Electronic equipment control method for self-discipline management

Abstract

The invention discloses an electronic equipment control method for self-discipline management, and particularly relates to the technical field of self-discipline risk management. Comprising the following steps: step S01: numbering use time periods of electronic equipment; step S02: collecting use time data of the electronic equipment; step S03: managing attention risk information; step S04: classifying attention risk information anomalies, step S05: analyzing task completion quality, step S06: intelligently evaluating task completion quality, step S07: monitoring and early warning in a self-discipline manner, and step S08: evaluating a self-discipline management effect. According to the method, the electronic equipment use basic data of each sub-period of the use time of the target electronic equipment is collected, the attention risk management index and the user task completion quality evaluation index are calculated, the learning efficiency early warning coefficient is analyzed, the use data of different sub-periods are comprehensively collected, deep calculation and analysis are performed, and the user experience is improved. The user self-discipline management effect is fed back in time, and the learning efficiency of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous risk management, and more specifically, to an electronic device control method for autonomous management. Background Art

[0002] With the popularity of tablet and mobile phone devices, people's lives and work are increasingly dependent on these portable technologies. According to data, teenagers spend an average of more than 7 hours a day on digital media. This long screen time not only affects their sleep quality, but may also have a negative impact on their social skills and mental health. Therefore, self-discipline management has become an issue that companies and educational institutions must face.

[0003] Self-discipline management refers to the process by which an individual achieves self-control and self-improvement through self-monitoring, self-motivation and self-regulation. In the digital age, self-discipline management can improve personal productivity and quality of life. The Alixiong App is based on this concept. By setting time limits and smart reminder functions, it encourages users to develop good usage habits and reduce unnecessary interference and distraction.

[0004] However, there are still some shortcomings in actual use. For example, with the existing self-regulatory control methods of electronic devices, users cannot clearly see their usage patterns and habits, and thus cannot make targeted adjustments; The existing self-discipline management functions of electronic devices lack a data-driven approach to help users manage their own learning efficiency, which makes it impossible for users to create intuitive self-discipline management reports. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an electronic device control method for autonomous management, which is used to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for controlling an electronic device for self-discipline management, comprising the following steps: Step S01: electronic device usage time period numbering: used to divide the target electronic device usage time into sub-time periods every t time periods, and number the sub-time periods of the target electronic device usage time as 1, 2, ...i, ...n in sequence.

[0007] Step S02: Electronic device usage time data collection: used to collect basic electronic device usage data of each sub-period of the target electronic device usage time, wherein the electronic device usage time data collection includes an attention data collection unit and a task completion quality data collection unit.

[0008] Step S03: Attention risk information management: Calculate the attention risk management index of each sub-period of the target electronic device usage time based on the attention data collected by the attention data collection unit.

[0009] Step S04: Classification of abnormal attention risk information: used to obtain the attention risk management index of each sub-period of the target electronic device usage time, compare it with the preset attention risk management index, and generate attention risk warning information.

[0010] Step S05: Task completion quality analysis: Based on the task completion quality data collected by the task completion quality data collection unit, calculate the user task completion quality evaluation index for each sub-period of the target electronic device usage time.

[0011] Step S06: Intelligent assessment of task completion quality: used to obtain the user task completion quality assessment index of each sub-period of the target electronic device usage time, compare it with the preset user task completion quality assessment index, and send a corresponding intelligent reminder to the user.

[0012] Step S07: Self-discipline management monitoring and warning: According to the attention risk management index and the user task completion quality evaluation index of each sub-period of the target electronic device usage time, the learning efficiency warning coefficient of each sub-period of the target electronic device usage time is calculated.

[0013] Step S08: Self-discipline management effect evaluation: used to obtain the learning efficiency warning coefficient of each sub-period of the target electronic device usage time, compare it with the preset learning efficiency warning coefficient, and screen out qualified and unqualified users in self-discipline management in each sub-period.

