CN112463268A - Application data processing method, device, equipment and storage medium - Google Patents
Application data processing method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN112463268A CN112463268A CN201910841265.7A CN201910841265A CN112463268A CN 112463268 A CN112463268 A CN 112463268A CN 201910841265 A CN201910841265 A CN 201910841265A CN 112463268 A CN112463268 A CN 112463268A
- Authority
- CN
- China
- Prior art keywords
- model
- information
- model information
- sticker
- determining
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 25
- 238000012545 processing Methods 0.000 claims abstract description 13
- 238000000034 method Methods 0.000 claims description 26
- 238000013507 mapping Methods 0.000 claims description 19
- 238000004590 computer program Methods 0.000 claims description 7
- 230000003044 adaptive effect Effects 0.000 claims description 6
- 238000011284 combination treatment Methods 0.000 claims description 5
- 238000013461 design Methods 0.000 description 22
- 238000010586 diagram Methods 0.000 description 13
- 241000282326 Felis catus Species 0.000 description 5
- 241000233948 Typha Species 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000013459 approach Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 210000005069 ears Anatomy 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 235000012469 Cleome gynandra Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/451—Execution arrangements for user interfaces
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/30—Creation or generation of source code
- G06F8/34—Graphical or visual programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/77—Retouching; Inpainting; Scratch removal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/24—Indexing scheme for image data processing or generation, in general involving graphical user interfaces [GUIs]
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Human Computer Interaction (AREA)
- Stored Programmes (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The disclosure provides an application data processing method, an application data processing device, application data processing equipment and a storage medium. The application data processing method provided by the embodiment comprises the following steps: determining model information corresponding to the sticker; determining the model information of the locally missing target model according to the model information; sending a model downloading request to a cloud end, wherein the model downloading request comprises model information of a target model; enabling the cloud to issue the target model to a terminal according to the model information of the target model; and receiving the target model issued by the cloud. By the application data processing method provided by the embodiment of the disclosure, the size of the application software can be effectively reduced, and the local storage space occupied by the application software is reduced.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for processing application data.
Background
With the development of terminal technology, more and more application software can be loaded in the terminal. In many application software, an identification algorithm is used for image identification, and then corresponding stickers are added to the images according to identification results, so that the effect of beautifying the image picture is achieved.
In the prior art, different stickers need to correspond to different recognition algorithms and model files, so the sticker material, the recognition algorithms and the model files are packaged in application software together.
However, the above approach may increase the size of the application software, so that it occupies a large amount of local storage space.
Disclosure of Invention
The present disclosure provides an application data processing method, apparatus, device and storage medium, which can effectively reduce the size of application software and reduce the local storage space occupied by the application software.
In a first aspect, the present disclosure provides an application data processing method, including:
determining model information corresponding to the sticker;
determining the model information of the locally missing target model according to the model information;
sending a model downloading request to a cloud end, wherein the model downloading request comprises model information of a target model; enabling the cloud to issue the target model to a terminal according to the model information of the target model;
and receiving the target model issued by the cloud.
In one possible design, determining model information corresponding to the sticker includes:
through a preset mapping relation table, searching model information corresponding to the paster, wherein the model information comprises: model name, and/or model number.
In one possible design, determining model information corresponding to the sticker includes:
acquiring label information of the sticker; the label information of the paster is used for representing the type of the paster, and different types of pasters correspond to different recognition algorithms; the recognition algorithm comprises: a face recognition algorithm, a gesture recognition algorithm and a three-dimensional image recognition algorithm;
determining a recognition algorithm corresponding to the sticker according to the label information of the sticker;
searching model information matched with the recognition algorithm, wherein the model information comprises: model name, and/or model number.
In one possible design, when the number of recognition algorithms corresponding to the sticker is greater than 1, the searching for the model information matching the recognition algorithms includes:
respectively searching the model information matched with each recognition algorithm;
and performing duplicate removal and combination treatment on all the model information matched with the recognition algorithm to obtain the model information corresponding to the paster.
