Video data processing method and device, equipment and medium

Abstract

The invention discloses a video data processing method and device, equipment and a medium. The method comprises the following steps: acquiring a video data set corresponding to a target video model; obtaining video demand information of each video group corresponding to the target video model; determining a video group matched with each video according to the video demand information of each video group and the length, resolution and aspect ratio of each video in the video data set, and storing each video in a storage position of the matched video group; for each video group, videos of an expected number of videos of the video group are obtained from a storage location of the video group and provided to a corresponding processor system. According to the embodiment of the invention, each video in the video data set can be quickly and accurately stored in the video group to which the video can belong, so that each video group corresponding to the video model is obtained and provided for the processor system, and the accuracy and efficiency of the video data processing process are improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a video data processing method, device, equipment and medium. Background Art

[0002] With the development of video generation technology, more and more companies are beginning to generate videos through video models. The video model can generate videos according to the input video generation request, generate videos with specified length, specified resolution and specified aspect ratio, and output the generated videos. Video models are usually trained based on multiple video groups with different lengths, resolutions and aspect ratios. Each video group needs to contain multiple videos with lengths, resolutions and aspect ratios in a specified numerical range. In the training process of the video model, it is necessary to process a large number of videos collected in advance to obtain multiple video groups with different lengths, resolutions and aspect ratios for training the video model, and then provide the videos in each video group to the processor system for training the video model.

[0003] In the related art, the commonly used video data processing scheme is: technicians process a large number of pre-collected videos to obtain multiple video groups with lengths, resolutions and aspect ratios in different numerical ranges for training video models, and then provide the videos in each video group to a processor system for training video models. The video data processing scheme in the related art requires manual operation to obtain multiple video groups with lengths, resolutions and aspect ratios in different numerical ranges for training video models, which has low processing efficiency, high labor and time costs, and is difficult to guarantee accuracy. Summary of the invention

[0004] The present invention provides a video data processing method, device, equipment and medium to solve the problems of low processing efficiency, high labor cost and time cost, and difficulty in ensuring accuracy of video data processing solutions in related technologies.

[0005] According to one aspect of the present invention, there is provided a video data processing method, comprising:

[0006] Obtain a video dataset corresponding to the target video model;

[0007] Acquire video demand information of each video group corresponding to the target video model; wherein the video demand information includes identification information, expected length, expected resolution, expected aspect ratio, probability of video length reduction, probability of video resolution reduction, storage location information, and expected number of videos;

[0008] Determine a video group that matches each video in the video data set according to video demand information of each video group, length, resolution, and aspect ratio of each video in the video data set, and store each video in the video data set in a storage location of the matched video group;

[0009] For each video group, an expected number of videos of the video group is obtained from a storage location of the video group, and the obtained expected number of videos of the video group is provided to a processor system corresponding to the target video model.

[0010] According to another aspect of the present invention, there is provided a video data processing device, comprising:

[0011] A data set acquisition module, used to acquire a video data set corresponding to a target video model;

[0012] An information acquisition module, used to acquire video demand information of each video group corresponding to the target video model; wherein the video demand information includes identification information, expected length, expected resolution, expected aspect ratio, probability of video length reduction, probability of video resolution reduction, storage location information and expected number of videos;

[0013] A video matching module, configured to determine a video group that matches each video in the video data set according to video requirement information of each video group, length, resolution, and aspect ratio of each video in the video data set, and store each video in the video data set in a storage location of the matched video group;

[0014] The video providing module is used to obtain the expected number of videos of the video group from the storage location of the video group for each video group, and provide the obtained expected number of videos of the video group to the processor system corresponding to the target video model.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor;

[0017] and a memory communicatively coupled to the at least one processor;

[0018] The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the video data processing method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the video data processing method described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention is to obtain a video data set corresponding to a target video model and video demand information of each video group corresponding to the target video model; wherein the video demand information includes identification information, expected length, expected resolution, expected aspect ratio, probability of video length reduction, probability of video resolution reduction, storage location information and expected number of videos; then, based on the video demand information of each video group, the length, resolution and aspect ratio of each video in the video data set, determine the video group that matches each video in the video data set, and store each video in the video data set in the storage location of the matched video group; finally, for each video group, obtain the expected number of videos of the video group from the storage location of the video group, and provide the obtained videos of the expected number of videos of the video group to the processor system corresponding to the target video model, thereby solving the problem that the processing efficiency of the video data processing scheme in the related art is low. The problem of high labor cost and time cost and difficulty in ensuring accuracy can be solved by automatically storing each video in the video data set into a video group to which a video can be attributed based on the expected length, expected resolution, expected aspect ratio, video length reduction probability, video resolution reduction probability in the video demand information of each video group corresponding to the video model, and the length, resolution and aspect ratio of the video in the video data set, so as to obtain each video group corresponding to the video model, that is, multiple video groups with different lengths, resolutions and aspect ratios in different numerical ranges required in the training process of the video model, and provide the videos in each video group to a processor system for training the video model, with low labor cost and time cost, improving the accuracy and efficiency of the video data processing process, and being able to quickly and accurately meet the data requirements of the training process of the video model and improve the training efficiency of the video model.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 The present invention provides a flowchart of a video data processing method according to the first embodiment of the present invention.

