Video polyphonic ringtone generation method, system and device and medium

Abstract

The invention discloses a video polyphonic ringtone generation method and apparatus, a device and a storage medium. The method comprises the steps of obtaining personalized parameters input by a user; acquiring address information of the user, and determining weather data of a region where the user is located according to the address information; inputting the weather data into a pre-trained deep learning model, and generating initial video data corresponding to the weather data through the deep learning model; wherein the initial video data comprises a plurality of video frames; according to the personalized parameters, adjusting the video frame to obtain target video data; and rendering the target video data to obtain a video polyphonic ringtone file, and sending the video polyphonic ringtone file to a polyphonic ringtone platform corresponding to the user. According to the method, more intelligent and personalized video polyphonic ringtones can be generated, and the video polyphonic ringtone use experience of users can be improved. The method can be widely applied to the technical field of video polyphonic ringtones.

Description

Technical Field

[0001] The present application relates to the technical field of video ringback tone, and in particular to a method, device, equipment and storage medium for generating a video ringback tone. Background Art

[0002] At present, with the development of information technology, more and more applications are being developed to provide services for users. For example, video ringback tone is a value-added service based on the communication network. When a user makes a voice call, it provides a short video to the called party, replacing the traditional audio ringback tone. When the caller calls the called party, he can see the personalized video content pre-set by the called party while waiting for the call to be answered, such as personal short videos, corporate promotions, holiday greetings, etc.

[0003] In the related technologies, traditional video ringback tones have problems such as single content, delayed updates, and lack of personalization. They are difficult to meet users' pursuit of freshness and personalization, and affect users' usage experience.

[0004] In summary, the problems existing in related technologies need to be solved urgently. Summary of the invention

[0005] The purpose of this application is to solve one of the technical problems existing in the related art to at least a certain extent.

[0006] To this end, an object of embodiments of the present application is to provide a method, apparatus, device and storage medium for generating a video ringback tone.

[0007] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present application include:

[0008] On the one hand, an embodiment of the present application provides a method for generating a video ringback tone, the method comprising:

[0009] Get the personalized parameters entered by the user;

[0010] Acquire the address information of the user, and determine the weather data of the area where the user is located according to the address information;

[0011] Inputting the weather data into a pre-trained deep learning model, and generating initial video data corresponding to the weather data through the deep learning model; wherein the initial video data includes a plurality of video frames;

[0012] According to the personalized parameters, the video frame is adjusted to obtain target video data;

[0013] The target video data is rendered to obtain a video ring back tone file, and the video ring back tone file is sent to a ring back tone platform corresponding to the user.

[0014] In addition, the method for generating a video ringback tone according to the above embodiment of the present application may also have the following additional technical features:

[0015] Furthermore, in one embodiment of the present application, the acquiring the address information of the user includes:

[0016] Get the address information entered by the user;

[0017] Alternatively, the terminal device used by the user is located to obtain the address information.

[0018] Furthermore, in one embodiment of the present application, the weather data includes at least one of temperature, humidity, precipitation, wind direction and speed, weather conditions and weather forecast information.

[0019] Furthermore, in one embodiment of the present application, the obtaining of the personalized parameters input by the user includes:

[0020] Displaying an interactive interface to the user;

[0021] In response to the interactive operation of the user in the interactive interface, the mood state of the user, the animal image, background pattern and font style selected by the user are determined.

[0022] Further, in an embodiment of the present application, adjusting the video frame according to the personalized parameter includes:

[0023] According to the mood state, performing a first adjustment on the elements of the video frame by using an emotion fusion algorithm to obtain a first video frame;

[0024] According to the animal image and the background pattern, performing a second adjustment on the elements of the first video frame by using an emotion fusion algorithm to obtain a second video frame;

[0025] The elements of the video frame include color, light and shadow, and background music.

