An intelligent terminal management method, system and readable storage medium

Abstract

The application provides a kind of intelligent terminal management method, system and readable storage medium, the method comprises: obtaining the application program that user has opened in current intelligent terminal;Determine target application program;From target application program, the remaining duration of the task being executed is obtained;Obtain the remaining power of current intelligent terminal at current time;The first endurance duration of current intelligent terminal under the first running environment is predicted by power endurance prediction model;The second endurance duration of current intelligent terminal under the second running environment is predicted by power endurance prediction model;When remaining duration is greater than the first endurance duration and less than the second endurance duration, the application program exit scheme is determined by preset management algorithm, so that the task being executed of target application program can be completed.The application program is intelligently managed to ensure that the task being executed of target application program is successfully completed, and the user experience of intelligent terminal is improved.

Description

Technical Field

[0001] This invention relates to the field of electronic information technology, and in particular to a smart terminal management method, system, and readable storage medium. Background Technology

[0002] With the advancement of science and technology, the development and expansion of mobile internet, smart terminals (such as smartphones) are becoming increasingly popular, and various application software is also widely used.

[0003] Currently, when users need to open certain apps on their smart devices, such as watching a video through a video app or using a map app for navigation, there is usually a playback time or navigation time limit—that is, the total time to play the video or the total time to reach the destination. While current smart devices have battery level reminders, these only notify the user whether to activate power-saving mode when the battery level drops below a certain threshold (e.g., 20%). However, for some long videos or navigation sessions, even if the smart device enters power-saving mode when the battery level is below a certain threshold, it may still not be able to finish playing the video or navigate to the destination, resulting in a poor user experience. Summary of the Invention

[0004] To address at least one of the aforementioned technical problems, this invention proposes a smart terminal management method, system, and readable storage medium. This method can intelligently manage other applications on the smart terminal based on the time taken by the target application to perform its task, thereby ensuring that the target application's task can be completed smoothly and improving the user's experience with the smart terminal.

[0005] The first aspect of this invention proposes a smart terminal management method, the method comprising:

[0006] Get the applications that the user has opened on the current smart terminal;

[0007] The target application is determined from the currently open applications based on the current interface display status of the smart terminal;

[0008] Obtain the remaining duration of the currently executing task from the target application;

[0009] Get the remaining battery level of the current smart terminal at the current time;

[0010] Based on the remaining battery power, the battery life prediction model predicts the first battery life of the current smart terminal in the first operating environment, where the first operating environment refers to the environment in which the current smart terminal keeps the opened applications running.

[0011] Based on the remaining battery power, the battery life prediction model predicts the second battery life of the current smart terminal in the second operating environment, where the second operating environment refers to the environment in which the current smart terminal keeps only the target application running.

[0012] Determine if the remaining time is greater than the first battery life and less than the second battery life. If so, for already running applications, determine an application exit plan using a preset management algorithm so that the target application's ongoing tasks can be completed.

[0013] In this solution, the battery life prediction model predicts the initial battery life of the current smart terminal under the first operating environment, specifically including:

[0014] Build a battery life prediction model;

[0015] Obtain parameter data that affects the current battery life of the smart terminal. The parameter data includes at least the current battery performance of the smart terminal at the current time, the power consumption attributes of each application in the first operating environment, and external environmental parameters.

[0016] The above parameter data and the remaining battery power are input into the battery life prediction model, and the battery life prediction model predicts the first battery life.

[0017] In this solution, after the first battery life prediction duration is determined by the battery life prediction model, the method further includes:

[0018] Acquire historical monitoring data from multiple reference smart terminals. Each historical monitoring data should include at least the battery performance, remaining power, external environmental parameters, power consumption attributes of running applications, and actual battery life of the reference smart terminal over a historical period.

[0019] Based on the historical monitoring data of each reference smart terminal, the battery performance and external environmental parameters of the reference smart terminal at historical time are characterized and the first feature value is obtained.

[0020] Obtain the battery performance and external environment parameters of the current smart terminal at the current time, and perform feature calculations to obtain the second feature value;

[0021] The difference between the first feature value of each reference smart terminal and the second feature value of the current smart terminal is calculated to obtain the difference value based on each reference smart terminal.

[0022] Historical monitoring data of reference smart terminals with difference values ​​less than the second threshold are added to the reference database;

[0023] Based on the historical monitoring data of each reference smart terminal in the reference database, the battery performance, remaining power, external environmental parameters, and power consumption attributes of the running applications of the reference smart terminal at historical time are input into the power life prediction model, and the predicted battery life is output.

[0024] Based on the historical monitoring data of each reference smart terminal in the reference database, the actual battery life is subtracted from the predicted battery life to obtain the battery life difference.

[0025] The differences in battery life duration of historical monitoring data of all reference smart terminals in the reference database are added together to obtain the total difference in battery life duration. The total difference in battery life duration is then divided by the total amount of historical monitoring data of reference smart terminals in the reference database to obtain the average value of the battery life duration difference, which is used as the battery life duration correction value.

[0026] The corrected first battery life is obtained by adding a battery life correction value to the first battery life predicted by the battery life prediction model.

