CN111081286B - Video editing system for artificial intelligence teaching - Google Patents
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- 238000013473 artificial intelligence Methods 0.000 title claims description 27
- 238000012216 screening Methods 0.000 claims abstract description 20
- 238000012545 processing Methods 0.000 claims description 84
- 230000000694 effects Effects 0.000 claims description 35
- 238000013528 artificial neural network Methods 0.000 claims description 12
- 238000011156 evaluation Methods 0.000 claims description 12
- 238000013441 quality evaluation Methods 0.000 claims description 12
- 238000012163 sequencing technique Methods 0.000 claims description 10
- 238000003062 neural network model Methods 0.000 claims description 9
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- 230000003044 adaptive effect Effects 0.000 claims description 3
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- 238000000034 method Methods 0.000 abstract description 14
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- G11B27/02—Editing, e.g. varying the order of information signals recorded on, or reproduced from, record carriers
- G11B27/031—Electronic editing of digitised analogue information signals, e.g. audio or video signals
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- G—PHYSICS
- G11—INFORMATION STORAGE
- G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
- G11B27/00—Editing; Indexing; Addressing; Timing or synchronising; Monitoring; Measuring tape travel
- G11B27/10—Indexing; Addressing; Timing or synchronising; Measuring tape travel
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
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Abstract
The invention provides a video editing system for artificial intelligent teaching, which sequentially finishes different editing processes in the video editing process by setting an editing source material determining module, a source material content learning determining module, a video editing mode determining module and a video editing executing module, wherein each functional module in the video editing system can finish the determination of editing source materials, intelligent screening and editing and synthesizing of the source materials according to a preset working mode and finally obtain required teaching videos, thereby improving the interactivity and diversity of the artificial intelligent teaching.
Description
Technical Field
The invention relates to the technical field of intelligent teaching, in particular to a video editing system for artificial intelligent teaching.
Background
The multimedia technology is widely applied to intelligent teaching, and various different teaching application scenes can be realized through the multimedia technology, so that multi-scene artificial intelligent teaching is realized. At present, the great majority of artificial intelligence teaching based on multimedia technology relies on corresponding teaching video to realize, because different teaching courses have different teaching content, in order to improve the interactivity and the diversity of artificial intelligence teaching, need adjust teaching video's content and presentation form in real time according to different teaching courses, prior art only edits and updates different teaching video through the manual editing mode, because teaching video usually has longer duration and higher video quality requirement, and still need carry out different processes such as collection screening, editing combination and proofreading of video material in the video editing process, the editing work volume of this teaching video is great and time-consuming, this reduces teaching video convenient efficiency and quality seriously. Therefore, the teaching video editing mode in the prior art cannot effectively reduce the editing workload and the editing time consumption and improve the convenient quality of the video.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a video editing system for artificial intelligence teaching, which finishes different editing processes in the video editing process in sequence by setting an editing source material determining module, a source material content learning determining module, a video editing mode determining module and a video editing executing module, each functional module in the video editing system can finish the determination of editing source materials, intelligent screening and the editing and synthesizing of the source materials according to a preset working mode and finally obtain the required teaching video, and as the video editing system finishes the corresponding editing process in the form of neural network learning processing, the screening efficiency and the screening accuracy of the editing source materials can be improved, the quality of the edited teaching video can be improved, and the video editing system can meet the requirements of different teaching courses, the editing operation of teaching videos of different teaching contents is completed quickly and efficiently, so that the interactivity and diversity of artificial intelligent teaching are improved.
