WO2020073317A1 - 文件管理方法及电子设备 - Google Patents

文件管理方法及电子设备 Download PDF

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Publication number
WO2020073317A1
WO2020073317A1 PCT/CN2018/110070 CN2018110070W WO2020073317A1 WO 2020073317 A1 WO2020073317 A1 WO 2020073317A1 CN 2018110070 W CN2018110070 W CN 2018110070W WO 2020073317 A1 WO2020073317 A1 WO 2020073317A1
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WO
WIPO (PCT)
Prior art keywords
picture
pictures
feature
score
electronic device
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Application number
PCT/CN2018/110070
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English (en)
French (fr)
Inventor
郭颂
刘宗超
高华江
宋海东
Original Assignee
华为技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to CN201880081385.5A priority Critical patent/CN111480158A/zh
Priority to PCT/CN2018/110070 priority patent/WO2020073317A1/zh
Publication of WO2020073317A1 publication Critical patent/WO2020073317A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/54—Browsing; Visualisation therefor

Definitions

  • This application relates to the field of electronic equipment, and in particular to a file management method and electronic equipment.
  • the electronic device manages the pictures including displaying the pictures and deleting the pictures.
  • the pictures can be scored, and the pictures can be displayed or deleted according to the scores.
  • the scoring mechanism in the prior art is unreasonable, and the score of the obtained picture often has a gap with the user's psychological expectation, resulting in the user being unable to quickly find the picture, or deleting the picture that the user does not want to delete, which affects the operation efficiency.
  • the embodiments of the present application provide a picture management method and an electronic device, which can make the scoring mechanism more reasonable, and the score of the obtained picture is more in line with the user's psychological expectations, so that the user can quickly find the picture, or improve the accuracy of deleting the picture. Improve operating efficiency.
  • an embodiment of the present application provides a picture management method, which is performed by an electronic device, and the method may include: acquiring each of at least two pictures stored in the electronic device and / or stored in a cloud album Scene features; determine the first feature, the second feature, the scoring criteria for the first feature, and the scoring criteria for the second feature according to the scene features of each picture; based on the value of the first feature of each picture, and The value of the second feature of each picture is calculated to obtain the score of each picture; wherein, the effect of the first feature on the score of each picture is greater than the effect of the second feature on the score of each picture; An operation; in response to the first operation, displaying S second pictures in at least two pictures; wherein, S is an integer greater than or equal to 1, and the score of the S second pictures is higher than at least two pictures except S The score of other pictures than the second picture.
  • the electronic device may determine the first feature and its scoring standard, the second feature and the scoring standard of each picture stored in the cloud album and / or stored in the cloud album according to the scene characteristics.
  • the value of one feature and the value of the second feature calculate the score of each picture, and display S second pictures with a high score.
  • the level of the image score can be used to determine the user's preference for the picture. The higher the score, the higher the preference. Therefore, displaying the second picture with a high score can make it easier for the user to view his favorite picture and improve the user's efficiency in finding the picture.
  • the above-mentioned scene features include one or any combination of the following: whether it is favorited, whether it is commented, whether it is associated with a wallpaper, whether it is uploaded to the cloud, or a storage location;
  • the above-mentioned first feature includes one or any combination of the following: Favorited, whether it is commented;
  • the above second feature includes one or any combination of the following: whether it is shared, shooting mode, picture content classification, browsing times, shooting time, last browsing time, picture size, aesthetic score.
  • the technical solution provided by the embodiment of the present application can preliminarily determine whether the user may like a certain picture based on the above-mentioned scene characteristics, and then extract the characteristics of the picture for calculation according to the judgment result.
  • Different interpretation results correspond to different features involved in calculating the score of the picture and the scoring criteria of the feature, which can more accurately calculate the score of the picture, make the score of the picture more in line with the user's psychological expectations, reduce user operations, improve operations effectiveness.
  • the method before detecting the user's first operation, the method further includes: the electronic device displays a status bar, a navigation bar, a time component icon, and one or more application icons, and the camera application icon
  • the first operation is the user's operation on the icon of the album application
  • the method further includes: in response to the first operation, displaying at least two pictures Among the pictures other than the S second pictures, the score of the S second pictures is higher than the score of the other pictures except the S second pictures; the S second pictures are in the above division The other pictures other than the S second pictures are displayed before, or the S second pictures are specially marked to be displayed differently from the other pictures except the S second pictures.
  • the technical solution provided by the embodiment of the present application may display the second picture with a high score in front of other pictures with a low score, or add a special mark to the second picture with a high score to distinguish it from other pictures with a low score , Can make it easier for users to view their favorite pictures, improve the efficiency of users to find pictures.
  • the S second pictures are arranged in order from high to low, and the other pictures except the S second pictures are arranged in order from high to low.
  • the pictures are arranged according to the order of the scores, and the pictures that the user may like are at the forefront, which makes it easier for the user to view the pictures he likes and improves the user's efficiency in finding the pictures.
  • the manner in which the S second pictures are specially marked includes one or any combination of the following: increased display, increased frame display, increased marked display, special color display, and special transparency display.
  • the technical solution provided by the embodiment of the present application adds a special mark to the second picture with a high score, so that the user can more easily view his favorite picture, and improve the efficiency of the user in finding the picture.
  • the method further includes: in response to the first operation, displaying folders according to categories, and displaying a search control, a first menu control, and a second menu control , A third menu control; wherein the above-mentioned first operation is a user's operation on the above-mentioned third menu control, and the above classification method includes one or any combination of the following: location, time, person; each folder includes one or more Picture, the one or more pictures belong to at least two pictures; after displaying folders according to categories and displaying search controls, first menu controls, second menu controls, and third menu controls, the method further includes: responding In the second operation of the user on the search control, a search bar, folders displayed according to categories, and the S second pictures are displayed.
  • the technical solution provided by the embodiment of the present application displays the second picture with a high score together with other folders displayed according to categories, so that the user can more easily view his favorite pictures, and improve the efficiency of the user in finding pictures.
  • the method before calculating the score of each picture according to the value of the first characteristic of each picture and the value of the second characteristic of each picture, the method further includes: judging that the optimization condition is satisfied, the The optimization conditions include one or any combination of the following: the remaining storage space of the electronic device is lower than the first set value, the set time has been reached, the remaining power of the electronic device is lower than the second set value, the electronic device is charging, the electronic device The screen is off.
  • the technical solution provided by the embodiment of the present application can calculate the score of each picture again when it is determined that the optimization condition is satisfied. Since the score calculation process occupies the running memory of the electronic device and consumes the power of the electronic device, the technical solution provided by the embodiments of the present application can ensure that the calculation process of the picture score does not affect the normal use of the user.
  • the method further includes: receiving the user ’s cancellation of the special marking of at least one second picture among the S second pictures Three operations, in response to the above-mentioned third operation, recalculating the score of the at least one second picture; or receiving the user adding a special mark to at least one of the pictures other than the S second pictures.
  • the fourth operation in response to the fourth operation, recalculating the score of the at least one picture; or the receiving user moves at least one second picture out of the S second pictures to the second picture except the S second pictures
  • the technical solution provided by the embodiment of the present application can recalculate the score of some pictures according to the user's feedback behavior to some pictures, so that the calculated result is more in line with the user's psychological expectations, so that the displayed result is more accurate, and the user search is further improved Picture efficiency.
  • an embodiment of the present application provides a picture management method, which is executed by an electronic device and includes: acquiring scene characteristics of each of at least two pictures stored in the electronic device and / or stored in a cloud album; The scene features of each picture determine the third feature, fourth feature, third feature scoring criterion, and fourth feature scoring criterion of each picture; based on the value of the third feature of each picture, and the first The value of the four features is calculated to obtain the score of each picture; wherein, the influence of the third feature on the score of each picture is greater than the influence of the fourth feature on the score of each picture; detecting the user's first operation; responding to The first operation displays the first folder; wherein, the first folder includes M first pictures of at least two pictures; wherein, M is an integer greater than or equal to 1, and the score of the M first pictures is lower than Scores of other pictures except the M first pictures in at least two pictures.
  • the electronic device may determine the third feature and the scoring standard, the fourth feature and the scoring standard of each picture stored in the cloud album and / or stored in the cloud album according to the scene characteristics.
  • the value of the three features and the value of the fourth feature calculate the score of each picture, and a folder including the M first pictures with a low score is displayed.
  • the picture score can be used to determine the user's dislike of the picture. The lower the score, the higher the dislike. Therefore, placing the M first pictures with a low score in the first folder eliminates the need for the user to select unfavourable pictures one by one, reducing user operations and improving operation efficiency.
  • the method further includes: detecting a second operation of the user; and in response to the second operation, deleting the M first pictures.
  • the user can delete all the M first pictures in the folder with one key, without the user deleting the unfavourable pictures one by one, reducing the operation of deleting pictures, and improving the efficiency of the user to delete pictures.
  • the above scene features include one or any combination of the following: whether it is favorited, whether it is commented, whether it is associated with a wallpaper, whether it is uploaded to the cloud, and the storage location; It is placed in the trash; the fourth feature mentioned above includes one or any combination of the following: shooting mode, picture content classification, browsing times, shooting time, last browsing time, picture size, aesthetic score.
  • the technical solution provided by the embodiment of the present application can preliminarily determine whether the user may like a certain picture based on the above-mentioned scene characteristics, and then extract the characteristics of the picture for calculation according to the judgment result.
  • Different interpretation results correspond to different features involved in calculating the score of the picture and the scoring criteria of the feature, which can more accurately calculate the score of the picture, make the score of the picture more in line with the user's psychological expectations, reduce user operations, improve operations effectiveness.
  • the method before calculating the score of each picture according to the value of the third feature of each picture and the value of the fourth feature of each picture, the method further includes: judging that the optimization condition is satisfied; wherein The optimization conditions include one or any combination of the following: the remaining storage space of the electronic device is lower than the first set value, the set time has been reached, the remaining power of the electronic device is lower than the second set value, the electronic device is charging, the electronic The device is off.
  • the technical solution provided by the embodiment of the present application can calculate the score of each picture again when it is determined that the optimization condition is satisfied. Since the score calculation process occupies the running memory of the electronic device and consumes the power of the electronic device, the technical solution provided by the embodiments of the present application can ensure that the calculation process of the picture score does not affect the normal use of the user.
  • the method further includes: receiving a third operation that the user moves at least one of the M first pictures out of the first folder, in response to The third operation, recalculating the at least one first score; or receiving the fourth operation that the user moves at least one of the pictures other than the M first pictures into the first folder In response to the fourth operation, recalculate the score of the at least one picture.
  • the technical solution provided by the embodiment of the present application can recalculate the score of some pictures according to the user's feedback behavior to some pictures, so that the calculated result is more in line with the user's psychological expectations, thereby making the displayed result more accurate and further improving user deletion Picture efficiency.
  • an embodiment of the present application provides a picture management method, which is executed by an electronic device and includes: acquiring scene characteristics of each of at least two pictures stored in the electronic device and / or stored in a cloud album; The scene features of each picture determine the third feature and the third feature scoring criterion, the fourth feature and the fourth feature scoring criterion of each picture; according to the value of the third feature of each picture, and the fourth feature of each picture. The value of the feature is calculated to obtain the score of each picture; where the effect of the third feature on the score of each picture is greater than the effect of the fourth feature on the score of each picture; delete the M One picture; wherein, M is an integer greater than or equal to 1, and the score of the M first pictures is lower than the other pictures except the M first pictures in at least two pictures.
  • the electronic device may determine the third feature and the scoring standard, the fourth feature and the scoring standard of each picture stored in the cloud album and / or stored in the cloud album according to the scene characteristics.
  • the value of the three features and the value of the fourth feature calculate the score of each picture, and the first picture with a low score is deleted according to the score.
  • the picture score can be used to determine the user's dislike of the picture. The lower the score, the higher the dislike. Therefore, deleting the first picture with a low score can reduce the user's operation of deleting pictures, and improve the efficiency of deleting pictures.
  • the above-mentioned scene features include one or any combination of the following: whether it is favorited, whether it is commented, whether it is associated with a wallpaper, whether it is uploaded to the cloud, and the storage location;
  • the above fourth feature includes one or any combination of the following: shooting mode, picture content classification, browsing times, shooting time, last browsing time, picture size, aesthetic score.
  • the technical solution provided by the embodiment of the present application can preliminarily determine whether the user may like a certain picture based on the above-mentioned scene characteristics, and then extract the characteristics of the picture for calculation according to the judgment result.
  • Different interpretation results correspond to different features involved in calculating the score of the picture and the scoring criteria of the feature, which can more accurately calculate the score of the picture, make the score of the picture more in line with the user's psychological expectations, reduce user operations, improve operations effectiveness.
  • the method before calculating the score of each picture according to the value of the third characteristic of each picture and the value of the fourth characteristic of each picture, the method further includes: It is determined that the optimization condition is satisfied, and the optimization condition includes one or any combination of the following: the remaining storage space of the electronic device is lower than the first set value, the set time has elapsed, and the remaining power of the electronic device is lower than the second Set value, the electronic device is charging, and the electronic device is in a screen-off state.
  • the technical solution provided by the embodiment of the present application may calculate the score of each picture if it is determined that the optimization conditions are satisfied. Since the score calculation process occupies the running memory of the electronic device and consumes the power of the electronic device, the technical solution provided by the embodiments of the present application can ensure that the calculation process of the picture score does not affect the normal use of the user.
  • the method further includes: receiving a first operation of downloading at least one first picture from the M first pictures by the user, and responding For the first operation, recalculate the score of the at least one first picture; or receive a second operation of the user to delete at least one picture among the other pictures except the M first pictures, in response to the In the second operation, the score of the at least one picture is recalculated.
  • the technical solution provided by the embodiment of the present application can recalculate the score of some pictures according to the user's feedback behavior to some pictures, so that the calculated result is more in line with the user's psychological expectations, so that the displayed result is more accurate, and the user search is further improved Picture efficiency.
  • an embodiment of the present application provides a picture score calculation method, which is executed by an electronic device and is applied to any of the first aspect or any implementation manner of the first aspect of the embodiments of the present application.
  • An image management method provided by an implementation manner the score calculation method includes: acquiring scene characteristics of at least two pictures stored in an electronic device and / or stored in a cloud album; determining each picture according to the scene characteristics of each picture Scoring criteria for the first feature and the first feature, scoring criteria for the second feature and the second feature; the first feature corresponds to the first value and the second value, the first value is greater than the second value, and the second feature corresponds to two or more Value; calculate the score of each picture according to the value of the first feature of each picture and the value of the second feature of each picture; the score is proportional to the value of the first feature, the score is The weighted sum of the two features is proportional to the value, the first value of the first feature is greater than the weighted sum of the second feature when the maximum value; the first value of the
  • the score of the picture is proportional to the value of the first feature
  • the score of the picture is proportional to the value of the weighted sum of the second feature
  • the first value of the first feature is greater than the second feature
  • an embodiment of the present application provides a picture score calculation method, which is executed by an electronic device and is applied to any one of the second aspect or any implementation manner of the second aspect, the third aspect, or a third party of the embodiment of the present application
  • the score calculation method includes: acquiring scene characteristics of at least two pictures stored in an electronic device and / or stored in a cloud album; determining each picture according to the scene characteristics of each picture The third feature and the third feature scoring criteria, the fourth feature and the fourth feature scoring criteria; the third feature corresponds to the first value and the second value, the first value is greater than the second value, the fourth feature corresponds to two or Multiple values; the score of each picture is calculated based on the value of the third feature of each picture and the value of the fourth feature of each picture; the score is inversely proportional to the value of the third feature, and the score is The value of the fourth feature weighted summation is proportional to the value of the fourth feature weighted summation, and the first value of
  • the score of the picture is inversely proportional to the value of the third feature, and the score of the picture is directly proportional to the value of the weighted sum of the fourth feature.
  • the weighted sum of the fourth feature is a negative number
  • the first value of the third feature is greater than the absolute value of the weighted sum when the fourth feature takes the minimum value, which can ensure that the score of the picture is determined by the third feature
  • the value mainly affects the secondary influence of the weighted sum of the fourth feature, highlighting the importance of the third feature, making the scoring mechanism more reasonable and more in line with the user's psychological expectations.
  • an embodiment of the present application provides a picture score calculation method, which is executed by an electronic device and applied to any one of the second aspect or any implementation manner of the second aspect, the third aspect, or a third party of the embodiment of the present application
  • the score calculation method includes: acquiring scene characteristics of at least two pictures stored in an electronic device and / or stored in a cloud album; determining each picture according to the scene characteristics of each picture The third feature and the third feature scoring criteria, the fourth feature and the fourth feature scoring criteria; the third feature corresponds to the first value and the second value, the first value is greater than the second value, the fourth feature corresponds to two or Multiple values; the score of each picture is calculated based on the value of the third feature of each picture and the value of the fourth feature of each picture; the score is inversely proportional to the value of the third feature, and the score is The value of the weighted sum of the fourth feature is proportional to the value of the weighted sum of the fourth feature.
  • the score of the picture is inversely proportional to the value of the third feature, and the score of the picture is directly proportional to the value of the weighted sum of the fourth feature.
  • the weighted sum of the fourth feature is a non-negative number
  • the first value of the third feature is less than the reciprocal of the weighted sum when the fourth feature takes the maximum value, which can ensure that the score of the picture is determined by the third feature
  • the value mainly affects the secondary influence of the weighted sum of the fourth feature, highlighting the importance of the third feature, making the scoring mechanism more reasonable and more in line with the user's psychological expectations.
  • an embodiment of the present application provides an electronic device, including: one or more processors, a memory, a display screen, a wireless communication module, and a mobile communication module; the aforementioned memory, display screen, wireless communication module, and mobile communication module Coupled with one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and when the one or more processors execute the computer instructions, the electronic device performs the first aspect or the first
  • the image management method provided in any aspect of the aspect, or the second aspect or any of the second aspect, or the third aspect or any of the third aspect.
  • an embodiment of the present application provides an electronic device, including: one or more processors and a memory; the above memory is coupled to one or more processors, the above memory is used to store computer program code, and the above computer program code Including computer instructions, when the above one or more processors execute the above computer instructions, the electronic device executes any implementation manner as the fourth aspect or the fourth aspect, or any implementation manner of the fifth aspect or the fifth aspect , Or the image score calculation method provided in the sixth aspect or any implementation manner of the sixth aspect.
  • an embodiment of the present application provides a computer storage medium, including computer instructions, which, when the computer instructions run on an electronic device, cause the electronic device to perform any one of the first aspect or any implementation manner of the first aspect, or The second aspect or any implementation manner of the second aspect, or the image management method provided in the third aspect or the third aspect.
  • an embodiment of the present application provides a computer storage medium, including computer instructions, which, when the computer instructions run on an electronic device, cause the electronic device to perform any of the fourth aspect or any implementation manner of the fourth aspect, or The image score calculation method provided in the fifth aspect or any implementation manner of the fifth aspect, or the sixth aspect or any implementation manner of the sixth aspect.
  • an embodiment of the present application provides a computer program product, which, when the computer program product runs on a computer, causes the computer to execute any one of the first aspect or any implementation manner of the first aspect, or the second aspect or the first aspect
  • the image management method provided by any implementation manner of the second aspect, or the third aspect or any implementation manner of the third aspect.
  • an embodiment of the present application provides a computer program product, which, when the computer program product runs on a computer, causes the computer to execute any implementation manner as the fourth aspect or the fourth aspect, or the fifth aspect or the first aspect
  • the image score calculation method provided by any implementation manner of the fifth aspect, or the sixth aspect or any implementation manner of the sixth aspect.
  • the electronic device described in the seventh aspect, the computer storage medium described in the ninth aspect, or the computer program product described in the eleventh aspect are all used to execute the first aspect, or the second aspect, or The picture management method provided by any one of the third aspect. Therefore, for the beneficial effects that can be achieved, refer to the beneficial effects in the corresponding method, which will not be repeated here.
  • the electronic device described in the eighth aspect, the computer storage medium described in the tenth aspect, or the computer program product described in the twelfth aspect are all used to execute the fourth aspect, or the fifth aspect, or The picture score calculation method provided in any of the sixth aspect. Therefore, for the beneficial effects that can be achieved, refer to the beneficial effects in the corresponding method, which will not be repeated here.
  • FIG. 1 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.
  • FIG. 2 is a block diagram of a software structure of an electronic device provided by an embodiment of the present application.
  • FIG. 3 is a schematic flowchart of a picture management method provided by an embodiment of this application.
  • 4A is a diagram 1 of a human-computer interaction interface provided by an embodiment of the present application.
  • 4B is a second diagram of a human-computer interaction interface provided by an embodiment of this application.
  • 5A is a schematic diagram 1 of an interface of an electronic device provided by an embodiment of the present application.
  • 5B is a second schematic diagram of an electronic device interface provided by an embodiment of the present application.
  • FIG. 6A is a first picture of a picture management scenario provided by an embodiment of this application.
  • 6B is a diagram 3 of a human-computer interaction interface provided by an embodiment of the present application.
  • FIG. 7 is a diagram 4 of a human-computer interaction interface provided by an embodiment of the present application.
  • FIG. 10 is a schematic diagram of image score ranking in a display scenario provided by an embodiment of the present application.
  • FIG. 11A is a schematic diagram 1 of picture display provided by an embodiment of the present application.
  • 11B is a second schematic diagram of picture display provided by an embodiment of the present application.
  • 11C is a schematic diagram 3 of picture display provided by an embodiment of the present application.
  • FIG. 12 is a schematic diagram 4 of picture display provided by an embodiment of the present application.
  • FIG. 13 is a schematic diagram 5 of picture display provided by an embodiment of the present application.
  • FIG. 14 is a schematic diagram of image score ranking in a deletion scenario provided by an embodiment of the present application.
  • 15 is a schematic diagram of picture deletion provided by an embodiment of the present application.
  • 16 is a schematic diagram 1 of user feedback provided by an embodiment of the present application.
  • FIG. 17 is a second schematic diagram of user feedback provided by an embodiment of the present application.
  • the scoring algorithm model is used to receive the characteristics of the input picture and output the score of the picture.
  • the picture is a picture stored in the memory of the electronic device (including internal memory and external memory card).
  • the number of first pictures can be obtained according to the deletion condition, and the deletion condition is used to determine the number of pictures that need to be deleted.
  • the number of second pictures may be an absolute value or a relative value, which may be factory settings or recommended by a mobile phone manufacturer, or may be user settings.
  • the aforementioned relative value is a percentage of the total number of pictures stored in the electronic device memory and the cloud album corresponding to the electronic device.
  • the relative value is S
  • the total number of pictures stored in the electronic device memory and the cloud album corresponding to the electronic device is 100
  • the number of second pictures is 100 ⁇ S.
  • the first feature a feature that may directly characterize the user's liking for the picture, and has a relatively major influence on the picture scoring result.
  • the first feature may include at least two states. Different states represent different degrees of user's love for pictures, and each state corresponds to a value.
  • the second feature a feature that may indirectly characterize the user's liking for the picture, and has a relatively minor influence on the scoring result of the picture.
  • the second feature may include at least two states. Different states represent different levels of user preference for pictures, and each state corresponds to a value. A state with a higher value indicates a higher level of user preference.
  • Each picture may contain one or more first features, and one or more second features. When the status of the first features of the two pictures is the same, the second feature is used to distinguish the user's preference for the two pictures .
  • the third feature a feature that may directly characterize the user ’s dislike of the picture, and has a relatively major influence on the picture scoring result.
  • the third feature may include at least two states. Different states represent different levels of dislike of the user, and each state corresponds to a value.
  • the fourth feature may include at least two states. Different states represent different levels of dislike of the user. Each state corresponds to a value. A state with a lower value indicates a higher level of dislike of the user.
  • Each picture may contain one or more third features, and one or more fourth features. When the status of the third features of the two pictures is the same, the fourth feature is used to distinguish the user's dislike of the two pictures degree.
  • Positive feedback behavior the user's operation behavior on the managed picture, which may indicate that the score calculated according to the scoring algorithm model is lower than the user's psychological expectation.
  • Reverse feedback behavior the user's operation behavior of the managed picture. This operation behavior may indicate that the score calculated according to the scoring algorithm model is higher than the user's psychological expectation.
  • the file management method provided in the embodiments of the present application may be applied to the management of pictures, audio files, video files, documents, applications, etc. by electronic devices.
  • the management of pictures will be described as an example.
  • the electronic devices involved in the embodiments of the present application may be mobile phones, tablet computers, desktops, laptops, laptops, ultra-mobile personal computers (Ultra-mobile Personal Computer (UMPC), handheld computers, netbooks, personal digital assistants (Personal Digital Assistant (PDA), wearable electronic devices, virtual reality devices, etc.
  • UMC Ultra-mobile Personal Computer
  • PDA Personal Digital Assistant
  • wearable electronic devices virtual reality devices, etc.
  • FIG. 1 shows a schematic structural diagram of an electronic device 10.
  • the electronic device 10 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 , Mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, sensor module 180, key 190, motor 191, indicator 192, camera 193, display 194, and Subscriber identification module (SIM) card interface 195, etc.
  • SIM Subscriber identification module
  • the sensor module 180 may include a pressure sensor 180A, a gyro 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, and ambient light Sensor 180L, bone conduction sensor 180M, etc.
  • the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the electronic device 10.
  • the electronic device 10 may include more or fewer components than shown, or combine some components, or split some components, or arrange different components.
  • the illustrated components can be implemented in hardware, software, or a combination of software and hardware.
  • the processor 110 may include one or more processing units.
  • the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), and an image signal processor. (image) signal processor (ISP), controller, memory, video codec, digital signal processor (DSP), baseband processor, and / or neural-network processing unit (NPU) Wait.
  • image image signal processor
  • ISP image signal processor
  • controller memory
  • video codec digital signal processor
  • DSP digital signal processor
  • NPU neural-network processing unit
  • different processing units may be independent devices, or may be integrated in one or more processors.
  • the controller may be the nerve center and command center of the electronic device 10.
  • the controller can generate the operation control signal according to the instruction operation code and the timing signal to complete the control of fetching instructions and executing instructions.
  • the processor 110 may also be provided with a memory for storing instructions and data.
  • the memory in the processor 110 is a cache memory.
  • the memory may store instructions or data that the processor 110 has just used or recycled.
  • the processor 110 may include one or more interfaces.
  • Interfaces can include integrated circuit (inter-integrated circuit, I2C) interface, integrated circuit built-in audio (inter-integrated circuit, sound, I2S) interface, pulse code modulation (pulse code modulation (PCM) interface, universal asynchronous transceiver (universal asynchronous) receiver / transmitter, UART) interface, mobile industry processor interface (MIPI), general-purpose input / output (GPIO) interface, subscriber identity module (SIM) interface, and / Or universal serial bus (USB) interface, etc.
  • I2C integrated circuit
  • I2S integrated circuit built-in audio
  • PCM pulse code modulation
  • PCM pulse code modulation
  • UART universal asynchronous transceiver
  • MIPI mobile industry processor interface
  • GPIO general-purpose input / output
  • SIM subscriber identity module
  • USB universal serial bus
  • the interface connection relationship between the modules illustrated in the embodiments of the present application is only a schematic description, and does not constitute a limitation on the structure of the electronic device 10.