[0027] In this solution, feature calculations are performed on the battery performance and external environmental parameters of the reference smart terminal over historical time to obtain a first feature value; the battery performance and external environmental parameters of the current smart terminal at the current time are obtained and feature calculations are performed to obtain a second feature value; the difference between the first feature value of each reference smart terminal and the second feature value of the current smart terminal is calculated to obtain a difference value based on each reference smart terminal, specifically including:

[0028] The optimal operating temperature and humidity for the battery are preset to T0 and R0, respectively.

[0029] If the battery performance of the reference smart terminal over a historical period is obtained as health Q1, and the external environmental parameters are temperature T1 and humidity H1, then the first feature value K1 is calculated using the formula K1=Q1 / F1, where F1=|T1-T0|*|H1-H0|.

[0030] If the current battery performance of the smart terminal at the current time is obtained as health Q2, and the external environmental parameters are temperature T2 and humidity H2, then the second characteristic value K2 is calculated by the formula K2=Q2 / F2, where F2=|T2-T0|*|H2-H0|;

[0031] The difference value R based on each reference smart terminal is calculated using the difference calculation formula R = |K2-K1| / K2.

[0032] In this solution, a preset management algorithm is used to determine the application exit strategy, which specifically includes:

[0033] It is assumed that there are n applications running besides the target application.

[0034] Exit each of the n applications in turn, and predict whether the current smart device’s battery life is greater than the remaining time when the remaining n-1 applications remain open. If so, mark the selected application as selected and add it as a candidate solution to the candidate database.

[0035] Remove the selected applications from the n applications, and then select 2 applications from the remaining applications to exit. Predict whether the current battery life of the smart device is greater than the remaining battery life when the remaining n-2 applications are still open. If so, mark the selected applications as selected and add them to the candidate database as a candidate solution.

[0036] The selected applications in the first i-1 steps are removed in the above manner. Then, each i application is selected from the remaining applications and exited. It is predicted whether the current battery life of the smart device is greater than the remaining battery life when the remaining ni applications are kept open. If so, the selected application is marked as selected and added to the candidate database as a candidate solution. This process continues until there are fewer than i applications remaining after the selected applications in the first i-1 steps are removed. Here, n is a positive integer greater than or equal to 1, and i is a positive integer greater than 1 and less than or equal to n.

[0037] The candidate solutions in the candidate database are filtered according to a preset optimization algorithm, and the best candidate solution is selected as the final application exit solution.

[0038] In this scheme, candidate schemes in the candidate database are screened according to a preset optimization algorithm, and the best candidate scheme is selected as the final application exit scheme, specifically including:

[0039] The number of applications that need to be exited in each candidate scheme state, the difference between the smart device's battery life and the remaining time, and the usage frequency of the applications that need to be exited are obtained respectively.

[0040] The number of applications that need to be terminated under each candidate scheme in the candidate database is compared with the number of applications that need to be terminated under the remaining candidate schemes. If the former is less than the latter, the score of the quantity surplus item of the former candidate scheme is increased by 1; otherwise, it remains unchanged.

[0041] The difference between the battery life of the smart device under each candidate scheme in the candidate database and the remaining time is compared with the difference between the battery life of the smart device under the remaining candidate schemes. If the former is less than the latter, the score of the time remaining item of the former candidate scheme is increased by 1; otherwise, it remains unchanged.

[0042] The total usage frequency of the application that needs to be exited under each candidate scheme in the candidate database is compared with the total usage frequency of the application that needs to be exited under the remaining candidate schemes. If the former is less than the latter, the score of the action stop-loss item of the former candidate scheme is increased by 1; otherwise, it remains unchanged.

[0043] The candidate solutions in the candidate database are compared pairwise to determine the number of applications that need to be exited, and the total score for the quantity surplus item of each candidate solution is calculated. The candidate solutions in the candidate database are compared pairwise to determine the difference between the battery life of the smart device and the remaining battery life, and the total score for the time surplus item of each candidate solution is calculated. The candidate solutions in the candidate database are compared pairwise to determine the usage frequency of the applications that need to be exited, and the total score for the action stop loss item of each candidate solution is calculated.

[0044] The weights of the preset quantity surplus, time surplus, and action stop loss items in selecting the best candidate solution are different;

[0045] The sum of scores for the quantity surplus item of each candidate solution is multiplied by the corresponding influence weight to obtain the sum of score weights for the quantity surplus item; the sum of scores for the time surplus item of each candidate solution is multiplied by the corresponding influence weight to obtain the sum of score weights for the time surplus item; and the sum of scores for the action stop-loss item of each candidate solution is multiplied by the corresponding influence weight to obtain the sum of score weights for the action stop-loss item.

[0046] The total score for each candidate solution is obtained by summing the weighted scores of the quantity surplus item, the time surplus item, and the action stop loss item.

[0047] The candidate solution with the highest overall score in the candidate database will be selected as the final application exit solution.

[0048] A second aspect of the present invention also provides an intelligent terminal management system, including a memory and a processor. The memory includes an intelligent terminal management method program, which, when executed by the processor, performs the following steps:

[0049] Get the applications that the user has opened on the current smart terminal;

[0050] The target application is determined from the currently open applications based on the current interface display status of the smart terminal;

[0051] Obtain the remaining duration of the currently executing task from the target application;

[0052] Get the remaining battery level of the current smart terminal at the current time;

[0053] Based on the remaining battery power, the battery life prediction model predicts the first battery life of the current smart terminal in the first operating environment, where the first operating environment refers to the environment in which the current smart terminal keeps the opened applications running.