The invention provides a video editing system for artificial intelligence teaching, which is characterized in that:
the video editing system for artificial intelligence teaching comprises an editing source material determining module, a source material content learning determining module, a video editing mode determining module and a video editing executing module; wherein,
the editing source material determining module is used for determining source material data to be edited for video editing operation from the source material resource set according to preset teaching conditions;
the source material content learning determination module is used for carrying out neural network learning processing in a preset mode on the source material data to be edited so as to obtain teaching content data related to the source material to be edited;
the video editing mode determining module is used for determining a currently applicable video editing mode according to the teaching content data;
the video editing execution module is used for carrying out adaptive video editing operation on the source material to be edited according to the currently applicable video editing mode;
further, the editing source material determining module comprises a source material database submodule, a database updating submodule, a source material matching submodule and a source material capturing submodule; wherein,
the source material database submodule is used for storing video source material data related to teaching videos;
the database updating submodule is used for carrying out data content updating processing and/or data storage position updating processing on the video source material data according to the data storage state of the source material database submodule;
the source material matching submodule is used for searching and positioning data matched with the preset teaching condition in the source material data submodule;
the source material capturing submodule is used for capturing and sequentially registering the data obtained by searching and positioning to obtain the source material data to be edited with a preset registering sequence;
further, the source material content learning determination module comprises a source material data screening submodule, a neural network learning submodule and a source material content identification submodule; wherein,
the source material data screening submodule is used for screening at least one of image resolution, image brightness and audio quality of the source material data to be edited so as to determine corresponding original input data of the neural network model;
the neural network learning submodule is used for learning and processing original input data of the neural network model through a preset neural network model so as to determine image content information and/or sound content information corresponding to the original input data;
the source material content identification submodule is used for identifying the image content information and/or the sound content information to obtain corresponding teaching content data;
further, the video editing mode determining module comprises a teaching content data feature extracting sub-module, an editing evaluation information determining sub-module and an editing mode matching sub-module; wherein,
the teaching content data feature extraction submodule is used for extracting a plurality of feature information about image quality, sound quality and data consistency from the teaching content data;
the editing evaluation information determining submodule is used for calculating editability evaluation information related to the teaching content data according to the characteristic information;
the editing mode matching sub-module is used for determining a certain video editing mode with the best matching degree with the current teaching content data from a plurality of video editing modes according to the editability evaluation information, and the certain video editing mode is used as the currently applicable video editing mode;
further, the video editing execution module comprises an image editing submodule, a sound editing submodule, a time axis editing submodule, an editing quality evaluation submodule and a video editing result determining submodule; wherein,
the image editing submodule is used for carrying out image editing processing on the source material to be edited according to the currently applicable video editing mode;
the sound editing submodule is used for carrying out sound editing processing on the source material to be edited according to the currently applicable video editing mode;
the time axis editing submodule is used for carrying out time axis editing processing on the source material to be edited according to the currently applicable video editing mode;
the editing quality evaluation submodule is used for carrying out corresponding quality evaluation processing on processing results of the image editing processing, the sound editing processing and the time axis editing processing;
the video editing result determining submodule is used for determining an output result of the video editing operation according to the quality evaluation processing result;
further, the image editing submodule comprises a color editing unit, a texture editing unit, a contour editing unit and a brightness editing unit; wherein,
the color editing unit, the texture editing unit, the contour editing unit and the brightness editing unit are used for respectively carrying out color editing processing, texture editing processing, contour editing processing and brightness editing processing on the source material to be edited;
or
The sound editing submodule comprises an intensity editing unit, a tone editing unit and a speed editing unit; wherein,
the intensity editing unit, the tone editing unit and the speech speed editing unit are used for respectively carrying out intensity editing processing, tone editing processing and speech speed editing processing on the source material to be edited.
Compared with the prior art, the video editing system for artificial intelligence teaching completes different editing processes in the video editing process in sequence by setting the editing source material determining module, the source material content learning determining module, the video editing mode determining module and the video editing executing module, each functional module in the video editing system can complete the determination of editing source materials, intelligent screening and the editing and synthesizing of the source materials according to the preset working mode and finally obtain the required teaching video, and as the video editing system completes the corresponding editing process in the form of neural network learning processing, the video editing system not only can improve the screening efficiency and the screening accuracy of the editing source materials, but also can improve the quality of the edited teaching video, and can meet the requirements of different teaching courses, the editing operation of teaching videos of different teaching contents is completed quickly and efficiently, so that the interactivity and diversity of artificial intelligent teaching are improved.
