CN112001930B - Picture asset processing method and device, storage medium and electronic device - Google Patents
Picture asset processing method and device, storage medium and electronic device Download PDFInfo
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Abstract
The invention provides a picture asset processing method and device, a storage medium and an electronic device, wherein the method comprises the following steps: monitoring the operation behavior of a first user account on a client aiming at a learning task; acquiring an operation result of the operation behavior, wherein the operation result is used for representing whether the first user account completes the learning task or not; and if the learning task is completed, distributing a picture splicing fragment of a picture asset to the user account, wherein the picture asset is composed of a plurality of picture splicing fragments. The invention solves the technical problem that the picture assets can not be collected through on-line learning in the related technology, can quantize the learning result of the user into the picture assets, provides a scheme for collecting the picture assets on line, realizes a diversified on-line learning incentive mode, and improves the learning enthusiasm of the user.
Description
Technical Field
The invention relates to the field of data processing, in particular to a picture asset processing method and device, a storage medium and an electronic device.
Background
In the related art, in order to master a certain language, such as english, a user uses learning software, such as dictionary software and translation software, to improve the language ability through a learning task.
Among the correlation technique, the user is in the answer process, and only the answer of simple, and reward mechanism is single, and some APP can unblock next course after the answer is to certain question, perhaps can reward some gold coin coupons and so on, and the user often can be running gold coin and study to deviated from the original intention of study, in case the gold coin reduces, the user can lacked the study on the contrary.
In view of the above problems in the related art, no effective solution has been found at present.
Disclosure of Invention
The embodiment of the invention provides a picture asset processing method and device, a storage medium and an electronic device.
According to an embodiment of the present invention, there is provided a picture asset processing method including: monitoring the operation behavior of a first user account on a client aiming at a learning task; acquiring an operation result of the operation behavior, wherein the operation result is used for representing whether the first user account completes the learning task or not; and if the learning task is completed, distributing a picture splicing fragment of a picture asset to the user account, wherein the picture asset is composed of a plurality of picture splicing fragments.
Optionally, the monitoring of the operation behavior of the first user account on the client for the learning task includes at least one of: monitoring the operation behavior of a first user account for identifying and memorizing a first corpus resource on a client; monitoring the operation behavior of the first user account for reading the second corpus resources on the client; monitoring the operation behavior of the first user account for listening to the third corpus resource on the client; and monitoring the operation behavior of the first user account for writing the fourth linguistic resource on the client.
Optionally, allocating one piece of jigsaw fragments of the picture asset to the first user account includes: determining a first jigsaw fragment according to the collection progress of the picture asset, wherein the first jigsaw fragment is the last jigsaw fragment collected by the first user account; and allocating a second jigsaw fragment for the first user account according to the preset jigsaw sequence of the picture assets, wherein the second jigsaw fragment is the next jigsaw fragment of the first jigsaw fragment.
Optionally, allocating one piece of jigsaw fragments of the picture asset to the first user account includes: finding a target tile fragment that matches the learning task, wherein the learning task comprises one of: words, phrases, sentences, articles; allocating the target tile fragments of a picture asset to the first user account.
Optionally, before allocating one piece of puzzle fragment of the picture asset to the first user account, the method further includes: determining an ability value of the first user account, wherein the ability value is used for representing the ability level of the first user account to learn languages; selecting a picture asset matching the capability value in a picture asset library.
Optionally, determining the capability value of the first user account includes at least one of: calculating the capacity value of the first user account according to the number of the corpus words in the corpus created by the first user account and the attribute value corresponding to each corpus word, wherein the attribute value is used for representing the mastering degree of the first user account on the corpus words; determining a difficulty coefficient of the learning task, and converting the difficulty coefficient into a capability value of the first user account, wherein the capability value is positively correlated with the difficulty coefficient.
Optionally, after allocating one piece of puzzle fragment of the picture asset to the first user account, the method further includes: judging whether the collection of the picture assets is finished or not; and if the collection of the picture assets is finished, unlocking and displaying the asset introduction information of the picture assets in a correlated manner, and storing the picture assets as the user assets of the first user account.
Optionally, the method further comprises at least one of: sending the fragment assets collected by the first user account on the client to a second user account, and deleting the fragment assets in an asset library of the first user account so that the second user account collects the fragment assets after completing a learning task corresponding to the fragment assets, wherein the fragment assets comprise at least one jigsaw fragment; and sending the picture assets collected by the first user account on the client to a third user account, and deleting the picture assets from an asset library of the first user account so that the third user account collects the picture assets after completing the learning task corresponding to the picture assets.