[0014] Preferably, the electronic device usage time data collection is specifically: Attention data collection unit: collects the mobile phone screen usage time and target application usage time of each sub-period of the target electronic device usage time, and marks them as , , where i=1,2,...n, i represents the number of the i-th sub-period; Task completion quality data collection unit: collects the task input time, task accuracy, and task completion quantity of each sub-period of the target electronic device usage time, and marks them as , , .

[0015] Preferably, the calculation formula of the attention risk management index is:

[0016] in, Expressed as the attention risk management index of the ith sub-period, It is represented as the mobile phone screen usage time in the i-th sub-period, is the target application usage time in the ith sub-period, n is the total number of sub-periods, It is expressed as the weight factor of the usage time ratio of the target application.

[0017] Preferably, the abnormal classification of attention risk information is specifically as follows: The shorter the target application usage time in the sub-period, the smaller the difference between the target application usage time ratio and the mean target application usage time ratio, the smaller the attention risk management index. The attention risk management index of each sub-period of the target electronic device usage time is obtained and compared with the preset attention risk management index. When the attention risk management index of a sub-period is less than the preset attention risk management index, it is determined that there is an attention risk abnormality in the sub-period, and attention risk warning information is generated through the electronic device terminal. Otherwise, it is determined that there is no attention risk abnormality in the sub-period.

[0018] Preferably, the task completion quality analysis is specifically as follows: Step S51: extract the maximum value of the task investment duration of the target electronic device usage time, and calculate the stability of the user task investment duration in each sub-period:

[0019] in, It is represented by the stability of the user's task time in the i-th sub-period, It is represented by the duration of the task in the ith sub-period. It is expressed as the maximum time spent on the task; Step S52: Calculate the user task completion efficiency of each sub-period based on the task accuracy rate and the number of tasks completed:

[0020] in, It is expressed as the user task completion efficiency in the i-th sub-period, It is expressed as the preset task accuracy rate, Represents the preset number of completed tasks. It is expressed as the task accuracy rate of the i-th sub-period, It is expressed as the number of tasks completed in the i-th sub-period; Step S53: The calculation formula of the user task completion quality evaluation index is:

[0021] in, It is represented as the user task completion quality evaluation index of the i-th sub-period, It is represented by the stability of the user's task time in the i-th sub-period, It is expressed as the user task completion efficiency in the i-th sub-period, , They respectively represent the correction factors for the stability of user task investment time and user task completion efficiency.

[0022] Preferably, the task completion quality intelligent assessment is specifically: The greater the stability of the time invested in user tasks, it indicates that there is a risk in the time invested in user tasks during this period. The lower the efficiency of user task completion, the worse the quality of user tasks during this period. When the user task completion quality evaluation index of a sub-period is less than the preset user task completion quality evaluation index, it indicates that the user's self-discipline management ability in this sub-period is poor, and intelligent reminders are provided to users through electronic device terminals. Otherwise, it indicates that the user's self-discipline management ability in this sub-period is good, and rewards are provided to users through electronic device terminals.

[0023] Preferably, the calculation formula of the learning efficiency warning coefficient is:

[0024] in, It is expressed as the learning efficiency warning coefficient of the i-th sub-period, Expressed as the attention risk management index of the ith sub-period, It is represented as the user task completion quality evaluation index of the i-th sub-period, It is expressed as the attention risk management index of the i-1th sub-period, It is represented as the user task completion quality evaluation index of the i-1th sub-period, Expressed as the average growth rate of the attention risk management index, It is expressed as the average growth rate of the user task completion quality evaluation index.

[0025] Preferably, the self-discipline management effect evaluation is specifically: The learning efficiency warning coefficient of each sub-period of the target electronic device usage time is obtained, and compared with the preset learning efficiency warning coefficient. If the learning efficiency warning coefficient of a sub-period is less than the preset learning efficiency warning coefficient, it indicates that the user's learning efficiency in this sub-period does not meet expectations, and there is an abnormality in the self-discipline management monitoring. The user is marked as an unqualified user for self-discipline management. Otherwise, it indicates that the user's learning efficiency in this sub-period is in line with expectations, and there is no abnormality in the self-discipline management monitoring, and the user is marked as a qualified user for self-discipline management.

