Reminding method, device and electronic equipment

Abstract

The application discloses a reminding method and 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 in the case of using a preset application program; obtaining prediction information of the use performance of the first user for the preset application program according to a preset model and the first physiological information, wherein the preset model is a model obtained by model training according to second physiological information and second use performance information of a second user in the case of using the preset application program; and outputting preset reminding information corresponding to the prediction information.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of electronic devices, and particularly relates to a reminding method and device and electronic device. BACKGROUND

[0002] For some application programs (or application software), the use performance of a user in using the same application program at different times usually differs. For example, for a competitive game application program, the same user has a higher win rate at a certain time, and a lower win rate at another time.

[0003] At present, a user usually decides whether to use an application program according to self-will. In this way, if the user uses the application program when the use performance is poor, the use experience is poor. SUMMARY

[0004] Embodiments of the present application provide a reminding method, device and electronic device, and can solve the problem of poor use experience.

[0005] In a first aspect, embodiments of the present application provide a reminding method, which comprises: obtaining first physiological information of a first user in using a preset application program; obtaining predicted information of use performance of the first user for the preset application program according to a preset model and the first physiological information, wherein the preset model is a model obtained by model training according to second physiological information and second use performance information of a second user in using the preset application program; and outputting preset reminding information corresponding to the predicted information.

[0006] In a second aspect, embodiments of the present application provide a reminding device, which comprises: an obtaining module, configured to obtain first physiological information of a first user in using a preset application program; a processing module, configured to obtain predicted information of use performance of the first user for the preset application program according to a preset model and the first physiological information, wherein the preset model is a model obtained by model training according to second physiological information and second use performance information of a second user in using the preset application program; and an output module, configured to output preset reminding information corresponding to the predicted information.

[0007] In a third aspect, embodiments of the present application provide an electronic device, which comprises a processor and a memory, the memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement the steps of the method according to the first aspect.

[0008] In a fourth aspect, an embodiment of the present application provides a readable storage medium, the readable storage medium storing a program or instructions, the program or instructions being executed by a processor to implement the steps of the method according to the first aspect.

[0009] In a fifth aspect, an embodiment of the present application provides a chip, the chip comprising a processor and a communication interface, the communication interface being coupled to the processor, and the processor being configured to run a program or instructions to implement the method according to the first aspect.

[0010] In a sixth aspect, an embodiment of the present application provides a computer program product, the program product being stored in a storage medium, and the program product being executed by at least one processor to implement the method according to the first aspect.

[0011] In the embodiment of the present application, the first physiological information of the first user in the case of using the preset application program is acquired; the prediction information of the use performance of the first user for the preset application program is obtained according to a preset model and the first physiological information, wherein the preset model is a model obtained by model training according to the second physiological information and the second use performance information of the second user in the case of using the preset application program; and preset reminding information corresponding to the prediction information is output. In the embodiment, the preset model is constructed by combining the physiological information and the use performance information of the user in the case of using the application program, so that in the case of using the application program by the current user, the use performance prediction information of the current user in the case of using the application program can be obtained according to the physiological information of the current user in the case of using the application program and by combining the preset model, and the corresponding reminding information is output according to the use performance prediction information, so that the current user can decide whether to continue to use the application program by combining the reminding information, and the use of the application program by the user in the case of poor use performance is avoided, thereby improving the use experience comprehensively. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 is a flowchart of a reminding method provided by the embodiment;

[0013] Figure 2 is a block schematic diagram of a reminding device provided by the embodiment;

[0014] Figure 3 is a hardware structure schematic diagram of an electronic device provided by the embodiment;

[0015] Figure 4 is a hardware structure schematic diagram of another electronic device provided by the embodiment. DETAILED DESCRIPTION

[0016] With reference to the drawings, the technical solutions in the embodiments of the present application will be clearly described below. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art are within the scope of the present application.

[0017] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of a kind and do not limit the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / ", generally indicates that the objects before and after are in a "or" relationship.

[0018] The reminding method provided by the embodiments of the present application will be described in detail below with reference to the drawings, specific embodiments and application scenarios.

[0019] Please refer to Figure 1 The reminding method provided by the embodiments of the present application can include the following steps 110-130:

[0020] Step 110, acquiring first physiological information of a first user in a case where the first user uses a preset application program.