Optionally, after allocating one piece of jigsaw fragments of the picture assets to the user account, the method further includes: sharing collection progress information of the photo asset on social media, wherein the collection progress information includes at least one of: the picture assets currently being collected, the puzzle fragments already collected, the puzzle fragments to be collected, and the corpus resource list learned in the collection process.
According to another embodiment of the present invention, there is provided a picture asset processing apparatus including: the monitoring module is used for monitoring the operation behavior of the first user account on the client aiming at the learning task; an obtaining module, configured to obtain an operation result of the operation behavior, where the operation result is used to characterize whether the first user account completes the learning task; and the distribution module is used for distributing a picture splicing fragment of a picture asset to the user account if the learning task is finished, wherein the picture asset is composed of a plurality of picture splicing fragments.
Optionally, the monitoring module includes at least one of: the first monitoring unit is used for monitoring the operation behavior of the first user account for memorizing the first corpus resource on the client; the second monitoring unit is used for monitoring the operation behavior of the first user account for reading the second corpus resources on the client; the third monitoring unit is used for monitoring the operation behavior of the first user account for listening to the third corpus resource on the client; and the fourth monitoring unit is used for monitoring the operation behavior of writing the fourth corpus resource on the client by the first user account.
Optionally, the allocating module includes: a determining unit, configured to determine a first tile fragment according to a collection progress of the picture asset, where the first tile fragment is a last tile fragment that has been collected by a first user account; the first allocation unit is configured to allocate a second puzzle piece to the first user account according to a preset puzzle sequence of the picture asset, where the second puzzle piece is a next puzzle piece of the first puzzle piece.
Optionally, the allocating module includes: a searching unit, configured to search for target tile fragments matching the learning task, wherein the learning task includes one of: words, phrases, sentences, articles; a second allocating unit, configured to allocate the target tile fragments of the picture assets to the first user account.
Optionally, the apparatus further comprises: the first determination module is used for determining the ability value of the first user account before the distribution module distributes a jigsaw fragment of a picture asset to the first user account, wherein the ability value is used for representing the ability level of the first user account to learn languages; and the selection module is used for selecting the picture assets matched with the capability value in the picture asset library.
Optionally, the determining module includes at least one of: the calculation unit is used for calculating the capacity value of the first user account according to the number of the linguistic data words in the corpus created by the first user account and the attribute value corresponding to each linguistic data word, wherein the attribute value is used for representing the mastery degree of the first user account on the linguistic data words; the conversion unit is used for determining a difficulty coefficient of the learning task and converting the difficulty coefficient into a capability value of the first user account, wherein the capability value is positively correlated with the difficulty coefficient.
Optionally, the apparatus further comprises: the judging module is used for judging whether the picture assets are completely collected or not after the distributing module distributes a picture mosaic fragment of the picture assets to the first user account; and the display module is used for outputting and displaying asset introduction information of the picture assets if the picture assets are collected, and storing the picture assets as the user assets of the first user account.
Optionally, the apparatus further comprises at least one of: the system comprises a first transfer module, a second transfer module and a third transfer module, wherein the first transfer module is used for sending a fragment asset collected by a first user account on a client to a second user account, and deleting the fragment asset in an asset library of the first user account so that the second user account collects the fragment asset after completing a learning task corresponding to the fragment asset, and the fragment asset comprises at least one jigsaw fragment; and the second transfer module is used for sending the picture assets collected by the first user account on the client to a third user account, and deleting the picture assets from the asset library of the first user account so that the third user account collects the picture assets after completing the learning task corresponding to the picture assets.
Optionally, the apparatus further comprises: the sharing module is configured to share the collection progress information of the picture asset on a social media after the distribution module distributes one piece of puzzle piece of the picture asset to the user account, where the collection progress information includes at least one of the following information: the picture assets currently being collected, the puzzle fragments already collected, the puzzle fragments to be collected, and the corpus resource list learned in the collection process.
According to a further embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps in any of the apparatus embodiments described above when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the method and the device, the operation behavior of the first user account on the client aiming at the learning task is monitored; the method comprises the steps of obtaining an operation result of an operation behavior, distributing a jigsaw fragment of a picture asset for a user account if a learning task is completed, and collecting pictures according to the completion condition of the user account for operating the learning task on a client, so that the technical problem that the picture asset cannot be collected through online learning in the related technology is solved, the learning result of the user can be quantized into the picture asset, a scheme for collecting the picture asset on line is provided, a diversified online learning incentive mode is realized, and the learning enthusiasm of the user is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a block diagram of the hardware architecture of a photo asset processing handset according to an embodiment of the invention;
FIG. 2 is a flow chart of a method of picture asset processing according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating the collection of puzzle pieces according to an embodiment of the present invention;
FIG. 4 is an effect diagram of customizing a heart-shaped picture according to an embodiment of the present invention;
FIG. 5 is an effect diagram of a custom picture asset according to an embodiment of the present invention;
fig. 6 is a block diagram of a picture asset processing device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above 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 application described herein are capable of operation in sequences other than those illustrated or 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, 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.