In one possible design, determining model information of a locally missing target model based on the model information includes:
obtaining model information of a local existing model;
comparing the model information of the local existing model with the model information corresponding to the stickers one by one, and determining the model information of the local missing target model.
In a possible design, the model downloading request further includes terminal model information and application software version information; and the cloud terminal issues the target model adaptive to the terminal and the application software to the terminal according to the terminal model information and the application software version information.
In one possible design, before determining the model information corresponding to the sticker, the method further includes:
displaying the sticker option on an application interface;
receiving operation information of a user for the sticker option;
and determining the paster according to the operation information.
In one possible design, further comprising:
after the target model is received, loading the target model;
and loading the paster.
In a second aspect, the present disclosure also provides an application data processing apparatus, including:
the first determining module is used for determining the model information corresponding to the paster;
the second determining module is used for determining the model information of the locally missing target model according to the model information;
the system comprises a sending module, a cloud terminal and a model downloading module, wherein the sending module is used for sending a model downloading request to the cloud terminal, and the model downloading request comprises model information of a target model; enabling the cloud to issue the target model to a terminal according to the model information of the target model;
and the receiving module is used for receiving the target model issued by the cloud.
In one possible design, the first determining module is specifically configured to:
through a preset mapping relation table, searching model information corresponding to the paster, wherein the model information comprises: model name, and/or model number.
In one possible design, the first determining module is further configured to:
acquiring label information of the sticker; the label information of the paster is used for representing the type of the paster, and different types of pasters correspond to different recognition algorithms; the recognition algorithm comprises: a face recognition algorithm, a gesture recognition algorithm and a three-dimensional image recognition algorithm;
determining a recognition algorithm corresponding to the sticker according to the label information of the sticker;
searching model information matched with the recognition algorithm, wherein the model information comprises: model name, and/or model number.
In one possible design, when the number of recognition algorithms corresponding to the sticker is greater than 1, the searching for the model information matching the recognition algorithms includes:
respectively searching the model information matched with each recognition algorithm;
and performing duplicate removal and combination treatment on all the model information matched with the recognition algorithm to obtain the model information corresponding to the paster.
In one possible design, the second determining module is specifically configured to:
obtaining model information of a local existing model;
comparing the model information of the local existing model with the model information corresponding to the stickers one by one, and determining the model information of the local missing target model.
In a possible design, the model downloading request further includes terminal model information and application software version information; and the cloud terminal issues the target model adaptive to the terminal and the application software to the terminal according to the terminal model information and the application software version information.
In one possible design, further comprising: a display module to:
displaying the sticker option on an application interface;
receiving operation information of a user for the sticker option;
and determining the paster according to the operation information.
In one possible design, further comprising: a loading module to:
after the target model is received, loading the target model;
and loading the paster.
In a third aspect, the present disclosure also provides an electronic device, including:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform any one of the application data processing methods of the first aspect via execution of the executable instructions.
In a fourth aspect, the disclosed embodiments also provide a storage medium, on which a computer program is stored, where the program, when executed by a processor, implements any one of the application data processing methods in the first aspect.
The present disclosure provides an application data processing method, apparatus, device and storage medium, by determining model information corresponding to a sticker; determining the model information of the locally missing target model according to the model information; sending a model downloading request to a cloud end, wherein the model downloading request comprises model information of a target model; enabling the cloud to issue the target model to a terminal according to the model information of the target model; and receiving the target model issued by the cloud. Therefore, the size of the application software can be effectively reduced, and the local storage space occupied by the application software is reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present disclosure, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is an application scenario diagram illustrating an application data processing method according to an example embodiment of the present disclosure;
FIG. 2 is a flow diagram illustrating an application data processing method according to an example embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating the principle of matching model information for an algorithm according to an example embodiment of the present disclosure;
FIG. 4 is a flow diagram illustrating an application data processing method according to another example embodiment of the present disclosure;
FIG. 5 is a flow diagram illustrating an application data processing method according to yet another example embodiment of the present disclosure;
FIG. 6 is a schematic block diagram of an application data processing apparatus according to an example embodiment of the present disclosure;
FIG. 7 is a schematic block diagram of an application data processing apparatus according to another example embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of an electronic device shown in accordance with an example embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present disclosure and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
With the development of terminal technology, more and more application software can be loaded in the terminal. In many application software, an identification algorithm is used for image identification, and then corresponding stickers are added to the images according to identification results, so that the effect of beautifying the image picture is achieved. For example, in video or photographing, a face can be recognized through a face recognition algorithm, then stickers are added at corresponding positions of the face, and materials such as cat ears, cat beards and the like are added, so that the requirement of beautifying a picture of a user is met. In the prior art, different stickers need to correspond to different recognition algorithms and model files, so the sticker material, the recognition algorithms and the model files are packaged in application software together. However, the above approach may increase the size of the application software, so that it occupies a large amount of local storage space. Moreover, this approach is not suitable for the hot repair technique, resulting in the inability to achieve flexible updating of the model.
