Equipment control method and device, electronic equipment and readable storage medium

Abstract

The invention discloses an equipment control method and device, electronic equipment and a readable storage medium, and relates to the technical field of equipment control, the method is applied to the electronic equipment, and the method comprises the following steps: obtaining biological characteristics of a target user using the electronic equipment; predicting work and rest information of the target user at the current moment based on a pre-acquired work and rest habit prediction model; acquiring position information of the target user at the current moment; target control operation for a target object is determined based on the biological characteristics, the position information and the work and rest information, and the target object comprises at least one of a hardware device and an application program configured by the electronic equipment; and executing the target control operation through the electronic equipment. A user can execute the illness control operation on the electronic equipment without actively intervening the operation, so that the convenience is high.

Description

Technical Field

[0001] The present application relates to the field of device control technology, and more specifically, to a device control method, apparatus, electronic device and readable storage medium. Background Art

[0002] At present, with the development of electronic information technology, people can use electronic devices in various scenarios. Although the hardware devices configured in the electronic devices or the application programs can be adjusted, the convenience of adjusting the hardware devices or the application programs is currently insufficient. Summary of the invention

[0003] The present application proposes a device control method, apparatus, electronic device and readable storage medium to improve the above-mentioned defects.

[0004] In a first aspect, an embodiment of the present application provides a device control method, which is applied to an electronic device, and the method includes: obtaining biometric features of a target user who uses the electronic device; predicting the work and rest information of the target user at the current moment based on a pre-acquired work and rest habit prediction model; obtaining the location information of the target user at the current moment; determining a target control operation for a target object based on the biometric features, location information, and the work and rest information, wherein the target object includes at least one of a hardware device and an application configured for the electronic device; and performing the target control operation through the electronic device.

[0005] In the second aspect, the embodiment of the present application also provides a device control device, which is applied to an electronic device, and the device includes: a first acquisition unit, a prediction unit, a second acquisition unit, a control operation determination unit, and an execution unit. Among them, the first acquisition unit is used to obtain the biometric characteristics of the target user who uses the electronic device; the prediction unit is used to predict the work and rest information of the target user at the current moment based on the pre-acquired work and rest habit prediction model; the second acquisition unit is used to obtain the location information of the target user at the current moment; the control operation determination unit is used to determine the target control operation for the target object based on the biometric characteristics, location information and the work and rest information, wherein the target object includes at least one of the hardware device and application configured by the electronic device; the execution unit is used to execute the target control operation through the electronic device.

[0006] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: one or more processors; a memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the method described in the first aspect.

[0007] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program code executable by a processor, and when the program code is executed by the processor, the processor executes the above method.

[0008] The device control method, device, electronic device and readable storage medium provided by the present application first obtain the biometrics of the target user who uses the electronic device; predict the work and rest information of the target user at the current moment based on the pre-acquired work and rest habit prediction model; then obtain the location information of the target user at the current moment; thereby determining the target control operation for the target object based on the biometrics, location information and the work and rest information, wherein the target object includes at least one of the hardware device and application configured by the electronic device; so as to perform the target control operation through the electronic device. If the user actively operates the electronic device to adjust the hardware device or application, the convenience is poor. In the present application, the target control operation for the target object can be determined based on the biometrics, location information and the work and rest information, so that the target control operation is performed through the electronic device, without the user actively intervening to adjust the hardware device or application, which increases the convenience of controlling the electronic device and improves the user's experience.

[0009] Other features and advantages of the embodiments of the present application will be described in the subsequent description, and partly become apparent from the description, or can be understood by practicing the embodiments of the present application. The purposes and other advantages of the embodiments of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 An application scenario diagram of the device control method provided in an embodiment of the present application is shown;

[0012] Figure 2 A method flow chart of a device control method provided by an embodiment of the present application is shown;

[0013] Figure 3 A method flow chart of a device control method provided by another embodiment of the present application is shown;

[0014] Figure 4A schematic diagram of determining age characteristics provided by an embodiment of the present application is shown;

[0015] Figure 5 A schematic diagram of constructing a work and rest habit prediction model provided in an embodiment of the present application is shown;

[0016] Figure 6 A schematic diagram of determining a spatial position provided by an embodiment of the present application is shown;

[0017] Figure 7 A method flow chart of a device control method provided by another embodiment of the present application is shown;

[0018] Figure 8 A method flow chart of a device control method provided by another embodiment of the present application is shown;

[0019] Fig. 9 The structure block diagram of the device control device provided in the embodiment of the present application is shown;

[0020] Fig.10 A structural block diagram of an electronic device provided in an embodiment of the present application is shown;

[0021] Fig.11 A structural block diagram of a computer-readable storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] In order to make those skilled in the art better understand the present application scheme, the technical scheme in the present application embodiment will be clearly and completely described below in conjunction with the drawings in the present application embodiment. Obviously, the described embodiment is only a part of the present application embodiment, rather than all the embodiments. The components of the present application embodiment usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application for protection, but merely represents the selected embodiment of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0023] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0024] At present, with the development of electronic information technology, people can use electronic devices in various scenarios. Although the hardware devices configured in the electronic devices or the applications can be adjusted, the convenience of adjusting the hardware devices or applications is currently insufficient. How to improve the convenience of adjusting the hardware devices or applications is a problem that needs to be solved urgently.

[0025] Currently, users can manually adjust the hardware devices configured in electronic devices or the application programs. For example, users can manually adjust the brightness of the display screen of the electronic device. In addition, currently, a sensor component can also be configured on the electronic device, so as to adjust the hardware devices configured in the electronic device based on the data collected by the sensor component. For example, the sensor component can be a light sensor.

[0026] However, the inventors found in their research that if the user manually adjusts the hardware devices or applications configured in the electronic device, it will reduce the convenience of adjusting the electronic device. If the electronic device is adjusted only by the data collected by the sensor component, the accuracy of adjusting the electronic device will be low due to the small amount of data collected by the sensor component, and the user's active intervention will still be required, and the problem of insufficient convenience still exists.

[0027] Therefore, in order to overcome the above-mentioned defects, the present application provides a device control method, apparatus, electronic device and readable storage medium.

[0028] See also Figure 1 , Figure 1 An application scenario diagram of a device control method is shown, namely, a device control scenario 100 , which includes an electronic device 110 and a target user 120 .

[0029] exist Figure 1 The electronic device 110 shown in the figure is a smart phone. The target user 120 can use the electronic device 110. The electronic device 110 can run an operating system, so that applications can be run through the operating system, and the hardware devices configured on the electronic device 110 can also be controlled. For a detailed introduction, please refer to the subsequent embodiments.

[0030] See also Figure 2 , Figure 2 The device control method provided by the embodiment of the present application is shown. The device control method can be applied to Figure 1 The electronic device shown in the figure may specifically use the processor of the electronic device as the execution subject of the device control method. The device control method may include steps S110 to S150.

[0031] Step S110: Acquire biometric features of a target user using the electronic device.

[0032] In order to conveniently adjust the hardware device or application configured in the electronic device through the electronic device, information of different dimensions may be collected first.