Example 1
The method provided by the first embodiment of the application can be executed in a mobile phone, a tablet, a computer, a wearable device or a similar electronic terminal. Taking the example of operating on a mobile phone, fig. 1 is a block diagram of a hardware structure of a picture asset processing mobile phone according to an embodiment of the present invention. As shown in fig. 1, the handset 10 may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally may also include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is merely illustrative and not limiting to the structure of the mobile phone. For example, the handset 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store a mobile phone program, for example, a software program and a module of an application software, such as a mobile phone program corresponding to a picture asset processing method in an embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the mobile phone program stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the handset 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of such networks may include wireless networks provided by the communications provider of the handset 10. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In the embodiment, a method for processing a picture asset is provided, and fig. 2 is a flowchart of a method for processing a picture asset according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, monitoring the operation behavior of the first user account on the client aiming at the learning task;
the user account in this embodiment is an account that a user logs in on an application program of a client, and corresponds to a target user, and the user logs in one account when learning on the client, and through the account, the user associates a corpus, user data, and learning progress on different clients.
The learning task is executed on line by the user through interactive information, and comprises course, note, exercise question and the like recommended by the system and selected by the user.
Step S204, obtaining an operation result of the operation behavior, wherein the operation result is used for representing whether the first user account completes a learning task;
the operation result of this embodiment is related to the type of the learning task, including the test question result, the completion degree of listening, speaking, reading and writing, etc., such as whether the user follows a certain english word on the client, whether the following reading of a certain word is accurately completed, i.e., the operation result, whether the user practices the pronunciation of the word on the client, whether the word is read through a standard phonetic symbol, i.e., the operation result, whether the user recognizes a certain word, and whether the word, i.e., the operation result, can be identified according to the paraphrasing content or the pronunciation.
Step S206, if the learning task is completed, distributing a picture splicing fragment of a picture asset to the user account, wherein the picture asset is composed of a plurality of picture splicing fragments;
the picture asset of this embodiment is blank in the initial stage, and each time the user completes one learning task, one piece of jigsaw fragments is allocated to the user account, and the jigsaw fragments are lit up at the blank position of the picture asset.
Optionally, the picture assets may be stored in the client and the cloud at the same time, so as to implement data migration and synchronization at different clients.
Through the steps, the operation behavior of the first user account on the client aiming at the learning task is monitored; obtaining an operation result of an operation behavior, and if the learning task is completed, allocating a jigsaw fragment of a picture asset to a user account, wherein the learning task of the embodiment comprises learning or testing of listening, speaking, reading and writing dimensions of linguistic resources such as words, and the like, for example, when a user follows and reads a certain English word on a client, the user accurately follows and reads the word on the client to complete the learning task of the word, the user exercises pronunciation of a phonetic symbol on the client, the learning task of the phonetic symbol is completed by reading the standard phonetic symbol on the client, the user recognizes and remembers a certain phrase, the phrase is distinguished on the client according to paraphrase content or pronunciation to complete the learning task, or a test question can be answered, the operation is continued and online learning software is operated, and the picture is collected according to the completion condition that the learning task is operated on the client by the user account, so that the technical problem that the picture asset cannot be collected through online learning in the related technology is solved, the method can quantize the learning result of the user into the picture assets, provides a scheme for collecting the picture assets on line, realizes a diversified on-line learning incentive mode, and improves the learning enthusiasm of the user.
In this embodiment, the monitoring of the operation behavior of the first user account on the client for the learning task includes at least one of: monitoring the operation behaviors of the first user account for learning the first corpus resource on the client, such as reading behavior, viewing behavior, selecting behavior and the like; monitoring the operation behaviors of the first user account on reading the second corpus resources on the client, such as reading behavior and follow-up reading behavior; monitoring the operation behaviors of the first user account on the client for listening to the third corpus resource, such as playing behavior, hearing test and the like; and monitoring the operation behaviors of the first user account for writing the fourth linguistic resource on the client, such as word input behaviors, articles, diaries, mindset writing behaviors and the like.
The corpus resources of this embodiment may be words, sentences, articles, exercises, news, and other resources. Aiming at a certain language word, such as a certain English word, a user can learn and master in different dimensions and through different channels, different mastering channels can be realized on a client by adopting different operation behaviors, such as clicking different buttons (playing, turning pages and the like), calling different hardware (such as a microphone, a loudspeaker, a touch screen and the like), and monitoring whether the user triggers and executes a learning task on the client by monitoring the behaviors through the system.