In view of the above technical problems, the present disclosure provides an application data processing method, apparatus, device, and storage medium, which can obtain a latest model from a cloud in time, thereby effectively reducing the size of application software, and reducing the local storage space occupied by the application software to implement flexible update of the model. Fig. 1 is an application scenario diagram illustrating an application data processing method according to an example embodiment of the present disclosure, and as shown in fig. 1, all sticker options may be displayed on a terminal 10. Then, the user can determine the target sticker through touch control and other operations. For example, on the terminal display interface, a sticker a, a sticker B, and a sticker C are displayed, and when the user clicks the sticker a, sticker information is displayed, and the sticker a is used as the target sticker. Then, the terminal 10 determines model information corresponding to the sticker from the sticker. There are various ways to determine the model from the sticker. For example, a mapping relation table of a sticker and a model is built in the terminal, and the application model is determined in a table look-up manner; or a matching algorithm can be set at the terminal for determining the corresponding application model according to the sticker. In one possible design, a mapping relation table of the sticker and the model can be set in the terminal. After the terminal determines the sticker, the model corresponding to the sticker can be determined by looking up the mapping relation table. The way of establishing the mapping relationship table needs to enumerate all relationships between the sticker and the model one by one. If the mapping relation table does not exist or needs to be updated, the latest mapping relation table can be downloaded from the cloud, and therefore the relation between the sticker and the model is updated. Therefore, for version upgrading and the like, generally, only the mapping relation table needs to be upgraded, and only when the sticker is used, the model information corresponding to the sticker is upgraded from the cloud. The method has the advantages that the existing code structure does not need to be changed, the memory space occupied by the program is reduced, and the upgrading and updating are more flexible. In another possible design, the model information may be matched by way of a matching algorithm. The input of the matching algorithm is the sticker label, and the output of the matching algorithm is the model corresponding to the sticker label. Specifically, each sticker is provided with a label, and the algorithm may determine the identification algorithm corresponding to the sticker based on the category indicated by the label. The recognition algorithm refers to an algorithm for recognizing an image and applying a model to a sticker, such as a face recognition algorithm, a gesture recognition algorithm, a three-dimensional image recognition algorithm, and the like. Different recognition algorithms may match different models. Therefore, the identification algorithm can be determined first by the label information of the sticker, and then the corresponding model information can be determined according to the identification algorithm. Then, the terminal 10 acquires model information of the locally existing model. Then, comparing the model information of the local existing model with the model information corresponding to the stickers one by one, and determining the model information of the local missing target model. For example, a model a, a model b, and a model c exist locally, and if the models corresponding to the stickers are determined to be the models a, c, and d in step 101, the missing models are determined to be the models d by one-to-one comparison, and only the model d needs to be the target model. Then, the terminal 10 may send a download request including the target model information, the terminal model information, and the application version information to the cloud 20. After receiving the download request, the cloud 20 extracts identification information such as a model name and a number, terminal model information, and application software version information, and then issues the corresponding target model to the terminal 10. After receiving the target model issued by the cloud 20, the terminal 10 updates and upgrades the local model library, and can load the target model and the sticker.
