Patent Application
20230222161
The present application claims the priority from Chinese Patent Application No. 202210023596.1, filed with the Chinese Patent Office on Jan. 10, 2022, the content of which is hereby incorporated herein by reference in its entirety.
The present disclosure relates to a technical field of video, and in particular, to a technical field of short video. Particularly, the present disclosure relates to a video title generation method and apparatus, an electronic device and a storage medium.
With the development of video technology and the popularity of short video platforms, a large amount of information is transmitted through video or short video. How to determine the title of a video or short video file has become a technical problem to be solved.
The present disclosure provides a video title generation method and apparatus, an electronic device and a storage medium.
According to one aspect of the present disclosure, provided is a video title generation method, including: obtaining a plurality of pieces of optional text information, for a first video file; determining central text information, from the plurality of pieces of optional text information, the central text information being optional text information with the highest similarity to content of the first video file; and determining the central text information as a title of the first video file.
According to another aspect of the present disclosure, provided is a video title generation apparatus including: an obtaining module configured to obtain a plurality of pieces of optional text information, for a first video file; a determination module configured to determine central text information, from the plurality of pieces of optional text information, the central text information being optional text information with the highest similarity to content of the first video file; and a title module configured to determine the central text information as a title of the first video file.
According to another aspect of the present disclosure, provided is an electronic device including: at least one processor; and a memory connected in communication with the at least one processor. The memory stores an instruction executable by the at least one processor, and the instruction, when executed by the at least one processor, enables the at least one processor to execute any one of the methods provided by the above aspects.
According to another aspect of the present disclosure, provided is a non-transitory computer-readable storage medium storing a computer instruction thereon. The computer instruction is used to enable a computer to execute any one of the methods provided by the above aspects.
According to another aspect of the present disclosure, provided is a computer program product including a computer program, where the computer program implements any one of the methods provided by the above aspects, when executed by a processor.
By adopting the present disclosure, interest points in an original video file according to user's interactive behavior data on the original video file can be determined, the original video file is clipped based on the interest points to obtain a plurality of clipped video files, that is, short videos. Because the clipped short video contains the most interesting part of the original video file, the quality of the short video obtained by this method is relatively high, particularly in line with the user's preferences, and has great application value.
It should be understood that the content described in this part is not intended to identify crucial or important features of embodiments of the present disclosure, or to limit the scope of the present disclosure. Other features of the present disclosure will be easily understood from the following description.
The accompanying drawings are used to better understand the present solution, and do not constitute a limitation to the present disclosure.
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the present disclosure are included to facilitate understanding, and should be considered as merely exemplary. Therefore, those of ordinary skill in the art should realize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Likewise, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.
Implementations of the present disclosure provide a video title generation method which may be used to generate a title for a video file or a title for a short video. Short video is a popular form of video in recent years. There is no uniform definition for the term “short video” in the art. Short videos are generally relative to long videos, and short videos and long videos are mainly divided from an aspect of the length of the video. For example, a video file with a length of tens of seconds, minutes or the like may be referred to as a short video, and a video file with a length of tens of minutes, hours or the like may be referred to as a long video (or a video in the traditional sense).
Generally, short-duration videos may be considered short videos, regardless of the shooting equipment, location and post-processing. For example, a video with a concentrated climax and duration of no more than 10 minutes may be considered a short video. From the perspective of dissemination, short videos have the potential of viral dissemination due to their short and sophisticated features. Compared with traditional pictures and texts, short videos have large amount of information, strong performance and good intuition, and thus people may use fragmented time to browse and share.
Short videos may be variously generated. For example, it is capable of shooting a short video file directly as a short video. Alternatively, a long video may be clipped to make short videos from selected clips of the long video. Specifically, one clip may be clipped from the long video, and the clip may be used as a short video. In addition, two or more clips may also be clipped from the long video, and a short video may be obtained by splicing the clipped clips.
The following describes the video title generation method provided by implementations of the present disclosure by taking generating the title of a short video as an example. It should be emphasized that the video title generation method provided by implementations of the present disclosure is not limited to title generation of short videos, but may also be applied to title generation of long videos (or video files in the traditional sense).