[0024] Figure 2 The present invention provides a flowchart of a video data processing method according to a second embodiment of the present invention.

[0025] Figure 3 A schematic diagram of the structure of a video data processing device provided in Embodiment 3 of the present invention.

[0026] Figure 4 A schematic diagram of the structure of an electronic device for implementing the video data processing method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "target", "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprise", "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 A flowchart of a video data processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to the case where a large number of pre-collected videos are processed during the training of a video model. The method can be executed by a video data processing device, which can be implemented in the form of hardware and / or software, and the video data processing device can be configured in an electronic device. The electronic device can be an electronic device set up in an enterprise for processing pre-collected videos. Figure 1 As shown, the method includes:

[0031] Step 101: Obtain a video dataset corresponding to a target video model.

[0032] Optionally, the video model may be a model obtained by training a deep learning model using a plurality of video groups with lengths, resolutions, and aspect ratios in different numerical ranges as training samples. The video model may generate a video according to an input video generation request, generate a video with a specified length, a specified resolution, and a specified aspect ratio, and output the generated video. The input of the video model is a video generation request, and the output of the video model is a video corresponding to the video generation request. The video generation request may be text for instructing the video model to generate a video with a specified length, a specified resolution, and a specified aspect ratio based on a specified text. The video corresponding to the video generation request may be a video generated by the video model according to the video generation request.

[0033] Optionally, the length of a video may refer to the number of video images contained in the video. For example, if a video contains 1 frame of video images, the length of the video is 1. If a video contains 8 frames of video images, the length of the video is 8. If a video contains 16 frames of video images, the length of the video is 16.

[0034] Optionally, the resolution of a video may refer to the resolution of a video image contained in the video. Exemplarily, if the resolution of a video image contained in the video is 480P, the resolution of the video is 480P. If the resolution of a video image contained in the video is 360P, the resolution of the video is 360P. If the resolution of a video image contained in the video is 720P, the resolution of the video is 720P.

[0035] Optionally, the aspect ratio of a video may refer to the ratio between the width and height of a video image contained in the video. Exemplarily, the ratio between the width and height of a video image contained in a video is 16:9, and the aspect ratio of the video is 16:9. The ratio between the width and height of a video image contained in a video is 4:3, and the aspect ratio of the video is 4:3. The ratio between the width and height of a video image contained in a video is 1:1, and the aspect ratio of the video is 1:1. The ratio between the width and height of a video image contained in a video is 3:4, and the aspect ratio of the video is 3:4. The ratio between the width and height of a video image contained in a video is 9:16, and the aspect ratio of the video is 9:16.

[0036] Optionally, the target video model may be a video model that needs to be trained. The video data set corresponding to the target video model is composed of a plurality of pre-collected videos for training the target video model. Exemplarily, the video data set corresponding to the target video model is composed of 2,000 pre-collected videos for training the target video model.

[0037] Optionally, obtaining a video data set corresponding to a target video model includes: obtaining a video data set corresponding to the target video model sent by a target user. The target user may be a technician responsible for managing the video model. The target user may send the video data set corresponding to the target video model to an electronic device through a terminal device. The electronic device obtains the video data set corresponding to the target video model sent by the target user.

[0038] Step 102: Obtain video demand information of each video group corresponding to the target video model.

[0039] The video demand information includes identification information, expected length, expected resolution, expected aspect ratio, probability of video length reduction, probability of video resolution reduction, storage location information and expected number of videos.

