CN114159781A

CN114159781A - Reminding method and device and electronic equipment

Info

Publication number: CN114159781A
Application number: CN202111365427.8A
Authority: CN
Inventors: 吴璐瑶
Original assignee: Vivo Mobile Communication Co Ltd
Current assignee: Vivo Mobile Communication Co Ltd
Priority date: 2021-11-16
Filing date: 2021-11-16
Publication date: 2022-03-11

Abstract

The application discloses a reminding method, a reminding device and electronic equipment, and belongs to the field of electronic equipment. The method comprises the following steps: acquiring first physiological information of a first user under the condition of using a preset application program; obtaining the prediction information of the use performance of the first user on the preset application program according to a preset model and the first physiological information, wherein the preset model is obtained by performing model training according to second physiological information and second use performance information of a second user under the condition of using the preset application program; and outputting preset reminding information corresponding to the prediction information.

Description

Reminding method and device and electronic equipment

Technical Field

The application belongs to the field of electronic equipment, and particularly relates to a reminding method and device and electronic equipment.

Background

For some applications (or application software), the performance of the user using the same application at different times will usually be different. For example, for an athletic game-like application, the same user may win more at one time and less at another time.

Currently, users usually rely on their own will to decide whether to use an application. Thus, if the user uses the application program when the use performance is poor, the use experience is poor.

Disclosure of Invention

The embodiment of the application aims to provide a reminding method, a reminding device and electronic equipment, and the problem that the use experience is poor can be solved.

In a first aspect, an embodiment of the present application provides a reminding method, where the method includes: acquiring first physiological information of a first user under the condition of using a preset application program; obtaining the prediction information of the use performance of the first user on the preset application program according to a preset model and the first physiological information, wherein the preset model is obtained by performing model training according to second physiological information and second use performance information of a second user under the condition of using the preset application program; and outputting preset reminding information corresponding to the prediction information.

In a second aspect, an embodiment of the present application provides a reminder device, where the reminder device includes: the acquisition module is used for acquiring first physiological information of a first user under the condition of using a preset application program; the processing module is used for obtaining the prediction information of the use performance of the first user on the preset application program according to a preset model and the first physiological information, wherein the preset model is obtained by performing model training according to second physiological information and second use performance information of a second user under the condition of using the preset application program; and the output module is used for outputting preset reminding information corresponding to the prediction information.

In a third aspect, embodiments of the present application provide an electronic device, which includes a processor and a memory, where the memory stores a program or instructions executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method according to the first aspect.

In a fourth aspect, embodiments of the present application provide a readable storage medium, on which a program or instructions are stored, which when executed by a processor implement the steps of the method according to the first aspect.

In a fifth aspect, an embodiment of the present application provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and the processor is configured to execute a program or instructions to implement the method according to the first aspect.

In a sixth aspect, embodiments of the present application provide a computer program product, stored on a storage medium, for execution by at least one processor to implement the method according to the first aspect.

In the embodiment of the application, first physiological information of a first user under the condition of using a preset application program is acquired; obtaining the prediction information of the use performance of the first user on the preset application program according to a preset model and the first physiological information, wherein the preset model is obtained by performing model training according to second physiological information and second use performance information of a second user under the condition of using the preset application program; and outputting preset reminding information corresponding to the prediction information. The embodiment combines the physiological information and the use performance information of the user when using the application program to construct the preset model, so that when the current user uses the application program, the use performance prediction information of the current user when using the application program can be obtained according to the physiological information of the current user when using the application program and by combining the preset model, and corresponding reminding information is output according to the use performance prediction information, so that the current user can combine the reminding information to determine whether to continue to use the application program, the application program is prevented from being used when the user uses the application program with poor performance, and the use experience is comprehensively improved.

Drawings

Fig. 1 is a flowchart of a reminding method provided in this embodiment;

FIG. 2 is a block diagram of a reminder device according to the present embodiment;

fig. 3 is a schematic diagram of a hardware structure of an electronic device provided in this embodiment;

fig. 4 is a schematic diagram of a hardware structure of another electronic device provided in this embodiment.

Detailed Description

The technical solutions in the embodiments of the present application will be described clearly below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present disclosure.