Further, the video editing mode determining module is configured to determine a currently applicable video editing mode according to the teaching content data, and further includes processing and calculating the teaching content data to obtain parameter values capable of sorting the video editing module, where the steps include:
step A1, obtaining the effect value M of different video editing modules for judging the teaching content data according to the formula (1)i
Wherein M isiRepresenting the judgment effect value of the ith video editing module on the teaching content data, m representing the total number of the video editing modules, η× EjRepresenting the processing action value of the ith video editing module on the jth material data in the teaching content data, η being the action effect value of data processing and being a randomly generated value between 0 and 1, n representing the total number of the material data in the teaching content data, EjDenotes the jth elementThe number of the material data accounts for the proportion of the total number of the material data in the teaching content data;
step A2, calculating the standard value M of the effect of the video editing module on the teaching content data processing according to the formula (2)
Wherein M represents a standard value of the effect of the video editing module on the teaching content data processing, vhA maximum effect value, v, representing the effect of the historical video editing module on the processing of the teaching content datalThe minimum effect value represents the effect of the historical video editing module on the teaching content data processing;
step A3, according to formula (3), calculating the sequencing parameter Si of the effect value of different video editing modules on the judgment of the teaching content data
And sequencing according to the value of Si from large to small, wherein the corresponding sequence is the sequence of the video editing modules, preferentially selecting the video editing module with the first sequence to determine the corresponding video editing mode, and if the video editing module with the first sequence cannot work, selecting downwards according to the sequence and determining.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a video editing system for artificial intelligence teaching according to the present invention.
Fig. 2 is a schematic structural diagram of an editing source material determination module in a video editing system for artificial intelligence teaching according to the present invention.
Fig. 3 is a schematic structural diagram of a source material content learning determination module in a video editing system for artificial intelligence teaching according to the present invention.
Fig. 4 is a schematic structural diagram of a video editing mode determining module in a video editing system for artificial intelligence teaching according to the present invention.
Fig. 5 is a schematic structural diagram of a video editing execution module in a video editing system for artificial intelligence teaching according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic structural diagram of a video editing system for artificial intelligence teaching according to an embodiment of the present invention. The video editing system for artificial intelligence teaching comprises an editing source material determining module, a source material content learning determining module, a video editing mode determining module and a video editing executing module; the editing source material determining module is used for determining source material data to be edited for video editing operation from the source material resource set according to preset teaching conditions;
the source material content learning determination module is used for carrying out neural network learning processing in a preset mode on the source material data to be edited so as to obtain teaching content data related to the source material to be edited;
the video editing mode determining module is used for determining a currently applicable video editing mode according to the teaching content data;
the video editing execution module is used for carrying out adaptive video editing operation on the source material to be edited according to the currently applicable video editing mode.
Fig. 2 is a schematic structural diagram of an editing source material determination module in a video editing system for artificial intelligence teaching according to the present invention. The editing source material determining module comprises a source material database submodule, a database updating submodule, a source material matching submodule and a source material capturing submodule; wherein,
the source material database submodule is used for storing video source material data related to teaching videos;
the database updating submodule is used for carrying out data content updating processing and/or data storage position updating processing on the video source material data according to the data storage state of the source material database submodule;
the source material matching submodule is used for searching and positioning data matched with the preset teaching condition in the source material data submodule;
the source material capturing submodule is used for capturing and sequentially registering the data obtained by searching and positioning so as to obtain the source material data to be edited with a preset registering sequence.
Fig. 3 is a schematic structural diagram of a source material content learning determination module in a video editing system for artificial intelligence teaching according to the present invention. The source material content learning determination module comprises a source material data screening submodule, a neural network learning submodule and a source material content identification submodule; wherein,
the source material data screening submodule is used for screening at least one of image resolution, image brightness and audio quality of the source material data to be edited so as to determine corresponding original input data of the neural network model;
the neural network learning submodule is used for learning and processing the original input data of the neural network model through a preset neural network model so as to determine image content information and/or sound content information corresponding to the original input data;
the source material content identification submodule is used for identifying the image content information and/or the sound content information so as to obtain corresponding teaching content data.