[0040] Optionally, each video group corresponding to the target video model is a video group with multiple lengths, resolutions and aspect ratios in different numerical ranges that need to be used in the training process of the target video model. Each video group needs to be composed of multiple videos, and the length, resolution and aspect ratio of each video in the video group need to be within a specified numerical range. The video requirement information of the video group may be information related to the video group and the videos that can be attributed to the video group. The identification information of the video group may be a string used to uniquely identify the video group. The expected length of the video group may be the length that the length of each video in the video group needs to be greater than. The expected resolution of the video group may be the resolution that the resolution of each video in the video group needs to be greater than. The expected aspect ratio of the video group may be the aspect ratio that each video in the video group needs to have. The video length reduction probability of the video group may be the probability of reducing the length of the video in the video group in order to reduce the amount of calculation during the model training process. The video resolution reduction probability of the video group may be the probability of reducing the resolution of the video in the video group in order to reduce the amount of calculation during the model training process. The storage location information of the video group may be a string used to identify the storage location of the video group. The storage location of the video group may be a pre-set memory or storage file for storing videos in the video group. The expected number of videos in a video group may be the number of videos in the video group that need to be used during the model training process.

[0041] Optionally, obtaining the video demand information of each video group corresponding to the target video model includes: obtaining the video demand information of each video group corresponding to the target video model sent by the target user. The target user may send the video demand information of each video group corresponding to the target video model to the electronic device through the terminal device. The electronic device obtains the video demand information of each video group corresponding to the target video model sent by the target user.

[0042] Step 103: Determine a video group that matches each video in the video data set according to the video demand information of each video group, the length, resolution, and aspect ratio of each video in the video data set, and store each video in the video data set in a storage location of the matched video group.

[0043] Optionally, for each video in the video data set, the video group that matches the video is the video group corresponding to the target video model to which the video can belong, and the video is stored in the storage location of the matched video group, thereby storing the video in the video group corresponding to the target video model to which the video can belong.

[0044] Optionally, based on the video demand information of each video group, the length, resolution and aspect ratio of each video in the video data set, determine the video group that matches each video in the video data set, and store each video in the video data set in the storage location of the matched video group, including: performing the following operations for each video in the video data set: determining each video group as each candidate video group corresponding to the video, determining each basic candidate video group in each candidate video group whose expected length is less than the length of the video and whose expected resolution is less than the resolution of the video; determining the maximum length among the expected lengths of each basic candidate video group and the maximum resolution among the expected resolutions of each basic candidate video group; obtaining a basic candidate video group in each basic candidate video group whose length is the maximum length and whose resolution is the maximum resolution as A target candidate video group corresponding to the video; generating a random number between 0 and 1, and determining whether the random number is greater than or equal to the probability of video length reduction of the target candidate video group; if the random number is greater than or equal to the probability of video length reduction of the target candidate video group, generating a new random number between 0 and 1, and determining whether the new random number is greater than or equal to the probability of video resolution reduction of the target candidate video group; if the new random number is greater than or equal to the probability of video resolution reduction of the target candidate video group, determining whether the aspect ratio of the video is equal to the expected aspect ratio of the target candidate video group; if the aspect ratio of the video is equal to the expected aspect ratio of the target candidate video group, determining the target candidate video group as a video group that matches the video, and storing the video in the storage position of the matched video group according to the storage position information of the target candidate video group.

[0045] Optionally, for each video in the video data set, the electronic device may detect the video to determine the length, resolution, and aspect ratio of the video.

[0046] Optionally, each candidate video group corresponding to a video is a video group to which the video may belong. A basic candidate video group is a candidate video group in each candidate video group whose expected length is less than the length of the video and whose expected resolution is less than the resolution of the video. Each video group is determined as a candidate video group corresponding to a video. The video is detected to determine the length, resolution and aspect ratio of the video. For each candidate video group, it is detected whether the expected length of the candidate video group is less than the length of the video and whether the expected resolution is less than the resolution of the video. The candidate video group in each candidate video group whose expected length is less than the length of the video and whose expected resolution is less than the resolution of the video is determined as a basic candidate video group, thereby obtaining each basic candidate video group in each candidate video group.

[0047] Optionally, the maximum length is the maximum value among the expected lengths of each basic candidate video group. The maximum resolution is the maximum value among the expected resolutions of each basic candidate video group. The electronic device can detect the expected length and expected resolution of each basic candidate video group to determine the maximum length among the expected lengths of each basic candidate video group and the maximum resolution among the expected resolutions of each basic candidate video group.