[0021] In detail, the electronic device of the user, such as a smart phone, can be installed with a preset application program. The preset application program can generally be a competitive game application program, a friend-making application program, etc., so that the use performance of the user using the same application program in different states can be different.

[0022] 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 using the application program can affect the use performance of the user on the application program. In order to be able to predict the use performance of the user on the application program in combination with the state of the user, the electronic device can acquire the physiological information of the user during the use of the application program while the user uses the application program.

[0023] In an embodiment of the present disclosure, the physiological information can include at least one of vital index information, fatigue index information, and attention index information.

[0024] In detail, the physiological information can be acquired by the electronic device itself and in cooperation with other smart wearable devices, and the obtained physiological information can reflect the physical and mental state of the user.

[0025] It is feasible to obtain vital signs information such as heart rate, heart rate variance, and blood pressure from users through device body sensors, smart terminal devices, etc.

[0026] Considering that users usually put down the device for a while or hold it with one hand while resting the other hand when they are tired, it is feasible to use the device's body sensors to obtain information on the frequency of users putting down the device and holding it with one hand to determine fatigue indicators.

[0027] Considering that users' attention is often not focused on the game, which usually breaks the full-screen immersion of the game, it is feasible to obtain information on the frequency of users leaving the game interface, checking and replying to messages in the small window during the game to determine attention indicators.

[0028] Step 120: Based on the preset model and the first physiological information, obtain the prediction information of the first user's usage performance of the preset application, wherein the preset model is a model obtained by training the model based on the second physiological information and the second usage performance information of the second user when using the preset application.

[0029] In detail, by incorporating the user's physiological information when using the application into the model, information can be predicted to indicate the user's performance in using the application within a given time period.

[0030] Considering the persistence of users' physiological changes, the aforementioned corresponding time period can include the time period corresponding to the acquired physiological information, as well as a period of time after that. For example, by acquiring the physiological information of a user when playing the first game, it is possible to predict the user's performance when playing the first N (N≥2) games.

[0031] In this embodiment, in order to accurately predict the user's performance in using the application based on the user's physiological information, the model used in this embodiment is obtained by training the model based on the user's physiological data and corresponding performance data when using the application.

[0032] Taking competitive gaming applications as an example, the model in this embodiment not only uses the user's physiological information while playing the game, but also user performance feedback information such as wins / losses and competitive performance levels. In other words, it uses the user's overall state in the game to train the model. The model trained in this way can be used to predict user performance and provide reminders, thereby helping users improve their competitive gaming performance, find their optimal gaming state, and comprehensively enhance their gaming experience.

[0033] In detail, considering the large amount of data required for model training and the universality of the model, the model can be trained based on data from a large number of general users.

[0034] For example, a certain number of game users can be selected from people of different ages and different occupations, and invited to play games. The physiological information and use performance information of these users are obtained in real time during their game playing. After obtaining enough model training data of people with different backgrounds, these data can be brought into a pre-built reinforcement learning model (such as a DNQ model, which combines the advantages of neural networks and Q-learning) for training to obtain an initial universal "game performance prediction" AI (Artificial Intelligence) model.

[0035] In detail, taking a competitive game application as an example, the use performance information can include at least one of the following: game win / loss record, number of enemy kills, number of deaths, and whether MVP (Most Valuable Player).

[0036] Step 130: outputting preset reminding information corresponding to the prediction information.

[0037] In detail, the prediction information can indicate the use performance of the user on the application in the corresponding time period, and therefore the corresponding reminding information can be output according to the specific content of the prediction information. For example, the reminding information can be information for the user to refer to in deciding whether to continue using the application.

[0038] For example, the prediction information can be use performance information, and the reminding information can be a use score corresponding to the use performance information. Generally, the higher the score, the better the use performance of the user on the application. By outputting the use score, the user can decide whether to continue using the application according to the specific score size viewed.

[0039] For another example, the prediction information can also be a predicted use score, and the reminding information can be information corresponding to the use score, which is used to indicate whether to suggest continuing to use the application. By outputting the suggestion information, the user can intuitively understand whether it is necessary to continue using the application.

[0040] Based on this, in an embodiment of the present disclosure, the prediction information includes a predicted score. Generally, the higher the predicted score, the better the use performance of the user on the application. In this embodiment, by substituting the physiological information of the user using the application into the model, a performance score corresponding to the application use performance affected by the physiological information can be directly obtained.