The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the application may be practiced in sequences other than those illustrated or described herein, and that the terms "first," "second," and the like are generally used herein in a generic sense and do not limit the number of terms, e.g., the first term can be one or more than one. In addition, "and/or" in the specification and claims means at least one of connected objects, a character "/" generally means that a preceding and succeeding related objects are in an "or" relationship.

The following describes in detail the reminding method provided by the embodiment of the present application through specific embodiments and application scenarios thereof with reference to the accompanying drawings.

Referring to fig. 1, a reminding method provided in this embodiment may include the following steps 110 to 130:

step 110, acquiring first physiological information of a first user under the condition of using a preset application program.

In detail, a preset application may be installed on an electronic device of a user, such as a smart phone. The preset application program can be a competitive game application program, a friend-making application program and the like, so that the use performance of a user using the same application program in different states can be different.

In detail, the state of the user can be reflected in combination with the physiological information of the user, and the state of the user when using the application can influence the use performance of the application by the user. In order to be able to predict the usage performance of the application program by the user in combination with the user state, the electronic device may acquire physiological information of the user during the usage of the application program while the user uses the application program.

In one embodiment of the present disclosure, the physiological information may include at least one of life index information, fatigue index information, attention index information.

In detail, the physiological information can be acquired by the electronic equipment and matching with other intelligent wearable equipment, and the acquired physiological information can reflect the physical and mental states of the user.

The method can be used for obtaining life index information such as heart rate, heart rate variance, blood pressure information and the like of a user in a mode of a device body sensor, intelligent terminal equipment and the like.

Considering that a user usually puts down the equipment for a moment or holds the equipment with one hand to let the other hand rest when the user is tired, the fatigue index can be conveniently judged by obtaining the frequency information of the user putting down the equipment and holding the equipment with one hand through the sensor of the body of the equipment.

Considering that the full-screen immersion state of the game is usually broken when the user is not focused on the game, the attention index can be judged by obtaining frequency information of leaving the game interface midway during the game, checking and replying messages through a small window and the like.

And step 120, obtaining the prediction information of the first user on the use performance of the preset application program according to a preset model and the first physiological information, wherein the preset model is obtained by performing model training according to second physiological information and second use performance information of a second user under the condition of using the preset application program.

In detail, by substituting physiological information of a user when using an application program into a model, information indicating usage performance of the application program by the user in a corresponding time period can be predicted.

Considering that the physiological change of the user has a certain persistence, the corresponding time period may include a time period corresponding to the acquired physiological information and a time period after the time period. For example, by obtaining the physiological information of the user playing the first game, the use performance of the user playing the first N (N is more than or equal to 2) games can be predicted.

In this embodiment, in order to accurately predict the use performance of the application program by the user according to the physiological information of the user when using the application program, the model used in this embodiment may be obtained by performing model training according to the physiological data of the user when using the application program and the corresponding use performance data.

Taking the application program of the competitive game as an example, the model in this embodiment not only uses the physiological information of the user playing the game, but also uses the use performance feedback information such as the win/loss of the user in the game, the superiority and inferiority of the competitive performance, and the like, i.e. uses the comprehensive state of the user in the game to train the model. The trained model can be used for realizing use performance prediction and giving a prompt, so that a user can be helped to improve game competitive performance, find the optimal game state and comprehensively improve game experience.

In detail, considering that the amount of data required for model training is large, and the universality of the model, the model can be trained according to the data of a large number of mass users.

For example, a certain number of game users may be drawn from people of all ages and professions, and invited to play the game. During the game playing process of the users, the physiological information and the use performance information of the users are acquired in real time. After model training data of people with enough different backgrounds are obtained, the data can be brought into a pre-built reinforcement learning model (such as a DNQ model, the model combines the advantages of a neural network and Q-learning) for training, and an initial game performance prediction (AI) model with universality is obtained.

In detail, taking an application of a competitive game as an example, the use performance information may include at least one of win-or-lose competition performance, the number of times of hitting and killing enemies, the number of times of death, whether or not MVP (Most Valuable Player).

And step 130, outputting preset reminding information corresponding to the prediction information.

