Data processing method, electronic device and storage medium

Abstract

The present application relates to the field of artificial intelligence, and in particular to a data processing method, electronic device, and storage medium, wherein the method comprises: detecting first data corresponding to a first operation of a user, such as image data corresponding to a shooting operation; determining a first prescribed processing time based on the user's current operation data, such as time data of a shooting operation, and the user's historical operation data, such as time data of a viewing operation after a user's historical shooting; and completing the processing of the first data before the first prescribed processing time, such as completing the post-processing of the image data, when the processing resources of the electronic device meet the processing conditions. Through the solution of the present application, the first data can be processed based on the prescribed processing time, so that the processed first data can be obtained as soon as possible before subsequent operations, such as obtaining post-processed image data before a viewing operation, to ensure the smoothness of subsequent operations.

Description

[0001] This application is a divisional application. The application number of the original application is 202311676327.6, and the original application date is December 8, 2023. The entire content of the original application is incorporated into this application by reference. Technical Field

[0002] The present application relates to the field of artificial intelligence, and in particular to a data processing method, electronic device, and storage medium. Background Art

[0003] Nowadays, more and more people use applications of electronic devices (for example, mobile phones, tablet computers and other terminal devices) to realize various functions, among which there may be a contextual relationship between functions, that is, the execution of the background process corresponding to the previous function will affect the realization of the next function. For example, after the user takes an image through the camera application, the electronic device needs to automatically process the captured image (for example, beautify it) to obtain the processed image before it can be presented to the user, so that the user can view, edit, share and other operations on the processed image through the album application. However, the process of post-processing the image by the electronic device takes time. For example, if the image is not processed before the user operates, then after the user performs the viewing operation, the electronic device needs to post-process the image for a period of time before the processed image can be displayed, resulting in the electronic device being unable to present the image to the user smoothly. Summary of the Invention

[0004] The purpose of this application is to provide a data processing method, an electronic device and a storage medium.

[0005] In a first aspect, the present application provides a data processing method, which includes: detecting first data corresponding to a first operation of a user, wherein the first operation has at least one subsequent operation associated with the user's history, and the at least one subsequent operation includes a second operation; determining a first specified processing time for the first data based on the user's current operation data and the user's historical operation data of performing the first operation and then the second operation; corresponding to the processing resources of the electronic device meeting the processing conditions, calling the processing resources to complete the processing of the first data before the first specified processing time to obtain the second data corresponding to the second operation.

[0006] That is, in an embodiment of the present application, a first operation can be executed in association with at least one subsequent operation. For example, the first operation can be an operation of taking an image, and the second operation can be an operation of viewing an image. Accordingly, the first data can be image data. The current operation data and the historical operation data of the user performing the first operation and then the second operation can be in the form of a time series, that is, including the time points at which the operations occurred, such as the time points at which the first operation was historically performed and the time points at which the second operation was historically performed. It can be understood that the processing resources of the electronic device include a processor and memory, that is, the electronic device can allocate processor resources and memory resources to the processing of the first data to obtain the second data corresponding to the second operation, for example, the image data required for viewing an image.

[0007] Through the embodiments of the present application, the prescribed processing time for the first data can be determined based on the historical data of the first and second operations, and then processing resources can be scheduled based on the prescribed processing time, so that the data processing of the first operation can be completed as soon as possible before the second operation, ensuring the smoothness of the user's subsequent second operation. For example, before the user performs a viewing operation, the electronic device can, if the processing resources meet the processing conditions, promptly schedule processing resources to complete the processing of the image data generated by the shooting operation, so that the user can smoothly view the image when performing the viewing operation.

[0008] In a possible implementation of the first aspect above, the first specified processing time of the first data is determined based on the user's current operation data and the historical operation data of the user performing the first operation and then the second operation, including: predicting the predicted operation time of the user performing the second operation based on the user's current operation data and the historical operation data of the user performing the first operation and then the second operation; and determining the first specified processing time of the first data based on the predicted operation time.

[0009] Through the embodiments of the present application, the time can be predicted according to the user operation, and then the processing resources can be scheduled based on the predicted time, so that the data processing of the first operation can be completed as soon as possible before the time required by the user.

[0010] In a possible implementation of the first aspect above, based on the user's current operation data and the user's historical operation data of performing the first operation and then performing the second operation, the predicted operation time for the user to perform the second operation is predicted, including: based on the user's current operation data, the user's current geographic location, and the user's historical operation data of performing the first operation and then performing the second operation, the predicted operation time for the user to perform the second operation at the current geographic location is predicted.

[0011] Through the embodiments of the present application, the predicted operation time of the second operation can be predicted in combination with the user's current geographical location, so that the predicted time can be more consistent with the user's operating habits in the current location, for example, at home, at a tourist attraction, at a restaurant, etc.

[0012] In a possible implementation of the first aspect above, the first specified processing time of the first data is determined based on the user's current operation data and the user's historical operation data of performing a first operation and then performing a second operation, including: inputting the processing time prediction model based on the user's current operation data, and outputting the first specified processing time of the first data corresponding to the current first operation, wherein the processing time prediction model is trained based on the user's historical operation data of performing a first operation and then performing a second operation.

[0013] That is, in the embodiment of the present application, the processing time prediction model can be a trained neural network model.

[0014] In a possible implementation of the first aspect above, a processing time prediction model is obtained by training; wherein, the processing time prediction model is obtained by training, including: collecting historical operation data; modeling the historical operation data as a time series to obtain training data; and training a preset neural network model based on the training data to obtain a processing time prediction model.

[0015] That is, in an embodiment of the present application, the time series can be modeled from historical operation data, which may specifically include historical operation tags (such as shooting, viewing, editing, sharing), and timestamps corresponding to the historical operation tags, with the tags and timestamps corresponding one to one.

[0016] In a possible implementation of the first aspect above, the processing resources of the electronic device satisfy the processing conditions including at least one of the following conditions: the temperature of the electronic device satisfies the temperature condition; the resource utilization rate of the electronic device satisfies the resource utilization rate condition; the memory utilization rate of the electronic device satisfies the memory utilization rate condition; and the power of the electronic device satisfies the power condition.

[0017] In a possible implementation of the first aspect above, the current operation data includes: startup data of a first application corresponding to the first operation; and operation data of the user on the electronic device after the first application is started.

[0018] That is, in the embodiment of the present application, the first operation may be a shooting operation, and the first application may be a camera application.

[0019] In a possible implementation of the first aspect above, the user's operation data on the electronic device is the user's operation data on the first application.