[0054] Based on the remaining battery power, the battery life prediction model predicts the second battery life of the current smart terminal in the second operating environment, where the second operating environment refers to the environment in which the current smart terminal keeps only the target application running.

[0055] Determine if the remaining time is greater than the first battery life and less than the second battery life. If so, for already running applications, determine an application exit plan using a preset management algorithm so that the target application's ongoing tasks can be completed.

[0056] In this solution, the battery life prediction model predicts the initial battery life of the current smart terminal under the first operating environment, specifically including:

[0057] Build a battery life prediction model;

[0058] Obtain parameter data that affects the current battery life of the smart terminal. The parameter data includes at least the current battery performance of the smart terminal at the current time, the power consumption attributes of each application in the first operating environment, and external environmental parameters.

[0059] The above parameter data and the remaining battery power are input into the battery life prediction model, and the battery life prediction model predicts the first battery life.

[0060] In this solution, after the first battery life prediction duration is determined by the battery life prediction model, the smart terminal management method program, when executed by the processor, also performs the following steps:

[0061] Acquire historical monitoring data from multiple reference smart terminals. Each historical monitoring data should include at least the battery performance, remaining power, external environmental parameters, power consumption attributes of running applications, and actual battery life of the reference smart terminal over a historical period.

[0062] Based on the historical monitoring data of each reference smart terminal, the battery performance and external environmental parameters of the reference smart terminal at historical time are characterized and the first feature value is obtained.

[0063] Obtain the battery performance and external environment parameters of the current smart terminal at the current time, and perform feature calculations to obtain the second feature value;

[0064] The difference between the first feature value of each reference smart terminal and the second feature value of the current smart terminal is calculated to obtain the difference value based on each reference smart terminal.

[0065] Historical monitoring data of reference smart terminals with difference values ​​less than the second threshold are added to the reference database;

[0066] Based on the historical monitoring data of each reference smart terminal in the reference database, the battery performance, remaining power, external environmental parameters, and power consumption attributes of the running applications of the reference smart terminal at historical time are input into the power life prediction model, and the predicted battery life is output.

[0067] Based on the historical monitoring data of each reference smart terminal in the reference database, the actual battery life is subtracted from the predicted battery life to obtain the battery life difference.

[0068] The differences in battery life duration of historical monitoring data of all reference smart terminals in the reference database are added together to obtain the total difference in battery life duration. The total difference in battery life duration is then divided by the total amount of historical monitoring data of reference smart terminals in the reference database to obtain the average value of the battery life duration difference, which is used as the battery life duration correction value.

[0069] The corrected first battery life is obtained by adding a battery life correction value to the first battery life predicted by the battery life prediction model.

[0070] A third aspect of the present invention also provides a computer-readable storage medium comprising a smart terminal management method program, wherein when the smart terminal management method program is executed by a processor, it implements the steps of the smart terminal management method described above.

[0071] The present invention proposes a smart terminal management method, system, and readable storage medium that can combine the remaining time of the target application's currently executing task and determine the exit scheme of other applications through a preset management algorithm, so that the target application's currently executing task can be completed smoothly, thereby improving the user's experience with the smart terminal.

[0072] Additional aspects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description or may be learned by practice of the invention. Attached Figure Description

[0073] Figure 1A flowchart of a smart terminal management method according to the present invention is shown;

[0074] Figure 2 A block diagram of an intelligent terminal management system according to the present invention is shown. Detailed Implementation

[0075] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0076] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0077] Figure 1 A flowchart of a smart terminal management method according to the present invention is shown.

[0078] like Figure 1 As shown, the first aspect of the present invention proposes a smart terminal management method, the method comprising:

[0079] S102, Obtain the applications that the user has opened on the current smart terminal;

[0080] S104, determine the target application from the applications that are currently open based on the current interface display status of the smart terminal;

[0081] S106, Obtain the remaining duration of the currently executing task from the target application;

[0082] S108, obtain the remaining battery power of the current smart terminal at the current time;

[0083] S110, based on the remaining power, predict the first battery life of the current smart terminal in the first operating environment through the power life prediction model, wherein the first operating environment refers to the environment in which the current smart terminal keeps the opened applications running.

[0084] S112, based on the remaining power, predict the second battery life of the current smart terminal in the second operating environment through the power life prediction model, wherein the second operating environment refers to the environment in which the current smart terminal keeps only the target application running.

[0085] S114, determine whether the remaining time is greater than the first battery life and less than the second battery life. If so, for the already opened application, determine the application exit plan through the preset management algorithm so that the target application's ongoing task can be completed.

[0086] The smart terminal management method of the present invention can combine the remaining time of the target application's currently executing task and determine the exit scheme of other applications through a preset management algorithm, so that the target application's currently executing task can be completed smoothly, thereby improving the user's experience with the smart terminal.

[0087] Intelligent management of other applications on smart terminals is implemented to ensure that the target application's ongoing tasks can be completed smoothly, thereby improving the user's experience with the smart terminal.

[0088] It is understandable that enabled applications can include those displayed on the interface, or those that are not displayed on the interface but run in the background.