  • the electronic device 10 may also use different interface connection methods in the foregoing embodiments, or a combination of multiple interface connection methods.
  • the charging management module 140 is used to receive charging input from the charger. While the charging management module 140 charges the battery 142, it can also supply power to the terminal through the power management module 141.
  • the power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110.
  • the power management module 141 receives input from the battery 142 and / or the charging management module 140, and supplies power to the processor 110, the internal memory 121, the external memory, the display screen 194, the camera 193, and the wireless communication module 160.
  • the power management module 141 can also be used to monitor battery capacity, battery cycle times, battery health status (leakage, impedance) and other parameters.
  • the power management module 141 may also be disposed in the processor 110.
  • the power management module 141 and the charging management module 140 may also be set in the same device.
  • the wireless communication function of the electronic device 10 can be realized through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor, and the baseband processor.
  • Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals.
  • the antenna 1 and the antenna 2 may be used to send data to the cloud server to back up the pictures stored in the memory of the electronic device 10 to the cloud.
  • Antenna 1 and antenna 2 can also be used to send download requests to the cloud server. The download requests are used to obtain pictures backed up in the cloud.
  • the antenna 1 and the antenna 2 may also be used to receive data sent by the cloud server in response to the download request sent by the electronic device 10.
  • the mobile communication module 150 may provide a wireless communication solution including 2G / 3G / 4G / 5G and the like applied to the electronic device 10.
  • the mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), and the like.
  • the mobile communication module 150 can receive the electromagnetic wave from the antenna 1, filter and amplify the received electromagnetic wave, and transmit it to the modem processor for demodulation.
  • the mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor and convert it to electromagnetic wave radiation through the antenna 1.
  • at least part of the functional modules of the mobile communication module 150 may be provided in the processor 110.
  • at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be provided in the same device.
  • the modem processor may include a modulator and a demodulator.
  • the modem processor may be an independent device.
  • the modem processor may be independent of the processor 110, and may be set in the same device as the mobile communication module 150 or other functional modules.
  • the wireless communication module 160 can provide wireless local area networks (wireless local area networks, WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (bluetooth, BT), and global navigation satellites that are applied to the electronic device 10 Wireless communication solutions such as global navigation (satellite system, GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc.
  • the wireless communication module 160 may be one or more devices integrating at least one communication processing module.
  • the wireless communication module 160 receives the electromagnetic wave via the antenna 2, frequency-modulates and filters the electromagnetic wave signal, and sends the processed signal to the processor 110.
  • the wireless communication module 160 may also receive the signal to be transmitted from the processor 110, frequency-modulate it, amplify it, and convert it to electromagnetic waves through the antenna 2 to radiate it out.
  • the antenna 1 of the electronic device 10 and the mobile communication module 150 are coupled, and the antenna 2 and the wireless communication module 160 are coupled so that the electronic device 10 can communicate with the network and other devices through wireless communication technology.
  • the wireless communication technology may include global mobile communication system (global system for mobile communications, GSM), general packet radio service (general packet radio service, GPRS), code division multiple access (code division multiple access, CDMA), broadband Code division multiple access (wideband code division multiple access, WCDMA), time division code division multiple access (time-division code division multiple access, TD-SCDMA), long-term evolution (LTE), BT, GNSS, WLAN, NFC , FM, and / or IR technology, etc.
  • GSM global system for mobile communications
  • GPRS general packet radio service
  • code division multiple access code division multiple access
  • CDMA broadband Code division multiple access
  • WCDMA wideband code division multiple access
  • TD-SCDMA time division code division multiple access
  • LTE long-term evolution
  • BT GNSS
  • WLAN NFC
  • the GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a beidou navigation system (BDS), and a quasi-zenith satellite system (quasi -zenith satellite system (QZSS) and / or satellite-based augmentation system (SBAS).
  • GPS global positioning system
  • GLONASS global navigation satellite system
  • BDS beidou navigation system
  • QZSS quasi-zenith satellite system
  • SBAS satellite-based augmentation system
  • the electronic device 10 realizes a display function through a GPU, a display screen 194, and an application processor.
  • the GPU is a microprocessor for image processing, connecting the display screen 194 and the application processor.
  • the processor 110 may include one or more GPUs that execute program instructions to generate or change display information.
  • the display screen 194 is used to display images, videos and the like.
  • the display screen 194 includes a display panel.
  • the display panel may use a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active matrix organic light-emitting diode or an active matrix organic light-emitting diode (active-matrix organic light) emitting diode, AMOLED), flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light emitting diode (QLED), etc.
  • the electronic device 10 may include 1 or N display screens 194, where N is a positive integer greater than 1.
  • the display screen 194 may be used to display pictures.
  • the electronic device 10 can realize a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.
  • the ISP processes the data fed back by the camera 193. For example, when taking a picture, the shutter is opened, the light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to the ISP for processing, and converts it into an image visible to the naked eye.
  • ISP can also optimize the algorithm of image noise, brightness and skin color. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, the ISP may be set in the camera 193.
  • 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 may be a charge coupled device (charge coupled device, CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor.
  • CCD charge coupled device
  • CMOS complementary metal-oxide-semiconductor
  • the photosensitive element converts the optical signal into an electrical signal, and then transmits the electrical signal to the ISP to convert it into a digital image signal.
  • the ISP outputs the digital image signal to the DSP for processing.
  • DSP converts digital image signals into standard RGB, YUV and other image signals.
  • the electronic device 10 may include 1 or N cameras 193, where N is a positive integer greater than 1.
  • the digital signal processor is used to process digital signals. In addition to digital image signals, it can also process other digital signals. For example, when the electronic device 10 is selected at a frequency point, the digital signal processor is used to perform Fourier transform on the energy at the frequency point.
  • Video codec is used to compress or decompress digital video.
  • the electronic device 10 may support one or more video codecs. In this way, the electronic device 10 can play or record videos in various encoding formats, such as: moving picture experts group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.
  • MPEG moving picture experts group
  • MPEG2 MPEG2, MPEG3, MPEG4, etc.
  • NPU is a neural-network (NN) computing processor.
  • NN neural-network
  • the NPU can realize applications such as intelligent recognition of the electronic device 10, such as image recognition, face recognition, voice recognition, and text understanding.
  • the external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to realize the expansion of the storage capacity of the terminal 201.
  • the external memory card communicates with the processor 110 through the external memory interface 120 to realize the data storage function.
  • pictures may be stored in an external memory card, and the processor 110 of the electronic device 10 may obtain the pictures stored in the external memory card through the external memory interface 120.
  • the internal memory 121 may be used to store computer executable program code, where the executable program code includes instructions.
  • the processor 110 executes instructions stored in the internal memory 121 to execute various functional applications and data processing of the electronic device 10.
  • the internal memory 121 may include a storage program area and a storage data area.
  • the storage program area may store an operating system, at least one function required application programs (such as sound playback function, image playback function, etc.) and so on.
  • the storage data area may store data (such as audio data, phone book, pictures, etc.) created during the use of the electronic device 10 and the like.
  • 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 memory (universal flash storage, UFS), and so on.
  • the internal memory 121 may be used to store multiple pictures. The multiple pictures may be taken by the electronic device 10 through the camera 193, or may be the electronic device 10 through the antenna 1 and the antenna 2 from other After receiving and downloading in applications (such as WeChat, Weibo, Facebook, etc.).
  • the electronic device 10 may implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone interface 170D, and an application processor. For example, music playback, recording, etc.
  • the pressure sensor 180A is used to sense the pressure signal and can convert the pressure signal into an electrical signal.
  • the pressure sensor 180A may be provided on the display screen 194.
  • the gyro sensor 180B may be used to determine the movement posture of the electronic device 10.
  • the gyro sensor 180B can also be used for navigation and somatosensory game scenes.
  • the air pressure sensor 180C is used to measure air pressure.
  • the magnetic sensor 180D includes a Hall sensor.
  • the acceleration sensor 180E can detect the magnitude of acceleration of the electronic device 10 in various directions (generally three axes).
  • the distance sensor 180F is used to measure the distance.
  • the proximity light sensor 180G may include, for example, a light emitting diode (LED) and a light detector, such as a photodiode.
  • the electronic device 10 can use the proximity light sensor 180G to detect that the user holds the electronic device 10 close to the ear to talk, so as to automatically turn off the screen to save power.
  • the proximity light sensor 180G can also be used in leather case mode, pocket mode automatically unlocks and locks the screen.
  • the ambient light sensor 180L is used to sense the brightness of ambient light.
  • the fingerprint sensor 180H is used to collect fingerprints.
  • the electronic device 10 can use the collected fingerprint characteristics to realize fingerprint unlocking, access to application lock, fingerprint photo taking, fingerprint answering call, and the like.
  • the temperature sensor 180J is used to detect the temperature.
  • Touch sensor 180K also known as "touch panel”.
  • the touch sensor 180K may be provided on the display screen 194, and the touch sensor 180K and the display screen 194 constitute a touch screen, also called a "touch screen”.
  • the touch sensor 180K is used to detect a touch operation acting on or near it.
  • the touch sensor can pass the detected touch operation to the application processor to determine the type of touch event.
  • the visual output related to the touch operation can be provided through the display screen 194.
  • the touch sensor 180K may also be disposed on the surface of the electronic device 10, which is different from the location where the display screen 194 is located.
  • the touch sensor 180K may be used to detect a user's touch operation on the first picture contained in the album, and pass the detected touch operation to the application processor to display the corresponding to the first picture The second picture.
  • the size of the first picture is smaller than the size of the second picture, and the number of pixels included in the first picture is smaller than the number of pixels included in the second picture.
  • the bone conduction sensor 180M can acquire vibration signals.
  • the bone conduction sensor 180M can also contact the pulse of the human body and receive a blood pressure beating signal.
  • the key 190 includes a power-on key, a volume key, and the like.
  • the key 190 may be a mechanical key. It can also be a touch button.
  • the electronic device 10 can receive key input and generate key signal input related to user settings and function control of the electronic device 10.
  • the motor 191 may generate a vibration prompt.
  • the motor 191 can be used for vibration notification of incoming calls and can also be used for touch vibration feedback.
  • the indicator 192 may be an indicator light, which may be used to indicate a charging state, a power change, and may also be used to indicate a message, a missed call, a notification, and the like.
  • the SIM card interface 195 is used to connect a SIM card.
  • the SIM card can be inserted into or removed from the SIM card interface 195 to achieve contact and separation with the electronic device 10.
  • the electronic device 10 may support 1 or N SIM card interfaces, where N is a positive integer greater than 1.
  • the electronic device 10 interacts with the network through the SIM card to realize functions such as call and data communication.
  • the software system of the electronic device 10 may adopt a layered architecture, an event-driven architecture, a micro-core architecture, a micro-service architecture, or a cloud architecture.
  • the embodiments of the present application take the Android system with a layered architecture as an example to exemplarily explain the software structure of the electronic device 10.
  • FIG. 2 is a block diagram of the software structure of the electronic device 10 according to an embodiment of the present application.
  • the layered architecture divides the software into several layers, and the layers communicate with each other through a software interface.
  • the Android system is divided into four layers, from top to bottom are the application layer, the application framework layer, the Android runtime and the system library, and the kernel layer.
  • the application layer may include a series of application packages.
  • the application package may include applications such as SMS, Facebook, QQ, maps, albums, calendar, WLAN, Twitter, music player, and Amazon.
  • the application framework layer provides an application programming interface (application programming interface) and programming framework for applications at the application layer.
  • the application framework layer includes some predefined functions.
  • the application framework layer may include a window manager, a content provider, a view system, a phone manager, a resource manager, a notification manager, and so on.
  • the window manager is used to manage window programs.
  • the window manager can obtain the size of the display screen, determine whether there is a status bar, lock the screen, intercept the screen, etc.
  • Content providers are used to store and retrieve data and make it accessible to applications.
  • the data may include videos, images, audio, calls made and received, browsing history and bookmarks, phone book, etc.
  • the view system includes visual controls, such as controls for displaying text and controls for displaying pictures.
  • the view system can be used to build applications.
  • the display interface can be composed of one or more views.
  • the phone manager is used to provide the communication function of the electronic device 10. For example, the management of the call state (including connection, hang up, etc.).
  • the resource manager provides various resources for the application, such as localized strings, icons, pictures, layout files, video files, and so on.
  • the notification manager enables applications to display notification information in the status bar, which can be used to convey notification-type messages, and can disappear after a short stay without user interaction.
  • Android Runtime includes core library and virtual machine. Android runtime is responsible for the scheduling and management of the Android system.
  • the core library contains two parts: one part is the function function that Java language needs to call, and the other part is the core library of Android.
  • the application layer and the application framework layer run in the virtual machine.
  • the virtual machine executes the java files of the application layer and the application framework layer into binary files.
  • the virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, security and exception management, and garbage collection.
  • the system library may include multiple functional modules. For example: surface manager (surface manager), media library (Media library), 3D graphics processing library (for example: OpenGL ES), 2D graphics engine (for example: SGL), etc.
  • surface manager surface manager
  • media library Media library
  • 3D graphics processing library for example: OpenGL ES
  • 2D graphics engine for example: SGL
  • the surface manager is used to manage the display subsystem and provides the fusion of 2D and 3D layers for multiple applications.
  • the media library supports a variety of commonly used audio, video format playback and recording, and still image files.
  • the media library can support multiple audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.
  • the 3D graphics processing library is used to realize 3D graphics drawing, image rendering, synthesis, and layer processing.
  • the 2D graphics engine is a drawing engine for 2D drawing.
  • the kernel layer is the layer between hardware and software.
  • the kernel layer contains at least the display driver, camera driver, audio driver, and sensor driver.
  • the technical solutions involved in the following embodiments can be implemented in the electronic device 10 having the above hardware architecture and software architecture.
  • the file management method provided by the embodiments of the present application will be described in detail below with reference to the drawings and application scenarios.
  • the picture management in the embodiment of the present application may include displaying and deleting pictures.
  • the electronic device 10 is a mobile phone as an example.
  • FIG. 3 is a schematic diagram of a picture management method provided by an embodiment of the present application. As shown in FIG. 3, the picture management method may include at least the following steps:
  • the touch sensor 180K of the electronic device 10 detects the user's first operation.
  • the above-mentioned first operation is the operation of the user on the shooting button of the camera application displayed on the display screen 194.
  • an interface 20 displayed on the display screen 194 the interface 20 includes a status bar 204, a navigation bar 205, a time component icon and a weather component icon, icons of multiple application programs such as a camera icon 201, a WeChat icon 202, Setting icon 203, album icon, Weibo icon, Alipay icon, etc.
  • the status bar 204 may include the name of the operator (for example, China Mobile), time WIFI icon, signal strength, and current remaining power.
  • the navigation bar 306 may include a return control, a home screen control, a control that displays a task window, and so on.
  • the display screen 194 displays the shooting interface a1.
  • the shooting interface a1 may include at least a framing frame b1 that displays the picture to be shot and Shoot button c1.
  • the first operation may be one-click, or double-click, long-press, etc.
  • the shooting interface a1 may further include a control d1 for opening an album and a control e1 for switching a camera.
  • the camera 193 After entering the photographing interface a1 or the photographing interface a2, the camera 193 is turned on to acquire the picture to be photographed in real time.
  • the touch sensor 180K receives the first operation of the user, in response to the first operation, the camera 193 acquires the viewfinder Shooting screen, convert the shooting screen into a picture, and save it to the internal memory 121.
  • the picture may also be downloaded by the electronic device 10 from the application server of other applications through the antenna 1 or the antenna 2, for example, the electronic device 10 may be downloaded from the application server of Weibo through the antenna 1, or may be the electronic device 10 through the antenna, for example. 2 Downloaded from the application server of Google Chrome.
  • the electronic device 10 may save the pictures downloaded from the application servers of other applications to the internal memory 121.
  • the picture may also be a picture stored in an external memory card connected to the electronic device 10, and the electronic device 10 may read the picture stored in the external memory card through the external memory interface 120.
  • S303 The touch sensor 180K of the electronic device 10 detects the user's second operation.
  • the second operation is the user's operation on the setting items in the system setting application or the album application of the electronic device 10.
  • This setting item is used to set the optimization mode on and off.
  • the processor 110 of the electronic device 10 can determine whether the optimization conditions are met. If the optimization conditions are met, the pictures stored in the internal memory 121 and the cloud album pictures corresponding to the electronic device 10 are obtained, and the respective pictures are calculated. Score, according to the score to optimize the display of the picture or suggest deletion.
  • the display screen 194 of the electronic device 10 may display the system setting interface 40.
  • the system setting interface 40 may include setting entries for multiple applications and components.
  • the display screen 194 of the electronic device 10 may display the setting interface of the application, and the setting interface may include various setting items related to the application.
  • the setting interface 40 may further include setting portals of other applications or components.
  • the display screen 194 may display setting portals of more applications or components. For example, when the electronic device 10 detects the user's operation on the album setting portal 401, the display screen 194 of the electronic device 10 may display the album setting interface 50, as shown in FIG. 5B.
  • the setting interface 50 may include a cloud account setting item 501, a cloud album setting item 502, other album setting items 503 that need to be synchronized, an optimization mode setting item 504, a shooting time setting item 505, and a shooting location setting item 506.
  • the cloud account setting item 501 is used to set up a cloud account.
  • the electronic device 10 can upload the pictures stored in the internal storage 121 to the album corresponding to the cloud account for backup.
  • the electronic device 10 can also retrieve the album corresponding to the cloud account ( (Referred to as a cloud album) Download pictures are saved to the internal memory 121.
  • the cloud album setting item 502 can be used to turn on or off the cloud album.
  • the electronic device 10 can perform data interaction with the cloud album, including: the electronic device 10 uploads the pictures stored in the internal storage 121 to the cloud album for backup, and the electronic device 10 The downloaded pictures in the album are saved in the internal memory 121.
  • the cloud album is closed, the electronic device 10 cannot perform data interaction with the cloud album.
  • Other album setting items 503 that need to be synchronized are used to set which albums in the album need to be uploaded to the cloud album.
  • the optimization mode setting item 504 is used to turn on or off the optimization mode. When the optimization mode is turned on, the processor 110 of the electronic device 10 runs the picture display and deletion method of the embodiment of the present invention (refer to the subsequent detailed description).
  • the sliding mode in the control 5041 included in the sliding optimization mode setting item 504 can be used to turn the optimization mode on or off.
  • the way to open and close the optimization mode is not limited to being realized by the above sliding button, and may also exist in other forms, which are not limited in the embodiments of the present application.
  • the shooting time setting item 405 is used to display the shooting time in the picture when the display 194 displays the picture in the album.
  • the shooting location setting item 506 is used to display the shooting location in the picture when the display 194 displays the picture in the album.
  • the setting items included in the setting interface 50 shown in FIG. 5B are only exemplary descriptions, and may also include other setting items in a specific implementation, which is not limited in this embodiment of the present application.
  • the processor 110 of the electronic device 10 starts to determine whether the optimization conditions are met, and if it is satisfied, the pictures stored in the internal memory 121 are obtained, and the pictures stored in the external memory card and the electronic device are obtained through the external memory interface 120 10.
  • step S305 The processor 110 of the electronic device 10 determines whether the optimization condition is satisfied. If the optimization condition is satisfied, step S306 is performed, and if it is not satisfied, step S305 is continued.
  • the remaining storage space in the internal memory 121 of the electronic device 10 when the remaining storage space in the internal memory 121 of the electronic device 10 is insufficient, it is to satisfy the optimization condition. For example, when the remaining storage space in the internal memory 121 of the electronic device 10 is less than 200M, it is to satisfy the optimization condition. For another example, when the remaining storage space in the internal memory 121 of the electronic device 10 is less than 10% of the total storage space, it is to satisfy the optimization condition.
  • the value of the above remaining storage space (200M) and the ratio of the above remaining storage space to the total storage space (10%) are only exemplary descriptions, and may have other values in a specific implementation, which is not limited in the embodiments of the present application.
  • step S306 when the processor 110 of the electronic device 10 detects that the current time is 22:00, it determines whether the optimization condition is satisfied, and if it does not satisfy, it delays 1 hour to 23:00 and determines whether it is satisfied again If the optimization conditions are satisfied, step S306 is executed.
  • the optimization conditions may include one or any combination of the following: the remaining power in the battery 142 of the electronic device 10 is higher than 20% of the total power, the charge management module 140 of the electronic device 10 is receiving charging input from the charger, the electronic device The display screen 194 of 10 is in a screen-off state, and the electronic device 10 has connected to the WiFi network through the wireless communication module 160.
  • the starting time (22:00) for determining whether the above optimization conditions are satisfied, the ratio of the remaining power to the total power (20%), and the delay time (1 hour) are all exemplary illustrations, and may be available in actual implementation. Other values are not limited in the embodiments of the present application.
  • a prompt box 701 is displayed to prompt the user to "start optimization”
  • a cancel optimization control 702 is provided for the user to input an instruction to cancel the optimization within a period of time, as shown in FIG. 6B.
  • the above period of time may be displayed in the cancellation optimization control 702 in the form of a countdown.
  • optimization starts.
  • the above period of time may be 10s, 15s, 30s, etc., for example.
  • Step S305 After the optimization mode of the processor 110 of the electronic device 10 is turned on, it is not necessary to perform step S305 to determine whether the optimization condition is satisfied. Steps S306-S308 are performed once for the pictures stored in and the pictures stored in the cloud album corresponding to the electronic device 10.
  • the processor 110 of the electronic device 10 obtains the features of the pictures stored in the internal memory 121, the pictures stored in the external memory card, and the pictures stored in the cloud album corresponding to the electronic device 10, and inputs the features into the scoring algorithm model to calculate the pictures Score.
  • each picture can contain many features, and the features included in the picture can be divided into the following four categories: the characteristics of usage habits, the characteristics of the user portrait, the characteristics of the picture itself, and the storage of the electronic device.
  • the characteristics of the usage record may include, but are not limited to: clicks, views, zooms, whether it has been edited, whether it has been shared or shared, whether it is associated with a wallpaper (eg, associated with a phone contact, or set as an electronic device) 10 Home screen wallpaper, etc.), whether it was searched or the number of times it was searched, whether it was placed in the trash can (moved manually into the "trash can" folder by the user, and still stored in the internal memory 121), whether it was favorited, whether Commented (for example, the user can choose to add a comment in the menu option of a certain picture, can record the mood when the picture is taken, or record the content of the taken picture, etc.), whether it is marked (for example, a picture includes multiple fruits, can Add a mark to each fruit in the picture to indicate the name of the fruit), whether it is downloaded (saved in a cloud album and downloaded to the internal memory 121 of the electronic device 10), the last browsing time, etc.
  • a wallpaper eg, associated with
  • the device 110 may analyze that the event corresponding to the click operation with coordinates (x, y) on the current display interface is browsing P1, and the processor 110 causes the display screen 194 to display the display interface 70 of P1 for the user to browse P1.
  • the processor 110 may save the "browse” event for P1 to the internal memory 121, and may also save the browsing time to the internal memory 121.
  • the processor 110 analyzes the "browse” event for P1 again, the current "browse” event and the browse time can be saved in the internal memory 121.
  • the processor 110 may obtain the number of "browse" events of P1 from the internal memory 121, and obtain the last recorded browsing time (last browsing time).
  • only the browsing times and current browsing time for P1 may be recorded in the internal memory. For example, after analyzing the “browsing” event for P1 at the m-th time, only the value m of browsing times and the browsing time can be saved to the internal memory 121, and after the next analysis of the “browsing” event for P1, only the internal In the memory 121, the value of the browsing times is updated to m + 1, and the browsing time is updated.
  • the processor 110 may obtain the browsing times and browsing time (last browsing time) of P1 from the internal memory 121. It should be noted that the demonstration of the occurrence of the "browse" event in FIG. 7 is only an exemplary illustration.
  • User profile features may include, but are not limited to: user classification and user preferences.
  • the user classification may be the user's gender, age range, and so on.
  • the user preference may be whether the user is a food photography enthusiast or a landscape photography enthusiast.
  • the user classification may be that the electronic device 10 obtains the previously filled-in gender from the personal information of the system account (for example, Huawei account center of Huawei terminal, Apple account center (Apple ID) of Apple terminal, etc.) And date of birth.
  • the user classification may be that the electronic device 10 calls a third-party application (such as QQ, WeChat, Youtube, etc.) to provide a data access interface for access rights, and obtains the user's gender and the user's gender from the server of the third-party application. Date of birth, etc.
  • the processor 110 of the electronic device 10 may calculate the age of the user according to the acquired date of birth, thereby determining the age group of the user.
  • the above-mentioned user classification acquisition method is only an exemplary description, and there may be other acquisition methods in a specific implementation, which is not limited in the embodiments of the present application.
  • the user preference may be obtained by analyzing the multiple pictures stored in the internal memory 121 and the external memory card obtained through the external memory interface 120 by the electronic device. For example, the content of 70 pictures in 100 pictures is gourmet, then the electronic device 10 considers the user as a gourmet photography enthusiast.
  • the electronic device 10 may provide a data access interface for access rights through a third-party application (such as Google Chrome, Zhihu, Baidu Forum, etc.) to obtain the user's browsing from the server of the third-party application It is recorded that the content that the user often browses is related to the food photography category, and the electronic device 10 considers the user to be a food photography enthusiast.
  • a third-party application such as Google Chrome, Zhihu, Baidu Forum, etc.
  • the characteristics of the picture itself can include, but are not limited to: the shooting time, the name of the picture, the shooting technique (such as whether the shooting angle is up or down, whether the shooting lens is close-up or distant, etc.), the shooting mode (such as high dynamic range imaging (high dynamic range imaging) range (imaging, HDR) mode, large aperture mode, night scene mode, panorama mode, black and white mode, slow motion mode, streamer shutter mode, etc.), picture format (such as JPG format, PNG format or BMP format, etc.), color, composition (E.g. three-point composition, diagonal composition, symmetric composition, golden spiral composition, etc.), aesthetic score, picture content classification, geographic location, picture size, resolution, storage location, device type (i.e.
  • the above storage location is the storage location of the picture when the processor 110 acquires the picture.
  • the above-mentioned shooting time, picture name, shooting mode, picture format, geographic location, picture size, resolution, device type and other characteristics are the parameters of the picture itself, and these parameters can be stored in the internal memory 121 or the external memory card together with the picture,
  • the processor 110 may acquire these features from the internal memory 121 or the external memory card when acquiring the picture.
  • the above shooting techniques, colors, composition, aesthetic score, picture content classification, similarity to other pictures, whether they are blurry, etc., can be performed by the processor 110 of the electronic device 10 on the content of the picture, or the parameters of the picture, or the parameters at the time of shooting Analyzed.
  • the storage situation of the electronic device may include, but is not limited to: the total storage capacity of the electronic device, the remaining available storage capacity, and the used storage capacity of the picture.
  • the above storage conditions of the electronic device can be obtained by the processor 110 querying the status of the internal memory 121.
  • the above process of calculating the score of the image may be at most once a day.
  • the specific conditions for triggering the calculation of the score may refer to the description in step S305, and may be set to determine whether the optimization condition is satisfied at 22:00 every night. From 1 hour to 23:00, determine whether it is satisfied again, until the optimization conditions are met, and the score calculation is triggered.