The title of the short video is generally required to reflect the content of the short video, and has a certain decorative effect. In the related art, a short video is generally generated by manual viewing of an annotator, and a title is manually created. This method of title generation requires a lot of manpower and time costs, may not adapt to massive short video updates, and has a high requirement to the annotator. In addition, it is difficult to ensure stable quality of the title of the short video created manually. Another common method of title generation is to count several words occurred at a high frequency, combine those words to a sentence, and use the sentence as the title of a video file. The title of the video file obtained in this way may not better reflect the subject of the video file, and the grammar, decoration and the like of the title are not good. It can be seen that the related video title generation methods above are not effective and efficient.
The embodiments of the present disclosure provide a video title generation method.
In S110, a plurality of pieces of optional text information are obtained, for a first video file.
In S120, central text information is determined, from the plurality of pieces of optional text information, the central text information being optional text information with the highest similarity to content of the first video file.
In S130, the central text information is determined as a title of the first video file.
In some implementations, the above optional text information includes at least one of: a bullet screen text of the first video file, a comment of the first video file, a subtitle of the first video file, or an introductory text of the first video file. By adopting a plurality of forms of files as the optional text information and selecting the central text information as the title of the first video file from the plurality of forms of files, it is capable of enriching the file source of the title, thereby obtaining the title that accurately represents the main content of the first video file.
In some implementations, the above first video file is a video file formed by using a clip selected from a second video file; and the bullet screen text of the first video file includes: a text of bullet comments posted by a plurality of users when the clip in the first video file is displayed during playing the second video file.
The above first video file may be referred to as the short video, and the above second video file may be referred to as the long video. The short video involved in implementations of the present disclosure may be any one among a video file with a shorter duration captured directly, a short video formed by a clip clipped from a video file, a short vide formed by combining two clips clipped from a video file, and a short video formed by combining at least two clips clipped from at least two video files.
In some implementations, the above obtaining of the interactive behavior data for the first video file includes: obtaining interactive behavior data for the second video file from a user behavior database. In an example, the user behavior database records the interactive behavior data of a plurality of video files from a video player terminal. The interactive behavior data of the plurality of video files includes at least one of: an identification of each video file, a total duration of the video file, an interactive behavior of the video file, or a time point of the interactive behavior in the video file.
For example, the above identification of the video file may include a video identifier (ID), which may be the unique label of the video file, and the Cloud terminal and the user behavior database thereof may uniquely determine a specific video file according to the video identifier.
The above total duration may include a total duration of the video file.
The above interactive behavior may include at least one of collecting, sharing, posting a bullet comment, posting a comment, rewarding and clicking a “like”.
The above time point of the interactive behavior in the video file may include a time point in the playing duration of the video file when the interactive behavior of the user occurs.
For example, ID of a video file is 001, and the total duration is two hours. User A clicks to a collection button to collect when the video file has been played for fifteen minutes, and posts a bullet comment when the video file has been played for twenty minutes. The video player terminal may report the interactive behavior data of the user A for the video file to the Cloud terminal. The interactive behavior data may at least include the following information:
[Video file ID=001;
total duration=two hours;
interactive behavior: collecting, corresponding time point thereof: 15th minute;
interactive behavior: posting a bullet comment, corresponding time point thereof: 20th minute].
For this video file, user B clicks a sharing button to share when the video file has been played for eighteen minutes, and sends a comment when the video file has been played for twenty-five minutes. The video player terminal may report the interactive behavior data of the user B for the video file to the Cloud terminal. The interactive behavior data may at least include the following information:
[Video file ID=001;
total duration=two hours;
interactive behavior: sharing, corresponding time point thereof: 18th minute;
interactive behavior: posting a comment, corresponding time point thereof: 25th minute].
The user behavior database in the Cloud terminal may count/analyze and save the interactive behavior data reported by each video player terminal. For example, for the above video file with ID of “001”, the user behavior database counts the interactive behavior data reported by user A and user B through the video player terminal, and the interactive behavior data may include the following information:
[Video file ID=001;
total duration=two hours;
interactive behavior: collecting, corresponding time point thereof: 15th minute;
interactive behavior: sharing, corresponding time point thereof: 18th minute;
interactive behavior: posting a bullet comment, corresponding time point thereof: 20th minute;
interactive behavior: posting a comment, corresponding time point thereof: 25th minute].