In detail, the prediction information may indicate the use performance of the application program in the corresponding time period, so that the corresponding reminding information may be output according to the specific content of the prediction information. For example, the reminder information may be information that is referenced by the user to decide whether to continue using the application.

For example, the predicted information may be use performance information, and the reminding information may be a use score corresponding to the use performance information. Generally, a higher score indicates a better performance of the application in use by the user. By outputting the usage score, the user can decide whether to continue using the application program according to the size of the specific score viewed.

For another example, the predicted information may be a predicted usage score, and the reminding information may be information indicating whether to recommend to continue using the application program, which corresponds to the usage score. By outputting the advice information, the user can intuitively know whether it is necessary to continue using the application.

Based on this, in one embodiment of the present disclosure, the prediction information includes a prediction score. Generally, the higher the prediction score, the better the user's performance of the application. In this embodiment, by substituting the physiological information of the user when using the application program into the model, the performance score corresponding to the application program usage performance affected by the physiological information can be directly obtained.

In a feasible implementation mode, the value range of the prediction score output by the model can be 0-100.

Correspondingly, the step 130 of outputting the preset reminding information corresponding to the prediction information may include the following steps 1301 to 1302:

in detail, a comparison result of the prediction score and a preset threshold may be acquired, and step 1301 or step 1302 may be performed based on the acquired comparison result.

Typically, a user will either continue to use or stop using an application after a period of use. Thus, the embodiment can reasonably set a score threshold value, and compare the obtained pre-measured value with the score threshold value. For example, when the value range of the prediction score output by the model may be 0 to 100, the preset threshold may be 30.

Step 1301, outputting first reminding information when the prediction score is greater than or equal to a preset threshold, wherein the first reminding information is preset reminding information for indicating that the preset application program is recommended to be continuously used.

In detail, when the predicted score is not less than the threshold, the predicted use performance of the user may be considered to be better, and the user may have good use performance when continuing to use the application, so that the user may be recommended to continue to use the application. Therefore, the user can generally have good use performance when continuing to use the application program, and the use experience of the user on the application program is good.

For example, if the score is higher than 70, the game state is judged to be excellent, the user is reminded that the state is excellent and the subsequent winning chance is large, and the game is recommended to continue.

Step 1302, outputting a second reminding message when the prediction score is smaller than the preset threshold, where the second reminding message is a preset reminding message for indicating that the use of the preset application program is suggested to be suspended.

In detail, when the prediction score is smaller than the threshold, the user may be considered to have poor predicted performance, and the user may not generally have good performance when continuing to use the application, so that the user may be recommended to stop (or suspend) using the application. Therefore, the situation that the user does not have good use performance when the user continues to use the application program can be avoided, and the use experience of the user on the application program is influenced.

For example, if the score is less than 30, the game state is determined to be very poor, the user is reminded that the state is not good, the subsequent winning chance is small, the game is suggested to be paused, and the user takes a break and plays again.

Based on the above, under the condition that the user basically follows the reminding suggestion information, the technical scheme provided by the embodiment can achieve the effect that the user has good use performance as long as the user uses the application program, so that the use experience of the user can be comprehensively improved. Furthermore, even if the user continues to use the application program with the suggestion of suspending the use, the user has an expectation on the use performance of the subsequent application program, so that the user can be prevented from being in a low mood due to poor use performance when using the application program.

In view of the above, the embodiment of the present disclosure provides a reminding method, where the method obtains first physiological information of a first user when the first user uses a preset application program; obtaining the prediction information of the use performance of the first user on the preset application program according to a preset model and the first physiological information, wherein the preset model is obtained by performing model training according to second physiological information and second use performance information of a second user under the condition of using the preset application program; and outputting preset reminding information corresponding to the prediction information. The embodiment combines the physiological information and the use performance information of the user when using the application program to construct the preset model, so that when the current user uses the application program, the use performance prediction information of the current user when using the application program can be obtained according to the physiological information of the current user when using the application program and by combining the preset model, and corresponding reminding information is output according to the use performance prediction information, so that the current user can combine the reminding information to determine whether to continue to use the application program, the application program is prevented from being used when the user uses the application program with poor performance, and the use experience is comprehensively improved.