  • the score of each picture can be saved in the internal memory 121, and each score can be associated with its corresponding picture. Specifically, the picture can be associated with its corresponding score through the identification of the picture. In the case where the score of each picture has been stored in the internal memory 121, after the score calculation is completed, the score of each picture may be updated.
  • the logo of the above picture may be automatically generated when the electronic device 10 shoots through the camera 193.
  • the logo of the picture may also be carried when the picture is downloaded from a server of another application, or the picture may be carried when the processor 110 obtains the picture from the external memory card through the external memory interface 120.
  • Each picture has a unique identification, which is used to enable the electronic device 10 to identify the picture through the identification.
  • the above-mentioned frequency of calculating scores (at most once a day) is only for illustrative purposes. In specific implementations, the frequency of calculating scores may be higher or lower, which may not be limited in the embodiments of the present application.
  • S307 The processor 110 of the electronic device 10 displays the picture according to the score.
  • the second picture is displayed preferentially, or displayed enlarged.
  • the interface 80 is a display interface of an album, and the interface may include three menu controls (photo, album, and discovery), and the display modes of the pictures under the three menus are different.
  • the currently selected menu type shown in FIG. 8 is "Photo".
  • the interface 80 can display multiple pictures, the electronic device 10 can receive the user's sliding operation on the interface 80 to browse more pictures, and the "photo" menu control can This is called the first menu control.
  • the interface 80 may display one or more folders (file collections), each folder may contain multiple pictures with common characteristics, the "album" menu
  • the control may be called a second menu control.
  • pictures in the same shooting mode such as panorama mode, HDR mode, etc.
  • pictures from the same source such as Weibo, WeChat, QQ, Facebook, etc.
  • the interface 80 may display multiple folders according to different categories, each category contains one or more folders, and each folder contains one or more Picture, the "discovery" menu control can be called the third menu control.
  • multiple folders can be displayed according to location and time. Under the location category, the pictures can be classified into different folders according to the shooting location (such as Beijing, Shanghai, New York, Tokyo, etc.). Under the time classification, the pictures can be classified into different folders according to the shooting time (for example, 2018, 2017, 2016, etc.).
  • the middle photos in the left picture of FIG. 8 may be arranged in the order of shooting time, etc. In this sequential arrangement, the scores calculated in the embodiment of the present invention are not considered. As shown in the left figure of FIG. 8, the position of (picture, P) P1 with a low score is at the forefront of all pictures, and the position of P16 with a high score is behind. After optimization, in the right picture of Figure 8, P16 with a high score has been moved to the front of all pictures for priority display, and P1 with a low score has been moved backward.
  • S308 The processor 110 of the electronic device 10 deletes the picture according to the score.
  • the first picture set is displayed so that the user can delete multiple first pictures with one click.
  • P1, P9, P12, and P20 with low scores are classified into the first folder.
  • the user can view the first picture contained in the folder, electronic
  • the device 10 may delete all the first pictures in the first folder based on the control 901 with one click.
  • the first picture can be displayed differently according to the score of each picture.
  • the chromaticity value of the first picture can be reduced, or the frame can be increased, or a specific mark can be marked to distinguish it from other pictures.
  • the above process of deleting pictures according to the score may be at most once a week, and it may be determined whether the optimization conditions described in step S305 are satisfied at 22:00 every Sunday night, if not satisfied, a delay of 1 hour to 23: 00 determines whether it is satisfied again, until the optimization conditions are met, triggering the deletion process at most once a week to ensure that the image deletion process will not affect the normal use of the user, which not only improves the deletion efficiency, but also improves the user experience.
  • deletion frequency (at most once a week) is only an exemplary description. In a specific implementation, the deletion frequency may be higher or lower, which is not limited in the embodiments of the present application.
  • the pictures in the first folder can be deleted according to the user's operation.
  • the frequency of deletion of pictures is not limited to the above maximum once a week.
  • S307 and S308 may be two independent steps, and the order of these two steps is not limited in the embodiment of the present invention.
  • the third operation may be the operation of the user on the optimized picture detected by the touch sensor 180K.
  • the operation may be, for example, canceling the enlarged display of the enlarged picture, and the operation may be, for example, deleting the “recommended deletion” file.
  • the first picture in the folder is moved out so that it is displayed in the interface 80 under the "Photo” menu, or it is classified into other folders under the album menu according to its characteristics.
  • the scoring algorithm model is adjusted according to the judgment result, so that the picture score is close to the user's psychological expectation, so that the results of picture display and deletion In line with user intent, improve the accuracy of the results of image display and deletion.
  • AI artificial intelligence
  • the processor 110 of the electronic device 10 can obtain all the pictures in its internal memory 121 and external memory card and the pictures stored in the cloud album corresponding to the electronic device 10.
  • the processor 110 of the electronic device 10 can also acquire the characteristics of each picture. After acquiring the characteristics of each picture, first of all, the available scenes of the picture may be determined as display scenes or deleted scenes according to at least one characteristic of the picture.
  • the aforementioned at least one feature is used to initially determine whether a certain picture may be the first picture or may be the second picture. If it is determined that the picture may be the first picture according to the at least one feature, the picture may be used to delete the scene; if it is determined that the picture may be the second picture according to the at least one feature, the picture may be used to display the scene.
  • At least one feature for determining a scene available for a picture may be referred to as a scene feature.
  • the purpose of determining the available scenes of a picture is to determine which features of the picture are extracted and the scoring criteria of these features is used to calculate the score of the picture. Calculating the score of a picture according to different features and the scoring criteria of these features can make the final score more in line with the user's psychological expectations, improve the accuracy of the score, and thus make the display or deletion of the picture more accurate, reducing user operations To improve operational efficiency.
  • the first feature and the second feature of the picture are extracted. Calculate the score of each picture according to the first feature and the second feature. Then display the picture according to the score. If the scene available for the picture is a deleted scene, the third feature and the fourth feature of the picture are extracted. Calculate the score of each picture according to the third feature and the fourth feature. Then delete the picture according to the score.
  • the following embodiments respectively introduce a picture display method and a picture deletion method according to available scenarios.
  • x j is the value of the second feature corresponding to the j value.
  • ⁇ j is the weight of the second feature corresponding to the j value.
  • f (x j ) is a feature function of the second feature corresponding to the j value, and is used to normalize the value of the second feature.
  • the values of the weights ⁇ j of the second features in the initial algorithm are all the same, set to 1, and the weights can be adjusted for certain features according to the test feedback results, to provide users with various weight configuration functions.
  • the weight ⁇ j of each second feature may be different, and the value of ⁇ j is not limited to 1. This embodiment of the present application does not limit this.
  • the first feature corresponding to the above k value can be obtained by searching a mapping relationship table.
  • the mapping relationship table may be stored in the internal memory 121 to indicate the mapping relationship between the k value and the first feature. For example, when the k value is 1, the corresponding first feature may be a collection, and when the k value is 2, the corresponding first feature may be a comment, etc.
  • the second feature corresponding to the j value can also be obtained by looking up the mapping relationship table.
  • ⁇ k is the value of the third feature corresponding to the k value;
  • n is the number of the fourth feature, and
  • j 1,..., n, j and different values respectively correspond to a fourth feature.
  • x j is the value of the fourth feature corresponding to the j value.
  • ⁇ j is the weight of the fourth feature corresponding to the j value.
  • f (x j ) is a feature function of the fourth feature corresponding to the j value, and is used to normalize the value of the fourth feature.
  • the weights ⁇ j of the fourth features in the initial algorithm are all the same, set to 1, and then the weights can be adjusted for certain features according to the test feedback results to provide users with various weight configuration functions.
  • the weight ⁇ j of each fourth feature may be different, and the value of ⁇ j is not limited to 1. This embodiment of the present application does not limit this.
  • the third feature corresponding to the k value and the fourth feature corresponding to the j value can be obtained by looking up a mapping relationship table, which is not described in detail here.
  • Table 1 lists P1-P20, the status of each feature contained in each picture.
  • the first column in Table 1 is used to indicate the picture number.
  • the first row in Table 1 is used to represent various features of the picture.
  • Upload cloud refers to whether a certain picture stored in the internal memory 121 has been uploaded to the cloud. If a picture has been uploaded to the cloud, it is determined that the user may not like this picture; if a picture has not been uploaded to the cloud, it is determined that the user may like this picture. If it has been uploaded to the cloud, the status of "Upload Cloud” is yes; if it has not been uploaded to the cloud, the status of "Upload Cloud” is no.
  • the processor 110 obtains a picture from the internal memory 121, the storage location of the picture is built-in, it is determined that the user may not like the picture, and the state of the "storage location" is built-in; if the processor 110 uses the external memory interface 120 to When a picture is obtained from an external memory card, the storage location of the picture is external. If the user may like the picture, the status of "storage location" is external.
  • a picture is favorited, it is determined that the user may like the picture, and the status of "favorite" is yes; if a picture is not favorited, it is determined that the user may not like the picture, and the status of "favorite" is no.
  • the status of "shooting mode” is yes; if a picture is not taken in any shooting mode, the user is determined May not like this picture, the status of "shooting mode" is no.
  • the number of times a picture is browsed is greater than a certain threshold, it is determined that the user may like the picture; if the number of times a picture is browsed is not greater than the threshold, it is determined that the user may not like the picture.
  • the threshold may be 5, for example. If a picture is viewed more than 5 times, the status of "view times" is> 5; if a picture is viewed no more than 5 times, the status of "view times" is ⁇ 5.
  • the shooting time of a certain picture is greater than a certain threshold, it is determined that the user may not like the picture; if the shooting time of a certain picture is not greater than the threshold, it is determined that the user may like the picture.
  • the threshold may be 30, for example. If the shooting time of a picture is greater than 30 days, the "shooting time" status is> 30; if the shooting time of a picture is not greater than 30 days, the "shooting time” status is ⁇ 30.
  • “Last browse time” refers to the last time the picture was viewed. You can determine whether a user likes a picture based on the last time a picture was viewed. If the last time a picture was viewed is greater than a certain threshold, it is determined that the user may not like the picture; if the last time a picture is viewed is not greater than the threshold, it is determined that the user may like the picture.
  • the threshold may be 30, for example. If the last time a picture was viewed is greater than 30 days, the status of "Last View Time" is> 30; if the last time a picture is viewed is not greater than 30 days, the status of "Last View Time” is ⁇ 30 .
  • Picture size refers to the storage space occupied by pictures.
  • the size of the storage space occupied by a picture can be used to determine whether the user likes the picture. If the storage space occupied by a certain picture is greater than a certain threshold, it is determined that the user may not like the picture; if the storage space occupied by a certain picture is not greater than the threshold, it is determined that the user may like the picture.
  • the threshold may be 5 trillion (M), for example. If the storage space occupied by a picture is greater than 5M, the status of "picture size" is> 5; if the storage space occupied by a picture is not greater than 5M, the status of "picture size” is ⁇ 5.
  • “Aesthetic score” refers to the score calculated based on the structure and color of the picture. You can use the aesthetic score of a picture to determine whether the user likes the picture. If the aesthetic score of a picture is greater than a threshold, it is determined that the user may like the picture; if the aesthetic score of a picture is not greater than the threshold, it is determined that the user may not like the picture.
  • the threshold may be 5 points, for example. If the aesthetic score of a picture is greater than 5, the status of "aesthetic score" is> 5; if the aesthetic score of a picture is not greater than 5, the status of "aesthetic score” is ⁇ 5.
  • Trash refers to a category of pictures, which can exist in the form of a folder.
  • the pictures contained in the "Trash” folder are all pictures that the user does not like and want to delete.
  • the picture in the "box” folder is still stored in the internal memory 121.
  • the picture will be deleted from the internal memory 121.
  • the status of the “trash can” is yes; if a picture is not assigned to the “trash can” folder, the status of the “trash can” is no.
  • the folder to which the pictures that the user does not like and want to delete belongs to is not limited to being named “trash can", but may also be "recycle bin", "recently deleted", etc., which is not limited in the embodiment of the present application.
  • the picture that may be the second picture may be used to display the scene, and the picture that may be the first picture may be used to delete the scene.
  • the above-mentioned at least one feature for determining that the available scene of the picture is a display scene or a deletion scene may indicate that a certain picture may be the first picture or the second picture.
  • the at least one feature may include: collection, remarks, associated wallpaper, storage location, Upload to the cloud. If a picture is not favorited, not commented, no associated wallpaper, the storage location is built-in and uploaded to the cloud, it is determined that the picture may be the first picture, and the picture can be used to delete the scene.
  • the picture may be the second picture, which can be used to display the scene; if the storage location of a picture is external, it means that the user may like this picture, The picture may be the second picture, which can be used to display the scene; if a picture is associated with a wallpaper, it means that the user may like the picture, then the picture may be the second picture, which can be used to display the scene; if a picture is Remarks, it means that the user may like this picture, the picture may be the second picture, which can be used to display the scene; if a picture is not uploaded to the cloud, it means that the user may like the picture, the picture may be the first Two pictures can be used to show the scene.
  • the second picture that may be the picture may be used to display the scene.
  • the above-mentioned features for determining the usage scene are not limited to the five listed above, but may also include other features, such as whether to be shared, and if it is shared, it may be a second picture, which can be used to display the scene.
  • the feature used to determine the usage scene may be any combination of the above-listed features, or may include other features, which can be used to determine whether a certain picture may be the first picture or a possible second picture, and determine each The picture can be used only in scenes, which is not limited in the embodiments of the present application.
  • P1, P4, P6, P7, P8, P10, P11, P13 , P15, P18, P19 can be used to display scenes
  • P2, P3, P5, P9, P12, P14, P16, P17, P20 can be used to delete scenes.
  • the selection and values of the first feature (or third feature) and the second feature (or fourth feature) under different scenarios are introduced.
  • the first feature and the second feature are used to characterize the user's liking for the picture.
  • the first feature may include, for example, favorites and notes. If it is bookmarked or commented, it is determined that the user likes the picture, and the score of the picture is increased by the value of the first feature.
  • the value of the feature is a larger value; if it is determined that the user may not like the picture according to the state of the first feature, the The value of the feature is the smaller value. For values corresponding to different states of the first feature, see Table 2 as an example. The larger value is 10 and the smaller value is 1.
  • the second feature may include, for example, sharing, shooting mode, picture content classification, browsing times, shooting time, last browsing time, picture size, and aesthetic score.
  • the value of the feature is a larger value; if it is determined that the user may not like the picture according to the state of the second feature, the The value of the feature is the smaller value.
  • Table 3 For values corresponding to different states of the second feature, see Table 3 as an example. The larger value is 1, and the smaller value is 0.
  • sharing may include sharing to a third-party platform through third-party software, such as, but not limited to, WeChat, Weibo, Tencent QQ, Tencent Weibo, Facebook, and so on. Sharing may also include sharing to other electronic devices through short-range wireless communication. If a picture has been shared, the value of the second feature "Share” is 1; if a picture has not been shared, the value of the second feature "Share” is 0. If a picture is taken in a certain shooting mode, the value of the second feature "shooting mode” is 1; if a picture is not taken in any shooting mode, the second feature "shooting mode” The value is 0.
  • the value of the second feature "picture content classification” is 1; if the content of a picture does not belong to any category, the value of the second feature "picture content classification” is 0. If a picture is viewed more than 5 times, the value of the second feature "view count” is 1; if a picture is viewed no more than 5 times, the second feature is "view count” 0. If the shooting time of a picture exceeds 30 days, the second feature "shooting time” is 0; if the shooting time of a picture does not exceed 30 days, the value of the second feature "shooting time” is 1.
  • the value of the second feature "Last View Time” is 0; if the last viewing time of a picture does not exceed 30 days, the second feature "Last View”
  • the value of "time” is 1. If the size of a picture exceeds 5 trillion, the value of the second feature "picture size” is 0; if the size of a picture does not exceed 5 trillion, the value of the second feature "picture size” is 1. If the aesthetic score of a picture exceeds 5 points, the value of the second feature "aesthetic score” is 1; if the size of a picture does not exceed 5 points, the value of the second feature "aesthetic score” is 0.
  • the value of the first feature "collection" is 10
  • the score of the weighted sum of the second feature is enlarged ten times, which greatly improves the score of the picture to ensure that the user likes
  • the picture score is high; if a picture is not favorited, the value of the first feature "collection” is 1, and the score of the weighted summation of the second feature is not changed. If a picture is commented, the value of the first feature "Remarks" is 10, and the score of the weighted summation of the second feature is enlarged ten times to greatly increase the score of the picture to ensure that the user likes the picture.
  • the value is high; if a picture is not commented, the value of the first feature "comment" is 1, and the score of the weighted summation of the second feature is not changed. In short, it is necessary to increase the score of the picture by a larger value of the first feature.
  • Table 2 shows the selection and value of the first feature in the scene
  • Table 3 shows the selection and value of the second feature in the scene
  • Table 4 shows exemplary mapping relationships between the k value and the first feature.
  • Table 5 shows an example of the mapping relationship between the j value and the second feature.
  • mapping relationship between the k value and the first feature and the mapping relationship between the j value and the second feature are only exemplary descriptions, and in fact, there may be other mapping relationships, which are not limited in the embodiment of the present application.
  • each first feature and the selection of the second feature are not limited to the choices shown in Table 2 and Table 3. In actual use, there may be other choices, and the first The feature and the second feature are selected, and the first feature and the second feature can also be manually selected by the user.
  • the larger and smaller values of the first and second features are not limited to the values shown in Table 2 and Table 3. In actual use, there are other options. There is no restriction on this.
  • the value of the second feature is not limited to 0 or 1 listed in Table 3. In a possible implementation manner, it may also be 0 or 2, 0 or 10, 1 or 10, and so on. In a possible implementation manner, the value of the second feature may also be continuous, for example, the value of the "view count" for the second feature may be, but not limited to, a value of> 5 times and a value of ⁇ 5 times 0, or it can increase linearly as the number of views increases. If the number of browsing times is 0, the value is 0; the number of browsing times is 1, the value is 0.1; the number of browsing times is 2, the value is 0.2; the number of browsing times is 10 or more, the value is 1. The same can be applied to the second feature "share", “shooting time”, “last browsing time”, “picture size,” aesthetic score ", etc. For the specific assignment method, please refer to the assignment method of the second feature" view count ", I will not repeat them here.
  • the larger value of the first feature depends on the score S ′ i calculated according to the weighted sum of the second feature:
  • each second feature has a larger value of 1, and each second feature has a weight ⁇ j of 1, then each second feature is weighted The maximum possible summation is 8, and the larger value of the first feature needs to be greater than 8.
  • the number of second features is 10
  • the larger value of each second feature is 2, and the weight ⁇ j occupied by each second feature is 1, then each second feature is weighted The maximum possible summation is 20, and the larger value of the first feature needs to be greater than 20.
  • the value of the second feature of picture A is a larger value, but the value of the first feature is a smaller value, and the value of the first feature of picture B is a smaller value, but the second feature
  • the value of is not a large value, so the score of picture B must be greater than the score of picture A. It can be seen from this example that when the value of the first feature is a larger value, the score of the weighted summation of the second feature can be directly magnified several times, greatly improving the score of the picture, reflecting the The score advantage of the picture, so that the score reflects the user's love for the picture.
  • the score of the first feature can be used to separate the score from the pictures that are not bookmarked and not commented, reflecting the user ’s love for these pictures. different. If the two pictures are collected or commented at the same time, or both are not bookmarked and not commented, the score of the second feature is used to widen the score difference, reflecting the difference in the user's preference for the two pictures.
  • Table 1 Combining Table 1, Table 2 and Table 3 can obtain the value of each feature of the above pictures applicable to the recommended scene, and calculate the score S i of each picture according to formula (1), as shown in Table 6.
  • Table 6 shows the value of each feature of the picture under the scene and the score of the picture
  • the first column is the number of each picture, and the first row is each feature of the picture, S ′ i and S i .
  • the ranking is increased to the first; only according to the first
  • the second feature weighted sum score ranks the 7th P15, after the larger value of the first feature increases the score, the ranking is raised to 5th; only the 8th P7 ranks according to the second feature weighted sum score
  • the ranking is improved to the sixth. It can be seen that the larger value of the first feature can directly enlarge the score of the weighted sum of the second feature several times, greatly improving the score of the picture, reflecting the advantage of the score of the picture, thus The score reflects the user's liking for the picture.
  • the electronic device 10 can display the picture according to the score.
  • the electronic device 10 may preferentially display pictures with high scores in the interface 80 under the “Photo” menu.
  • the picture with the highest score may be ranked first, and the pictures are arranged in order from left to right, from top to bottom according to the order of score from high to low. For the arrangement, refer to FIG. 8 As shown in the figure on the right, it will not be repeated here.
  • the electronic device 10 may increase the area of the second picture in the interface 80 under the “Photo” menu.
  • the second picture may refer to a picture with the highest score, and the picture with the highest score is P11, which increases the area of the interface 80, at least the area of P11 in the interface 80 It is larger than the area of other non-second pictures.
  • the size of the second picture P11 is at least 4 times the area of other non-second pictures.
  • the electronic device 10 may frame a second picture in the interface 80 under the "Photo” menu, as shown in FIG. 11B.
  • the electronic device 10 may also display a second picture with a star in the interface 80 under the "Photo” menu, as shown in FIG. 11C.
  • the second picture may also be displayed in other display modes, for example, the second picture is displayed in a special color, or the second picture is displayed in a special transparency, etc.
  • This embodiment of the present application does not limit this.
  • the pictures displayed on the interface 80 under the "Photo" menu are all pictures in the internal memory 121 and the external memory card.
  • the electronic device 10 may collectively display the second picture, as shown in FIGS. 12 and 13.
  • the interface 80 may include the first category 8021 in addition to the location category and time category.
  • the location category contains multiple folders, and each folder can contain multiple pictures.
  • the shooting locations of the multiple pictures in the folder are the same, for example, Beijing, Shanghai, New York, Tokyo, etc.
  • the time category contains multiple folders, and each folder can contain multiple pictures.
  • the shooting time of the multiple pictures contained in the folder is the same, for example, it can be 2018, 2017, 2016, 2015, etc.
  • the first category 8021 may contain multiple second pictures.
  • the "first category” can also be displayed as "guess you like” or “Favorite” or “Fav” in the interface 80, which is not limited thereto, and can also have other category names, which are not limited in the embodiment of the present application.
  • the above classification methods may also have other classification methods, for example, classification according to people, and the specific classification method is not limited in the embodiment of the present application.
  • the interface 80 may further include a search control 804.
  • the touch sensor 180K of the electronic device 10 detects the user's operation on the search control 804
  • the display screen 194 of the electronic device 10 displays the search interface.
  • the search interface 90 at least It may include: a search bar 901, a display interface 902, and a status bar.
  • the status bar is similar to the status bar 204 listed in FIG. 4A and will not be repeated here.
  • the search bar 901 is used to receive a user's search instruction, and is used to search for pictures from the internal memory 121, external memory card, and cloud album of the electronic device 10.
  • an input method interface 9022 is displayed on the display interface 902, and the user can input a picture to be searched on the input method interface 9022, such as "blue sky", Then, the electronic device 10 can search for the picture with the content of "blue sky” from the internal memory 121, the external memory card, and the cloud album.
  • the display interface 902 includes a plurality of different categories.
  • the location category and the second category 9021 are shown in FIG. 13.
  • the folder included in the location category is similar to the folder included in the location category in FIG. 12.
  • the second category 9021 The second picture is included, which is similar to the second picture included in the first category 8021 in FIG. 12 and will not be repeated here.
  • the above-mentioned collection display form for the second picture is not limited to the classification form listed above, and in actual implementation, there may be other display forms, which are not limited in the embodiment of the present application.
  • the embodiment of the present application first determines the available scenes of the picture through some features, and then through the combined action of the first feature and the second feature, the score calculated according to the algorithm model provided in the embodiment of the present application is more in line with the psychological expectation of the user, which can make The user quickly searches for pictures.
  • the pictures displayed in the embodiments of the present application are more in line with the user's operating habits, and improve the user's operating efficiency.
  • the third feature and the fourth feature are used to characterize users' dislike of pictures.
  • the third feature may include, for example, a trash can.
  • the pictures containing the "trash can” feature are deleted by the user, and are currently classified in the "trash can” folder but the pictures are still stored in the internal memory 121. If a picture is classified in the "trash" folder, it is determined that the user does not like the picture, and the score of the picture is reduced by the value of the third feature.
  • the value of the feature is a larger value; if it is determined that the user may like the picture according to the state of the third feature, the The value of the feature is the smaller value.
  • the fourth feature may include, for example, shooting mode, picture content classification, shooting time, last browsing time, picture size, and aesthetic score.
  • the value of the feature is a smaller value; if it is determined that the user may like the picture according to the state of the second feature, the The value of the feature is the larger value.
  • Table 8 For the values of the different states of the fourth characteristic, see Table 8 for an example.
  • the larger value of the third feature “trash can” is 10, due to each
  • the score of weighted summation of the fourth feature may be negative, and the score of weighted summation of the fourth feature may be enlarged ten times in the negative direction by reducing the larger value of the third feature to 10, which greatly reduces the picture
  • the score of the image ensures that the picture that the user does not like is ranked low; if a picture is not classified in the "trash" folder and it is determined that the user may like this picture, the third feature "trash” is smaller The value of is 1, without changing the score of the fourth feature weighted summation.
  • the score of the weighted summation of the fourth features in the deletion scenario is not negative, then if it is determined that the user may not like the picture according to the state of the third feature, the feature Is the smaller value.
  • the smaller value can be a decimal, it can also be 0, or it can be a negative number.
  • the score of the picture needs to be reduced by the value of the third feature.
  • Table 9 shows exemplary mapping relationships between the k value and the third feature.
  • Table 10 shows exemplary mapping relationships between the j value and the fourth feature.
  • mapping relationship between the j value and the fourth feature is only an exemplary description, and in fact, there may be other mapping relationships, which are not limited in this embodiment of the present application.
  • each third feature and the selection of the fourth feature are not limited to the choices shown in Table 7 and Table 8. In actual use, there may be other choices, and the third The feature and the fourth feature are selected, and the third feature and the fourth feature can also be manually selected by the user.
  • the larger and smaller values of the third and fourth features are not limited to the assignments shown in Table 7 and Table 8. In actual use, there may be other options. This embodiment of the present application There is no restriction on this.
  • the value of the fourth feature is not limited to the cases listed in Table 8.
  • the smaller value of the fourth feature may be a positive number, and the larger value of the fourth feature is also a positive number.
  • the smaller value of the fourth feature may be a negative number, and the larger value of the fourth feature is also a negative number.
  • the assignment of the fourth feature may also be continuous. For example, the value of the fourth feature "shooting time" may decrease linearly as the shooting time increases.
  • the value is -1 points; when the shooting time is greater than 20 days and less than or equal to 30 days, the value is -0.5; when the shooting time is greater than 10 days and less than or equal to 20 days, the value is 0; the shooting time is greater than 5 When the day is less than or equal to 10 days, the value is 0.5; when the shooting time is less than or equal to 5 days, the value is 1.
  • the fourth feature “Browse this number", “Last browse time", “Picture size," Aesthetic score ", etc.
  • the assignment method of the fourth feature Shooting time ", which is no longer here Repeat.
  • the larger value of the third feature depends on weighting the calculated score S ′ i according to the fourth feature.