As the interactive behavior data reported by video player terminal gradually increase, the interactive behavior data for each video file counted in the user behavior database of the Cloud terminal may also be gradually enriched.
By using the interactive behavior data for the second video file counted and saved in the user behavior database, the interaction heat at each time point of the second video file may be determined, and the second video file (i.e. the long video) may be clipped according to the interaction heat to obtain the first video file (i.e. short video) with a high interaction heat. Then, the video title generation method provided in implementations of the present disclosure may be used to generate the title for the short video.
In some implementations, the above first video file is a video file formed by using a clip selected from the second video file, and the bullet screen text of the first video file includes: a text of bullet comments posted by a plurality of users when the clip in the first video file is displayed during playing the second video file.
For example, clip 1, clip 2, and clip 3 are selected from the long video, and then connected sequentially to obtain the short video. Upon generating the title of the short video, the title of the short video may be generated according to the bullet comments posted by the user at the time points of playing the clip 1, the clip 2 and the clip 3 during playing the short video, or the title of the short video may be generated by using the bullet comments posted by the user at the time points of playing the clip 1, the clip 2 and the clip 3, and other relevant information during playing the short video. Other relevant information may include: comments sent by the user at the time points of playing the clip 1, the clip 2 and the clip 3, subtitles in the short video, introductory text for the long video or the clip 1, the clip 2 and the clip 3, and the like.
It can be seen that the bullet screen text regarding the first video file in the second video file may be directly searched from the interactive behavior data sent by each user upon playing the second video file according to implementations of the present disclosure. This bullet screen obtaining method is more convenient, and may obtain a text of bullet comments posted by a large number of users for the first video file. Therefore, the title of the first video file may be determined through richer text materials, and the accuracy of the title may be improved.
As shown in
In S410, each piece of optional text information of the plurality of pieces of optional text information is converted into a corresponding text vector.
In S420, for each text vector, a sum of distances between the text vector and each other text vector is determined.
In S430, a piece of optional text information corresponding to a text vector with the minimum sum of distances is determined as the central text information.
Optionally, a manner in which a distance between the text vector and the other text vector is determined includes: calculating angle cosine between the text vector and the other text vector, to take the angle cosine as the distance between the text vector and the other text vector, or calculating a Euclidean distance between the text vector and the other text vector, to take the Euclidean distance as the distance between the text vector and the other text vector.
For example, taking the bullet screen text as the optional text information, in some possible implementations, the above determining of the central text information from the plurality of pieces of optional text information, and the using of the central text information as the title of the first video file specifically include the following steps.
In a first step, the bullet screen text of the first video file is extracted, and is recorded as: txt=[txt(1), txt(2), . . . , txt(i), . . . , txt(n)].
For example, n bullet screen texts are extracted, which are txt(1), txt(2), . . . , txt(n).
In a second step, each bullet screen text is converted into a corresponding text vector.
After conversion, n corresponding text vectors are obtained, and are recorded as: vec=[vec(1), vec(2), . . . , vec(i), . . . , vec(n)].
For example, n corresponding text vectors may be obtained after conversion, which are vec(1), vec(2), . . . , vec(n).
The conversion method of text vector may be implemented by a neural network model such as a BERT (Bidirectional Encoder Representations from Transformers) model, an ERNIE (Enhanced Language Representation with Informative Entities) model, etc.
The BERT model as a whole is an autoencoder language model (Autoencoder LM), in which two tasks are designed to pre-train the model. The first task is to train the language model. Upon inputting a sentence, some words to be predicted are randomly selected, and then a special symbol [MASK] is used to replace the words to be predicted. The BERT model predicts the words to be replaced by MASK according to a given tag. The second task is to add a sentence-level continuity prediction task based on a bidirectional language model, that is, to predict whether two texts input to BERT are continuous texts. The introduction of the second task may make the trained BERT model better predict a relationship between consecutive text clips.
Comparatively, the BERT model may execute concurrently while extracting relational features of words in a sentence, and may extract the relational features at a plurality of different levels, thereby more comprehensively reflecting sentence semantics. Moreover, the BERT model may obtain the meanings of a word according to the context of the sentence, thereby avoiding ambiguity.