[0026] Further, in one embodiment of the present application, sending the video ringback tone file to the ringback tone platform corresponding to the user includes:

[0027] Obtaining predetermined channel setting parameters;

[0028] Determining specification restriction information corresponding to the video ringback tone file according to the channel setting parameters;

[0029] The video ring back tone file is compressed according to the specification restriction information, and the compressed video ring back tone file is sent to the ring back tone platform corresponding to the user.

[0030] Furthermore, in one embodiment of the present application, the method further includes:

[0031] Check whether the current time point is the scheduled update time point;

[0032] If the current time point is the predetermined time point, return to the step of obtaining the address information of the user to update the video ringback tone file.

[0033] On the other hand, an embodiment of the present application provides a device for generating a video ringback tone, the device comprising:

[0034] An acquisition unit, used to acquire personalized parameters input by a user;

[0035] A processing unit, configured to obtain address information of the user, and determine weather data of a region where the user is located according to the address information;

[0036] A generating unit, configured to input the weather data into a pre-trained deep learning model, and generate initial video data corresponding to the weather data through the deep learning model; wherein the initial video data includes a plurality of video frames;

[0037] An adjustment unit, configured to adjust the video frame according to the personalized parameter to obtain target video data;

[0038] The sending unit is used to render the target video data to obtain a video ring back tone file, and send the video ring back tone file to the ring back tone platform corresponding to the user.

[0039] On the other hand, an embodiment of the present application provides an electronic device, including:

[0040] at least one processor;

[0041] at least one memory for storing at least one program;

[0042] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method for generating a video color ring back tone.

[0043] On the other hand, an embodiment of the present application further provides a computer-readable storage medium, which stores a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the above-mentioned method for generating a video ringback tone.

[0044] The advantages and benefits of the present application will be partially given in the following description, and partially become apparent from the following description, or be understood through the practice of the present application:

[0045] The method, device, equipment and storage medium for generating video ringback tone disclosed in the embodiment of the present application obtain personalized parameters input by the user; obtain the address information of the user, and determine the weather data of the area where the user is located according to the address information; input the weather data into a pre-trained deep learning model, and generate initial video data corresponding to the weather data through the deep learning model; wherein the initial video data includes several video frames; according to the personalized parameters, adjust the video frames to obtain target video data; render the target video data to obtain a video ringback tone file, and send the video ringback tone file to the ringback tone platform corresponding to the user. The method can obtain the weather data of the area where the user is located in real time, generate video content matching the weather conditions by using a deep learning model, and customize it in combination with the user's personalized preferences to generate a more intelligent and personalized video ringback tone, which is conducive to improving the user's video ringback tone usage experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present application or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present application. For technical personnel in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A schematic diagram of an implementation environment of a method for generating a video ringback tone provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of a flow chart of a method for generating a video ringback tone provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of the structure of a device for generating a video ringback tone provided in an embodiment of the present application;

[0050] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The present application is further described below in conjunction with the accompanying drawings and specific embodiments. The described embodiments should not be regarded as limiting the present application, and all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0052] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0054] 1) Video ringback tone: refers to a video played instead of the traditional audio ringback tone during a mobile phone call, usually a short video content selected by the ringback tone user.

[0055] 2) Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0056] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. Basic artificial intelligence technologies generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, pre-trained model technology, operation / interaction systems, mechatronics, etc. Among them, pre-trained models are also called large models and basic models. After fine-tuning, they can be widely used in downstream tasks in various major directions of artificial intelligence. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0057] 3) Machine Learning (ML) is a multi-disciplinary interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in studying how computers simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications are spread across all areas of artificial intelligence. Machine learning and deep learning usually include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and self-learning. The pre-trained model is the latest development in deep learning, which integrates the above technologies.

[0058] 4) Deep Learning is a subfield of machine learning that simulates the way the human brain processes information by building and training multi-layer neural networks. Deep learning models can automatically extract complex features from large amounts of data and are used for a variety of tasks, such as image recognition, natural language processing, speech recognition, recommendation systems, etc.