  • each fourth feature is 10
  • the smaller value of each fourth feature is -2
  • the weight ⁇ j occupied by each fourth feature is 1, then each fourth feature The minimum possible weighted sum is -20, and the larger value of the third feature needs to be greater than 20.
  • the smaller value of the third feature needs to be smaller than the reciprocal of the maximum possible weighted summation of the fourth features, and the maximum possible weighted summation of the fourth features is the individual
  • the score S ′ i obtained when the values of the fourth feature are all larger values.
  • each fourth feature has a larger value of 1, and each fourth feature has a weight ⁇ j of 1, then each fourth feature is weighted
  • the maximum possible summation is 8, and the smaller value of the third feature needs to be less than 1/8.
  • each fourth feature is weighted
  • the maximum possible summation is 20, and the smaller value of the third feature needs to be less than 1/20.
  • the values of the fourth feature of the picture A are all smaller values, but it is determined that the user may like a certain picture according to the state of the third feature, and it is determined that the user does not like a certain picture according to the third feature of the picture B, but If the value of the fourth feature is not a small value, the score of picture B must be smaller than the score of picture A.
  • the weighted summation score of the fourth feature can be directly enlarged or reduced several times in the negative direction, which is very large
  • the score of the picture is reduced to reflect the disadvantage of the score of the picture, so that the score shows the user's dislike of the picture.
  • Table 7 and Table 8 can obtain the value of each feature of the above pictures suitable for deleting the scene, and calculate the score S i of each picture according to formula (1), as shown in Table 11.
  • the first column is the number of each picture, and the first row is each feature of the picture, S ′ i and S i .
  • the ranking is reduced to the sixth; only according to the The four feature weighted sum scores rank the fourth P12, after the larger value of the third feature reduces the score, the ranking is reduced to the seventh; only the fourth feature weighted sum score is ranked the sixth P20 , After the larger value of the third feature lowers the score, the ranking is reduced to eighth; only the weighted sum of the fourth feature is used to rank the eighth P14, and the larger value of the third feature lowers the score After that, the ranking is reduced to number 9.
  • the larger value of the third feature can directly magnify the score of the fourth feature by a few times in the negative direction, greatly reducing the score of the picture, reflecting the disadvantage of the score of the picture , So that the score shows the user's dislike of the picture.
  • the electronic device 10 may prompt the user to delete or automatically delete the first picture directly according to the score and combining the deletion conditions.
  • the deletion condition is one of the basis for the electronic device 10 to determine the first picture.
  • the deletion condition may be that the remaining available capacity of the internal memory 121 of the electronic device 10 is not lower than a certain threshold, then the electronic device 10 may determine the first picture to be deleted according to the deletion condition and the score of each picture to ensure After a picture is deleted, the remaining available capacity of the internal memory 121 of the electronic device 10 is not lower than the threshold.
  • “delete” mentioned in the embodiment of the present application is different from categorizing pictures in the “trash” folder, and “prompt the user to delete” in the embodiment of the present application means that it is still stored in the internal memory 121 Or in the external memory card, until the user's delete instruction is received, it is deleted from the internal memory 121 or the external memory card.
  • “Delete directly” refers to deleting pictures from the internal memory 121 or the external memory card to release the storage capacity of the electronic device 10.
  • the first picture may be determined according to the deletion condition and the score of each picture, prompting the user to delete the first picture, or directly deleting the first picture automatically.
  • the first picture may be calculated and determined according to the total storage capacity Q of the user's mobile phone, the available storage capacity Q left , and the number N of pictures.
  • the target remaining storage capacity can be set:
  • the target remaining storage capacity Q is 2G; when the total storage capacity Q is not greater than 20G, the target remaining storage capacity Q left. Threshold is 10% of the total storage capacity Q. That is, the target remaining storage capacity is at most 2G.
  • the to-be-removable score threshold S t can be obtained by formula (5):
  • S i is the score of the picture i
  • the total capacity of the picture with the score less than or equal to the deleteable score threshold S t is greater than or equal to the difference between the target remaining storage capacity Q left.threshold and the current remaining capacity Q left .
  • the difference between the target remaining storage capacity Q left.threshold and the current remaining capacity Q left is 200 trillion (M)
  • the picture with the lowest score is selected as the picture to be deleted, until the total capacity of the selected image to be deleted is greater than or exactly equal to 200M
  • these images to be deleted in the highest score is the score of the image may be deleted score threshold of S t.
  • the total size of the deleted image Q delete.threshold is 100M each time; when the target remaining storage capacity Q left.threshold and the current remaining When the difference of the capacity Q left is not less than 100M, the total size Q delete.threshold of the image deleted each time is the difference between the target remaining storage capacity Q left.threshold and the current remaining capacity Q left .
  • the total capacity of deleted pictures is at least 100M each time. For example, if the deleteable score threshold S t calculated according to equation (4) shows that the capacity of the picture to be deleted is 80M, then several pictures with the lowest score can be deleted until the capacity of the deleted picture is just greater than or equal to 100M.
  • the deleteable score threshold S t is calculated according to equation (4), the number of pictures that need to be deleted is 20, and the total number of pictures stored in the electronic device 10 is 150, so the number of pictures that are finally deleted is 15 Pictures, the first picture is the 15 pictures with the lowest score.
  • the maximum value of the target remaining storage capacity is not limited to the above listed 2G
  • the minimum value of the total capacity of the pictures deleted each time is also not limited to the above listed 100M
  • the threshold of the number of pictures deleted each time is not limited to the total number of the above listed 10% of N can be other values in the actual implementation process, and the user can also manually set the above parameters.
  • the embodiments of the present application are only exemplary descriptions, which are not limited.
  • the album display interface 80 displays the "Weibo” folder, "WeChat” folder, and "Facebook” folder in addition to the "Weibo” folder.
  • the first folder 8022 may be displayed, as shown in FIG. 15.
  • the first folder 8022 may include multiple first pictures.
  • the first folder can also be displayed in the interface 80 as a suggestion to delete more intuitively, which is not limited to this, and may also have other names, which are not limited in the embodiment of the present application.
  • the display screen 194 of the electronic device 10 may display the interface 100, and the interface 100 may include the first picture, the control 901, and the status bar.
  • the status bar is similar to the status bar 204 in FIG. 4A, and will not be repeated here.
  • the storage capacity of the internal memory 121 of the electronic device 10 can be released to 20% of the total capacity, and the user can easily operate during the deletion process.
  • the first picture is comprehensively determined according to the third feature and the fourth feature, and the accuracy of deleting the picture is high, which reduces the probability that the user downloads the picture from the cloud again, and improves the user experience.
  • the above-mentioned method of collectively displaying the first picture is not limited to being placed in the first folder 8022. In actual implementation, there may be other methods of collectively displaying, which is not limited in this embodiment of the present application.
  • the electronic device 10 can also directly delete the first picture without manually deleting it by the user, further reducing user operations.
  • the above picture display and picture deletion can be performed separately. That is, in the picture management process shown in FIG. 3, only S301-S307 may be executed to display pictures, or only S301-S306, S308 may be executed to delete pictures.
  • the display of the above picture may be that the electronic device 10 calculates a score for the picture suitable for the display scene once a day.
  • the processor 110 of the electronic device 10 may calculate the score according to the score Update the display of the picture, and the deletion of the picture may be that the electronic device 10 calculates the score of the picture suitable for the deletion scene once a week, displays the first picture according to the score and the deletion strategy set to prompt the user to delete, or automatically delete the first A picture.
  • the electronic device 10 may calculate the scores of all the pictures stored in its internal memory 121 and / or cloud albums once a day, and then update the display of the pictures according to the scores, and perform according to the scores and deletion strategies A weekly collection displays the first picture to prompt the user to delete, or automatically delete the first picture.
  • the embodiment of the present application first determines the scene to which the picture is applicable through some features, and then through the combined effect of the third feature and the fourth feature, the score calculated according to the algorithm model provided by the embodiment of the present application is more in line with the user's psychological expectations and can be quickly The picture is deleted accurately.
  • the first picture determined in the embodiment of the present application is more accurate, reduces user operations, and improves user operation efficiency.
  • the overall scores of all pictures can be arranged in order from high to low, and the The sequence from left to right and from top to bottom is shown in the right figure of Figure 8.
  • the user feedback may include the user's positive feedback behavior and reverse feedback behavior.
  • the positive feedback behavior may include: moving the picture behind in the picture displayed on the interface 80 under the “Photo” menu forward, zooming in on the normally displayed picture, framing the normally displayed picture, displaying The normally displayed pictures are marked with stars, and the pictures classified in the first folder 8022 are moved out to the interface 80 under the “Photo” menu. If the above user feedback behavior occurs, it can be determined that the score of the picture should be higher.
  • the reverse feedback behavior may include: moving the front picture in the picture displayed on the interface 80 under the “photo” menu backward, canceling the enlarged picture display, and canceling the boxed picture display Display, cancel the star display of the picture displayed by the star, the user categorizes a picture in the "trash" folder. If the above user feedback behavior occurs, it is determined that the score of the picture should be lower.
  • Fig. 16 introduces the optimization algorithm model based on the reverse feedback behavior
  • Fig. 17 introduces the optimization algorithm model based on the positive feedback behavior.
  • the weight of the feature that the user may like a picture may be determined, and in the second feature of P8, the weight of the feature that the user may not like a picture may be determined.
  • the second feature of P8 can determine that the features that the user may like for a picture are "share”, “picture content classification” and “last browsing time”, it can be determined that the user may not like a certain
  • the characteristics of the pictures are "shooting mode”, “viewing times”, “shooting time”, “picture size”, “aesthetic score”, and from the description of formula (1), it can be seen that the weights of the second features are 1.
  • the feature that can determine that the user may like a certain picture is "shared"
  • the weights of "Picture Content Classification” and “Last View Time” are both reduced from 1 to 0.5, and at the same time, the characteristics that can determine that the user may not like a picture are the weights of "shooting mode", “views", and “shooting time” If it is increased to 1.5, then the score S i of P8 is reduced from 30 to 15.
  • the magnitude of the decrease and increase of the weight of the above second feature are only illustrative, and the choice of reducing the weight can determine that the user may like the features of a picture and the choice of increasing the weight can determine that the user may not like the features of a picture It is also an exemplary description, which is not limited in the embodiments of the present application.
  • the electronic device 10 may collect the user's reverse feedback behavior for multiple pictures in the internal memory 121 and the external memory card within a period of time (for example, one week, one month, etc.), and then extract Common among the first features of these multiple pictures can determine the features that the user may like a certain picture, common among the second features can determine the features that the user may like a certain picture and can determine the features that the user may not like a certain picture .
  • the common feature is that the user may like a picture and the common feature that the user may not like a picture is not strictly a feature of each picture, only most of the multiple pictures If the pictures are consistent, it can be determined that the user may like the characteristics of a certain picture or that the user may not like the characteristics of a certain picture.