The ERNIE model uses informative entity in a knowledge graph as external knowledge to improve language representation. In order to add knowledge information into a model, the ERNIE model solves the following two problems. First, structured knowledge encoding: for abstract knowledge information, it is required to be encoded so that knowledge may be used for language representation. Second, heterogeneous information fusion: obviously, the encoding of words is different from that of knowledge when BERT pre-trains. Although the words and knowledge both are converted into vectors, the vectors are in different vector spaces. Therefore, it is required to design the model to implement the fusion of vocabulary, syntax and knowledge information. The ERNIE model is divided into two parts: extracting knowledge information and training the language model. In order to obtain a structured knowledge encoding, the ERNIE model adopts a knowledge embedding algorithm, and then integrates the encoded knowledge information into the semantic information. In order to better fuse semantic information and knowledge information, the architecture of BERT model is improved and a new pre-training task is designed, so that the fusion of knowledge information and semantic information may be implemented.
Implementations of the present disclosure adopt the BERT model or the ERNIE model to determine that a piece of optional text information is converted into a corresponding text vector, and may obtain the accurate text vector information of the piece of optional text information, so as to provide a basis for correctly determining the central text vector.
The training method of the BERT model or the ERNIE model will not be repeated in implementations of the present disclosure. By using a pre-trained BERT model or ERNIE model, it is capable of converting the optional text information into the corresponding text vector.
In a third step, for each text vector, a sum of distances between the text vector and each other text vector is determined.
For example, for each text vector vec(i) in the above example, the distances from vec(i) to other text vectors are calculated and the calculated distances are summed, recorded as sum(i), in which sum(i)=Σj=1M cos ine(vec(i), vec(j)).
Here, cosine(vec(i), vec(j)) represents the distance between the text vector vec(i) and the text vector vec(j); the text vector vec(j) represents another text vector other than the text vector vec(i); and M represents the number of text vectors other than the text vector vec(i).
In an example, the calculation method of the distance between the text vector vec(i) and the text vector vec(j) may adopt a cosine formula:
In the above cosine formula. Xi and Yi represent an i-th element in the two text vectors (text vector X and text vector Y), respectively; n represents the number of elements in text vector X or text vector Y; and cos(θ) represents angle cosine between the text vector X and the text vector Y, and the angle cosine may represent the distance between the text vector X and the text vector Y.
The calculation method of the distance is not limited to the above method, and a method such as a Euclidean distance calculation method may also be used, which will not be described in detail in this disclosure. According to implementations of the present disclosure, the distance between text vectors may be determined in various ways, and the degree of difference of the text vectors may be quantified, thereby providing a basis for determining the bullet comment content that may represent the central text vector of the video content.
After calculation, the sum of the distances between each text vector and other text vectors may be recorded as: sum=[sum(1), sum(2), . . . , sum(i), . . . , sum(n)].
In a fourth step, based on the list of the sum of the distance between each text vector and other text vectors determined in the above steps, the bullet screen text corresponding to the text vector with the minimum sum of the distance is determined, and the determined bullet screen text is used as the title of the short video.
The above process may be considered as determining a central vector of the bullet comment.
Implementations of the present disclosure also provide other methods of generating titles. For example, word segmentation processing is performed on each piece of optional text information of the plurality of pieces of optional text information, to obtain a plurality of segmented words, and an occurrence number that each segmented word appears in the plurality of pieces of optional text information is counted; L segmented words with the most occurrence number are selected from the plurality of segmented words as an important segmented word, and a word segmentation score is set for the important segmented word according to an occurrence number of the important segmented word, where L is a positive integer; for each piece of optional text information of the plurality of pieces of optional text information, the important segmented word contained in the piece of optional text information is determined, and a text score of the piece of optional text information is determined according to the word segmentation score of the important segmented word; and a piece of optional text information with the highest text score is determined as the central text information. The central text information may be taken as the title of the first video file.
