Video acquisition method and related equipment

Abstract

The invention discloses a video acquisition method and related equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a target theme of a to-be-generated video in response to a video acquisition instruction, selecting a material associated with the target theme from a preset video material library, generating a plurality of groups of initial videos, and predicting the playing amount of the to-be-generated video based on a preset video playing amount prediction model. Predicting the future playing amount of the plurality of groups of initial videos, determining the stream casting weight of each group of initial videos, publishing the plurality of groups of initial videos to a short video platform and performing stream casting, detecting the real-time playing amount of the initial videos after stream casting until the real-time playing amount is greater than or equal to a preset playing amount threshold value, and obtaining a target video. According to the method, the preset video playing amount prediction model is used, the playing amount of the initial video is predicted, and the stream is cast based on the prediction result, so that the video quality is ensured, the video production cost is reduced, the market and user requirements are quickly responded, and the video creation cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a video acquisition method and related equipment. Background Art

[0002] With the rapid development of science and technology, AI video generation technology is moving from the experimental stage to practical application, and various technical routes are constantly emerging. The development and application of AI video generation technology are showing strong potential and trends in multiple fields and levels, heralding a major change in video creation.

[0003] In the existing technology, the creation of videos by staff depends on the experience and skills of professional editors, which cannot quickly respond to market and user needs. In addition, the high cost of professional video editing and post-production limits the scale and frequency of content creation. Therefore, how to quickly respond to market and user needs and reduce the cost of video creation is a technical problem that needs to be solved urgently.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a video acquisition method and related equipment, aiming to solve the technical problem of how to quickly respond to market and user needs and reduce the cost of video creation.

[0006] To achieve the above purpose, the present application proposes a video acquisition method, which includes:

[0007] In response to the video acquisition instruction, acquiring a target subject of the video to be generated;

[0008] Based on a preset video material library, selecting multiple groups of image materials and multiple groups of audio materials associated with the target theme, and generating multiple groups of initial videos based on the multiple groups of image materials and audio materials;

[0009] Based on a preset video playback volume prediction model, predict the future playback volume of the multiple groups of initial videos, determine the streaming weight of each group of initial videos based on the future playback volume, and publish the multiple groups of initial videos to a short video platform and stream them based on the streaming weight, wherein the preset video playback volume prediction model is a trained video playback volume prediction model;

[0010] The real-time playback volume of the initial video after streaming is detected until the real-time playback volume is greater than or equal to a preset playback volume threshold, thereby obtaining the target video.

[0011] In one embodiment, before the step of selecting multiple sets of image materials and multiple sets of audio materials associated with the target theme based on the preset video material library, the step further includes:

[0012] Based on a preset video material generation model, image material and audio material are generated, and copyright verification is performed on the image material and the audio material to obtain a copyright verification result, wherein the preset video material generation model is a trained video material generation model;

[0013] The image material and audio material for which the copyright verification result indicates that there is no copyright risk are saved in a preset video material library.

[0014] In one embodiment, the step of selecting multiple sets of image materials and multiple sets of audio materials associated with the target theme based on a preset video material library further includes:

[0015] Analyzing the image materials and audio materials in the preset video material library to determine a first theme of each group of image materials and a second theme of each group of audio materials;

[0016] Determining a first relevance of the first topic to a target topic, and determining a second relevance of the second topic to the target topic;

[0017] Based on the first association and the second association, multiple groups of image materials and multiple groups of audio materials associated with the target theme are selected from a preset video material library.

[0018] In one embodiment, the step of generating multiple groups of initial videos based on the multiple groups of image materials and audio materials further includes:

[0019] Based on any one of the multiple groups of image materials and any one of the multiple groups of audio materials, a video generation operation is performed to obtain multiple groups of first videos;

[0020] Performing quality evaluation on the multiple groups of first videos to obtain multiple groups of quality parameters;

[0021] Determine whether the quality parameter is greater than or equal to a preset quality threshold, and if the quality parameter is greater than or equal to the preset quality threshold, set the first video corresponding to the quality parameter as the second video;

[0022] The quality of the second video is optimized to obtain multiple groups of initial videos.

[0023] In one embodiment, the step of determining the streaming weight of each group of initial videos based on the future playback volume, and publishing the multiple groups of initial videos to the short video platform and streaming them based on the streaming weight, further includes:

[0024] Determining whether the future playback volume is greater than or equal to a preset minimum playback volume;

[0025] Setting the initial video corresponding to the future playback amount that is greater than or equal to the preset minimum playback amount as the third video;

[0026] Based on the sum of the future playback volumes of the multiple groups of third videos, determine the proportion of the future playback volume of each group of third videos, and set the proportion as the streaming weight of the third video corresponding to the proportion;

[0027] Based on the streaming weights, the multiple groups of initial videos are published to a short video platform and streamed.

