CN117395475B - High-availability low-repetition store video production method and system - Google Patents
High-availability low-repetition store video production method and system Download PDFInfo
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Abstract
The application relates to a high-availability low-repetition video production method and a system for a store, wherein the high-availability low-repetition video production method comprises the following steps: uploading the video clips serving as the materials to a preset material video library; numbering video clips in a material video library; selecting video clips in a material video library and sequencing to generate a new finished video; obtaining the video segment numbers and ordered mapping combinations of the new finished video, and matching the mapping combinations with the existing mapping combinations of the finished video in a preset finished video library; if the same mapping combination is matched, a finished video which is repeated with the new finished video exists, and a repeated prompt is sent out; if the same mapping combination is not matched, no finished video which is repeated with the new finished video exists, and the new finished video is input into a finished video library. The method has the effect of improving the production efficiency of the video of the store.
Description
Technical Field
The application relates to the technical field of video generation, in particular to a method and a system for producing a high-availability low-repetition store video.
Background
Along with the development and progress of science and technology, the popularization mode of merchants becomes various, is not limited to a leaflet and a billboard erected at the shop front, and gradually changes from off-line popularization to on-line popularization, and propagates advertisement information through the Internet, so that the advertisement range is not limited to regions.
When a merchant promotes online, pictures or videos are needed to serve as advertisement information, the existing short video platform is one important promotion platform, and a store-probing person publishes store-probing videos related to the merchant and propagates and publicizes by means of self flow so as to help the merchant promote; however, in actual popularization, the shop assistant does not physically get to the shop of the merchant to shoot, but issues according to the video provided by the merchant.
With respect to the related art in the above, there are the following drawbacks: along with the increase of merchant popularization demand, the quantity demand to the store video also increases, and the merchant drops into the limited energy of production store video, leads to current existence a plurality of people to use the condition of same store video, reduces the propaganda effect, consequently needs the improvement.
Disclosure of Invention
In order to improve the production efficiency of the store video, the application provides a high-availability low-repetition store video production method and system.
The first object of the present application is achieved by the following technical solutions:
A high-availability low-repetition video production method for a store comprises the following steps: uploading the video clips serving as the materials to a preset material video library;
numbering video clips in a material video library;
Selecting video clips in a material video library and sequencing to generate a new finished video;
obtaining the video segment numbers and ordered mapping combinations of the new finished video, and matching the mapping combinations with the existing mapping combinations of the finished video in a preset finished video library;
if the same mapping combination is matched, a finished video which is repeated with the new finished video exists, and a repeated prompt is sent out;
if the same mapping combination is not matched, no finished video which is repeated with the new finished video exists, and the new finished video is input into a finished video library.
By adopting the technical proposal, the material video is uploaded to the material video library for editing by the store-in-person, the store-in-person produces the store-in-person video through the secondary creation of the material video, compared with the prior art that the merchant directly provides the store video for the store-detecting person, the scheme reduces the repetition rate of the store-detecting video through the secondary creation of the store-detecting person, and the store-detecting video is composed of different combinations of a plurality of materials, the quantity of available store video is increased to realize the high utilization ratio and the low repetition rate of store video, in this scheme, different store visitors all possess the ability of creating the store video, compare current store video by merchant production, improved the production efficiency of store video, and the finished product video can retrieve in order to detect whether there is repeated finished product video, effectively avoid different store visitors to produce the condition of same store video, improve the quality of store video production.
The present application may be further configured in a preferred example to: after the step of uploading the video clips as the material to a preset material video library, the method comprises the following steps:
dubbing and text configuration are carried out on the material video;
and classifying and marking the material video based on the big data model through artificial intelligence calculation.
Through adopting above-mentioned technical scheme, add dubbing and join in marriage the material video, increase the integrality of store video for the cell-phone user that part does not have sound custom also can improve browsing experience through the content description of text understanding material video, classify the marking to the material video, be convenient for select suitable material video when producing the store video, improve the production efficiency of store video.
The present application may be further configured in a preferred example to: the step of dubbing and text configuration of the material video comprises the following steps:
Detecting whether the material video is provided with an audio;
if the material video is provided with the audio, identifying and analyzing the audio in the material video and generating a corresponding text;
If the audio is needed to be added to the material video, generating the added audio, and synthesizing the added audio and the video material.