[0059] At present, with the development of information technology, more and more applications are being developed to provide services for users. For example, video ringback tone is a value-added service based on the communication network. When a user makes a voice call, it provides a short video to the called party, replacing the traditional audio ringback tone. When the caller calls the called party, he can see the personalized video content pre-set by the called party while waiting for the call to be answered, such as personal short videos, corporate promotions, holiday greetings, etc.

[0060] In the related technologies, traditional video ringback tones have problems such as single content, delayed updates, and lack of personalization. They are difficult to meet users' pursuit of freshness and personalization, and affect users' usage experience.

[0061] In view of this, a method for generating a video ringback tone is provided in an embodiment of the present application, which includes obtaining personalized parameters input by a user; obtaining the address information of the user, and determining the weather data of the area where the user is located according to the address information; inputting the weather data into a pre-trained deep learning model, and generating initial video data corresponding to the weather data through the deep learning model; wherein the initial video data includes a plurality of video frames; adjusting the video frames according to the personalized parameters to obtain target video data; rendering the target video data to obtain a video ringback tone file, and sending the video ringback tone file to the ringback tone platform corresponding to the user. The method can obtain the weather data of the area where the user is located in real time, generate video content matching the weather conditions using a deep learning model, and customize it in combination with the user's personalized preferences to generate a more intelligent and personalized video ringback tone, which is conducive to improving the user's video ringback tone usage experience.

[0062] Please refer to Figure 1 , Figure 1 The schematic diagram of the implementation environment of a method for generating a video ring back tone provided in an embodiment of the present application is shown. In the implementation environment, the main hardware and software entities involved include a terminal device 110 and a backend server 120. The terminal device 110 and the backend server 120 are connected in communication.

[0063] Specifically, the method for generating a video ringback tone provided in the embodiment of the present application can be executed alone on the terminal device 110 side, or can be executed alone on the background server 120 side, or can be executed based on data interaction between the terminal device 110 and the background server 120.

[0064] The terminal device 110 of the above embodiment may include a mobile phone, a computer, a smart wearable device, a PDA device, an intelligent voice interaction device, a smart home appliance, a vehicle-mounted terminal, etc., but is not limited thereto. The backend server 120 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0065] The terminal device 110 and the backend server 120 may establish a communication connection via a wireless network or a wired network. The wireless network or wired network uses standard communication technology and / or protocols, and the network may be set to the Internet or any other network, such as but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or any combination of a virtual private network.

[0066] Of course, it is understandable that Figure 1 The implementation environment in the embodiment of the present application is only some optional application scenarios of the method for generating video ringback tone provided in the embodiment of the present application, and the actual application is not fixed. Figure 1 The hardware and software environment shown.

[0067] Next, in combination with the introduction of the aforementioned implementation environment, a method for generating a video ringback tone provided in an embodiment of the present application is introduced and illustrated.

[0068] Please refer to Figure 2 , Figure 2: is a schematic diagram of a method for generating a video ringback tone provided in an embodiment of the present application. The method for generating a video ringback tone includes but is not limited to:

[0069] Step 210: Obtain personalized parameters input by the user;

[0070] Step 220: Acquire the address information of the user, and determine the weather data of the area where the user is located according to the address information;

[0071] Step 230: input the weather data into a pre-trained deep learning model, and generate initial video data corresponding to the weather data through the deep learning model; wherein the initial video data includes a plurality of video frames;

[0072] Step 240: adjusting the video frame according to the personalized parameters to obtain target video data;

[0073] Step 250: Render the target video data to obtain a video ring back tone file, and send the video ring back tone file to the ring back tone platform corresponding to the user.

[0074] In an embodiment of the present application, a method for generating a video ringback tone is provided. The method can obtain weather data of the user's location in real time, use a deep learning model to generate video content that matches the weather conditions, and customize it in combination with the user's personalized preferences to generate a more intelligent and personalized video ringback tone, which is conducive to improving the user's video ringback tone usage experience.