[0028] In one embodiment, before the step of predicting the future playback volumes of the multiple groups of initial videos based on a preset video playback volume prediction model, the step further includes:

[0029] Obtaining a prediction model for the playback volume of a sample video and a current video, wherein the future playback volume of the sample video is a first playback volume;

[0030] Based on the current video playback volume prediction model, predict the future playback volume of the sample video to obtain a second playback volume;

[0031] Calculate the difference between the first playback amount and the second playback amount, and determine whether the difference is greater than a preset difference threshold;

[0032] If it is greater, adjust the parameters of the current video playback volume prediction model, and based on the adjusted current video playback volume prediction model, return to the step of predicting the future playback volume of the sample video based on the current video playback volume prediction model to obtain the second playback volume, until the playback volume difference is less than or equal to the preset difference threshold, and obtain the preset video playback volume prediction model.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a video acquisition device, the video acquisition device comprising:

[0034] An acquisition module, the acquisition module is used to acquire a target subject of a video to be generated in response to a video acquisition instruction;

[0035] A selection module, the selection module is used to select multiple groups of image materials and multiple groups of audio materials associated with the target theme based on a preset video material library, and generate multiple groups of initial videos based on the multiple groups of image materials and audio materials;

[0036] A prediction module, the prediction module is used to predict the future playback volume of the multiple groups of initial videos based on a preset video playback volume prediction model, determine the streaming weight of each group of initial videos based on the future playback volume, and publish the multiple groups of initial videos to the short video platform and stream them based on the streaming weight, wherein the preset video playback volume prediction model is a trained video playback volume prediction model;

[0037] The detection module is used to detect the real-time playback volume of the initial video after streaming, until the real-time playback volume is greater than or equal to a preset playback volume threshold, to obtain the target video.

[0038] In one embodiment, the selection module includes:

[0039] A generation unit, configured to generate image material and audio material based on a preset video material generation model, and perform copyright verification on the image material and audio material to obtain a copyright verification result, wherein the preset video material generation model is a trained video material generation model;

[0040] The storage unit is used to store the image material and the audio material whose copyright verification result indicates that there is no copyright risk in a preset video material library.

[0041] In one embodiment, the selection module further includes:

[0042] A parsing unit, used for parsing the image materials and audio materials in the preset video material library, determining a first theme of each group of image materials, and determining a second theme of each group of audio materials;

[0043] a determining unit, configured to determine a first relevance between the first topic and a target topic, and to determine a second relevance between the second topic and the target topic;

[0044] A selection unit is used to select multiple groups of image materials and multiple groups of audio materials associated with the target theme from a preset video material library based on the first association and the second association.

[0045] In one embodiment, the selection module further includes:

[0046] An execution unit, configured to execute an operation of generating a video based on any one of the multiple sets of image materials and any one of the multiple sets of audio materials, to obtain multiple sets of first videos;

[0047] An evaluation unit, configured to perform quality evaluation on the plurality of groups of first videos to obtain a plurality of groups of quality parameters;

[0048] A first judgment unit, configured to judge whether the quality parameter is greater than or equal to a preset quality threshold, and if the quality parameter is greater than or equal to the preset quality threshold, setting the first video corresponding to the quality parameter as the second video;

[0049] The optimization unit is used to optimize the quality of the second video to obtain multiple groups of initial videos.

[0050] In one embodiment, the prediction module includes:

[0051] A second judgment unit is used to judge whether the future playback volume is greater than or equal to a preset minimum playback volume;

[0052] A first setting unit, configured to set an initial video corresponding to the future playback amount that is greater than or equal to a preset minimum playback amount as a third video;

[0053] A second setting unit is used to determine a proportion of the future playback volume of each group of third videos based on the sum of the future playback volumes of the multiple groups of third videos, and set the proportion as the streaming weight of the third video corresponding to the proportion;

[0054] A publishing unit is used to publish the multiple groups of initial videos to a short video platform and stream them based on the streaming weights.

[0055] In one embodiment, the prediction module further includes:

[0056] An acquisition unit, used to acquire a prediction model of the playback volume of a sample video and a current video, wherein the future playback volume of the sample video is a first playback volume;

[0057] A prediction unit, configured to predict the future playback volume of the sample video based on a current video playback volume prediction model to obtain a second playback volume;

[0058] A calculation unit, configured to calculate a difference between the first playback amount and the second playback amount, and determine whether the difference is greater than a preset difference threshold;

[0059] The training unit is used to adjust the parameters of the current video playback volume prediction model if it is greater than, and return to the step of predicting the future playback volume of the sample video based on the current video playback volume prediction model based on the adjusted current video playback volume prediction model to obtain the second playback volume, until the playback volume difference is less than or equal to the preset difference threshold, and obtain the preset video playback volume prediction model.

[0060] In addition, to achieve the above objectives, the present application also proposes a video acquisition device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the video acquisition method described above.

[0061] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the video acquisition method described above are implemented.

[0062] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the video acquisition method described above are implemented.

[0063] One or more technical solutions proposed in this application have at least the following technical effects:

[0064] The present application proposes a video acquisition method and related equipment. In the related technology, video production cannot take into account the rapid response to market and user needs and the reduction of video creation costs. In the present application, first, in response to a video acquisition instruction, a target theme of the video to be generated is acquired. Then, based on a preset video material library, multiple groups of image materials and multiple groups of audio materials associated with the target theme are selected. Based on the multiple groups of image materials and audio materials, multiple groups of initial videos are generated. Further, based on a preset video playback volume prediction model, the future playback volume of the multiple groups of initial videos is predicted. Based on the future playback volume, the streaming weight of each group of initial videos is determined. Based on the streaming weight, the multiple groups of initial videos are published to a short video platform and streamed. The preset video playback volume prediction model is a trained video playback volume prediction model. Finally, the real-time playback volume of the initial video after streaming is detected until the real-time playback volume is greater than or equal to the preset playback volume threshold, and the target video is obtained.