[0026] Technical effects and advantages of the present invention: 1. The present invention provides an electronic device control method for self-discipline management. According to the attention data collected by the attention data collection unit, the attention risk management index of each sub-period of the target electronic device usage time is calculated and compared with the preset attention risk management index. When the attention risk management index of a sub-period is less than the preset attention risk management index, it is determined that the sub-period has an abnormal attention risk, and an attention risk warning information is generated through the electronic device terminal. On the contrary, it is determined that the attention risk of the sub-period is normal. By intelligently identifying and managing the user's electronic device usage behavior, and using the electronic device terminal to automatically generate attention risk warning information, excessive distraction is avoided, thereby improving the self-discipline management ability and improving the user's The user's concentration is calculated according to the task completion quality data collected by the task completion quality data collection unit to obtain the user task completion quality evaluation index of each sub-period of the target electronic device use time. When the user task completion quality evaluation index of a sub-period is less than the preset user task completion quality evaluation index, it indicates that the user's self-discipline management ability of the sub-period is poor, and an intelligent reminder is provided to the user through the electronic device terminal. On the contrary, it indicates that the user's self-discipline management ability of the sub-period is good, and a reward is provided to the user through the electronic device terminal. Through real-time task completion quality data collection, it is conducive to evaluating the user's self-discipline management ability in different time periods, and providing intelligent reminders through the electronic device terminal, thereby optimizing the use of electronic devices and improving self-discipline management ability; 2. The present invention provides an electronic device control method for self-discipline management, which collects basic data on the use of electronic devices in each sub-period of the target electronic device's use time, calculates the attention risk management index and the user task completion quality evaluation index in each sub-period of the target electronic device's use time, and further analyzes to obtain the learning efficiency warning coefficient of each sub-period of the target electronic device's use time, and compares it with the preset learning efficiency warning coefficient. If the learning efficiency warning coefficient of a sub-period is less than the preset learning efficiency warning coefficient, it indicates that the user's learning efficiency in this sub-period does not meet expectations, and there is an abnormality in the self-discipline management monitoring, and the user is marked as an unqualified user for self-discipline management. Otherwise, it indicates that the user's learning efficiency in this sub-period meets expectations, and there is no abnormality in the self-discipline management monitoring, and the user is marked as a qualified user for self-discipline management. By comprehensively collecting the use data of the target electronic device in different sub-periods, the attention risk management index, the user task completion quality evaluation index and the learning efficiency warning coefficient are deeply calculated and analyzed, so as to realize instant feedback on the user's self-discipline management effect and improve the user's learning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention is a flowchart of a method for controlling an electronic device for self-discipline management.

[0028] Figure 2It is a structural schematic diagram of the electronic equipment usage time data collection of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 As shown, the present invention provides an electronic device control method for self-discipline management, including step S01: electronic device usage time period numbering, step S02: electronic device usage time data collection, step S03: attention risk information management, step S04: attention risk information abnormality classification, step S05: task completion quality analysis, step S06: task completion quality intelligent assessment, step S07: self-discipline management monitoring and early warning, step S08: self-discipline management effect evaluation.

[0031] The step S01: electronic device usage time period numbering is connected with the step S02: electronic device usage time data collection, step S02: electronic device usage time data collection is connected with the step S03: attention risk information management, step S03: attention risk information management is connected with the step S04: attention risk information abnormality classification, step S04: attention risk information abnormality classification is connected with the step S05: task completion quality analysis, step S05: task completion quality analysis is connected with the step S06: task completion quality intelligent assessment, step S06: task completion quality intelligent assessment is connected with the step S07: self-discipline management monitoring and early warning, step S07: self-discipline management monitoring and early warning is connected with the step S08: self-discipline management effect evaluation.