By adopting the technical scheme, dubbing and text are added to the material video, so that the integrity of the store video is improved, and part of mobile phone users without voice habits can also know the content description of the material video through characters, so that browsing experience is improved.
The present application may be further configured in a preferred example to: the step of identifying and analyzing the audio in the material video and generating the corresponding text if the material video is provided with the audio comprises the following steps:
If the material video is provided with the audio, selecting to keep the audio, keep part of the audio or delete all the audio;
if the self-contained audio is reserved, the self-contained audio is identified, and a text corresponding to the audio is automatically generated;
if part of the audio is reserved, identifying the reserved audio part, producing a text corresponding to the audio, and readjusting the audio missing part in the video;
and if all the audios are deleted, readjusting the audio part of the material video.
By adopting the technical scheme, the audio is added according to the requirement, and the modification is carried out on the material audio again so as to meet the requirement of video production of a store.
The present application may be further configured in a preferred example to: after the step of classifying and marking the material videos based on the big data model and through artificial intelligence calculation, the step of selecting video fragments in the material video library and sequencing to generate new finished video comprises the following steps:
Determining the overall style of the finished video;
Selecting different material videos according to the overall style of the finished video;
Sequencing the selected material videos, and setting a transition effect between two continuous materials to finish splicing a plurality of material videos;
Checking and confirming dubbing and matching text of the spliced material video;
and selecting a cover photo of the finished video.
By adopting the technical scheme, the video script is set, and the overall style of the finished video is determined, so that the video creation process is clear, and the production efficiency of the video of the store is improved.
The present application may be further configured in a preferred example to: the step of classifying and marking the material video based on the big data model through artificial intelligence calculation comprises the following steps:
setting classification labels according to regions, consumer group ages and store types;
acquiring keywords in a material video;
and classifying the material videos into different labels according to the acquired keywords.
By adopting the technical scheme, the keywords are matched with the labels, if the obtained keywords are the same as the labels, the material video is divided into the labels, and further explanation is needed that the obtained keywords of the single material video can be multiple, so that the single material video can be divided into different labels.
The present application may be further configured in a preferred example to: the step of classifying and marking the material video based on the big data model through artificial intelligence calculation comprises the following steps:
Recording the selected times of the material video;
Sequentially arranging and pushing according to the selected times from high to low;
And the selected part is forward pushed by ten material videos with the frequency of being selected and used at the back ten times of the ranking reciprocal, and the click rate and the use rate of forward pushed are recorded.
By adopting the technical scheme, the quality of the material video is judged according to the selected times, and if the alternative times are more, the material video is displayed at the front, so that a video producer can select the material video preferentially, and the content quality of the store video is improved; the potential high-quality videos are also subjected to enough exposure, so that a video producer can conveniently select high-quality material videos to generate a shop video, the quality of the shop video is improved, meanwhile, the situation that the material videos with high use times are selected for multiple times is avoided, different material videos are provided for pushing, the shop video producer can conveniently select different material videos, the repetition rate of video finished products is reduced, and the availability of the shop video finished products is improved.
The present application may be further configured in a preferred example to: after the step of selecting video clips within the material video library and ordering to generate new finished video, the steps are as follows:
And issuing comments or message suggestions to the selected material video, wherein the comments comprise the advantages and disadvantages of the material video, and the message suggestions comprise the use scene and the use sequence of the material video as selection references of other users.
By adopting the technical scheme, an exchange platform is provided for different store video producers in a mode of issuing comments or message suggestions, so that the different video producers can learn and exchange with each other, the use of the material video is optimized, each store video producer is prevented from independently trying out errors, and the production efficiency of the store video is improved.
The second object of the present application is achieved by the following technical solutions:
a high-availability low-repetition video production method for a store comprises a material video library module, a finished product video library module and a retrieval function module,
The material video library module is used for establishing a preset material video database;
the finished video library module is used for establishing a preset finished video library;
And the retrieval function module is used for matching the mapping combination of the new finished video with the mapping combination of the finished video in the finished video library.
By adopting the technical scheme, the material videos are selected from the material video library and edited, the store video is produced, whether the repeated videos exist or not is judged by searching through the searching functional module, the production efficiency of the store video is improved, and the repetition rate of the store video is reduced.
Optionally, the system further comprises a big data module and an artificial intelligent computing module, wherein the big data module and the artificial intelligent computing module are both provided with a self-learning function, and the big data module and the artificial intelligent computing module are both used for classifying and marking the material videos.
