ k，j is the personalized growth matrix output by the artificial intelligence platform.

[0065] Then, calculate the personalized recommended duration of each homework under different growth models

[0066]

[0067] In the third step, predict the growth curves of different growth models. The time required for the personalized recommended homework durations of different growth models to reach the ideal homework duration is different. This system can predict the growth curves of different growth models as follows:

[0068] Calculate the distance D between the personalized recommended duration of each homework under different growth models and the ideal homework duration k，j .

[0069]

[0070] In the fourth step, under different growth models, the duration of each homework will change over time. Therefore, the distance D k，j (t) between each homework and the ideal homework duration will also change over time.

[0071]

[0072] t = 1, 2, …, m represent the 1st day, 2nd day, …, mth day after this analysis, and β k，j is the growth curve index matrix output by the artificial intelligence platform.

[0073] Step 5, calculate the overall distance D of the duration of each operation and the ideal operation duration changing with time j .

[0074]

[0075] When D j is 0, this growth mode reaches the ideal operation duration.

[0076] The visualization output unit. Finally, perform visual display, such as using a column adjustment chart to show the adjustment duration of each operation, a pie chart of operation distribution to show the current and ideal states and the operation distribution recommended by the three growth modes, a growth curve graph to predict the overall distance of the three growth modes and the ideal operation duration changing with time, and the time required to reach the ideal operation duration.

[0077] After the data processing unit obtains the ideal operation duration of the subject, it is stored. Specifically, when the same user is evaluated multiple times, his own dataset forms a personalized subclass, and the artificial intelligence training platform can call the previously stored data. The artificial intelligence training platform will make a preliminary subclass division of the population according to factors such as the age, gender, physique, personality, and occupation of the subject, and perform learning and training based on the health big data to output a personalized ideal operation duration. It is possible to perform training iterations through the artificial intelligence training platform multiple times to obtain a better personalized ideal operation duration, as Figure 1 shown.

[0078] This system analyzed the daily operation duration of 93 subjects, with a cumulative usage times of 12,863 times; the results show that this method can recommend a personalized ideal operation duration distribution for users and help users adjust step by step to improve health.

[0079] Figures 2 - 8 are the analysis and feedback results of the real records of a certain user. Among them, Figure 2 is the current operation duration distribution. Figure 3 is the ideal operation duration distribution, Figure 4 is the adjustment result of the duration of each operation, Figures 5 - 7 is the recommended operation duration distribution map of the three growth modes, Figure 8 is the predicted growth curve of the three growth modes.

[0080] It can be seen from the current job duration distribution diagram that there is room for optimization and adjustment in both the current key jobs and non-key jobs of this user. The solution proposed by the present invention can personalized recommend the job duration distribution suitable for the ideal physical and mental health state of this user, recommend 3 different growth modes, and predict the user's growth curve, helping the user to adjust step by step, shorten the distance from the ideal physical and mental health state, and continuously improve the health state through iteration.

[0081] A personalized job duration intelligent analysis method corresponds one-to-one with a personalized job duration intelligent analysis system. For specific description, see a personalized job duration intelligent analysis system.

[0082] The beneficial effects that may be brought about by the embodiments of the present application include but are not limited to:

[0083] 1. Based on the human body system model, according to the significance for the physical and mental health state, the daily jobs are divided into key jobs and non-key jobs. This helps users achieve focused, streamlined, and efficient management of time.

[0084] 2. By means of machine learning, the job duration and growth matrix parameters under the user's ideal physical and mental health state are generated, and the distribution modes of job duration at different growth speeds are personalized recommended for the user. It realizes the intelligent analysis and recommendation of personalized job duration, and through visual display, helps users see clear improvement goals and routes in terms of time management.

[0085] 3. Based on the artificial intelligence training platform, as the number of times the user uses increases, the personalized growth matrix parameters and growth curve matrix will become more and more accurate, thus achieving the effect that the recommended key job duration and the predicted growth curve are getting more and more accurate. This is conducive to clinical promotion and industrial application.

[0086] It should be noted that the beneficial effects that may be produced by different embodiments are different. In different embodiments, the beneficial effects that may be produced can be any one or several combinations of the above, or any other beneficial effects that may be obtained.

[0087] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to the present application.

[0088] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numbers, letters, or other names in the present application is not used to limit the order of the processes and methods of the present application. Although some currently considered useful invention embodiments are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of the present application.

[0089] Similarly, it should be noted that, in order to simplify the description disclosed in the present application and thus assist in the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of the present application, multiple features are sometimes grouped into one embodiment, drawing or description thereof. However, this method of disclosure does not imply that the features required by the subject matter of the present application are more than those recited in the claims.