[0065] It can be understood that the present application uses a preset video playback volume prediction model to predict the playback volume of the initial video, and casts the stream based on the prediction results, thereby reducing the video production cost while ensuring the video quality, achieving a rapid response to market and user needs, and reducing the cost of video creation. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0067] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0068] Figure 1 A schematic diagram of a process flow provided for the first embodiment of the video acquisition method of the present application;

[0069] Figure 2 A schematic diagram of a flow chart provided for the second embodiment of the video acquisition method of the present application;

[0070] Figure 3A flowchart of the third embodiment of the video acquisition method of the present application is provided;

[0071] Figure 4 This is a schematic diagram of the module structure of the video acquisition device according to an embodiment of the present application;

[0072] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the video acquisition method in the embodiment of the present application.

[0073] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0074] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0075] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0076] The main solutions of the embodiments of this application are:

[0077] In this embodiment, for the convenience of description, the following description is made by taking the identification video acquisition device as the execution subject.

[0078] Since existing technical staff rely on the experience and skills of professional editors to create videos, they cannot respond quickly to market and user needs. In addition, the cost of professional video editing and post-production is high, which limits the scale and frequency of content creation. Therefore, how to quickly respond to market and user needs and reduce the cost of video creation is a technical problem that needs to be solved urgently.

[0079] The present application provides a solution, which enables: in response to a video acquisition instruction, to obtain the target theme of the video to be generated, to select materials associated with the target theme from a preset video material library, to generate multiple groups of initial videos, to predict the future playback volume of the multiple groups of initial videos based on a preset video playback volume prediction model, to determine the streaming weight of each group of initial videos, to publish the multiple groups of initial videos to a short video platform and stream them, to detect the real-time playback volume of the initial videos after streaming, until the real-time playback volume is greater than or equal to the preset playback volume threshold, to obtain the target video. The present application uses a preset video playback volume prediction model to predict the playback volume of the initial video, to stream based on the prediction result, to reduce the video production cost while ensuring the video quality, to achieve both rapid response to market and user needs, and to reduce the cost of video creation.

[0080] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a video acquisition device, etc. The following takes the video acquisition device as an example to illustrate this embodiment and the following embodiments.

[0081] Based on this, the embodiment of the present application provides a video acquisition method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the video acquisition method of the present application.

[0082] In this embodiment, the video acquisition method includes steps S10 to S40:

[0083] Step S10, in response to the video acquisition instruction, acquiring the target subject of the video to be generated;

[0084] In this embodiment, the specific application scenario may be:

[0085] The user sends a request to the video acquisition device to generate a video in some way (such as voice command, clicking a button, filling out a form, etc.). Furthermore, the video acquisition device needs to understand the user's instructions, which may include recognizing voice, parsing text, or understanding the user's operating intention.

[0086] After understanding the user's instructions, the video acquisition device needs to determine the theme or content of the video. This may involve identifying keywords, understanding the context, or inferring based on the user's historical preferences.

[0087] After determining the theme of the video, the video acquisition device can obtain information related to the theme, such as pictures, audio, etc., from a database.

[0088] Using the acquired information, the video acquisition device will start to generate the video. The process of generating the video may include steps such as video editing, special effects addition, and audio synthesis.

[0089] Finally, the video acquisition device outputs the generated video to the user, and the user further processes the generated video.

[0090] Step S20, based on a preset video material library, selecting multiple groups of image materials and multiple groups of audio materials associated with the target theme, and generating multiple groups of initial videos based on the multiple groups of image materials and audio materials;

[0091] It should be noted that the preset video material library is a pre-prepared resource collection, which includes a variety of image materials (such as video clips, pictures) and audio materials (such as background music, sound effects). These materials are classified and stored for subsequent retrieval and use.

[0092] It is understandable that in this embodiment, the video acquisition device retrieves the materials associated with the target theme specified by the user from the material library. This may involve keyword matching, content recognition or a recommendation algorithm based on the user's historical preferences. Among them, these associated materials will be used for video generation.

[0093] After the retrieval is completed, the video acquisition device generates multiple sets of initial videos using the selected images and audio materials. The process of generating multiple sets of initial videos may include: video editing, audio synthesis and special effects processing.

[0094] It should be noted that in the process of generating the initial video, the video acquisition device may generate multiple versions of the video, each of which may differ in material selection, editing order, special effects application, etc., to provide diversified video output. These initial videos may also require further editing and optimization, such as adjusting the editing rhythm, optimizing audio synchronization, adding subtitles, etc., to meet the final needs of users.

[0095] Specifically, the step of selecting multiple groups of image materials and multiple groups of audio materials associated with the target theme based on the preset video material library further includes steps S21 to S23:

[0096] Step S21, parsing the image materials and audio materials in the preset video material library, determining a first theme of each group of image materials, and determining a second theme of each group of audio materials;

[0097] It should be noted that image material analysis refers to the analysis of image materials (such as pictures, video clips) in the video material library, which may include using image recognition technology to identify objects, scenes, colors, textures and other features in the image.