Therefore, based on the implementation manner provided by the embodiment, when the user uses the terminal device to play the competitive electronic game, the game competitive performance can be predicted through the physiological and psychological index information of the user, and the user is given corresponding prompt, so that the game experience of the user can be comprehensively improved.

In detail, considering that the trained model has universality and personal differences easily exist among different users, the trained model can be optimized and adjusted according to personal data of the users, so that the optimized model has certain pertinence to the users, more accurate use performance prediction can be realized, and more accurate reminding can be provided for the users.

Based on this, in an embodiment of the present disclosure, after obtaining the predicted information of the usage performance of the preset application program by the first user, the method may further include the following steps 140 to 150:

step 140, obtaining first use performance information of the first user under the condition of using the preset application program.

In detail, during the application program usage period of the user, not only the physiological information of the user in the period can be obtained, but also the usage performance information of the user in the period can be obtained, and the physiological information in the same period can correspondingly influence the usage performance information in the period.

For example, after the user plays a game, the use performance information of the user playing the game can be obtained, such as the win-win or-lose battle performance, the number of enemy attack, the number of death, whether the MVP is available or not and the like when the user plays the game.

In a possible implementation, the electronic device may access an SDK (Software Development Kit) of the application when the application is used by the user to obtain usage performance information of the user when using the application.

Correspondingly, according to the physiological information of the user in the period, the use performance information of the user in the period can be predicted based on the model so as to obtain the predicted information.

Under the condition that the model can realize accurate prediction, the obtained actual use performance and the predicted use performance are not much different, otherwise, the model can be considered to be further optimized so as to improve the pertinence prediction of the model to the current user.

And 150, optimizing the preset model according to the first use performance information and the prediction information.

In detail, the model used in this embodiment may be a reinforcement learning model, and the machine learning model has the characteristics of being able to continuously optimize self prediction parameters according to new data and improve prediction capability.

In detail, the model can be continuously self-learned and optimized through a machine learning method, so that the optimized model can be more intelligently and accurately matched with the personal characteristics of a single user.

In this step, parameters of the model may be optimized and adjusted according to the obtained actual usage performance and the predicted usage performance, so that the predicted usage performance more conforming to the actual usage performance may be obtained based on the optimized model. Further, prediction is always performed subsequently based on the most recently optimized model. By cycling through this, the optimized model can be made more targeted to the user by continuously optimizing the model based on the user data.

Since the model optimization operation can be repeated for many times, the preset model in step 120 may be a model obtained by training or a model obtained after a certain optimization.

Therefore, the model is optimized by combining the predicted use performance and the actual use performance of the user, so that the model is more pertinent, and the prediction accuracy is further improved. And along with the long-term continuous use of the application program by the user, the electronic equipment can realize the continuous learning and optimization of the model, so that the reminding mechanism gradually becomes more intelligent and more accurate to match the personal characteristics of a single user.

As can be seen from the above, the present embodiment can calculate a set of general game performance prediction AI models under which physiological states the user can win in the game more easily by collecting information of the mass user and using the reinforcement learning model. The set of AI models is then applied to the terminal electronic equipment, can be used for predicting the game performance of a single user, and gives corresponding prompts to the user to prompt the user to play a game in the optimal game state and pause the game in a poor state, so that the game experience is comprehensively improved. Based on the principle of the machine learning model, the set of AI model can gradually change from a universal AI model to an AI model customized for the user along with the continuous increase of the use time of the user, and the game performance prediction of the user is more and more accurate.

In this embodiment, a model with universality can be trained according to physiological information and corresponding use performance information of a large number of mass users. In detail, the physiological information and the use performance information may be quantized, and the model may be trained based on the quantized values.

Based on this, in one embodiment of the present disclosure, the second physiological information includes at least one index information. Any data included in the physiological information may be continuously changing data. For example, the physiological information may include at least one of life index information, fatigue index information, and attention index information.

Correspondingly, before obtaining the predicted information of the first user's usage performance of the preset application program according to the preset model and the first physiological information in the step 120, the method may further include the following steps a1 to a 4:

step A1, obtaining a time interval corresponding to the second physiological information.

For example, during a game played by a mass user, physiological information of the mass user during the game may be acquired. Therefore, the time period corresponding to the game played by the user is the time interval corresponding to the physiological information.