[0089] According to a specific embodiment of the present invention, the target application is determined from the applications that have been opened based on the current interface display state of the smart terminal. Specifically, this includes: obtaining the application that is currently being displayed on the interface of the smart terminal, and determining whether the application being displayed is performing a time-consuming task (such as navigation). If so, the application displayed on the interface is the target application.

[0090] According to a specific embodiment of the present invention, if the remaining time is greater than the second battery life, the user is reminded that the current battery level is insufficient to complete the task; if the remaining time is less than the first battery life, no other application exit operation is required.

[0091] According to an embodiment of the present invention, predicting the first battery life of a current smart terminal under a first operating environment using a battery life prediction model specifically includes:

[0092] Build a battery life prediction model;

[0093] Obtain parameter data that affects the current battery life of the smart terminal. The parameter data includes at least the current battery performance of the smart terminal at the current time, the power consumption attributes of each application in the first operating environment, and external environmental parameters.

[0094] The above parameter data and the remaining battery power are input into the battery life prediction model, and the battery life prediction model predicts the first battery life.

[0095] It is understood that external environmental parameters can include, but are not limited to, temperature and humidity values. Battery performance can refer to the health status of the smart terminal's battery, for example, a battery health status of 90%. However, it is not limited to these.

[0096] According to an embodiment of the present invention, after the first battery life is predicted by the battery life prediction model, the method further includes:

[0097] Acquire historical monitoring data from multiple reference smart terminals. Each historical monitoring data should include at least the battery performance, remaining power, external environmental parameters, power consumption attributes of running applications, and actual battery life of the reference smart terminal over a historical period.

[0098] Based on the historical monitoring data of each reference smart terminal, the battery performance and external environmental parameters of the reference smart terminal at historical time are characterized and the first feature value is obtained.

[0099] Obtain the battery performance and external environment parameters of the current smart terminal at the current time, and perform feature calculations to obtain the second feature value;

[0100] The difference between the first feature value of each reference smart terminal and the second feature value of the current smart terminal is calculated to obtain the difference value based on each reference smart terminal.

[0101] Historical monitoring data of reference smart terminals with difference values ​​less than the second threshold are added to the reference database;

[0102] Based on the historical monitoring data of each reference smart terminal in the reference database, the battery performance, remaining power, external environmental parameters, and power consumption attributes of the running applications of the reference smart terminal at historical time are input into the power life prediction model, and the predicted battery life is output.

[0103] Based on the historical monitoring data of each reference smart terminal in the reference database, the actual battery life is subtracted from the predicted battery life to obtain the battery life difference.

[0104] The differences in battery life duration of historical monitoring data of all reference smart terminals in the reference database are added together to obtain the total difference in battery life duration. The total difference in battery life duration is then divided by the total amount of historical monitoring data of reference smart terminals in the reference database to obtain the average value of the battery life duration difference, which is used as the battery life duration correction value.

[0105] The corrected first battery life is obtained by adding a battery life correction value to the first battery life predicted by the battery life prediction model.

[0106] As can be understood, this invention uses the same battery life prediction model to predict the battery life based on historical monitoring data of reference smart terminals. It then calculates the difference between the predicted battery life and the actual battery life, and further averages these differences across the historical monitoring data of multiple reference smart terminals to obtain a corrected battery life value. Finally, the corrected battery life value is added to the first battery life predicted by the battery life prediction model to obtain the corrected first battery life. This invention effectively improves the accuracy of the first battery life prediction, further facilitating more precise determination of application exit strategies.

[0107] In addition, the present invention also uses feature calculation and comparison to find data that is similar to the feature value of the current smart terminal from the historical monitoring data of multiple reference smart terminals, thereby eliminating the interference of historical monitoring data of other parameter smart terminals on the calculation of battery life correction value, and further improving the accuracy of battery life correction value.

[0108] It is understandable that after predicting the second battery life, a correction method similar to that used for the first battery life can be used to correct it, thereby obtaining the corrected second battery life.

[0109] According to an embodiment of the present invention, feature calculations are performed on the battery performance and external environmental parameters of a reference smart terminal at a historical time to obtain a first feature value; the battery performance and external environmental parameters of the current smart terminal at the current time are obtained, and feature calculations are performed to obtain a second feature value; the difference between the first feature value of each reference smart terminal and the second feature value of the current smart terminal is calculated to obtain a difference value based on each reference smart terminal, specifically including:

[0110] The optimal operating temperature and humidity for the battery are preset to T0 and R0, respectively.

[0111] If the battery performance of the reference smart terminal over a historical period is obtained as health Q1, and the external environmental parameters are temperature T1 and humidity H1, then the first feature value K1 is calculated using the formula K1=Q1 / F1, where F1=|T1-T0|*|H1-H0|.

[0112] If the current battery performance of the smart terminal at the current time is obtained as health Q2, and the external environmental parameters are temperature T2 and humidity H2, then the second characteristic value K2 is calculated by the formula K2=Q2 / F2, where F2=|T2-T0|*|H2-H0|;

[0113] The difference value R based on each reference smart terminal is calculated using the difference calculation formula R = |K2-K1| / K2.

[0114] According to an embodiment of the present invention, an application exit scheme is determined through a preset management algorithm, specifically including:

[0115] It is assumed that there are n applications running besides the target application.