[0098] It is understandable that the video acquisition device determines the main theme of each set of image materials through the features obtained by image analysis. For example, if the image contains a beach and palm trees, the first theme may be determined as "beach".

[0099] Similarly, audio material parsing refers to the analysis of audio material (such as music, sound effects), which may include the use of audio analysis technology to identify audio characteristics such as rhythm, pitch, volume, emotional color, etc.

[0100] Through the features obtained from the audio analysis, the video acquisition device determines the main theme of each set of audio materials. For example, if the audio is a fast-paced music, the second theme may be determined as "energy" or "sports".

[0101] This process may involve audio sentiment analysis techniques to identify the emotion or atmosphere conveyed by the audio material.

[0102] Step S22, determining a first correlation between the first topic and the target topic, and determining a second correlation between the second topic and the target topic;

[0103] Step S23: Based on the first correlation and the second correlation, multiple groups of image materials and multiple groups of audio materials associated with the target theme are selected from a preset video material library.

[0104] It is understandable that the video acquisition device uses a specific algorithm to filter out the image and audio materials that best match the target theme from the material library based on the first relevance and the second relevance scores.

[0105] Since there may be multiple materials that have varying degrees of relevance to the target subject, the system will select multiple sets of image materials and multiple sets of audio materials to provide diverse choices or alternatives.

[0106] In addition, it should be noted that the selected image materials may include pictures or video clips of different styles, scenes, colors or emotional expressions to meet the needs of different video productions. The selected audio materials may include music and sound effects of different rhythms, tones, styles or emotional colors to enhance the auditory effect of the video.

[0107] Based on the evaluation results of the relevance between image and audio materials and the target theme, relevant materials are intelligently selected from the preset video material library. This helps ensure that the video content is visually and audibly consistent with the target theme, improving the quality and relevance of the video content.

[0108] Additionally, the step of generating multiple groups of initial videos based on the multiple groups of image materials and audio materials further includes steps S24 to S27:

[0109] Step S24, based on any group of image materials among the multiple groups of image materials and any group of audio materials among the multiple groups of audio materials, an operation of generating a video is performed to obtain multiple groups of first videos;

[0110] In this embodiment, the video acquisition device will perform an operation involving combining any set of image materials with any set of audio materials to generate a video.

[0111] The process of generating a video includes, but is not limited to, video editing, clipping, synthesis and other techniques to ensure that the image and audio materials can be harmoniously combined.

[0112] Step S25, performing quality evaluation on the multiple groups of first videos to obtain multiple groups of quality parameters;

[0113] It should be noted that in this application, quality assessment refers to video quality assessment (VQA). Video quality assessment aims to quantitatively describe the quality of a video, including subjective quality assessment and objective quality assessment. Subjective quality assessment collects subjective scores through psychophysical experiments, while objective quality assessment automatically predicts video quality through algorithms.

[0114] It should also be noted that quality parameters are a series of indicators used to measure video quality, which can be objective or subjective. Objective parameters may include visual information fidelity (VIF), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), etc. Subjective parameters involve the audience's direct evaluation of video quality, such as mean subjective score (MOS) and mean difference in subjective score (DMOS).

[0115] Among them, the video quality assessment methods include the Double Stimulus Continuous Quality Scale (DSCQS), the Double Stimulus Impairment Scale (DSIS), the Single Stimulus Continuous Quality Evaluation (SSCQE) and the Paired Comparison (PC) method. These methods present videos to subjects in different ways and collect their evaluations of video quality.

[0116] Step S26, determining whether the quality parameter is greater than or equal to a preset quality threshold, and if the quality parameter is greater than or equal to the preset quality threshold, setting the first video corresponding to the quality parameter as the second video;

[0117] It should be noted that the preset quality threshold is a pre-set standard used to define the minimum acceptable level of video quality. This threshold can be set based on different application scenarios and requirements.

[0118] In this embodiment, the video acquisition device compares the quality parameter of each video with the preset quality threshold to determine whether the video meets this standard. If the quality parameter of the video is greater than or equal to the preset quality threshold, it means that the quality of the video is considered to be acceptable or high-quality, and then the video will be selected as the "second video".

[0119] Step S27, optimizing the quality of the second video to obtain multiple groups of initial videos.

[0120] It is understandable that the purpose of optimizing the quality of the second video is to further enhance the viewing experience of the video, including visual and auditory effects.

[0121] Specific optimization methods include adjusting visual parameters such as video resolution, contrast, color balance, brightness, sharpness, and adjusting auditory parameters such as audio volume, sound quality, and sound effects.

[0122] After quality optimization, each set of second videos may generate multiple versions of videos, each of which may be optimized under different parameter settings to adapt to different playback environments or user preferences. These optimized videos are called "initial videos", which are the video versions before final release or distribution.

[0123] In this embodiment, the process of optimizing the quality of the second video to obtain multiple groups of initial videos is to ensure that the video content achieves the best viewing experience before being released. This process involves using various video and audio processing technologies to improve the overall quality of the video, and may include automated optimization and user customization steps.