[0041] In a feasible implementation manner, the value range of the predicted score output by the model can be 0-100.

[0042] Correspondingly, the step 130 of outputting preset reminding information corresponding to the prediction information can include the following steps 1301-1302.

[0043] In detail, a comparison result of the prediction score and the preset threshold value can be obtained, and the step 1301 or the step 1302 can be performed based on the obtained comparison result.

[0044] Generally, after using an application program for a period of time, a user will continue to use the application program or stop using the application program. Thus, the embodiment can reasonably set a score threshold value, and compare the obtained prediction score with the score threshold value. For example, when the prediction score output by the model ranges from 0 to 100, the preset threshold value can be 30.

[0045] The step 1301 outputs first reminding information in a case where the prediction score is greater than or equal to the preset threshold value, and the first reminding information is preset reminding information for indicating a suggestion to continue to use the preset application program.

[0046] In detail, when the prediction score is not less than the threshold value, it can be considered that the predicted use performance of the user is good, and the user can have a good use performance when continuing to use the application program, and thus the user can be suggested to continue to use the application program. Thus, the user can generally have a good use performance when continuing to use the application program, and the user has a good use experience of the application program.

[0047] For example, if the score is higher than 70, it is determined that the game state is excellent, the user is reminded that the state is excellent, the subsequent winning opportunity is large, and the user is suggested to continue to play the game.

[0048] The step 1302 outputs second reminding information in a case where the prediction score is less than the preset threshold value, and the second reminding information is preset reminding information for indicating a suggestion to pause using the preset application program.

[0049] In detail, when the prediction score is less than the threshold value, it can be considered that the predicted use performance of the user is poor, and the user generally does not have a good use performance when continuing to use the application program, and thus the user can be suggested to stop (or pause) using the application program. Thus, it can be avoided that the user does not have a good use performance when continuing to use the application program, thereby affecting the use experience of the user on the application program.

[0050] For example, if the score is lower than 30, it is determined that the game state is extremely poor, the user is reminded that the state is not good, the subsequent winning opportunity is small, and the user is suggested to pause the game and play after a rest.

[0051] Based on the above, in the case that the user basically follows the suggested information of the reminder, the technical solution provided by the embodiment can have the effect that the user has good use performance as long as the user uses the application program, and thus the user experience can be comprehensively improved. Moreover, even if the user continues to use in the case that the user is suggested to pause the use, the user has an expectation for the subsequent use performance of the application program, and thus the user can avoid being in a low mood due to poor use performance when using the application program.

[0052] As can be seen, the embodiment of the present disclosure provides a reminding method, which obtains first physiological information of a first user in the case of using a preset application program; obtains prediction information of use performance of the first user for the preset application program according to a preset model and the first physiological information, wherein the preset model is a model obtained by model training according to second physiological information and second use performance information of a second user in the case of using the preset application program; and outputs preset reminder 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 obtain 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 in combination with the preset model, and the corresponding reminder information is outputted accordingly, so that the current user can decide whether to continue to use the application program in combination with the reminder information, the user can avoid using the application program when the use performance is poor, and thus the use experience can be comprehensively improved.

[0053] As can be seen, based on the implementation manner provided by the embodiment, the game performance can be predicted by the physiological and psychological index information of the user when the user uses the terminal device to play the competitive electronic game, and the user can be given the corresponding reminder, and thus the game experience of the user can be comprehensively improved.

[0054] In detail, considering that the trained model has universality, and there are personal differences between different users, the trained model can be optimized and adjusted according to the personal data of the user, so that the optimized model has a certain pertinence for the user, and thus more accurate use performance prediction can be realized, and more accurate reminders can be provided for the user.

[0055] Based on this, in an embodiment of the present disclosure, after obtaining the prediction information of the use performance of the first user for the preset application program, the method can further include the following steps 140 to step 150:

[0056] Step 140: obtaining first use performance information of the first user in the case of using the preset application program.

[0057] In detail, during the use of the application program by the user, not only the physiological information of the user during this period can be obtained, but also the use performance information of the user during this period can be obtained, and the physiological information of the same period will correspondingly affect the use performance information of the same period.