In one embodiment of this embodiment, allocating one piece of tile fragment of the picture asset to the first user account includes:
s11, determining a first jigsaw fragment according to the collection progress of the picture assets, wherein the first jigsaw fragment is the last jigsaw fragment collected by the first user account;
and S12, allocating a second jigsaw fragment for the first user account according to the preset jigsaw sequence of the picture assets, wherein the second jigsaw fragment is the next jigsaw fragment of the first jigsaw fragment.
The collection progress user indicates how many tile fragments have been collected for the current picture asset, and may arrange a sequence number, such as 12345, in time or order for each tile fragment collected, with the largest sequence number being the last tile fragment that has been collected by the first user account. The tile order is used to indicate the tile path for completing the entire picture asset using tile fragments, such as a first tile fragment placed in a first position of the picture asset and a second tile fragment placed in a second position of the picture asset until the entire picture asset is tiled, and the tile order may be random or pre-configured, with each tile fragment corresponding to a fixed position of the picture asset.
In another embodiment of this embodiment, a learning task corresponds to a tile fragment, and a tile fragment can be allocated after a specific learning task is completed. Assigning a tile fragment of the picture asset to the first user account comprises:
s21, searching target jigsaw fragments matched with the learning task, wherein the learning task comprises one of the following: words, phrases, sentences, articles;
in one example, the learning task is a back word, each learning task corresponds to a word by setting a mapping relationship, each word corresponds to a piece of jigsaw puzzle fragment, word 1 corresponds to jigsaw puzzle fragment 1, word 2 corresponds to jigsaw puzzle fragment 2, and word 3 corresponds to jigsaw puzzle fragment 3 …, a user successfully backs up a word 1 (if the word is marked as "mature" and successfully writes the word implicitly, the word is recognized according to chinese) in the learning process, the system assigns jigsaw puzzle fragment 1 corresponding to word 1 to the user account by searching, and matches with the word 1.
And S22, distributing target jigsaw fragments of the picture assets for the first user account.
Fig. 3 is a schematic diagram of collecting picture mosaic fragments, 5 picture mosaic fragments are spliced, and a picture asset is a girl with pearl earrings, and consists of 256 picture mosaic fragments, wherein each picture mosaic fragment corresponds to a fixed position in the picture asset.
Optionally, after allocating one piece of puzzle fragment of the picture asset to the first user account, the method further includes: judging whether the collection of the picture assets is finished or not; and if the collection of the picture assets is finished, outputting and displaying asset introduction information of the picture assets, and storing the picture assets as the user assets of the first user account.
In one example, the picture assets are famous pictures, the system can be provided with a plurality of famous pictures, each famous picture is composed of a plurality of puzzles, one puzzles of the puzzles is lightened until the whole famous picture is lightened, and the user can collect the famous pictures and display Chinese and English introduction information corresponding to the famous pictures. Each picture is divided into a plurality of puzzles, each puzzles is used as answer motivation, a user can obtain one puzzles when answering one question in the course of learning or reviewing the course, (one puzzle can be removed by wrong answering and not deducted), the complete picture is pieced into one step according to a preset puzzle path, the picture can be lightened after being gathered, and the Chinese and English brief introduction of the picture can be checked after lightening. Each picture tile corresponds to a question answering task (the question answering task can be system distributed or selected by a user), and once the task is completed, a picture tile can be collected.
In this embodiment, the system is provided with a picture library, in which a plurality of picture assets are stored, and a text, audio and video content library corresponding to the picture library, where each picture asset in the picture library is associated with one piece of asset introduction information (text, audio and video in the content library). The asset introduction information for unlocking and associating the displayed picture asset may be, but is not limited to: unlocking and associating the reading text for displaying the picture assets; unlocking and playing the audio content of the picture asset in a correlated manner; and unlocking and associating the video content of the picture assets.
In this embodiment, the system prestores a plurality of picture assets in the database in advance, and the picture assets are composed of a jigsaw module (which may be a whiteboard or a grayscale version of an original picture asset) and a plurality of jigsaw fragments. Before the user starts to group together, the system allocates a picture asset to the user account, or the user selects a favorite picture in a picture asset library. Before distributing a piece of jigsaw fragments of the picture assets for the first user account, the method further comprises the following steps:
s31, determining the ability value of the first user account, wherein the ability value is used for representing the ability level of the first user account to the learning language;
in one embodiment of this embodiment, determining the capability value of the first user account may be, but is not limited to, by:
the first method is as follows: calculating the capacity value of the first user account according to the number of the corpus words in the corpus created by the first user account and the attribute value corresponding to each corpus word, wherein the attribute value is used for representing the mastering degree of the first user account on the corpus words;
the second method comprises the following steps: and determining a difficulty coefficient of the learning task, and converting the difficulty coefficient into a capability value of the first user account, wherein the capability value and the difficulty coefficient are in positive correlation.