Implementations of the present disclosure may adopt a word segmentation algorithm or a machine learning algorithm to implement the word segmentation processing of the optional text information. For example, the word segmentation processing is performed using a dictionary-based word segmentation algorithm or a counting-based machine learning algorithm. In an example, the dictionary-based word segmentation algorithm is also called a string matching word segmentation algorithm. This algorithm matches a string to be matched with a word in an established “sufficiently large” dictionary according to a certain strategy. If a corresponding entry is found, the matching is successful and the word is identified. Common dictionary-based word segmentation algorithms include a forward maximum matching method, a reverse maximum matching method, a bidirectional matching word segmentation method, and the like. The dictionary-based word segmentation algorithm is the most widely used and fastest word segmentation algorithm. For a long time, researchers have been optimizing the string-based matching method, such as maximum length setting, string storage and search method, and an organizational structure of the vocabulary, such as the usage of TRIE index trees, hash indexes, etc. Counting-based machine learning algorithms mainly include a Hidden Markov Model (HMM), a Conditional Random Field (CRF) algorithm, a Support Vector Machine (SVM) algorithm, a deep learning algorithm, and the like. Taking CRF as an example, the basic idea is to tag and train Chinese characters, which not only considers the occurrence number of words, but also considers the context. CRF has a better learning ability, and thus it has a good effect on the identification of ambiguous words and unregistered words. Common tokenizers use a combination of the machine learning algorithm and a dictionary, which may improve the accuracy of word segmentation, and improve adaptability to different fields. With development of deep learning, neural network-based tokenizers have also been developed, such as a bidirectional LSTM+CRF implementation tokenizer, which is essentially sequence annotations, and thus it is versatile and may be used for named entity identification.
After the above word segmentation processing, the implementations of the present disclosure may remove the segmented words that do not carry the actual meaning, such as modal particles, termination words, and the like, and determine a segmented word with the most occurrence number from the remaining segmented words as an important segmented word. Termination words may also be called stop words, out-of-use words, etc. Modal particles and termination words usually do not carry useful information. For example, meaningless words such as “le”, “ah” and “me” may be regarded as modal particles or termination words. The deletion of the terminator in a sentence does not affect comprehension.
Taking the above optional text information being the bullet screen text as an example, for example, after word segmentation and counting, p segmented words with the most occurrence number are determined as the important segmented words, including W1, W2, . . . , and WP. For example, the occurrence number of W1 is 100, the occurrence number of W2 is 80, . . . , and the occurrence number of Wp is 20. The word segmentation score is determined for each important segmented word according to the occurrence number. For example, the word segmentation scores of W1, W2, . . . , and Wp are determined to be 100, 80, . . . , and 20, respectively. Then, for each bullet screen text, the important segmented words contained in each bullet screen text may be determined, and the word scores of the important segmented words are used as the text score of the bullet screen text. For example, the word segmentation scores of the important segmented word contained in the bullet screen text may be added to obtain the text score of the entire bullet screen text. The foregoing method of determining the text score according to the word segmentation scores is only an example, and implementations of the present disclosure do not limit thereto. For example, the important segmented words may be divided into different levels according to the word segmentation scores, and weights of the important segmented words of different levels may be set. Upon calculating the text score, the weights of the important segmented words contained in the text vector are added to obtain the text score of the text vector.
It can be seen that the bullet screen text with the highest text score contains the most important comments issued by the user group in the system for the short video, and thus it may reflect the content of the short video or the users views on the short video. Therefore, it has the highest similarity to the content of the short video, and is very suitable for being the title of the short video.
It can be seen that the above video title generation method provided in implementations of the present disclosure selects a piece of optional text information that may best represent its content from a plurality of pieces of optional text information of the first video file, and takes the selected optional text information as the title of the first video file. Due to the selection of the existing optional text information, it is avoided to clip and piece together a plurality of pieces of text information to generate the title, and thus the problem of poor grammar and rhetoric of generating the title is avoided.
The present disclosure also provides a video title generation apparatus.
Another embodiment of the present disclosure provides an apparatus for generating a video title, and the apparatus includes one or more features of the above embodiments of the video title generation apparatus. In one possible implementation, the optional text information includes at least one of a bullet screen text of the first video file, a comment of the first video file, a subtitle of the first video file, or an introductory text of the first video file.
The first video file is a video file formed by using a clip selected from the second video file; and the bullet screen text of the first video file includes; a text of bullet comments posted by a plurality of users when the clip in the first video file is displayed during playing the second video file.