  • the electronic device 10 collects the user's reverse feedback behavior for 100 pictures within a month, if there are 60 pictures in the first feature of the 100 pictures, it can be determined that the features that the user may like for a picture are all "favorites" ", Then" collection "can be used as the common feature among the first features of these 100 pictures to determine that the user may like a certain picture. The same applies to the feature among the second features that can determine that the user may like a certain picture And it can be determined that the user may not like the characteristics of a certain picture.
  • the algorithm model can be optimized more accurately by collecting the common features extracted by the user's reverse feedback behavior for multiple pictures.
  • the display screen 194 of the electronic device 10 may display the interface 200, and the interface 200 may include the picture display area 2001 and the recovery The control 2002, the picture display area 2001 is used to display P3, and the recovery control 2002 is used to receive the user's recovery instruction, move P3 out of the first folder 8022, and after moving P3 out of the first folder 8022, when the electronic device 10
  • the user's instruction to view the "recommended deletion” folder 8022 is received again, P3 is no longer displayed in the interface 100, and when the electronic device 10 receives the user's operation on the "album” menu control again, P3 may be displayed in the " In the interface 80 under the "Photo” menu, if it is determined that the user wishes to increase the score of P3, there are various ways to increase the score of P3, for example:
  • the weight of the feature that the user may like a certain feature of the picture can be determined.
  • the fourth feature of P3 can be determined that the characteristics of the user may not like a picture are “shooting mode”, “shooting time”, “last browsing time”, “picture size”, you can It is determined that the characteristics of a picture that the user may like are “picture content classification”, “views”, and “aesthetic score”, and it can be seen from the description of formula (1) that the weight of each fourth feature is 1.
  • the feature that can determine that the user may not like a picture can be set to "shooting mode""," Shooting time “,” last browsing time “,” picture size "weights are reduced from 1 to 0.5, and at the same time, the characteristics that can determine that the user may like a picture are” picture content classification "," views ", And the weight of the "aesthetic score” increases to 1.5, then the score S i of P3 is increased from -10 to 15.
  • the magnitude of the weight reduction and the magnitude of the increase in the above fourth feature are only exemplary descriptions, and those who choose to reduce the weight can determine that the user may not like the features of a picture and those who choose to increase the weight can determine that the user may like the features of a picture It is also an exemplary description, which is not limited in the embodiments of the present application.
  • the electronic device 10 may collect the user's positive feedback behavior for multiple pictures within a period of time (such as a week or a month, etc.), and then extract the third feature of the multiple pictures Commonly, it can be determined that the user may not like the feature of a certain picture.
  • the common feature can determine the feature that the user may like a certain picture and can determine the feature that the user may not like a certain picture.
  • the common feature here is that the user may like a picture and the common feature that the user may not like a picture is not strictly a feature of each picture, only most of the multiple pictures If the pictures are consistent, it can be determined that the user may like the characteristics of a certain picture or that the user may not like the characteristics of a certain picture.
  • the electronic device 10 collects the positive feedback behavior of the user for 100 pictures within a month
  • the third feature “trash can” of 60 pictures in the 100 pictures can be determined that the user may not like a certain picture Feature
  • the characteristics of the picture may determine that the user may not like the characteristics of a certain picture.
  • the algorithm model can be optimized more accurately by collecting the common features extracted by the user's positive feedback behavior for multiple pictures.
  • the algorithm model when the algorithm model is optimized according to the above two feedback behaviors, it can also be achieved by adjusting the first feature (third feature) or the second feature (or fourth feature). Specifically, you can adjust a certain first feature (third) to a second feature (fourth feature), or adjust a certain second feature (fourth feature) to a first feature (third feature), or add a A feature (third feature), either reduce the first feature (third feature), or increase the second feature (fourth feature), or reduce the second feature (fourth feature) to achieve.
  • the embodiments of the present application are not described in detail.
  • the selection of the first feature (third feature), the adjustment of the larger value and the smaller value of the first feature (third feature), the selection of each second feature (fourth feature), the second The adjustment of the larger value and the smaller value of the feature (fourth feature) can also be manually selected or manually input by the user.
  • the feature manually selected by the user and the value of each feature manually entered can more accurately represent the user's intention.
  • the selection of each feature and the value of each feature are not limited in the embodiments of the present application.
  • the algorithm model involved in the embodiments of the present application is not limited to the formula (1) proposed above, and may actually be an AI machine learning algorithm model, such as naive Bayes, support vector machine, deep neural network, and so on.
  • the initial training sample of the AI machine learning algorithm model may be various users scoring a large number of pictures, and these pictures may include various features listed in the above embodiments, which will not be listed here.
  • the score of the picture can be calculated according to these feature data, and finally the score of the picture i is output.
  • the electronic device 10 can manage the pictures according to the output score.
  • the algorithm model can be continuously optimized to improve the accuracy of the output score.
  • Embodiments of the present application also provide a computer-readable storage medium, in which instructions are stored in the computer-readable storage medium, which when executed on a computer or processor, causes the computer or processor to perform any one of the above methods Or multiple steps.
  • An embodiment of the present application also provides a computer program product containing instructions.
  • the computer program product runs on a computer or processor, it causes the computer or processor to perform one or more steps in any of the above methods.
  • the computer program product includes one or more computer instructions.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
  • the computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium.
  • the computer instructions can be sent from one website site, computer, server, or data center to another website site, computer, via wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.). Server or data center for transmission.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device including a server, a data center, and the like integrated with one or more available media.
  • the usable medium may be a magnetic medium (eg, floppy disk, hard disk, magnetic tape), optical medium (eg, DVD), or semiconductor medium (eg, solid state disk (SSD)), or the like.
  • the process may be completed by a computer program instructing relevant hardware.
  • the program may be stored in a computer-readable storage medium.
  • When the program is executed May include the processes of the foregoing method embodiments.
  • the foregoing storage media include various media that can store program codes, such as ROM or random storage memory RAM, magnetic disks, or optical disks.

Abstract

一种图片管理方法及电子设备，能够提高查找图片的效率。该方法包括：获取电子设备中存储的和/或云相册存储的至少两张图片中每张图片的场景特征；根据场景特征确定每张图片的第一特征及其打分标准、第二特征及其打分标准；根据每张图片的第一特征的值和第二特征的值计算得到其分值，其中第一特征对该图片的分值的影响大于第二特征对该图片的分值的影响；检测用户的第一操作；响应于第一操作，显示至少两张图片中的S张第二图片，其中，S为大于或者等于1的整数，S张第二图片的分值高于至少两张图片中除S张第二图片之外的其他图片的分值。

Description

文件管理方法及电子设备 技术领域
本申请涉及电子设备领域，尤其涉及一种文件管理方法及电子设备。
背景技术
目前，电子设备的存储容量越来越大，可存储的文件(例如图片、音频、视频等)也越来越多，因此，需要对文件进行有效管理。
电子设备对图片进行管理包括对图片的展示以及图片的删除。现有技术中可以通过对图片打分，并根据分值对图片进行展示或者删除。但是现有技术中的打分机制不合理，得到的图片的分值往往与用户的心理预期有差距，导致用户无法快速查找图片，或者删除了用户不想删除的图片，影响操作效率。
发明内容
本申请实施例提供了一种图片管理方法及电子设备，可以使打分机制更合理，得到的图片的分值更加符合用户的心理预期，使用户可以快速查找图片，或者提高删除图片的准确性，提高操作效率。
第一方面，本申请实施例提供了一种图片管理方法，该方法由电子设备执行，该方法可以包括：获取电子设备中存储的和/或云相册存储的至少两张图片中每张图片的场景特征；根据每张图片的场景特征确定每张图片的第一特征、第二特征、第一特征的打分标准、和第二特征的打分标准；根据每张图片的第一特征的值，和每张图片的第二特征的值计算得到每张图片的分值；其中，第一特征对每张图片的分值的影响大于第二特征对每张图片的分值的影响；检测用户的第一操作；响应于第一操作，显示至少两张图片中的S张第二图片；其中，S为大于或等于1的整数，S张第二图片的分值高于至少两张图片中除S张第二图片之外的其他图片的分值。
本申请实施例提供的技术方案，电子设备可以根据场景特征，确定其存储的和/或云相册中存储的每张图片的第一特征及其打分标准、第二特征及其打分标准，根据第一特征的值和第二特征的值计算出每张图片的分值，显示分值高的S张第二图片。图片分值的高低可以用来确定用户对该图片的喜爱程度，分值越高，喜爱程度越高。因此，显示分值高的第二图片可以使用户更加容易查看到自己喜爱的图片，提高用户查找图片的效率。
在一种可能的实现方式中，上述场景特征包括以下一个或任意组合：是否被收藏、是否被备注、是否关联壁纸、是否上传云端、存储位置；上述第一特征包括以下一个或任意组合：是否被收藏、是否被备注；上述第二特征包括以下一个或任意组合：是否被分享、拍摄模式、图片内容分类、浏览次数、拍摄时间、最后浏览时间、图片大小、美学评分。
本申请实施例提供的技术方案，根据上述场景特征可以初步判断用户是否可能喜欢某张图片，再根据判断的结果提取图片的特征进行计算。不同的判读结果对应不同的参与计算图片分值的特征以及该特征的打分标准，可以更加准确的计算图片的分值，使图片的分值更加符合用户的心理预期，减少用户的操作，提高操作效率。
在一种可能的实现方式中，上述检测用户的第一操作之前，该方法还包括：电子设备显示状态栏、导航栏、时间组件图标及一个或多个应用程序的图标，上述相机应用的图标属于上述一个或多个应用程序的图标，上述第一操作为用户对相册应用的图标的操作；上述检测用户的第一操作后，该方法还包括：响应于第一操作，显示至少两张图片中除S张第二图片之外的其他图片，上述S张第二图片的分值高于上述除S张第二图片之外的其他图片的分值；上述S张第二图片在上述除所述S张第二图片之外的其他图片之前显示，或者上述S张第二图片被特殊标记以与上述除S张第二图片之外的其他图片区别显示。
本申请实施例提供的技术方案，可以将分值高的第二图片在分值低的其他图片前面显示，或者将分值高的第二图片增加特殊标记以与其他分值低的图片区别显示，可以使用户更加容易查看到自己喜爱的图片，提高用户查找图片的效率。
在一种可能的实现方式中，上述S张第二图片按照分值从高到低顺序排列，上述除S张第二图片之外的其他图片按照分值从高到低排列。
本申请实施例提供的技术方案，将图片按照分值高低顺序排列，将用户可能喜爱的图片排在最前面，可以使用户更加容易查看到自己喜爱的图片，提高用户查找图片的效率。
在一种可能的实现方式中，上述S张第二图片被特殊标记的方式包括以下一种或任意组合：增大显示、增加边框显示、增加标记显示、特殊颜色显示、特殊透明度显示。
本申请实施例提供的技术方案，对分值高的第二图片增加特殊标记，使用户可以更加容易地查看到自己喜爱的图片，提高用户查找图片的效率。
在一种可能的实现方式中，上述检测用户的第一操作后，该方法还包括：响应于上述第一操作，按照分类显示文件夹，并显示搜索控件、第一菜单控件、第二菜单控件、第三菜单控件；其中，上述第一操作为用户对上述第三菜单控件的操作，上述分类的方式包括以下一个或任意组合：地点、时间、人物；每个文件夹包括一张或者多张图片，上述一张或多张图片属于上述至少两张图片；上述按照分类显示文件夹，并显示搜索控件、第一菜单控件、第二菜单控件、第三菜单控件后，该方法还包括：响应于用户对该搜索控件的第二操作，显示搜索栏、按照分类显示的文件夹和所述S张第二图片。
本申请实施例提供的技术方案，将分值高的第二图片与其他按照分类显示的文件夹一起显示，使用户可以更加容易地查看到自己喜爱的图片，提高用户查找图片的效率。
在一种可能的实现方式中，上述根据每张图片的第一特征的值，和每张图片的第二特征的值计算得到每张图片的分值之前，还包括：判断满足优化条件，该优化条件包括以下一个或任意组合：电子设备的剩余存储空间低于第一设定值，已达到设定的时间，电子设备的剩余电量低于第二设定值，电子设备正在充电，电子设备处于熄屏状态。
本申请实施例提供的技术方案，可以在判断出满足优化条件的情况下再计算每张图片的分值。由于分值计算过程会占用电子设备的运行内存，消耗电子设备的电量，本申请实施例提供的技术方案可以保证图片分值的计算过程不影响用户的正常使用。
在一种可能的实现方式中，上述显示至少两张图片中的S张第二图片之后，该方法还包括：接收用户取消上述S张第二图片中至少一张第二图片的特殊标记的第三操作，响应于上述第三操作，重新计算上述至少一张第二图片的分值；或者接收用户为上述除上述S张第二图片之外的其他图片中的至少一张图片增加特殊标记的第四操作，响应于上述第四 操作，重新计算上述至少一张图片的分值；或者接收用户将上述S张第二图片中的至少一张第二图片移动至上述除上述S张第二图片之外的其他图片中的至少一张图片之后显示的第五操作，响应于上述第五操作，重新计算上述S张第二图片中的至少一张第二图片的分值；或者接收用户将上述除所述S张第二图片之外的其他图片中的至少一张图片移动至上述S张第二图片中的至少一张第二图片之前显示的第六操作，响应于上述第六操作，重新计算上述除上述S张第二图片之外的其他图片中的至少一张图片的分值。本申请实施例提供的技术方案，可以根据用户对部分图片的反馈行为重新计算这部分图片的分值，使计算的结果更加符合用户的心理预期，从而使显示的结果更加准确，进一步提高用户查找图片的效率。
第二方面，本申请实施例提供了一种图片管理方法，由电子设备执行，包括：获取电子设备中存储的和/或云相册存储的至少两张图片中每张图片的场景特征；根据每张图片的场景特征确定每张图片的第三特征、第四特征、第三特征的打分标准、和第四特征的打分标准；根据每张图片的第三特征的值，和每张图片的第四特征的值计算得到每张图片的分值；其中，第三特征对每张图片的分值的影响大于第四特征对每张图片的分值的影响；检测用户的第一操作；响应于第一操作，显示第一文件夹；其中，第一文件夹包括至少两张图片中的M张第一图片；其中，M为大于或等于1的整数，M张第一图片的分值低于至少两张图片中除M张第一图片之外的其他图片的分值。
本申请实施例提供的技术方案，电子设备可以根据场景特征，确定其存储的和/或云相册中存储的每张图片的第三特征及其打分标准、第四特征及其打分标准，根据第三特征的值和第四特征的值计算出每张图片的分值，显示包括分值低的M张第一图片的文件夹。图片分值的高低可以用来确定用户对该图片的不喜爱程度，分值越低，不喜爱程度越高。因此，将分值低的M张第一图片置于第一文件夹内，无需用户逐个选择不喜爱的图片，减少用户操作，提升操作效率。
在一种可能的实现方式中，上述显示第一文件夹之后，还包括：检测用户的第二操作；响应于上述第二操作，删除上述M张第一图片。
本申请实施例提供的技术方案，用户可以一键删除该文件夹内所有的M张第一图片，无需用户逐个删除不喜爱的图片，减少删除图片的操作，提高用户删除图片的效率。
在一种可能的实现方式中，上述场景特征包括以下一个或任意组合：是否被收藏、是否被备注、是否关联壁纸，是否上传云端，存储位置；上述第三特征包括以下一个或任意组合：是否被置于垃圾箱；上述第四特征包括以下一个或任意组合：拍摄模式、图片内容分类、浏览次数、拍摄时间、最后浏览时间、图片大小、美学评分。
本申请实施例提供的技术方案，根据上述场景特征可以初步判断用户是否可能喜欢某张图片，再根据判断的结果提取图片的特征进行计算。不同的判读结果对应不同的参与计算图片分值的特征以及该特征的打分标准，可以更加准确的计算图片的分值，使图片的分值更加符合用户的心理预期，减少用户的操作，提高操作效率。
在一种可能的实现方式中，上述根据每张图片的第三特征的值，和每张图片的第四特征的值计算得到每张图片的分值之前，还包括：判断满足优化条件；其中，优化条件包括以下一个或任意组合：电子设备的剩余存储空间低于第一设定值，已到达设定的时间，电 子设备的剩余电量低于第二设定值，电子设备正在充电，电子设备处于熄屏状态。
本申请实施例提供的技术方案，可以在判断出满足优化条件的情况下再计算每张图片的分值。由于分值计算过程会占用电子设备的运行内存，消耗电子设备的电量，本申请实施例提供的技术方案可以保证图片分值的计算过程不影响用户的正常使用。
在一种可能的实现方式中，上述显示第一文件夹之后，还包括：接收用户将上述M张第一图片中的至少一张第一图片移出上述第一文件夹的第三操作，响应于上述第三操作，重新计算上述至少一张第一的分值；或者接收用户将上述除上述M张第一图片之外的其他图片中的至少一张图片移入上述第一文件夹的第四操作，响应于上述第四操作，重新计算上述至少一张图片的分值。
本申请实施例提供的技术方案，可以根据用户对部分图片的反馈行为重新计算这部分图片的分值，使计算的结果更加符合用户的心理预期，从而使显示的结果更加准确，进一步提高用户删除图片的效率。
第三方面，本申请实施例提供了一种图片管理方法，由电子设备执行，包括：获取电子设备中存储的和/或云相册存储的至少两张图片中每张图片的场景特征；根据每张图片的场景特征确定每张图片的第三特征及第三特征的打分标准、第四特征及第四特征的打分标准；根据每张图片的第三特征的值，和每张图片的第四特征的值计算得到每张图片的分值；其中，第三特征对每张图片的分值的影响大于第四特征对每张图片的分值的影响；删除上述至少两张图片中M张第一图片；其中，M为大于或者等于1的整数，M张第一图片的分值低于至少两张图片中除M张第一图片之外的其他图片。
本申请实施例提供的技术方案，电子设备可以根据场景特征，确定其存储的和/或云相册中存储的每张图片的第三特征及其打分标准、第四特征及其打分标准，根据第三特征的值和第四特征的值计算出每张图片的分值，根据分值删除分值低的第一图片。图片分值的高低可以用来确定用户对该图片的不喜爱程度，分值越低，不喜爱程度越高。因此，删除分值低的第一图片可以减少用户删除图片的操作，提升删除图片的效率。
在一种可能的实现方式中，上述场景特征包括以下一个或任意组合：是否被收藏、是否被备注、是否关联壁纸，是否上传云端，存储位置；上述第三特征包括：是否被置于垃圾箱；上述第四特征包括以下一个或任意组合：拍摄模式、图片内容分类、浏览次数、拍摄时间、最后浏览时间、图片大小、美学评分。
本申请实施例提供的技术方案，根据上述场景特征可以初步判断用户是否可能喜欢某张图片，再根据判断的结果提取图片的特征进行计算。不同的判读结果对应不同的参与计算图片分值的特征以及该特征的打分标准，可以更加准确的计算图片的分值，使图片的分值更加符合用户的心理预期，减少用户的操作，提高操作效率。
在一种可能的实现方式中，上述根据所述每张图片的第三特征的值，和所述每张图片的第四特征的值计算得到所述每张图片的分值之前，还包括：判断满足优化条件，所述优化条件包括以下一个或任意组合：所述电子设备的剩余存储空间低于第一设定值，已到达设定的时间，所述电子设备的剩余电量低于第二设定值，所述电子设备正在充电，所述电子设备处于熄屏状态。
本申请实施例提供的技术方案，可以在判断出满足优化条件的情况下再计算每张图片 的分值。由于分值计算过程会占用电子设备的运行内存，消耗电子设备的电量，本申请实施例提供的技术方案可以保证图片分值的计算过程不影响用户的正常使用。
在一种可能的实现方式中，上述删除上述至少两张图片中M张第一图片之后，还包括：接收用户下载上述M张第一图片中的至少一张第一图片的第一操作，响应于该第一操作，重新计算上述至少一张第一图片的分值；或者接收用户删除上述除上述M张第一图片之外的其他图片中的至少一张图片的第二操作，响应于该第二操作，重新计算上述至少一张图片的分值。
本申请实施例提供的技术方案，可以根据用户对部分图片的反馈行为重新计算这部分图片的分值，使计算的结果更加符合用户的心理预期，从而使显示的结果更加准确，进一步提高用户查找图片的效率。
第四方面，本申请实施例提供了一种图片分值计算方法，由电子设备执行，应用于本申请实施例第一方面或第一方面的任意一种实现方式的任意一种实现方式的任意一种实现方式提供的图片管理方法，该分值计算方法包括：获取电子设备中存储的和/或云相册存储的至少两张图片的场景特征；根据每张图片的场景特征确定每张图片的第一特征及第一特征的打分标准、第二特征及第二特征的打分标准；第一特征对应第一值和第二值，第一值大于第二值，第二特征对应两个或多个值；根据每张图片的第一特征的值，和每张图片的第二特征的值计算得到每张图片的分值；该分值与第一特征的值成正比，该分值与第二特征加权求和的值成正比，第一特征的第一值大于第二特征取最大值时加权求和的值；第一特征的第一值大于第二特征取最大值时加权求个的值。
本申请实施例提供的技术方案，图片的分值与第一特征的值成正比，图片的分值与第二特征加权求和的值成正比，且第一特征的第一值大于第二特征取最大值时加权求和的值，可以保证图片的分值由第一特征的值主要影响，由第二特征加权求和的值次要影响，突出第一特征的重要性，使打分机制更加合理，更加符合用户的心理预期。
第五方面，本申请实施例提供了一种图片分值计算方法，由电子设备执行，应用于本申请实施例第二方面或第二方面的任意一种实现方式、第三方面或第三方的任意一种实现方式提供的图片管理方法，该分值计算方法包括：获取电子设备中存储的和/或云相册存储的至少两张图片的场景特征；根据每张图片的场景特征确定每张图片的第三特征及第三特征的打分标准、第四特征及第四特征的打分标准；第三特征对应第一值和第二值，第一值大于第二值，第四特征对应两个或多个值；根据每张图片的第三特征的值，和每张图片的第四特征的值计算得到每张图片的分值；该分值与第三特征的值成反比，该分值与第四特征加权求和的值成正比，第四特征加权求和的值为负数，第三特征的第一值大于第四特征取最小值时加权求和的值的绝对值。
本申请实施例提供的技术方案，图片的分值与第三特征的值成反比，图片的分值与第四特征加权求和的值成正比。在第四特征加权求和的值为负数的情况下，第三特征的第一值大于第四特征取最小值时加权求和的值的绝对值，可以保证图片的分值由第三特征的值主要影响，由第四特征加权求和的值次要影响，突出第三特征的重要性，使打分机制更加合理，更加符合用户的心理预期。
第六方面，本申请实施例提供了一种图片分值计算方法，由电子设备执行，应用于本 申请实施例第二方面或第二方面的任意一种实现方式、第三方面或第三方的任意一种实现方式提供的图片管理方法，该分值计算方法包括：获取电子设备中存储的和/或云相册存储的至少两张图片的场景特征；根据每张图片的场景特征确定每张图片的第三特征及第三特征的打分标准、第四特征及第四特征的打分标准；第三特征对应第一值和第二值，第一值大于第二值，第四特征对应两个或多个值；根据每张图片的第三特征的值，和每张图片的第四特征的值计算得到每张图片的分值；该分值与第三特征的值成反比，该分值与第四特征加权求和的值成正比，第四特征加权求和的值为非负数，第三特征的第一值小于第四特征取最大值时加权求和的值的倒数。
本申请实施例提供的技术方案，图片的分值与第三特征的值成反比，图片的分值与第四特征加权求和的值成正比。在第四特征加权求和的值为非负数的情况下，第三特征的第一值小于第四特征取最大值时加权求和的值的倒数，可以保证图片的分值由第三特征的值主要影响，由第四特征加权求和的值次要影响，突出第三特征的重要性，使打分机制更加合理，更加符合用户的心理预期。
第七方面，本申请实施例提供了一种电子设备，包括：一个或多个处理器、存储器、显示屏、无线通信模块以及移动通信模块；上述存储器、显示屏、无线通信模块以及移动通信模块与一个或多个处理器耦合，上述存储器用于存储计算机程序代码，上述计算机程序代码包括计算机指令，当上述一个或多个处理器执行上述计算机指令时，电子设备执行如第一方面或第一方面的任意一种实现方式、或者第二方面或第二方面的任意一种实现方式、或者第三方面或第三方面的任意一种实现方式提供的图片管理方法。
第八方面，本申请实施例提供了一种电子设备，包括：一个或多个处理器、存储器；上述存储器与一个或多个处理器耦合，上述存储器用于存储计算机程序代码，上述计算机程序代码包括计算机指令，当上述一个或多个处理器执行上述计算机指令时，电子设备执行如第四方面或第四方面的任意一种实现方式、或者第五方面或第五方面的任意一种实现方式、或者第六方面或第六方面的任意一种实现方式提供的图片分值计算方法。
第九方面，本申请实施例提供了一种计算机存储介质，包括计算机指令，当计算机指令在电子设备上运行时，使得电子设备执行如第一方面或第一方面的任意一种实现方式、或者第二方面或第二方面的任意一种实现方式、或者第三方面或第三方面提供的图片管理方法。
第十方面，本申请实施例提供了一种计算机存储介质，包括计算机指令，当计算机指令在电子设备上运行时，使得电子设备执行如第四方面或第四方面的任意一种实现方式、或者第五方面或第五方面的任意一种实现方式、或者第六方面或第六方面的任意一种实现方式提供的图片分值计算方法。
第十一方面，本申请实施例提供一种计算机程序产品，当计算机程序产品在计算机上运行时，使得计算机执行如第一方面或第一方面的任意一种实现方式、或者第二方面或第二方面的任意一种实现方式、或者第三方面或第三方面的任意一种实现方式提供的图片管理方法。
第十二方面，本申请实施例提供一种计算机程序产品，当计算机程序产品在计算机上运行时，使得计算机执行如第四方面或第四方面的任意一种实现方式、或者第五方面或第 五方面的任意一种实现方式、或者第六方面或第六方面的任意一种实现方式提供的图片分值计算方法。
可以理解地，上述提供的第七方面所述的电子设备、第九方面所述的计算机存储介质或者第十一方面所述的计算机程序产品均用于执行第一方面、或第二方面、或第三方面中任一所提供的图片管理方法。因此，其所能达到的有益效果可参考对应方法中的有益效果，此处不再赘述。
可以理解地，上述提供的第八方面所述的电子设备、第十方面所述的计算机存储介质或者第十二方面所述的计算机程序产品均用于执行第四方面、或第五方面、或第六方面中任一所提供的图片分值计算方法。因此，其所能达到的有益效果可参考对应方法中的有益效果，此处不再赘述。
附图说明
图1为本申请实施例提供的一种电子设备的结构示意图；
图2为本申请实施例提供的一种电子设备的软件结构框图；
图3为本申请实施例提供的图片管理方法流程示意图；
图4A为本申请实施例提供的人机交互界面图一；
图4B为本申请实施例提供的人机交互界面图二；
图5A为本申请实施例提供的电子设备界面示意图一；
图5B为本申请实施例提供的电子设备界面示意图二；
图6A为本申请实施例提供的图片管理场景图一；
图6B为本申请实施例提供的人机交互界面图三；
图7为本申请实施例提供的人机交互界面图四；
图8为本申请实施例提供的优化前后的对比图一；
图9为本申请实施例提供的优化前后的对比图二；
图10为本申请实施例提供的展示场景下的图片分值排序示意图；
图11A为本申请实施例提供的图片展示示意图一；
图11B为本申请实施例提供的图片展示示意图二；
图11C为本申请实施例提供的图片展示示意图三；
图12为本申请实施例提供的图片展示示意图四；
图13为本申请实施例提供的图片展示示意图五；
图14为本申请实施例提供的删除场景下的图片分值排序示意图；
图15为本申请实施例提供的图片删除示意图；
图16为本申请实施例提供的用户反馈示意图一；
图17为本申请实施例提供的用户反馈示意图二。
具体实施方式
下面将结合附图对本申请实施例中的技术方案进行描述。
为了便于理解，示例的给出了部分与本申请实施例相关概念的说明以供参考。如下所 示：
第一图片：根据打分算法模型对图片打分后，分值最低的一张或几张图片。其中，打分算法模型用于接收输入的图片的特征，并输出该图片的分值。图片为电子设备存储器(包括内部存储器及外部存储卡)中存储的图片。第一图片的张数可以根据删除条件得到，删除条件用于确定需要删除的图片的张数。在一种可能的实施例中，删除条件为电子设备的剩余存储容量为X 1。若电子设备当前的剩余存储容量为X 2，其中X 2＜X 1，则需要删除的图片占用的存储容量为ΔX＝X 1-X 2。若第一图片的张数为M，则分值最低的M张图片占用的存储容量大于或者等于ΔX，且分值最低的M-1张图片占用的存储容量小于ΔX。在另一种可能的实施例中，删除条件为删除电子设备存储的图片总数量的Y％。若电子设备当前存储的图片总数量为100，则需要删除的图片的数量为100×Y％，即M＝100×Y％。
第二图片：根据打分算法模型对图片打分后，分值最高的一张或几张图片。在一种可能的实施例中，第二图片的张数可以是绝对值也可以是相对值，可以为出厂设置或者手机厂商推荐，也可以是用户设置。前述的相对值为电子设备存储器和该电子设备对应的云相册中存储的图片总数量的百分比。例如，相对值为S，电子设备存储器和该电子设备对应的云相册中存储的图片总数量为100，则第二图片的张数为100×S。
第一特征：可能直接表征用户对图片的喜爱程度的特征，对图片打分结果有相对主要影响。第一特征可以包含至少两个状态，不同的状态表征用户对图片不同的喜爱程度，每个状态分别对应一个值。
第二特征：可能间接表征用户对图片的喜爱程度的特征，对图片的打分结果有相对次要影响。第二特征可以包含至少两个状态，不同的状态表征用户对图片不同的喜爱程度，每个状态分别对应一个值，值越高的状态表征用户的喜爱程度越高。每张图片可以包含一个或多个第一特征，及一个或多个第二特征，当两张图片的第一特征的状态相同时，通过第二特征来区分用户对这两张图片的喜爱程度。
第三特征：可能直接表征用户对图片的不喜爱程度的特征，对图片打分结果有相对主要影响。第三特征可以包含至少两个状态，不同的状态表征用户对图片不同的不喜爱程度，每个状态分别对应一个值。
第四特征：可能间接表征用户对图片的不喜爱程度的特征，对图片打分结果有相对次要影响的特征。第四特征可以包含至少两个状态，不同的状态表征用户对图片不同的不喜爱程度，每个状态分别对应一个值，值越低的状态表征用户的不喜爱程度越高。每张图片可以包含一个或多个第三特征，及一个或多个第四特征，当两张图片的第三特征的状态相同时，通过第四特征来区分用户对这两张图片的不喜爱程度。
正向反馈行为：用户对管理后的图片的操作行为，该操作行为可能表明与用户的心理预期相比，根据打分算法模型计算的分值偏低。
反向反馈行为：用户为管理后的图片的操作行为，该操作行为可能表明与用户的心理预期相比，根据打分算法模型计算的分值偏高。
本申请实施例提供的文件管理方法可以应用于电子设备对图片、音频文件、视频文件、文档、应用程序等的管理，接下来的实施例中将以对图片的管理为例进行说明。
本申请实施例中涉及的电子设备可以是手机、平板电脑、桌面型、膝上型、笔记本电 脑、超级移动个人计算机(Ultra-mobile Personal Computer，UMPC)、手持计算机、上网本、个人数字助理(Personal Digital Assistant，PDA)、可穿戴电子设备、虚拟现实设备等。
请参考图1，示出了电子设备10的结构示意图。
电子设备10可以包括处理器110，外部存储器接口120，内部存储器121，通用串行总线(universal serial bus，USB)接口130，充电管理模块140，电源管理模块141，电池142，天线1，天线2，移动通信模块150，无线通信模块160，音频模块170，扬声器170A，受话器170B，麦克风170C，耳机接口170D，传感器模块180，按键190，马达191，指示器192，摄像头193，显示屏194，以及用户标识模块(subscriber identification module，SIM)卡接口195等。其中传感器模块180可以包括压力传感器180A，陀螺仪传感器180B，气压传感器180C，磁传感器180D，加速度传感器180E，距离传感器180F，接近光传感器180G，指纹传感器180H，温度传感器180J，触摸传感器180K，环境光传感器180L，骨传导传感器180M等。
可以理解的是，本申请实施例示意的结构并不构成对电子设备10的具体限定。在本申请另一些实施例中，电子设备10可以包括比图示更多或更少的部件，或者组合某些部件，或者拆分某些部件，或者不同的部件布置。图示的部件可以以硬件，软件或软件和硬件的组合实现。
处理器110可以包括一个或多个处理单元，例如：处理器110可以包括应用处理器(application processor，AP)，调制解调处理器，图形处理器(graphics processing unit，GPU)，图像信号处理器(image signal processor，ISP)，控制器，存储器，视频编解码器，数字信号处理器(digital signal processor，DSP)，基带处理器，和/或神经网络处理器(neural-network processing unit，NPU)等。其中，不同的处理单元可以是独立的器件，也可以集成在一个或多个处理器中。
其中，控制器可以是电子设备10的神经中枢和指挥中心。控制器可以根据指令操作码和时序信号，产生操作控制信号，完成取指令和执行指令的控制。
处理器110中还可以设置存储器，用于存储指令和数据。在一些实施例中，处理器110中的存储器为高速缓冲存储器。该存储器可以保存处理器110刚用过或循环使用的指令或数据。
在一些实施例中，处理器110可以包括一个或多个接口。接口可以包括集成电路(inter-integrated circuit，I2C)接口，集成电路内置音频(inter-integrated circuit sound，I2S)接口，脉冲编码调制(pulse code modulation，PCM)接口，通用异步收发传输器(universal asynchronous receiver/transmitter，UART)接口，移动产业处理器接口(mobile industry processor interface，MIPI)，通用输入输出(general-purpose input/output，GPIO)接口，用户标识模块(subscriber identity module，SIM)接口，和/或通用串行总线(universal serial bus，USB)接口等。
可以理解的是，本申请实施例示意的各模块间的接口连接关系，只是示意性说明，并不构成对电子设备10的结构限定。在本申请另一些实施例中，电子设备10也可以采用上述实施例中不同的接口连接方式，或多种接口连接方式的组合。
充电管理模块140用于从充电器接收充电输入。充电管理模块140为电池142充电的 同时，还可以通过电源管理模块141为终端供电。
电源管理模块141用于连接电池142，充电管理模块140与处理器110。电源管理模块141接收电池142和/或充电管理模块140的输入，为处理器110，内部存储器121，外部存储器，显示屏194，摄像头193，和无线通信模块160等供电。电源管理模块141还可以用于监测电池容量，电池循环次数，电池健康状态(漏电，阻抗)等参数。在其他一些实施例中，电源管理模块141也可以设置于处理器110中。在另一些实施例中，电源管理模块141和充电管理模块140也可以设置于同一个器件中。
电子设备10的无线通信功能可以通过天线1，天线2，移动通信模块150，无线通信模块160，调制解调处理器以及基带处理器等实现。
天线1和天线2用于发射和接收电磁波信号。例如，本申请实施例中，天线1和天线2可以用于向云服务器发送数据，以将保存在电子设备10存储器内的图片备份至云端。天线1和天线2还可以用于向云服务器发送下载请求，下载请求用于获取备份在云端的图片。天线1和天线2还可以用于接收云服务器响应于电子设备10发送的下载请求而发送的数据。
移动通信模块150可以提供应用在电子设备10上的包括2G/3G/4G/5G等无线通信的解决方案。移动通信模块150可以包括至少一个滤波器，开关，功率放大器，低噪声放大器(low noise amplifier，LNA)等。移动通信模块150可以由天线1接收电磁波，并对接收的电磁波进行滤波，放大等处理，传送至调制解调处理器进行解调。移动通信模块150还可以对经调制解调处理器调制后的信号放大，经天线1转为电磁波辐射出去。在一些实施例中，移动通信模块150的至少部分功能模块可以被设置于处理器110中。在一些实施例中，移动通信模块150的至少部分功能模块可以与处理器110的至少部分模块被设置在同一个器件中。
调制解调处理器可以包括调制器和解调器。在一些实施例中，调制解调处理器可以是独立的器件。在另一些实施例中，调制解调处理器可以独立于处理器110，与移动通信模块150或其他功能模块设置在同一个器件中。
无线通信模块160可以提供应用在电子设备10上的包括无线局域网(wireless local area networks，WLAN)(如无线保真(wireless fidelity，Wi-Fi)网络)，蓝牙(bluetooth，BT)，全球导航卫星系统(global navigation satellite system，GNSS)，调频(frequency modulation，FM)，近距离无线通信技术(near field communication，NFC)，红外技术(infrared，IR)等无线通信的解决方案。无线通信模块160可以是集成至少一个通信处理模块的一个或多个器件。无线通信模块160经由天线2接收电磁波，将电磁波信号调频以及滤波处理，将处理后的信号发送到处理器110。无线通信模块160还可以从处理器110接收待发送的信号，对其进行调频，放大，经天线2转为电磁波辐射出去。
在一些实施例中，电子设备10的天线1和移动通信模块150耦合，天线2和无线通信模块160耦合，使得电子设备10可以通过无线通信技术与网络以及其他设备通信。所述无线通信技术可以包括全球移动通讯系统(global system for mobile communications，GSM)，通用分组无线服务(general packet radio service，GPRS)，码分多址接入(code division multiple access，CDMA)，宽带码分多址(wideband code division multiple access，WCDMA)，时分码分多址(time-division code division multiple access，TD-SCDMA)，长期演进(long term  evolution，LTE)，BT，GNSS，WLAN，NFC，FM，和/或IR技术等。所述GNSS可以包括全球卫星定位系统(global positioning system，GPS)，全球导航卫星系统(global navigation satellite system，GLONASS)，北斗卫星导航系统(beidou navigation satellite system，BDS)，准天顶卫星系统(quasi-zenith satellite system，QZSS)和/或星基增强系统(satellite based augmentation systems，SBAS)。
电子设备10通过GPU，显示屏194，以及应用处理器等实现显示功能。GPU为图像处理的微处理器，连接显示屏194和应用处理器。处理器110可包括一个或多个GPU，其执行程序指令以生成或改变显示信息。
显示屏194用于显示图像，视频等。显示屏194包括显示面板。显示面板可以采用液晶显示屏(liquid crystal display，LCD)，有机发光二极管(organic light-emitting diode，OLED)，有源矩阵有机发光二极体或主动矩阵有机发光二极体(active-matrix organic light emitting diode的，AMOLED)，柔性发光二极管(flex light-emitting diode，FLED)，Miniled，MicroLed，Micro-oLed，量子点发光二极管(quantum dot light emitting diodes，QLED)等。在一些实施例中，电子设备10可以包括1个或N个显示屏194，N为大于1的正整数。例如，在本申请实施例中，显示屏194可以用于显示图片。
电子设备10可以通过ISP，摄像头193，视频编解码器，GPU，显示屏194以及应用处理器等实现拍摄功能。
ISP用于处理摄像头193反馈的数据。例如，拍照时，打开快门，光线通过镜头被传递到摄像头感光元件上，光信号转换为电信号，摄像头感光元件将所述电信号传递给ISP处理，转化为肉眼可见的图像。ISP还可以对图像的噪点，亮度，肤色进行算法优化。ISP还可以对拍摄场景的曝光，色温等参数优化。在一些实施例中，ISP可以设置在摄像头193中。
摄像头193用于捕获静态图像或视频。物体通过镜头生成光学图像投射到感光元件。感光元件可以是电荷耦合器件(charge coupled device，CCD)或互补金属氧化物半导体(complementary metal-oxide-semiconductor，CMOS)光电晶体管。感光元件把光信号转换成电信号，之后将电信号传递给ISP转换成数字图像信号。ISP将数字图像信号输出到DSP加工处理。DSP将数字图像信号转换成标准的RGB，YUV等格式的图像信号。在一些实施例中，电子设备10可以包括1个或N个摄像头193，N为大于1的正整数。
数字信号处理器用于处理数字信号，除了可以处理数字图像信号，还可以处理其他数字信号。例如，当电子设备10在频点选择时，数字信号处理器用于对频点能量进行傅里叶变换等。
视频编解码器用于对数字视频压缩或解压缩。电子设备10可以支持一种或多种视频编解码器。这样，电子设备10可以播放或录制多种编码格式的视频，例如：动态图像专家组(moving picture experts group，MPEG)1，MPEG2，MPEG3，MPEG4等。
NPU为神经网络(neural-network，NN)计算处理器，通过借鉴生物神经网络结构，例如借鉴人脑神经元之间传递模式，对输入信息快速处理，还可以不断的自学习。通过NPU可以实现电子设备10的智能认知等应用，例如：图像识别，人脸识别，语音识别，文本理解等。
外部存储器接口120可以用于连接外部存储卡，例如Micro SD卡，实现扩展终端201的存储能力。外部存储卡通过外部存储器接口120与处理器110通信，实现数据存储功能。例如，本申请实施例中可以将图片保存在外部存储卡中，电子设备10的处理器110可以通过外部存储器接口120获取保存在外部存储卡中的图片。
内部存储器121可以用于存储计算机可执行程序代码，所述可执行程序代码包括指令。处理器110通过运行存储在内部存储器121的指令，从而执行电子设备10的各种功能应用以及数据处理。内部存储器121可以包括存储程序区和存储数据区。其中，存储程序区可存储操作系统，至少一个功能所需的应用程序(比如声音播放功能，图像播放功能等)等。存储数据区可存储电子设备10使用过程中所创建的数据(比如音频数据，电话本，图片等)等。此外，内部存储器121可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件，闪存器件，通用闪存存储器(universal flash storage，UFS)等。例如，在本申请实施例中，内部存储器121可以用于存储多张图片，这多张图片可以是电子设备10通过摄像头193拍摄得到的，也可以是电子设备10通过天线1和天线2从其他应用(例如微信、微博、Facebook等)中接收后并下载得到的。
电子设备10可以通过音频模块170，扬声器170A，受话器170B，麦克风170C，耳机接口170D，以及应用处理器等实现音频功能。例如音乐播放，录音等。
压力传感器180A用于感受压力信号，可以将压力信号转换成电信号。在一些实施例中，压力传感器180A可以设置于显示屏194。
陀螺仪传感器180B可以用于确定电子设备10的运动姿态。陀螺仪传感器180B还可以用于导航，体感游戏场景。气压传感器180C用于测量气压。磁传感器180D包括霍尔传感器。加速度传感器180E可检测电子设备10在各个方向上(一般为三轴)加速度的大小。距离传感器180F，用于测量距离。接近光传感器180G可以包括例如发光二极管(LED)和光检测器，例如光电二极管。电子设备10可以利用接近光传感器180G检测用户手持电子设备10贴近耳朵通话，以便自动熄灭屏幕达到省电的目的。接近光传感器180G也可用于皮套模式，口袋模式自动解锁与锁屏。环境光传感器180L用于感知环境光亮度。环境光传感器180L也可用于拍照时自动调节白平衡。
指纹传感器180H用于采集指纹。电子设备10可以利用采集的指纹特性实现指纹解锁，访问应用锁，指纹拍照，指纹接听来电等。
温度传感器180J用于检测温度。
触摸传感器180K，也称“触控面板”。触摸传感器180K可以设置于显示屏194，由触摸传感器180K与显示屏194组成触摸屏，也称“触控屏”。触摸传感器180K用于检测作用于其上或附近的触摸操作。触摸传感器可以将检测到的触摸操作传递给应用处理器，以确定触摸事件类型。可以通过显示屏194提供与触摸操作相关的视觉输出。在另一些实施例中，触摸传感器180K也可以设置于电子设备10的表面，与显示屏194所处的位置不同。例如，在本申请实施例中，触摸传感器180K可以用于检测用户对相册内包含的第一图片的触摸操作，并将检测到的触摸操作传递给应用处理器，以便显示与第一图片对应的第二图片。其中，第一图片的尺寸小于第二图片的尺寸，第一图片包含的像素点的个数小于第二图片包含的像素点的个数。
骨传导传感器180M可以获取振动信号。骨传导传感器180M也可以接触人体脉搏，接收血压跳动信号。
按键190包括开机键，音量键等。按键190可以是机械按键。也可以是触摸式按键。电子设备10可以接收按键输入，产生与电子设备10的用户设置以及功能控制有关的键信号输入。
马达191可以产生振动提示。马达191可以用于来电振动提示，也可以用于触摸振动反馈。
指示器192可以是指示灯，可以用于指示充电状态，电量变化，也可以用于指示消息，未接来电，通知等。
SIM卡接口195用于连接SIM卡。SIM卡可以通过插入SIM卡接口195，或从SIM卡接口195拔出，实现和电子设备10的接触和分离。电子设备10可以支持1个或N个SIM卡接口，N为大于1的正整数。电子设备10通过SIM卡和网络交互，实现通话以及数据通信等功能。
电子设备10的软件系统可以采用分层架构，事件驱动架构，微核架构，微服务架构，或云架构。本申请实施例以分层架构的Android系统为例，示例性说明电子设备10的软件结构。
图2是本申请实施例的电子设备10的软件结构框图。
分层架构将软件分成若干个层，层与层之间通过软件接口通信。在一些实施例中，将Android系统分为四层，从上至下分别为应用程序层，应用程序框架层，安卓运行时(Android runtime)和系统库，以及内核层。
应用程序层可以包括一系列应用程序包。
如图2所示，应用程序包可以包括，短信息，Facebook，QQ，地图，相册，日历，WLAN，推特(Twitter)，音乐播放器，亚马逊等应用程序。
应用程序框架层为应用程序层的应用程序提供应用编程接口(application programming interface，API)和编程框架。应用程序框架层包括一些预先定义的函数。
如图2所示，应用程序框架层可以包括窗口管理器，内容提供器，视图系统，电话管理器，资源管理器，通知管理器等。
窗口管理器用于管理窗口程序。窗口管理器可以获取显示屏大小，判断是否有状态栏，锁定屏幕，截取屏幕等。
内容提供器用来存放和获取数据，并使这些数据可以被应用程序访问。所述数据可以包括视频，图像，音频，拨打和接听的电话，浏览历史和书签，电话簿等。
视图系统包括可视控件，例如显示文字的控件，显示图片的控件等。视图系统可用于构建应用程序。显示界面可以由一个或多个视图组成的。
电话管理器用于提供电子设备10的通信功能。例如通话状态的管理(包括接通，挂断等)。
资源管理器为应用程序提供各种资源，比如本地化字符串，图标，图片，布局文件，视频文件等等。
通知管理器使应用程序可以在状态栏中显示通知信息，可以用于传达告知类型的消息，可以短暂停留后自动消失，无需用户交互。
Android Runtime包括核心库和虚拟机。Android runtime负责安卓系统的调度和管理。
核心库包含两部分：一部分是java语言需要调用的功能函数，另一部分是安卓的核心库。
应用程序层和应用程序框架层运行在虚拟机中。虚拟机将应用程序层和应用程序框架层的java文件执行为二进制文件。虚拟机用于执行对象生命周期的管理，堆栈管理，线程管理，安全和异常的管理，以及垃圾回收等功能。
系统库可以包括多个功能模块。例如：表面管理器(surface manager)，媒体库(Media Libraries)，三维图形处理库(例如：OpenGL ES)，2D图形引擎(例如：SGL)等。
表面管理器用于对显示子系统进行管理，并且为多个应用程序提供了2D和3D图层的融合。
媒体库支持多种常用的音频，视频格式回放和录制，以及静态图像文件等。媒体库可以支持多种音视频编码格式，例如：MPEG4，H.264，MP3，AAC，AMR，JPG，PNG等。
三维图形处理库用于实现三维图形绘图，图像渲染，合成，和图层处理等。
2D图形引擎是2D绘图的绘图引擎。
内核层是硬件和软件之间的层。内核层至少包含显示驱动，摄像头驱动，音频驱动，传感器驱动。
示例性的，以下实施例中所涉及的技术方案均可以在具有上述硬件架构和软件架构的电子设备10中实现。以下结合附图和应用场景对本申请实施例提供的文件管理方法进行详细介绍。接下来将结合图3-图8示例性介绍对图片进行管理的过程。本申请实施例中图片管理可以包括图片的展示和删除等。以下实施例中以电子设备10是手机为例进行说明。
首先，请参阅图3。图3为本申请实施例提供的一种图片管理方法示意图。如图3所示，图片管理方法至少可以包括以下几个步骤：
S301：电子设备10的触摸传感器180K检测用户的第一操作。
可选地，上述第一操作是用户对显示屏194上显示的相机应用的拍摄按钮的操作。如图4A所示，显示屏194中显示的界面20，该界面20包括状态栏204、导航栏205、时间组件图标和天气组件图标、多个应用程序的图标例如相机图标201、微信图标202、设置图标203、相册图标、微博图标、支付宝图标等。其中，状态栏204中可以包括运营商的名称(例如中国移动)、时间WIFI图标、信号强度和当前剩余电量。导航栏306可以包括返回控件、主屏幕控件和显示任务窗口的控件等。当电子设备10基于界面20中的相机图标201检测到用户的点击操作时，显示屏194显示拍摄界面a1，如图4B所示，拍摄界面a1至少可以包括显示待拍摄的画面的取景框b1及拍摄按钮c1。第一操作可以是单击，还可以是双击、长按等操作。此外，拍摄界面a1还可以包括用于打开相册的控件d1及用于切换摄像头的控件e1。
S302：响应于上述第一操作，电子设备10的摄像头193获取一张图片，保存至内部存储器121中。
具体地，进入拍摄界面a1或者拍摄界面a2后，摄像头193开启，实时获取待拍摄的 画面，当触摸传感器180K接收到用户的第一操作后，响应于该第一操作，摄像头193获取取景框内的拍摄画面，将该拍摄画面转换成图片，并保存至内部存储器121中。
此外，图片还可以是电子设备10通过天线1或天线2从其他应用的应用服务器下载得到，例如可以是电子设备10通过天线1从微博的应用服务器下载得到，又例如可以是电子设备通过天线2从谷歌浏览器的应用服务器下载得到。电子设备10可以将从其他应用的应用服务器下载得到的图片保存至内部存储器121中。
此外，图片还可以是与电子设备10连接的外部存储卡中存储的图片，电子设备10可以通过外部存储器接口120读取外部存储卡中存储的图片。
S303：电子设备10的触摸传感器180K检测用户的第二操作。
具体地，第二操作是用户对电子设备10的系统设置应用或相册应用中的设置项的操作。该设置项用于设置优化模式的开启和关闭。当优化模式开启后，电子设备10的处理器110可以判断是否满足优化条件，若满足优化条件，则获取内部存储器121中存储的图片及电子设备10对应的云相册的图片，并计算各个图片的分值，根据分值对图片进行优化的显示或者建议删除。
接下来以系统设置应用为例进行说明。当电子设备10检测到用户对设置控件203的操作时，电子设备10的显示屏194可以显示系统设置界面40，如图5A所示，系统设置界面40可以包括多个应用和组件的设置入口，当电子设备10检测到用户对某个应用的设置入口的操作时，电子设备10的显示屏194可以显示该应用的设置界面，设置界面中可以包括该应用相关的各种设置项。图5A中示出的应用程序及组件的设置入口包括：相册的设置入口401、微博的设置入口、支付宝的设置入口、微信的设置入口、相机的设置入口、电话的设置入口、短信的设置入口、通讯录的设置入口、天气的设置入口等。设置界面40还可以包括其他应用程序或组件的设置入口，当电子设备10检测到用户的上滑或者下滑操作时，显示屏194可以显示更多的应用的设置入口或者组件的设置入口。例如当电子设备10检测到用户对相册的设置入口401的操作时，电子设备10的显示屏194可以显示相册的设置界面50，如图5B所示。设置界面50中可以包括云端账户设置项501、云相册设置项502、其他需要同步的相册设置项503、优化模式设置项504、拍摄时间设置项505、拍摄地点设置项506。其中，云端账户设置项501用于设置云端账户，电子设备10可以将存储在内部存储器121中的图片上传至该云端账户对应的相册以备份，电子设备10也可以从该云端账户对应的相册(称为云相册)下载图片保存至内部存储器121中。云相册设置项502可以用于开启或关闭云相册。当云相册的功能被开启时，电子设备10可以与上述云相册进行数据交互，包括：电子设备10将存储在内部存储器121中的图片上传至云相册中以备份，以及电子设备10从上述云相册中下载图片保存至内部存储器121中。当云相册被关闭时，电子设备10无法与云相册进行数据交互。其他需要同步的相册设置项503用于设置需要将相册中的哪些相册上传至云相册中。优化模式设置项504用于开启或关闭优化模式，在优化模式开启的情况下，电子设备10的处理器110运行本发明实施例的图片展示和删除方法(参考后续的具体描述)。具体地，可以通过滑动优化模式设置项504包含的控件5041中的滑动按钮开启或关闭优化模式。当滑动按钮从左往右滑动时，可以将优化模式从关闭切换为开启，当滑动按钮从右往左滑动时，可以将优化模式从开启切换为关闭。优化模式的开启 和关闭方式不限于通过上述滑动按钮来实现，还可以存在其他形式，本申请实施例对此不做限制。拍摄时间设置项405用于使显示屏194显示相册中的图片时在图片中显示拍摄时间。拍摄地点设置项506用于使显示屏194显示相册中的图片时在图片中显示拍摄地点。图5B示出的设置界面50中包括的设置项仅为示例性说明，在具体实现中还可以包括其他的设置项，本申请实施例对此不作限定。
S304：响应于上述第二操作，电子设备10的处理器110开启优化模式。
具体地，优化模式开启后，电子设备10的处理器110开始判断是否满足优化条件，若满足则获取内部存储器121中存储的图片、通过外部存储器接口120获取外部存储卡中存储的图片及电子设备10对应的云相册中存储的图片的特征，并计算各个图片的分值，根据分值对图片进行优化的显示或者建议删除。
S305：电子设备10的处理器110判断是否满足优化条件，若满足，执行步骤S306，若不满足，继续执行步骤S305。