[0032] The step S01: the electronic device usage time period number is used to divide the target electronic device usage time into sub-time periods every t time periods, and the sub-time periods of the target electronic device usage time are numbered 1, 2, ...i, ...n in sequence.

[0033] In this embodiment, it should be specifically noted that the electronic device is a mobile phone or tablet based on the Android system.

[0034] The step S02: electronic device usage time data collection is used to collect basic electronic device usage data for each sub-period of the target electronic device usage time, the electronic device usage time data collection includes an attention data collection unit and a task completion quality data collection unit, and the basic electronic device usage data includes attention data and task completion quality data.

[0035] See also Figure 2 As shown, in a possible design, the electronic device usage time data collection is specifically as follows: Attention data collection unit: collects the mobile phone screen usage time and target application usage time of each sub-period of the target electronic device usage time, and marks them as , , where i=1,2,...n, i represents the number of the i-th sub-period; Task completion quality data collection unit: collects the task input time, task accuracy, and task completion quantity of each sub-period of the target electronic device usage time, and marks them as , , .

[0036] The step S03: attention risk information management calculates the attention risk management index of each sub-period of the target electronic device usage time according to the attention data collected by the attention data collection unit.

[0037] In a possible design, the calculation formula of the attention risk management index is:

[0038] in, Expressed as the attention risk management index of the ith sub-period, It is represented as the mobile phone screen usage time in the i-th sub-period, is the target application usage time in the ith sub-period, n is the total number of sub-periods, It is expressed as the weight factor of the usage time ratio of the target application.

[0039] The step S04: the abnormal classification of attention risk information is used to obtain the attention risk management index of each sub-period of the target electronic device usage time, compare it with the preset attention risk management index, and generate attention risk warning information.

[0040] In a possible design, the abnormal classification of attention risk information is specifically as follows: The shorter the target application usage time in the sub-period, the smaller the difference between the target application usage time ratio and the mean target application usage time ratio, the smaller the attention risk management index. The attention risk management index of each sub-period of the target electronic device usage time is obtained and compared with the preset attention risk management index. When the attention risk management index of a sub-period is less than the preset attention risk management index, it is determined that there is an attention risk abnormality in the sub-period, and attention risk warning information is generated through the electronic device terminal. Otherwise, it is determined that there is no attention risk abnormality in the sub-period.

[0041] The step S05: Task completion quality analysis calculates the user task completion quality evaluation index for each sub-period of the target electronic device usage time based on the task completion quality data collected by the task completion quality data collection unit.

[0042] In a possible design, the task completion quality analysis is specifically as follows: Step S51: extract the maximum value of the task investment duration of the target electronic device usage time, and calculate the stability of the user task investment duration in each sub-period:

[0043] in, It is represented by the stability of the user's task time in the i-th sub-period, It is represented by the duration of the task in the ith sub-period. It is expressed as the maximum time spent on the task; Step S52: Calculate the user task completion efficiency of each sub-period based on the task accuracy rate and the number of tasks completed:

[0044] in, It is expressed as the user task completion efficiency in the i-th sub-period, It is expressed as the preset task accuracy rate, Represents the preset number of completed tasks. It is expressed as the task accuracy rate of the i-th sub-period, It is represented as the number of tasks completed in the i-th sub-period; Step S53: The calculation formula of the user task completion quality evaluation index is:

[0045] in, It is represented as the user task completion quality evaluation index of the i-th sub-period, It is represented by the stability of the user's task time in the i-th sub-period, It is expressed as the user task completion efficiency in the i-th sub-period, , They respectively represent the correction factors for the stability of user task investment time and user task completion efficiency.