Step a2, obtaining each time slice included in the time interval.

In this step, the time interval may be divided into time segments (for example, 1s is a time segment), so that index information under each time segment may be obtained subsequently.

Step A3, according to the second physiological information, obtaining an index value of each index information corresponding to each time slice.

In this step, index values of various index information at each time slice can be obtained based on the obtained physiological information. For example, a vital index value, a fatigue index value, and an attention index value at each time slice can be obtained.

As described above, the present embodiment realizes the quantification of continuously changing data, i.e., physiological information, and can perform model training using index values of various index information at each time segment as independent variables of a model and corresponding use performance information as dependent variables of the model.

For example, to make a quantized index, N types of index data are sorted and quantized, high-order data are obtained through conversion in a certain form on the basis of the N types of index data, an independent variable Ant is formed, wherein N is an nth independent variable, T is a tth time section, and finally, a T × N independent variable matrix is obtained for model training.

Step a4, performing model training according to the second use performance information and the index value of each kind of index information corresponding to each time slice, respectively, to obtain the preset model.

In detail, the obtained usage performance information may also be quantized to obtain usage performance quantization data, and the model may be trained based on the user quantization index data and the usage performance quantization data.

In this embodiment, the physiological information may be continuously changing data, so that information quantization is implemented by constructing an independent variable matrix of T × N (T is the number of time segments, and N is the index information type) for model training. Because the model can be trained based on the change trend of the physiological information, the model training effect can be improved, and the model capable of realizing accurate prediction can be obtained.

In one embodiment of the present disclosure, the second usage performance information includes at least one preset information. Any of the data included in the usage performance information may be numerical data. Taking the competitive game type application as an example, the use performance information may include at least one of win-or-lose battle performance, enemy hitting frequency, death frequency and whether MVP is available when the user plays the game.

Correspondingly, before obtaining the predicted information of the first user's usage performance of the preset application program according to the preset model and the first physiological information in the step 120, the method may further include the following steps B1 to B3:

step B1, converting each of the preset information into a corresponding score according to a preset score conversion rule, so as to obtain a score corresponding to each of the preset information.

In detail, a mapping relationship between the preset information and the score may be preset, and each preset information in the obtained use performance information may be converted into a corresponding score according to the mapping relationship, so as to implement preliminary quantization of the use performance information.

And step B2, obtaining the score of the second use performance information according to the preset weight of each preset information and the corresponding score of each preset information.

In detail, the weights of different preset information may be different, and the specific weights may be accurately configured in combination with experience. Based on the weight and the score of the preset information, the obtained scores can be summarized to obtain a unified score for embodying the use performance information.

As described above, the present embodiment achieves quantization of numerical data, which is performance information, to obtain performance quantization data, so that model training can be performed using the performance quantization data as a dependent variable of a model and corresponding physiological information as an independent variable of the model.

For example, to make a quantized index, n types of data are sorted and quantized to form dependent variables Bt1, Bt2, Bt3, … … Btn. The total weight is integrated into a use performance score index B which is in the range of 0-100. Higher scores represent better performance in use.

And step B3, according to the scores of the second physiological information and the second use performance information, performing model training to obtain the preset model.

In detail, the obtained physiological information may also be quantized to obtain user quantitative indicator data, and the model may be trained based on the user quantitative indicator data and using the performance quantitative data.

In this embodiment, the performance information is used as numerical data, and the quantification of the performance information is realized by converting the comprehensive performance information into the unified score, so that the performance information is used for model training.

In summary, the game performance of the game player can be intelligently predicted and prompted according to the physical and mental states of the game player, so that the game player can know when the game player is more suitable or unsuitable for playing the game, the game player can be helped to improve the game competitive performance, the optimal game state can be found, and the game experience can be comprehensively improved. In addition, the embodiment can output the reminding of suspending the game when the player plays the game for a long time and the state is not good, thereby having the effects of protecting the life safety of the user and preventing enthrallment.

According to the reminding method provided by the embodiment of the application, the execution main body can be a reminding device. In the embodiment of the present application, a reminding device executing a reminding method is taken as an example, and the reminding device provided in the embodiment of the present application is described.