By adopting the technical scheme, automatic classification marking of the material video is realized, and the target material video can be selected according to classification when the store video is produced conveniently.
In summary, the present application includes at least one of the following beneficial technical effects:
1. Uploading the material video to a material video library for editing by a store-sponsor, producing the store-sponsor video by the store-sponsor for the secondary creation of the material video, compared with the prior merchant which directly provides the store-sponsor with the store-sponsor video, the proposal reduces the repetition rate of the store video through the secondary creation of the store-reach person, has a large number of materials, the store video is composed of different combinations of a plurality of materials, increases the number of available store videos, therefore, the high utilization rate and the low repetition rate of the video of the store detection are realized, in the scheme, different store detection persons have the capability of creating the video of the store detection, compared with the prior art that the store detection video is produced by a merchant, the production efficiency of the video of the store detection is improved, and the finished video can be searched to detect whether the repeated finished video exists or not, so that the condition that the same video of the store detection is produced by different store detection persons is effectively avoided, and the production quality of the video of the store detection is improved;
2. The dubbing and the text are added to the material video, so that the integrity of the store video is improved, and part of mobile phone users without voice habits can know the content description of the material video through characters, so that browsing experience is improved;
3. classifying and marking the material videos, so that a proper material video can be conveniently selected when the store video is produced, and the production efficiency of the store video is improved;
4. Judging the quality of the material video through the selected times, if the number of the alternative times is large, displaying the material video at the front, facilitating the preferential selection of the material video by a video producer, and improving the content quality of the store video; the potential high-quality videos are also subjected to enough exposure, so that a video producer can conveniently select high-quality material videos to generate a shop video, the quality of the shop video is improved, meanwhile, the situation that the material videos with high use times are selected for multiple times is avoided, different material videos are provided for pushing, the shop video producer can conveniently select different material videos, the repetition rate of video finished products is reduced, and the availability of the shop video finished products is improved.
Drawings
FIG. 1 is a schematic flow chart of the steps in a highly available and low repetition video production method for a store in accordance with the present application;
FIG. 2 is a schematic flow chart of the method for producing high availability low repetition store video according to the present application after step S10;
FIG. 3 is a schematic flow chart of a method for producing highly available and low repetition store video in step S101 according to the present application;
FIG. 4 is a schematic flow chart of a method for producing highly available and low repetition store video in step S1013 according to the present application;
FIG. 5 is a schematic flow chart of a method for producing highly available and low repetition store video in step S102 of the present application;
FIG. 6 is a schematic flow chart of a method for producing highly available and low repetition store video in step S102 of the present application;
FIG. 7 is a schematic flow chart of a method for producing highly available and low repetition store video in step S103 of the present application;
FIG. 8 is a flow chart of the method for producing highly available and low repetition store video according to the present application after step S30;
FIG. 9 is a schematic block diagram of a highly available low repetition store video production system of the present application;
Reference numerals illustrate: 1. a material video library module; 2. a finished video library module; 3. a retrieval function module; 4. a big data module; 5. and an artificial intelligence calculation module.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
In one embodiment, as shown in fig. 1-8, the application discloses a method for producing a highly available and low-repetition video of a store, which specifically comprises the following steps:
s10: uploading the video clips serving as the materials to a preset material video library;
Specifically, a plurality of videos related to the merchant are shot as materials, including store introduction, product introduction, and the presence condition of the store during business operation, and the feedback access of the customer; the shop introduction can be shot from the angles of shop decoration style, shop layout and the like, and the product introduction comprises key introduction of a signboard product, integral introduction of a plurality of products in the signboard category and the like; the material videos are shot from multiple dimensions as much as possible, so that the material videos in the material video library are as rich as possible, the repetition rate when a plurality of material videos are clipped and spliced to generate video finished products is increased, and the number of the video finished products is increased along with the increase of available materials.
S20: numbering video clips in a material video library;
Specifically, the video clips in the material video library are numbered, so that each material video has single identity recognition, and can be recognized according to the naming and the number of the material video when selected, and repeated material videos selected from the same video finished product are not easy to generate.
S30: selecting video clips in a material video library and sequencing to generate a new finished video;
Specifically, determining a video script of a store to be created, selecting a material video meeting the demand of the video script of the store from a material video library according to the video script of the store to be created, and editing and sorting the selected material video according to the demand to generate a new finished video, wherein the new finished video is the video of the store.