[0046] The step S06: the task completion quality intelligent assessment is used to obtain the user task completion quality assessment index of each sub-period of the target electronic device usage time, compare it with the preset user task completion quality assessment index, and send a corresponding intelligent reminder to the user.

[0047] In a possible design, the task completion quality intelligent assessment is specifically as follows: The greater the stability of the time invested in user tasks, it indicates that there is a risk in the time invested in user tasks during this period. The lower the efficiency of user task completion, the worse the quality of user tasks during this period. When the user task completion quality evaluation index of a sub-period is less than the preset user task completion quality evaluation index, it indicates that the user's self-discipline management ability in this sub-period is poor, and intelligent reminders are provided to users through electronic device terminals. Otherwise, it indicates that the user's self-discipline management ability in this sub-period is good, and rewards are provided to users through electronic device terminals.

[0048] The step S07: the self-discipline management monitoring warning calculates the learning efficiency warning coefficient of each sub-period of the target electronic device usage time according to the attention risk management index and the user task completion quality evaluation index of each sub-period of the target electronic device usage time.

[0049] In a possible design, the calculation formula of the learning efficiency warning coefficient is:

[0050] in, It is expressed as the learning efficiency warning coefficient of the i-th sub-period, Expressed as the attention risk management index of the ith sub-period, It is represented as the user task completion quality evaluation index of the i-th sub-period, It is expressed as the attention risk management index of the i-1th sub-period, It is represented as the user task completion quality evaluation index of the i-1th sub-period, Expressed as the average growth rate of the attention risk management index, It is expressed as the average growth rate of the user task completion quality evaluation index.

[0051] In this embodiment, it should be specifically stated that the average growth rate of the attention risk management index is: Based on the attention risk management index of each sub-period of the target electronic device usage time, the average growth rate of the attention risk management index of each sub-period of the target electronic device usage time is analyzed: ; The average growth rate of the user task completion quality evaluation index is specifically: Based on the user task completion quality evaluation index of each sub-period of the target electronic device usage time, the average growth rate of the user task completion quality evaluation index of each sub-period of the target electronic device usage time is analyzed: .

[0052] The step S08: the self-discipline management effect evaluation is used to obtain the learning efficiency warning coefficient of each sub-period of the target electronic device usage time, compare it with the preset learning efficiency warning coefficient, and screen out qualified and unqualified users in self-discipline management in each sub-period.

[0053] In a possible design, the self-discipline management effect evaluation is specifically as follows: The learning efficiency warning coefficient of each sub-period of the target electronic device usage time is obtained, and compared with the preset learning efficiency warning coefficient. If the learning efficiency warning coefficient of a sub-period is less than the preset learning efficiency warning coefficient, it indicates that the user's learning efficiency in this sub-period does not meet expectations, and there is an abnormality in the self-discipline management monitoring. The user is marked as an unqualified user for self-discipline management. Otherwise, it indicates that the user's learning efficiency in this sub-period is in line with expectations, and there is no abnormality in the self-discipline management monitoring, and the user is marked as a qualified user for self-discipline management.