As shown in fig. 2, the present embodiment provides a reminder device 200, which may include an obtaining module 210, a processing module 220, and an output module 230.

The obtaining module 210 is configured to obtain first physiological information of a first user using a preset application program. The processing module 220 is configured to obtain predicted information of the usage performance of the preset application program by the first user according to a preset model and the first physiological information, where the preset model is obtained by performing model training according to second physiological information and second usage performance information of a second user in a case of using the preset application program. The output module 230 is configured to output preset reminding information corresponding to the prediction information.

In an embodiment of the present disclosure, the reminding apparatus 200 further includes: the optimization module is used for acquiring first use performance information of the first user under the condition of using the preset application program; and optimizing the preset model according to the first use performance information and the prediction information.

In one embodiment of the present disclosure, the second physiological information includes at least one index information. The reminder device 200 further includes: the model training module is used for acquiring a time interval corresponding to the second physiological information; acquiring each time slice included in the time interval; according to the second physiological information, obtaining an index value of each index information corresponding to each time slice; and performing model training according to the second use performance information and the index value of each index information corresponding to each time segment to obtain the preset model.

In one embodiment of the present disclosure, the second usage performance information includes at least one preset information. The reminder device 200 further includes: the model training module is used for respectively converting each preset information into corresponding scores according to a preset score conversion rule so as to obtain the score corresponding to each preset information; obtaining the score of the second use performance information according to the preset weight of each preset information and the corresponding score of each preset information; and performing model training according to the scores of the second physiological information and the second use performance information to obtain the preset model.

In one embodiment of the present disclosure, the prediction information includes a prediction score. The output module 230 is configured to output a first reminding message when the predicted score is greater than or equal to the preset threshold, where the first reminding message is a preset reminding message for indicating that the preset application program is recommended to continue to be used; and outputting second reminding information under the condition that the prediction score is smaller than the preset threshold, wherein the second reminding information is preset reminding information used for indicating that the preset application program is recommended to be suspended.

The reminding device in the embodiment of the present application may be an electronic device, or may be a component in an electronic device, such as an integrated circuit or a chip. The electronic device may be a terminal, or may be a device other than a terminal. The electronic Device may be, for example, a Mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic Device, a Mobile Internet Device (MID), an Augmented Reality (AR)/Virtual Reality (VR) Device, a robot, a wearable Device, an ultra-Mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like, and may also be a server, a Network Attached Storage (NAS), a Personal Computer (PC), a Television (TV), a teller machine, a self-service machine, and the like, and the embodiments of the present application are not particularly limited.

The reminding device in the embodiment of the application can be a device with an operating system. The operating system may be an Android (Android) operating system, an ios operating system, or other possible operating systems, and embodiments of the present application are not limited specifically.

The reminding device provided by the embodiment of the application can realize each process realized by the method embodiment of fig. 1, and is not described herein again in order to avoid repetition.

Optionally, as shown in fig. 3, an electronic device 300 is further provided in the embodiment of the present application, and includes a processor 310 and a memory 320, where the memory 320 stores a program or an instruction that can be executed on the processor 310, and when the program or the instruction is executed by the processor 310, the steps of the above-mentioned reminding method embodiment are implemented, and the same technical effects can be achieved, and are not described again to avoid repetition.

Fig. 4 is a schematic hardware structure diagram of an electronic device 1000 implementing the embodiment of the present application.

The electronic device 1000 includes, but is not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, and a processor 1010.

Those skilled in the art will appreciate that the electronic device 1000 may further comprise a power source (e.g., a battery) for supplying power to various components, and the power source may be logically connected to the processor 1010 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The electronic device structure shown in fig. 4 does not constitute a limitation of the electronic device, and the electronic device may include more or less components than those shown, or combine some components, or arrange different components, and thus, the description is omitted here.

The processor 1010 is configured to acquire first physiological information of a first user when the first user uses a preset application program; obtaining the prediction information of the use performance of the first user on the preset application program according to a preset model and the first physiological information, wherein the preset model is obtained by performing model training according to second physiological information and second use performance information of a second user under the condition of using the preset application program; and outputting preset reminding information corresponding to the prediction information.