S40: obtaining the video segment numbers and ordered mapping combinations of the new finished video, and matching the mapping combinations with the existing mapping combinations of the finished video in a preset finished video library;
Specifically, the serial numbers of the selected material videos in the finished video are obtained, namely, the unique identity identification of the selected material videos is obtained, and the sequence of the material videos in the finished video is obtained, so that the mapping combination of the video segment numbers and the sequence of the new finished video is obtained; and the finished video in the finished video library is provided with the mapping combination corresponding to the finished video, and the mapping combination of the newly generated finished video and the mapping combination in the finished video database are calculated through traversal so as to search whether the mapping combination in the finished video library is matched with the mapping combination of the new finished video.
S50: if the same mapping combination is matched, a finished video which is repeated with the new finished video exists, and a repeated prompt is sent out;
Specifically, when the finished video which is repeated with the new finished video exists in the traversing searching mode, a repeated prompt is sent out to inform a video creator that the new finished video exists, repeated release is avoided, and therefore the repetition rate of the video production of a store is reduced.
S60: if the same mapping combination is not matched, no finished video which is repeated with the new finished video exists, and the new finished video is input into a finished video library.
Specifically, if the same mapping combination is not matched, the new finished video does not have the existing repeated store video, so that the repetition rate of the store video in production is reduced.
At S10: after the step of uploading the video clips serving as the materials to a preset material video library, the method comprises the following steps:
S101: dubbing and text configuration are carried out on the material video;
Specifically, dubbing and text matching are added to the material video, so that the integrity of the store video is improved, and part of mobile phone users without voice habit can also know the content description of the material video through characters, so that browsing experience is improved.
S102: and classifying and marking the material video based on the big data model through artificial intelligence calculation.
Further, at S101: the step of dubbing and text configuration of the material video comprises the following steps:
S1011: detecting whether the material video is provided with an audio;
Specifically, the sound channel in the video is extracted, and whether the audio is self-contained in the material video is identified, wherein the audio refers to background music or human voice.
S1012: if the material video is provided with the audio, identifying and analyzing the audio in the material video and generating a corresponding text;
specifically, the voice of the audio is identified, and the voice dubbing is converted into a text format to serve as a corresponding text, so that the automatic identification of the audio is realized.
S1013: if the audio is needed to be added to the material video, generating the added audio, and synthesizing the added audio and the video material.
In the embodiment of the application, the mode of adding audio comprises custom dubbing and intelligent dubbing, wherein the custom dubbing is an audio file recorded by oneself, and a file corresponding to dubbing content is generated according to the recorded audio file; in the scheme of the application, the time corresponding to the characters is set, specifically 5 words/second, namely, the time length of one second is corresponding to each five characters, so that the time length of the dubbing and the text is controlled; when synthesizing the audio and the video, the method needs to meet the condition that the audio duration is not longer than the duration of the video material, otherwise, a synthesis abnormality prompt is sent.
At S1013: if the material video is provided with audio, the steps of identifying and analyzing the audio in the material video and generating the corresponding text, comprising the following steps:
S10131: if the material video is provided with the audio, selecting to keep the audio, keep part of the audio or delete all the audio;
Through the technical scheme, various audio processing methods in the material video are realized, different requirements of store-in-store operators during video creation are met, different processing results generate different material video numbers, and the diversity of the material video is further increased.
S10132: if the self-contained audio is reserved, the self-contained audio is identified, and a text corresponding to the audio is automatically generated;
S10133: if part of the audio is reserved, identifying the reserved audio part, producing a text corresponding to the audio, and readjusting the audio missing part in the video;
s10134: and if all the audios are deleted, readjusting the audio part of the material video.
Specifically, part of the audio is reserved or all the audio is deleted, the audio is added according to the requirement, and the modification is carried out on the material audio again so as to meet the requirement of video production of a store.
At S102: the method comprises the following steps of:
s1021: setting classification labels according to regions, consumer group ages and store types;
specifically, the method is provided with major classes such as regions, consumer group ages, store types and the like, and each major class is subdivided into a plurality of minor classes; wherein, coastal, inland and the like are subdivided according to geographic positions in regional major categories, and minor categories can be set according to provinces; the consumer group age can be subdivided into students and adults, or divided according to specific age intervals; the store types can be subdivided according to the setting of industries, such as retail, diet, accommodation, amusement and travel, etc.