[0075] Specifically, in the embodiment of the present application, when generating a personalized video ringback tone, the personalized parameters input by the user may be obtained. Exemplarily, in some embodiments, the obtaining of the personalized parameters input by the user includes:

[0076] Displaying an interactive interface to the user;

[0077] In response to the interactive operation of the user in the interactive interface, the mood state of the user, the animal image, background pattern and font style selected by the user are determined.

[0078] In the embodiment of the present application, when obtaining the personalized parameters input by the user, it can be achieved by means of interface interaction. For example, the user can open the client or H5 page (i.e., the interactive interface) installed on a smart phone or other terminal device, and then perform relevant interactive operations on these interactive interfaces, such as selection operations or input operations, which are not limited in the present application. According to the interactive operations of the user in the interactive interface, the terminal device can determine the personalized parameters input by the user. In the embodiment of the present application, the type of personalized parameters can be flexibly set according to demand, for example, it can include mood state, animal image, background pattern and font style, etc. Among them, the mood state can be happy, excited, melancholy, sad and other categories; the animal image is the animal style video ringtone that the user wants to generate, such as cats, dogs or other animal types; the background pattern can be used to limit the background of the generated video ringtone, for example, the user can set some city images or landscape images as background patterns, of course, in the embodiment of the present application, other images can also be selected as background patterns. The font style is used to set the font style that the user likes, which can be flexibly set according to the needs of the user.

[0079] In the embodiment of the present application, there is no restriction on the personalized parameters determined by the user in the interactive interface, and the user can flexibly arrange various personalized parameters according to his or her own needs. In some other embodiments, some basic information of the user, such as gender, age, occupation and hobbies, can also be obtained, and the personalized parameter categories that can be selected by the user are recommended based on this basic information, and the present application does not limit this.

[0080] In an embodiment of the present application, the user's address information can also be obtained, and then the weather data of the user's location can be determined based on the user's address information. Specifically, when obtaining the user's address information, the user can enter the address information by himself, or the terminal device used by the user can be located, and the address information can be determined based on the positioning result. In an embodiment of the present application, there is no restriction on the specific algorithm used for positioning and the granularity of the address information. For example, the user can enter the city name as the address information, or enter a street or community name as the address information. In particular, it should be noted that in the case where the user enters the address information, the address information entered by the user can be the current address of the user, or it can be other addresses (for example, the user is currently in place A and can enter the address information of place B), and this application does not limit this.

[0081] After obtaining the user's address information, the weather data of the user's location can be determined according to the address information. In the embodiment of the present application, the weather data may include at least one of temperature, humidity, precipitation, wind direction and speed, weather conditions (such as sunny, cloudy, rainy, snowy, foggy, etc.) and weather forecast information, and is not limited thereto. For weather data, it can be input into a pre-trained deep learning model, and the initial video data corresponding to the weather data can be generated by the deep learning model. In the embodiment of the present application, the deep learning model can adopt a generative model, such as GANs and StyleGANs, wherein GANs (Generative Adversarial Networks) is a deep learning model that generates realistic data samples through adversarial training of two neural networks. The two networks are: Generator: responsible for generating fake samples that look like real data. Discriminator: responsible for distinguishing generated data from real data. In the embodiment of the present application, the trained generator can be used to generate initial video data corresponding to the weather data. StyleGANs is an improved generative adversarial network that introduces a style control mechanism (style-based generator architecture) to make the generated images have more natural changes at different scales. Of course, in the embodiments of the present application, the types of deep learning models are not limited to these models. For example, in some other embodiments, variational autoencoders, diffusion models, Transformer-based generative models (such as ViT-GAN, DALLE, GLIDE), etc. can also be selected, and the present application does not limit this.