[0058] For example, after the user plays a game, the use performance information of the user when playing the game can be obtained, such as the game win / loss record, the number of enemy kills, the number of deaths, whether MVP, and the like.

[0059] In a feasible implementation manner, the electronic device can access the SDK (Software Development Kit) of the application program when the user uses the application program to obtain the use performance information of the user when using the application program.

[0060] Correspondingly, according to the physiological information of the user during this period, the use performance information of the user during this period can also be predicted based on the model to obtain the predicted information.

[0061] In the case that the model can achieve accurate prediction, the actual use performance and the predicted use performance should not differ much, otherwise it can be considered that the model needs to be further optimized to improve the targeted prediction of the model on the current user.

[0062] Step 150, optimizing the preset model according to the first use performance information and the predicted information.

[0063] In detail, the model used in this embodiment can be a reinforcement learning model, which has the characteristics of being able to continuously optimize its own prediction parameters according to new data and improve the prediction ability.

[0064] In detail, the model can continuously learn and optimize itself through machine learning methods, so that the optimized model can more intelligently and accurately match the personal characteristics of individual users.

[0065] In this step, the parameters of the model can be optimized and adjusted according to the actual use performance and the predicted use performance, so that the predicted use performance based on the optimized model can be more consistent with the actual use performance. Furthermore, subsequent predictions are always based on the latest optimized model. In this way, through continuous optimization of the model based on user data, the optimized model can be more targeted for users.

[0066] Since the model optimization operation can be repeated multiple times, the preset model in the above step 120 can be a model trained, or a model obtained after a certain optimization.

[0067] It can be seen that, by combining the predicted use performance and the actual use performance of the user, the model is optimized, the model is more targeted, and the prediction accuracy is further improved. With the long-term use of the application program by the user, the electronic device can realize continuous learning and optimization of the model, so that the reminding mechanism gradually becomes more intelligent and accurate to match the personal characteristics of the individual user.

[0068] As can be seen from the above, by collecting information of the public users and using the reinforcement learning model, a set of universal game performance prediction AI model that can calculate the physiological state of the user in which the user is more likely to win in the game can be obtained. Then, the AI model is applied to the terminal electronic device, which can be used to predict the game performance of the individual user and give the user corresponding reminders to prompt the user to play the game in the best game state and pause the game in the poor state, thereby comprehensively improving the game experience. Moreover, based on the principle of the machine learning model, the AI model can gradually change from a universal AI model to a tailor-made AI model for the user as the user's use time increases, and the prediction of the user's game performance will also become more and more accurate.

[0069] In the embodiment, a universal model can be trained according to the physiological information and the corresponding use performance information of a large number of public users. In detail, the physiological information and the use performance information can be quantified, and the model is trained based on the quantified values.

[0070] 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 can be continuous change data. For example, the physiological information can include at least one of life index information, fatigue index information, and attention index information.

[0071] Correspondingly, before the step 120, the method can further include the following steps A1-A4:

[0072] Step A1, obtaining a time interval corresponding to the second physiological information.

[0073] For example, the physiological information of a public user during playing a game can be obtained. In this way, the time period corresponding to the user playing the game is the time interval corresponding to the physiological information.

[0074] Step A2, obtaining each time segment included in the time interval.

[0075] In this step, the time interval can be split into time segments (for example, 1s as a time segment) so that the index information of each time segment can be obtained subsequently.

[0076] In step A3, according to the second physiological information, the index value of each kind of index information corresponding to each time segment is obtained.

[0077] In this step, according to the obtained physiological information, the index value of each kind of index information in each time segment can be obtained. For example, the life index value, fatigue index value, and attention index value in each time segment can be obtained.

[0078] As described above, the embodiment realizes the quantification of the physiological information which is continuous change data, so that the index value of each kind of index information in each time segment can be used as the independent variable of the model, and the corresponding use performance information can be used as the dependent variable of the model to perform model training.

[0079] For example, to formulate the quantified index, n kinds of index data are sorted and quantified, and based on the n kinds of index data, high-order data is obtained through certain form of transformation to form the independent variable Ant, where n is the nth independent variable, t is the tth time section, and finally a TxN independent variable matrix is obtained for model training.

[0080] In step A4, according to the second use performance information and the index value of each kind of index information corresponding to each time segment, the model is trained to obtain the preset model.