Another embodiment of the present disclosure provides a video title generation apparatus, and the apparatus includes one or more features of the above embodiments of the video title generation apparatus. In one possible implementation, a manner in which a distance between the text vector and the other text vector is determined includes: calculating an angle cosine between the text vector and the other text vector, to take the angle cosine as the distance between the text vector and the other text vector; or calculating a Euclidean distance between the text vector and the other text vector, to take the Euclidean distance as the distance between the text vector and the other text vector.
Another embodiment of the present disclosure provides a video title generation apparatus, and the apparatus includes one or more features of the above embodiments of the video title generation apparatus. In one possible implementation, the conversion sub module is further configured to: convert the piece of optional text information into the corresponding text vector, by using at least one of a bidirectional encoder representation from transformers (BERT) model and an enhanced language representation with informative entities (ERNIE) model.
In the technical solutions of the present disclosure, collection, storage and application of user's personal information involved herein are all in compliance with the provisions of relevant laws and regulations, and do not violate public order and good customs.
According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
As shown in
A plurality of components in the electronic device 900 are connected to the I/O interface 905, and include an input unit 906 such as a keyboard, a mouse, and the like, an output unit 907 such as various types of displays, speakers, and the like, a storage unit 908 such as a magnetic disk, an optical disk, and the like, and a communication unit 909 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 909 allows the electronic device 900 to exchange information/data with other devices through computer networks such as Internet and/or various telecommunication networks.
The computing unit 901 may be various general-purpose and/or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPS), and any appropriate processors, controllers, microcontrollers, and the like. The calculation unit 901 performs various methods and processes described above, such as a video title generation method. For example, in some embodiments, the video title generation method may be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed on the electronic device 900 via ROM 902 and/or the communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the video title generation method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the video title generation method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and technologies described above herein may be implemented in a digital electronic circuit system, an integrated circuit system, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), an application special standard product (ASSP), a system on chip (SOC), a load programmable logic device (CPLD), a computer hardware, firmware, software, and/or a combination thereof. These various implementations may include implementing in one or more computer programs that may be executed and/or interpreted on a programmable system including at least one programmable processor. The programmable processor may be a special-purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and may transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
The program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to the processor or controller of general-purpose computer, special-purpose computer or other programmable data processing device, so that when executed by the processor or controller, the program code enables the functions/operations specified in the flow chart and/or block diagram to be implemented. The program code may be executed completely on a machine, partially on a machine, partially on a machine and partially on a remote machine, or completely on a remote machine or server as a separate software package.
In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store programs for use by or in combination with an instruction execution system, device, or equipment. The machine-readable medium may be machine-readable signal medium or machine-readable storage medium. The machine readable medium may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the above. More specific examples of the machine-readable storage medium may include electrical connections based on one or more lines, portable computer disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
In order to provide interaction with the user, the systems and technologies described herein may be implemented on a computer. The computer has a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user may provide input to the computer. Other types of devices may also be used to provide interaction with the user. For example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and it is capable of receiving input from the user in any form (including acoustic input, voice input, or tactile input).
The systems and technologies described herein may be implemented in a computing system that includes a back-end component (e.g., as a data server), a computing system that includes a middleware component (e.g., as an application server), a computing system that includes a front-end component (e.g., as a user computer with a graphical user interface or web browser through which the user may interact with implementations of the systems and technologies described herein), or a computing system that includes any combination of the back-end component, the middleware component, or the front-end component. The components of the system may be connected each other through any form or kind of digital data communication (e.g., a communication network). Examples of the communication network include a local area network (LAN), a wide area network (WAN), and Internet.
A computer system may include a client and a server. The client and the server are generally far away from each other and usually interact through a communication network. The server may also be a server of a distributed system or a server combined with a blockchain, and the relationship between the client and the server is generated through computer programs performed on a corresponding computer and having a client-server relationship with each other.
It should be understood that various forms of processes shown above may be used to reorder, add or delete steps. For example, steps described in the present disclosure may be executed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in the present disclosure may be achieved, but is not limited herein.
The foregoing specific implementations do not constitute a limitation to the protection scope of the present disclosure. Those of ordinary skill in the art should understand that various modifications, combinations, sub-combinations and substitutions may be made according to design requirements and other factors. Any modification, equivalent replacement and improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202210023596.1 | Jan 2022 | CN | national |