Optionally, the processor 1010 is configured to, after obtaining the predicted information of the usage performance of the preset application program by the first user, obtain first usage performance information of the first user in a case of using the preset application program; and optimizing the preset model according to the first use performance information and the prediction information.

Optionally, the second physiological information comprises at least one index information; a processor 1010, configured to obtain a time interval corresponding to the second physiological information before obtaining, according to a preset model and the first physiological information, predicted information of a usage performance of the preset application program by the first user; acquiring each time slice included in the time interval; according to the second physiological information, acquiring an index value of each time slice, which corresponds to each index information; and according to the second use performance information and the index value of each time segment corresponding to each index information, performing model training to obtain the preset model.

Optionally, the second usage performance information includes at least one preset information; a processor 1010, configured to, before obtaining the predicted information of the usage performance of the preset application program by the first user according to the preset model and the first physiological information, respectively convert each type of the preset information into a corresponding score according to a preset score conversion rule, so as to obtain a score corresponding to each type of the preset information; obtaining the score of the second use performance information according to the preset weight of each preset information and the corresponding score of each preset information; and performing model training according to the scores of the second physiological information and the second use performance information to obtain the preset model.

Optionally, the prediction information comprises a prediction score; optionally, the processor 1010 is configured to output first reminding information when the predicted score is greater than or equal to the preset threshold, where the first reminding information is preset reminding information for indicating that the preset application program is recommended to continue to be used; and outputting second reminding information under the condition that the prediction score is smaller than the preset threshold, wherein the second reminding information is preset reminding information used for indicating that the preset application program is recommended to be suspended.

It should be understood that in the embodiment of the present application, the input Unit 1004 may include a Graphics Processing Unit (GPU) 10041 and a microphone 10042, and the Graphics Processing Unit 10041 processes image data of still pictures or videos obtained by an image capturing device (such as a camera) in a video capturing mode or an image capturing mode. The display unit 1006 may include a display panel 10061, and the display panel 10061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 1007 includes at least one of a touch panel 10071 and other input devices 10072. The touch panel 10071 is also referred to as a touch screen. The touch panel 10071 may include two parts, a touch detection device and a touch controller. Other input devices 10072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail herein.

The memory 1009 may be used to store software programs as well as various data. The memory 1009 may mainly include a first storage area storing a program or an instruction and a second storage area storing data, wherein the first storage area may store an operating system, an application program or an instruction (such as a sound playing function, an image playing function, and the like) required for at least one function, and the like. Further, the memory 1009 may include volatile memory or nonvolatile memory, or the memory x09 may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. The volatile Memory may be a Random Access Memory (RAM), a Static Random Access Memory (Static RAM, SRAM), a Dynamic Random Access Memory (Dynamic RAM, DRAM), a Synchronous Dynamic Random Access Memory (Synchronous DRAM, SDRAM), a Double Data Rate Synchronous Dynamic Random Access Memory (Double Data Rate SDRAM, ddr SDRAM), an Enhanced Synchronous SDRAM (ESDRAM), a Synchronous Link DRAM (SLDRAM), and a Direct Memory bus RAM (DRRAM). The memory 1009 in the embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.

Processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor, which primarily handles operations related to the operating system, user interface, and applications, and a modem processor, which primarily handles wireless communication signals, such as a baseband processor. It will be appreciated that the modem processor described above may not be integrated into processor 1010.

The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the program or the instruction implements the processes of the above-mentioned reminding method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.

The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read only memory ROM, a random access memory RAM, a magnetic or optical disk, and the like.

The embodiment of the present application further provides a chip, where the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to run a program or an instruction to implement each process of the above-mentioned reminding method embodiment, and can achieve the same technical effect, and for avoiding repetition, the description is omitted here.

It should be understood that the chips mentioned in the embodiments of the present application may also be referred to as system-on-chip, system-on-chip or system-on-chip, etc.

The embodiments of the present application provide a computer program product, where the program product is stored in a storage medium, and the program product is executed by at least one processor to implement the processes of the above-mentioned reminding method embodiments, and can achieve the same technical effects, and in order to avoid repetition, details are not described here again.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Further, it should be noted that the scope of the methods and apparatus of the embodiments of the present application is not limited to performing the functions in the order illustrated or discussed, but may include performing the functions in a substantially simultaneous manner or in a reverse order based on the functions involved, e.g., the methods described may be performed in an order different than that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.