It should be noted that the cloud end can receive model downloading requests sent by the multiple terminals, and issue the target model to the corresponding cloud end.
Fig. 2 is a schematic flowchart of an application data processing method according to an exemplary embodiment of the present disclosure, and as shown in fig. 2, the application data processing method provided in this embodiment includes:
In this embodiment, after the user selects the sticker from the terminal, the terminal determines the model information corresponding to the sticker according to the sticker. There are various ways to determine the model from the sticker. For example, a mapping relation table of a sticker and a model is built in the terminal, and the application model is determined in a table look-up manner; or a matching algorithm can be set at the terminal for determining the corresponding application model according to the sticker.
Optionally, determining model information corresponding to the sticker includes: through a preset mapping relation table, searching model information corresponding to the paster, wherein the model information comprises: model name, and/or model number.
Specifically, a mapping relation table of the sticker and the model may be set in the terminal. After the terminal determines the sticker, the model corresponding to the sticker can be determined by looking up the mapping relation table. The way of establishing the mapping relationship table needs to enumerate all relationships between the sticker and the model one by one. If the mapping relation table does not exist or needs to be updated, the latest mapping relation table can be downloaded from the cloud, and therefore the relation between the sticker and the model is updated. Therefore, for version upgrading and the like, generally, only the mapping relation table needs to be upgraded, and only when the sticker is used, the model information corresponding to the sticker is upgraded from the cloud. The method has the advantages that the existing code structure does not need to be changed, the memory space occupied by the program is reduced, and the upgrading and updating are more flexible.
Optionally, determining model information corresponding to the sticker includes: acquiring label information of a sticker; the label information of the stickers is used for representing the types of the stickers, and different types of stickers correspond to different recognition algorithms; the recognition algorithm comprises: a face recognition algorithm, a gesture recognition algorithm and a three-dimensional image recognition algorithm; determining a recognition algorithm corresponding to the sticker according to the label information of the sticker; : model name, and/or model number.
In particular, the model information may also be matched by means of a matching algorithm. The input of the matching algorithm is the sticker label, and the output of the matching algorithm is the model corresponding to the sticker label. Specifically, each sticker is provided with a label, and the algorithm may determine the identification algorithm corresponding to the sticker based on the category indicated by the label. The recognition algorithm refers to an algorithm for recognizing an image and applying a model to a sticker, such as a face recognition algorithm, a gesture recognition algorithm, a three-dimensional image recognition algorithm, and the like. Different recognition algorithms may match different models. Therefore, the identification algorithm can be determined first by the label information of the sticker, and then the corresponding model information can be determined according to the identification algorithm.
Optionally, when the number of recognition algorithms corresponding to the sticker is greater than 1, searching for model information matching the recognition algorithms includes: respectively searching the model information matched with each recognition algorithm; and carrying out duplication removal and combination treatment on the model information matched with all the recognition algorithms to obtain the model information corresponding to the paster.
Specifically, when the number of the recognition algorithms corresponding to the stickers is greater than 1, the terminal obtains the model files required by each algorithm, and merges and removes the duplicate to obtain the set which needs to be downloaded finally. Fig. 3 is a schematic diagram illustrating a principle of matching model information by an algorithm according to an example embodiment of the present disclosure, and as shown in fig. 3, a recognition algorithm corresponding to a sticker resource includes: algorithm A, algorithm B, algorithm C, and algorithm D. Different recognition algorithms require different models, for example, algorithm a corresponds to model a and model B, and algorithm B corresponds to model B and model c. Then, after the model information is subjected to deduplication and merging processing, the model b is actually downloaded once.
And step 102, determining the model information of the locally missing target model according to the model information.
In this embodiment, the model information of the local existing model may be obtained first. Then, comparing the model information of the local existing model with the model information corresponding to the stickers one by one, and determining the model information of the local missing target model. For example, a model a, a model b, and a model c exist locally, and if the models corresponding to the stickers are determined to be the models a, c, and d in step 101, the missing models are determined to be the models d by one-to-one comparison, and only the model d needs to be the target model. Therefore, only the update model d needs to be downloaded, so that network resources are saved, and update can be realized more flexibly.