[0033] It is understandable that users of different age characteristics have different requirements for the working state of the hardware device of the electronic device when using the electronic device. For example, if the hardware device includes a display screen, older users are generally more sensitive to light, so for older users, the brightness of the display screen of the electronic device can be greatly reduced when using the electronic device.

[0034] Therefore, in the embodiment provided in the present application, the biometric feature of the target user using the electronic device can be first obtained. The biometric feature can be used to characterize the age of the target user, so the biometric feature is an age feature; for example, the biometric feature can also be used to characterize the gender of the target user, so the biometric feature can be a gender feature.

[0035] For some implementations, the target user using the electronic device may be a user whose identity information matches the account currently logged into the electronic device. Exemplarily, the electronic device may store multiple accounts and relevant data information of each account, such as account A, account B, and account C, wherein the user whose identity information matches the account A is user A; similarly, the user whose identity information matches the account B is user B; and the user whose identity information matches the account C is user C. If the electronic device is currently logged into account A, user A whose identity information matches the account A can be used as the target user.

[0036] Wherein, the biometric feature can be inferred by the electronic device based on the collected relevant data of the target user. Exemplarily, the biometric feature is an age feature, and the electronic device can infer the input relevant data of the target user through an age prediction model, thereby outputting the age feature. Wherein, the relevant data of the target user may include the voice data, image data of the target user, and the interactive feature data of the target user's interactive operation with the electronic device. For a detailed description, please refer to the subsequent embodiments. Another exemplary embodiment, the biometric feature may also include the identity feature of the target user, wherein the identity feature can be used to characterize whether the target user is the owner of the electronic device.

[0037] Step S120: predicting the work and rest information of the target user at the current moment based on the work and rest habit prediction model acquired in advance.

[0038] It is understandable that users often use electronic devices before going to sleep at night, for example, browsing videos, reading text content, etc. When using electronic devices before going to sleep, the brightness of the surrounding environment of the user is generally darker, so the display screen can be adjusted at this time.

[0039] Therefore, in some embodiments, the work and rest information of the target user at the current moment can also be obtained. Exemplarily, the work and rest information may include sleep information, which is used to characterize the prediction of whether the target user has fallen asleep at the current moment, for example, the sleep information may include already asleep or waiting to fall asleep. Another exemplary example is that the work and rest information may also include work information, which is used to characterize the prediction of whether the target user is working at the current moment, for example, the work information may include working or not working.

[0040] The work and rest information can be predicted by a pre-acquired work and rest habit prediction model. The input parameter of the work and rest habit prediction model can be the current moment, and the output parameter is used to characterize the predicted work and rest information corresponding to the target user at the current moment.

[0041] It should be noted that the work and rest information obtained in this step is not the actual measured information obtained by collecting the tasks that the target user is currently performing, but the predicted information obtained by predicting the work and rest habit prediction model.

[0042] For some embodiments, taking the work and rest information including sleep information as an example, a habit prediction model can be constructed in advance based on the characteristics of the target user in some time periods, so that the work and rest habit prediction model learns whether the target user falls asleep in some time periods, and then can directly predict whether the target user falls asleep at the current moment through the work and rest habit prediction model, thereby obtaining sleep information. Among them, the characteristics in some time periods can include the body movement feature vector, breathing feature vector and habit feature vector of the target user, etc., and a detailed introduction can be referred to the subsequent embodiments.

[0043] Step S130: Acquire the location information of the target user at the current moment.

[0044] Furthermore, the current location of the target user is also important for determining how to adjust the electronic device in the future. Therefore, in the embodiment provided by the present application, the location information of the target user at the current moment can also be obtained.

[0045] Exemplarily, the location information may include room information for characterizing a room type. For example, a room type may include a bedroom, a living room, or a balcony, etc. Thus, the location information may characterize a specific room type where the user is currently located.

[0046] It is understandable that even if the spatial position of the target user at the current moment is determined by positioning technology, it is still necessary to determine the room type that matches the spatial position. For some implementations, a room position comparison table can be obtained in advance, and then the room information is determined by the spatial position and the room position comparison table. For detailed description, please refer to the subsequent embodiments.

[0047] Step S140: determining a target control operation for a target object based on the biometric feature, the location information, and the work and rest information, wherein the target object includes at least one of a hardware device and an application configured in the electronic device.

[0048] Furthermore, after the biometric features, location information and the work and rest information are acquired through the aforementioned steps, the target control operation for the target object can be determined. The target object may include at least one of a hardware device and an application configured in the electronic device.

[0049] Exemplarily, if the biometric feature includes an age feature, if the age feature is relatively small, it can be determined that the target control operation includes switching to a game monitoring mode or switching to a youth protection mode; if the age feature is relatively large, it can be determined that the target control operation includes switching to an elder care mode. In another exemplary embodiment, if it is detected that the age feature of the target user is relatively large, and the location information is in a public place, it can be determined that the target control operation includes switching to an elderly-friendly mode. In another exemplary embodiment, if it is detected that the age feature of the target user is relatively small, and the location information is in a teaching building, and the schedule information includes being in class, it can be determined that the target control operation includes switching to a learning mode.

[0050] Among them, if the target object is a hardware device configured in the electronic device, specifically for example, the hardware device is a display screen, the target control operation can be to adjust the brightness, color temperature, etc. of the display screen. In addition, if the target object is an application, specifically for example, the application is an audio player, the target control operation can be to play the target audio at the playback volume through the audio player. For a detailed description, please refer to the subsequent embodiments.

[0051] For some embodiments, the target control operation for the target object can be determined by a pre-acquired comparison table. The comparison table includes multiple reference biometrics, reference location information, and reference work and rest information, and also includes reference target control operations corresponding to any combination of reference biometrics, reference location information, and reference work and rest information. Thus, after obtaining the biometrics, location information, and the work and rest information, the biometrics can be matched with the reference biometrics, the location information can be matched with the reference location information, and the work and rest information can be matched with the reference work and rest information to obtain the corresponding reference target control operation as the target control operation for the target object.

[0052] For some other embodiments, the decision model can also output a feature vector, so that the target control operation can be determined by the feature vector. Exemplarily, the biometrics, location information, and work and rest information can be used as input parameters of the decision model to obtain a feature vector output by the decision model. Among them, the feature vector can be used to characterize whether certain operations need to be performed, such as whether eye protection is needed, whether to perform sleep wake-up operations, etc.; in addition, the feature vector can also be used to characterize the state of the target user, such as the length of time the target user uses the electronic device, whether the target user may be in a location area with low ambient brightness, etc. Therefore, the target control operation that needs to be performed later can be determined based on the feature vector.

[0053] Optionally, before determining the target control operation, the user's behavior preference may be obtained, so that the behavior preference is used as one of the reference parameters for determining the target control operation, so that the determined target control operation can refer to the behavior preference. The behavior preference may include the control operations performed by the target user on the hardware device or application of the electronic device at various moments before the current moment.

[0054] For example, the target user prefers to set the brightness of the display screen to the lowest when he is about to fall asleep. Based on the behavior preference, when it is detected that the target user is about to fall asleep at the current moment, the brightness of the display screen is set to the lowest.