[0081] In detail, the obtained use performance information can also be quantified to obtain use performance quantification data, and then the model is trained based on the user quantification index data and the use performance quantification data.

[0082] In the embodiment, the physiological information can be continuous change data, so that the TxN (T is the number of time segments, and N is the number of kinds of index information) independent variable matrix is constructed to realize information quantification for model training. Since the embodiment can train the model based on the change trend of the physiological information, the model training effect can be improved, so that the model capable of realizing accurate prediction can be obtained.

[0083] In one embodiment of the disclosure, the second use performance information includes at least one preset information. Any data included in the use performance information can be numerical data. Taking a competitive game application as an example, the use performance information can include at least one of the following information: the game result, the number of enemy kills, the number of deaths, and whether MVP when the user plays the game.

[0084] Correspondingly, before the step 120, the method can further include the following steps B1-B3 to obtain the prediction information of the first user on the use performance of the preset application according to the preset model and the first physiological information.

[0085] In the step B1, each of the preset information is converted into a corresponding score according to a preset score conversion rule, so as to obtain a score corresponding to each of the preset information.

[0086] In detail, a mapping relationship between the preset information and the score can be preset in advance, and then each of the obtained use performance information can be converted into a corresponding score according to the mapping relationship, so as to realize the preliminary quantification of the use performance information.

[0087] In the step B2, a score of the second use performance information is obtained according to a preset weight of each of the preset information and the score corresponding to each of the preset information.

[0088] In detail, the weights of different preset information can be different, and the specific weight can be accurately configured in combination with experience. Based on the weight and the score of the preset information, the obtained score can be summarized to obtain a unified score for reflecting the use performance information.

[0089] As described above, the embodiment realizes the quantification of the use performance information, so as to obtain use performance quantification data, and then the use performance quantification data can be used as the dependent variable of the model, and the corresponding physiological information can be used as the independent variable of the model to perform model training.

[0090] For example, to formulate the quantified index, n types of data are sorted and quantified to form dependent variables Bt1, Bt2, Bt3, …, Btn. The dependent variables are weighted and integrated into a use performance score index B in the range of 0-100. The higher the score is, the better the use performance is.

[0091] In the step B3, the model is trained according to the second physiological information and the score of the second use performance information to obtain the preset model.

[0092] In detail, the obtained physiological information can also be quantified to obtain user quantification index data, and then the model can be trained based on the user quantification index data and the use performance quantification data.

[0093] In the embodiment, the use performance information is numerical data, and the use performance information is quantified by converting the comprehensive score into a unified score, so as to be used for model training. This implementation manner can improve the model training effect, so as to obtain a model capable of realizing accurate prediction, and improve the model training efficiency.

[0094] To sum up, the embodiment can intelligently predict the game performance of a game player through the game physical and mental state of the game player and give a prompt, which can facilitate the player to know when he is more suitable / unsuitable for playing the game, help the player to improve the game competitive performance, find the best game state, and comprehensively improve the game experience. In addition, the embodiment can output a prompt to pause the game when the player plays the game for a long time and the state is not good, so as to have the effect of protecting the life safety of the user and preventing the user from being addicted.

[0095] The execution subject of the reminding method provided in the embodiment of the application can be a reminding device. The reminding device provided in the embodiment of the application is described by taking the reminding device as an example.

[0096] As shown in Figure 2 The embodiment provides a reminding device 200, which can include an acquisition module 210, a processing module 220, and an output module 230.

[0097] The acquisition module 210 is configured to acquire first physiological information of a first user in a case where the first user uses a preset application program. The processing module 220 is configured to obtain prediction information of a use performance of the first user for the preset application program according to a preset model and the first physiological information, where the preset model is a model obtained by model training according to second physiological information and second use performance information of a second user in a case where the second user uses the preset application program. The output module 230 is configured to output preset reminding information corresponding to the prediction information.

[0098] In the embodiment of the application, first physiological information of a first user in a case where the first user uses a preset application program is acquired. Prediction information of a use performance of the first user for the preset application program is obtained according to a preset model and the first physiological information, where the preset model is a model obtained by model training according to second physiological information and second use performance information of a second user in a case where the second user uses the preset application program. Preset reminding information corresponding to the prediction information is output. The embodiment combines the physiological information and the use performance information of the user when the user uses 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 in combination with the preset model, and the corresponding reminding information is output accordingly, so that the current user can decide whether to continue to use the application program in combination with the reminding information, thereby avoiding the user from using the application program when the use performance is poor, and comprehensively improving the use experience.