[0054] In the present embodiment, it should be specifically explained that the present invention calculates the attention risk management index of each sub-period of the target electronic device usage time based on the attention data collected by the attention data collection unit, and compares it with the preset attention risk management index. When the attention risk management index of a sub-period is less than the preset attention risk management index, it is determined that there is an attention risk abnormality in the sub-period, and attention risk warning information is generated through the electronic device terminal. On the contrary, it is determined that there is no abnormality in the attention risk of the sub-period. By intelligently identifying and managing the user's electronic device usage behavior, and using the electronic device terminal to automatically generate attention risk warning information, excessive distraction can be avoided, thereby improving self-discipline management ability and improving the user's attention. Attention concentration, based on the task completion quality data collected by the task completion quality data collection unit, calculate the user task completion quality evaluation index of each sub-period of the target electronic device use time. When the user task completion quality evaluation index of a sub-period is less than the preset user task completion quality evaluation index, it indicates that the user's self-discipline management ability in the sub-period is poor, and an intelligent reminder is provided to the user through the electronic device terminal. On the contrary, it indicates that the user's self-discipline management ability in the sub-period is good, and a reward is provided to the user through the electronic device terminal. Through real-time task completion quality data collection, it is conducive to evaluating the user's self-discipline management ability in different time periods, and providing intelligent reminders through the electronic device terminal, thereby optimizing the use of electronic devices and improving self-discipline management ability; The present invention collects basic data on the use of electronic devices in each sub-period of the use time of the target electronic device, calculates the attention risk management index and the user task completion quality evaluation index in each sub-period of the use time of the target electronic device, further analyzes to obtain the learning efficiency warning coefficient in each sub-period of the use time of the target electronic device, and compares it with the preset learning efficiency warning coefficient. If the learning efficiency warning coefficient of a sub-period is less than the preset learning efficiency warning coefficient, it indicates that the learning efficiency of the user in the sub-period does not meet expectations, and there is an abnormality in the self-discipline management monitoring, and the user is marked as an unqualified user for self-discipline management. Otherwise, it indicates that the learning efficiency of the user in the sub-period meets expectations, and there is no abnormality in the self-discipline management monitoring, and the user is marked as a qualified user for self-discipline management. By comprehensively collecting the use data of the target electronic device in different sub-periods, the attention risk management index, the user task completion quality evaluation index and the learning efficiency warning coefficient are deeply calculated and analyzed, so as to realize instant feedback on the user's self-discipline management effect and improve the user's learning efficiency.

[0055] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling an electronic device for self-discipline management, characterized in that: include: Step S01: electronic device usage time period numbering: used to divide the target electronic device usage time into sub-time periods every t time periods, and number the sub-time periods of the target electronic device usage time as 1, 2, ...i, ...n in sequence; Step S02: electronic device usage time data collection: used to collect basic electronic device usage data of each sub-period of the target electronic device usage time, wherein the electronic device usage time data collection includes an attention data collection unit and a task completion quality data collection unit; Step S03: attention risk information management: calculating the attention risk management index of each sub-period of the target electronic device usage time according to the attention data collected by the attention data collection unit; Step S04: Classification of abnormal attention risk information: used to obtain the attention risk management index of each sub-period of the target electronic device usage time, compare it with the preset attention risk management index, and generate attention risk warning information; Step S05: Task completion quality analysis: according to the task completion quality data collected by the task completion quality data collection unit, calculate the user task completion quality evaluation index of each sub-period of the target electronic device usage time; Step S06: Intelligent evaluation of task completion quality: used to obtain the user task completion quality evaluation index of each sub-period of the target electronic device usage time, compare it with the preset user task completion quality evaluation index, and send a corresponding intelligent reminder to the user; Step S07: Self-discipline management monitoring and early warning: according to the attention risk management index and the user task completion quality evaluation index of each sub-period of the target electronic device usage time, the learning efficiency early warning coefficient of each sub-period of the target electronic device usage time is calculated; Step S08: Self-discipline management effect evaluation: used to obtain the learning efficiency warning coefficient of each sub-period of the target electronic device usage time, compare it with the preset learning efficiency warning coefficient, and screen out qualified and unqualified users in self-discipline management in each sub-period.

2. The electronic device control method for self-discipline management according to claim 1, characterized in that: The electronic device usage time data collection is specifically as follows: Attention data collection unit: collects the mobile phone screen usage time and target application usage time of each sub-period of the target electronic device usage time, and marks them as , , where i=1,2,...n, i represents the number of the i-th sub-period; Task completion quality data collection unit: collects the task input time, task accuracy, and task completion quantity of each sub-period of the target electronic device usage time, and marks them as , , .