The method has the advantages that the material videos are classified and marked according to the classification, so that when the store video is produced, video content is conveniently set according to industries instead of simply sequencing the material videos, the quality of the store video is improved, and consumers are attracted and popularization is facilitated.
S1022: acquiring keywords in a material video;
specifically, one of the acquisition modes is to fragment the text of the text in the material video and intercept the text in the material video so as to automatically acquire the keywords in the material video; and the other mode is to summarize according to the whole content of the material video and automatically generate keywords related to the whole content.
S1023: classifying the material videos into different labels according to the acquired keywords;
Specifically, the matching is performed according to the keywords and the labels, if the obtained keywords are the same as the labels, the material video is divided into the labels, and it is further required to explain that the obtained keywords for a single material video may be multiple, so that the single material video may be divided into different labels.
S102: the method comprises the following steps of:
S1024: recording the selected times of the material video;
Specifically, according to the released finished video, the material video number image in the finished video is obtained, the number occurrence times of the number are recorded through a preset big data model, and compared with the number times in the production process, the number times of the material video in the finished video are recorded directly, so that errors caused by multiple selections in video production can be effectively reduced.
S1025: sequentially arranging and pushing according to the selected times from high to low;
Specifically, the quality of the material video is judged according to the selected times, if the selected times are more, the material video is displayed at the front, so that a video producer can select the material video preferentially, and the content quality of the store video is improved.
S1026: and the selected part is forward pushed by ten material videos with the frequency of being selected and used at the back ten times of the ranking reciprocal, and the click rate and the use rate of forward pushed are recorded.
Specifically, part of the newly uploaded material videos are limited in time, although the material videos are high in quality, the situation that the number of times of use is small exists, at the moment, the material videos of which the part is selected to be used for ten times at the back of the ranking reciprocal are pushed forward, the influence of the uploading time on preferential pushing of the material videos can be effectively reduced, the potential high-quality videos are also exposed enough, a video producer can conveniently select the high-quality material videos to generate a shop video, the quality of the shop video is improved, meanwhile, the situation that the material videos with high use times are selected for many times is avoided, different material videos are provided for pushing, the shop video producer can conveniently select different material videos, the repetition rate of video products is reduced, and the availability of the shop video products is improved.
At S102: after the step of classifying and marking the material video based on the big data model and through artificial intelligence calculation, S103: the method comprises the following steps of selecting video clips in a material video library and sequencing to generate a new finished video:
S1031: determining the overall style of the finished video;
Specifically, a video script is set, and the overall style of the finished video is determined, so that the video creation process is clear, and the production efficiency of the video of the store is improved.
S1032: selecting different material videos according to the overall style of the finished video;
s1033: sequencing the selected material videos, and setting a transition effect between two continuous materials to finish splicing a plurality of material videos;
specifically, a plurality of transition effects are provided for a video producer, and the continuity among a plurality of material videos is improved through the transition effects, so that the quality of the videos is improved.
S1034: checking and confirming dubbing and matching text of the spliced material video;
s1035: and selecting a cover photo of the finished video.
Specifically, after the text matching, dubbing and transition effects of the finished video are completed, the top title and the cover text of the video are acquired; a certain frame in the video can be intercepted to be used as a cover, and the video cover can be customized.
In the embodiment of the present application, it is to be illustrated that: in the video production, a time axis is used for integrating the material video to finish the video cover, a cover document and a top title are put in a first frame of the time axis, which is about 1/12 second, and a corresponding document style is set; and placing the advertisement identification text in the cover, finally synthesizing the video, recording the combination mode of the used material videos, and recording the use times of each material.