Fig. 4 is a schematic structural diagram of a video editing mode determining module in a video editing system for artificial intelligence teaching according to the present invention. The video editing mode determining module comprises a teaching content data characteristic extracting sub-module, an editing evaluation information determining sub-module and an editing mode matching sub-module; wherein,
the teaching content data feature extraction submodule is used for extracting a plurality of feature information about image quality, sound quality and data consistency from the teaching content data;
the editing evaluation information determining submodule is used for calculating and obtaining editability evaluation information related to the teaching content data according to the characteristic information;
the editing mode matching sub-module is used for determining a certain video editing mode with the best matching degree with the current teaching content data from a plurality of video editing modes according to the editability evaluation information, and the certain video editing mode is used as the currently applicable video editing mode.
Fig. 5 is a schematic structural diagram of a video editing execution module in a video editing system for artificial intelligence teaching according to the present invention. The video editing execution module comprises an image editing submodule, a sound editing submodule, a time axis editing submodule, an editing quality evaluation submodule and a video editing result determining submodule; wherein,
the image editing submodule is used for carrying out image editing processing on the source material to be edited according to the currently applicable video editing mode;
the sound editing submodule is used for carrying out sound editing processing on the source material to be edited according to the currently applicable video editing mode;
the time axis editing submodule is used for carrying out time axis editing processing on the source material to be edited according to the currently applicable video editing mode;
the editing quality evaluation submodule is used for carrying out corresponding quality evaluation processing on the processing results of the image editing processing, the sound editing processing and the time axis editing processing;
the video editing result determining submodule is used for determining an output result of the video editing operation according to the result of the quality evaluation processing.
Preferably, the image editing submodule comprises a color editing unit, a texture editing unit, a contour editing unit and a brightness editing unit; wherein,
the color editing unit, the texture editing unit, the contour editing unit and the brightness editing unit are used for respectively carrying out color editing processing, texture editing processing, contour editing processing and brightness editing processing on the source material to be edited;
preferably, the sound editing sub-module comprises an intensity editing unit, a tone editing unit and a speed editing unit; wherein,
the intensity editing unit, the tone editing unit and the speech speed editing unit are used for respectively carrying out intensity editing processing, tone editing processing and speech speed editing processing on the source material to be edited.
It can be known from the content of the above embodiment that the video editing system for artificial intelligence teaching completes different editing processes in the video editing process in sequence by setting the editing source material determining module, the source material content learning determining module, the video editing mode determining module and the video editing executing module, each functional module in the video editing system can complete the determination of editing source materials, the intelligent screening and the editing and synthesizing of source materials according to the preset working mode, and finally obtain the required teaching video, because the video editing system completes the corresponding editing process in the form of neural network learning processing, it not only can improve the screening efficiency and the screening accuracy of editing source materials, but also can improve the quality of the edited teaching video, it can be seen that the video editing system can complete different editing processes according to the requirements of different teaching courses, the editing operation of teaching videos of different teaching contents is completed quickly and efficiently, so that the interactivity and diversity of artificial intelligent teaching are improved.
Preferably, the video editing mode determining module is configured to determine a currently applicable video editing mode according to the teaching content data, and further includes processing and calculating the teaching content data to obtain parameter values capable of sorting the video editing module, where the steps include:
wherein the applicable video editing modes are controlled according to different video editing modules corresponding to the video editing modes,
step A1, obtaining the effect value M of different video editing modules for judging the teaching content data according to the formula (1)i
Wherein M isiRepresenting the judgment effect value of the ith video editing module on the teaching content data, m representing the total number of the video editing modules, η× EjRepresenting the processing action value of the ith video editing module on the jth material data in the teaching content data, η being the action effect value of data processing and being a randomly generated value between 0 and 1, n representing the total number of the material data in the teaching content data, EjThe proportion of the number of the jth material data in the total number of the material data in the teaching content data is represented;
step A2, calculating the standard value M of the effect of the video editing module on the teaching content data processing according to the formula (2)
Wherein M represents a standard value of the effect of the video editing module on the teaching content data processing, vhRepresenting historical video compilationsMaximum effect value v of effect of editing module on teaching content data processinglThe minimum effect value represents the effect of the historical video editing module on the teaching content data processing;
a3, solving the sequencing parameter Si of the effect value of different video editing modules on the teaching content data judgment according to the formula (3);
and sequencing according to the value of Si from large to small, wherein the corresponding sequence is the sequence of the video editing modules, preferentially selecting the video editing module with the first sequence to determine the corresponding video editing mode, and if the video editing module with the first sequence cannot work, selecting downwards according to the sequence and determining.