In some examples, the system assigns different pictures according to user rank, e.g., a fourth-rank user may assign different pictures than a sixth-rank user, and the order in which tiles are collected may be different for the same pair of pictures. By dividing the same secondary picture into jigsaw fragments with different quantities, different quantities of jigsaw fragments can be configured according to user grades aiming at the same picture, the user grade is higher, because the learning speed of the user is higher, the more jigsaw fragments are collected in the same learning time (correspondingly, the size of each jigsaw fragment is correspondingly adjusted, the more the jigsaw fragments are, the smaller the size of the jigsaw fragments are), and the achievement feeling of the user in the learning process is provided
And S32, selecting the picture assets matched with the capability values in the picture asset library. The content of the picture assets corresponds to the ability value of each user, and the personalized and diversified picture splicing experience of the user is met.
In some examples, the system assigns the photo asset by analyzing a user representation of the user account. The method comprises the steps of presetting a gallery (comprising a plurality of famous paintings), generating a user portrait by collecting attribute information of a user, further analyzing the user portrait, distributing matched famous paintings for each user, distributing ancient paintings for users who like history, enjoying modern paintings after distribution, and classifying the paintings in the gallery according to attributes such as authors, years and styles.
In some examples, a picture uploading interface is set on the client, the interface is called by a background, the user uploads a self-selected or self-defined picture asset, and before a piece of jigsaw fragments of the picture asset is allocated to the first user account, the method further includes: receiving a first picture uploaded by a first user account at a client; cutting the first picture based on a preset size to obtain a second picture; cutting the second picture into a plurality of jigsaw fragments, and sequencing the jigsaw fragments according to a jigsaw sequence; and determining the plurality of jigsaw fragments as the picture assets to be collected by the first user account.
Optionally, the original picture may be cut according to a system template and a user-defined template. Cutting the first picture based on a preset size to obtain a second picture, wherein the second picture comprises one of the following pictures: cutting the first picture based on a preset system size to obtain a second picture; and analyzing the user-defined picture template, and cutting the first picture based on the picture template to obtain a second picture.
In some embodiments of this embodiment, after the user uploads the self-selected picture, the system may also match the associated asset introduction information for the user to display to the user after the user collects the puzzle pieces that complete the picture. After receiving a first picture uploaded by a first user account on a client, the method further comprises the following steps: searching reading corpora matched with the first picture through a local corpus or a search engine; and determining the reading corpus as the asset introduction information of the picture asset.
The first picture is a picture uploaded by a user, such as a landscape, a plant, a figure and the like, the system or the user can cut the uploaded picture according to a certain format and size, the uploaded picture is made to accord with the jigsaw rule, then the picture is cut into a plurality of fragments, meanwhile, the system automatically identifies the picture and related information of the picture, such as GPS positioning information, weather information, time and the like, the corresponding introduction information is matched in a content library, the picture is displayed after being assembled, and when the local content library does not have the matched asset introduction information, the system searches the matched asset introduction information on the internet through a search engine. In one example, the picture uploaded by the user comprises elements such as 'tadpole', 'frog', 'pond', and the like, by identifying the content of the picture, the story background of the picture is identified as 'tadpole looking for mom', the system searches story materials related to 'tadpole looking for mom' on the internet, and uses the story materials as asset introduction information of the picture asset, or matches the asset introduction information according to the overall style and pixel color of the picture, bright pictures match the content which is positive and upward, and dark pictures match the content which is deep and melancholy.
In the cutting process, the flashing point (namely C bit) in the picture can be positioned firstly, the layout of each object in the picture is analyzed, then the picture is cut into a plurality of complete objects as much as possible, for example, a person can be cut into arms, legs, a trunk, a neck, a head and the like, the fragments are identified and stored according to time sequence, the object which is taken as the flashing point in the picture is placed behind, and a user can assemble the fragments step by step in the answering process and finally draw a dragon-point eye.
In the example of the user-defined picture template of the embodiment, the whole first picture can be cut into the second picture (e.g. the second picture conforming to the predetermined shape) according to the predetermined shape
Heart, circle, etc.) or a predetermined shape (e.g., a picture asset) composed of a plurality of first pictures (which may be the same picture or different pictures) by taking the first picture as a material of a tile fragment
Heart, circle, etc.), fig. 4 is an effect diagram of customizing a heart-shaped picture according to an embodiment of the present invention, a left heart-shaped picture 1 is a picture obtained by cutting a complete original picture into a heart shape, and a right heart-shaped picture 2 is a picture obtained by splicing and cutting a plurality of original pictures into a heart shape.