[0048] Optionally, after determining the maximum length in the expected length of each basic candidate video group and the maximum resolution in the expected resolution of each basic candidate video group, a basic candidate video group with a length of the maximum length and a resolution of the maximum resolution in each basic candidate video group is obtained as the target candidate video group corresponding to the video. Then, a random number generation component is used to generate a random number between 0 and 1, and it is determined whether the random number is greater than or equal to the video length reduction probability of the target candidate video group. The random number generation component can be a component set in an electronic device for generating a random number between 0 and 1. If the random number is greater than or equal to the video length reduction probability of the target candidate video group, a new random number between 0 and 1 is generated, and it is determined whether the new random number is greater than or equal to the video resolution reduction probability of the target candidate video group. If the new random number is greater than or equal to the video resolution reduction probability of the target candidate video group, it is determined whether the aspect ratio of the video is equal to the expected aspect ratio of the target candidate video group. If the aspect ratio of the video is equal to the expected aspect ratio of the target candidate video group, the target candidate video group is determined as a video group that matches the video, and according to the storage location information of the target candidate video group, the video is stored in the storage location of the matched video group. The electronic device can determine the storage location of the target candidate video group according to the storage location information of the target candidate video group, and store the video in the storage location of the target candidate video group, thereby storing the video in the storage location of the matched video group.

[0049] Optionally, after determining whether the random number is greater than or equal to the video length reduction probability of the target alternative video group, it also includes: if the random number is less than the video length reduction probability of the target alternative video group, and there is a basic alternative video group in each basic alternative video group whose expected length is less than the expected length of the target alternative video group and whose expected resolution is equal to the expected resolution of the target alternative video group, then after removing the target alternative video group from each basic alternative video group, return to execute the operation of determining the maximum length of each basic alternative video group and the maximum resolution of each basic alternative video group.

[0050] Optionally, if the random number is less than the video length reduction probability of the target candidate video group, then detect whether there is a basic candidate video group in each basic candidate video group whose expected length is less than the expected length of the target candidate video group and whose expected resolution is equal to the expected resolution of the target candidate video group. If there is a basic candidate video group in each basic candidate video group whose expected length is less than the expected length of the target candidate video group and whose expected resolution is equal to the expected resolution of the target candidate video group, then remove the target candidate video group from each basic candidate video group, and after removing the target candidate video group from each basic candidate video group, return to perform the operation of determining the maximum length in the expected length of each basic candidate video group and the maximum resolution in the expected resolution of each basic candidate video group.

[0051] Optionally, it also includes: if the random number is smaller than the probability of video length reduction of the target alternative video group, then after removing the target alternative video group from each basic alternative video group, returning to execute the operation of determining the maximum length among the expected lengths of each basic alternative video group and the maximum resolution among the expected resolutions of each basic alternative video group.

[0052] Optionally, after determining whether the new random number is greater than or equal to the video resolution reduction probability of the target alternative video group, it also includes: if the new random number is less than the video resolution reduction probability of the target alternative video group, and there is a basic alternative video group in each basic alternative video group whose expected resolution is less than the expected resolution of the target alternative video group and whose expected length is equal to the expected length of the target alternative video group, then after removing the target alternative video group from each basic alternative video group, return to execute the operation of determining the maximum length of each basic alternative video group and the maximum resolution of each basic alternative video group.

[0053] Optionally, if the new random number is less than the probability of video resolution reduction of the target candidate video group, then it is detected whether there is a basic candidate video group in each basic candidate video group whose expected resolution is less than the expected resolution of the target candidate video group and whose expected length is equal to the expected length of the target candidate video group. If there is a basic candidate video group in each basic candidate video group whose expected resolution is less than the expected resolution of the target candidate video group and whose expected length is equal to the expected length of the target candidate video group, then the target candidate video group is removed from each basic candidate video group, and after the target candidate video group is removed from each basic candidate video group, the operation of determining the maximum length in the expected length of each basic candidate video group and the maximum resolution in the expected resolution of each basic candidate video group is returned to.

[0054] Optionally, it also includes: if the new random number is smaller than the probability of video resolution reduction of the target alternative video group, then after removing the target alternative video group from each basic alternative video group, returning to execute the operation of determining the maximum length among the expected lengths of each basic alternative video group and the maximum resolution among the expected resolutions of each basic alternative video group.

[0055] Optionally, if the aspect ratio of the video is not equal to the expected aspect ratio of the target alternative video group, the target alternative video group is removed from each basic alternative video group. After removing the target alternative video group from each basic alternative video group, the operation of determining the maximum length among the expected lengths of each basic alternative video group and the maximum resolution among the expected resolutions of each basic alternative video group is returned to be executed.

[0056] Therefore, based on the generated random number between 0 and 1, the expected length, expected resolution, expected aspect ratio, video length reduction probability, video resolution reduction probability, and video length, resolution, and aspect ratio in the video requirement information of each video group corresponding to the target video model, each video in the video data set can be randomly stored in a video group to which a video can belong, thereby obtaining each video group corresponding to the target video model, that is, multiple video groups with lengths, resolutions, and aspect ratios in different numerical ranges that need to be used in the training process of the target video model.