[0116] Exit each of the n applications in turn, and predict whether the current smart device’s battery life is greater than the remaining time when the remaining n-1 applications remain open. If so, mark the selected application as selected and add it as a candidate solution to the candidate database.

[0117] Remove the selected applications from the n applications, and then select 2 applications from the remaining applications to exit. Predict whether the current battery life of the smart device is greater than the remaining battery life when the remaining n-2 applications are still open. If so, mark the selected applications as selected and add them to the candidate database as a candidate solution.

[0118] The selected applications in the first i-1 steps are removed in the above manner. Then, each i application is selected from the remaining applications and exited. It is predicted whether the current battery life of the smart device is greater than the remaining battery life when the remaining ni applications are kept open. If so, the selected application is marked as selected and added to the candidate database as a candidate solution. This process continues until there are fewer than i applications remaining after the selected applications in the first i-1 steps are removed. Here, n is a positive integer greater than or equal to 1, and i is a positive integer greater than 1 and less than or equal to n.

[0119] The candidate solutions in the candidate database are filtered according to a preset optimization algorithm, and the best candidate solution is selected as the final application exit solution.

[0120] In a specific embodiment, when the selected applications marked in the first i-1 steps are removed in the manner described above, and each i application is selected from the remaining applications for exit, n is preset to be 10. After removing the selected applications marked in the first 4 steps, it is assumed that there are only 3 applications remaining. At this time, the 3 remaining applications are not enough to perform the operation in the 5th step, that is, each 5 applications are selected for exit in turn. Then, the selection of candidate schemes ends in the 5th step, and the candidate database consists of the candidate schemes selected in the first 4 steps.

[0121] In a specific embodiment, when exiting by selecting every two applications from the remaining applications, n is preset to be 5, and is application A, application B, application C, application D, and application E respectively; the applications marked in step 1 are preset to be application B and application C, then the remaining applications in step 2 (i.e., unmarked) are 3, namely application A, application D, and application E; at this time, there are 3 combinations of selecting every two applications, namely combination 1: application A and application D, combination 2: application A and application E, and combination 3: application D and application E. The process requires evaluating three combinations. First, combination 1 is selected, where applications A and D exit simultaneously. The remaining open applications include B, C, and E. The system predicts whether the remaining battery life of the smart device will exceed the remaining battery life if these three applications remain open. If so, the two applications in combination 1 are associated and marked as selected. Similarly, combinations 2 and 3 are selected sequentially, and the system checks whether the remaining battery life of the smart device will exceed the remaining battery life if the applications in combination 2 or 3 exit. If so, the two applications in combination 2 or 3 are associated and marked as selected; otherwise, no marking is performed. In step 3, all applications marked as selected in the first two steps are removed, and three applications are selected from the remaining applications for combination 3. Thus, the invention's first step involves exiting one application, the second step involves exiting two applications, and then gradually increases the number of applications. It is understandable that in step 3, three applications need to be selected to exit. If an application is marked as selected, it means that the application has already achieved a battery life longer than the remaining time by exiting fewer than three applications in the first two steps. Therefore, based on the principle of reducing excessive intervention in the smart terminal, it is naturally unnecessary to include the applications marked as selected in the first two steps in the selection of application exit schemes in step 3.

[0122] According to an embodiment of the present invention, candidate solutions in the candidate database are screened according to a preset optimization algorithm, and the best candidate solution is selected as the final application exit solution, specifically including:

[0123] The number of applications that need to be exited in each candidate scheme state, the difference between the smart device's battery life and the remaining time, and the usage frequency of the applications that need to be exited are obtained respectively.

[0124] The number of applications that need to be terminated under each candidate scheme in the candidate database is compared with the number of applications that need to be terminated under the remaining candidate schemes. If the former is less than the latter, the score of the quantity surplus item of the former candidate scheme is increased by 1; otherwise, it remains unchanged.

[0125] The difference between the battery life of the smart device under each candidate scheme in the candidate database and the remaining time is compared with the difference between the battery life of the smart device under the remaining candidate schemes. If the former is less than the latter, the score of the time remaining item of the former candidate scheme is increased by 1; otherwise, it remains unchanged.

[0126] The total usage frequency of the application that needs to be exited under each candidate scheme in the candidate database is compared with the total usage frequency of the application that needs to be exited under the remaining candidate schemes. If the former is less than the latter, the score of the action stop-loss item of the former candidate scheme is increased by 1; otherwise, it remains unchanged.

[0127] The candidate solutions in the candidate database are compared pairwise to determine the number of applications that need to be exited, and the total score for the quantity surplus item of each candidate solution is calculated. The candidate solutions in the candidate database are compared pairwise to determine the difference between the battery life of the smart device and the remaining battery life, and the total score for the time surplus item of each candidate solution is calculated. The candidate solutions in the candidate database are compared pairwise to determine the usage frequency of the applications that need to be exited, and the total score for the action stop loss item of each candidate solution is calculated.

[0128] The weights of the preset quantity surplus, time surplus, and action stop loss items in selecting the best candidate solution are different;

[0129] The sum of scores for the quantity surplus item of each candidate solution is multiplied by the corresponding influence weight to obtain the sum of score weights for the quantity surplus item; the sum of scores for the time surplus item of each candidate solution is multiplied by the corresponding influence weight to obtain the sum of score weights for the time surplus item; and the sum of scores for the action stop-loss item of each candidate solution is multiplied by the corresponding influence weight to obtain the sum of score weights for the action stop-loss item.