And 103, sending a model downloading request to the cloud, wherein the model downloading request comprises model information of the target model.
In this embodiment, the terminal may send a download request including the target model information to the cloud. And after receiving the downloading request, the cloud extracts identification information such as model names, serial numbers and the like, and then issues the corresponding target model to the terminal.
Optionally, the model downloading request further includes terminal model information and application software version information; and the cloud terminal issues a target model adaptive to the terminal and the application software to the terminal according to the terminal model information and the application software version information.
Specifically, the download request may further include terminal model information and application software version information. And the cloud terminal issues a target model adaptive to the terminal and the application software to the terminal according to the terminal model information and the application software version information. For example, the android system and the apple system can have different models, and the model suitable for the android system is released for the terminal of the android system.
And 104, receiving a target model issued by the cloud.
In this embodiment, the terminal receives the target model delivered by the cloud, and updates and upgrades the local model library.
In the embodiment, the model information corresponding to the paster is determined; determining the model information of the locally missing target model according to the model information; sending a model downloading request to a cloud end, wherein the model downloading request comprises model information of a target model; the cloud end sends the target model to the terminal according to the model information of the target model; and receiving a target model issued by the cloud. Therefore, the size of the application software can be effectively reduced, and the local storage space occupied by the application software is reduced.
Fig. 4 is a flowchart illustrating an application data processing method according to another exemplary embodiment of the present disclosure, and as shown in fig. 4, the application data processing method provided in this embodiment includes:
And step 203, determining the paster according to the operation information.
In this embodiment, all sticker options may be displayed on the terminal. Then, the user can determine the sticker by touch or the like. For example, on the terminal display interface, a sticker a, a sticker B, and a sticker C are displayed, and when the user clicks the sticker a, sticker information is displayed, and the sticker a is used as the target sticker. In this way, only the model corresponding to the sticker A needs to be downloaded in the subsequent steps, but the models corresponding to the stickers B and C do not need to be downloaded, so that the local storage space can be saved, the network resources are saved, and more flexible updating is realized.
And step 204, determining the model information corresponding to the paster.
And step 205, determining the model information of the locally missing target model according to the model information.
And step 206, sending a model downloading request to the cloud, wherein the model downloading request comprises model information of the target model.
And step 207, receiving the target model issued by the cloud.
In this embodiment, please refer to the related description in step 101 to step 104 in the method shown in fig. 2 for the specific implementation process and technical principle of step 204 to step 207, which is not described herein again.
In the embodiment, the model information corresponding to the paster is determined; determining the model information of the locally missing target model according to the model information; sending a model downloading request to a cloud end, wherein the model downloading request comprises model information of a target model; the cloud end sends the target model to the terminal according to the model information of the target model; and receiving a target model issued by the cloud. Therefore, the size of the application software can be effectively reduced, and the local storage space occupied by the application software is reduced.
In addition, the implementation can also display the sticker option on the application interface, and receive the operation information of the user aiming at the sticker option, so as to determine the target sticker. Therefore, only the model corresponding to the target sticker needs to be downloaded, the local storage space can be saved, the network resources are saved, and more flexible upgrading and updating are realized.
Fig. 5 is a flowchart illustrating an application data processing method according to still another exemplary embodiment of the present disclosure, and as shown in fig. 5, the application data processing method provided in this embodiment includes:
and 301, determining model information corresponding to the paster.
And step 302, determining the model information of the locally missing target model according to the model information.
And step 304, receiving a target model issued by the cloud.
In this embodiment, please refer to the related description in step 101 to step 104 in the method shown in fig. 2 for the specific implementation process and technical principle of step 301 to step 304, which is not described herein again.
In this embodiment, the target model may be loaded after the target model is received, and then the sticker may be loaded, thereby achieving flexible update of the model. For example, the cartoon sticker includes a cat ear model, a cat beard model. After the version is upgraded, a cat tail model is added to the cartoon sticker, and after the cat tail model is downloaded, the cat tail model is loaded and the sticker is loaded, so that a complete sticker pattern is displayed, namely, images containing cat ears, cat whiskers and a cat tail are displayed.