[0082] In an embodiment of the present application, the generated initial video data may include several video frames, which are generated according to the input weather data and correspond to the weather data. In an embodiment of the present application, the deep learning model is trained with a large amount of video data labeled with weather data, and can accurately capture weather characteristics and generate realistic video images. It should be noted that in an embodiment of the present application, the number of video frames in the initial video data can be arbitrary, and the present application does not limit the length of the generated video, which can be flexibly adjusted according to actual needs.

[0083] After obtaining the initial video data, the video frame can be adjusted through personalized parameters to obtain the target video data.

[0084] Specifically, in some embodiments, adjusting the video frame according to the personalized parameter includes:

[0085] According to the mood state, performing a first adjustment on the elements of the video frame by using an emotion fusion algorithm to obtain a first video frame;

[0086] According to the animal image and the background pattern, performing a second adjustment on the elements of the first video frame by using an emotion fusion algorithm to obtain a second video frame;

[0087] The elements of the video frame include color, light and shadow, and background music.

[0088] In the embodiment of the present application, when adjusting the video frame according to the personalized parameters, it can be performed in two steps. First, the emotional atmosphere can be integrated into the video frame. Specifically, the elements of the video frame can be adjusted by the emotional fusion algorithm in combination with the mood state input by the user, and the color, light and shadow, music and other elements in the video can be adjusted by the emotional analysis algorithm to create an atmosphere that conforms to the user's emotions. In the embodiment of the present application, the core of the emotional fusion algorithm used is to match the user's emotional state with the elements in the video, and to adjust the color, light and shadow, music and other elements of the video frame so that the video content can better reflect the user's emotional changes. Exemplarily, according to the user's mood state, the emotional fusion algorithm maps these emotions to the visual and auditory elements of the video. For example, when the user feels sad, the tone of the video may become softer, the light and shadow contrast is weakened, and the background music becomes soothing; when the user is excited, the color of the video may be brighter, the light and shadow contrast is enhanced, and the music rhythm is accelerated.

[0089] In an embodiment of the present application, the first adjusted video frame is recorded as the first video frame. After obtaining the first video frame, personalized elements can be added thereto. According to personalized parameters such as animal images, background patterns, font styles, etc. selected by the user, the generated first video frame is intelligently integrated with these elements to form video ringtone content with user characteristics. In an embodiment of the present application, this process is recorded as the second adjustment to obtain the second video frame, which can constitute the target video data.

[0090] After obtaining the target video data, it can be rendered to obtain the corresponding video ringback tone file. Then, the video ringback tone file can be sent to the ringback tone platform so that the user can play it when making or receiving a call. In an embodiment of the present application, the rendered video ringback tone file is uploaded to the cloud storage platform to achieve cross-device synchronization and backup. When the user answers the call, the video ringback tone playback terminal quickly obtains the latest video ringback tone file through the cloud distribution platform and plays it. The present application supports the playback and display of video ringback tone on multiple types of devices, including smart phones, tablets, smart watches, smart TVs, etc. The playback terminal has built-in video decoder and player components, which can smoothly play video ringback tone files from the cloud distribution platform.

[0091] It can be understood that the method for generating a video ringback tone provided in the embodiment of the present application obtains the personalized parameters input by the user; obtains the address information of the user, and determines the weather data of the user's location based on the address information; inputs the weather data into a pre-trained deep learning model, and generates the initial video data corresponding to the weather data through the deep learning model; wherein the initial video data includes several video frames; according to the personalized parameters, the video frames are adjusted to obtain the target video data; the target video data is rendered to obtain a video ringback tone file, and the video ringback tone file is sent to the ringback tone platform corresponding to the user. This method can obtain the weather data of the user's location in real time, generate video content matching the weather conditions using a deep learning model, and customize it in combination with the user's personalized preferences to generate a more intelligent and personalized video ringback tone, which is conducive to improving the user's video ringback tone usage experience.

[0092] Specifically, in some embodiments, sending the video ring back tone file to the ring back tone platform corresponding to the user includes:

[0093] Obtaining predetermined channel setting parameters;

[0094] Determining specification restriction information corresponding to the video ringback tone file according to the channel setting parameters;

[0095] The video ring back tone file is compressed according to the specification restriction information, and the compressed video ring back tone file is sent to the ring back tone platform corresponding to the user.