[0124] Step S30, based on a preset video playback volume prediction model, predict the future playback volume of the multiple groups of initial videos, determine the streaming weight of each group of initial videos based on the future playback volume, and publish the multiple groups of initial videos to the short video platform and stream them based on the streaming weight, wherein the preset video playback volume prediction model is a trained video playback volume prediction model;

[0125] It should be noted that the preset video playback volume prediction model is a trained model that can predict the future playback volume of a video based on its features. This model may use machine learning or deep learning technology to learn the relationship between video features and playback volume by analyzing historical data.

[0126] The video acquisition device uses the above-mentioned preset video playback volume prediction model to predict the future playback volume of each group of initial videos. This involves inputting the characteristics of the video into the model and obtaining a prediction value, which represents the playback volume that the model estimates the video may obtain in the future.

[0127] Furthermore, based on the predicted future playback volume, a streaming weight is determined for each group of initial videos. The streaming weight is a numerical value that indicates the priority or amount of resources allocated to the video when streaming on the platform. Videos with higher predicted playback volume may receive higher streaming weights because they are considered more likely to attract viewers.

[0128] According to the weight of each group of initial videos, these videos are published to the short video platform and streamed according to the weight. This means that videos with high predicted playback volume may get more exposure opportunities, such as being recommended to more users or displayed in better positions.

[0129] Specifically, the step of determining the streaming weight of each group of initial videos based on the future playback volume, and publishing the multiple groups of initial videos to the short video platform and streaming them based on the streaming weight, further includes steps S31 to S34:

[0130] Step S31, determining whether the future playback volume is greater than or equal to a preset minimum playback volume;

[0131] It should be noted that the preset minimum number of views is a pre-set threshold, which represents the minimum number of views that the video content needs to reach. This standard can be set based on a variety of factors, such as market research, business goals, content type, etc.

[0132] Step S32, setting the initial video corresponding to the future playback volume that is greater than or equal to the preset minimum playback volume as the third video;

[0133] Step S33, based on the sum of the future playback volumes of the multiple groups of third videos, determining the proportion of the future playback volume of each group of third videos, and setting the proportion as the streaming weight of the third video corresponding to the proportion;

[0134] First, the video acquisition device adds up the future playback volume of multiple groups of third videos to obtain the sum of the future playback volume of all videos. Then, the ratio of the future playback volume of each group of third videos to the total playback volume is calculated. This ratio reflects the expected popularity or influence of each group of videos relative to other videos.

[0135] The calculated future playback volume ratio of each group of third videos is set as the streaming weight of the video. The streaming weight determines the promotion strength of the video on the platform. The higher the ratio, the more promotion resources the video will receive.

[0136] It is understandable that the traffic weight is the basis for the platform to allocate promotional resources. Videos with high weights may be pushed to more users or get a higher priority position in the user's information flow.

[0137] Step S34: Based on the streaming weights, the multiple groups of initial videos are published to the short video platform and streamed.

[0138] In this embodiment, the video acquisition device uploads these initial videos that have been optimized in quality and weighted to the short video platform so that they can be viewed by users. The publishing process may include uploading the video, filling in metadata (such as title, description, tags, etc.), and setting the publishing time.

[0139] It should be noted that the weight of the traffic can be adjusted dynamically based on the actual performance of the video after it is released. If the actual number of views of a video is higher than the predicted value, its weight may be increased to further increase exposure; conversely, if the performance is poor, its weight may be reduced.

[0140] It is understandable that publishing multiple groups of initial videos to short video platforms and streaming them based on streaming weights is a complex process involving content publishing, promotion, and dynamic adjustment. This process aims to optimize resource allocation, increase content exposure and viewing rates, and improve user experience and the commercial benefits of the platform.

[0141] Step S40, detecting the real-time playback volume of the initial video after streaming, until the real-time playback volume is greater than or equal to a preset playback volume threshold, and obtaining the target video.

[0142] It should be noted that the preset playback volume threshold is a pre-defined value used as a criterion for judging whether a video is successful. This threshold is set based on business goals, market research or historical data.

[0143] In this embodiment, the video acquisition device continuously monitors the playback volume of each group of initial videos after streaming. Furthermore, the video acquisition device compares the real-time playback volume of each group of videos with a preset playback volume threshold. If the real-time playback volume is greater than or equal to the threshold, then the video is considered to have reached the expected success standard.

[0144] Once the real-time playback volume of a video reaches or exceeds the preset playback volume threshold, the video will be marked as a "target video". Target videos may refer to those videos that perform well and are worthy of further promotion or analysis.

[0145] The present application proposes a video acquisition method and related equipment. In the related technology, video production cannot take into account the rapid response to market and user needs and the reduction of video creation costs. In the present application, first, in response to a video acquisition instruction, a target theme of the video to be generated is acquired. Then, based on a preset video material library, multiple groups of image materials and multiple groups of audio materials associated with the target theme are selected. Based on the multiple groups of image materials and audio materials, multiple groups of initial videos are generated. Further, based on a preset video playback volume prediction model, the future playback volume of the multiple groups of initial videos is predicted. Based on the future playback volume, the streaming weight of each group of initial videos is determined. Based on the streaming weight, the multiple groups of initial videos are published to a short video platform and streamed. The preset video playback volume prediction model is a trained video playback volume prediction model. Finally, the real-time playback volume of the initial video after streaming is detected until the real-time playback volume is greater than or equal to the preset playback volume threshold, and the target video is obtained.