While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise embodiments described above, which are meant to be illustrative and not restrictive, and that various changes may be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A reminding method is characterized by comprising the following steps:

acquiring first physiological information of a first user under the condition of using a preset application program;

obtaining the prediction information of the use performance of the first user on the preset application program according to a preset model and the first physiological information, wherein the preset model is obtained by performing model training according to second physiological information and second use performance information of a second user under the condition of using the preset application program;

and outputting preset reminding information corresponding to the prediction information.

2. The method of claim 1, wherein after obtaining the predicted information of the first user's performance of use of the preset application, the method further comprises:

acquiring first use performance information of the first user under the condition of using the preset application program;

and optimizing the preset model according to the first use performance information and the prediction information.

3. The method of claim 1, wherein the second physiological information includes at least one metric information;

before obtaining the predicted information of the use performance of the preset application program by the first user according to the preset model and the first physiological information, the method further includes:

acquiring a time interval corresponding to the second physiological information;

acquiring each time slice included in the time interval;

according to the second physiological information, obtaining an index value of each index information corresponding to each time slice;

and performing model training according to the second use performance information and the index value of each index information corresponding to each time segment to obtain the preset model.

4. The method according to claim 1, wherein the second usage performance information includes at least one preset information;

converting each preset information into a corresponding score according to a preset score conversion rule so as to obtain a score corresponding to each preset information;

obtaining the score of the second use performance information according to the preset weight of each preset information and the corresponding score of each preset information;

and performing model training according to the scores of the second physiological information and the second use performance information to obtain the preset model.

5. The method of claim 1, wherein the prediction information comprises a prediction score;

the outputting of the preset reminding information corresponding to the prediction information comprises:

outputting first reminding information under the condition that the prediction score is greater than or equal to a preset threshold value, wherein the first reminding information is preset reminding information for indicating that the preset application program is recommended to be continuously used;

and outputting second reminding information under the condition that the prediction score is smaller than the preset threshold, wherein the second reminding information is preset reminding information used for indicating that the preset application program is recommended to be suspended.

6. A reminder device, comprising:

the acquisition module is used for acquiring first physiological information of a first user under the condition of using a preset application program;

the processing module is used for obtaining the prediction information of the use performance of the first user on the preset application program according to a preset model and the first physiological information, wherein the preset model is obtained by performing model training according to second physiological information and second use performance information of a second user under the condition of using the preset application program; and the number of the first and second groups,

and the output module is used for outputting preset reminding information corresponding to the prediction information.

7. The apparatus of claim 6, further comprising:

the optimization module is used for acquiring first use performance information of the first user under the condition of using the preset application program; and optimizing the preset model according to the first use performance information and the prediction information.

8. The apparatus of claim 6, wherein the second physiological information comprises at least one indicator information;

the device further comprises:

the model training module is used for acquiring a time interval corresponding to the second physiological information; acquiring each time slice included in the time interval; according to the second physiological information, obtaining an index value of each index information corresponding to each time slice; and performing model training according to the second use performance information and the index value of each index information corresponding to each time segment to obtain the preset model.

9. The apparatus according to claim 6, wherein the second usage performance information includes at least one preset information;

the device further comprises:

the model training module is used for respectively converting each preset information into corresponding scores according to a preset score conversion rule so as to obtain the score corresponding to each preset information; obtaining the score of the second use performance information according to the preset weight of each preset information and the corresponding score of each preset information; and performing model training according to the scores of the second physiological information and the second use performance information to obtain the preset model.

10. The apparatus of claim 6, wherein the prediction information comprises a prediction score;

the output module is used for outputting first reminding information under the condition that the prediction score is greater than or equal to a preset threshold value, wherein the first reminding information is preset reminding information for indicating that the preset application program is recommended to be continuously used; and outputting second reminding information under the condition that the prediction score is smaller than the preset threshold, wherein the second reminding information is preset reminding information used for indicating that the preset application program is recommended to be suspended.

11. An electronic device comprising a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions when executed by the processor implementing the steps of the reminder method according to any one of claims 1 to 5.