[0096] It should be noted that in the embodiment of the present application, due to the channel reasons of the playback platform, the file size of the video ringback tone played to the user in real time needs to reach a certain limit. Therefore, in the embodiment of the present application, a predetermined channel setting parameter can be obtained, and the channel setting parameter here can be a technical parameter pre-set for transmitting the video ringback tone file. These parameters may include bandwidth, transmission rate, encoding format, resolution, frame rate, etc. According to the channel setting parameters, the specification restriction information corresponding to the video ringback tone file can be determined, such as resolution, encoding format, file size, etc. The specification restriction information can ensure that the video file meets the network conditions during transmission and can be played normally on the user device, while maintaining high quality as much as possible.

[0097] In the embodiment of the present application, the video ring back tone file can be compressed according to the specification restriction information, and the compressed video ring back tone file is sent to the ring back tone platform corresponding to the user. Specifically, during compression, the resolution of the video can be reduced from a higher resolution (such as 1080p, 4K) to a lower resolution (such as 720p or lower), which can significantly reduce the file size. Alternatively, the resolution of the video can be reduced from a higher resolution (such as 1080p, 4K) to a lower resolution (such as 720p or lower), which is not limited by the present application.

[0098] Specifically, in some embodiments, the method further includes:

[0099] Check whether the current time point is the scheduled update time point;

[0100] If the current time point is the predetermined time point, return to the step of obtaining the address information of the user to update the video ringback tone file.

[0101] The scheme in the embodiment of the present application can be automatically executed at a predetermined time point. For example, the weather ringback tone can be regenerated and distributed in the whole process at 6 o'clock, 12 o'clock, and 18 o'clock respectively. Specifically, it can be detected in real time whether the current time point is the predetermined update time point. If not, the most recently generated ringback tone can be used; if the predetermined update time point is reached, the user's address information can be obtained again, and the steps in the aforementioned embodiment can be executed to update the video ringback tone file. In the embodiment of the present application, the predetermined time point can be set flexibly, and there is no limitation on its specific situation.

[0102] It is understandable that the solutions in the embodiments of the present application have at least the following technical effects:

[0103] 1. Highly personalized experience: Users can customize unique video ringtone content according to their mood and preferences to meet personalized needs and improve the user experience.

[0104] 2. Real-time update of weather and freshness: The system can obtain weather data in real time and generate matching video ringback tone content to ensure that users can feel freshness every time they answer a call, enhance interactivity, and obtain weather information.

[0105] 3. Intelligent processing and efficiency improvement: Use AI technologies such as deep learning to automatically generate and update video content, reduce manual intervention costs and improve processing efficiency.

[0106] 4. Emotional transmission and social interaction: Through the emotion mapping algorithm and personalized parameter fusion mechanism, video ringtones become a new way for users to transmit emotions and interact socially, enhancing the emotional connection between users.

[0107] Reference Figure 3 In an embodiment of the present application, a device for generating a video ringback tone is also provided, including:

[0108] An acquisition unit 310 is used to acquire personalized parameters input by a user;

[0109] The processing unit 320 is used to obtain the address information of the user, and determine the weather data of the area where the user is located according to the address information;

[0110] A generating unit 330 is used to input the weather data into a pre-trained deep learning model, and generate initial video data corresponding to the weather data through the deep learning model; wherein the initial video data includes a plurality of video frames;

[0111] An adjustment unit 340, configured to adjust the video frame according to the personalized parameter to obtain target video data;

[0112] The sending unit 350 is used to render the target video data to obtain a video ring back tone file, and send the video ring back tone file to the ring back tone platform corresponding to the user.

[0113] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0114] Reference Figure 4 , an embodiment of the present application provides an electronic device, including:

[0115] at least one processor 410;

[0116] At least one memory 420, used to store at least one program;

[0117] When at least one program is executed by at least one processor 410, at least one processor 410 implements the above-mentioned method for generating a video ringback tone.