[0057] Step 104: for each video group, obtain an expected number of videos of the video group from a storage location of the video group, and provide the obtained expected number of videos of the video group to a processor system corresponding to the target video model.

[0058] Optionally, the processor system corresponding to the target video model may be a processor system for training a deep learning model using the videos in each video group corresponding to the target video model as training samples to obtain the target video model. The processor system corresponding to the target video model may be composed of multiple graphics processing units (GPUs).

[0059] Optionally, for each video group, an expected number of videos of the video group are obtained from a storage location of the video group, and the obtained expected number of videos of the video group are provided to a processor system corresponding to the target video model, including: for each video group, an expected number of videos of the video group are obtained from a storage location of the video group, the obtained videos are randomly arranged to form a video sequence, and the video sequence is shuffled to obtain a shuffled video sequence of the video group, and the shuffled video sequence of the video group is provided to a processor system corresponding to the target video model.

[0060] Optionally, the scrambled video sequence of the video group can be a scrambled video sequence obtained by scrambling a video sequence formed by videos of an expected number of videos of the video group. The videos of the expected number of videos of the video group are obtained from the storage location of the video group, and the obtained videos are randomly arranged to form a video sequence. Then, according to a preset scrambling rule, the video sequence is scrambled to obtain the scrambled video sequence, thereby obtaining a scrambled video sequence of the video group. Finally, the scrambled video sequence of the video group is provided to a processor system corresponding to the target video model. Exemplarily, the expected number of videos of the video group is 20. 20 videos are obtained from the storage location of the video group, the obtained 20 videos are randomly arranged to form a video sequence, and then according to a preset scrambling rule, the video sequence is scrambled to obtain a scrambled video sequence, thereby obtaining a scrambled video sequence of the video group. Finally, the scrambled video sequence of the video group is provided to a processor system corresponding to the target video model.

[0061] Optionally, the preset scrambling rule may be a preset rule for scrambling the video sequence to obtain a scrambled video sequence. The electronic device may scramble the video sequence according to the preset scrambling rule to obtain a scrambled video sequence.

[0062] Optionally, providing the scrambled video sequence of the video group to a processor system corresponding to the target video model includes: sending the scrambled video sequence of the video group to the processor system corresponding to the target video model.

[0063] Optionally, in a specific example, the processor system corresponding to the target video model includes a first GPU, a second GPU, and a third GPU. The first GPU, the second GPU, and the third GPU are three different GPUs. The first GPU is used to train the deep learning model using the videos in the video group including videos with a length greater than 1, a resolution greater than 480P, and an aspect ratio equal to 16:9 as training samples. The second GPU is used to train the deep learning model using the videos in the video group including videos with a length greater than 8, a resolution greater than 360P, and an aspect ratio equal to 1:1 as training samples. The third GPU is used to train the deep learning model using the videos in the video group including videos with a length greater than 16, a resolution greater than 720P, and an aspect ratio equal to 3:4 as training samples.

[0064] The technical solution of the embodiment of the present invention is to obtain a video data set corresponding to a target video model and video demand information of each video group corresponding to the target video model; wherein the video demand information includes identification information, expected length, expected resolution, expected aspect ratio, probability of video length reduction, probability of video resolution reduction, storage location information and expected number of videos; then, based on the video demand information of each video group, the length, resolution and aspect ratio of each video in the video data set, determine the video group that matches each video in the video data set, and store each video in the video data set in the storage location of the matched video group; finally, for each video group, obtain the expected number of videos of the video group from the storage location of the video group, and provide the obtained videos of the expected number of videos of the video group to the processor system corresponding to the target video model, thereby solving the problem that the processing efficiency of the video data processing scheme in the related art is low. The problem of high labor cost and time cost and difficulty in ensuring accuracy can be solved by automatically storing each video in the video data set into a video group to which a video can be attributed based on the expected length, expected resolution, expected aspect ratio, video length reduction probability, video resolution reduction probability in the video demand information of each video group corresponding to the video model, and the length, resolution and aspect ratio of the video in the video data set, so as to obtain each video group corresponding to the video model, that is, multiple video groups with different lengths, resolutions and aspect ratios in different numerical ranges required in the training process of the video model, and provide the videos in each video group to a processor system for training the video model, with low labor cost and time cost, improving the accuracy and efficiency of the video data processing process, and being able to quickly and accurately meet the data requirements of the training process of the video model and improve the training efficiency of the video model.