[0146] It can be understood that the present application uses a preset video playback volume prediction model to predict the playback volume of the initial video, and casts the stream based on the prediction results, thereby reducing the video production cost while ensuring the video quality, achieving a rapid response to market and user needs, and reducing the cost of video creation.

[0147] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 2 Before step S20, the video acquisition method further includes steps A201 to A202:

[0148] Step A201, based on a preset video material generation model, generating image material and audio material, and performing copyright verification on the image material and audio material to obtain a copyright verification result, wherein the preset video material generation model is a trained video material generation model;

[0149] It should be noted that the preset video material generation model is a trained video material generation model that can generate image materials and audio materials based on preset parameters or inputs. According to the search results, for example, LTX Video is such an open source AI video generation model that uses text encoders, DiT (Diffusion Transformer) models, 3D VAE and other technologies to generate video content.

[0150] Using the above preset video material generation model, the required image and audio materials can be generated. These materials can be completely new creations or variations based on existing materials.

[0151] It is understandable that copyright verification is required for the generated images and audio materials to ensure that these materials do not infringe the copyright of others. Copyright verification can be achieved through various technical means, such as comparing copyright information in the database, using the digital copyright chain (DCI system 3.0), etc.

[0152] After the copyright check is completed, you will get a result indicating whether the material has passed the copyright check. If the materials pass the check, it means they are safe to use; if they do not pass, the materials may need to be replaced or modified to avoid copyright issues.

[0153] Step A202, saving the image material and audio material whose copyright verification result indicates no copyright risk to a preset video material library.

[0154] It is understandable that if the results of the copyright check show that a set of materials does not have copyright risks, that is, they are original, authorized, or belong to the public domain, then these materials are considered safe and can be used legally.

[0155] Save these copyright-verified materials into a pre-set video material library. This material library may be a physical storage device, such as a hard disk or server, or a cloud storage service, which is used to store and manage various materials required for video production.

[0156] It should be noted that the preset video material library is a library specifically used to store video production materials, which may contain various types of materials, such as images, audio, video clips, etc. This library can be used for a variety of purposes such as video editing, content creation, and advertising production.

[0157] In this embodiment, by saving materials with copyright verification results indicating no copyright risk to a preset video material library, a safe and legal source of materials can be provided for video production, thereby improving the efficiency and compliance of video production.

[0158] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those of the first and second embodiments can be referred to the above description, and will not be described in detail later. Figure 3 Before step S30, the video acquisition method further includes steps A301 to A304:

[0159] Step A301, obtaining a prediction model of the playback volume of a sample video and a current video, wherein the future playback volume of the sample video is a first playback volume;

[0160] Step A302, predicting the future playback volume of the sample video based on the current video playback volume prediction model to obtain a second playback volume;

[0161] It can be understood that the second playback volume is the prediction result of the current video playback volume prediction model on the sample video processing, and the first playback volume is the actual playback volume of the sample video. There may be a difference between the first playback volume and the second playback volume. Therefore, if there is a difference, the current video playback volume prediction model is trained.

[0162] Step A303, calculating the difference between the first playback volume and the second playback volume, and determining whether the difference is greater than a preset difference threshold;

[0163] Step A304, if it is greater than, adjust the parameters of the current video playback volume prediction model, and based on the adjusted current video playback volume prediction model, return to the current video playback volume prediction model to predict the future playback volume of the sample video and obtain the second playback volume, until the playback volume difference is less than or equal to the preset difference threshold, and obtain the preset video playback volume prediction model.

[0164] If the two play counts are found to be different, this may indicate that the model has inconsistencies or instability in the prediction process. In this case, the parameters of the video play count prediction model need to be adjusted. The parameters may include: learning rate, regularization coefficient, model architecture, optimization algorithm, and loss function weight.

[0165] It is understandable that the purpose of adjusting parameters is to improve the accuracy, consistency and generalization ability of the model, ensuring that the model can produce stable and reliable output when faced with the same input. By adjusting parameters, the performance of the model can be optimized to better suit a specific task or data set.

[0166] This process is usually iterative, requiring multiple comparisons of inference results and adjustments of parameters until the model's performance reaches a satisfactory level.

[0167] In this embodiment, the video play volume prediction model is trained based on sample videos, which effectively improves the reliability of the prediction results.

[0168] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the video acquisition method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0169] This application also provides a video acquisition device, please refer to Figure 4 , the video acquisition device comprises:

[0170] An acquisition module 10, the acquisition module is used to respond to the video acquisition instruction and acquire the target subject of the video to be generated;

[0171] A selection module 20, the selection module is used to select multiple groups of image materials and multiple groups of audio materials associated with the target theme based on a preset video material library, and generate multiple groups of initial videos based on the multiple groups of image materials and audio materials;

[0172] A prediction module 30, the prediction module is used to predict the future playback volume of the multiple groups of initial videos based on a preset video playback volume prediction model, determine the streaming weight of each group of initial videos based on the future playback volume, and publish the multiple groups of initial videos to the short video platform and stream them based on the streaming weight, wherein the preset video playback volume prediction model is a trained video playback volume prediction model;

[0173] The detection module 40 is used to detect the real-time playback volume of the initial video after streaming, until the real-time playback volume is greater than or equal to a preset playback volume threshold, to obtain the target video.