[0099] In an embodiment of the present disclosure, the reminding device 200 further comprises an optimization module configured to acquire first use performance information of the first user in the case of using the preset application program; and optimize the preset model according to the first use performance information and the prediction information.

[0100] In an embodiment of the present disclosure, the second physiological information comprises at least one index information. The reminding device 200 further comprises a model training module configured to acquire a time interval corresponding to the second physiological information; acquire each time segment included in the time interval; acquire an index value of each index information corresponding to each time segment according to the second physiological information; and perform 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.

[0101] In an embodiment of the present disclosure, the second use performance information comprises at least one preset information. The reminding device 200 further comprises a model training module configured to convert each preset information into a corresponding score according to a preset score conversion rule to obtain a score corresponding to each preset information; obtain a score of the second use performance information according to a preset weight of each preset information and the score corresponding to each preset information; and perform model training according to the second physiological information and the score of the second use performance information to obtain the preset model.

[0102] In an embodiment of the present disclosure, the prediction information comprises a prediction score. The output module 230 is configured to output first reminding information in the case that the prediction score is greater than or equal to the preset threshold, the first reminding information being preset reminding information for indicating that it is suggested to continue using the preset application program; and output second reminding information in the case that the prediction score is less than the preset threshold, the second reminding information being preset reminding information for indicating that it is suggested to suspend using the preset application program.

[0103] The reminding apparatus in the embodiments of the present application can be an electronic device, or a component in the electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or other devices other than the terminal. For example, the electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm 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 can also be a server, a Network Attached Storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, and the like, and the embodiments of the present application are not limited in this regard.

[0104] The reminding apparatus in the embodiments of the present application can be an apparatus with an operating system. The operating system can be an Android operating system, an ios operating system, or other possible operating systems, and the embodiments of the present application are not limited in this regard.

[0105] The reminding apparatus provided in the embodiments of the present application can implement the method embodiments, and each process of the method embodiments is not repeated here. Figure 1 The method embodiments implement each process, and each process is not repeated here.

[0106] Optionally, as shown in Figure 3 The embodiments of the present application further provide an electronic device 300, which includes a processor 310 and a memory 320, and the memory 320 stores programs or instructions that can be run on the processor 310. When the programs or instructions are executed by the processor 310, each step of the above reminding method embodiments is implemented, and the same technical effects are achieved. Each step is not repeated here.

[0107] Figure 4 A hardware structure schematic diagram of an electronic device 1000 for implementing the embodiments of the present application.

[0108] 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, and the like.

[0109] Those skilled in the art can understand that the electronic device 1000 can further include a power supply (such as a battery) for supplying power to each component, and the power supply can be logically connected to the processor 1010 through a power management system, so that the power management system can realize the functions of managing charging, discharging, and power consumption management. Figure 4 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device can include more or fewer components than the figure, or combine certain components, or different component arrangements, which are not described here.

[0110] The processor 1010 is configured to obtain first physiological information of a first user in a case of using a preset application; obtain predicted information of a use performance of the first user for the preset application according to a preset model and the first physiological information, wherein the preset model is a model obtained by model training according to second physiological information and second use performance information of a second user in a case of using the preset application; and output preset reminding information corresponding to the predicted information.

[0111] In the embodiment of the present application, the first physiological information of the first user in the case of using the preset application is obtained; the predicted information of the use performance of the first user for the preset application is obtained according to the preset model and the first physiological information, wherein the preset model is a model obtained by model training according to the second physiological information and the second use performance information of the second user in the case of using the preset application; and the preset reminding information corresponding to the predicted information is output. The embodiment combines the physiological information and the use performance information of the user when using the application to construct the preset model, so that when the current user uses the application, the use performance prediction information of the current user when using the application can be obtained according to the physiological information of the current user when using the application and in combination with the preset model, and the corresponding reminding information is output accordingly, so that the current user can decide whether to continue using the application in combination with the reminding information, thereby avoiding the user from using the application when the use performance is poor, and thereby improving the use experience comprehensively.

[0112] Optionally, the processor 1010 is configured to obtain first use performance information of the first user in the case of using the preset application after obtaining the predicted information of the use performance of the first user for the preset application; and optimize the preset model according to the first use performance information and the predicted information.