At S30: after the step of selecting video clips in the material video library and sorting to generate a new finished video, the method comprises the following steps:
s301: and issuing comments or message suggestions to the selected material video, wherein the comments comprise the advantages and disadvantages of the material video, and the message suggestions comprise the use scene and the use sequence of the material video as selection references of other users.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
In one embodiment, a high availability low repetition video production system is provided, which corresponds one-to-one to one with one of the high availability low repetition video production methods described in the above embodiments. As shown in fig. 9, the high availability low repetition store video production system comprises a material video library module 1, a finished product video library module 2, a retrieval function module 3, a big data module 4 and an artificial intelligence calculation module 5,
The material video library module 1 is used for establishing a preset material video database;
the finished video library module 2 is used for establishing a preset finished video library;
the retrieval function module 3 is used for matching the mapping combination of the new finished video with the mapping combination of the finished video in the finished video library;
the big data module 4 and the artificial intelligence computing module 5 are provided with self-learning functions, and the big data module 4 and the artificial intelligence computing module 5 are used for classifying and marking the material videos.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (9)
1. A high-availability low-repetition video production method for a store is characterized by comprising the following steps of: the method comprises the following steps:
uploading the video clips serving as the materials to a preset material video library;
numbering video clips in a material video library;
Selecting video clips in a material video library and sequencing to generate a new finished video;
obtaining the video segment numbers and ordered mapping combinations of the new finished video, and matching the mapping combinations with the existing mapping combinations of the finished video in a preset finished video library;
if the same mapping combination is matched, a finished video which is repeated with the new finished video exists, and a repeated prompt is sent out;
if the same mapping combination is not matched, no finished video which is repeated with the new finished video exists, and the new finished video is input into a finished video library;
the method comprises the following steps of selecting video clips in a material video library and sequencing to generate a new finished video:
Determining the overall style of the finished video;
Selecting different material videos according to the overall style of the finished video;
Sequencing the selected material videos, and setting a transition effect between two continuous materials to finish splicing a plurality of material videos;
Checking and confirming dubbing and matching text of the spliced material video;
and selecting a cover photo of the finished video.
2. The method for producing high availability low repetition video of a store according to claim 1, wherein: after the step of uploading the video clips as the material to a preset material video library, the method comprises the following steps:
dubbing and text configuration are carried out on the material video;
and classifying and marking the material video based on the big data model through artificial intelligence calculation.
3. The method for producing the high-availability low-repetition video of the store according to claim 2, wherein the method comprises the following steps of: the step of dubbing and text configuration of the material video comprises the following steps:
Detecting whether the material video is provided with an audio;
if the material video is provided with the audio, identifying and analyzing the audio in the material video and generating a corresponding text;
If the audio is needed to be added to the material video, generating the added audio, and synthesizing the added audio and the video material.
4. A method of producing a highly available low repetition video of a store according to claim 3, wherein: the step of identifying and analyzing the audio in the material video and generating the corresponding text if the material video is provided with the audio comprises the following steps:
If the material video is provided with the audio, selecting to keep the audio, keep part of the audio or delete all the audio;
if the self-contained audio is reserved, the self-contained audio is identified, and a text corresponding to the audio is automatically generated;
if part of the audio is reserved, identifying the reserved audio part, producing a text corresponding to the audio, and readjusting the audio missing part in the video;
and if all the audios are deleted, readjusting the audio part of the material video.
5. The method for producing the high-availability low-repetition video of the store according to claim 2, wherein the method comprises the following steps of: the step of classifying and marking the material video based on the big data model through artificial intelligence calculation comprises the following steps:
setting classification labels according to regions, consumer group ages and store types;
acquiring keywords in a material video;
and classifying the material videos into different labels according to the acquired keywords.
6. The method for producing the high-availability low-repetition video of the store according to claim 2, wherein the method comprises the following steps of: the step of classifying and marking the material video based on the big data model through artificial intelligence calculation comprises the following steps:
Recording the selected times of the material video;
Sequentially arranging and pushing according to the selected times from high to low;
And the selected part is forward pushed by ten material videos with the frequency of being selected and used at the back ten times of the ranking reciprocal, and the click rate and the use rate of forward pushed are recorded.
7. The method for producing high availability low repetition video of a store according to claim 1, wherein: after the step of selecting video clips within the material video library and ordering to generate new finished video, the steps are as follows:
And issuing comments or message suggestions to the selected material video, wherein the comments comprise the advantages and disadvantages of the material video, and the message suggestions comprise the use scene and the use sequence of the material video as selection references of other users.
8. A high availability low repetition video production system employing the high availability low repetition video production method of any one of claims 1-7, characterized in that: comprises a material video library module (1), a finished product video library module (2) and a retrieval function module (3),
The material video library module (1) is used for establishing a preset material video database;
The finished video library module (2) is used for establishing a preset finished video library;
and the retrieval function module (3) is used for matching the mapping combination of the new finished video with the mapping combination of the finished video in the finished video library.
9. The high availability low repetition video production system of claim 8, wherein: still include big data module (4) and artificial intelligence calculation module (5), big data module (4) with artificial intelligence calculation module (5) all possess self-learning function, just big data module (4) with artificial intelligence calculation module (5) all are used for carrying out classification marking with the material video.
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