3. The electronic device control method for self-discipline management according to claim 1, characterized in that: The calculation formula of the attention risk management index is: in, Expressed as the attention risk management index of the ith sub-period, It is represented as the mobile phone screen usage time in the i-th sub-period, is the target application usage time in the ith sub-period, n is the total number of sub-periods, It is expressed as the weight factor of the usage time ratio of the target application.

4. The electronic device control method for self-discipline management according to claim 1, characterized in that: The abnormal classification of attention risk information is specifically as follows: The shorter the target application usage time in the sub-period, the smaller the difference between the target application usage time ratio and the mean target application usage time ratio, the smaller the attention risk management index. The attention risk management index of each sub-period of the target electronic device usage time is obtained and compared with the preset attention risk management index. When the attention risk management index of a sub-period is less than the preset attention risk management index, it is determined that there is an attention risk abnormality in the sub-period, and attention risk warning information is generated through the electronic device terminal. Otherwise, it is determined that there is no attention risk abnormality in the sub-period.

5. The electronic device control method for self-discipline management according to claim 1, characterized in that: The task completion quality analysis is specifically as follows: Step S51: extract the maximum value of the task investment duration of the target electronic device usage time, and calculate the stability of the user task investment duration in each sub-period: in, It is represented by the stability of the user's task time in the i-th sub-period, It is represented by the duration of the task in the ith sub-period. It is expressed as the maximum time spent on the task; Step S52: Calculate the user task completion efficiency of each sub-period based on the task accuracy rate and the number of completed tasks: in, It is expressed as the user task completion efficiency in the i-th sub-period, It is expressed as the preset task accuracy rate, Represents the preset number of completed tasks. It is expressed as the task accuracy rate of the i-th sub-period, It is represented as the number of tasks completed in the i-th sub-period; Step S53: The calculation formula of the user task completion quality evaluation index is: in, It is represented as the user task completion quality evaluation index of the i-th sub-period, It is represented by the stability of the user's task time in the i-th sub-period, It is expressed as the user task completion efficiency in the i-th sub-period, , They respectively represent the correction factors for the stability of user task investment time and user task completion efficiency.

6. The electronic device control method for self-discipline management according to claim 1, characterized in that: The intelligent assessment of task completion quality is specifically as follows: The greater the stability of the time invested in user tasks, it indicates that there is a risk in the time invested in user tasks during this period. The lower the efficiency of user task completion, the worse the quality of user tasks during this period. When the user task completion quality evaluation index of a sub-period is less than the preset user task completion quality evaluation index, it indicates that the user's self-discipline management ability in this sub-period is poor, and intelligent reminders are provided to users through electronic device terminals. Otherwise, it indicates that the user's self-discipline management ability in this sub-period is good, and rewards are provided to users through electronic device terminals.

7. The electronic device control method for self-discipline management according to claim 1, characterized in that: The calculation formula of the learning efficiency warning coefficient is: in, Expressed as the learning efficiency warning coefficient of the i-th sub-period, Expressed as the attention risk management index of the ith sub-period, It is represented as the user task completion quality evaluation index of the i-th sub-period, It is expressed as the attention risk management index of the i-1th sub-period, It is represented as the user task completion quality evaluation index of the i-1th sub-period, Expressed as the average growth rate of the attention risk management index, It is expressed as the average growth rate of the user task completion quality evaluation index.

8. The electronic device control method for self-discipline management according to claim 1, characterized in that: The self-discipline management effect evaluation is specifically as follows: The learning efficiency warning coefficient of each sub-period of the target electronic device usage time is obtained, and compared with the preset learning efficiency warning coefficient. If the learning efficiency warning coefficient of a sub-period is less than the preset learning efficiency warning coefficient, it indicates that the user's learning efficiency in this sub-period does not meet expectations, and there is an abnormality in the self-discipline management monitoring. The user is marked as an unqualified user for self-discipline management. Otherwise, it indicates that the user's learning efficiency in this sub-period is in line with expectations, and there is no abnormality in the self-discipline management monitoring, and the user is marked as a qualified user for self-discipline management.

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