[0090] Finally, it should be understood that the embodiments described in the present application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, by way of example and not limitation, alternative configurations of the embodiments of the present application may be regarded as consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments specifically introduced and described in the present application.

Claims

1. A personalized intelligent analysis system for homework duration, characterized in that, Including: A data acquisition unit, a data preprocessing unit, and an artificial intelligence training platform; The data acquisition unit is used to obtain user information; The data preprocessing unit is used to preprocess the obtained user information to obtain the average duration of a range of days for current various types of tasks; The artificial intelligence training platform is used to output personalized ideal task durations based on the average durations of various tasks after preprocessing; It also includes a data processing unit. In the first step, it calculates the average duration of the user's current tasks and the ideal task duration to obtain the distance D k , and evaluates the gap between the current task duration and the ideal state. Step 2: Adjust the personalized assignment duration and calculate the adjusted duration A of each assignment under different personalized growth models k,j ; A k,j = D k * θ k,j where θ k,j is the personalized growth matrix output by the artificial intelligence platform; Then, calculate the personalized recommended duration of each assignment under different growth modes In the third step, predict the growth curves of different growth models. The time required for the personalized recommended task durations of different growth models to reach the ideal task duration is different. This system can predict the growth curves of different growth models, specifically as follows: Calculate the distance D between the personalized recommended duration and the ideal assignment duration under different growth models for each assignment k,j ; Step 4: Under different growth models, the duration of each operation changes over time. Therefore, the distance D k,j (t) between each operation and the ideal operation duration also changes over time; t = 1, 2…, m represent the 1st day, 2nd day… mth day after this analysis, β k,j is the growth curve index matrix output by the artificial intelligence platform; Step 5, calculate the overall distance D of the duration of each operation and the ideal operation duration varying with time j ; When D j is 0, the growth mode reaches the ideal operation duration.

2. The system according to claim 1, wherein The user information obtained by the data acquisition unit includes gender, age, residential area, occupation, and past medical history.

3. The system according to claim 1, characterized in that The user information obtained by the data acquisition unit includes the durations of various tasks per day of the user. The various tasks include but are not limited to sleep, three quiet states, reading classics, work, and summary.

4. The system according to claim 1, wherein The artificial intelligence training platform is used to output a personalized growth matrix and a growth curve index matrix based on the average durations of various tasks after preprocessing.

5. The system according to claim 1, wherein It further includes a visual output unit, which is used to display one or more of the following, including but not limited to: the adjusted duration of each task, the current and ideal states, the task distribution recommended by different growth models, the overall distance between different growth models and the ideal task duration over time, and the time required to reach the ideal task duration.

6. A personalized intelligent analysis method for homework duration, characterized in that, Including: Obtain user information; Preprocess the obtained user information to obtain the average duration of a range of days for current various types of tasks; Output personalized ideal task durations through the artificial intelligence training platform based on the average durations of various tasks after preprocessing; Obtain the ideal operation duration in the growth mode; specifically including: the first step is to calculate the current average operation duration of the user and the ideal operation duration distance D k , and evaluate the gap existing in the operation duration between the current and the ideal state; Step 2: Adjust the personalized homework duration and calculate the adjusted duration A of each homework under different personalized growth models k,j ; A k,j = D k * θ k,j where θ k,j is the personalized growth matrix output by the artificial intelligence platform; Then, calculate the personalized recommended duration of each assignment under different growth models In the third step, predict the growth curves of different growth models. The time required for the personalized recommended task durations of different growth models to reach the ideal task duration is different. This system can predict the growth curves of different growth models, specifically as follows: Calculate the distance D between the personalized recommended duration and the ideal assignment duration under different growth models for each assignment k,j ; In the fourth step, under different growth models, the duration of each operation changes over time. Therefore, the distance D k,j (t) between each operation and the ideal operation duration also changes over time; t = 1, 2, …, m represent the 1st day, 2nd day, …, mth day after this analysis, and β k,j is the growth curve index matrix output by the artificial intelligence platform; Step 5, calculate the overall distance D of the duration of each operation and the ideal operation duration changing with time j ; When D j is 0, the growth mode reaches the ideal operation duration.

7. The method according to claim 6, wherein The obtained user information includes gender, age, residential area, occupation, and past medical history.

8. The method according to claim 6, wherein The obtained user information includes the durations of various tasks per day of the user. The various tasks include but are not limited to sleep, three quiet states, reading classics, work, and summary.

9. The method according to claim 6, wherein Output a personalized growth matrix and a growth curve index matrix based on the average durations of various tasks after preprocessing.

10. The method according to claim 6, wherein Visual display includes one or more of the following, including but not limited to: the adjusted duration of each task, the current and ideal states, the task distribution recommended by different growth models, the overall distance between different growth models and the ideal task duration over time, and the time required to reach the ideal task duration.

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