[0130] The total score for each candidate solution is obtained by summing the weighted scores of the quantity surplus item, the time surplus item, and the action stop loss item.

[0131] The candidate solution with the highest overall score in the candidate database will be selected as the final application exit solution.

[0132] It is understandable that when it is necessary to exit other applications to ensure that the target application's task can be completed smoothly, it is necessary to exit as few other applications as possible. On the one hand, this is evaluated by comparing the number of applications that need to be exited under the candidate schemes, and on the other hand, it is evaluated by comparing the difference between the smart device's battery life and the remaining time under each candidate scheme. That is, if exiting 2 applications already satisfies the requirement that the smart device's battery life is greater than the remaining time, if more applications are exited, although the smart device's battery life can be extended even further, it is based on the premise that the target application's task can be completed smoothly, so as to minimize excessive intervention in the state of the smart device's currently enabled applications.

[0133] It is understandable that when exiting an application, the frequency of user usage of each application should be considered, minimizing the exit from frequently used applications. Therefore, this invention, based on a preset optimization algorithm, combines multiple factors and the weight of each factor's influence on the selected application, to select the candidate solution with the highest overall score as the final application exit solution.

[0134] Figure 2 A block diagram of an intelligent terminal management system according to the present invention is shown.

[0135] like Figure 2 As shown, a second aspect of the present invention also proposes an intelligent terminal management system 2, including a memory 21 and a processor 22. The memory includes an intelligent terminal management method program, which, when executed by the processor, performs the following steps:

[0136] Get the applications that the user has opened on the current smart terminal;

[0137] The target application is determined from the currently open applications based on the current interface display status of the smart terminal;

[0138] Obtain the remaining duration of the currently executing task from the target application;

[0139] Get the remaining battery level of the current smart terminal at the current time;

[0140] Based on the remaining battery power, the battery life prediction model predicts the first battery life of the current smart terminal in the first operating environment, where the first operating environment refers to the environment in which the current smart terminal keeps the opened applications running.

[0141] Based on the remaining battery power, the battery life prediction model predicts the second battery life of the current smart terminal in the second operating environment, where the second operating environment refers to the environment in which the current smart terminal keeps only the target application running.

[0142] Determine if the remaining time is greater than the first battery life and less than the second battery life. If so, for already running applications, determine an application exit plan using a preset management algorithm so that the target application's ongoing tasks can be completed.

[0143] According to an embodiment of the present invention, predicting the first battery life of a current smart terminal under a first operating environment using a battery life prediction model specifically includes:

[0144] Build a battery life prediction model;

[0145] Obtain parameter data that affects the current battery life of the smart terminal. The parameter data includes at least the current battery performance of the smart terminal at the current time, the power consumption attributes of each application in the first operating environment, and external environmental parameters.

[0146] The above parameter data and the remaining battery power are input into the battery life prediction model, and the battery life prediction model predicts the first battery life.

[0147] According to an embodiment of the present invention, after the first battery life is predicted by the battery life prediction model, the smart terminal management method program, when executed by the processor, further implements the following steps:

[0148] Acquire historical monitoring data from multiple reference smart terminals. Each historical monitoring data should include at least the battery performance, remaining power, external environmental parameters, power consumption attributes of running applications, and actual battery life of the reference smart terminal over a historical period.

[0149] Based on the historical monitoring data of each reference smart terminal, the battery performance and external environmental parameters of the reference smart terminal at historical time are characterized and the first feature value is obtained.

[0150] Obtain the battery performance and external environment parameters of the current smart terminal at the current time, and perform feature calculations to obtain the second feature value;

[0151] The difference between the first feature value of each reference smart terminal and the second feature value of the current smart terminal is calculated to obtain the difference value based on each reference smart terminal.

[0152] Historical monitoring data of reference smart terminals with difference values ​​less than the second threshold are added to the reference database;

[0153] Based on the historical monitoring data of each reference smart terminal in the reference database, the battery performance, remaining power, external environmental parameters, and power consumption attributes of the running applications of the reference smart terminal at historical time are input into the power life prediction model, and the predicted battery life is output.

[0154] Based on the historical monitoring data of each reference smart terminal in the reference database, the actual battery life is subtracted from the predicted battery life to obtain the battery life difference.

[0155] The differences in battery life duration of historical monitoring data of all reference smart terminals in the reference database are added together to obtain the total difference in battery life duration. The total difference in battery life duration is then divided by the total amount of historical monitoring data of reference smart terminals in the reference database to obtain the average value of the battery life duration difference, which is used as the battery life duration correction value.

[0156] The corrected first battery life is obtained by adding a battery life correction value to the first battery life predicted by the battery life prediction model.

[0157] A third aspect of the present invention also provides a computer-readable storage medium comprising a smart terminal management method program, wherein when the smart terminal management method program is executed by a processor, it implements the steps of the smart terminal management method described above.