[0113] Optionally, the second physiological information comprises at least one index information; the processor 1010 is configured to, before the step of obtaining the prediction information of the first user on the use performance of the preset application program according to the preset model and the first physiological information, acquire a time interval corresponding to the second physiological information; acquire each time segment included in the time interval; acquire an index value corresponding to each index information of each time segment according to the second physiological information; and perform model training according to the second use performance information and the index value corresponding to each index information of each time segment to obtain the preset model.

[0114] Optionally, the second use performance information comprises at least one preset information; the processor 1010 is configured to, before the step of obtaining the prediction information of the first user on the use performance of the preset application program according to the preset model and the first physiological information, convert each preset information into a corresponding score according to a preset score conversion rule to obtain a score corresponding to each preset information; acquire a score of the second use performance information according to a preset weight of each preset information and the score corresponding to each preset information; and perform model training according to the second physiological information and the score of the second use performance information to obtain the preset model.

[0115] Optionally, the prediction information comprises a prediction score; and the processor 1010 is configured to, in a case where the prediction score is greater than or equal to the preset threshold, output first reminding information, the first reminding information being preset reminding information for indicating a suggestion to continue using the preset application program; and in a case where the prediction score is less than the preset threshold, output second reminding information, the second reminding information being preset reminding information for indicating a suggestion to suspend using the preset application program.

[0116] It should be understood that in the embodiments of the present application, the input unit 1004 can include a graphics processor (GPU) 10041 and a microphone 10042. The graphics processor 10041 processes image data of a still picture or a video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 1006 can include a display panel 10061, which can 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 can include two parts of a touch detection device and a touch controller. The other input devices 10072 can include, but are not limited to, a physical keyboard, function keys (such as volume control keys, on-off keys, etc.), a trackball, a mouse, a joystick, and the like, which will not be described here.

[0117] The memory 1009 can be used to store software programs and various data. The memory 1009 can mainly include a first storage area storing programs or instructions and a second storage area storing data, wherein the first storage area can store an operating system, application programs or instructions required by at least one function (such as a sound playing function, an image playing function, etc.), and the like. In addition, the memory 1009 can include a volatile memory or a non-volatile memory, or the memory 1009 can include both a volatile memory and a non-volatile memory. The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Synch link DRAM (SLDRAM), and a Direct Rambus 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.

[0118] The processor 1010 can include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and an application program, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 1010.

[0119] The embodiments of the present application also provide a readable storage medium, the readable storage medium stores programs or instructions, the programs or instructions are executed by a processor to realize various processes of the above-mentioned reminding method embodiments, and the same technical effects can be achieved. To avoid repetition, details are not described here.

[0120] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes a computer readable storage medium, such as a computer readable only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0121] The embodiment of the present application further provides a chip, which comprises a processor and a communication interface, the communication interface is coupled with the processor, the processor is used for running programs or instructions to realize the processes of the reminding method embodiments and achieve the same technical effects. To avoid repetition, details are not described herein.

[0122] It should be understood that the chip mentioned in the embodiment of the present application can also be referred to as a system chip, a system chip, a chip system or a system on chip, etc.

[0123] The embodiment of the present application provides a computer program product, which is stored in a storage medium, and the program product is executed by at least one processor to realize the processes of the reminding method embodiments and achieve the same technical effects. To avoid repetition, details are not described herein.

[0124] It should be noted that in this document, the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "comprises a" does not exclude the presence of another identical element in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiment of the present application is not limited to the order of performing the functions as shown or discussed, but can also include performing the functions in a substantially simultaneous manner or in the opposite order, for example, the described method can be performed in an order different from the described order, and various steps can also be added, omitted or combined. In addition, the features described with reference to some examples can be combined in other examples.

[0125] Through the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned example methods can be realized by means of software and a necessary general hardware platform, and of course, can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a computer software product in essence or in the form of a part that contributes to the prior art, which is stored in a storage medium (such as a ROM / RAM, a magnetic disc, an optical disc), and includes a plurality of instructions for causing a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application.

[0126] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above-mentioned specific embodiments, and the above-mentioned specific embodiments are only illustrative and not restrictive. Those skilled in the art can make many forms under the inspiration of the present application without departing from the scope of the present application and the scope protected by the claims.