[0065] Embodiment 2

[0066] Figure 2This is a flowchart of a video data processing method provided by Embodiment 2 of the present invention. This embodiment of the present invention can be combined with each optional solution in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0067] Step 201: Obtain a video dataset corresponding to a target video model.

[0068] Step 202: Obtain video demand information of each video group corresponding to the target video model.

[0069] The video demand information includes identification information, expected length, expected resolution, expected aspect ratio, probability of video length reduction, probability of video resolution reduction, storage location information and expected number of videos.

[0070] Step 203: Determine a video group that matches each video in the video data set according to the video demand information of each video group, the length, resolution, and aspect ratio of each video in the video data set, and store each video in the video data set in a storage location of the matched video group.

[0071] Step 204: for each video group, obtain an expected number of videos of the video group from a storage location of the video group, randomly arrange the obtained videos to form a video sequence, shuffle the video sequence to obtain a shuffled video sequence of the video group, and provide the shuffled video sequence of the video group to a processor system corresponding to the target video model.

[0072] The technical solution of the embodiment of the present invention can automatically store each video in the video data set into a video group to which a video can belong based on the expected length, expected resolution, expected aspect ratio, video length reduction probability, video resolution reduction probability in the video demand information of each video group corresponding to the video model, and the length, resolution, and aspect ratio of the videos in the video data set, so as to obtain each video group corresponding to the video model, that is, multiple video groups with different lengths, resolutions, and aspect ratios in different numerical ranges required for the training process of the video model, and based on the expected number of videos in each video group, obtain a scrambled video sequence of each video group, and provide the scrambled video sequence of the video group to the processor system corresponding to the video model, so as to provide the videos in each video group to the processor system for training the video model based on the video demand for each video group, with low labor and time costs, and improve the accuracy and efficiency of the video data processing process, and can quickly and accurately meet the data requirements of the video model training process, and improve the training efficiency of the video model.

[0073] Embodiment 3

[0074] Figure 3 FIG. 1 is a schematic diagram of the structure of a video data processing device provided in Embodiment 3 of the present invention. The device may be configured in an electronic device. Figure 3 As shown, the device includes: a data set acquisition module 301, an information acquisition module 302, a video matching module 303 and a video providing module 304.

[0075] Among them, the data set acquisition module 301 is used to acquire a video data set corresponding to the target video model; the information acquisition module 302 is used to acquire the video demand information of each video group corresponding to the target video model; wherein the video demand information includes identification information, expected length, expected resolution, expected aspect ratio, video length reduction probability, video resolution reduction probability, storage location information and expected number of videos; the video matching module 303 is used to determine the video group that matches each video in the video data set according to the video demand information of each video group, the length, resolution and aspect ratio of each video in the video data set, and store each video in the video data set in the storage location of the matched video group; the video providing module 304 is used to acquire the expected number of videos of the video group from the storage location of the video group for each video group, and provide the acquired videos of the expected number of videos of the video group to the processor system corresponding to the target video model.

[0076] The technical solution of the embodiment of the present invention is to obtain a video data set corresponding to a target video model and video demand information of each video group corresponding to the target video model; wherein the video demand information includes identification information, expected length, expected resolution, expected aspect ratio, probability of video length reduction, probability of video resolution reduction, storage location information and expected number of videos; then, based on the video demand information of each video group, the length, resolution and aspect ratio of each video in the video data set, determine the video group that matches each video in the video data set, and store each video in the video data set in the storage location of the matched video group; finally, for each video group, obtain the expected number of videos of the video group from the storage location of the video group, and provide the obtained videos of the expected number of videos of the video group to the processor system corresponding to the target video model, thereby solving the problem that the processing efficiency of the video data processing scheme in the related art is low. The problem of high labor cost and time cost and difficulty in ensuring accuracy can be solved by automatically storing each video in the video data set into a video group to which a video can be attributed based on the expected length, expected resolution, expected aspect ratio, video length reduction probability, video resolution reduction probability in the video demand information of each video group corresponding to the video model, and the length, resolution and aspect ratio of the video in the video data set, so as to obtain each video group corresponding to the video model, that is, multiple video groups with different lengths, resolutions and aspect ratios in different numerical ranges required in the training process of the video model, and provide the videos in each video group to a processor system for training the video model, with low labor cost and time cost, improving the accuracy and efficiency of the video data processing process, and being able to quickly and accurately meet the data requirements of the training process of the video model and improve the training efficiency of the video model.