[0118] Similarly, the contents of the above method embodiments are all applicable to the electronic device embodiments. The functions specifically implemented by the electronic device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0119] The embodiment of the present application further provides a computer-readable storage medium, in which a program executable by the processor 410 is stored. When the program executable by the processor 410 is executed by the processor 410, it is used to execute the above-mentioned method for generating a video ringback tone.

[0120] Similarly, the contents of the above method embodiments are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0121] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the application is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part described as a larger operation is performed independently.

[0122] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present application. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional techniques of the engineer. Therefore, those skilled in the art can implement the present application set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the attached claims and their equivalents.

[0123] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0125] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0126] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0127] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0128] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0129] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Technical personnel familiar with the field can make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A method for generating a video ring back tone, characterized in that: The method comprises: Get the personalized parameters entered by the user; Acquire the address information of the user, and determine the weather data of the area where the user is located according to the address information; Inputting the weather data into a pre-trained deep learning model, and generating initial video data corresponding to the weather data through the deep learning model; wherein the initial video data includes a plurality of video frames; According to the personalized parameters, the video frame is adjusted to obtain target video data; The target video data is rendered to obtain a video ring back tone file, and the video ring back tone file is sent to a ring back tone platform corresponding to the user.

2. The method for generating a video ringback tone according to claim 1, characterized in that: The obtaining the address information of the user includes: Get the address information entered by the user; Alternatively, the terminal device used by the user is located to obtain the address information.

3. The method for generating a video ringback tone according to claim 1 or 2, characterized in that: The weather data includes at least one of temperature, humidity, precipitation, wind direction and speed, weather conditions and weather forecast information.

4. The method for generating a video ring back tone according to claim 1, characterized in that: The obtaining of the personalized parameters input by the user includes: Displaying an interactive interface to the user; In response to the interactive operation of the user in the interactive interface, the mood state of the user, the animal image, background pattern and font style selected by the user are determined.

5. The method for generating a video ring back tone according to claim 4, characterized in that: The step of adjusting the video frame according to the personalized parameter includes: According to the mood state, performing a first adjustment on the elements of the video frame by using an emotion fusion algorithm to obtain a first video frame; According to the animal image and the background pattern, performing a second adjustment on the elements of the first video frame by using an emotion fusion algorithm to obtain a second video frame; The elements of the video frame include color, light and shadow, and background music.

6. The method for generating a video ring back tone according to claim 1, characterized in that: The step of sending the video ring back tone file to the ring back tone platform corresponding to the user comprises: Obtaining predetermined channel setting parameters; Determining specification restriction information corresponding to the video ringback tone file according to the channel setting parameters; The video ring back tone file is compressed according to the specification restriction information, and the compressed video ring back tone file is sent to the ring back tone platform corresponding to the user.

7. The method for generating a video ring back tone according to claim 1, characterized in that: The method further comprises: Check whether the current time point is the scheduled update time point; If the current time point is the predetermined time point, return to the step of obtaining the address information of the user to update the video ringback tone file.

8. A device for generating a video ring back tone, characterized in that: The device comprises: An acquisition unit, used to acquire personalized parameters input by a user; A processing unit, configured to obtain address information of the user, and determine weather data of a region where the user is located according to the address information; A generating unit, configured to input the weather data into a pre-trained deep learning model, and generate initial video data corresponding to the weather data through the deep learning model; wherein the initial video data includes a plurality of video frames; An adjustment unit, configured to adjust the video frame according to the personalized parameter to obtain target video data; The sending unit is used to render the target video data to obtain a video ring back tone file, and send the video ring back tone file to the ring back tone platform corresponding to the user.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a method for generating a video ringback tone as described in any one of claims 1-7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to implement a method for generating a video ringback tone as described in any one of claims 1 to 7 when executed by the processor.

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