Claims

1. A reminding method characterized by, The method comprises: obtaining first physiological information of a first user in a case of using a preset application, wherein the first physiological information comprises at least two of life index information, fatigue index information and attention index information, and the first physiological information is used to reflect a physical and mental state of the first user; obtaining prediction information of a use performance of the first user for the preset application according to a preset model and the first physiological information, wherein the preset model is obtained by model training according to second physiological information and second use performance information of a second user in a case of using the preset application; outputting preset reminding information corresponding to the prediction information, wherein the preset reminding information is used to indicate a suggestion to continue using the preset application or a suggestion to suspend using the preset application; the prediction information comprises a prediction score; the outputting of the preset reminding information corresponding to the prediction information comprises: in a case that the prediction score is greater than or equal to a preset threshold, outputting first reminding information, wherein the first reminding information is preset reminding information used to indicate a suggestion to continue using the preset application; in a case that the prediction score is less than the preset threshold, outputting second reminding information, wherein the second reminding information is preset reminding information used to indicate a suggestion to suspend using the preset application.

2. The method of claim 1, wherein, after the obtaining of the prediction information of the use performance of the first user for the preset application, the method further comprises: obtaining first use performance information of the first user in a case of using the preset application; 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 comprises at least one index information; before the obtaining of the prediction information of the use performance of the first user for the preset application according to the preset model and the first physiological information, the method further comprises: obtaining a time interval corresponding to the second physiological information; obtaining each time segment included in the time interval; obtaining an index value of each index information corresponding to each time segment according to the second physiological information; performing model training to obtain the preset model according to the second use performance information and the index value of each index information corresponding to each time segment.

4. The method of claim 1, wherein, the second use performance information comprises at least one preset information; before the obtaining of the prediction information of the use performance of the first user for the preset application according to the preset model and the first physiological information, the method further comprises: converting each preset information into a corresponding score according to a preset score conversion rule to obtain a score corresponding to each preset information; obtaining a score of the second use performance information according to a preset weight of each preset information and the score corresponding to each preset information; performing model training to obtain the preset model according to the second physiological information and the score of the second use performance information.

5. A reminding device, characterized in that The method comprises: The acquisition module is configured to acquire first physiological information of a first user in a case of using a preset application, wherein the first physiological information comprises at least two of life index information, fatigue index information, and attention index information, and the first physiological information is used to reflect a physical and mental state of the first user. The processing module is configured to obtain predicted information of a use performance of the first user for the preset application according to a preset model and the first physiological information, wherein the preset model is a model obtained by model training according to second physiological information and second use performance information of a second user in a case of using the preset application; and The output module is configured to output preset reminding information corresponding to the predicted information, wherein the preset reminding information is used to indicate a suggestion to continue using the preset application or a suggestion to suspend using the preset application. The predicted information comprises a predicted score. The output module is configured to output first reminding information in a case that the predicted score is greater than or equal to a preset threshold, the first reminding information being preset reminding information used to indicate a suggestion to continue using the preset application; and output second reminding information in a case that the predicted score is less than the preset threshold, the second reminding information being preset reminding information used to indicate a suggestion to suspend using the preset application.

6. The apparatus of claim 5, wherein, The device further comprises: The optimization module is configured to acquire first use performance information of the first user in a case of using the preset application, and optimize the preset model according to the first use performance information and the predicted information.

7. The apparatus of claim 5, wherein, The second physiological information comprises at least one index information. The device further comprises: The model training module is configured to acquire a time interval corresponding to the second physiological information, acquire each time segment included in the time interval, acquire an index value of each index information corresponding to each time segment according to the second physiological information, and perform model training to obtain the preset model according to the second use performance information and the index value of each index information corresponding to each time segment.

8. The apparatus of claim 5, wherein, The second use performance information comprises at least one preset information. The device further comprises: The model training module is configured to convert each preset information into a corresponding score according to a preset score conversion rule to obtain a score corresponding to each preset information, obtain a score of the second use performance information according to a preset weight of each preset information and the score corresponding to each preset information, and perform model training to obtain the preset model according to the second physiological information and the score of the second use performance information.

9. An electronic device, comprising: The device comprises a processor and a memory, wherein the memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement steps of the reminding method according to any one of claims 1-4.

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