[0077] In an optional implementation of an embodiment of the present invention, optionally, the video matching module 303 is specifically used to: perform the following operations for each video in the video data set: determine each video group as each candidate video group corresponding to the video, determine each basic candidate video group in each candidate video group whose expected length is less than the length of the video and whose expected resolution is less than the resolution of the video; determine the maximum length value in the expected length of each basic candidate video group and the maximum resolution value in the expected resolution of each basic candidate video group; obtain a basic candidate video group in each basic candidate video group whose length is the maximum length and whose resolution is the maximum resolution as the target candidate video group corresponding to the video; generate a random number between 0 and 1, and judge the whether the random number is greater than or equal to the video length reduction probability of the target alternative video group; if the random number is greater than or equal to the video length reduction probability of the target alternative video group, generate a new random number between 0 and 1, and judge whether the new random number is greater than or equal to the video resolution reduction probability of the target alternative video group; if the new random number is greater than or equal to the video resolution reduction probability of the target alternative video group, judge whether the aspect ratio of the video is equal to the expected aspect ratio of the target alternative video group; if the aspect ratio of the video is equal to the expected aspect ratio of the target alternative video group, determine the target alternative video group as the video group that matches the video, and store the video in the storage position of the matched video group according to the storage position information of the target alternative video group.

[0078] In an optional implementation of an embodiment of the present invention, optionally, the video matching module 303 is also used for: if the random number is smaller than the video length reduction probability of the target alternative video group, and there is a basic alternative video group in each basic alternative video group whose expected length is smaller than the expected length of the target alternative video group and whose expected resolution is equal to the expected resolution of the target alternative video group, then after removing the target alternative video group from each basic alternative video group, returning to perform the operation of determining the maximum length in the expected lengths of each basic alternative video group and the maximum resolution in the expected resolutions of each basic alternative video group.

[0079] In an optional implementation of an embodiment of the present invention, optionally, the video matching module 303 is also used for: if the new random number is smaller than the video resolution reduction probability of the target alternative video group, and there exists a basic alternative video group in each basic alternative video group whose expected resolution is smaller than the expected resolution of the target alternative video group and whose expected length is equal to the expected length of the target alternative video group, then after removing the target alternative video group from each basic alternative video group, returning to execute the operation of determining the maximum length among the expected lengths of each basic alternative video group and the maximum resolution among the expected resolutions of each basic alternative video group.

[0080] In an optional implementation of an embodiment of the present invention, optionally, the video providing module 304 is specifically used to: for each video group, obtain an expected number of videos of the video group from a storage location of the video group, randomly arrange the obtained videos to form a video sequence, and shuffle the video sequence to obtain a shuffled video sequence of the video group, and provide the shuffled video sequence of the video group to a processor system corresponding to the target video model.

[0081] In an optional implementation of the embodiment of the present invention, optionally, the data set acquisition module 301 is specifically used to: acquire a video data set corresponding to the target video model sent by the target user.

[0082] In an optional implementation of the embodiment of the present invention, optionally, the information acquisition module 302 is specifically used to: acquire video demand information of each video group corresponding to the target video model sent by the target user.

[0083] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0084] The above-mentioned video data processing device can execute the video data processing method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the video data processing method.

[0085] Embodiment 4

[0086] Figure 4 The schematic diagram of the structure of the electronic device 10 that can be used to implement the video data processing method of the embodiment of the present invention is shown. The components shown herein, their connections and relationships, and their functions are only for example, and are not intended to limit the implementation of the present invention described and / or required herein.

[0087] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program built from the storage unit 18 to the random access memory (RAM) 13. In the random access memory (RAM) 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the read-only memory (ROM) 12 and the random access memory (RAM) 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0088] A number of components in the electronic device 10 are connected to an input / output (I / O) interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, speakers, etc., a storage unit 18, such as a disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0089] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a video data processing method.

[0090] In some embodiments, the video data processing method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via a read-only memory (ROM) 12 and / or a communication unit 19. When the computer program is built into a random access memory (RAM) 13 and executed by the processor 11, one or more steps of the video data processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the video data processing method in any other appropriate manner (e.g., by means of firmware).

[0091] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] The computer program for implementing the video data processing method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0093] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0094] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0095] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0096] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0097] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0098] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A video data processing method, characterized in that: include: Obtain a video dataset corresponding to the target video model; Acquire video demand information of each video group corresponding to the target video model; wherein the video demand information includes identification information, expected length, expected resolution, expected aspect ratio, probability of video length reduction, probability of video resolution reduction, storage location information, and expected number of videos; Determine a video group that matches each video in the video data set according to video demand information of each video group, length, resolution, and aspect ratio of each video in the video data set, and store each video in the video data set in a storage location of the matched video group; For each video group, an expected number of videos of the video group is obtained from a storage location of the video group, and the obtained expected number of videos of the video group is provided to a processor system corresponding to the target video model.