[0158] The intelligent terminal management method, system, and readable storage medium proposed in this invention can combine the remaining time of the target application's currently executing task and determine the exit scheme of other applications through a preset management algorithm, so that the target application's currently executing task can be completed smoothly, thereby improving the user's experience with the intelligent terminal.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0160] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0161] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0162] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0163] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0164] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for managing intelligent terminals, characterized in that, The method includes: Get the applications that the user has opened on the current smart terminal; The target application is determined from the currently open applications based on the current interface display status of the smart terminal; Obtain the remaining duration of the currently executing task from the target application; Get the remaining battery level of the current smart terminal at the current time; Based on the remaining battery power, the battery life prediction model predicts the first battery life of the current smart terminal in the first operating environment, where the first operating environment refers to the environment in which the current smart terminal keeps the opened applications running. Based on the remaining battery power, the battery life prediction model predicts the second battery life of the current smart terminal in the second operating environment, where the second operating environment refers to the environment in which the current smart terminal keeps only the target application running. Determine if the remaining time is greater than the first battery life and less than the second battery life. If so, for the already opened applications, determine the application exit plan through a preset management algorithm so that the tasks being executed by the target application can be completed. The battery life prediction model predicts the initial battery life of a smart terminal under the first operating environment, specifically including: Build a battery life prediction model; Obtain parameter data that affects the current battery life of the smart terminal. The parameter data includes at least the current battery performance of the smart terminal at the current time, the power consumption attributes of each application in the first operating environment, and external environmental parameters. Input the above parameter data and the remaining battery power into the battery life prediction model, and the battery life prediction model will predict the first battery life. Acquire historical monitoring data from multiple reference smart terminals. Each historical monitoring data should include at least the battery performance, remaining power, external environmental parameters, power consumption attributes of running applications, and actual battery life of the reference smart terminal over a historical period. Based on the historical monitoring data of each reference smart terminal, the battery performance and external environmental parameters of the reference smart terminal at historical time are characterized and the first feature value is obtained. Obtain the battery performance and external environment parameters of the current smart terminal at the current time, and perform feature calculations to obtain the second feature value; The difference between the first feature value of each reference smart terminal and the second feature value of the current smart terminal is calculated to obtain the difference value based on each reference smart terminal. Historical monitoring data of reference smart terminals with difference values ​​less than the second threshold are added to the reference database; Based on the historical monitoring data of each reference smart terminal in the reference database, the battery performance, remaining power, external environmental parameters, and power consumption attributes of the running applications of the reference smart terminal at historical time are input into the power life prediction model, and the predicted battery life is output. Based on the historical monitoring data of each reference smart terminal in the reference database, the actual battery life is subtracted from the predicted battery life to obtain the battery life difference. The differences in battery life duration of historical monitoring data of all reference smart terminals in the reference database are added together to obtain the total difference in battery life duration. The total difference in battery life duration is then divided by the total amount of historical monitoring data of reference smart terminals in the reference database to obtain the average value of the battery life duration difference, which is used as the battery life duration correction value. The corrected first battery life is obtained by adding a battery life correction value to the first battery life predicted by the battery life prediction model.

2. The intelligent terminal management method according to claim 1, characterized in that, The battery performance and external environmental parameters of the reference smart terminal over a historical period are used to perform feature calculations to obtain the first feature value; Obtain the battery performance and external environment parameters of the current smart terminal at the current time, and perform feature calculations to obtain the second feature value; The difference between the first feature value of each reference smart terminal and the second feature value of the current smart terminal is calculated to obtain the difference value based on each reference smart terminal, specifically including: The optimal operating temperature and humidity for the battery are preset to T0 and R0, respectively. If the battery performance of the reference smart terminal in historical time is obtained as health Q1, and the external environmental parameters are temperature T1 and humidity H1, then the first feature value K1 is calculated by the formula K1=Q1 / F1, where F1=|T1-T0|*|H1-H0|. If the current battery performance of the smart terminal at the current time is obtained as health Q2, and the external environmental parameters are temperature T2 and humidity H2, then the second characteristic value K2 is calculated using the formula K2=Q2 / F2, where F2 =|T2-T0|*|H2-H0|. The difference value R based on each reference smart terminal is calculated using the difference calculation formula R=|K2- K1| / K2.

3. The intelligent terminal management method according to claim 1, characterized in that, The application exit strategy is determined through a preset management algorithm, specifically including: It is assumed that there are n applications running besides the target application. Exit each of the n applications in turn, and predict whether the current smart device’s battery life is greater than the remaining time when the remaining n-1 applications remain open. If so, mark the selected application as selected and add it as a candidate solution to the candidate database. Remove the selected applications from the n applications, and then select 2 applications from the remaining applications to exit. Predict whether the current battery life of the smart device is greater than the remaining battery life when the remaining n-2 applications are still open. If so, mark the selected applications as selected and add them to the candidate database as a candidate solution. The selected applications in the first i-1 steps are removed in the above manner. Then, each i application is selected from the remaining applications and exited. It is predicted whether the current battery life of the smart device is greater than the remaining battery life when the remaining ni applications are kept open. If so, the selected application is marked as selected and added to the candidate database as a candidate solution. This process continues until there are fewer than i applications remaining after the selected applications in the first i-1 steps are removed. Here, n is a positive integer greater than or equal to 1, and i is a positive integer greater than 1 and less than or equal to n. The candidate solutions in the candidate database are filtered according to a preset optimization algorithm, and the best candidate solution is selected as the final application exit solution.