Fig. 5 is an effect diagram of the customized picture assets of the embodiment of the present invention, wherein a user takes an outdoor scene of a castle outdoors, and the original picture is made to conform to the picture rules of the system by clipping and format conversion, and then is cut into a plurality of jigsaw fragments.
In this embodiment, after collecting the puzzle pieces or the completed picture assets, the user account stores the puzzle pieces and the picture assets as digital assets in an asset library of the user account, and the user can give away these assets, including at least one of the following: sending the fragment assets collected by the first user account on the client to the second user account, and deleting the fragment assets in an asset library of the first user account so that the second user account collects the fragment assets after completing the learning tasks corresponding to the fragment assets, wherein the fragment assets comprise at least one jigsaw fragment; and if different user accounts are simultaneously used for sharing the same picture asset, sending the picture asset collected by the first user account on the client to a third user account, and deleting the picture asset in an asset library of the first user account so that the third user account collects the picture asset after completing a learning task corresponding to the picture asset.
After receiving the fragment assets and the picture assets transferred by the first user account, the second user account or the third user account completes corresponding learning tasks, namely, the fragment assets and the picture assets can be synchronously collected.
In an application scenario, a first user account number with small brightness finishes a learning task of a word "absurd" on a client, an exquisite jigsaw fragment (jigsaw fragment 1) is collected, the first user account number with small brightness considers that the jigsaw fragment 1 has a collection value, the jigsaw fragment 1 is sent to a second user account number with small strength, after the small strength receives the jigsaw fragment 1, the user finds that the user is really exquisite and wants to collect the jigsaw fragment by himself, the user with small strength can start the learning task of the word "absurd" on the client, and after the user with small strength finishes the learning task, the user with small strength also collects the jigsaw fragment 1.
In another application scenario, a xiaoming first user account completes a learning task of 5 words (abcde) on a client, collects an exquisite picture asset (picture 1), the xiaoming considers that the picture 1 has a very high collection value, and the xiaoling may want to collect as well, sends the picture 1 to a minihong third user account, finds that the picture 1 is really exquisite after the minihong receives the picture 1, then starts to collect, the minihong starts the learning task of the words (abcde) on the client and completes the learning tasks corresponding to the words in sequence, and the minihong also collects the picture 1 after the minihong completes the learning task.
The different users can exchange or present the collected jigsaw puzzle and the jigsaw picture, and the different users can exchange or present the jigsaw puzzle and the jigsaw picture. In some examples, after assigning a piece of tile fragment of the picture asset to the user account, further comprising: sharing the collection progress information of the picture assets on the social media, wherein the collection progress information comprises the current picture assets being collected, the collected jigsaw fragments, the jigsaw fragments to be collected, the number of the jigsaw fragments required by the completion of the collection, the corpus list learned by the first user account in the collection process and other information. For example, a user is currently collecting a picture asset 1, learns a course abc, and collects corresponding tile fragments a, b, and c, where the picture asset 1 is composed of five tiles including tile fragment a, b, c, d, and e, and the user can share collection progress information on social platforms such as instant messaging software and content sharing software, including: the picture assets 1 are currently collected, the jigsaw fragments a, b and c are collected, the jigsaw fragments d and e are to be collected, and three courses abc are learned in the collection process.
Furthermore, the collection progress information can be packaged into an entrance through a network link, a two-dimensional code and the like, and other users can open or download the associated application program or enter an interface of a learning task, a display interface of a picture asset and the like by clicking the entrance.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
The embodiment also provides a picture asset processing device and a system, which are used for implementing the above embodiments and preferred embodiments, and are not described again after being described. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 6 is a block diagram of a picture asset processing apparatus according to an embodiment of the present invention, as shown in fig. 6, the apparatus including: a monitoring module 60, an acquisition module 62, an assignment module 64, wherein,
a monitoring module 60, configured to monitor an operation behavior of the first user account on the client for the learning task;
an obtaining module 62, configured to obtain an operation result of the operation behavior, where the operation result is used to characterize whether the first user account completes the learning task;
and an allocating module 64, configured to allocate a piece of jigsaw fragments of a picture asset to the user account if the learning task is completed, where the picture asset is composed of a plurality of jigsaw fragments.