[0174] In one embodiment, the selection module includes:

[0175] A generation unit, configured to generate image material and audio material based on a preset video material generation model, and perform copyright verification on the image material and audio material to obtain a copyright verification result, wherein the preset video material generation model is a trained video material generation model;

[0176] The storage unit is used to store the image material and the audio material whose copyright verification result indicates that there is no copyright risk in a preset video material library.

[0177] In one embodiment, the selection module further includes:

[0178] A parsing unit, used for parsing the image materials and audio materials in the preset video material library, determining a first theme of each group of image materials, and determining a second theme of each group of audio materials;

[0179] a determining unit, configured to determine a first relevance between the first topic and a target topic, and to determine a second relevance between the second topic and the target topic;

[0180] A selection unit is used to select multiple groups of image materials and multiple groups of audio materials associated with the target theme from a preset video material library based on the first association and the second association.

[0181] In one embodiment, the selection module further includes:

[0182] An execution unit, configured to execute an operation of generating a video based on any one of the multiple sets of image materials and any one of the multiple sets of audio materials, to obtain multiple sets of first videos;

[0183] An evaluation unit, configured to perform quality evaluation on the plurality of groups of first videos to obtain a plurality of groups of quality parameters;

[0184] A first judgment unit, configured to judge whether the quality parameter is greater than or equal to a preset quality threshold, and if the quality parameter is greater than or equal to the preset quality threshold, setting the first video corresponding to the quality parameter as the second video;

[0185] The optimization unit is used to optimize the quality of the second video to obtain multiple groups of initial videos.

[0186] In one embodiment, the prediction module includes:

[0187] A second judgment unit is used to judge whether the future playback volume is greater than or equal to a preset minimum playback volume;

[0188] A first setting unit, configured to set an initial video corresponding to the future playback amount that is greater than or equal to a preset minimum playback amount as a third video;

[0189] A second setting unit is used to determine a proportion of the future playback volume of each group of third videos based on the sum of the future playback volumes of the multiple groups of third videos, and set the proportion as the streaming weight of the third video corresponding to the proportion;

[0190] A publishing unit is used to publish the multiple groups of initial videos to a short video platform and stream them based on the streaming weights.

[0191] In one embodiment, the prediction module further includes:

[0192] An acquisition unit, used to acquire a prediction model of the playback volume of a sample video and a current video, wherein the future playback volume of the sample video is a first playback volume;

[0193] A prediction unit, configured to predict the future playback volume of the sample video based on a current video playback volume prediction model to obtain a second playback volume;

[0194] A calculation unit, configured to calculate a difference between the first playback amount and the second playback amount, and determine whether the difference is greater than a preset difference threshold;

[0195] The training unit is used to adjust the parameters of the current video playback volume prediction model if it is greater than, and return to the step of predicting the future playback volume of the sample video based on the current video playback volume prediction model based on the adjusted current video playback volume prediction model to obtain the second playback volume, until the playback volume difference is less than or equal to the preset difference threshold, and obtain the preset video playback volume prediction model.

[0196] The video acquisition device provided by the present application adopts the video acquisition method in the above embodiment, which can solve the technical problem of video acquisition. Compared with the prior art, the beneficial effects of the video acquisition device provided by the present application are the same as the beneficial effects of the video acquisition method provided by the above embodiment, and other technical features in the video acquisition device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0197] The present application provides a video acquisition device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the video acquisition method in the above-mentioned embodiment 1.

[0198] Reference below Figure 5, which shows a schematic diagram of the structure of a video acquisition device suitable for implementing the embodiment of the present application. The video acquisition device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The video acquisition device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0199] like Figure 5 As shown, the video acquisition device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the video acquisition device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the video acquisition device to communicate with other devices wirelessly or by wire to exchange data. Although the video acquisition device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0200] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0201] The video acquisition device provided by the present application adopts the video acquisition method in the above embodiment to solve the technical problem of video acquisition. Compared with the prior art, the beneficial effects of the video acquisition device provided by the present application are the same as the beneficial effects of the video acquisition method provided by the above embodiment, and other technical features in the video acquisition device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0202] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0203] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0204] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the video acquisition method in the above-mentioned embodiment.

[0205] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0206] The computer-readable storage medium may be included in the video acquisition device; or may exist independently without being assembled into the video acquisition device.

[0207] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the video acquisition device, the video acquisition device:

[0208] In response to the video acquisition instruction, acquiring a target subject of the video to be generated;

[0209] Based on a preset video material library, selecting multiple groups of image materials and multiple groups of audio materials associated with the target theme, and generating multiple groups of initial videos based on the multiple groups of image materials and audio materials;

[0210] Based on a preset video playback volume prediction model, predict the future playback volume of the multiple groups of initial videos, determine the streaming weight of each group of initial videos based on the future playback volume, and publish the multiple groups of initial videos to a short video platform and stream them based on the streaming weight, wherein the preset video playback volume prediction model is a trained video playback volume prediction model;

[0211] The real-time playback volume of the initial video after streaming is detected until the real-time playback volume is greater than or equal to a preset playback volume threshold, thereby obtaining the target video.