In the embodiment, the model information corresponding to the paster is determined; determining the model information of the locally missing target model according to the model information; sending a model downloading request to a cloud end, wherein the model downloading request comprises model information of a target model; the cloud end sends the target model to the terminal according to the model information of the target model; and receiving a target model issued by the cloud. Therefore, the size of the application software can be effectively reduced, and the local storage space occupied by the application software is reduced.
In addition, the embodiment can also load the target model and the sticker after the target model is received. Therefore, the size of the application software can be effectively reduced, and the local storage space occupied by the application software is reduced.
Fig. 6 is a schematic structural diagram of an application data processing apparatus according to an example embodiment of the present disclosure. As shown in fig. 6, the application data processing apparatus 30 provided in the present embodiment includes:
a first determining module 301, configured to determine model information corresponding to a sticker;
the second determining module 302 is used for determining the model information of the locally missing target model according to the model information;
the sending module 303 is configured to send a model downloading request to the cloud, where the model downloading request includes model information of the target model; the cloud end sends the target model to the terminal according to the model information of the target model;
the receiving module 304 is configured to receive a target model issued by a cloud.
In one possible design, the first determining module 301 is specifically configured to:
through a preset mapping relation table, searching model information corresponding to the paster, wherein the model information comprises: model name, and/or model number.
In one possible design, the first determining module 301 is further configured to:
acquiring label information of a sticker; the label information of the stickers is used for representing the types of the stickers, and different types of stickers correspond to different recognition algorithms; the recognition algorithm comprises: a face recognition algorithm, a gesture recognition algorithm and a three-dimensional image recognition algorithm;
determining a recognition algorithm corresponding to the sticker according to the label information of the sticker;
searching model information matched with the recognition algorithm, wherein the model information comprises: model name, and/or model number.
In one possible design, when the number of recognition algorithms corresponding to the sticker is greater than 1, finding the model information matching the recognition algorithms includes:
respectively searching the model information matched with each recognition algorithm;
and carrying out duplication removal and combination treatment on the model information matched with all the recognition algorithms to obtain the model information corresponding to the paster.
In one possible design, the second determining module 302 is specifically configured to:
obtaining model information of a local existing model;
comparing the model information of the local existing model with the model information corresponding to the stickers one by one, and determining the model information of the local missing target model.
In one possible design, the model downloading request further includes terminal model information and application software version information; and the cloud terminal issues a target model adaptive to the terminal and the application software to the terminal according to the terminal model information and the application software version information.
The apparatus provided in this embodiment may be used to implement the technical solution of the method embodiment shown in fig. 2, and the implementation principle and the technical effect are similar, which are not described herein again.
In the embodiment, the model information corresponding to the paster is determined; determining the model information of the locally missing target model according to the model information; sending a model downloading request to a cloud end, wherein the model downloading request comprises model information of a target model; the cloud end sends the target model to the terminal according to the model information of the target model; and receiving a target model issued by the cloud. Therefore, the size of the application software can be effectively reduced, and the local storage space occupied by the application software is reduced.
On the basis of the embodiment shown in fig. 6, fig. 7 is a schematic structural diagram of an application data processing apparatus shown in the present disclosure according to another exemplary embodiment, as shown in fig. 7, the application data processing apparatus provided in this embodiment further includes:
a display module 305 for:
displaying a sticker option on an application interface;
receiving operation information of a user for the sticker option;
and determining the paster according to the operation information.
In one possible design, further comprising: a loading module 306 configured to:
after the target model is received, loading the target model;
and (6) loading the paster.
The apparatus provided in this embodiment may be used to execute the technical solutions of the method embodiments shown in fig. 2, fig. 4, and fig. 5, and the implementation principles and technical effects are similar, which are not described herein again.
In the embodiment, the model information corresponding to the paster is determined; determining the model information of the locally missing target model according to the model information; sending a model downloading request to a cloud end, wherein the model downloading request comprises model information of a target model; the cloud end sends the target model to the terminal according to the model information of the target model; and receiving a target model issued by the cloud. Therefore, the size of the application software can be effectively reduced, and the local storage space occupied by the application software is reduced.