Optionally, the monitoring module includes at least one of: the first monitoring unit is used for monitoring the operation behavior of the first user account for memorizing the first corpus resource on the client; the second monitoring unit is used for monitoring the operation behavior of the first user account for reading the second corpus resources on the client; the third monitoring unit is used for monitoring the operation behavior of the first user account for listening to the third corpus resource on the client; and the fourth monitoring unit is used for monitoring the operation behavior of writing the fourth corpus resource on the client by the first user account.
Optionally, the allocating module includes: a determining unit, configured to determine a first tile fragment according to a collection progress of the picture asset, where the first tile fragment is a last tile fragment that has been collected by a first user account; the first allocation unit is configured to allocate a second puzzle piece to the first user account according to a preset puzzle sequence of the picture asset, where the second puzzle piece is a next puzzle piece of the first puzzle piece.
Optionally, the allocating module includes: a searching unit, configured to search for target tile fragments matching the learning task, wherein the learning task includes one of: words, phrases, sentences, articles; a second allocating unit, configured to allocate the target tile fragments of the picture assets to the first user account.
Optionally, the apparatus further comprises: the first determination module is used for determining the ability value of the first user account before the distribution module distributes a jigsaw fragment of a picture asset to the first user account, wherein the ability value is used for representing the ability level of the first user account to learn languages; and the selection module is used for selecting the picture assets matched with the capability value in the picture asset library.
Optionally, the determining module includes at least one of: the calculation unit is used for calculating the capacity value of the first user account according to the number of the linguistic data words in the corpus created by the first user account and the attribute value corresponding to each linguistic data word, wherein the attribute value is used for representing the mastery degree of the first user account on the linguistic data words; the conversion unit is used for determining a difficulty coefficient of the learning task and converting the difficulty coefficient into a capability value of the first user account, wherein the capability value is positively correlated with the difficulty coefficient.
Optionally, the apparatus further comprises: the receiving module is used for receiving a first picture uploaded by the first user account at the client before the distribution module distributes a picture mosaic fragment of a picture asset to the first user account; the cutting module is used for cutting the first picture based on a preset size to obtain a second picture; the processing module is used for cutting the second picture into a plurality of jigsaw fragments and sequencing the jigsaw fragments according to a jigsaw sequence; a second determining module, configured to determine the plurality of tile fragments as the picture assets to be collected by the first user account.
Optionally, the cutting module includes one of: the first clipping unit is used for clipping the first picture based on a system preset size to obtain a second picture; and the second cutting unit is used for analyzing the user-defined picture template, cutting the first picture based on the picture template and obtaining a second picture.
Optionally, the apparatus further comprises: the searching module is used for searching reading linguistic data matched with the first picture through a local corpus or a search engine after the receiving module receives the first picture uploaded by the first user account at the client; and the third determining module is used for determining the reading corpus as the asset introduction information of the picture asset.
Optionally, the apparatus further comprises: the judging module is used for judging whether the picture assets are completely collected or not after the distributing module distributes a picture mosaic fragment of the picture assets to the first user account; and the display module is used for outputting and displaying asset introduction information of the picture assets if the picture assets are collected, and storing the picture assets as the user assets of the first user account.
Optionally, the apparatus further comprises at least one of: the system comprises a first transfer module, a second transfer module and a third transfer module, wherein the first transfer module is used for sending a fragment asset collected by a first user account on a client to a second user account, and deleting the fragment asset in an asset library of the first user account so that the second user account collects the fragment asset after completing a learning task corresponding to the fragment asset, and the fragment asset comprises at least one jigsaw fragment; and the second transfer module is used for sending the picture assets collected by the first user account on the client to a third user account, and deleting the picture assets from the asset library of the first user account so that the third user account collects the picture assets after completing the learning task corresponding to the picture assets.
Optionally, the apparatus further comprises: the sharing module is used for sharing the collection progress information of the picture assets on the social media after the distribution module distributes one jigsaw fragment of the picture assets to the user account.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Example 3
Embodiments of the present invention also provide a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, monitoring the operation behavior of the first user account on the client aiming at the learning task;
s2, obtaining an operation result of the operation behavior, wherein the operation result is used for representing whether the first user account completes the learning task;
s3, if the learning task is completed, allocating a piece of jigsaw puzzle fragment of a picture asset to the user account, where the picture asset is composed of a plurality of jigsaw puzzle fragments.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, monitoring the operation behavior of the first user account on the client aiming at the learning task;
s2, obtaining an operation result of the operation behavior, wherein the operation result is used for representing whether the first user account completes the learning task;
s3, if the learning task is completed, allocating a piece of jigsaw puzzle fragment of a picture asset to the user account, where the picture asset is composed of a plurality of jigsaw puzzle fragments.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (11)
1. A picture asset processing method is characterized by comprising the following steps:
monitoring the operation behavior of a first user account on a client aiming at a learning task;
acquiring an operation result of the operation behavior, wherein the operation result is used for representing whether the first user account completes the learning task or not;
if the learning task is completed, distributing a picture splicing fragment of a picture asset to the user account, wherein the picture asset is composed of a plurality of picture splicing fragments;
the method further comprises at least one of: sending the fragment assets collected by the first user account on the client to a second user account, and deleting the fragment assets in an asset library of the first user account so that the second user account collects the fragment assets after completing a learning task corresponding to the fragment assets, wherein the fragment assets comprise at least one jigsaw fragment;
and sending the picture assets collected by the first user account on the client to a third user account, and deleting the picture assets from an asset library of the first user account so that the third user account collects the picture assets after completing the learning task corresponding to the picture assets.