[0020] In a possible implementation of the first aspect above, at least one subsequent operation also includes a third operation; the method also includes: determining a second specified processing time for the first data based on the user's current operation data and the user's historical operation data of performing the first operation and then the third operation; corresponding to the processing resources of the electronic device meeting the processing conditions, calling the processing resources to complete the processing of the first data before the second specified processing time, and obtaining the third data corresponding to the third operation.

[0021] That is, in the embodiment of the present application, the second operation and the third operation can be two of the viewing operation, the editing operation, and the sharing operation respectively.

[0022] In a possible implementation of the first aspect, the third operation is an operation after the second operation, wherein the second prescribed processing time is later than the first prescribed processing time.

[0023] That is, in the embodiment of the present application, the second operation and the third operation may be a viewing operation and an editing operation, or a viewing operation and a sharing operation, or an editing operation and a sharing operation, respectively.

[0024] In a possible implementation of the first aspect above, the third operation is an operation after the second operation; completing the processing of the first data to obtain third data corresponding to the third operation includes: processing the second data to obtain the third data.

[0025] That is, in the embodiment of the present application, the second operation and the third operation can be an editing operation and a sharing operation respectively, and the second data (the editing image data of the editing operation) can be processed to obtain the third data (sharing image data) for the sharing operation.

[0026] In a possible implementation of the first aspect, at least one subsequent operation is an operation on an application that manages the first data.

[0027] That is, in the embodiment of the present application, the first data is image data or video data, and the application that manages the first data is an album application or a gallery application.

[0028] In a possible implementation of the first aspect above, the processing resources of the electronic device do not meet the processing conditions, and in response to the user performing the second operation, the processing resources are called to complete the processing of the first data.

[0029] In a possible implementation of the first aspect, the first data includes at least one of image data and video data.

[0030] In a possible implementation of the first aspect above, the first operation includes a shooting operation, and the second operation includes at least one of a viewing operation, an editing operation, and a sharing operation.

[0031] In a second aspect, an embodiment of the present application provides an electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, which enables the electronic device to execute the above-mentioned data processing method when the processor executes the instructions in the memory.

[0032] In a third aspect, an embodiment of the present application provides a storage medium having instructions stored thereon. When the instructions are executed on an electronic device, the electronic device executes the data processing method of the first aspect.

[0033] In a fourth aspect, an embodiment of the present application provides a computer program product, comprising: a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium contains a computer program code for executing the data processing method of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0035] Figure 1A According to the present application, a first interface schematic diagram of data processing is shown;

[0036] Figure 1B According to the present application, a second interface schematic diagram of data processing is shown;

[0037] Figure 1C According to the present application, a third interface schematic diagram of data processing is shown;

[0038] Figure 2A According to an embodiment of the present application, a first interface schematic diagram of an electronic device 100 is shown;

[0039] Figure 2B According to an embodiment of the present application, a schematic diagram of a second interface of an electronic device 100 is shown;

[0040] Figure 2C According to an embodiment of the present application, a third interface diagram of an electronic device 100 is shown;

[0041] Figure 2D According to an embodiment of the present application, a fourth interface diagram of an electronic device 100 is shown;

[0042] Figure 2E A fifth interface diagram of an electronic device 100 is shown according to an embodiment of the present application;

[0043] Figure 2F A sixth interface diagram of an electronic device 100 is shown according to an embodiment of the present application;

[0044] Figure 3 According to an embodiment of the present application, a first flow chart of a data processing method is shown;

[0045] Figure 4 According to an embodiment of the present application, a structural diagram of a data processing system is shown;

[0046] Figure 5 According to an embodiment of the present application, a second flow chart of a data processing method is shown;

[0047] Figure 6 According to an embodiment of the present application, a third flow chart of a data processing method is shown;

[0048] Figure 7 A fourth flow chart of a data processing method is shown according to an embodiment of the present application;

[0049] Figure 8 According to an embodiment of the present application, a hardware framework diagram of an electronic device 100 is shown;

[0050] Figure 9 According to an embodiment of the present application, a software structure block diagram of an electronic device 100 is shown. DETAILED DESCRIPTION

[0051] The illustrative embodiments of the present application include, but are not limited to, a data processing method, an electronic device, and a storage medium.

[0052] It should be understood that the embodiments of the present application do not limit the type of data. For example, in some embodiments, the data can be multimedia data related to a camera application or an album application, such as images and videos. As mentioned above, before the user views, edits, shares, or performs other operations on the original captured image, the electronic device needs to complete post-processing of the original captured image and other multimedia data. Otherwise, it will affect the smoothness of the use of the electronic device. For example, the original captured image may be temporarily processed during the user's operation, resulting in the inability to smoothly present the processed image to the user.

[0053] For example, in some other embodiments, the data may be data related to an email application. After the user clicks the email application icon, the electronic device may generate the attachment data of the latest unread email, so that when the user subsequently opens the latest unread email and clicks the attachment icon of the latest unread email, the electronic device may directly display the above-mentioned attachment content file to the user, allowing the user to view the attachment smoothly.

[0054] For example, in some other embodiments, the data may be audio data. After the user completes the recording operation, the electronic device may perform post-processing based on the original recorded audio file to obtain a processed audio file, allowing the user to listen to, edit, share, etc. based on the processed audio file.

[0055] For ease of description, the following embodiments take multimedia data as an example to introduce the technical solutions of the present application. It is understood that multimedia data may include image data and video data.

[0056] Please refer to Figure 1A , the user can take a picture by clicking the shooting button 01 on the camera application interface of the electronic device 100. Figure 1A Click the album button 02 to enter Figure 1B The photo album application interface shown in Figure 1B As shown, the camera application interface includes an image thumbnail 03A. If the user clicks on the image thumbnail 03A, the electronic device 100 has already completed the post-processing of the original image, that is, obtained the processed image, and the user can immediately view the processed image. For example, after the user clicks on the image thumbnail 03A, the electronic device 100 immediately displays the following Figure 1C The interface shown includes the processed image 03B.

[0057] However, in some scenarios, when the user clicks on the thumbnail 03A of the image, the electronic device has not yet completed the image processing, that is, the processed image 03B has not yet been obtained, resulting in the electronic device 100 being unable to immediately display the image 03B. Figure 1C The interface shown causes the photo album application to respond slowly to user operations, that is, the user's experience in using the electronic device 100 is poor.