4. The intelligent terminal management method according to claim 3, characterized in that, The candidate solutions in the candidate database are filtered according to a preset optimization algorithm, and the best candidate solution is selected as the final application exit solution. Specifically, this includes: The number of applications that need to be exited in each candidate scheme state, the difference between the smart device's battery life and the remaining time, and the usage frequency of the applications that need to be exited are obtained respectively. The number of applications that need to be terminated under each candidate scheme in the candidate database is compared with the number of applications that need to be terminated under the remaining candidate schemes. If the former is less than the latter, the score of the quantity surplus item of the former candidate scheme is increased by 1; otherwise, it remains unchanged. The difference between the battery life of the smart device under each candidate scheme in the candidate database and the remaining time is compared with the difference between the battery life of the smart device under the remaining candidate schemes. If the former is less than the latter, the score of the time remaining item of the former candidate scheme is increased by 1; otherwise, it remains unchanged. The total usage frequency of the application that needs to be exited under each candidate scheme in the candidate database is compared with the total usage frequency of the application that needs to be exited under the remaining candidate schemes. If the former is less than the latter, the score of the action stop-loss item of the former candidate scheme is increased by 1; otherwise, it remains unchanged. The candidate solutions in the candidate database are compared pairwise to determine the number of applications that need to be exited, and the total score for the quantity surplus item of each candidate solution is calculated. The candidate solutions in the candidate database are compared pairwise to determine the difference between the battery life of the smart device and the remaining battery life, and the total score for the time surplus item of each candidate solution is calculated. The candidate solutions in the candidate database are compared pairwise to determine the usage frequency of the applications that need to be exited, and the total score for the action stop loss item of each candidate solution is calculated. The weights of the preset quantity surplus, time surplus, and action stop loss items in selecting the best candidate solution are different; The sum of scores for the quantity surplus item of each candidate solution is multiplied by the corresponding influence weight to obtain the sum of score weights for the quantity surplus item; the sum of scores for the time surplus item of each candidate solution is multiplied by the corresponding influence weight to obtain the sum of score weights for the time surplus item; and the sum of scores for the action stop-loss item of each candidate solution is multiplied by the corresponding influence weight to obtain the sum of score weights for the action stop-loss item. The total score for each candidate solution is obtained by summing the weighted scores of the quantity surplus item, the time surplus item, and the action stop loss item. The candidate solution with the highest overall score in the candidate database will be selected as the final application exit solution.

5. An intelligent terminal management system, characterized in that, The system includes a memory and a processor. The memory includes a smart terminal management method program, which, when executed by the processor, performs the following steps: Get the applications that the user has opened on the current smart terminal; The target application is determined from the currently open applications based on the current interface display status of the smart terminal; Obtain the remaining duration of the currently executing task from the target application; Get the remaining battery level of the current smart terminal at the current time; Based on the remaining battery power, the battery life prediction model predicts the first battery life of the current smart terminal in the first operating environment, where the first operating environment refers to the environment in which the current smart terminal keeps the opened applications running. Based on the remaining battery power, the battery life prediction model predicts the second battery life of the current smart terminal in the second operating environment, where the second operating environment refers to the environment in which the current smart terminal keeps only the target application running. Determine if the remaining time is greater than the first battery life and less than the second battery life. If so, for the already opened applications, determine the application exit plan through a preset management algorithm so that the tasks being executed by the target application can be completed. The battery life prediction model predicts the initial battery life of a smart terminal under the first operating environment, specifically including: Build a battery life prediction model; Obtain parameter data that affects the current battery life of the smart terminal. The parameter data includes at least the current battery performance of the smart terminal at the current time, the power consumption attributes of each application in the first operating environment, and external environmental parameters. Input the above parameter data and the remaining battery power into the battery life prediction model, and the battery life prediction model will predict the first battery life. Acquire historical monitoring data from multiple reference smart terminals. Each historical monitoring data should include at least the battery performance, remaining power, external environmental parameters, power consumption attributes of running applications, and actual battery life of the reference smart terminal over a historical period. Based on the historical monitoring data of each reference smart terminal, the battery performance and external environmental parameters of the reference smart terminal at historical time are characterized and the first feature value is obtained. Obtain the battery performance and external environment parameters of the current smart terminal at the current time, and perform feature calculations to obtain the second feature value; The difference between the first feature value of each reference smart terminal and the second feature value of the current smart terminal is calculated to obtain the difference value based on each reference smart terminal. Historical monitoring data of reference smart terminals with difference values ​​less than the second threshold are added to the reference database; Based on the historical monitoring data of each reference smart terminal in the reference database, the battery performance, remaining power, external environmental parameters, and power consumption attributes of the running applications of the reference smart terminal at historical time are input into the power life prediction model, and the predicted battery life is output. Based on the historical monitoring data of each reference smart terminal in the reference database, the actual battery life is subtracted from the predicted battery life to obtain the battery life difference. The differences in battery life duration of historical monitoring data of all reference smart terminals in the reference database are added together to obtain the total difference in battery life duration. The total difference in battery life duration is then divided by the total amount of historical monitoring data of reference smart terminals in the reference database to obtain the average value of the battery life duration difference, which is used as the battery life duration correction value. The corrected first battery life is obtained by adding a battery life correction value to the first battery life predicted by the battery life prediction model.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a smart terminal management method program, which, when executed by a processor, implements the steps of a smart terminal management method as described in any one of claims 1 to 4.

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