在一种具体的实施例中，当电子设备10的内部存储器121中的剩余存储空间不足时，即为满足优化条件。例如，当电子设备10的内部存储器121中的剩余存储空间不足200M时，即为满足优化条件。又例如，当电子设备10的内部存储器121中的剩余存储空间不足总存储空间的10％时，即为满足优化条件。上述剩余存储空间的值(200M)及上述剩余存储空间与总存储空间的比值(10％)仅为示例性说明，在具体实现中还可以有其他的值，本申请实施例对此不作限定。
在另一种具体的实施例中，当电子设备10的处理器110检测到当前时间为22:00时，判断是否满足优化条件，若不满足则延时1小时到23:00再次判断是否满足优化条件，直至满足优化条件，则执行步骤S306。其中，优化条件可以包括以下的一项或任意组合：电子设备10的电池142内的剩余电量高于总电量的20％、电子设备10的充电管理模块140正从充电器接收充电输入、电子设备10的显示屏194处于熄屏状态、电子设备10通过无线通信模块160已接入WiFi网络。上述判断是否满足优化条件的起始时间(22:00)、上述剩余电量与总电量的比值(20％)及上述延时时长(1小时)均为示例性说明，在实际实现中还可以有其他的值，本申请实施例对此不作限定。
示例性地，如图6A所示，当满足优化条件，则开始优化(即后续步骤S306-步骤S308)。此外，还可以在判断出满足优化条件后，显示提示框701提示用户“开始优化”，并提供取消优化控件702供用户在一段时间内输入取消优化的指令，如图6B所示。上述一段时间可以以倒计时的形式在取消优化控件702内展示，当倒计时结束后未接收到用户的取消优化指令，则开始优化。上述一段时间例如可以是10s、15s、30s等。
此外，当电子设备10的处理器110开启优化模式后，也可以无需执行步骤S305来判断是否满足优化条件，实际上可以直接在开启优化模式后，对内部存储器121中存储的图片、外部存储卡中存储的图片以及电子设备10对应的云相册中存储的图片执行一次步骤S306-步骤S308。
S306：电子设备10的处理器110获取内部存储器121中存储的图片、外部存储卡中存储的图片以及电子设备10对应的云相册中存储的图片的特征，并将特征输入打分算法模型，计算图片的分值。
具体地，每张图片可以包含很多特征，可以将图片包含的特征分为以下四类：使用习惯记录的特征、用户画像特征、图片本身的特征及电子设备存储情况。
使用习惯记录的特征可以包括但不限于：点击次数、浏览次数、缩放次数、是否被编辑、是否被分享或者被分享的次数、是否关联壁纸(例如与电话联系人关联、或者被设置为电子设备10主屏幕壁纸等)、是否被搜索或者被搜索的次数、是否被置于垃圾箱(被用户手动移入“垃圾箱”文件夹内，且依然保存在内部存储器121中)、是否被收藏、是否被备注(例如用户可以在某张图片的菜单选项中选择添加备注，可以记录拍摄图片时的心情，或者记录拍摄图片的内容等)、是否被标记(例如某张图片中包括多种水果，可以在该图片中给每种水果添加标记表明该水果的名称)、是否被下载(保存在云相册中且被下载至电子设备10的内部存储器121中)、最后浏览时间等。上述点击次数、浏览次数、缩放次数、是否被编辑、是否被分享或者分享的次数、是否被搜索或者被搜索的次数是否被置于垃圾箱、是否被收藏、是否被备注、是否被标记、是否被下载、最后浏览时间等特征都可以通过记录用户在一段时间内的操作获取界面。例如，若通过触摸传感器180K检测用户的操作为单击，且该操作的坐标位置为(x，y)，处理器110获取显示屏194当前显示的界面为60，如图7所示，则处理器110可以分析出在当前显示界面坐标为(x，y)的单击操作对应的事件为浏览P1，则处理器110使显示屏194显示P1的展示界面70，供用户浏览P1。处理器110可以将针对P1的“浏览”事件保存至内部存储器121中，另外还可以将该浏览时间也保存至内部存储器121中。当处理器110再一次分析出针对P1的“浏览”事件时，可以将此次的“浏览”事件及浏览时间保存至内部存储器121中。当处理器110判断出满足优化条件时，处理器110可以从内部存储器121中获取P1的“浏览”事件的次数，并获取最后一次记录的浏览时间(最后浏览时间)。在另外一种可能的实施例中，可以只记录针对P1的浏览次数及当前浏览的时间至内部存储器中。例如在第m次分析出针对P1的“浏览”事件后，可以只保存浏览次数的值m及浏览时间至内部存储器121中，在下一次分析出针对P1的“浏览”事件后，只需在内部存储器121中将浏览次数的值更新为m+1，并更新浏览时间即可。当处理器110判断出满足优化条件时，处理器110可以从内部存储器121中获取P1的浏览次数及浏览时间(最后浏览时间)。需要说明的是，图7中对于“浏览”事件发生的演示仅为示例性说明，实际上还可以在某图片的展示界面中接收用户的左滑或者右滑操作使“浏览”事件发生，还可以通过在第三方应用(例如微信、QQ、微博)中进入相册，选择图片分享时使“浏览”事件发生。
用户画像特征可以包括但不限于：用户分类、用户喜好。例如用户分类可以是用户的性别、年龄段等。用户喜好可以是用户是美食摄影爱好者还是风景摄影爱好者等。在一种可能实施例中，用户分类可以是电子设备10从系统账号(例如华为终端的华为账号中心、苹果终端的苹果账号中心(Apple ID)等)的个人信息中获取到用户之前填写的性别和出生日期等。在另一种可能的实施例中，用户分类可以是电子设备10调用第三方应用(例如QQ、微信、Youtube等)提供访问权限的数据访问接口，从第三方应用的服务器上获取用户的性别及出生日期等。电子设备10的处理器110可以根据获取到的出生日期计算该用户的年龄，从而确定该用户的年龄段。上述用户分类的获取方式仅为示例性说明，在具体实现中还可以有其他的获取方式，本申请实施例对此不作限定。在一种可能的实施例中，用 户喜好可以是电子设备根据内部存储器121及通过外部存储器接口120获取的外置存储卡中保存的多张图片分析得到。例如100张图片其中70张图片的内容为美食，则电子设备10认为该用户为美食摄影爱好者。在另一种可能的实施例中，电子设备10可以通过第三方应用(例如谷歌浏览器、知乎、百度论坛等)提供访问权限的数据访问接口，从第三方应用的服务器上获取用户的浏览记录，得到用户经常浏览的内容与美食摄影类相关，则电子设备10认为该用户为美食摄影爱好者。上述用户分类及用户喜好的获取方式仅为示例性说明，在具体实现中还可以有其他的获取方式，本申请实施例对此不作限定。
图片本身的特征可以包括但不限于是：拍摄时间、图片名字、拍摄手法(例如拍摄角度是俯拍还是仰拍、拍摄镜头是近景还是远景等)、拍摄模式(例如高动态范围成像(high dynamic range imaging，HDR)模式、大光圈模式、夜景模式、全景模式、黑白模式、慢动作模式、流光快门模式等等)、图片格式(例如是JPG格式、PNG格式还是BMP格式等)、色彩、构图(例如三分法构图、对角线构图、对称构图、黄金螺旋构图等)、美学评分、图片内容分类、地理位置、图片大小、分辨率、存储位置、设备类型(即拍摄该图片的设备的类型，例如可以是Huawei P20等)、与其他图片是否相似、是否模糊。上述存储位置为处理器110获取图片时该图片的存储位置。上述拍摄时间、图片名字、拍摄模式、图片格式、地理位置、图片大小、分辨率、设备类型等特征是图片本身的参数，这些参数可以与该图片一起存储在内部存储器121或者外部存储卡中，处理器110在获取图片时可从内部存储器121或者外部存储卡中获取这些特征。上述拍摄手法、色彩、构图、美学评分、图片内容分类、与其他图片是否相似、是否模糊等都可以通过电子设备10的处理器110对图片的内容，或者图片的参数，或者拍摄时的参数进行分析得到。
电子设备存储情况可以包括但不限于是：电子设备总存储容量、剩余可用存储容量、图片已占用存储容量。上述电子设备存储情况可以由处理器110查询内部存储器121的状态获得。
具体地，上述计算图片分值的过程可以是每天最多一次，具体触发分值计算的条件可以参考步骤S305中的描述，可设置为每天晚上22:00判断优化条件是否满足，若不满足则延时1小时至23:00再次判断是否满足，直至满足优化条件，触发分值计算。分值计算完毕后，可以将各个图片的分值保存至内部存储器121中，并将每个分值与其对应的图片关联起来，具体可以通过图片的标识将该图片与其对应的分值关联起来。在内部存储器121中已保存了各个图片分值的情况下，分值计算完毕后，可以更新各个图片的分值。上述图片的标识可以是电子设备10通过摄像头193拍摄时自动生成的。图片的标识还可以是从其他应用的服务器下载图片时携带的，或者是处理器110通过外部存储器接口120从外部存储卡中获取图片时该图片携带的。每张图片存在一个唯一的标识，用于使电子设备10通过标识识别图片。上述计算分值的频率(每天最多一次)仅为示例性说明，在具体实现中计算分值的频率可以更高或者更低，本申请实施例可以不作限定。
S307：电子设备10的处理器110根据分值展示图片。
具体地，根据各图片的分值，将第二图片优先展示，或者放大展示。如图8所示，界面80为相册的显示界面，该界面可以包括三种菜单控件(照片、相册、发现)，这三种菜单下图片的显示方式不同。图8中示出的当前选择的菜单种类即为“照片”。当电子设备 10检测到用户针对“照片”菜单控件的操作时，界面80可以展示多张图片，电子设备10可以在界面80中接收用户的滑动操作浏览更多的图片，“照片”菜单控件可以称为第一菜单控件。当电子设备10检测到用户针对“相册”菜单控件的操作时，界面80可以展示一个或多个文件夹(文件集合)，每个文件夹可以包含多张具有共同特征的图片，“相册”菜单控件可以称为第二菜单控件。例如，可以将同一拍摄模式(例如全景模式、HDR模式等)的图片归属于文件夹一，可以将同一来源(例如微博、微信、QQ、Facebook等)的图片归属于文件夹二，用户也可以自定义文件夹三，将多个图片归属于文件夹三。当电子设备10检测到用户针对“发现”菜单控件的操作时，界面80可以按照不同的分类展示多个文件夹，每个分类包含一个或多个文件夹，每个文件夹包含一个或多个图片，“发现”菜单控件可以称为第三菜单控件。例如可以按照地点和时间分类展示多个文件夹。地点分类下，可以具体根据拍摄地点(例如北京市、上海市、New York、Tokyo等)将图片归属于不同的文件夹。时间分类下，可以具体根据拍摄时间(例如2018年、2017年、2016年等)将图片归属于不同的文件夹。
图8左图的中照片可以是按照拍摄时间的先后等顺序排列，这种顺序排列方式，没有考虑本发明实施例中计算得到的分值。图8左图所示，分值低的(picture，P)P1位置在所有图片的最前面，分值高的P16位置靠后。经过优化后，图8右图中，分值高的P16已经被移动到所有图片的最前面优先展示，分值低的P1被往后移。
S308：电子设备10的处理器110根据分值删除图片。
可选地，根据各图片的分值，将第一图片集合展示，使用户可以一键删除多张第一图片。如图9所示，优化后将分值低的P1、P9、P12、P20都归类于第一文件夹，通过点击第一文件夹，用户可以查看该文件夹内包含的第一图片，电子设备10可以基于控件901一键删除第一文件夹中所有的第一图片。
可选地，可以根据各图片的分值，对第一图片区别显示，例如可以将第一图片的色度值降低，或增加外框，或打上特定的标记，以使其区别于其他图片。
具体地，上述根据分值删除图片的过程可以是每周最多一次，可以在每周日晚上22:00判断是否满足步骤S305中描述的优化条件，若不满足，则延时1小时至23:00再次判断是否满足，直至满足优化条件则触发每周最多一次的删除过程，以保证图片删除过程中不会影响用户正常使用，既提升删除效率，又提升用户体验。
上述删除频率(每周最多一次)仅为示例性说明，在具体实现中，删除频率可以更高也可以更低，本申请实施例对此不作限定。
此外，除了上述在满足优化条件的情况下自动执行删除过程之外，实际上还可以根据用户对第一文件夹的操作来删除其中的图片。此时图片的删除频率不限于上述每周最多一次。
此外，S307和S308可以是两个独立的步骤，本发明实施例对这两个步骤的先后顺序没有限定。
S309：电子设备10的触摸传感器180K检测用户的第三操作。
具体地，上述第三操作可以是触摸传感器180K检测到的用户对优化后的图片的操作，该操作例如可以是将放大显示的图片取消放大显示，该操作例如可以是将“建议删除”的文 件夹里的第一图片移出，使其显示在“照片”菜单下的界面80中，或者使其按照其特征分类归属于相册菜单下的其他文件夹内。
S310：响应于上述第三操作，电子设备10的处理器110调整上述打分算法模型。
具体地，根据第三操作判断图片分值与用户心理预期相比是偏低还是偏高，根据判断结果调整打分算法模型，使图片分值接近用户心理预期，从而使图片展示和删除的结果更加符合用户意图，提升图片展示和删除的结果的准确性。
此外，还可以在Beta用户测试中搜集不同的用户针对不同电子设备10的相册中图片的反馈数据，从而根据反馈数据对算法模型进行调整。
进一步地，可以收集用户大数据，引入人工智能(artificial intelligence，AI)算法对打分算法模型进行调整。通过对打分算法模型进行调整，可以使其输出的图片的分值更接近用户心理预期，从而使图片展示和删除的结果更加符合用户意图，提升图片展示和删除的准确性。
接下来结合具体实例详细介绍本申请实施例提供的图片展示和删除的方法。
具体地，电子设备10的处理器110可以获取其内部存储器121及外部存储卡中的所有图片以及电子设备10对应的云相册中存储的图片。电子设备10的处理器110还可以获取各个图片的特征。在获取各个图片的特征后，首先可以根据图片的至少一个特征确定该图片可用的场景为展示场景或者删除场景。前述至少一个特征用于初步判断某张图片可能是第一图片或者可能是第二图片。若根据前述至少一个特征判断出该图片可能是第一图片，则确定该图片可用于删除场景；若根据前述至少一个特征判断出该图片可能是第二图片，则确定该图片可用于展示场景。此处用于确定图片可用的场景的至少一个特征可称为场景特征。确定图片可用的场景的目的在于确定提取图片的哪些特征以及这些特征的打分标准用于计算该图片的分值。根据不同的特征以及这些特征的打分标准来计算图片的分值可以使最终的分值更加符合用户的心理预期，提高分值的准确性，从而使图片的展示或者删除更加准确，减少用户的操作，提升操作效率。
若该图片可用的场景为展示场景，则提取图片的第一特征及第二特征。根据第一特征及第二特征计算每张图片的分值。再根据分值对图片进行展示。若该图片可用的场景为删除场景，则提取图片的第三特征及第四特征。根据第三特征及第四特征计算每张图片的分值。再根据分值对图片进行删除。以下实施例根据可用的场景分别介绍图片展示方法以及图片删除方法。
具体地，图片P i的分值S i计算公式如下：
Figure PCTCN2018110070-appb-000001
展示场景下，公式(1)中，k、m、j、n为正整数，m为第一特征的个数，k＝1，...，m，k不同的值分别对应一个第一特征。α k为k值对应的第一特征的值；n为第二特征的个数，j＝1，...，n，j不同的值分别对应一个第二特征。x j为j值对应的第二特征的值。ω j为j值对应的第二特征的权重。f(x j)为j值对应的第二特征的特征函数，用于将第二特征的值进行归一化。本申请实施例中，初始算法中各第二特征的权重ω j的值均相同，设置为1， 后续可根据测试反馈结果，针对某些特征调整权重，为用户提供各权重配置功能。在实际实现中，各第二特征的权重ω j可以不同，ω j的取值也不限于为1。本申请实施例对此不作限制。
上述k值对应的第一特征可以通过查找映射关系表获得。映射关系表可以保存在内部存储器121中，用于表明k值与第一特征的映射关系。例如，k值为1时，对应的第一特征可以是收藏，k值为2时，对应的第一特征可以是备注等。类似的，j值对应的第二特征也可以通过查找映射关系表获得。
删除场景下，公式(1)中，k、m、j、n为正整数，m为第三特征的个数，k＝1，...，m，k不同的值分别对应一个第三特征。α k为k值对应的第三特征的值；n为第四特征的个数，j＝1，...，n，j不同的值分别对应一个第四特征。x j为j值对应的第四特征的值。ω j为j值对应的第四特征的权重。f(x j)为j值对应的第四特征的特征函数，用于将第四特征的值进行归一化。本申请实施例中，初始算法中各第四特征的权重ω j的值均相同，设置为1，后续可根据测试反馈结果，针对某些特征调整权重，为用户提供各权重配置功能。在实际实现中，各第四特征的权重ω j可以不同，ω j的取值也不限于为1。本申请实施例对此不作限制。
与展示场景下类似的，k值对应的第三特征、j值对应的第四特征均可通过查找映射关系表获得，在此不详述。
接下来将结合表1，示例性列举了20张图片(P1-P20)包含的各个特征对应的状态，后续实施例将以这20张图片为基础进行说明。
表1P1-P20各特征状态表
Figure PCTCN2018110070-appb-000002
Figure PCTCN2018110070-appb-000003
表1列出了P1-P20，每张图片包含的各个特征的状态。表1中第一列用于表示图片的编号。表1中的第一行用于表示图片的各个特征。
接下来介绍表1中各个特征的含义以及各个特征对应的状态的含义。
“上传云端”指的是某张保存在内部存储器121中的图片，是否已被上传至云端。若某张图片已被上传至云端，则确定用户可能不喜欢这张图片；若某张图片未被上传至云端，则确定用户可能喜欢这张图片。若已被上传至云端，则“上传云端”的状态为是；若未被上传至云端，则“上传云端”的状态为否。
若处理器110从内部存储器121中获取某图片，则该图片的存储位置为内置，确定用户可能不喜欢这张图片，“存储位置”的状态为内置；若处理器110通过外部存储器接口120从外部存储卡中获取某图片，则该图片的存储位置为外置，确定用户可能喜欢这张图片，则“存储位置”的状态为外置。
若某张图片被关联壁纸，则确定用户可能喜欢这张图片，“关联壁纸”的状态为是；若某张图片未被关联壁纸，则确定用户可能不喜欢这张图片“关联壁纸”的状态为否。
若某张图片被收藏，则确定用户可能喜欢这张图片，“收藏”的状态为是；若某张图片未被收藏，则确定用户可能不喜欢这张图片，“收藏”的状态为否。
若某张图片被备注，则确定用户可能喜欢这张图片，“备注”的状态为是；若某张图片未被备注，则确定用户可能不喜欢这张图片，“备注”的状态为否。
若某张图片被分享，则确定用户可能喜欢这张图片，“分享”的状态为是；若某张图片未被分享，则确定用户可能不喜欢这张图片，“分享”的状态为否。
若某张图片是在某种拍摄模式下拍摄的，则确定用户可能喜欢这张图片，“拍摄模式”的状态为是；若某张图片不是在任意一种拍摄模式下拍摄的，则确定用户可能不喜欢这张图片，“拍摄模式”的状态为否。
若某张图片的内容属于某种分类，则确定用户可能喜欢这张图片，“图片内容分类”的状态为是；若某张图片的内容不属于任意一种分类，则确定用户可能不喜欢这张图片，“图片内容分类”的状态为否。
若某张图片被浏览的次数大于某阈值，确定用户可能喜爱该图片；若某张图片被浏览的次数不大于该阈值，确定用户可能不喜爱该图片。该阈值例如可以是5。若某张图片被浏览的次数大于5次，“浏览次数”的状态为＞5；若某张图片被浏览的次数不大于5次，“浏览次数”的状态为≤5。
若某张图片的拍摄时间大于某阈值，确定用户可能不喜爱该图片；若某张图片的拍摄时间不大于该阈值，确定用户可能喜爱该图片。该阈值例如可以是30。若某张图片的拍摄时间大于30天，“拍摄时间”的状态为＞30；若某张图片的拍摄时间不大于30天，“拍摄时间”的状态为≤30。
“最后浏览时间”指的是图片上一次被浏览的时间。可以通过某张图片上一次被浏览的时间确定用户是否喜爱该图片。若某张图片上一次被浏览的时间大于某阈值，确定用户可能不喜爱该图片；若某张图片上一次被浏览的时间不大于该阈值，确定用户可能喜爱该图片。该阈值例如可以是30。若某张图片上一次被浏览的时间大于30天，“最后浏览时间”的状态为＞30；若某张图片上一次被浏览的时间不大于30天，“最后浏览时间”的状态为≤30。
“图片大小”指的是图片占用的存储空间。可以通过某张图片占用的存储空间的大小确定用户是否喜爱该图片。若某张图片占用的存储空间大于某阈值，确定用户可能不喜爱 该图片；若某张图片占用的存储空间不大于该阈值，确定用户可能喜爱该图片。该阈值例如可以是5兆(M)。若某张图片占用的存储空间大于5M，“图片大小”的状态为＞5；若某张图片占用的存储空间不大于5M，“图片大小”的状态为≤5。
“美学评分”指的是根据图片的结构、色彩等计算的分值。可以通过某张图片的美学评分确定用户是否喜爱该图片。若某张图片的美学评分大于某阈值，确定用户可能喜爱该图片；若某张图片的美学评分不大于该阈值，确定用户可能不喜爱该图片。该阈值例如可以是5分。若某张图片的美学评分大于5分，“美学评分”的状态为＞5；若某张图片的美学评分不大于5分，“美学评分”的状态为≤5。
“垃圾箱”指的是图片的一种分类，可以以文件夹的形式存在，“垃圾箱”文件夹内包含的图片均为用户不喜欢的且想要删除的图片，此时归属于“垃圾箱”文件夹的图片依然保存在内部存储器121中，当图片从“垃圾箱”文件夹中被清理或者被删除时，该图片会从内部存储器121中删除。若某张图片被归属于“垃圾箱”文件夹，“垃圾箱”的状态为是；若某张图片未被归属于“垃圾箱”文件夹时，“垃圾箱”的状态为否。此外，用户不喜欢且想要删除的图片归属的文件夹不限定于命名为“垃圾箱”，还可以是“回收站”、“最近删除”等，本申请实施例对此不作限定。
具体地，可能是第二图片的图片可用于展示场景，可能是第一图片的图片可用于删除场景。上述用于确定图片可用的场景为展示场景或者删除场景的至少一个特征可以表示某图片可能是第一图片或者是第二图片，上述至少一个特征可以包括：收藏、备注、关联壁纸、存储位置、上传云端。若某张图片没有被收藏、没有被备注、没有被关联壁纸、存储位置为内置且为已上传云端，则确定该图片可能是第一图片，该图片可用于删除场景。若某张图片被收藏，则代表用户可能喜爱这张图片，则该图片可能是第二图片，可用于展示场景；若某张图片的存储位置为外置，则代表用户可能喜爱这张图片，则该图片可能是第二图片，可用于展示场景；若某张图片被关联壁纸，则代表用户可能喜爱这张图片，则该图片可能是第二图片，可用于展示场景；若某张图片被备注，则代表用户可能喜爱这张图片，则该图片可能是第二图片，可用于展示场景；若某张图片未被上传至云端，则代表用户可能喜爱这张图片，则该图片可能是第二图片，可用于展示场景。即若某一张图片被收藏，或者被备注，或者被关联壁纸，或者存储位置为外置，或者为未被上传至云端，则该图片可能的第二图片，可用于展示场景。上述用于确定使用场景的特征不限于上述列出的五种，还可以包括其他特征，例如是否被分享，若被分享则可能是第二图片，可用于展示场景。在具体实现中，用于确定使用场景的特征可以是上述列举的几种特征的任意组合，也可以包括其他特征，能够用于确定某图片可能是第一图片或者可能的第二图片，确定各个图片的使用场景即可，本申请实施例对此不作限制。
因此，从表1中第二列至第六列的特征(上传云端、存储位置、关联壁纸、收藏、备注)的状态可以看出，P1、P4、P6、P7、P8、P10、P11、P13、P15、P18、P19可用于展示场景，P2、P3、P5、P9、P12、P14、P16、P17、P20可用于删除场景。
接下来针对不同的场景，介绍不同场景下第一特征(或第三特征)及第二特征(或第四特征)的选择及值。首先介绍展示场景，然后介绍删除场景。
在展示场景下：
第一特征和第二特征用于表征用户对图片的喜爱程度。第一特征例如可以包括：收藏和备注。若被收藏或者被备注，则确定用户喜爱这张图片，通过该第一特征的值，提升该图片的分值。结合表1的描述，若根据第一特征的状态确定用户可能喜欢这张图片，则该特征的值为较大的值；若根据第一特征的状态确定用户可能不喜欢这张图片，则该特征的值为较小的值。对于第一特征不同状态对应的值，示例性的可见表2，较大的值为10，较小的值为1。第二特征例如可以包括：分享、拍摄模式、图片内容分类、浏览次数、拍摄时间、最后浏览时间、图片大小、美学评分。结合表1的描述，若根据第二特征的状态确定用户可能喜欢这张图片，则该特征的值为较大的值；若根据第二特征的状态确定用户可能不喜欢这张图片，则该特征的值为较小的值。对于第二特征不同状态对应的值，示例性的可见表3，较大的值为1，较小的值为0。
具体地，分享可以包括通过第三方软件分享至第三方平台，第三方平台例如可以但不限于是微信、微博、腾讯QQ、腾讯微博、Facebook等。分享也可以包括通过短距离无线通信方式分享至其他电子设备。若某张图片被分享过，则第二特征“分享”的值为1；若某张图片未被分享过，则则第二特征“分享”的值为0。若某张图片的在某种拍摄模式下拍摄的，则第二特征“拍摄模式”的值为1；若某张图片不是在任意一种拍摄模式下拍摄的，则第二特征“拍摄模式”的值为0。若某张图片的内容属于某种分类，则第二特征“图片内容分类”的值为1；若某张图片的内容不属于任意一种分类，则第二特征“图片内容分类”的值为0。若某张图片被浏览的次数超过5次，则第二特征“浏览次数”的值为1；若某张图片被浏览的次数不超过5次，则第二特在“浏览次数”的值为0。若某张图片的拍摄时间超过30天，则第二特征“拍摄时间”的为0；若某张图片的拍摄时间不超过30天，则第二特征“拍摄时间”的值为1。若某张图片的上次浏览时间超过30天，则第二特征“上次浏览时间”的值为0；若某张图片的上次浏览时间不超过30天，则第二特征“上次浏览时间”的值为1。若某张图片的大小超过5兆，则第二特征“图片大小”的值为0；若某张图片的大小不超过5兆，则第二特征“图片大小”的值为1。若某张图片的美学评分超过5分，则第二特征“美学评分”的值为1；若某张图片的大小不超过5分，则第二特征“美学评分”的值为0。
具体地，若某张图片被收藏，则第一特征“收藏”的值为10，第二特征加权求和的分值放大十倍，极大程度上提高该图片的分值，确保用户喜欢的图片分值靠前；若某张图片未被收藏，则第一特征“收藏”的值为1，不改变第二特征加权求和的分值。若某张图片被备注，则第一特征“备注”的值为10，将第二特征加权求和的分值放大十倍，极大程度上提高该图片的分值，确保用户喜欢的图片分值靠前；若某张图片未被备注，则第一特征“备注”的值为1，不改变第二特征加权求和的分值。总之需要通过第一特征的较大的值提升图片的分值。
表2展示场景下第一特征的选择及值
第一特征 收藏 备注
是 10 10
否 1 1
表3展示场景下第二特征的选择及值
Figure PCTCN2018110070-appb-000004
结合表2和表3可以看出，公式(1)中m＝2，n＝8。k值与第一特征的映射关系表示例性的如表4所示。j值与第二特征的映射关系表示例性的如表5所示。
表4 k值与第一特征的映射关系表
k值 第一特征
k＝1 收藏
k＝2 备注
表5 j值与第二特征的映射关系表
j值 第二特征
j＝1 分享
j＝2 拍摄模式
j＝3 图片内容分类
j＝4 浏览次数
j＝5 拍摄时间
j＝6 最后浏览时间
j＝7 图片大小
j＝8 美学评分
上述k值与第一特征的映射关系以及j值与第二特征的映射关系仅为示例性说明，实际上还可以存在其他的映射关系，本申请实施例对此不作限定。
具体地，各个第一特征的选择及第二特征的选择不限于表2和表3中示出的选择，在实际使用过程中，还可以有其他的选择，后续可以根据用户的反馈对第一特征及第二特征进行选择，还可以由用户手动选择第一特征及第二特征。此外，各个第一特征及第二特征的较大的值及较小的值也不限于表2和表3中示出的值，在实际使用过程中，还可以有其他选择，本申请实施例对此不作限定。
在一些实施例中，第二特征的值不限于是表3列出的0或1。在一种可能的实现方式中，还可以是0或2、0或10、1或10等。在一种可能的实现方式中，第二特征的值还可以是连续的，例如对于第二特征“浏览次数”的值可以但不限于是＞5次时值为1，≤5次时值为0，还可以是随着浏览次数的增加，其值线性增加。如浏览次数为0次，则值为0；浏览次数为1次，则值为0.1；浏览次数为2次，则值为0.2；浏览次数为10次及以上，则值为 1。同理可适用于第二特征“分享”、“拍摄时间”、“最后浏览时间”、“图片大小、“美学评分”等，具体赋值的方式可参考第二特征“浏览次数”的赋值方式，在此不再赘述。
在一些实施例中，第一特征的较大值取决于根据第二特征加权求和计算得到的分值S′ i：
Figure PCTCN2018110070-appb-000005
式(2)中j、n、ω j、f(x j)的含义与式(1)一致，在此不详述。从式(2)可以看出，根据各个第二特征计算得到的分值S′ i取决于第二特征的个数及各第二特征的值。若想要通过第一特征的较大的值提升图片的分值，突出第一特征对图片分值的决定性作用，需要使第一特征的较大的值大于各个第二特征加权求和可能的最大值，各个第二特征加权求和可能的最大值即为各个第二特征的值均为较大的值时求得的分值S′ i。示例性地，若第二特征的个数为8个，每个第二特征的较大的值均为1，且每个第二特征占的权重ω j均为1，则各个第二特征加权求和可能的最大值为8，第一特征的较大的值需大于8即可。示例性地，若第二特征的个数为10个，每个第二特征的较大的值均为2，且每个第二特征占的权重ω j均为1，则各个第二特征加权求和可能的最大值为20，第一特征的较大的值需大于20即可。
示例性的，图片A的第二特征的的值均为较大的值，但第一特征的值为较小的值，图片B的第一特征的值为较小的值，但第二特征的值不全为较大的值，则图片B的分值一定会大于图片A的分值。从该示例中可以看出，第一特征的值为较大的值时，可以直接使第二特征加权求和的分值放大若干倍，很大程度上提升该图片的分值，体现出该图片的分值优势，从而通过分值体现出用户对该图片的喜爱程度。
具体地，若某些图片被收藏或者被备注，则仅通过第一特征的值即可将其与未被收藏且未被备注的图片的分值拉开差距，体现用户对这些图片喜爱程度的不同。若两张图片同时被收藏或被备注，或者都未被收藏且未被备注，则通过第二特征的值将其分值拉开差距，体现用户对这两张图片喜爱程度的不同。
结合表1、表2及表3可以获得上述适用于推荐场景下的各图片的各个特征的值，并根据公式(1)计算出各个图片的分值S i，如表6所示。
表6展示场景下的图片各个特征的取值及图片的分值
Figure PCTCN2018110070-appb-000006
Figure PCTCN2018110070-appb-000007
表6中，第一列为各个图片的编号，第一行为图片的各个特征、S′ i及S i。
具体地，根据表6中的分值计算结果，可以看出若根据第二特征加权求和的分值S′ i对表6中列举的图片进行排序(分值由高到低)，与根据第一特征及第二特征共同作用求得的分值S i对表6中列举的图片进行排序(分值由高到低)的对比图如图10所示。结合表6和图10可以看出，仅根据第二特征加权求和的分值排序第6的P11，经过第一特征的较大的值提高分值后，排序提升至第1；仅根据第二特征加权求和的分值排序第7的P15，经过第一特征的较大的值提高分值后，排序提升至第5；仅根据第二特征加权求和的分值排序第8的P7，经过第一特征的较大的值提高分值后，排序提升至第6。可以看出，经过第一特征的较大的值可以直接使第二特征加权求和的分值放大若干倍，很大程度上提升该图片的分值，体现出该图片的分值优势，从而通过分值体现出用户对该图片的喜爱程度。
具体地，在计算得到各图片的分值后，电子设备10可以根据分值对图片进行展示。
在一种可能的实施例中，电子设备10可以在“照片”菜单下的界面80中优先展示分值高的图片。在一种具体地实现方式中，可以将分值最高的图片排在第一，按照分值从高到低的顺序从上到下，先左后右的排列依次图片，排列方式可参考图8右图所示，在此不赘述。
在另一种可能的实施例中，电子设备10可以在“照片”菜单下的界面80中将第二图片的面积变大。示例性的，第二图片可以是指分值最高的1张图片，则分值最高的一张图片为P11，将其在界面80中的面积变大，至少可以使P11在界面80中的面积大于其他非第二图片的面积。如图11A所示，第二图片P11的尺寸为其他非第二图片的面积的至少4倍。
在另一种可能的实施例中，电子设备10可以在“照片”菜单下的界面80中加框显示第二图片，如图11B所示。
在另一种可能的实施例中，电子设备10还可以在“照片”菜单下的界面80中标星显示第二图片，如图11C所示。
在具体实现中，还可以有其他显示方式显示第二图片，例如特殊颜色显示第二图片，或者特殊透明度显示第二图片等，本申请实施例对此不作限制。“照片”菜单下界面80中展示的图片为内部存储器121及外部存储卡中的所有图片。
在另一种可能的实施例中，电子设备10可以集合展示第二图片，如图12和图13所示。 图12中，电子设备10检测到用户针对“发现”菜单控件的操作时，界面80中除了包括地点分类、时间分类之外，还可以包括第一分类8021。地点分类中包含多个文件夹，每个文件夹中可以包含多张图片，该文件夹中包含的多张图片的拍摄地点相同，例如可以是北京市、上海市、New York、Tokyo等。时间分类中包含多个文件夹，每个文件夹中可以包含多张图片，该文件夹中包含的多张图片的拍摄时间相同，例如可以是2018年、2017年、2016年、2015年等。第一分类8021中可以包含多张第二图片。“第一分类”在界面80中还可以显示为“猜你喜欢”或者“Favorite”或者“Fav”，不限于此，还可以有其他的类别名称，本申请实施例对此不做限制。以上分类方式除了按照地点分类、时间分类外，还可以有其他的分类方式，例如按照人物分类，本申请实施例对具体的分类方式不做限定。界面80中还可以包括搜索控件804，当电子设备10的触摸传感器180K检测到用户对搜索控件804的操作时，电子设备10的显示屏194显示搜索界面，如图13所示，搜索界面90至少可以包括：搜索栏901、展示界面902及状态栏。其中状态栏与图4A中列出的状态栏204类似，在此不赘述。搜索栏901用于接收用户的搜索指令，用于从电子设备10的内部存储器121、外部存储卡及云相册中搜索图片。当电子设备10的触摸传感器180K检测到用户对搜索栏901的操作后，在展示界面902中显示输入法界面9022，用户可以在输入法界面9022中输入想要搜索的图片，如“蓝天”，则电子设备10可从内部存储器121、外部存储卡及云相册中搜索出内容为“蓝天”的图片。展示界面902中包括多个不同的分类，图13中示出了地点分类及第二分类9021，地点分类中包含的文件夹与图12中地点分类中包含的文件夹类似，第二分类9021中包含第二图片，与图12中第一分类8021中包含的第二图片类似，在此不赘述。