In addition, the embodiment can also load the target model and the sticker after the target model is received. Therefore, the size of the application software can be effectively reduced, and the local storage space occupied by the application software is reduced.
Fig. 8 is a schematic structural diagram of an electronic device shown in accordance with an example embodiment of the present disclosure. As shown in fig. 8, the present embodiment provides an electronic device 40, including:
a processor 401; and the number of the first and second groups,
a memory 402 for storing executable instructions of the processor, which may also be a flash (flash memory);
wherein the processor 401 is configured to perform the respective steps of the above-described method via execution of executable instructions. Reference may be made in particular to the description relating to the preceding method embodiment.
Alternatively, the memory 402 may be separate or integrated with the processor 401.
When the memory 402 is a device independent of the processor 401, the electronic device 40 may further include:
a bus 403 for connecting the processor 401 and the memory 402.
The present embodiment also provides a readable storage medium, in which a computer program is stored, and when at least one processor of the electronic device executes the computer program, the electronic device executes the methods provided by the above various embodiments.
The present embodiment also provides a program product comprising a computer program stored in a readable storage medium. The computer program can be read from a readable storage medium by at least one processor of the electronic device, and the execution of the computer program by the at least one processor causes the electronic device to implement the methods provided by the various embodiments described above.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (11)
1. An application data processing method, comprising:
determining model information corresponding to the sticker;
determining the model information of the locally missing target model according to the model information;
sending a model downloading request to a cloud end, wherein the model downloading request comprises model information of a target model; enabling the cloud to issue the target model to a terminal according to the model information of the target model;
and receiving the target model issued by the cloud.
2. The method of claim 1, wherein determining model information corresponding to the sticker comprises:
through a preset mapping relation table, searching model information corresponding to the paster, wherein the model information comprises: model name, and/or model number.
3. The method of claim 1, wherein determining model information corresponding to the sticker comprises:
acquiring label information of the sticker; the label information of the paster is used for representing the type of the paster, and different types of pasters correspond to different recognition algorithms; the recognition algorithm comprises: a face recognition algorithm, a gesture recognition algorithm and a three-dimensional image recognition algorithm;
determining a recognition algorithm corresponding to the sticker according to the label information of the sticker;
searching model information matched with the recognition algorithm, wherein the model information comprises: model name, and/or model number.
4. The method according to claim 3, wherein when the number of recognition algorithms corresponding to the sticker is greater than 1, the searching for the model information matching the recognition algorithms comprises:
respectively searching the model information matched with each recognition algorithm;
and performing duplicate removal and combination treatment on all the model information matched with the recognition algorithm to obtain the model information corresponding to the paster.
5. The method of claim 1, wherein determining model information for a locally missing target model based on the model information comprises:
obtaining model information of a local existing model;
comparing the model information of the local existing model with the model information corresponding to the stickers one by one, and determining the model information of the local missing target model.
6. The method according to claim 1, wherein the model download request further includes terminal model information and application software version information; and the cloud terminal issues the target model adaptive to the terminal and the application software to the terminal according to the terminal model information and the application software version information.
7. The method of any of claims 1-6, further comprising, prior to determining the model information corresponding to the sticker:
displaying the sticker option on an application interface;
receiving operation information of a user for the sticker option;
and determining the paster according to the operation information.
8. The method according to any one of claims 1-6, further comprising:
after the target model is received, loading the target model;
and loading the paster.
9. An application data processing apparatus, comprising:
the first determining module is used for determining the model information corresponding to the paster;
the second determining module is used for determining the model information of the locally missing target model according to the model information;
the system comprises a sending module, a cloud terminal and a model downloading module, wherein the sending module is used for sending a model downloading request to the cloud terminal, and the model downloading request comprises model information of a target model; enabling the cloud to issue the target model to a terminal according to the model information of the target model;
and the receiving module is used for receiving the target model issued by the cloud.