[0212] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0213] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0214] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0215] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned video acquisition method, and can solve the technical problem of video acquisition. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the video acquisition method provided in the above-mentioned embodiment, and will not be repeated here.

[0216] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned video acquisition method when executed by a processor.

[0217] The computer program product provided by the present application can solve the technical problem of video acquisition. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the video acquisition method provided by the above embodiment, which will not be repeated here.

[0218] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A video acquisition method, characterized in that: The video acquisition method comprises: In response to the video acquisition instruction, acquiring a target subject of the video to be generated; Based on a preset video material library, selecting multiple groups of image materials and multiple groups of audio materials associated with the target theme, and generating multiple groups of initial videos based on the multiple groups of image materials and audio materials; Based on a preset video playback volume prediction model, predict the future playback volume of the multiple groups of initial videos, determine the streaming weight of each group of initial videos based on the future playback volume, and publish the multiple groups of initial videos to a short video platform and stream them based on the streaming weight, wherein the preset video playback volume prediction model is a trained video playback volume prediction model; The real-time playback volume of the initial video after streaming is detected until the real-time playback volume is greater than or equal to a preset playback volume threshold, thereby obtaining the target video.

2. The video acquisition method according to claim 1, characterized in that: Before the step of selecting a plurality of sets of image materials and a plurality of sets of audio materials associated with the target theme based on the preset video material library, the step further includes: Based on a preset video material generation model, image material and audio material are generated, and copyright verification is performed on the image material and the audio material to obtain a copyright verification result, wherein the preset video material generation model is a trained video material generation model; The image material and audio material for which the copyright verification result indicates that there is no copyright risk are saved in a preset video material library.

3. The video acquisition method according to claim 1, characterized in that: The step of selecting multiple groups of image materials and multiple groups of audio materials associated with the target theme based on the preset video material library also includes: Analyzing the image materials and audio materials in the preset video material library to determine a first theme of each group of image materials and a second theme of each group of audio materials; Determining a first relevance of the first topic to a target topic, and determining a second relevance of the second topic to the target topic; Based on the first association and the second association, multiple groups of image materials and multiple groups of audio materials associated with the target theme are selected from a preset video material library.

4. The video acquisition method according to claim 1, characterized in that: The step of generating multiple groups of initial videos based on the multiple groups of image materials and audio materials further includes: Based on any one of the multiple groups of image materials and any one of the multiple groups of audio materials, a video generation operation is performed to obtain multiple groups of first videos; Performing quality evaluation on the multiple groups of first videos to obtain multiple groups of quality parameters; Determine whether the quality parameter is greater than or equal to a preset quality threshold, and if the quality parameter is greater than or equal to the preset quality threshold, set the first video corresponding to the quality parameter as the second video; The quality of the second video is optimized to obtain multiple groups of initial videos.

5. The video acquisition method according to claim 1, characterized in that: The step of determining the streaming weight of each group of initial videos based on the future playback volume, and publishing the multiple groups of initial videos to the short video platform and streaming them based on the streaming weight, further includes: Determining whether the future playback volume is greater than or equal to a preset minimum playback volume; Setting the initial video corresponding to the future playback amount that is greater than or equal to the preset minimum playback amount as the third video; Based on the sum of the future playback volumes of the multiple groups of third videos, determine the proportion of the future playback volume of each group of third videos, and set the proportion as the streaming weight of the third video corresponding to the proportion; Based on the streaming weights, the multiple groups of initial videos are published to a short video platform and streamed.

6. The video acquisition method according to claim 1, characterized in that: Before the step of predicting the future playback volumes of the multiple groups of initial videos based on the preset video playback volume prediction model, the method further includes: Obtaining a prediction model for the playback volume of a sample video and a current video, wherein the future playback volume of the sample video is a first playback volume; Based on the current video playback volume prediction model, predict the future playback volume of the sample video to obtain a second playback volume; Calculate the difference between the first playback amount and the second playback amount, and determine whether the difference is greater than a preset difference threshold; If it is greater, adjust the parameters of the current video playback volume prediction model, and based on the adjusted current video playback volume prediction model, return to the step of predicting the future playback volume of the sample video based on the current video playback volume prediction model to obtain the second playback volume, until the playback volume difference is less than or equal to the preset difference threshold, and obtain the preset video playback volume prediction model.

7. A video acquisition device, characterized in that: The video acquisition device comprises: An acquisition module, the acquisition module is used to acquire a target subject of a video to be generated in response to a video acquisition instruction; A selection module, the selection module is used to select multiple groups of image materials and multiple groups of audio materials associated with the target theme based on a preset video material library, and generate multiple groups of initial videos based on the multiple groups of image materials and audio materials; A prediction module, the prediction module is used to predict the future playback volume of the multiple groups of initial videos based on a preset video playback volume prediction model, determine the streaming weight of each group of initial videos based on the future playback volume, and publish the multiple groups of initial videos to the short video platform and stream them based on the streaming weight, wherein the preset video playback volume prediction model is a trained video playback volume prediction model; The detection module is used to detect the real-time playback volume of the initial video after streaming, until the real-time playback volume is greater than or equal to a preset playback volume threshold, to obtain the target video.

8. A video acquisition device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the video acquisition method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the video acquisition method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the video acquisition method according to any one of claims 1 to 6 are implemented.

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