2. The video data processing method according to claim 1, characterized in that: Determining a video group matching each video in the video data set according to video demand information of each video group, length, resolution, and aspect ratio of each video in the video data set, and storing each video in the video data set to a storage location of the matching video group, including: For each video in the video dataset, perform the following operations: Determine each video group as each candidate video group corresponding to the video, and determine each basic candidate video group in each candidate video group whose expected length is less than the length of the video and whose expected resolution is less than the resolution of the video; Determine a maximum value of lengths among expected lengths of each basic candidate video group and a maximum value of resolutions among expected resolutions of each basic candidate video group; Acquire a basic candidate video group whose length is the maximum length and whose resolution is the maximum resolution among the basic candidate video groups as a target candidate video group corresponding to the video; Generate a random number between 0 and 1, and determine whether the random number is greater than or equal to the video length reduction probability of the target candidate video group; If the random number is greater than or equal to the probability of video length reduction of the target candidate video group, generate a new random number between 0 and 1, and determine whether the new random number is greater than or equal to the probability of video resolution reduction of the target candidate video group; If the new random number is greater than or equal to the probability of video resolution reduction of the target candidate video group, determining whether the aspect ratio of the video is equal to the expected aspect ratio of the target candidate video group; If the aspect ratio of the video is equal to the expected aspect ratio of the target candidate video group, the target candidate video group is determined as a video group matching the video, and the video is stored in a storage location of the matching video group according to storage location information of the target candidate video group.

3. The video data processing method according to claim 2, characterized in that: After determining whether the random number is greater than or equal to the video length reduction probability of the target candidate video group, the method further includes: If the random number is smaller than the probability of video length reduction of the target alternative video group, and there exists a basic alternative video group in each basic alternative video group whose expected length is smaller than the expected length of the target alternative video group and whose expected resolution is equal to the expected resolution of the target alternative video group, then after removing the target alternative video group from each basic alternative video group, the operation of determining the maximum value of the expected lengths of each basic alternative video group and the maximum value of the expected resolutions of each basic alternative video group is returned.

4. The video data processing method according to claim 2, characterized in that: After determining whether the new random number is greater than or equal to the video resolution reduction probability of the target candidate video group, the method further includes: If the new random number is smaller than the probability of video resolution reduction of the target alternative video group, and there exists a basic alternative video group in each basic alternative video group whose expected resolution is smaller than the expected resolution of the target alternative video group and whose expected length is equal to the expected length of the target alternative video group, then after removing the target alternative video group from each basic alternative video group, the operation of determining the maximum length of each basic alternative video group and the maximum resolution of each basic alternative video group is returned.

5. The video data processing method according to claim 1, characterized in that: For each video group, obtaining an expected number of videos of the video group from a storage location of the video group, and providing the obtained expected number of videos of the video group to a processor system corresponding to the target video model, including: For each video group, an expected number of videos of the video group are obtained from a storage location of the video group, the obtained videos are randomly arranged to form a video sequence, and the video sequence is shuffled to obtain a shuffled video sequence of the video group, and the shuffled video sequence of the video group is provided to a processor system corresponding to the target video model.

6. The video data processing method according to claim 1, characterized in that: Get the video dataset corresponding to the target video model, including: Obtain a video dataset corresponding to the target video model sent by the target user.

7. The video data processing method according to claim 1, characterized in that: Acquiring video demand information of each video group corresponding to the target video model, including: The video demand information of each video group corresponding to the target video model sent by the target user is obtained.

8. A video data processing device, characterized in that: include: A data set acquisition module, used to acquire a video data set corresponding to a target video model; An information acquisition module, used to acquire video demand information of each video group corresponding to the target video model; wherein the video demand information includes identification information, expected length, expected resolution, expected aspect ratio, probability of video length reduction, probability of video resolution reduction, storage location information and expected number of videos; A video matching module, configured to determine a video group that matches each video in the video data set according to video requirement information of each video group, length, resolution, and aspect ratio of each video in the video data set, and store each video in the video data set in a storage location of the matched video group; The video providing module is used to obtain the expected number of videos of the video group from the storage location of the video group for each video group, and provide the obtained expected number of videos of the video group to the processor system corresponding to the target video model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the video data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the video data processing method according to any one of claims 1 to 7 when executed.

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