10. An electronic device, comprising:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the application data processing method of any of claims 1 to 8 via execution of the executable instructions.
11. A storage medium on which a computer program is stored, characterized in that the program, when executed by a processor, implements the application data processing method of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910841265.7A CN112463268A (en) | 2019-09-06 | 2019-09-06 | Application data processing method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910841265.7A CN112463268A (en) | 2019-09-06 | 2019-09-06 | Application data processing method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112463268A true CN112463268A (en) | 2021-03-09 |
Family
ID=74807701
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910841265.7A Pending CN112463268A (en) | 2019-09-06 | 2019-09-06 | Application data processing method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112463268A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114995899A (en) * | 2022-05-16 | 2022-09-02 | 上海商汤智能科技有限公司 | Program running method and device, electronic equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108986042A (en) * | 2018-06-15 | 2018-12-11 | Oppo广东移动通信有限公司 | Paster sharing method and device |
| CN109087376A (en) * | 2018-07-31 | 2018-12-25 | Oppo广东移动通信有限公司 | Image processing method, device, storage medium and electronic equipment |
| CN109147007A (en) * | 2018-08-01 | 2019-01-04 | Oppo（重庆）智能科技有限公司 | Paster loading method, device, terminal and computer readable storage medium |
-
2019
- 2019-09-06 CN CN201910841265.7A patent/CN112463268A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108986042A (en) * | 2018-06-15 | 2018-12-11 | Oppo广东移动通信有限公司 | Paster sharing method and device |
| CN109087376A (en) * | 2018-07-31 | 2018-12-25 | Oppo广东移动通信有限公司 | Image processing method, device, storage medium and electronic equipment |
| CN109147007A (en) * | 2018-08-01 | 2019-01-04 | Oppo（重庆）智能科技有限公司 | Paster loading method, device, terminal and computer readable storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114995899A (en) * | 2022-05-16 | 2022-09-02 | 上海商汤智能科技有限公司 | Program running method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108074177B (en) | Data reconciliation method, system and computer-readable storage medium | |
| CN109471653B (en) | Application program updating method, electronic device, server and storage medium | |
| CN108829868B (en) | Data display method and device, storage medium and electronic device | |
| CN110543318A (en) | control device program updating method and device, storage medium and control device | |
| CN109376126B (en) | File or folder processing method, mobile terminal and storage medium | |
| CN108733370B (en) | Stylized display method, device, terminal and storage medium of native APP | |
| CN112995806A (en) | Method, device, system and storage medium for upgrading optical network unit | |
| CN114546588A (en) | Task deployment method and device, storage medium and electronic device | |
| CN112463268A (en) | Application data processing method, device, equipment and storage medium | |
| CN109032612B (en) | Interface calling method and device of hybrid application and computer readable storage medium | |
| CN109976744B (en) | Visual programming method, system and terminal equipment | |
| CN108363574B (en) | Front-end customization method and device based on SDK, terminal equipment and storage medium | |
| JP7257554B2 (en) | Model file distribution method, platform, system, terminal and readable storage medium | |
| CN111752797B (en) | Terminal log processing method and device, terminal and storage medium | |
| CN108322542B (en) | Data updating method, system, device and computer readable storage medium | |
| CN116909634A (en) | Radio frequency drive identification method and device, storage medium and electronic equipment | |
| CN113010195B (en) | System upgrading method, storage medium and terminal equipment | |
| CN110995462B (en) | Cloud host function expansion method, system, equipment and computer storage medium | |
| CN114895933A (en) | System upgrade method, network device, medium, and electronic device | |
| CN113946357A (en) | Cloud application updating method, device, system, electronic equipment and computer medium | |
| CN115437660A (en) | Firmware updating method and device, electronic equipment and storage medium | |
| CN113419760A (en) | Method, apparatus, computer device and medium for opening/saving project file | |
| CN110825406A (en) | Software upgrading method and related equipment | |
| CN111124456A (en) | Method and device for upgrading firmware of single chip microcomputer through master control and readable storage medium | |
| CN113934453B (en) | Risk detection method, risk detection device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |