CN115514969A - Video big data storage and transcoding optimization system - Google Patents

Abstract

The invention relates to the technical field of data storage, in particular to a video big data storage and transcoding optimization system which uploads video data to be stored to a reading module of the optimization system. And uploading the video data to be stored to a reading module of the optimization system. The reading module collects various data of the video and writes the data into the remarks of the video. The marking module marks various data of the video in a sub mode and marks the video in a mother mode. The integration module integrates the bound data and finally outputs the data to the storage module, so that the repeated video storage amount in the storage space is effectively reduced in the video marking process and the integration process, and the storage pressure of the video data is effectively relieved. Meanwhile, the storage module pulls and deletes the video data with the overlapped original parent markers, so that the space occupation of repeated data is reduced, and the processed data is integrated for storage. The transcoding module performs compression transcoding on data in the storage module, so that the use efficiency of a storage space is improved.

Description

Video big data storage and transcoding optimization system

Technical Field

The invention relates to the technical field of data storage, in particular to a video big data storage and transcoding optimization system.

Background

In the information era, the amount of data to be stored is greatly increased, and the cost of hardware is increased due to the large redundancy of the stored data, so that a large amount of manpower and material resources are consumed. For video data, how to reduce the consumption of storage space by video is a problem of current research. In the prior art, generally, video transcoding is used to convert a video code stream that has been compressed and encoded into another video code stream, so as to adapt to different network bandwidths, different terminal processing capabilities and different user requirements, and achieve the effect of compressing a video or reversely restoring a video.

The invention with publication number CN112104869B discloses a video big data storage and transcoding optimization system, which comprises: the image acquisition module is used for acquiring an original image; the key frame selecting module is used for selecting a key frame, a to-be-determined background frame and an irrelevant frame according to the total Euclidean distance of corresponding feature points in the ROI of the original images of the adjacent frames; the background frame selection module is used for judging whether the to-be-determined background frame is a background frame or not; the transcoding optimization module is used for determining the coding quantization parameter QP of each frame; the data storage module is used for converting the key frame and the background frame into a key frame gray image and a background frame YUV image respectively and dividing the key frame gray image and the background frame YUV image into a plurality of image groups for storage; and the color reconstruction module is used for recovering the color of the key frame gray level image in the same image group according to the background frame YUV image in the image group.

However, the above technical solutions have the following disadvantages: the video storage and transcoding system mainly reduces the frame number of the video by image acquisition and key frame selection in the video data and sacrifices the video color and the image to realize the compression storage of the video. In the entertainment industry with higher requirements on video quality, the video storage system is not beneficial to protecting the quality and pictures of the video, and the time cost consumed in later-period color reconstruction is also too high.

Disclosure of Invention

The invention provides a video big data storage and transcoding optimization system aiming at the technical problems in the background technology.

The technical scheme of the invention is as follows: a video big data storage and transcoding optimization system comprises a reading module, a marking module, a comparison module, an integration module, a storage module and a transcoding module;

the reading module is used for reading the video and acquiring parameters such as resolution, code rate type, scurry type, image quality, frame rate and the like of the video;

the marking module is used for respectively carrying out sub-marking on the read video aiming at each group of information according to the information collected by the reading module, carrying out parent marking on the video by combining a plurality of groups of sub-marks, and arranging all the sub-marks behind the parent mark;

the comparison module is used for sequentially comparing the sub-marks of the new video entering the system with the sub-marks of the video which is already recorded into the system;

the integration module is used for judging the coincidence rate of the sub-marks and integrating two groups of videos with the coincidence rate of more than 95%;

the storage module is used for storing the video with the superposition rate of the incoming system sub-markers being less than 95%, and calling out the stored video sub-markers and the stored video parent-markers by matching with the integration module after the next video is incoming;

and the transcoding module is used for carrying out identity ID remark on each group of videos included in the storage module, compressing and transcoding the videos with the same type and storing the videos in the corresponding video transcoding sub-modules, and the video transcoding sub-modules in the transcoding module restore the video transcoding and call the videos when the videos need to be called.

Preferably, the reading module comprises an information acquisition sub-module and an information writing sub-module.

Preferably, the marking module comprises a sub-marking sub-module, a parent marking sub-module and a marking sorting sub-module.

Preferably, the comparison module comprises a sub-mark classification sub-module, a sub-mark comparison sub-module, a sub-mark coincidence rate calculation sub-module, a parent mark extraction sub-module and a parent mark bundling sub-module.

Preferably, the integration module comprises a binding data processing sub-module, a data processing sub-module and a data output sub-module.

Preferably, the storage module comprises a data storage submodule, a data pulling submodule and a storage database.

Preferably, the transcoding module comprises a data transcoding sub-module and a data restoration sub-module.

Preferably, the system operates as follows:

s1, uploading video data to be stored to a reading module of an optimization system;

s2, the reading module collects all data of the video and writes the data into notes of the video;

s3, the marking module carries out sub-marking on all data of the video and carries out mother marking on the whole video; the mark sorting module sorts and sorts the sub-marks of each video;

s4, the comparison module carries out classification comparison on the video of the new input system and each group of sub-marks of the video of the input system, data accumulation is carried out on the repeated sub-marks, when the accumulated data exceeds 95% of the data quantity of the sub-marks, the sub-mark coincidence rate calculation module sends the main mark of the video to a main mark extraction sub-module, the main mark extraction sub-module integrates the videos of the same type in the original database with the video, and the main mark binding sub-module binds the coincident video and sends the coincident video to the integration module;

s5, the integration module integrates the binding data and finally outputs the data to the storage module;

s6, the storage module pulls and deletes the video data with the original primary marks overlapped, and stores the integrated data;

and S7, the transcoding module performs compression transcoding on the data in the storage module, performs classification transcoding on the video according to the sub-label of the video, and the data restoration sub-module restores the video when the video needs to be exported.

Compared with the prior art, the technical scheme of the invention has the following beneficial technical effects: and uploading the video data to be stored to a reading module of the optimization system. And uploading the video data to be stored to a reading module of the optimization system. The reading module collects various data of the video and writes the data into the remarks of the video. The marking module marks various data of the video in a sub mode and marks the video in a mother mode. The integration module integrates the bound data and finally outputs the data to the storage module, so that the repeated video storage capacity in the storage space is effectively reduced in the video marking process and the integration process, and the storage pressure of the video data is effectively relieved. Meanwhile, the storage module pulls and deletes the original video data with the overlapped parent markers, so that the space occupation of repeated data is reduced, and the integrated data is stored. The transcoding module performs compression transcoding on data in the storage module, so that the use efficiency of a storage space is improved. The optimized storage system disclosed by the invention can integrate and optimize repeated data on the premise of not damaging video quality data information, and improves the storage capacity of video information.

Drawings

Fig. 1 is a schematic diagram of system modules according to an embodiment of the present invention.

Fig. 2 is a schematic diagram of a sub-module of the read module according to an embodiment of the present invention.

FIG. 3 is a sub-module diagram of a labeling module according to an embodiment of the present invention.

FIG. 4 is a sub-module of a comparison module in an embodiment of the invention.

FIG. 5 is a block diagram of an integrated module according to an embodiment of the present invention.

FIG. 6 is a block diagram of a sub-module of a memory module according to an embodiment of the invention.

Fig. 7 is a schematic diagram of sub-modules of the transcoding module according to an embodiment of the present invention.

Detailed Description

Example one

The video big data storage and transcoding optimization system provided by the embodiment comprises a reading module, a marking module, a comparison module, an integration module, a storage module and a transcoding module.

As shown in fig. 1, the reading module is configured to read a video and acquire parameters of the video, such as resolution, bitrate type, trick type, image quality, and frame rate. And the marking module is used for respectively performing sub-marking on the read video aiming at each group of information according to the information acquired by the reading module, combining a plurality of groups of sub-marks to perform parent marking on the video, and setting all the sub-marks behind the parent mark. And the comparison module is used for sequentially comparing the sub-mark of the new video entering the system with the sub-mark of the video which is already recorded into the system. And the integration module is used for judging the coincidence rate of the sub-marks and integrating two groups of videos with the coincidence rate of more than 95%. And the storage module is used for storing the video with the superposition rate of the incoming system sub-markers being less than 95%, and calling out the stored video sub-markers and the stored video parent-markers by matching with the integration module after the next video is incoming. And the transcoding module is used for carrying out identity ID remark on each group of videos included in the storage module, compressing and transcoding the videos with the same type and storing the videos in the corresponding video transcoding sub-modules, and the video transcoding sub-modules in the transcoding module restore the video transcoding and call the videos when the videos need to be called.

In this embodiment, the video data to be stored is uploaded to the reading module of the optimization system. And uploading the video data to be stored to a reading module of the optimization system. The reading module collects various data of the video and writes the data into the remarks of the video. The marking module marks various data of the video in a sub mode and marks the video in a mother mode. The integration module integrates the binding data and finally outputs the data to the storage module. The storage module pulls and deletes the video data with the original mother marks overlapped, and stores the integrated data. And the transcoding module performs compression transcoding on the data in the storage module.

Example two

The video big data storage and transcoding optimization system provided by the embodiment comprises a reading module, a marking module, a comparison module, an integration module, a storage module and a transcoding module.

As shown in fig. 1 and fig. 2, the reading module is configured to read a video, and acquire parameters of the video, such as resolution, bitrate type, trick type, image quality, and frame rate. And the marking module is used for respectively carrying out sub-marking on the read video aiming at each group of information according to the information collected by the reading module, carrying out master marking on the video by combining a plurality of groups of sub-marks, and setting all the sub-marks behind the master mark. And the comparison module is used for sequentially comparing the sub-mark of the new video entering the system with the sub-mark of the video which is already recorded into the system. And the integration module is used for judging the coincidence rate of the sub-marks and integrating two groups of videos with the coincidence rate of more than 95%. And the storage module is used for storing the video with the sub-mark coincidence rate of less than 95% entering the system, and the stored video sub-mark and the stored video parent mark are called out by matching with the integration module after the next video enters. And the transcoding module is used for carrying out identity ID remark on each group of videos included in the storage module, compressing and transcoding the videos with the same type ID and storing the videos into the corresponding video transcoding sub-modules, and the video transcoding sub-modules in the transcoding module restore the video transcoding and call the videos out when the videos need to be called out.

Furthermore, the reading module comprises an information acquisition submodule and an information writing submodule. The information acquisition submodule acquires characteristic data of the video, and the information writing submodule writes the information data of the video into the video remarks.

In this embodiment, the video data to be stored is uploaded to the reading module of the optimization system. And uploading the video data to be stored to a reading module of the optimization system. The reading module collects various data of the video and writes the data into the remarks of the video. The marking module carries out sub-marking on each item of data of the video and carries out mother marking on the whole video; and the mark sorting module sorts and sorts the sub-marks of each video. The comparison module carries out classification comparison on the video of the new input system and each group of sub-labels of the video of the input system, and the coincident video is bound and sent to the integration module. The integration module integrates the binding data and finally outputs the data to the storage module. The storage module pulls and deletes the video data with the original mother marks overlapped, and stores the integrated data. The transcoding module performs compression transcoding on data in the storage module, performs classification transcoding on the video according to the sub-label of the video, and the data restoration sub-module restores the video when the video needs to be exported.

EXAMPLE III

The video big data storage and transcoding optimization system provided by the embodiment comprises a reading module, a marking module, a comparison module, an integration module, a storage module and a transcoding module.

As shown in fig. 1 and fig. 3, the reading module is configured to read a video, and acquire parameters of the video, such as resolution, bitrate type, trick type, image quality, and frame rate. And the marking module is used for respectively carrying out sub-marking on the read video aiming at each group of information according to the information collected by the reading module, carrying out master marking on the video by combining a plurality of groups of sub-marks, and setting all the sub-marks behind the master mark. And the comparison module is used for sequentially comparing the sub-mark of the new video entering the system with the sub-mark of the video which is already recorded into the system. And the integration module is used for judging the coincidence rate of the sub-marks and integrating two groups of videos with the coincidence rate of more than 95%. And the storage module is used for storing the video with the superposition rate of the incoming system sub-markers being less than 95%, and calling out the stored video sub-markers and the stored video parent-markers by matching with the integration module after the next video is incoming. And the transcoding module is used for carrying out identity ID remark on each group of videos included in the storage module, compressing and transcoding the videos with the same type and storing the videos in the corresponding video transcoding sub-modules, and the video transcoding sub-modules in the transcoding module restore the video transcoding and call the videos when the videos need to be called.

Furthermore, the marking module comprises a sub-marking sub-module, a parent marking sub-module and a marking sorting sub-module. The marking module carries out sub-marking on each item of data of the video and carries out mother marking on the whole video; and the mark sorting module sorts and sorts the sub-marks of each video. The comparison module carries out classification comparison on the videos of the new input system and each group of sub-marks of the videos of the input system, data accumulation is carried out on the repeated sub-marks, when the accumulated data exceeds 95% of the data quantity of the sub-marks, the sub-mark coincidence rate calculation module sends the main marks of the videos to the main mark extraction sub-module, the main mark extraction sub-module integrates the videos of the same type in the original database and the videos, and the main mark binding sub-module binds the coincident videos and sends the coincident videos to the integration module.

In this embodiment, the video data to be stored is uploaded to the reading module of the optimization system. And uploading the video data to be stored to a reading module of the optimization system. The reading module collects various data of the video and writes the data into the remarks of the video. The marking module carries out sub-marking on each item of data of the video and carries out mother marking on the whole video; and the mark sorting module sorts and sorts the sub-marks of each video. The comparison module carries out classification comparison on the video of the new input system and each group of sub-marks of the video of the input system, data accumulation is carried out on the repeated sub-marks, when the accumulated data exceeds 95% of the data quantity of the sub-marks, the sub-mark coincidence rate calculation module sends the main mark of the video to the main mark extraction sub-module, the main mark extraction sub-module integrates the video of the same type in the original database with the video, and the main mark binding sub-module binds and sends the coincident video to the integration module. The integration module integrates the binding data and finally outputs the data to the storage module. The storage module pulls and deletes the video data with the original mother marks overlapped, and stores the integrated data. The transcoding module performs compression transcoding on data in the storage module, performs classification transcoding on the video according to the sub-markers of the video, and the data restoration sub-module restores the video when the video needs to be exported.

Example four

The video big data storage and transcoding optimization system provided by the embodiment comprises a reading module, a marking module, a comparison module, an integration module, a storage module and a transcoding module.

As shown in fig. 1 and fig. 4, the reading module is configured to read a video, and acquire parameters of the video, such as resolution, code rate type, blurry type, image quality, and frame rate. And the marking module is used for respectively carrying out sub-marking on the read video aiming at each group of information according to the information collected by the reading module, carrying out master marking on the video by combining a plurality of groups of sub-marks, and setting all the sub-marks behind the master mark. And the comparison module is used for sequentially comparing the sub-mark of the new video entering the system with the sub-mark of the video which is already recorded into the system. And the integration module is used for judging the coincidence rate of the sub-marks and integrating two groups of videos with the coincidence rate of more than 95%. And the storage module is used for storing the video with the superposition rate of the incoming system sub-markers being less than 95%, and calling out the stored video sub-markers and the stored video parent-markers by matching with the integration module after the next video is incoming. And the transcoding module is used for carrying out identity ID remark on each group of videos included in the storage module, compressing and transcoding the videos with the same type and storing the videos in the corresponding video transcoding sub-modules, and the video transcoding sub-modules in the transcoding module restore the video transcoding and call the videos when the videos need to be called.

Furthermore, the comparison module comprises a sub-mark classification sub-module, a sub-mark comparison sub-module, a sub-mark coincidence rate calculation sub-module, a parent mark extraction sub-module and a parent mark binding sub-module. The comparison module carries out classification comparison on the videos of the new input system and each group of sub-marks of the videos of the input system, data accumulation is carried out on the repeated sub-marks, when the accumulated data exceeds 95% of the data quantity of the sub-marks, the sub-mark coincidence rate calculation module sends the main marks of the videos to the main mark extraction sub-module, the main mark extraction sub-module integrates the videos of the same type in the original database and the videos, and the main mark binding sub-module binds the coincident videos and sends the coincident videos to the integration module.

In this embodiment, the video data to be stored is uploaded to the reading module of the optimization system. And uploading the video data to be stored to a reading module of the optimization system. The reading module collects various data of the video and writes the data into the remarks of the video. The marking module carries out sub-marking on each item of data of the video and carries out mother marking on the whole video; and the mark sorting module sorts and sorts the sub-marks of each video. The comparison module carries out classification comparison on the videos of the new input system and each group of sub-marks of the videos of the input system, data accumulation is carried out on the repeated sub-marks, when the accumulated data exceeds 95% of the data quantity of the sub-marks, the sub-mark coincidence rate calculation module sends the main marks of the videos to the main mark extraction sub-module, the main mark extraction sub-module integrates the videos of the same type in the original database and the videos, and the main mark binding sub-module binds the coincident videos and sends the coincident videos to the integration module. The integration module integrates the binding data and finally outputs the data to the storage module. The storage module pulls and deletes the video data with the original mother marks overlapped, and stores the integrated data. The transcoding module performs compression transcoding on data in the storage module, performs classification transcoding on the video according to the sub-markers of the video, and the data restoration sub-module restores the video when the video needs to be exported.

EXAMPLE five

The video big data storage and transcoding optimization system provided by the embodiment comprises a reading module, a marking module, a comparison module, an integration module, a storage module and a transcoding module.

As shown in fig. 1 and fig. 5, the reading module is configured to read a video, and acquire parameters of the video, such as resolution, bitrate type, trick type, image quality, and frame rate. And the marking module is used for respectively carrying out sub-marking on the read video aiming at each group of information according to the information collected by the reading module, carrying out master marking on the video by combining a plurality of groups of sub-marks, and setting all the sub-marks behind the master mark. And the comparison module is used for sequentially comparing the sub-mark of the new video entering the system with the sub-mark of the video which is recorded into the system. And the integration module is used for judging the coincidence rate of the sub-marks and integrating two groups of videos with the coincidence rate of more than 95%. And the storage module is used for storing the video with the superposition rate of the incoming system sub-markers being less than 95%, and calling out the stored video sub-markers and the stored video parent-markers by matching with the integration module after the next video is incoming. And the transcoding module is used for carrying out identity ID remark on each group of videos included in the storage module, compressing and transcoding the videos with the same type and storing the videos in the corresponding video transcoding sub-modules, and the video transcoding sub-modules in the transcoding module restore the video transcoding and call the videos when the videos need to be called.

Furthermore, the integration module comprises a binding data processing submodule, a data processing submodule and a data output submodule. The binding data processing submodule integrates the binding data, the data processing submodule processes the data to avoid the data from being damaged, and the data output submodule finally outputs the data to the storage module.

In this embodiment, the video data to be stored is uploaded to the reading module of the optimization system. And uploading the video data to be stored to a reading module of the optimization system. The reading module collects various data of the video and writes the data into the remarks of the video. The marking module carries out sub-marking on each item of data of the video and carries out mother marking on the whole video; and the mark sorting module sorts and sorts the sub-marks of each video. The comparison module carries out classification comparison on the videos of the new input system and each group of sub-marks of the videos of the input system, data accumulation is carried out on the repeated sub-marks, when the accumulated data exceeds 95% of the data quantity of the sub-marks, the sub-mark coincidence rate calculation module sends the main marks of the videos to the main mark extraction sub-module, the main mark extraction sub-module integrates the videos of the same type in the original database and the videos, and the main mark binding sub-module binds the coincident videos and sends the coincident videos to the integration module. The integration module integrates the binding data and finally outputs the data to the storage module. The storage module pulls and deletes the video data with the original mother marks overlapped, and stores the integrated data. The transcoding module performs compression transcoding on data in the storage module, performs classification transcoding on the video according to the sub-markers of the video, and the data restoration sub-module restores the video when the video needs to be exported.

EXAMPLE six

The video big data storage and transcoding optimization system provided by the embodiment comprises a reading module, a marking module, a comparison module, an integration module, a storage module and a transcoding module.

As shown in fig. 1 and fig. 6, the reading module is configured to read a video, and acquire parameters of the video, such as resolution, bitrate type, trick type, image quality, and frame rate. And the marking module is used for respectively carrying out sub-marking on the read video aiming at each group of information according to the information collected by the reading module, carrying out master marking on the video by combining a plurality of groups of sub-marks, and setting all the sub-marks behind the master mark. And the comparison module is used for sequentially comparing the sub-mark of the new video entering the system with the sub-mark of the video which is recorded into the system. And the integration module is used for judging the coincidence rate of the sub-marks and integrating two groups of videos with the coincidence rate of more than 95%. And the storage module is used for storing the video with the superposition rate of the incoming system sub-markers being less than 95%, and calling out the stored video sub-markers and the stored video parent-markers by matching with the integration module after the next video is incoming. And the transcoding module is used for carrying out identity ID remark on each group of videos included in the storage module, compressing and transcoding the videos with the same type and storing the videos in the corresponding video transcoding sub-modules, and the video transcoding sub-modules in the transcoding module restore the video transcoding and call the videos when the videos need to be called.

Further, the storage module comprises a data storage sub-module, a data pulling sub-module and a storage database in the storage database storage module for storing data. And the data pulling submodule pulls and deletes the video data with the original mother marks overlapped. And the data storage submodule stores the integrated data.

In this embodiment, the video data to be stored is uploaded to the reading module of the optimization system. And uploading the video data to be stored to a reading module of the optimization system. The reading module collects various data of the video and writes the data into the remarks of the video. The marking module carries out sub-marking on each item of data of the video and carries out mother marking on the whole video; and the mark sorting module sorts and sorts the sub-marks of each video. The comparison module carries out classification comparison on the videos of the new input system and each group of sub-marks of the videos of the input system, data accumulation is carried out on the repeated sub-marks, when the accumulated data exceeds 95% of the data quantity of the sub-marks, the sub-mark coincidence rate calculation module sends the main marks of the videos to the main mark extraction sub-module, the main mark extraction sub-module integrates the videos of the same type in the original database and the videos, and the main mark binding sub-module binds the coincident videos and sends the coincident videos to the integration module. The integration module integrates the binding data and finally outputs the data to the storage module. The storage module pulls and deletes the video data with the original mother marks overlapped, and stores the integrated data. The transcoding module performs compression transcoding on data in the storage module, performs classification transcoding on the video according to the sub-markers of the video, and the data restoration sub-module restores the video when the video needs to be exported.

EXAMPLE seven

The video big data storage and transcoding optimization system provided by the embodiment comprises a reading module, a marking module, a comparison module, an integration module, a storage module and a transcoding module.

As shown in fig. 1 and fig. 7, the reading module is configured to read a video, and acquire parameters of the video, such as resolution, bitrate type, trick type, image quality, and frame rate. And the marking module is used for respectively carrying out sub-marking on the read video aiming at each group of information according to the information collected by the reading module, carrying out master marking on the video by combining a plurality of groups of sub-marks, and setting all the sub-marks behind the master mark. And the comparison module is used for sequentially comparing the sub-mark of the new video entering the system with the sub-mark of the video which is already recorded into the system. And the integration module is used for judging the coincidence rate of the sub-marks and integrating two groups of videos with the coincidence rate of more than 95%. And the storage module is used for storing the video with the superposition rate of the incoming system sub-markers being less than 95%, and calling out the stored video sub-markers and the stored video parent-markers by matching with the integration module after the next video is incoming. And the transcoding module is used for carrying out identity ID remark on each group of videos included in the storage module, compressing and transcoding the videos with the same type ID and storing the videos into the corresponding video transcoding sub-modules, and the video transcoding sub-modules in the transcoding module restore the video transcoding and call the videos out when the videos need to be called out.

Furthermore, the transcoding module comprises a data transcoding sub-module and a data restoring sub-module. The transcoding module performs compression transcoding on data in the storage module, performs classification transcoding on the video according to the sub-markers of the video, and the data restoration sub-module restores the video when the video needs to be exported.

In this embodiment, the video data to be stored is uploaded to the reading module of the optimization system. And uploading the video data to be stored to a reading module of the optimization system. The reading module collects various data of the video and writes the data into the remarks of the video. The marking module carries out sub-marking on each item of data of the video and carries out mother marking on the whole video; and the mark sorting module sorts and sorts the sub-marks of each video. The comparison module carries out classification comparison on the videos of the new input system and each group of sub-marks of the videos of the input system, data accumulation is carried out on the repeated sub-marks, when the accumulated data exceeds 95% of the data quantity of the sub-marks, the sub-mark coincidence rate calculation module sends the main marks of the videos to the main mark extraction sub-module, the main mark extraction sub-module integrates the videos of the same type in the original database and the videos, and the main mark binding sub-module binds the coincident videos and sends the coincident videos to the integration module. The integration module integrates the binding data and finally outputs the data to the storage module. The storage module pulls and deletes the video data with the original mother marks overlapped, and stores the integrated data. The transcoding module performs compression transcoding on data in the storage module, performs classification transcoding on the video according to the sub-markers of the video, and the data restoration sub-module restores the video when the video needs to be exported.

A video big data storage and transcoding optimization system is operated as follows:

s1, uploading video data to be stored to a reading module of an optimization system.

S2, the reading module collects all data of the video and writes the data into notes of the video. S3, the marking module carries out sub-marking on all data of the video and carries out mother marking on the whole video; and the mark sorting module sorts and sorts the sub-marks of each video.

And S4, the comparison module performs classified comparison on the video of the new input system and each group of sub-marks of the video of the input system, data accumulation is performed on the repeated sub-marks, when the accumulated data exceeds 95% of the data amount of the sub-marks, the sub-mark coincidence rate calculation module sends the main mark of the video to the main mark extraction sub-module, the main mark extraction sub-module integrates the videos of the same type in the original database with the video, and the main mark binding sub-module binds the coincident video and sends the coincident video to the integration module.

And S5, the integration module integrates the binding data and finally outputs the data to the storage module.

And S6, the storage module pulls and deletes the video data with the overlapped original parent marks and stores the data after the integration processing.

And S7, the transcoding module performs compression transcoding on the data in the storage module, performs classification transcoding on the video according to the sub-label of the video, and the data restoration sub-module restores the video when the video needs to be exported.

It should be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.

Claims (8)

1. A video big data storage and transcoding optimization system is characterized by comprising a reading module, a marking module, a comparison module, an integration module, a storage module and a transcoding module;

the reading module is used for reading the video and acquiring parameters such as resolution, code rate type, scurry type, image quality, frame rate and the like of the video;

the marking module is used for respectively carrying out sub-marking on the read video aiming at each group of information according to the information collected by the reading module, carrying out parent marking on the video by combining a plurality of groups of sub-marks, and arranging all the sub-marks behind the parent mark;

the comparison module is used for sequentially comparing the sub-marks of the new video entering the system with the sub-marks of the video which is recorded into the system;

the integration module is used for judging the coincidence rate of the sub-marks and integrating two groups of videos with the coincidence rate of more than 95%;

the storage module is used for storing the video with the superposition rate of the incoming system sub-markers being less than 95%, and calling out the stored video sub-markers and the stored video parent-markers by matching with the integration module after the next video is incoming;

and the transcoding module is used for carrying out identity ID remark on each group of videos included in the storage module, compressing and transcoding the videos with the same type ID and storing the videos into the corresponding video transcoding sub-modules, and the video transcoding sub-modules in the transcoding module restore the video transcoding and call the videos out when the videos need to be called out.

2. The video big data storage and transcoding optimization system of claim 1, wherein the reading module comprises an information acquisition sub-module and an information writing sub-module.

3. The video big data storage and transcoding optimization system of claim 1, wherein the tagging module comprises a sub-tagging sub-module, a parent-tagging sub-module, and a tag sorting sub-module.

4. The video big data storage and transcoding optimization system of claim 1, wherein the comparison module comprises a sub-label classification sub-module, a sub-label comparison sub-module, a sub-label coincidence rate calculation sub-module, a parent label extraction sub-module, and a parent label bundling sub-module.

5. The video big data storage and transcoding optimization system of claim 1, wherein the integration module comprises a bundled data processing sub-module, a data processing sub-module, and a data output sub-module.

6. The video big data storage and transcoding optimization system according to claim 1, wherein the storage module comprises a data storage sub-module, a data pull sub-module and a storage database.

7. The video big data storage and transcoding optimization system of claim 1, wherein the transcoding module comprises a data transcoding sub-module and a data restoration sub-module.

8. The video big data storage and transcoding optimization system according to any one of claims 1 to 7, wherein the system operates as follows:

s1, uploading video data to be stored to a reading module of an optimization system;

s2, the reading module collects various data of the video and writes the data into remarks of the video;

s3, the marking module carries out sub-marking on all data of the video and carries out mother marking on the whole video; the mark sorting module sorts and sorts the sub-marks of each video;

s4, the comparison module carries out classification comparison on the video of the new input system and each group of sub-marks of the video of the input system, data accumulation is carried out on the repeated sub-marks, when the accumulated data exceeds 95% of the data quantity of the sub-marks, the sub-mark coincidence rate calculation module sends the main mark of the video to a main mark extraction sub-module, the main mark extraction sub-module integrates the videos of the same type in the original database with the video, and the main mark binding sub-module binds the coincident video and sends the coincident video to the integration module;

s5, the integration module integrates the binding data and finally outputs the data to the storage module;

s6, the storage module pulls and deletes the video data with the original primary marks overlapped, and stores the integrated data;

and S7, the transcoding module performs compression transcoding on the data in the storage module, performs classification transcoding on the video according to the sub-label of the video, and the data restoration sub-module restores the video when the video needs to be exported.

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