[0055] Step S150: executing the target control operation through the electronic device.

[0056] The target control operation can then be performed by the electronic device.

[0057] In some embodiments, after acquiring the target control operation, the electronic device may automatically execute the target control operation in the background, so that the target user in the foreground of the electronic device does not observe the execution process of the target control operation, thereby reducing interference with the target user's use of the electronic device.

[0058] In other embodiments, the electronic device may prompt the user whether to execute the target control operation after acquiring the target control operation, and wait to receive the user's feedback, so as to determine whether to execute the target control operation in response to the user's feedback. In addition, it may be set that the target control operation may be executed after no feedback from the user is received after waiting for a specified time, for example, the specified time may be 30s, 15s, etc.

[0059] The device control method provided in the present application first obtains the biometrics of the target user who uses the electronic device; predicts the work and rest information of the target user at the current moment based on a pre-acquired work and rest habit prediction model; then obtains the location information of the target user at the current moment; thereby determining the target control operation for the target object based on the biometrics, location information, and the work and rest information, wherein the target object includes at least one of the hardware device and application configured by the electronic device; so as to perform the target control operation through the electronic device. If the user actively operates the electronic device to adjust the hardware device or application, the convenience is poor. In the present application, the target control operation for the target object can be determined based on the biometrics, location information, and the work and rest information, so that the target control operation is performed through the electronic device, without the user actively intervening to adjust the hardware device or application, which increases the convenience of controlling the electronic device and improves the user's experience.

[0060] See also Figure 3 , Figure 3 The device control method provided by the embodiment of the present application is shown. The device control method can be applied to Figure 1 The electronic device shown in the figure may specifically use the processor of the electronic device as the execution subject of the device control method. The device control method may include steps S210 to S2110.

[0061] Step S210: collecting voice data of the target user and image data of the target user.

[0062] Step S220: Acquire interaction feature data of interaction operations performed between the target user and the electronic device.

[0063] In some implementations, the biometric feature may include an age feature. Thus, the voice data of the target user and the image data including the target user may be collected first.

[0064] Among them, the image data including the target user can be collected by the electronic device. Exemplarily, currently electronic devices are generally equipped with a front camera, so that the image can be collected at the current moment by the front camera of the electronic device. It is understandable that the image collected by the electronic device through the front camera may not include the target user, or only include a partial image of the target user. Therefore, when collecting images, the electronic device can also collect multiple images, and then subsequently perform image analysis on each collected image to determine an image that includes the target user and the face of the target user as the image data.

[0065] In addition, when the target user faces the electronic device, there may be differences in angle or position, which results in low accuracy in obtaining the age feature only through image data. Therefore, in order to improve the accuracy of determining the age feature, voice data may be further obtained.

[0066] The voice data may include the user's voice or the user's breathing sound. In some implementations, the electronic device may be equipped with a microphone, so that the voice data may be collected through the microphone equipped with the electronic device.

[0067] Furthermore, target users with different age characteristics generally have interactive feature data that matches their age characteristics when they perform interactive operations on electronic devices. For some embodiments, the interactive feature data may include operation feature data and vibration feature data. Among them, the operation feature data can be used to characterize the feature data of the user's interactive operation on the electronic device. In addition, since the target user performs interactive operations on the electronic device, the electronic device will generate micro-vibrations, so that the vibration feature data caused by the micro-vibrations generated by the target user's interactive operations on the electronic device can also be obtained. Among them, the micro-vibrations generated by target users with different age characteristics have different vibration feature data. Exemplarily, the vibration features generated by the interactive operations of target users with smaller or larger age characteristics may be smaller; while the vibration features generated by the interactive operations of target users with moderate age characteristics may be larger. Among them, the age feature can be used to characterize a specific age number, such as 30 years old, 40 years old, etc.; it can also be used to characterize age groups, such as 30 to 40 years old.

[0068] Specifically, step S220 may also include steps S221 to S223.

[0069] Step S221: Acquire operation characteristic data of the target user's interactive operation with the electronic device.

[0070] Step S222: Acquire vibration characteristic data collected by the inertial measurement unit of the electronic device based on the interactive operation.

[0071] Step S223: using the operation characteristic data and the vibration characteristic data as the interaction characteristic data.

[0072] First, the operation characteristic data of the target user's interactive operation with the electronic device can be obtained. The interactive operation may include the target user's interactive operation on the display screen of the electronic device, such as pressing, touching, or continuously clicking. Thus, illustratively, the operation characteristic data may include the target user's touch pressure on the display screen, the length of the sliding track on the display screen, and the area of ​​the touch display screen.

[0073] It should be noted that the operation characteristic data may be data of the target user's interactive operation with the electronic device acquired in a first time period before the current moment. In some embodiments, the electronic device may record, store and analyze the target user's operation on the display screen in the first time period before the current moment, thereby analyzing the acquired operation to obtain the interactive characteristic data.

[0074] Exemplarily, the interaction feature may be modeled so as to represent the interaction feature data by an interaction feature vector.

[0075] In addition, since the target user performs interactive operations on the electronic device, the electronic device will generate micro-vibrations, so that vibration characteristic data collected by the electronic device based on the interactive operations can also be obtained.

[0076] For some implementations, the electronic device is generally equipped with an inertial measurement unit (IMU), so that the vibration characteristic data generated by the micro-vibration of the interactive operation can be collected through the inertial measurement unit. The operation characteristic data and the vibration characteristic data can be used as the interactive characteristic data.

[0077] Exemplarily, the micro-vibration may be modeled so as to characterize the vibration characteristic data by a vibration characteristic vector.

[0078] Step S230: Determine the age characteristics of the target user using the electronic device based on the pre-acquired age prediction model through the voice data, image data and interaction feature data.

[0079] Furthermore, the age characteristics of the target user using the electronic device can be determined based on the pre-acquired age prediction model through the voice data, image data, and interaction feature data. For some implementations, the voice data, image data, operation feature data, and vibration feature data can be directly used as input parameters of the age prediction model, so as to regress the age characteristics of the target user through the age prediction model.

[0080] It is also possible to first obtain a first age feature to be confirmed based on voice data regression, and a first confidence level corresponding to the first age feature to be confirmed; obtain a second age feature to be confirmed based on image data regression, and a second confidence level corresponding to the second age feature to be confirmed; obtain a third age feature to be confirmed based on operation feature data regression, and a third confidence level corresponding to the third age feature to be confirmed; and obtain a fourth age feature to be confirmed based on vibration feature data regression, and a fourth confidence level corresponding to the fourth age feature to be confirmed.

[0081] Then, the first age feature to be confirmed, the first confidence level, the second age feature to be confirmed, the second confidence level, the third age feature to be confirmed, the third confidence level, the fourth age feature to be confirmed and the fourth confidence level are used as input parameters of the age prediction model for fusion regression, thereby regressing the age characteristics of the target user through the age prediction model.

[0082] Optionally, the fifth age feature to be confirmed and the fifth confidence level corresponding to the fifth age feature to be confirmed can also be regressed based on the operation feature data and the vibration feature data. For example, the operation feature data and the vibration feature data can be inferred by a cross-attention model to regress the fifth age feature to be confirmed and the fifth confidence level. Then, the first age feature to be confirmed, the first confidence level, the second age feature to be confirmed, the second confidence level, the fifth age feature to be confirmed and the fifth confidence level are used as input parameters of the age prediction model for fusion regression, so as to regress the age characteristics of the target user through the age prediction model.

[0083] For example, see Figure 4 , Figure 4 A schematic diagram of determining age characteristics provided in an embodiment of the present application is shown. Figure 4 Includes steps S231 to S237.

[0084] Step S231: Acquire image data.

[0085] Step S232: Acquire voice data.

[0086] Step S233: Acquire operation characteristic data.

[0087] Step S234: Acquire vibration characteristic data.

[0088] Step S235: Reasoning through cross-attention.

[0089] Step S236: Input age prediction model.

[0090] Step S237: Regress to obtain age characteristics.

[0091] Among them, the detailed introduction of steps S231 to S237 can refer to the introduction of the aforementioned steps, which will not be repeated here.

[0092] Step S240: obtaining a body motion feature vector corresponding to the body motion data and a breathing feature vector corresponding to the breathing data of the target user within the first time period.

[0093] Step S250: determining a habit feature vector of the target user based on usage data of the electronic device by the target user in a plurality of second time periods.

[0094] Step S260: constructing the work and rest habit prediction model based on the body movement feature vector, the breathing feature vector and the habit feature vector, wherein the input parameter of the work and rest habit prediction model is the time, and the output parameter of the work and rest habit prediction model is used to characterize the predicted sleep information of the target user at the time.

[0095] Furthermore, a work and rest habit prediction model can be constructed. For some embodiments, the work and rest information includes sleep information, so that the work and rest habit prediction model can be constructed based on the target user's breathing-related data, body movement-related data, and electronic device usage habit data.

[0096] Specifically, the body motion feature vector corresponding to the body motion data of the target user in the first time period and the breathing feature vector corresponding to the breathing data can be first obtained. Among them, the first time period can be the time period before the current moment. It can be understood that in order to subsequently construct a work and rest habit prediction model, multiple body motion feature vectors and breathing feature vectors corresponding to the first time period are generally required. Thus, exemplarily, the first time period can be set to a time period every night, such as 20:00 to 8:00.

[0097] Among them, body motion data can be used to characterize the motion state of a human body. For example, when a user is sleeping, body motion data may include data such as turning over, turning sideways, and limb movements. For some embodiments, the body motion data can be collected by an inertial measurement unit configured in an electronic device, so that after obtaining the body motion data, the electronic device can determine a corresponding body motion feature vector based on the body motion data.

[0098] It is understandable that sleep can be divided into multiple different sleep stages, including light sleep, deep sleep, rapid eye movement and wakefulness, and the body motion data of the user in each sleep stage is generally different. Therefore, the body motion data of the target user in each sleep stage can be collected separately, and then the body motion feature vector corresponding to each sleep stage can be obtained.

[0099] Exemplarily, the target user's sleep stage can be determined by the target user's breathing sound data, so that a corresponding stage label is set for the acquired body motion data, and the stage label is used to characterize the sleep stage corresponding to the body motion data.

[0100] Furthermore, breathing data can also be collected, so that after the electronic device obtains the breathing data, it can determine the corresponding breathing feature vector based on the breathing data. Exemplarily, the breathing data of the target user can be collected by a microphone configured in the electronic device. Then, the breathing feature vector corresponding to the breathing data is obtained. Among them, similar to obtaining body movement data, the breathing data can also be obtained within the first time period.

[0101] The breathing data may include snoring data, breathing frequency data or breathing depth data.

[0102] In addition, the habit feature vector of the target user may be determined based on the usage data of the target user for the electronic device in multiple second time periods. The usage data may be used to characterize whether the target user is using the electronic device. For example, the habit feature vector combined with the second time period may determine the latest time period when the target user uses the electronic device at night and the earliest time period when the target user uses the electronic device during the day.

[0103] It should be noted that the embodiment determines whether the target user is using the electronic device, that is, whether the target user displays the application running in the foreground through the display screen of the electronic device. For example, if the target user runs a video playback application in the foreground through the electronic device, and thus watches a video through the display screen of the electronic device, it can be determined that the target user is using the electronic device. If it is only detected that the electronic device is running an application in the background at this time, and the display screen of the electronic device is not working, it can be determined that the target user is not using the electronic device.

[0104] Exemplarily, the second time period may be a whole-day time period, so that the habit feature vector of the target user in the whole-day time period may be obtained.

[0105] Furthermore, the work and rest habit prediction model can be constructed based on the body motion feature vector, the breathing feature vector and the habit feature vector, wherein the input parameter of the work and rest habit prediction model is the time, and the output parameter of the work and rest habit prediction model is used to characterize the predicted sleep information of the target user at the time, and the sleep information can be used to characterize the user being asleep, waking up, waiting to fall asleep, etc. Among them, waiting to fall asleep can characterize the user being in a certain time period before falling asleep, for example, if the target user is in the time period of 10 minutes before falling asleep, it can be determined that the target user is waiting to fall asleep.

[0106] Optionally, the work and rest habit prediction model can also output the confidence level corresponding to the sleep information.

[0107] In some implementations, the first sleep feature of the target user can be regressed through the body motion feature vector and the breathing feature vector based on the classification model. If the first sleep feature is falling asleep, the sleep stage matched by the first sleep feature is further determined. Then, the second sleep feature of the target user is obtained through the regression model through the habit feature vector. Thus, the work and rest habit prediction model is constructed by combining the first sleep feature, the sleep stage and the second sleep feature.

[0108] See also Figure 5 , Figure 5 A schematic diagram of constructing a work and rest habit prediction model provided in an embodiment of the present application is shown. Figure 5 Specifically includes steps S261 to S266.

[0109] Step S261: Obtain body motion feature vector.

[0110] Step S262: Obtain a breathing feature vector.

[0111] Step S263: Obtain the habit feature vector.

[0112] Step S264: Classify sleep stages.

[0113] Step S265: regression model regression.

[0114] Step S266: construct a work and rest habit prediction model.

[0115] Among them, the detailed introduction of steps S261 to S266 can be found in the above introduction and will not be repeated here.

[0116] Optionally, the work and rest information may also include work information, so that the work and rest habit prediction model may be used to regress and obtain whether the target user is working or not working at the input time.

[0117] Optionally, the work and rest information may also include learning information, so that the work and rest habit prediction model may be used to regress whether the target user is in school or not at the input time. Thus, the perception of multiple scenarios may be achieved.

[0118] It should be noted that steps S240 to S260 are performed before the current moment to obtain the body movement feature vector, breathing feature vector and habit feature vector based on the collected data, so as to construct a work and rest habit prediction model, rather than obtaining the body movement feature vector, breathing feature vector and habit feature vector at the current moment.

[0119] Step S270: predicting the work and rest information of the target user at the current moment based on the work and rest habit prediction model acquired in advance.

[0120] Among them, step S270 has been introduced in detail in the above embodiment and will not be repeated here.

[0121] Step S280: Acquire the spatial position of the target user at the current moment.

[0122] Step S290: Determine room information matching the spatial position based on a pre-acquired room position comparison table, wherein the room position comparison table includes at least one reference room information and a reference spatial position corresponding to each reference room information.

[0123] For some implementations, the location information may include room information for characterizing the room type. Therefore, in order to obtain the room information, the spatial location of the target user at the current moment may be first obtained.

[0124] Exemplarily, the spatial location information can be determined based on the signal characteristics of the wireless signal received by the electronic device at the current moment. Specifically, for example, the signal characteristics may include a radio frequency fingerprint. Thus, different location areas can be distinguished by radio frequency fingerprints to determine the location area where the target user is located at the current moment, and the location area can be used as the spatial location. Among them, the radio frequency fingerprint can be matched with the location area, so that the radio frequency fingerprint determined by the electronic device based on the wireless signal can obtain the corresponding location area. Among them, the radio frequency fingerprint can include the fingerprint of cellular mobile communication, the fingerprint of wireless communication technology Wi-Fi, and the fingerprint of Bluetooth, etc.

[0125] For example, see Figure 6 , Figure 6 A schematic diagram of determining a spatial position provided in an embodiment of the present application is shown. Figure 6 6 shows a space 600, which includes a location area 601 and a location area 602, wherein the location area 601 matches the radio frequency fingerprint A, and the location area 602 matches the location area B. Thus, the electronic device can determine the spatial location of the target user through the radio frequency fingerprint.

[0126] Optionally, the current location of the target user may be directly determined by satellite positioning technology, and then the location obtained by satellite positioning technology may be adjusted by radio frequency fingerprinting to obtain the spatial location.

[0127] Furthermore, room information matching the spatial position may be determined based on a pre-acquired room position comparison table, wherein the room position comparison table includes at least one reference room information and a reference spatial position corresponding to each reference room information.

[0128] That is to say, after obtaining the spatial position, the spatial position can be matched with the reference spatial position in the room position comparison table, so that the reference room information corresponding to the reference empty position matching the spatial position is used as the room information matching the spatial position.

[0129] In some implementations, before step S290, a room location comparison table may be constructed. Specifically, before step S290, steps S281 to S283 may be included.

[0130] Step S281: Predicting the work and rest information of the target user at multiple designated moments based on the work and rest habit prediction model.

[0131] Step S282: Determine the reference space position of the target user at each of the specified moments.

[0132] Step S283: Based on the work and rest information of the target user at the specified time, determine the type of the reference space position that matches the specified time as the reference room information corresponding to the reference space position.

[0133] In order to construct the room location comparison table, the spatial location and room of the target user at the specified time can be obtained. In addition, the room type matching the room can be determined, so as to determine the room information corresponding to the room. The specified time can be a pre-set time, such as 3 o'clock, 6 o'clock, 9 o'clock, 12 o'clock, 15 o'clock, 18 o'clock, 21 o'clock and 24 o'clock.

[0134] Specifically, first, the work and rest information of the target user at multiple specified moments can be predicted based on the work and rest habit prediction model. From the above introduction, it can be known that the input parameter of the work and rest habit prediction model can be a moment, so that multiple specified moments can be used as input parameters of the work and rest habit prediction model respectively, so as to obtain the work and rest information of the target user matching each specified moment.

[0135] In addition, the reference space position of the target user at each designated time may also be determined. Specifically, step S282 may also include step S2821 and step S2822.

[0136] Step S2821: Acquire signal characteristics of the wireless signal received by the electronic device at each specified time.

[0137] Step S2822: Determine the reference space position of the target user at each of the specified moments based on the signal characteristics.

[0138] The signal characteristics of the wireless signal received by the electronic device at each specified time can be obtained. For example, the signal characteristics can be a radio frequency fingerprint. Therefore, the reference space position of the target user at each specified time is subsequently determined based on the signal characteristics, that is, the reference space position of the target user at each specified time is determined by radio frequency fingerprint. Specifically, the location area of ​​the target user at each specified time is determined by radio frequency fingerprint technology as the reference space position corresponding to the specified time. Exemplarily, the reference space position can be room A, room B, etc.

[0139] Optionally, the location of the target user at each designated time may also be directly determined by satellite positioning technology as a reference spatial location, which will not be described in detail here.

[0140] Thus, after obtaining the work and rest information of the target user at each specified time and the reference space position corresponding to each specified time, the type of the reference space position matching the specified time can be determined. Specifically, based on the work and rest information of the target user at the specified time, the type of the reference space position matching the specified time can be determined as the reference room information corresponding to the reference space position.

[0141] Exemplarily, a type label can be determined through work and rest information, and the type label can be used to characterize the type of the reference space location. The type label is then used to mark the reference space location that matches the specified moment, so that the reference space location can be characterized by the marked type label, and the type is the reference room information.

[0142] For example, if the work and rest information corresponding to a specified time is falling asleep, the type label can be determined as bedroom through the work and rest information, and then the reference space position matching the specified time is marked, so that the reference room information corresponding to the reference space position is bedroom.

[0143] Step S2100: Determine a target control operation for a target object based on the biometric feature, the location information, and the work and rest information, wherein the target object includes at least one of a hardware device and an application configured in the electronic device.

[0144] Step S2110: Execute the target control operation through the electronic device.

[0145] Among them, step S2100 and step S2110 have been introduced in detail in the above embodiments and will not be repeated here.

[0146] In the embodiment provided by the present application, the voice data of the target user and the image data of the target user are collected; the interactive feature data of the target user interacting with the electronic device is obtained; the age feature of the target user using the electronic device is determined based on the voice data, image data and interactive feature data based on the pre-acquired age prediction model. The age feature is determined by combining multiple types of data, with high accuracy. In addition, in the embodiment of the present application, the body motion feature vector corresponding to the body motion data of the target user in the first time period and the breathing feature vector corresponding to the breathing data are obtained; the habit feature vector of the target user is determined based on the usage data of the target user for the electronic device in multiple second time periods; the work and rest habit prediction model is constructed based on the body motion feature vector, the breathing feature vector and the habit feature vector. Thus, the work and rest habit prediction model constructed by the feature vector of more types predicts the sleep information of the target user with high accuracy and confidence. Furthermore, the spatial position of the target user at the current moment is obtained; and the room information matching the spatial position is determined based on the pre-acquired room position comparison table, wherein the room position comparison table includes at least one reference room information and the reference spatial position corresponding to each reference room information. Therefore, the room information can be determined based on the room position comparison table, rather than simply obtaining the spatial position of the target user, so as to facilitate accurate target control operations.

[0147] See also Figure 7 , Figure 7 The device control method provided by the embodiment of the present application is shown. The device control method can be applied to Figure 1 The electronic device shown in the figure may specifically use the processor of the electronic device as the execution subject of the device control method. The device control method may include steps S310 to S370.

[0148] Step S310: Acquire biometric features of a target user using the electronic device.

[0149] Step S320: predicting the work and rest information of the target user at the current moment based on the pre-acquired work and rest habit prediction model.

[0150] Step S330: Obtain the location information of the target user at the current moment.

[0151] Among them, step S310 to step S330 have been introduced in detail in the above embodiments and will not be repeated here.

[0152] Step S340: When the sleep information is waiting to fall asleep and the room information is a bedroom, determine whether the target user is a first type of user or a second type of user based on the age feature, wherein the first type of user is more sensitive to light than the second type of user.

[0153] The embodiment of the present application introduces in detail the target control operation for the target object based on the biometric feature, location information and the work and rest information through a specific example. In the embodiment of the present application, the biometric feature includes age feature, the location information includes room information, the work and rest information includes sleep information, the target object is a hardware device configured for the electronic device, and the hardware device includes a display screen.

[0154] It is understandable that if the target user's current sleep information is "to fall asleep", it can indicate that the target user is about to fall asleep, so that the display parameters of the display screen of the electronic device can be adjusted later so that the target user can fall asleep better.

[0155] In some embodiments, in order to determine the accuracy of the target user's upcoming sleep, the display parameters may be adjusted later when the sleep information is waiting to fall asleep and the room information is a bedroom. Specifically, when the sleep information is waiting to fall asleep and the room information is a bedroom, the target user may be further determined to be a first type of user or a second type of user based on the age feature, wherein the first type of user is more sensitive to light than the second type of user. Exemplarily, the first type of user may include users with larger age features and users with smaller age features. Specifically, for example, users with larger age features are elderly people, and users with smaller age features are children. The second type of user may include users with moderate age features. Specifically, for example, users with moderate age features are young people or middle-aged people.

[0156] Therefore, step S341 can be executed: whether the target user is a first type user. It is judged whether the target user is a first type user, if the target user is a first type user, the process jumps to step S350; if the target user is a second type user, the process jumps to step S360.

[0157] Step S350: When the target user is a first type of user, adjust the display parameters of the display screen based on a first adjustment strength, wherein the display parameters include at least one of brightness, color temperature, contrast, and blue light filtering strength.

[0158] Step S360: When the target user is a second type of user, adjusting the display parameters of the display screen based on a second adjustment strength, wherein the second adjustment strength is less than the first adjustment strength.

[0159] When the target user is a first type of user, the display parameters of the display screen are adjusted based on a first adjustment strength, wherein the display parameters include at least one of brightness, color temperature, contrast, and blue light filter strength. When the target user is a second type of user, the display parameters of the display screen are adjusted based on a second adjustment strength, wherein the second adjustment strength is less than the first adjustment strength.

[0160] That is, for the first type of users who are more sensitive to light, the display parameters can be adjusted with a greater intensity, and for the second type of users who are less sensitive to light, the display parameters can be adjusted with a smaller intensity, thereby providing a better user experience for both the first type of users and the second type of users.

[0161] In some embodiments, when the display parameters include brightness, color temperature, contrast, and blue light filtering intensity, step S350 may further include step S351, and step S360 may further include step S361.

[0162] Step S351: when the target user is a first type of user, the brightness and contrast of the display screen are reduced based on the first adjustment intensity, and the color temperature and blue light filtering intensity of the display screen are increased based on the first adjustment intensity.

[0163] Step S361: when the target user is a second type of user, the brightness and contrast of the display screen are reduced based on the second adjustment intensity, and the color temperature and blue light filtering intensity of the display screen are increased based on the second adjustment intensity.

[0164] It is understandable that, since the target user is about to fall asleep, the brightness and contrast of the display screen, as well as the color temperature and blue light filtering strength of the display screen can generally be reduced. Therefore, when the target user is a first type of user, the brightness and contrast of the display screen can be reduced based on the first adjustment strength, and the color temperature and blue light filtering strength of the display screen can be increased based on the first adjustment strength.

[0165] When the target user is a second type of user, the brightness and contrast of the display screen can be reduced based on the second adjustment intensity, and the color temperature and blue light filtering intensity of the display screen can be increased based on the second adjustment intensity.

[0166] It should be noted that the above-mentioned determination of reducing the brightness and contrast of the display screen and increasing the color temperature and blue light filtering intensity of the display screen are determined target control operations.

[0167] This can provide the target users with a gentler display screen with lower contrast and lower brightness, effectively reduce the brightness and blue light of the display screen, and effectively promote human melatonin secretion, so that the target users can fall asleep better afterwards.

[0168] Step S370: Execute the target control operation through the electronic device.

[0169] Among them, step S370 has been introduced in detail in the above embodiment and will not be repeated here.

[0170] In the embodiments provided in the present application, a gentle display screen with lower contrast and lower brightness can be provided to the target user, thereby effectively reducing the brightness and blue light of the display screen, and effectively promoting the secretion of human melatonin, so that the target user can fall asleep better later, and can also protect the eyes of the target user.

[0171] See also Figure 8 , Figure 8 The device control method provided by the embodiment of the present application is shown. The device control method can be applied to Figure 1 The electronic device shown in the figure may specifically use the processor of the electronic device as the execution subject of the device control method. The device control method may include steps S410 to S480.

[0172] Step S410: Acquire biometric features of a target user using the electronic device.

[0173] Step S420: predicting the work and rest information of the target user at the current moment based on the pre-acquired work and rest habit prediction model.

[0174] Step S430: Obtain the location information of the target user at the current moment.

[0175] Among them, step S410 to step S430 have been described in detail in the above embodiments and will not be repeated here.

[0176] Step S440: when the sleep information is that the user has fallen asleep and the room information is a bedroom, obtain the sleep stage corresponding to the user having fallen asleep at the current moment.

[0177] Step S450: Determine target audio that matches the sleep stage.

[0178] Step S460: determining a playback volume based on the age feature, wherein the playback volume is inversely correlated with the age feature.

[0179] Step S470: playing the target audio at the playback volume through the audio playback program as a target control operation.

[0180] The embodiment of the present application introduces in detail the target control operation for the target object based on the biometric feature, location information and the work and rest information through a specific example. In the embodiment of the present application, the biometric feature includes age feature, the location information includes room information, the work and rest information includes sleep information, the target object is the application, and the application includes an audio playback program.

[0181] It is understandable that, when the target user's sleep information indicates that the user has fallen asleep, music, such as sleep-aiding music, can be played for the target user through an electronic device, thereby improving the target user's sleep quality.

[0182] Therefore, in some implementations, when the sleep information is that the user has fallen asleep and the room information is that the user has a bedroom, playing the target audio through the audio playback program may be used as a target control operation.

[0183] In addition, when the sleep information is "already asleep" and the room information is "bedroom", the sleep stage corresponding to the current moment of "already asleep" can also be obtained. As can be seen from the above introduction, sleep stages can include light sleep, deep sleep, rapid eye movement, and wakefulness, and different music can be played in different sleep stages. Thus, the target audio that matches the sleep stage can be determined.

[0184] Exemplarily, the alternative audio corresponding to each sleep stage can be preset, so that after obtaining the sleep stage corresponding to the current moment of sleep, the alternative audio corresponding to the sleep stage can be searched as the target audio. For example, the target audio corresponding to the light sleep stage can be determined as a sleep-aiding audio; for another example, the target audio corresponding to the awakening stage can be determined as a soothing type of audio. It is understandable that since the target user in the awakening stage will soon wake up, playing a soothing type of audio to wake the user can prevent the user from waking up.

[0185] Optionally, the preset candidate audio may further include multiple audios, so that after the candidate audio is determined, one of the audios in the candidate audio may be randomly determined as the target audio.

[0186] In addition, the playback volume may be determined based on the age feature, wherein the playback volume is inversely correlated with the age feature. For example, for a user with an older age feature, a smaller playback volume may be determined; and for a user with a younger age feature, a larger playback volume may be determined.

[0187] Therefore, playing the target audio at the playback volume through the audio playback program can be regarded as a target control operation. The subsequent execution of the target control operation is that the electronic device plays the target audio at the playback volume through the audio playback program.

[0188] Optionally, the sleep information may further include "to wake up". "To wake up" may be used to indicate that the user is about to wake up from sleep, and the time period corresponding to "to wake up" may be a second time period before the user wakes up from sleep, for example, the second time period is 10 minutes.

[0189] Therefore, in some embodiments, when the sleep information is waiting to wake up and the room information is bedroom, the target audio can be played at the playback volume through the audio playback program as a target control operation. The playback volume in this embodiment can be a slowly increasing gradual volume, and the target audio can be an alarm clock, for example, the target audio can be an alarm clock pre-set by the target user. Thus, the user can wake up quickly.

[0190] Optionally, when the sleep information indicates that the user has fallen asleep and the room information indicates that the user is in a bedroom, triggering the low disturbance mode may be used as a target control operation, thereby preventing the target user from being disturbed.

[0191] Step S480: Execute the target control operation through the electronic device.

[0192] Among them, step S480 has been introduced in detail in the above embodiment and will not be repeated here.

[0193] In an embodiment of the present application, target audio can be played for target users according to different sleep stages, and the playback volume is determined based on the age characteristics of the target users, which can improve the sleep quality of the target users and better help the target users sleep.

[0194] See also Fig. 9 , which shows a structural block diagram of a device control apparatus provided in an embodiment of the present application, including: a first acquisition unit 910, a prediction unit 920, a second acquisition unit 930, a control operation determination unit 940 and an execution unit 950.

[0195] The first acquisition unit 910 is configured to acquire biometric features of a target user using the electronic device.

[0196] Optionally, the first acquisition unit 910 can also be used to collect voice data of the target user and image data of the target user; obtain interaction feature data of the target user's interaction with the electronic device; and determine the age characteristics of the target user using the electronic device based on the voice data, image data and interaction feature data based on a pre-acquired age prediction model.

[0197] Optionally, the first acquisition unit 910 can also be used to obtain operation characteristic data of the target user's interactive operation with the electronic device; obtain vibration characteristic data collected by the inertial measurement unit of the electronic device based on the interactive operation; and use the operation characteristic data and the vibration characteristic data as the interactive characteristic data.

[0198] The prediction unit 920 is used to predict the work and rest information of the target user at the current moment based on the pre-acquired work and rest habit prediction model.

[0199] Optionally, the prediction unit 920 can also be used to obtain the body motion feature vector corresponding to the body motion data of the target user in the first time period and the respiratory feature vector corresponding to the respiratory data; determine the habit feature vector of the target user based on the usage data of the electronic device by the target user in multiple second time periods; construct the work and rest habit prediction model based on the body motion feature vector, the respiratory feature vector and the habit feature vector, wherein the input parameter of the work and rest habit prediction model is the time, and the output parameter of the work and rest habit prediction model is used to characterize the predicted sleep information of the target user at the time.

[0200] The second acquisition unit 930 is used to acquire the location information of the target user at the current moment.

[0201] Optionally, the second acquisition unit 930 can also be used to obtain the spatial position of the target user at the current moment; determine the room information matching the spatial position based on a pre-acquired room position comparison table, wherein the room position comparison table includes at least one reference room information and a reference spatial position corresponding to each reference room information.

[0202] Optionally, the second acquisition unit 930 can also be used to predict the work and rest information of the target user at multiple specified times based on the work and rest habit prediction model; determine the reference spatial position of the target user at each specified time; based on the work and rest information of the target user at the specified time, determine the type of reference spatial position that matches the specified time as the reference room information corresponding to the reference spatial position.

[0203] Optionally, the second acquisition unit 930 can also be used to acquire signal characteristics of the wireless signal received by the electronic device at each of the specified moments; and determine the reference spatial position of the target user at each of the specified moments based on the signal characteristics.

[0204] The control operation determination unit 940 is used to determine a target control operation for a target object based on the biometric feature, the location information, and the work and rest information, wherein the target object includes at least one of a hardware device and an application configured in the electronic device.

[0205] Optionally, the control operation determination unit 940 can also be used to determine, when the sleep information is waiting to fall asleep and the room information is a bedroom, whether the target user is a first type of user or a second type of user based on the age characteristics, wherein the first type of user is more sensitive to light than the second type of user; when the target user is a first type of user, adjusting the display parameters of the display screen based on a first adjustment intensity, wherein the display parameters include at least one of brightness, color temperature, contrast, and blue light filtering intensity; when the target user is a second type of user, adjusting the display parameters of the display screen based on a second adjustment intensity, wherein the second adjustment intensity is less than the first adjustment intensity.

[0206] Optionally, the control operation determination unit 940 can also be used to, when the target user is a first type of user, reduce the brightness and contrast of the display screen based on the first adjustment intensity, and increase the color temperature and blue light filtering intensity of the display screen based on the first adjustment intensity; when the target user is a second type of user, reduce the brightness and contrast of the display screen based on the second adjustment intensity, and increase the color temperature and blue light filtering intensity of the display screen based on the second adjustment intensity.

[0207] Optionally, the control operation determination unit 940 can also be used to obtain the sleep stage corresponding to the current moment of falling asleep when the sleep information is that the person has fallen asleep and the room information is a bedroom; determine the target audio that matches the sleep stage; determine the playback volume based on the age feature, wherein the playback volume is inversely correlated with the age feature; and play the target audio at the playback volume through the audio playback program as a target control operation.

[0208] The execution unit 950 is used to execute the target control operation through the electronic device.

[0209] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0210] In several embodiments provided in the present application, the coupling between the units may be electrical, mechanical or other forms of coupling.

[0211] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0212] See also Fig.10 , Fig.10 The block diagram of a structure of an electronic device provided by an embodiment of the present application is shown. The electronic device 110 may be a smart phone, a desktop computer, a server or a tablet computer, etc. The electronic device 110 in the present application may include one or more of the following components: a processor 111, a memory 112 and one or more application programs, wherein the processor 111 is electrically connected to the memory 112, and the one or more program configurations are used to execute the methods described in the aforementioned embodiments.

[0213] The processor 111 may include one or more processing cores. The processor 111 uses various interfaces and lines to connect various parts of the entire electronic device 110, and executes various functions and processes data of the electronic device 110 by running or executing instructions, programs, code sets or instruction sets stored in the memory 112, and calling data stored in the memory 112. Optionally, the processor 111 can be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor 111 can integrate one or a combination of a central processing unit, an image processor, and a modem. Among them, the CPU mainly processes the operating system, the user interface, and the computer program; the GPU is responsible for rendering and drawing the display content; and the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 111, but may be implemented separately through a communication chip. Specifically, the method described in the above embodiment can be executed by one or more processors 111.

[0214] For some embodiments, the memory 112 may include a random access memory (RAM) or a read-only memory (ROM). The memory 112 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 112 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the following various method embodiments, etc. The data storage area may also store data created by the electronic device 110 during use, etc.

[0215] Please refer to Fig.11 , which shows a structural block diagram of a computer-readable storage medium provided in an embodiment of the present application. The computer-readable medium 1100 stores program codes, which can be called by a processor to execute the method described in the above method embodiment.

[0216] The computer readable storage medium 1100 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk, or a ROM. Optionally, the computer readable storage medium 1100 includes a non-transitory computer-readable storage medium. The computer readable storage medium 1100 has storage space for program codes 1110 that perform any of the method steps in the above method. These program codes may be read from or written to one or more computer program products. The program code 1110 may be compressed, for example, in an appropriate form.

[0217] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A device control method, characterized in that: Applied to electronic equipment, the method comprises: Acquiring biometric characteristics of a target user using the electronic device; Predicting the work and rest information of the target user at the current moment based on the pre-acquired work and rest habit prediction model; Obtaining the location information of the target user at the current moment; Determine a target control operation for a target object based on the biometric feature, the location information, and the work and rest information, wherein the target object includes at least one of a hardware device and an application configured in the electronic device; The target control operation is performed by the electronic device.

2. The method according to claim 1, characterized in that The biometric feature includes an age feature, and the step of obtaining the biometric feature of a target user using the electronic device includes: Collecting voice data of the target user and image data including the target user; Acquiring interactive feature data of interactive operations performed between the target user and the electronic device; Based on the pre-acquired age prediction model, the age characteristics of the target user using the electronic device are determined through the voice data, image data and interaction feature data.

3. The method according to claim 2, characterized in that The acquiring of interaction feature data of the interaction operation between the target user and the electronic device includes: Acquire operation characteristic data of the target user's interactive operation with the electronic device; Acquiring vibration characteristic data collected by an inertial measurement unit of the electronic device based on the interactive operation; The operation characteristic data and the vibration characteristic data are used as the interaction characteristic data.

4. The method according to claim 1, characterized in that The work and rest information includes sleep information. Before predicting the work and rest information of the target user at the current moment based on the pre-acquired work and rest habit prediction model, the method further includes: Acquire a body motion feature vector corresponding to the body motion data and a breathing feature vector corresponding to the breathing data of the target user in a first time period; Determining a habit feature vector of the target user based on usage data of the electronic device by the target user in a plurality of second time periods; The work and rest habit prediction model is constructed based on the body movement feature vector, the breathing feature vector and the habit feature vector, wherein the input parameter of the work and rest habit prediction model is the time, and the output parameter of the work and rest habit prediction model is used to characterize the predicted sleep information of the target user corresponding to the time.

5. The method according to claim 1, characterized in that The location information includes room information used to characterize a room type, and the acquiring the location information of the target user at the current moment includes: Obtaining the spatial position of the target user at the current moment; The room information matching the spatial position is determined based on a pre-acquired room position comparison table, wherein the room position comparison table includes at least one reference room information and a reference spatial position corresponding to each reference room information.

6. The method according to claim 5, characterized in that Before determining the room information matching the spatial position based on the pre-acquired room position comparison table, the method further includes: Predicting the work and rest information of the target user at multiple designated moments based on the work and rest habit prediction model; Determining the reference spatial position of the target user at each of the specified moments; Based on the work and rest information of the target user at the specified time, the type of the reference space position matching the specified time is determined as the reference room information corresponding to the reference space position.

7. The method according to claim 6, characterized in that The determining the reference space position of the target user at each designated time comprises: Acquire signal characteristics of wireless signals received by the electronic device at each of the specified times; The reference space position of the target user at each designated moment is determined based on the signal characteristics.

8. The method according to claim 1, characterized in that: The biometric feature includes an age feature, the location information includes room information, the work and rest information includes sleep information, the target object is a hardware device configured for the electronic device, and the hardware device includes a display screen; The determining of the target control operation for the target object based on the biometric feature, the location information, and the work and rest information includes: When the sleep information is "to fall asleep" and the room information is "bedroom", judging the target user as a first type of user or a second type of user based on the age feature, wherein the first type of user is more sensitive to light than the second type of user; When the target user is a first type of user, adjusting a display parameter of the display screen based on a first adjustment strength, wherein the display parameter includes at least one of brightness, color temperature, contrast, and blue light filtering strength; In a case where the target user is a second type of user, the display parameters of the display screen are adjusted based on a second adjustment strength, wherein the second adjustment strength is less than the first adjustment strength.

9. The method according to claim 8, characterized in that The display parameters include brightness, color temperature, contrast, and blue light filtering strength, and when the target user is a first type of user, adjusting the display parameters of the display screen based on a first adjustment strength includes: When the target user is a first type of user, the brightness and contrast of the display screen are reduced based on the first adjustment strength, and the color temperature and blue light filtering strength of the display screen are increased based on the first adjustment strength; When the target user is a second type of user, adjusting the display parameters of the display screen based on a second adjustment strength includes: When the target user is a second type of user, the brightness and contrast of the display screen are reduced based on the second adjustment intensity, and the color temperature and blue light filtering intensity of the display screen are increased based on the second adjustment intensity.

10. The method according to claim 1, characterized in that The biometric feature includes an age feature, the location information includes room information, the work and rest information includes sleep information, the target object is the application, and the application includes an audio playback program; The determining of the target control operation for the target object based on the biometric feature, the location information, and the work and rest information includes: When the sleep information is that the sleep has fallen asleep and the room information is a bedroom, obtaining the sleep stage corresponding to the sleep has fallen asleep at the current moment; determining a target audio frequency that matches the sleep stage; determining a playback volume based on the age feature, wherein the playback volume is inversely correlated with the age feature; Playing the target audio at the playback volume through the audio playback program is used as a target control operation.

11. A device control device, characterized in that: Applied to electronic equipment, the device comprises: A first acquisition unit, configured to acquire biometric features of a target user using the electronic device; A prediction unit, configured to predict the work and rest information of the target user at the current moment based on a pre-acquired work and rest habit prediction model; A second acquisition unit, used to acquire the location information of the target user at the current moment; a control operation determination unit, configured to determine a target control operation for a target object based on the biometric feature, the location information, and the work and rest information, wherein the target object includes at least one of a hardware device and an application configured in the electronic device; An execution unit is used to execute the target control operation through the electronic device.

12. An electronic device, characterized in that: include: one or more processors; Memory; One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the method according to any one of claims 1-10.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 10.

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