上述对于第二图片的集合展现形式不限于上述列出的分类的形式，在实际实现中还可以有其他的展现形式，本申请实施例对此不作限制。
本申请实施例首先通过部分特征确定图片可用的场景，再通过第一特征及第二特征的共同作用，使得根据本申请实施例提供的算法模型计算的分值更加符合用户的心理预期，可以使用户快速查找图片，与现有技术相比，本申请实施例展示的图片更加符合用户的操作习惯，提升了用户的操作效率。
在删除场景下：
第三特征和第四特征用于表征用户对图片的不喜爱程度。第三特征例如可以包括：垃圾箱。此处包含有“垃圾箱”特征的图片为用户主动删除，当前被归类于“垃圾箱”文件夹中但图片依旧保存在内部存储器121中的图片。若某张图片被归类于“垃圾箱”文件夹，则确定用户不喜爱这张图片，通过该第三特征的值，降低该图片的分值。结合表1的描述，若根据第三特征的状态确定用户可能不喜欢这张图片，则该特征的值为较大的值；若根据第三特征的状态确定用户可能喜欢这张图片，则该特征的值为较小的值。对于第三特征不同状态对应的值，示例性的可见表7，较大的值为10，较小的值为1。第四特征例如可以包括：拍摄模式、图片内容分类、拍摄时间、最后浏览时间、图片大小、美学评分。结合表1的描述，若根据第四特征的状态确定用户可能不喜欢这张图片，则该特征的值为较小的值；若根据第二特征的状态确定用户可能喜欢这张图片，则该特征的值为较大的值。对 于第四特征不同状态的值，示例性的可见表8。
具体地，若某张图片被归类于“垃圾箱”文件夹，确定用户很可能不喜欢这张图片，则第三特征“垃圾箱”的较大的值为10，由于在删除场景下各第四特征加权求和的分值可能为负，则通过将第三特征的较大的值为10可以将第四特征加权求和的分值负向放大十倍，极大程度上降低该图片的分值，确保用户不喜欢的图片分值靠后；若某张图片未被归类于“垃圾箱”文件夹，确定用户可能喜欢这张图片，则第三特征“垃圾箱”的较小的值为1，不改变第四特征加权求和的分值。在一种可能的实施例中，若删除场景下各第四特征加权求和的分值不为负，则在根据第三特征的状态确定用户可能不喜欢这张图片的情况下，则该特征的值为较小的值。其中，较小的值可以是小数，也可以是0，还可以是负数。总之需要通过第三特征的值降低图片的分值。本申请实施例主要以在删除场景下各第四特征加权求和的分值可能为负的情况为例进行说明。
表7删除场景下第三特征的选择及值
第三特征 垃圾箱
是 10
否 1
表8删除场景下第四第四特征的选择及值
Figure PCTCN2018110070-appb-000008
结合表7和表8可以看出，公式(1)m＝1，n＝7。k值与第三特征的映射关系表示例性的如表9所示。j值与第四特征的映射关系表示例性的如表10所示。
表9 k值与第三特征的映射关系表
k值 第三特征
k＝1 垃圾箱
表10 j值与第四特征的映射关系表
j值 第二特征
j＝1 拍摄模式
j＝2 图片内容分类
j＝3 浏览次数
j＝4 拍摄时间
j＝5 最后浏览时间
j＝6 图片大小
j＝7 美学评分
上述j值与第四特征的映射关系仅为示例性说明，实际上还可以存在其他的映射关系，本申请实施例对此不作限定。
具体地，各个第三特征的选择及第四特征的选择不限于表7和表8中示出的选择，在实际使用过程中，还可以有其他的选择，后续可以根据用户的反馈对第三特征及第四特征进行选择，还可以由用户手动选择第三特征及第四特征。此外，各个第三特征及第四特征的较大的值及较小的值也不限于表7和表8中示出的赋值，在实际使用过程中，还可以有其他选择，本申请实施例对此不作限定。
在一些实施例中，第四特征的值不限于是表8列出的几种情况。在一种可能的实现方式中，第四特征的较小的值可以为正数，第四特征的较大的值也为正数。在另一种可能的实现方式中，第四特征的较小的值可以是负数，第四特征的较大的值也为负数。在一种可能的实现方式中，第四特征的赋值还可以是连续的，例如对于第四特征“拍摄时间”的值可以是随着拍摄时间的增加，其值线性降低。如拍摄时间为30天以上时，值为-1分；拍摄时间大于20天且小于等于30天时，值为-0.5；拍摄时间大于10天且小于等于20天时，值为0；拍摄时间大于5天且小于等于10天时，值为0.5；拍摄时间小于等于5天时，值为1。同理可适用于第四特征“浏览此数”、“最后浏览时间”、“图片大小、“美学评分”等，具体赋值方式可参考第四特征“拍摄时间”的赋值方式，在此不再赘述。
在一些实施例中，第三特征的较大的值取决于根据第四特征加权求个计算得到的分值S′ i。
从式(2)可以看出，根据各个第四特征计算得到的分值S′ i取决于第四特征的个数及各第四特征的值。若想要通过第三特征的较大的值降低图片的分值，突出第三特征对图片分值的决定性作用。
在S′ i为负数的情况下，需要使第三特征的较大的值大于各个第四特征加权求和可能的最小值的绝对值，各个第四特征加权求和可能的最小值即为各个第四特征的值均为较小的值时求得的分值S′ i。示例性地，若第四特征的个数为8个，每个第四特征的较小的值均为-1，且每个第四特征占的权重ω j均为1，则各个第四特征加权求和可能的最小值为-8，第三特征的较大的值需大于8即可。示例性地，若第四特征的个数为10个，每个第四特征的较小的值均为-2，且每个第四特征占的权重ω j均为1，则各个第四特征加权求和可能的最小值为-20，第三特征的较大的值需大于20即可。
在S′ i为非负数的情况下，需要使第三特征的较小的值小于各个第四特征加权求和可能的最大值的倒数，各个第四特征加权求和可能的最大值即为各个第四特征的值均为较大的值时求得的分值S′ i。示例性地，若第四特征的个数为8个，每个第四特征的较大的值均为1，且每个第四特征占的权重ω j均为1，则各个第四特征加权求和可能的最大值为8，第三特征的较小的值需小于1/8即可。示例性地，若第四特征的个数为10个，每个第四特征的较大的值均为2，且每个第四特征占的权重ω j均为1，则各个第四特征加权求和可能的最大值为20，第三特征的较小的值需小于1/20即可。
总之，无论在S′ i为负数还是S′ i为非负数的情况下，需要通过根据第三特征的状态确定用 户不喜爱某张图片时该第三特征的值降低图片的分值，保证根据第三特征的状态确定用户不喜爱某张图片且的S′ i值较高的图片的分值低于根据第三特征确定用户可能喜爱某张图片且S′ i值较低的图片的分值。
示例性的，图片A的第四特征的值均为较小的值，但根据第三特征的状态确定用户可能喜爱某张图片，根据图片B的第三特征确定用户不喜爱某张图片，但第四特征的值不全为较小的值，则图片B的分值一定会小于图片A的分值。从该示例中可以看出，在根据第三特征的状态确定用户不喜爱某张图片的情况下，可以直接使第四特征加权求和的分值负向放大若干倍或者缩小若干倍，很大程度上降低该图片的分值，体现出该图片的分值劣势，从而通过分值体现出用户对该图片的不喜爱程度。
结合表1、表7及表8可以获得上述适用于删除景下的各图片的各个特征的取值，并根据公式(1)计算出各个图片的分值S i，如表11所示。
表11删除场景下的图片各个特征的值及图片的分值
Figure PCTCN2018110070-appb-000009
表11中，第一列为各个图片的编号，第一行为图片的各个特征、S′ i及S i。
具体地，根据表11中的分值计算结果，可以看出若根据第四特征加权求和的分值S′ i对表11中列举的图片进行排序(分值由高到低)，与根据第三特征及第四特征共同作用求得的分值S i对表11中列举的图片进行排序(分值由高到低)的对比图如图14所示。结合表11和图14可以看出，仅根据第四特征加权求和的分值排序第2的P3，经过第三特征的较大的值降低分值后，排序降低至第6；仅根据第四特征加权求和的分值排序第4的P12，经过第三特征的较大的值降低分值后，排序降低至第7；仅根据第四特征加权求和的分值排序第6的P20，经过第三特征的较大的值降低分值后，排序降低至第8；仅根据第四特征加权求和的分值排序第8的P14，经过第三特征的较大的值降低分值后，排序降低至第9。可 以看出，经过第三特征的较大的值可以直接使第四特征加权求和的分值负向放大若干倍，很大程度上降低该图片的分值，体现出该图片的分值劣势，从而通过分值体现出用户对该图片的不喜爱程度。
具体地，在计算得到各图片的分值后，电子设备10可以根据分值并结合删除条件提示用户删除或者直接自动删除第一图片。删除条件即为电子设备10确定第一图片的依据之一。例如删除条件可以是使电子设备10的内部存储器121的剩余可用容量不低于某阈值，则电子设备10可以根据该删除条件以及各图片的分值，确定需要删除的第一图片，以保证第一图片被删除后，电子设备10的内部存储器121剩余可用容量不低于上述阈值。需要说明的是，本申请实施例中提及的“删除”与将图片归类于“垃圾箱”文件夹中不同，本申请实施例中的“提示用户删除”是指依然保存在内部存储器121或者外部存储卡中，直至接收用户的删除指令，则从内部存储器121中或者外部存储卡中删除。“直接删除”是指将图片从内部存储器121中或者外部存储卡中删除，以释放电子设备10的存储容量。
具体地，可以根据删除条件及各图片的分值确定第一图片，提示用户删除第一图片，或者直接自动删除第一图片。
在一种可能的实施例中，第一图片可以根据用户手机总存储容量Q、可用存储容量Q left、图片张数N计算确定。
具体地，可以设置目标剩余存储容量：
Q left.threshold＝min(2G，Q×10％) (3)
即当总存储容量Q大于20G时，目标剩余存储容量Q为2G；当总存储容量Q不大于20G时，目标剩余存储容量Q left.threshold为总存储容量Q的10％。即目标剩余存储容量至多为2G。
待求的可删除分值阈值S t可通过公式(5)求得：
Figure PCTCN2018110070-appb-000010
其中，S i为图片i的分值，分值小于等于可删除分值阈值S t的图片的总容量大于或者等于目标剩余存储容量Q left.threshold与当前剩余容量Q left的差值。假设，目标剩余存储容量Q left.threshold与当前剩余容量Q left的差值为200兆(M)，即待释放的存储空间为200M，则依次选定分值最低的图片为待删除的图片，直至选定的待删除的图片的总容量刚好等于或者大于200M，则这些待删除的图片中分值最高的图片的分值即为可删除分值阈值S t的。
此外，可以设置每次删除图片的总容量大小Q delete.threshold为：
Q delete.threshold＝max(100M，(Q left.threshold-Q left)) (5)
即当目标剩余存储容量Q left.threshold与当前剩余容量Q left的差值小于100M时，每次删除图片的总容量大小Q delete.threshold为100M；当目标剩余存储容量Q left.threshold与当前剩余容量Q left的差值不小于100M时，每次删除图片的总容量大小Q delete.threshold为目标剩余存储容量Q left.threshold与当前剩余容量Q left的差值。换言之，每次删除图片总容量至少为 100M。例如，若根据式(4)计算出的可删除分值阈值S t可知需要删除的图片的容量为80M，则可再删除若干张分值最低的图片，直至删除的图片的容量刚好大于或者等于100M。
此外，还可以设置每次图片删除的数量阈值N threshold为最多为总数量N的10％，即
N threshold≤N×10％       (6)
若根据式(4)计算出可删除分值阈值S t可知需要删除的图片的数量为20张，而电子设备10内存储的图片的总量为150张，则最终删除的图片的数量为15张，则第一图片即为分值最低的15张图片。
具体地，目标剩余存储容量的最大值不限于上述列举的2G，每次删除图片总容量的最小值也不限于上述列举的100M，每次删除的图片的数量阈值也不限于上述列举的总数量N的10％，在实际的实现过程中还可以是其他值，用户也可以手动设置上述参数，本申请实施例仅为示例性说明，对此不作限定。
示例性地，若电子设备10内部存储器121的当前剩余容量为总容量的10％，而目标剩余容量为总容量的20％，则根据删除策略确定出表11中列举的图片中分值最低的5张图片为第一图片，即确定出P17、P3、P12、P20、P14为第一图片。当电子设备10的触摸传感器180K检测到用户针对“相册”菜单控件的操作时，相册的显示界面80除了显示“微博”文件夹、“微信”文件夹及“Facebook”文件夹之外，还可以显示第一文件夹8022，如图15所示。第一文件夹8022可以包括多张第一图片。第一文件夹在界面80中还可以更直观的显示为建议删除，不限于此，还可以有其他的名称，本申请实施例对此不做限制。电子设备10的触摸传感器180K检测到用户针对第一文件夹8022的操作后，电子设备10的显示屏194可以显示界面100，界面100中可以包括第一图片，控件901以及状态栏。其中，状态栏与图4A中的状态栏204类似，在此不赘述。当电子设备10的触摸传感器180K检测到用户针对控件901的操作将所有第一图片删除后，电子设备10内部存储器121的存储容量即可释放至总容量的20％，删除过程中用户操作简便，且第一图片根据第三特征及第四特征综合决定，删除图片的准确性高，降低用户再次从云端下载图片的概率，提升用户体验。上述集合展示第一图片的方式不限于置于第一文件夹8022内在实际实现过程中还可以有其他集合展示的方式，本申请实施例对此不作限定。
此外，电子设备10在确认第一图片之后，还可以直接删除第一图片，无需用户再手动删除，进一步减少用户操作。
在一种可能的实施例中，上述图片的展示及图片的删除可以分别执行。即在图3示出的图片管理流程中，可以只执行S301-S307进行图片的展示，也可以只执行S301-S306、S308进行图片的删除。例如，上述图片的展示可以是电子设备10对适用于展示场景下的图片每天计算一次分值，当电子设备10接收到用户进入相册应用的指令时，电子设备10的处理器110可以根据分值更新图片的展示，而图片的删除则可以是电子设备10对适用于删除场景下的图片每周计算一次分值，根据分值及删除策略集合展示第一图片以提示用户删除，或者自动删除第一图片。又例如，电子设备10可以对所有其内部存储器121中存储的和/或云相册存储的所有图片进行每天一次的分值计算，然后根据分值更新图片的展示，并根据分值及删除策略进行每周一次的集合展示第一图片以提示用户删除，或者自动删除第一图片。
本申请实施例首先通过部分特征确定图片适用的场景，再通过第三特征及第四特征的共同作用，使得根据本申请实施例提供的算法模型计算的分值更加符合用户的心理预期，可以快速准确地删除图片，与现有技术相比，本申请实施例确定的第一图片更加准确，减少用户操作，提升了用户的操作效率。
此外，在上述展示场景和删除场景下计算出内部存储器121中存储的和/或云相册存储的所有图片的分值后，可以将所有图片的分值整体从高到低顺序排列，并按照从左到右、从上到下的顺序在图8右图中显示。
以上是分场景介绍了通过算法模型对计算图片的分值，并对图片进行管理的过程。接下来将介绍电子设备10对图片进行管理后，通过用户反馈对算法模块进行优化调整的过程。
具体地，用户反馈可以包括用户的正向反馈行为及反向反馈行为。
具体地，正向反馈行为可以包括：将“照片”菜单下的界面80中显示的图片中靠后的图片往前移动、将正常显示的图片放大显示、将正常显示的图片加框显示、将正常显示的图片标星显示、将归类于第一文件夹8022内的图片移出至“照片”菜单下的界面80中。如果发生上述的用户的反馈行为，可以确定该图片的分值应该更高。
具体地，反向反馈行为可以包括：将“照片”菜单下的界面80中显示的图片中靠前的图片往后移动、将放大显示的图片取消放大显示、将加框显示的图片取消加框显示、将标星显示的图片取消标星显示、用户将某张图片归类于“垃圾箱”文件夹中。如果发生上述的用户的反馈行为，确定该图片的分值应该更低。
接下来结合图16和图17介绍如何根据两种反馈行为对算法模型进行优化。其中，图16介绍了根据反向反馈行为优化算法模型，图17介绍了根据正向反馈行为优化算法模型。
从图16中可以看出，用户将图片显示队列中位于第7的图片P8往后移动至第11，确定用户希望P8的分值降低，则可以通过各种方式降低P8的分值，例如：
1、降低P8的第一特征中可以确定用户可能喜爱某张图片的特征的较大的值。
2、降低P8的第二特征中可以确定用户可能喜爱某张图片的特征的权重，增加P8的第二特征中可以确定用户可能不喜爱某张图片的特征的权重。
其次详细介绍如何通过上述两种方式降低P8的分值。
1、从表6中可以看出，P8的第一特征中可以确定用户可能喜欢某张图片的特征为“收藏”，其较大的值为10，可以将其降低，例如降低成5，则此时P8的分值S i从30降低为15。上述将“收藏”的较大的值降低为5仅为示例性说明，本申请实施例对此不作限定。
2、从表6中可以看出，P8的第二特征中可以确定用户可能喜欢某张图片的特征为“分享”、“图片内容分类”及“最后浏览时间”，可以确定用户可能不喜欢某张图片的特征为“拍摄模式”、“浏览次数”、“拍摄时间”、“图片大小”、“美学评分”，且从式(1)的描述中可以看出各个第二特征的权重均为1。为了保证所有图片包含的所有第二特征权重总和为固定值，确保各个图片的评分标准一致，保证各个图片分值的可比性，可以将可以确定用户可能喜欢某张图片的特征为“分享”、“图片内容分类”及“最后浏览时间”的权重均从1降低为0.5，同时将可以确定用户可能不喜欢某张图片的特征为“拍摄模式”、“浏览次数”、“拍摄时间”的权重增加为1.5，则此时P8的分值S i从30降低为15。上述第二特征的权重降低的幅 度以及增加的幅度仅为示例性说明，且选择降低权重的可以确定用户可能喜欢某张图片的特征和选择增加权重的可以确定用户可能不喜欢某张图片的特征也为示例性说明，本申请实施例对此均不作限定。
在另外一种可能的实施例中，可以电子设备10可以搜集一段时间(例如一星期、一个月等)内用户针对内部存储器121及外部存储卡中的多张图片的反向反馈行为，再提取这多张图片的第一特征中共同的可以确定用户可能喜欢某张图片的特征，第二特征中共同的可以确定用户可能喜欢某张图片的特征及可以确定用户可能不喜欢某张图片的特征。此处共同的可以确定用户可能喜欢某张图片的特征及共同的可以确定用户可能不喜欢某张图片的特征并不是严格意义上每张图片都有的特征，只需这多张图片中大多数图片存在一致的可以确定用户可能喜欢某张图片的特征或可以确定用户可能不喜欢某张图片的特征即可。例如，电子设备10搜集了一个月内用户针对100张图片的反向反馈行为，则这100张图片的第一特征中有60张图片的可以确定用户可能喜欢某张图片的特征均为“收藏”，则可将“收藏”作为这100张图片的第一特征中共同的可以确定用户可能喜欢某张图片的特征，同理适用于第二特征中的可以确定用户可能喜欢某张图片的特征和可以确定用户可能不喜欢某张图片的特征。通过搜集用户针对多张图片的反向反馈行为提取的共同特征可以更加准确地对算法模型进行优化。
从图17中可以看出，当电子设备10的触摸传感器180K检测到用户针对界面100中P3的操作后，电子设备10的显示屏194可以显示界面200，界面200可以包括图片显示区域2001及恢复控件2002，图片显示区域2001用于显示P3，恢复控件2002用于接收用户的恢复指令，将P3从第一文件夹8022内移出，在将P3从第一文件夹8022内移出后，当电子设备10再次接收用户的查看该“建议删除”文件夹8022的指令时，界面100中不再显示P3，且在电子设备10再次接收到用户针对“相册”菜单控件的操作时，P3可显示在“照片”菜单下的界面80中，确定用户希望P3的分值提高，则可以通过多种方式提高P3的分值，例如：
1、降低P3的第三特征中可以确定用户可能不喜欢某张图片的特征的较大的值。
2、降低P3的第四特征中可以确定用户可能不喜欢某张图片的特征的权重，增大P3的第四特征中可以确定用户可能喜欢某张图片的特征的权重。
其次详细介绍如何通过上述两种方式提高P3的分值。
1、从表7中可以看出，P3的第三特征“垃圾箱”的较大的值为10，可以将其降低，例如降低成5，则此时P3的分值S i从-10提高为-5。上述将“垃圾箱”的较大的值降低为5仅为示例性说明，本申请实施例对此不作限定。
2、从表7中可以看出，P3的第四特征中可以确定用户可能不喜欢某张图片的特征为“拍摄模式”、“拍摄时间”、“最后浏览时间”、“图片大小”，可以确定用户可能喜欢某张图片的特征为“图片内容分类”、“浏览次数”、及“美学评分”，且从式(1)的描述中可以看出各个第四特征的权重均为1。为了保证所有图片包含的所有第四特征权重总和为固定值，确保各个图片的评分标准一致，保证各个图片分值的可比性，可以将可以确定用户可能不喜欢某张图片的特征为“拍摄模式”、“拍摄时间”、“最后浏览时间”、“图片大小”的权重均从1降低为0.5，同时将可以确定用户可能喜欢某张图片的特征为“图片内容分类”、“浏览次数”、 及“美学评分”的权重增加为1.5，则此时P3的分值S i从-10提高为15。上述第四特征的权重降低的幅度以及增加的幅度仅为示例性说明，且选择降低权重的可以确定用户可能不喜欢某张图片的特征和选择增加权重的可以确定用户可能喜欢某张图片的特征也为示例性说明，本申请实施例对此均不作限定。
在另外一种可能的实施例中，可以电子设备10可以搜集一段时间(例如一个星期或者一个月等)内用户针对多张图片的正向反馈行为，再提取这多张图片的第三特征中共同的可以确定用户可能不喜欢某张图片的特征，第四特征中共同的可以确定用户可能喜欢某张图片的特征及可以确定用户可能不喜欢某张图片的特征。此处共同的可以确定用户可能喜欢某张图片的特征及共同的可以确定用户可能不喜欢某张图片的特征并不是严格意义上每张图片都有的特征，只需这多张图片中大多数图片存在一致的可以确定用户可能喜欢某张图片的特征或可以确定用户可能不喜欢某张图片的特征即可。例如，电子设备10搜集了一个月内用户针对100张图片的正向反馈行为，则这100张图片中有60张图片的第三特征“垃圾箱”均为可以确定用户可能不喜欢某张图片的特征，则可将“垃圾箱”作为这100张图片的第三特征中共同的可以确定用户可能不喜欢某张图片的特征，同理适用于第四特征中的可以确定用户可能喜欢某张图片的特征或可以确定用户可能不喜欢某张图片的特征。通过搜集用户针对多张图片的正向反馈行为提取的共同特征可以更加准确地对算法模型进行优化。
此外，根据上述两种反馈行为对算法模型进行优化时，还可以通过调整第一特征(第三特征)或者第二特征(或第四特征)来实现。具体可以通过将某个第一特征(第三)调整成第二特征(第四特征)，或者将某个第二特征(第四特征)调整成第一特征(第三特征)，或者增加第一特征(第三特征)，或者减少第一特征(第三特征)，或者增加第二特征(第四特征)，或者减少第二特征(第四特征)来实现。本申请实施例不详述。
本申请实施例中第一特征(第三特征)的选择、第一特征(第三特征)较大的值及较小的值的调整、各第二特征(第四特征)的选择、第二特征(第四特征)较大的值及较小的值的调整，还可由用户手动选择或者手动输入，用户手动选择的特征及手动输入的各个特征的值可以更加准确表示用户的意图。对于各个特征的选择及各个特征的值，本申请实施例不作限定。
本申请实施例中涉及的算法模型不限于是上述提出的公式(1)，实际上还可以是AI机器学习算法模型，比如朴素贝叶斯、支持向量机、深度神经网络等等。AI机器学习算法模型的初始训练样本可以是各种用户对于大量图片的打分，这些图片可以包括以上实施例中列举的各种特征，在此不再列举。模型训练完成后，在接收到新输入的图片i的各个特征数据后，可以根据这些特征数据计算图片的分值，最终输出图片i的分值。电子设备10可以根据输出的分值对图片进行管理。此外，该算法模型可以不断优化，提高输出分值的准确性。
本申请实施例还提供了一种计算机可读存储介质，该计算机可读存储介质中存储有指令，当其在计算机或处理器上运行时，使得计算机或处理器执行上述任一个方法中的一个或多个步骤。
本申请实施例还提供了一种包含指令的计算机程序产品。当该计算机程序产品在计算 机或处理器上运行时，使得计算机或处理器执行上述任一个方法中的一个或多个步骤。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者通过所述计算机可读存储介质进行传输。所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线)或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质(例如，固态硬盘(solid state disk，SSD))等。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，该流程可以由计算机程序来指令相关的硬件完成，该程序可存储于计算机可读取存储介质中，该程序在执行时，可包括如上述各方法实施例的流程。而前述的存储介质包括：ROM或随机存储记忆体RAM、磁碟或者光盘等各种可存储程序代码的介质。
以上所述，仅为本申请实施例的具体实施方式，但本申请实施例的保护范围并不局限于此，任何在本申请实施例揭露的技术范围内的变化或替换，都应涵盖在本申请实施例的保护范围之内。因此，本申请实施例的保护范围应以所述权利要求的保护范围为准。

Claims (19)

  1. 一种图片管理方法，由电子设备执行，其特征在于，包括：
    获取所述电子设备中存储的和/或云相册存储的至少两张图片中每张图片的场景特征；
    根据每张图片的场景特征确定所述每张图片的第一特征、第二特征、第一特征的打分标准、和第二特征的打分标准；
    根据所述每张图片的第一特征的值，和所述每张图片的第二特征的值计算得到所述每张图片的分值；其中，所述第一特征对每张图片的分值的影响大于所述第二特征对每张图片的分值的影响；
    检测用户的第一操作；
    响应于所述第一操作，显示所述至少两张图片中的S张第二图片；其中，S为大于或等于1的整数，所述S张第二图片的分值高于所述至少两张图片中除所述S张第二图片之外的其他图片的分值。
  2. 如权利要求1所述的方法，其特征在于，所述场景特征包括以下一个或任意组合：是否被收藏、是否被备注、是否关联壁纸，是否上传云端，存储位置；所述第一特征包括以下一个或任意组合：是否被收藏、是否被备注；所述第二特征包括以下一个或任意组合：是否被分享、拍摄模式、图片内容分类、浏览次数、拍摄时间、最后浏览时间、图片大小、美学评分。
  3. 如权利要求1或2所述的方法，其特征在于，
    所述检测用户的第一操作之前，所述方法还包括：所述电子设备显示状态栏、导航栏、时间组件图标及一个或多个应用程序的图标，所述相机应用的图标属于所述一个或多个应用程序的图标，所述第一操作为用户对所述相册应用的图标的操作；
    所述检测用户的第一操作后，所述方法还包括：响应于所述第一操作，显示所述至少两张图片中除所述S张第二图片之外的其他图片；其中，所述S张第二图片在所述除所述S张第二图片之外的其他图片之前显示，或者所述S张第二图片被特殊标记以与所述除所述S张第二图片之外的其他图片区别显示。
  4. 如权利要求3所述的方法，其特征在于，所述S张第二图片按照分值从高到低顺序排列，所述除所述S张第二图片之外的其他图片按照分值从高到低排列。
  5. 如权利要求3或4所述的方法，其特征在于，所述S张第二图片被特殊标记的方式包括以下一种或任意组合：增大显示、增加边框显示、增加标记显示、特殊颜色显示、特殊透明度显示。
  6. 如权利要求1或2所述的方法其特征在于，所述检测用户的第一操作后，所述方法还包括：
    响应于所述第一操作，按照分类显示文件夹，并显示搜索控件、第一菜单控件、第二菜单控件、第三菜单控件；其中，所述第一操作为用户对所述第三菜单控件的操作，所述分类的方式包括以下一个或任意组合：地点、时间、人物；每个文件夹包括一张或者多张图片，所述一张或多张图片属于所述至少两张图片；
    所述按照分类显示文件夹，并显示搜索控件、第一菜单控件、第二菜单控件、第三菜 单控件后，所述方法还包括：响应于用户对所述搜索控件的第二操作，显示搜索栏、按照分类显示的文件夹和所述S张第二图片。
  7. 如权利要求1至6任一项所述的方法，所述根据所述每张图片的第一特征的值，和所述每张图片的第二特征的值计算得到所述每张图片的分值之前，还包括：
    判断满足优化条件；其中，所述优化条件包括以下一个或任意组合：所述电子设备的剩余存储空间低于第一设定值，已到达设定的时间，所述电子设备的剩余电量低于第二设定值，所述电子设备正在充电，所述电子设备处于熄屏状态。
  8. 如权利要求3至5任一项所述的方法，其特征在于，所述显示所述至少两张图片中的S张第二图片之后，所述方法还包括：
    接收用户取消所述S张第二图片中至少一张第二图片的特殊标记的第三操作，响应于所述第三操作，重新计算所述至少一张第二图片的分值；或者
    接收用户为所述除所述S张第二图片之外的其他图片中的至少一张图片增加特殊标记的第四操作，响应于所述第四操作，重新计算所述至少一张图片的分值；或者
    接收用户将所述S张第二图片中的至少一张第二图片移动至所述除所述S张第二图片之外的其他图片中的至少一张图片之后显示的第五操作，响应于所述第五操作，重新计算所述S张第二图片中的至少一张第二图片的分值；或者
    接收用户将所述除所述S张第二图片之外的其他图片中的至少一张图片移动至所述S张第二图片中的至少一张第二图片之前显示的第六操作，响应于所述第六操作，重新计算所述除所述S张第二图片之外的其他图片中的至少一张图片的分值。
  9. 一种图片管理方法，由电子设备执行，其特征在于，包括：
    获取所述电子设备中存储的和/或云相册存储的至少两张图片中每张图片的场景特征；
    根据每张图片的场景特征确定所述每张图片的第三特征、第四特征、第三特征的打分标准、和第四特征的打分标准；
    根据所述每张图片的第三特征的值，和所述每张图片的第四特征的值计算得到所述每张图片的分值；其中，所述第三特征对每张图片的分值的影响大于所述第四特征对每张图片的分值的影响；
    检测用户的第一操作；
    响应于所述第一操作，显示第一文件夹；其中，所述第一文件夹包括所述至少两张图片中的M张第一图片；其中，M为大于或等于1的整数，所述M张第一图片的分值低于所述至少两张图片中除所述M张第一图片之外的其他图片的分值。
  10. 如权利要求9所述的方法，其特征在于，所述显示第一文件夹之后，还包括：
    检测用户的第二操作；
    响应于所述第二操作，删除所述M张第一图片。
  11. 如权利要求9或10所述的方法，由电子设备执行，其特征在于，所述场景特征包括以下一个或任意组合：是否被收藏、是否被备注、是否关联壁纸，是否上传云端，存储位置；所述第三特征包括以下一个或任意组合：是否被置于垃圾箱；所述第四特征包括以下一个或任意组合：拍摄模式、图片内容分类、浏览次数、拍摄时间、最后浏览时间、图片大小、美学评分。
  12. 如权利要求9至11任一项所述的方法，所述根据所述每张图片的第三特征的值，和所述每张图片的第四特征的值计算得到所述每张图片的分值之前，还包括：
    判断满足优化条件；其中，所述优化条件包括以下一个或任意组合：所述电子设备的剩余存储空间低于第一设定值，已到达设定的时间，所述电子设备的剩余电量低于第二设定值，所述电子设备正在充电，所述电子设备处于熄屏状态。
  13. 如权利要求9至12任一项所述的方法，其特征在于，所述显示第一文件夹之后，还包括：
    接收用户将所述M张第一图片中的至少一张第一图片移出所述第一文件夹的第三操作，响应于所述第三操作，重新计算所述至少一张第一图片的分值；或者
    接收用户将所述除所述M张第一图片之外的其他图片中的至少一张图片移入所述第一文件夹的第四操作，响应于所述第四操作，重新计算所述至少一张图片的分值。
  14. 一种图片管理方法，由电子设备执行，其特征在于，包括：
    获取所述电子设备中存储的和/或云相册存储的至少两张图片中每张图片的场景特征；
    根据每张图片的场景特征确定所述每张图片的第三特征、第四特征、第三特征的打分标准、第四特征的打分标准；
    根据所述每张图片的第三特征的值，和所述每张图片的第四特征的值计算得到所述每张图片的分值；其中，所述第三特征对每张图片的分值的影响大于所述第四特征对每张图片的分值的影响；
    删除所述至少两张图片中M张第一图片；其中，所述M为大于或者等于1的整数，所述M张第一图片的分值低于所述至少两张图片中除所述M张第一图片之外的其他图片。
  15. 如权利要求14所述的方法，其特征在于，所述场景特征包括以下一个或任意组合：是否被收藏、是否被备注、是否关联壁纸，是否上传云端，存储位置；所述第三特征包括：是否被置于垃圾箱；所述第四特征包括以下一个或任意组合：拍摄模式、图片内容分类、浏览次数、拍摄时间、最后浏览时间、图片大小、美学评分。
  16. 如权利要求14或至15任一项所述的方法，其特征在于，所述根据所述每张图片的第三特征的值，和所述每张图片的第四特征的值计算得到所述每张图片的分值之前，还包括：判断满足优化条件；其中，所述优化条件包括以下一个或任意组合：所述电子设备的剩余存储空间低于第一设定值，已到达设定的时间，所述电子设备的剩余电量低于第二设定值，所述电子设备正在充电，所述电子设备处于熄屏状态。
  17. 如权利要求14至16任一项所述的方法，其特征在于，所述删除所述至少两张图片中M张第一图片之后，还包括：
    接收用户下载所述M张第一图片中的至少一张第一图片的第一操作，响应于所述第一操作，重新计算所述至少一张第一图片的分值；或者
    接收用户删除所述除所述M张第一图片之外的其他图片中的至少一张图片的第二操作，响应于所述第二操作，重新计算所述至少一张图片的分值。
  18. 一种电子设备，其特征在于，包括：一个或多个处理器、存储器、显示屏、无线通信模块以及移动通信模块；
    所述存储器、所述显示屏、所述无线通信模块以及所述移动通信模块与所述一个或多 个处理器耦合，所述存储器用于存储计算机程序代码，所述计算机程序代码包括计算机指令，当所述一个或多个处理器执行所述计算机指令时，所述电子设备执行如权利要求1-17中任一项所述的图片管理方法。
  19. 一种计算机存储介质，其特征在于，包括计算机指令，当所述计算机指令在电子设备上运行时，使得所述电子设备执行如权利要求1-17中任一项所述的图片管理方法。
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