2. The method of claim 1, wherein monitoring operational behavior of the first user account on the client for the learning task comprises at least one of:
monitoring the operation behavior of a first user account for identifying and memorizing a first corpus resource on a client;
monitoring the operation behavior of the first user account for reading the second corpus resources on the client;
monitoring the operation behavior of the first user account for listening to the third corpus resource on the client;
and monitoring the operation behavior of the first user account for writing the fourth linguistic resource on the client.
3. The method of claim 1, wherein assigning a tile fragment of a picture asset to the first user account comprises:
determining a first jigsaw fragment according to the collection progress of the picture asset, wherein the first jigsaw fragment is the last jigsaw fragment collected by the first user account;
and allocating a second jigsaw fragment for the first user account according to the preset jigsaw sequence of the picture assets, wherein the second jigsaw fragment is the next jigsaw fragment of the first jigsaw fragment.
4. The method of claim 1, wherein assigning a tile fragment of a picture asset to the first user account comprises:
finding a target tile fragment that matches the learning task, wherein the learning task comprises one of: words, phrases, sentences, articles;
allocating the target tile fragments of a picture asset to the first user account.
5. The method of claim 1, wherein prior to assigning a tile fragment of a picture asset to the first user account, the method further comprises:
determining an ability value of the first user account, wherein the ability value is used for representing the ability level of the first user account to learn languages;
selecting a picture asset matching the capability value in a picture asset library.
6. The method of claim 5, wherein determining the capability value of the first user account comprises at least one of:
calculating the capacity value of the first user account according to the number of the corpus words in the corpus created by the first user account and the attribute value corresponding to each corpus word, wherein the attribute value is used for representing the mastering degree of the first user account on the corpus words;
determining a difficulty coefficient of the learning task, and converting the difficulty coefficient into a capability value of the first user account, wherein the capability value is positively correlated with the difficulty coefficient.
7. The method of claim 1, wherein after assigning a tile fragment of a picture asset to the first user account, the method further comprises:
judging whether the collection of the picture assets is finished or not;
and if the collection of the picture assets is finished, unlocking and displaying the asset introduction information of the picture assets in a correlated manner, and storing the picture assets as the user assets of the first user account.
8. The method of claim 1, wherein after assigning a tile fragment of a picture asset to the user account, the method further comprises:
sharing collection progress information of the photo asset on social media, wherein the collection progress information includes at least one of: the picture assets currently being collected, the puzzle fragments already collected, the puzzle fragments to be collected, and the corpus resource list learned in the collection process.
9. A picture asset processing apparatus, comprising:
the monitoring module is used for monitoring the operation behavior of the first user account on the client aiming at the learning task;
an obtaining module, configured to obtain an operation result of the operation behavior, where the operation result is used to characterize whether the first user account completes the learning task;
the distribution module is used for distributing a picture splicing fragment of a picture asset to the user account if the learning task is finished, wherein the picture asset is composed of a plurality of picture splicing fragments;
the apparatus further comprises at least one of: the system comprises a first transfer module, a second transfer module and a third transfer module, wherein the first transfer module is used for sending a fragment asset collected by a first user account on a client to a second user account, and deleting the fragment asset in an asset library of the first user account so that the second user account collects the fragment asset after completing a learning task corresponding to the fragment asset, and the fragment asset comprises at least one jigsaw fragment; and the second transfer module is used for sending the picture assets collected by the first user account on the client to a third user account, and deleting the picture assets from the asset library of the first user account so that the third user account collects the picture assets after completing the learning task corresponding to the picture assets.
10. A storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 8 when executed.
11. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 8.
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| CN202010692866.9A CN112001930B (en) | 2020-07-17 | 2020-07-17 | Picture asset processing method and device, storage medium and electronic device |
| PCT/CN2020/130488 WO2022011917A1 (en) | 2020-07-17 | 2020-11-20 | Picture asset processing method and device, and storage medium |
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