[0058] To solve the above problems, the present application provides a data processing method. After detecting the first data newly added based on the user's first operation, the method predicts the specified processing time of the first data based on the user's current operation data (such as the time when the first operation was performed, other operations performed after the first operation and the time), and the historical operation data of the second operation performed by the user after the first operation. The second operation requires the use of the second data obtained by processing the first data, that is, the first data processing is completed before the second operation can be carried out smoothly. For example, before the user performs the viewing operation, the image data generated by the shooting operation needs to be processed and completed before the image can be viewed smoothly. Then, when the processing resources of the electronic device meet the processing conditions, the processing resources are called to complete the processing of the first data before the specified processing time to obtain the second data, so that the user's second operation can be executed smoothly, for example, the processed image is obtained in time, so that the processed image can be displayed immediately when the user performs the viewing operation. Through the above method, the time can be predicted according to the user operation, and then the processing resources can be scheduled based on the predicted time, so that the data processing of the first operation can be completed as soon as possible before the time required by the user, ensuring the smoothness of the user's subsequent second operation.

[0059] In some optional embodiments, a processing time prediction model can be trained based on the historical data of the user's first operation and second operation (such as the time of executing the first operation and the second operation). When the first data newly added based on the user's first operation is detected, the user's current operation data is input into the trained processing time prediction model to obtain the processing prediction time of the first data.

[0060] In some optional embodiments, the current location of the electronic device can also be input into the processing time prediction model, that is, the user's current operation data and the current location of the electronic device are input into the processing time prediction model together to obtain the processing prediction time corresponding to the current location of the electronic device. It can be understood that the user's current operation data, historical operation data and other operation data all correspond to the location of the electronic device, indicating that the user has different usage habits when in different locations. In this way, the predicted time can be closer to the time when the user performs the corresponding operation at the current location, for example, when the user clicks on the Figure 1B Thumbnails of images in 03 to view Figure 1C The processed image 04 is shown in the figure.

[0061] In some optional embodiments, the data processing of multimedia data may include at least one of beautification processing, beauty processing, blurring processing, and enhancement processing.

[0062] In some optional embodiments, the multimedia data may be image data, and corresponding to the first operation being a shooting operation, the user's current operation data may include: shooting time (the time point at which each image is shot), number of shots (the total number of shots taken since the camera application was opened), shooting frequency (how often an image is shot), and post-shooting operations (other operations after the image is shot and the time points of the operations). It is understandable that, if Figure 1A As shown in the figure, the shooting time is the time when the user clicks the shooting button 01 in the camera application interface.

[0063] In some optional embodiments, the user's historical operation data may include: image viewing habits, such as the time difference between viewing and capturing an image, i.e., how long after capturing an image, the user views the image; image editing habits, i.e., the temporal relationship between editing an image, capturing an image, and viewing an image; and image sharing habits, i.e., the temporal relationship between sharing an image, capturing an image, viewing an image, and editing an image. It will be appreciated that the historical operation data may correspond to a set historical time period, such as the past month, the past three months, etc.

[0064] In some optional embodiments, multimedia data processing can be divided into multiple processing stages, each corresponding to a user action. For example, the multiple processing stages include: a processing stage corresponding to viewing, a processing stage corresponding to editing, and a processing stage corresponding to sharing. In other words, the multimedia data required for viewing, editing, and sharing are different, corresponding to different data processing stages. In this embodiment, the post-shooting operations in the user's current operation data may include: viewing, editing, and sharing operations, and the processing time prediction model can output the processing prediction time for each processing stage.

[0065] It can be understood that the user's current operation data may include a time series, and the time series includes a one-to-one corresponding timestamp and operation label, wherein the timestamp can be the timestamp of the above-mentioned shooting operation, viewing operation, editing operation, and sharing operation, and the operation label can be shooting, viewing, editing, and sharing. For example, in one time series, the timestamp is 12:00 (i.e., the timestamp of the shooting operation), and the operation label is shooting; in another time series, the timestamp is 12:01 (i.e., the timestamp of the viewing operation), and the operation label is viewing, and so on.

[0066] In some optional embodiments, the processing resources of the electronic device meet the processing conditions, that is, the current operating parameters of the electronic device meet the processing conditions. Among them, the current operating parameters of the electronic device may include parameters such as the current power level, resource usage rate of the central processing unit (CPU) and the graphics processing unit (GPU), and temperature. Optionally, when the processing prediction time corresponding to the multimedia data processing is reached, if the current power level meets the power level condition, and the CPU and GPU meet the usage rate condition and the temperature condition respectively, it means that the processing resources of the electronic device meet the processing conditions, that is, the electronic device can be controlled to perform the task of multimedia data processing.

[0067] The following combination Figure 2A-2F The interface of the electronic device 100 in the embodiment of the present application is introduced.

[0068] according to Figure 2A As shown, the user clicks the camera control 000 on the desktop to open the camera application interface, as shown in Figure 2B As shown, it includes a shooting button 010A and an album control 020A.

[0069] As an example, the first operation in the embodiment of the present application is that the user clicks the shooting button 010A to shoot an image or video.

[0070] Understandably, users Figure 2D Click on the album control 020A in the camera application interface shown to open the Figure 2E The photo album application interface shown. For example, refer to Figure 2C , users can click on the album control 020B from the desktop to open Figure 2E The camera application interface is shown in the figure. Figure 2E As shown, the camera application interface includes a thumbnail 030A of the image or video.

[0071] As an example, the second operation in the embodiment of the present application can be a user viewing operation of an image or video, that is, the user clicks on the thumbnail 030A to view the image or video. It can be understood that after the user clicks on the thumbnail 030A, the user can open the Figure 2F The viewing interface shown displays a processed image or video 030B, a sharing control 040 , and an editing control 050 .

[0072] As an example, the second operation in the embodiment of the present application may be a user's sharing operation of an image or video, that is, the user clicks Figure 2F Use the sharing control 040 in the image or video to share the image or video.

[0073] As an example, the second operation in the embodiment of the present application may be a user's editing operation on an image or video, that is, the user clicks Figure 2F Use the editing controls 050 in the editor to edit images or videos.

[0074] It should be noted that the above is an exemplary interface of the data processing method applied to an application and a gallery application. In other optional embodiments, the interface of the electronic device 100 in the embodiment of the present application can also be the interface of an application corresponding to other context-related operations (e.g., a first operation and a second operation that requires the use of post-processed data of the first operation), and the embodiment of the present application is not limited to this.

[0075] In the embodiment of the present application, the data processing method can be applied to the electronic device 100, which may include a smart phone, a desktop computer, a tablet computer, a laptop computer, a smart speaker, a digital assistant, an augmented reality (AR) / virtual reality (VR) device, a smart wearable device, and other types of electronic devices 100. Optionally, the operating system running on the electronic device 100 may include but is not limited to Android, IOS, Linux, Windows, etc. The data processing method mentioned in the embodiment of the present application is introduced below. Reference Figure 3 , an exemplary process of a data processing method according to an embodiment of the present application includes:

[0076] S301: Detecting first data corresponding to a first operation of a user.

[0077] The first operation has at least one subsequent operation associated with it in the user's history. Associated execution means that the user performs the subsequent operation after performing the first operation, and there is an association between the first data of the first operation and the data of the subsequent operation. For example, the subsequent operation includes a second operation, and the second data of the second operation is associated with the first data of the first operation. That is, the second data used by the second operation is data processed from the first data of the first operation.

[0078] In an optional embodiment, the user's first operation is to take a picture. Figure 2B For example, clicking the capture button 010 takes a picture of the surrounding environment. In this embodiment, the first data is the captured image data. It is understood that the captured image data needs to be processed to obtain the processed image data before the user can perform a second operation associated with the first operation, such as viewing, editing, or sharing.

[0079] S302: Determine a prescribed processing time for first data corresponding to the first operation based on the user's current operation data and historical operation data of the user performing a first operation and then a second operation.

[0080] The specified processing time for the first data may be the predicted time when the user performs the second operation. It will be appreciated that if the processing of the first data is completed before the specified processing time, the user can smoothly perform the second operation using the electronic device 100. In other optional embodiments, the specified processing time may also be a time period starting at the time when the first operation is performed, with the end of the time period being the predicted time when the user performs the second operation; if the processing of the first data is completed within the time period, the user can smoothly perform the second operation.

[0081] It is understood that the user's current operation data includes the time when the user performs the current operation. The current operation can be all operations associated with the first operation, that is, all user operations starting from the application opening operation corresponding to the first operation and including the first operation. For example, the current operation data can include the camera application opening operation, the capture operation, and other operations after the capture operation; the other operations after the capture operation can include adjusting settings, operations on applications other than the camera application, and so on.

[0082] It is understood that historical operation data for a user performing a first operation followed by a second operation may include the time the user performed the first operation and the time the user performed the second operation. It may also include the time the user performed an operation after performing the first operation and before performing the second operation. For example, the time it takes to adjust filter parameters after taking the first photo and before viewing the first photo.

[0083] Optionally, the historical operation data of a user performing a first operation and then a second operation may include data of a fourth operation associated with the first operation, and the fourth operation may be performed before the first operation is performed. For example, the data of the operation of opening the camera application; the data of the fourth operation may include the time data of opening the camera application, and may also include data of the opening method, such as switching to another application, opening from the desktop (such as Figure 2A (Enter the interface shown).

[0084] In an optional embodiment, the user's current operation data can be input into a processing time prediction model to output the specified processing time of the first data corresponding to the current first operation; wherein the processing time prediction model is trained based on the historical operation data of the user performing the first operation and then the second operation.

[0085] In this embodiment, the current operation data may be a time series of the user's current operation. The time series includes one-to-one corresponding timestamps and operation events. The operation events may serve as labels for the time series. For example, the label corresponding to the timestamp when the user performs the first operation in the time series is the first operation. Similarly, the historical operation data of the user performing the first operation and then the second operation may also be a time series, including one-to-one corresponding timestamps and operation events. For example, the label corresponding to the timestamp when the user performs the first operation in the history is the first operation, and the label corresponding to the timestamp when the user performs the second operation in the history is the second operation. The processing time prediction model may predict the specified processing time for the first operation based on the input current operation data and the time series of historical operation data. In this embodiment, the processing time prediction model may be trained based on the user's historical operation data.

[0086] Typically, users have different operating habits at different locations. To make the specified processing time more closely aligned with the user's operations at the current location, in an optional embodiment, step S302 may also include determining the specified processing time for the first data corresponding to the first operation based on the user's current operating data, the user's current geographic location, and historical operating data of the user performing the first operation followed by the second operation. It will be understood that the specified processing time determined based on the user's current geographic location is the predicted time for the user to perform the second operation at the current geographic location.

[0087] Optionally, the user's current operation data, the user's current geographic location, and historical operation data of the user performing a first operation followed by a second operation can be input into a processing time prediction model to output a predetermined processing time for the first data corresponding to the first operation. That is, the processing time prediction model can predict the predetermined processing time based on the user's current geographic location. In this embodiment, the processing time prediction model can be trained based on the user's historical operation data and the historical geographic location corresponding to the historical operation data.

[0088] S303: When the processing resource of the electronic device satisfies the processing condition, the processing resource is called to complete the processing of the first data before the specified processing time, and second data corresponding to the second operation is obtained.

[0089] It is understood that if the processing resources of the electronic device meet the processing conditions, that is, the current state parameters of the electronic device meet the processing conditions. The current state parameters may include current power, CPU / GPU resource usage, memory usage, temperature, and other state parameters.

[0090] In an optional embodiment, the current state parameters of the electronic device meet the processing conditions, which may include one or more of the following: the current power meets the power condition, the CPU resource usage meets the first resource usage condition, the GPU resource usage meets the second resource usage condition, the temperature meets the temperature condition, and the memory usage meets the memory usage condition.

[0091] In another optional embodiment, a status score can be calculated based on status parameters such as current power, CPU / GPU resource usage, memory usage, temperature, etc. If the status score meets the scoring conditions, it is determined that the current status parameters of the electronic device meet the processing conditions.

[0092] It can be understood that calling processing resources may include calling CPU, GPU, and running memory.

[0093] It is understood that in embodiments where the first data is an image or video, the processing of the first data may be post-processing, wherein the post-processing may include beautification processing, enhancement processing (clarification, sharpening, color enhancement, etc.), blurring processing, facial beautification processing, body beautification processing, etc. After the first data processing is completed, the processed first data, i.e., the second data, is obtained. When the user performs the second operation, the electronic device 100 can directly use the processed second data.

[0094] In an optional embodiment, the first data may be processed differently for different types of second operations. For example, the image displayed to the user by the electronic device 100 after the user performs a viewing operation and the image provided for editing by the electronic device after the user performs an editing operation may have different image characteristics. For example, editing may require higher clarity. In other words, the image data processing methods required for viewing and editing operations may be different.

[0095] The following is based on Figure 4 A data processing system provided in an embodiment of the present application is introduced.

[0096] like Figure 4 As shown, a data processing system according to an embodiment of the present application includes a user behavior perception module 406 , a filming time prediction model 405 , a device state perception module 407 , a scheduling module 408 , and a media processing engine 409 .

[0097] In an optional embodiment, the filming time prediction model 405 can be trained by an artificial intelligence learning algorithm 404 based on the time series samples of user operations and the label 403 of the arrival time (time of the second operation).

[0098] Among them, the time series samples of user operations can be modeled based on user behavior habits 401 and geographic location information 402. It can be understood that user behavior habits 401 and geographic location information 402 are both historical data. Figure 7 As shown in the figure, user behavior habits 401 include photo-taking habits 4011 (number of photos, frequency of photos, and operations after photos), photo-viewing habits 4012 (long-term photo-viewing habits and short-term photo-viewing habits, including immediate viewing, delayed viewing, and viewing order), photo-editing habits 4013 (immediate editing, delayed editing, and time difference between editing and viewing), photo-sharing habits 4014 (immediate sharing, delayed sharing, and time difference between sharing and viewing or editing), etc.; geographic location information includes permanent location information 4021 (the historical location with the most statistical times) and current location information 4022 (the geographic location of each user's operation).

[0099] In an optional embodiment, the user behavior perception module 406 inputs the time series of user operations and the user's current geographic location into the film time prediction model 405, and the film time prediction model 405 outputs the specified processing time to the scheduling module 408. The scheduling module 408 controls the media processing engine 409 to process the first data based on the status information of the electronic device 100 sent by the device status perception module 407 and the specified processing time to obtain the second data.

[0100] It should be noted that a data processing system provided in an embodiment of the present application and a data processing method provided in an embodiment of the present application have the same concept.

[0101] The following is based on Figure 5 An exemplary process of step S302 of an embodiment of the present application is further introduced.

[0102] S3021: Determine the time sequence of user operations through the user behavior perception module 406.

[0103] It can be understood that the user behavior perception module 406 can perceive the time information of the user's current operation, for example, a shooting operation is performed at 8:08, and obtain the time sequence of the user operation.

[0104] As an example, a time series of user operations may include multiple timestamps, where each of the multiple timestamps corresponds to a label for an operation event. For example, the first timestamp corresponds to the label for the operation of opening a camera application, the second timestamp corresponds to the label for the operation of taking a first photo, and the third timestamp corresponds to the label for the operation of taking a second photo.

[0105] It should be noted that the above is only an example. In other optional implementations, the time series of user operations may include timestamps of other operations associated with the first operation and operation event labels, which is not limited in this application.

[0106] Optionally, the user behavior perception module 406 may also perceive the user's current geographic location.

[0107] S3022: Input the time series of user operations into the processing time prediction model and output the specified processing time.

[0108] It can be understood that the processing time prediction model is a model trained based on historical operation data. For a description of the historical operation data, reference can be made to the above description of step S302.

[0109] In an optional embodiment, the processing time prediction model can be a neural network based model, such as Figure 4 The illustrated slice time prediction model 405 .

[0110] Optionally, the time series of user operations, together with the user's current geographic location, can be input into a processing time prediction model to output the specified processing time at the current geographic location.

[0111] Alternatively, if the user behavior perception module 406 cannot sense the user's current geographic location, for example, if the user has disabled the positioning function of the electronic device 100, the time series of the user's operations can be directly input into the processing time prediction model. In this case, the processing time prediction model can directly output the specified processing time for the user's permanent location, based on the fact that the input data does not include the current geographic location. In this embodiment, the processing time prediction model can be trained based on historical operation data corresponding to historical geographic locations and the user's permanent location information.

[0112] The following is based on Figure 6 An exemplary process of step S303 in the embodiment of the present application is further introduced.

[0113] S3031: Determine the status information of the electronic device 100 through the device status perception module 407.

[0114] In an optional embodiment, the status information of the electronic device 100 may be sensed by a device status sensing module.

[0115] It can be understood that the status information of the electronic device 100, namely the current status parameters described above, may include current power, CPU / GPU resource usage, memory usage, temperature and other status parameters.

[0116] S3032: Input the specified processing time and status information into the scheduling module 408.

[0117] In an optional embodiment, the prescribed processing time obtained in step S302 and the status information obtained in step S501 may be input into the scheduling module 408 .

[0118] It is understood that the scheduling module 408 can schedule the process of processing the first data and other processes of the electronic device 100 based on the specified processing time and status information. For example, the scheduling timing, resource allocation, frequency setting, etc. of the first data in the background processing process are determined to complete the background scheduling of the first data. Among them, the scheduling timing is the time to start, interrupt or continue processing the first data, the resource allocation is to allocate CPU / GPU resources for the processing of the first data, and the frequency setting is to set the frequency of the CPU / GPU when processing the first data.

[0119] It can be understood that the specified processing time of the first data is fixed data and can be input into the scheduling module 408 once; the status information is real-time changing data and can be input into the scheduling module 408 in real time and multiple times, so that the scheduling module 408 performs scheduling based on the real-time status information.

[0120] S3033: The scheduling module 408 controls the operation of the media processing engine 409 according to the scheduling policy.

[0121] In an optional embodiment, the scheduling strategy may be: if the status information does not meet the processing conditions, the first data is not processed; if the status information meets the processing conditions, the media processing engine 409 is called to complete the processing of the first data before the specified processing time of the first data.

[0122] As an example, if the state information of the electronic device 100 continues to meet the processing condition after the user completes the first operation, the scheduling module 408 can call the media processing engine 409 to directly complete the processing of the first data.

[0123] As an example, if at the first moment after the user completes the first operation, the status information of the electronic device 100 meets the processing condition, the scheduling module 408 controls the electronic device 100 to process the first data. Until the second moment, the status of the electronic device 100 changes from meeting the processing condition to not meeting the processing condition, then the scheduling module 408 controls the electronic device 100 to suspend processing of the first data, and waits until the status information of the electronic device 100 meets the processing condition, or the user performs a second operation, and then controls the electronic device 100 to continue processing the first data. In this embodiment, the user's current operation data may also include data of the second operation, that is, the scheduling module 408 controls the electronic device 100 to process the first data based on the data of the second operation included in the current operation data.

[0124] As an example, if the status information of the electronic device 100 does not meet the processing conditions after the user completes the first operation and before performing the second operation, when the user performs the second operation, the scheduling module 408 controls the electronic device 100 to process the first data. In this embodiment, the user's current operation data may also include data for the second operation, that is, the scheduling module 408 controls the electronic device 100 to process the first data based on the data for the second operation included in the current operation data.

[0125] It can be understood that when the scheduling module 408 controls the electronic device 100 to process the first data, it can determine the resource allocation and frequency setting of the first data processing process based on the status information of the electronic device 100. That is, resources are allocated to the processing of the first data and the frequency is set, for example, Figure 4 The media processing engine 409 shown allocates CPU / GPU resources and sets the CPU / GPU frequency when processing the first data.

[0126] In an optional embodiment, at least one subsequent operation further includes a third operation. Optionally, the second operation and the third operation may be two of a viewing operation, an editing operation, and a sharing operation, respectively. After step S303, the exemplary process further includes: determining a second specified processing time for the first data based on the user's current operation data and the user's historical operation data of performing the first operation and then the third operation; corresponding to the processing resources of the electronic device meeting the processing conditions, calling the processing resources to complete the processing of the first data before the second specified processing time, and obtaining the third data corresponding to the third operation.

[0127] Optionally, the third operation is an operation after the second operation, wherein the second specified processing time is later than the first specified processing time. For example, the second operation and the third operation can be a viewing operation and an editing operation, or a viewing operation and a sharing operation, or an editing operation and a sharing operation, respectively.

[0128] Optionally, the second data may be processed to obtain third data. For example, image data corresponding to a viewing operation may be processed to obtain image data corresponding to an editing operation; or image data corresponding to an editing operation may be processed to obtain image data corresponding to a sharing operation. It is understood that the image data before and after processing may have different image parameters, such as different image specifications and image clarity, and the image parameters of the image data required for sharing, viewing, and editing operations may be different.

[0129] A data processing method according to an embodiment of the present application can predict the time based on user operations and then schedule processing resources based on the predicted time, thereby completing the data processing of the first operation as soon as possible before the user's desired time, ensuring the smoothness of the user's subsequent second operation. For example, after capturing an image, the user can smoothly view, share, edit, and other operations. Furthermore, by mapping user operations to geographic locations, operation times can be predicted for different user locations, making the specified processing time more realistic.

[0130] In the above-mentioned embodiment of determining the prescribed processing time based on the processing time prediction model, optionally, before step S301, a step of training the processing time prediction model may be included, for example, step S300: training the processing time prediction model. Figure 6 In an optional embodiment, an exemplary process of training a processing time prediction model includes:

[0131] S3001: Collect historical operation data through the collection module.

[0132] Optionally, the historical operation data may include time data of the user's historical execution of the first operation and the second operation, etc.

[0133] As an example, historical operation data may include data of a fourth operation that was historically executed, where the fourth operation may have been executed before the first operation. Optionally, the fourth operation may be an operation associated with the first operation, such as the first operation being a shooting operation and the fourth operation being an operation to open a camera application. For example, the data of the fourth operation may include the operation method, such as switching from another application or entering from the desktop. Optionally, the fourth operation may be an operation unrelated to the first operation. For example, the fourth operation may be other operations performed by the user on the electronic device 100, and the data of the fourth operation may be the user's operation speed, such as click frequency.

[0134] As an example, historical operation data may include data on historical execution of a first operation. In an embodiment where the first operation is a capture operation, the data on the first operation may include data such as the number of captures, the capture frequency, and the capture time. It is understood that the number of captures refers to the number of consecutive captures taken by the user in the camera application interface, the capture frequency refers to the frequency of consecutive captures taken by the user in the camera application interface, and the capture time refers to the time at which each capture is taken by the user.

[0135] As an example, the historical operation data may include data of the user's historical execution of the second operation. Optionally, the second operation may include at least one of a viewing operation, an editing operation, and a sharing operation. The data of the second operation may include the time of the viewing operation, the viewing order, the time of the editing operation, and the time of the sharing operation. It may also include the time difference between the shooting operation, the viewing operation, the editing operation, and the sharing operation. For example, the data of the user executing the second operation may include the viewing operation and the editing operation immediately after the shooting operation, and the sharing operation immediately after the editing operation is completed; or, the viewing operation may be delayed after a first time period after the shooting operation is completed, the editing operation may be delayed after a second time period after the viewing operation is completed, and the sharing operation may be delayed after a third time period after the editing operation is completed.

[0136] As an example, historical operation data may include data on other operations performed by the user after performing a first operation and before performing a second operation, such as operations performed after exiting a photo album application or opening other applications (such as a chat application, etc.).

[0137] Optionally, the above historical operation data can be divided into long-term data and short-term data. For example, long-term data refers to data from the past six months, and short-term data refers to data from the past week. This application does not limit the data period.

[0138] Optionally, the collection module may also collect the user's historical geographic location, including permanent location information and the historical geographic location corresponding to each set of historical operation data. Each set of historical operation data corresponds to each first data item, i.e., each set of historical operation data corresponds to each time the user performed a first operation and then a second operation. It is understood that the user completes each execution of the first operation and then the second operation at a historical geographic location.

[0139] For other contents about historical operation data, please refer to the above explanation and will not be repeated here.

[0140] S3002: Model the historical operation data as a time series and construct a data set.

[0141] It is understood that the time-related data in the historical operation data can be modeled as a time series, such as a time series of historical operations. The time series can have multiple timestamps, each of which corresponds to a historical operation event, such as a first timestamp corresponding to a first operation and a second timestamp corresponding to a second operation. Historical operation events can serve as labels for timestamps in the time series, such as labeling the first timestamp as the first operation and the second timestamp as the second operation.

[0142] In an optional embodiment, the data set may also include other data besides the time series, such as the number of shots and the shooting frequency data.

[0143] In an optional embodiment, each time series may correspond to a historical geographical location, which is collected by a collection module.

[0144] S3003: Train the neural network based on the data set to obtain a processing time prediction model.

[0145] It can be understood that the neural network can be trained multiple times based on the data set to obtain a processing time prediction model.

[0146] In an optional embodiment, the data set may include multiple sets of training data, wherein each set of training data corresponds to a time series, i.e., each time the user performs a first operation in history to obtain first data, and then performs a second operation, i.e., each set of training data corresponds to each first data.

[0147] It can be understood that in each set of training data, the time when the user historically performs the second operation can be used as a reference processing time, and in the training data, other data except the time when the user historically performs the second operation can be used as data to be predicted.

[0148] Optionally, the data to be predicted in the training data can be input into the neural network to obtain the output predicted processing time. The loss value is determined by the loss function based on the predicted processing time and the reference processing time. When the loss value is greater than the loss threshold, the parameters of the neural network are updated, and the step of inputting the predicted data into the neural network is repeated until the determined loss value is less than the loss threshold, thereby obtaining a trained processing time prediction model.

[0149] In an embodiment of the present application, a neural network model is trained based on the time series of user operation data. The learning ability of the neural network model can be utilized and the user operation data can be used as a reference to obtain a model that can be used for efficient prediction, so as to accurately predict the time of the second operation (the arrival time of the processed first data and the second data), that is, to predict the specified processing time.

[0150] It should be noted that the above implementation is only an example of training a processing time prediction model, and this application does not limit the method of training a processing time prediction model.

[0151] Figure 8 A schematic structural diagram of the electronic device 100 is shown.

[0152] The electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, etc.

[0153] It should be understood that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0154] The processor 110 may include one or more processing units. For example, the processor 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU). The different processing units may be independent devices or integrated into one or more processors.

[0155] The controller can generate operation control signals according to the instruction operation code and timing signal to complete the control of instruction fetching and execution.

[0156] Processor 110 may also include a memory for storing instructions and data. In some embodiments, the memory in processor 110 is a cache memory. This memory can store instructions or data that have just been used or are being recycled by processor 110. If processor 110 needs to use the same instruction or data again, it can directly access the memory. This avoids duplicate accesses, reduces processor 110 latency, and thus improves system efficiency.

[0157] In some embodiments, the processor 110 of the electronic device 100 calls program instructions stored in the memory to execute the data processing method mentioned in this application according to the obtained program instructions. For example, first data corresponding to a first operation of a user is detected, where the first operation has a second operation associated with the user's history; based on the user's current operation data and the user's historical operation data of performing the first operation and then the second operation, the specified processing time of the first data corresponding to the first operation is determined; corresponding to the processing resources of the electronic device meeting the processing conditions, the processing resources are called to complete the processing of the first data before the specified processing time to obtain the second data corresponding to the second operation.

[0158] In some embodiments, processor 110 may include one or more interfaces.

[0159] The MIPI interface can be used to connect the processor 110 to peripheral devices such as the display 194 and the camera 193. MIPI interfaces include the camera serial interface (CSI) and the display serial interface (DSI). In some embodiments, the processor 110 and the camera 193 communicate via the CSI interface to implement the camera function of the electronic device 100. The processor 110 and the display 194 communicate via the DSI interface to implement the display function of the electronic device 100.

[0160] Electronic device 100 implements display functionality through a GPU, display screen 194, and an application processor. A GPU is a microprocessor for image processing that connects display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 110 may include one or more GPUs that execute program instructions to generate or modify display information.

[0161] Display screen 194 is used to display images, videos, and the like. Display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a MiniLED, a MicroLED, a Micro-oLed, or a quantum dot light-emitting diode (QLED). In some embodiments, electronic device 100 may include one or N display screens 194, where N is a positive integer greater than one.

[0162] The electronic device 100 can implement a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, and an application processor.

[0163] The ISP processes data fed back by camera 193. For example, when taking a photo, the shutter is opened, and light is transmitted through the lens to the camera's photosensitive element. The light signal is converted into an electrical signal, which is then passed to the ISP for processing and converted into a visible image. The ISP can also perform algorithmic optimization on image noise, brightness, and skin tone. It can also optimize parameters such as exposure and color temperature of the captured scene. In some embodiments, the ISP can be located within camera 193.

[0164] The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, and then passes the electrical signal to the ISP for conversion into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV or other format. In some embodiments, the electronic device 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.

[0165] The digital signal processor is used to process digital signals. In addition to processing digital image signals, it can also process other digital signals. For example, when the electronic device 100 selects a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy.

[0166] Video codecs are used to compress or decompress digital video. Electronic device 100 may support one or more video codecs. This allows electronic device 100 to play or record videos in various encoding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, and MPEG4.

[0167] The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100. The external memory card communicates with the processor 110 via the external memory interface 120 to implement data storage functions. For example, files such as music and videos can be stored on the external memory card.

[0168] The internal memory 121 can be used to store computer executable program codes, which include instructions. The internal memory 121 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area may store data created during the use of the electronic device 100 (such as audio data, a phone book, etc.), etc. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc. The processor 110 executes various functional applications and data processing of the electronic device 100 by running instructions stored in the internal memory 121 and / or instructions stored in a memory provided in the processor.

[0169] The touch sensor 180K is also called a "touch-sensitive device." The touch sensor 180K can be disposed on the display screen 194. The touch sensor 180K and the display screen 194 form a touch screen, also called a "touch screen." The touch sensor 180K is used to detect touch operations applied thereto or in the vicinity thereof. The touch sensor can transmit the detected touch operations to the application processor to determine the type of touch event. Visual output related to the touch operations can be provided via the display screen 194. In other embodiments, the touch sensor 180K can also be disposed on the surface of the electronic device 100, at a location different from that of the display screen 194.

[0170] Figure 91 is a software structure block diagram of the electronic device 100 according to an embodiment of the present invention.

[0171] A layered architecture divides software into several layers, each with distinct roles and responsibilities. Layers communicate with each other through software interfaces. In some embodiments, the Android system is divided into four layers: the application layer, the application framework layer, the Android runtime and system libraries, and the kernel layer.

[0172] The application layer can include a series of application packages. Figure 9 As shown, the application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, dual SIM and mobile network. Among them, the camera application can provide the function of shooting images and videos, and the gallery application, also known as the album application, can provide the functions of viewing, editing and sharing images and videos.

[0173] The application framework layer provides an application programming interface (API) and programming framework for the applications in the application layer. The application framework layer includes some predefined functions.

[0174] like Figure 9 As shown, the application framework layer may include a window manager, a content provider, a view system, a phone manager, a resource manager, a media processing engine, and the like.

[0175] In some embodiments, the media processing engine is used to process media data such as images and videos to obtain processed data. For example, for unprocessed media data, the media processing engine can process it through different processing processes to obtain viewing image data corresponding to viewing operations, editing image data corresponding to editing operations, and editing image data corresponding to sharing operations, so that users can perform viewing operations, editing operations, and sharing operations respectively.

[0176] Android Runtime includes core libraries and a virtual machine. Android runtime is responsible for scheduling and management of the Android system.

[0177] The system library can include multiple functional modules, such as surface manager, media library, 3D graphics processing library (such as OpenGL ES), 2D graphics engine (such as SGL), etc.

[0178] The kernel layer is the layer between hardware and software. The kernel layer includes at least display driver, camera driver, audio driver, and sensor driver.

[0179] Accordingly, an embodiment of the present application provides an electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device, and a processor for executing instructions of the above-mentioned data processing method.

[0180] Accordingly, an embodiment of the present application provides a storage medium having instructions stored thereon, which, when executed on an electronic device, causes the electronic device to execute the above-mentioned data processing method.

[0181] This specification provides method or process operation steps as shown in the embodiments or flowcharts, but may include more or fewer operation steps based on routine or non-creative work. The order of steps listed in the embodiments is only one of many execution orders and does not represent the only execution order. In actual execution, the method or process shown in the embodiments or drawings may be executed sequentially or in parallel (for example, in a parallel controller or multi-threaded processing environment).

[0182] The various embodiments disclosed in this application can be implemented in hardware, software, firmware, or a combination of these implementation methods. The embodiments of the present application can be implemented as a computer program or program code executed on a programmable system, which includes at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0183] Program code can be applied to input instructions to perform the functions described herein and generate output information. The output information can be applied to one or more output devices in a known manner. For purposes of this application, a processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor.

[0184] Program code can be implemented with a high-level programming language or an object-oriented programming language to communicate with the processing system. Where necessary, program code can also be implemented in assembly language or machine language. In fact, the mechanism described in this application is not limited to the scope of any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0185] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed over a network or through other computer storage media. Therefore, machine storage media may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including but not limited to floppy disks, optical disks, optical discs, read-only memories (CD-ROMs), magneto-optical disks, read-only memories (ROMs), random access memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or tangible machine-readable memories for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in electrical, optical, acoustic, or other forms of propagation signals. Therefore, machine storage media include any type of machine storage media suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

[0186] As used herein, the term "module" may refer to, be part of, or include: memory (shared, dedicated, or group) for running one or more software or firmware programs, application-specific integrated circuits (ASICs), electronic circuits and / or processors (shared, dedicated, or group), combinational logic circuits, and / or other suitable components that provide the functionality.

[0187] In the accompanying drawings, some structural or method features may be shown in a particular arrangement and / or order. However, it should be understood that such a particular arrangement and / or order is not required. Rather, in some embodiments, these features may be illustrated in a manner and / or order different from that shown in the illustrative drawings. In addition, the inclusion of structural or method features in a particular drawing does not mean that all embodiments need to include such features. In some embodiments, these features may not be included, or they may be combined with other features.

[0188] The embodiments of the present application are described in detail above with reference to the accompanying drawings. However, the application of the technical solution of the present application is not limited to the various applications mentioned in the embodiments of the present patent. Various structures and variations can be easily implemented with reference to the technical solution of the present application to achieve the various beneficial effects mentioned herein. Various changes made within the knowledge of ordinary technicians in this field without departing from the purpose of the present application should fall within the scope of coverage of the patent application.

Claims

1. A data processing method, characterized in that: The method is applied to an electronic device, and includes: detecting first data captured by a user based on a first operation, the first data including at least one of image data and video data, the first operation being associated with at least one subsequent operation performed by the user in history, the at least one subsequent operation including a second operation and a third operation; Determining a first prescribed processing time for the first data based on current operation data of the user, the current geographic location of the user, and historical operation data of the user performing the first operation and then the second operation; If the processing resource of the electronic device meets the processing condition, calling the processing resource to complete processing of the first data before the first prescribed processing time, and obtaining second data corresponding to the second operation; If the processing resources of the electronic device do not meet the processing condition, in response to the second operation, calling the processing resources to complete the processing of the first data; determining a second prescribed processing time for the first data based on the user's current operation data and the user's historical operation data of performing the first operation and then the third operation; If the processing resource of the electronic device meets the processing condition, calling the processing resource to complete processing of the first data before the second specified processing time, and obtaining third data corresponding to the third operation; The current operation data includes the shooting time, the number of shots, the shooting frequency, the fifth operation after shooting, and the operation time of the fifth operation; The historical operation data of the user performing the first operation and then the second operation includes the time when the user performed the first operation and the time when the user performed the second operation; The historical operation data of the user performing the third operation after performing the first operation in history includes the time when the user performed the first operation in history and the time when the user performed the third operation in history.

2. The method according to claim 1, characterized in that The determining of the first prescribed processing time of the first data based on the user's current operation data, the user's current geographic location, and historical operation data of the user performing the first operation and then the second operation includes: Determining a predicted operation time for the user to perform the second operation based on the user's current operation data and historical operation data of the user performing the first operation and then the second operation; Based on the predicted operation time, a first prescribed processing time for the first data is determined.

3. The method according to claim 1, characterized in that The determining of the first prescribed processing time of the first data based on the user's current operation data, the user's current geographic location, and historical operation data of the user performing the first operation and then the second operation includes: The user's current operation data is input into a processing time prediction model, and a first specified processing time of the first data is output, wherein the processing time prediction model is trained based on the historical operation data of the user performing a first operation and then a second operation.

4. The method according to claim 1, wherein The processing resource of the electronic device satisfies at least one of the following conditions: The temperature of the electronic device meets the temperature condition; The resource usage rate of the electronic device meets the resource usage rate condition; The memory usage rate of the electronic device meets the memory usage rate condition; The power level of the electronic device meets the power level condition.

5. The method according to claim 1, wherein The current operation data includes: startup data of a first application corresponding to the first operation.

6. The method according to claim 1, characterized in that The third operation is an operation subsequent to the second operation, wherein the second prescribed processing time is later than the first prescribed processing time.

7. The method according to claim 1, characterized in that The third operation is an operation subsequent to the second operation; The completing the processing of the first data to obtain third data corresponding to the third operation includes: The second data is processed to obtain the third data.

8. The method according to any one of claims 1 to 5, characterized in that The second operation includes at least one of a viewing operation, an editing operation, and a sharing operation.

9. An electronic device, characterized in that: include: a memory for storing instructions to be executed by one or more processors of the electronic device, and The processor, when executing the instructions in the memory, can enable the electronic device to execute the method according to any one of claims 1 to 8.

10. A storage medium, characterized in that: The storage medium stores instructions, which, when executed on an electronic device, enable the electronic device to execute the method according to any one of claims 1 to 8.

11. A computer program product, characterized in that A non-volatile computer-readable storage medium is included, wherein the non-volatile computer-readable storage medium contains instructions for executing the method according to any one of claims 1 to 8.

Images