The beneficial effects of the above technical scheme are: the effect value of the video editing module is obtained from the current teaching content data, so that the video editing module can well reflect the effect on the teaching content data according to the effect value of the video editing module, the effect value of the video editing module can be sequenced by using the standard effect value, whether the video editing module is suitable for the teaching content data can be accurately and wavelessly reflected, and the video editing module at the front end of the sequencing can be switched according to the sequencing sequence when the video editing module cannot work, so that the working reliability is ensured to be rapidity
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (6)
1. A video editing system for artificial intelligence teaching, characterized by:
the video editing system for artificial intelligence teaching comprises an editing source material determining module, a source material content learning determining module, a video editing mode determining module and a video editing executing module; wherein,
the editing source material determining module is used for determining source material data to be edited for video editing operation from the source material resource set according to preset teaching conditions;
the source material content learning determination module is used for carrying out neural network learning processing in a preset mode on the source material data to be edited so as to obtain teaching content data related to the source material to be edited;
the video editing mode determining module is used for determining a currently applicable video editing mode according to the teaching content data;
the video editing execution module is used for carrying out adaptive video editing operation on the source material to be edited according to the currently applicable video editing mode;
the editing source material determining module comprises a source material database submodule, a database updating submodule, a source material matching submodule and a source material capturing submodule; wherein,
the source material database submodule is used for storing video source material data related to teaching videos;
the database updating submodule is used for carrying out data content updating processing and/or data storage position updating processing on the video source material data according to the data storage state of the source material database submodule;
the source material matching submodule is used for searching and positioning data matched with the preset teaching condition in the source material data submodule;
and the source material capturing submodule is used for capturing and sequentially registering the data obtained by searching and positioning so as to obtain the source material data to be edited with a preset registering sequence.
2. The video editing system for artificial intelligence teaching of claim 1, wherein: the source material content learning determination module comprises a source material data screening submodule, a neural network learning submodule and a source material content identification submodule; wherein,
the source material data screening submodule is used for screening at least one of image resolution, image brightness and audio quality of the source material data to be edited so as to determine corresponding original input data of the neural network model;
the neural network learning submodule is used for learning and processing original input data of the neural network model through a preset neural network model so as to determine image content information and/or sound content information corresponding to the original input data;
the source material content identification submodule is used for identifying the image content information and/or the sound content information to obtain the corresponding teaching content data.
3. The video editing system for artificial intelligence teaching of claim 1, wherein: the video editing mode determining module comprises a teaching content data characteristic extracting sub-module, an editing evaluation information determining sub-module and an editing mode matching sub-module; wherein,
the teaching content data feature extraction submodule is used for extracting a plurality of feature information about image quality, sound quality and data consistency from the teaching content data;
the editing evaluation information determining submodule is used for calculating editability evaluation information related to the teaching content data according to the characteristic information;
and the editing mode matching sub-module is used for determining a certain video editing mode with the best matching degree with the current teaching content data from a plurality of video editing modes according to the editability evaluation information, and the certain video editing mode is used as the currently applicable video editing mode.
4. The video editing system for artificial intelligence teaching of claim 1, wherein: the video editing execution module comprises an image editing submodule, a sound editing submodule, a time axis editing submodule, an editing quality evaluation submodule and a video editing result determining submodule; the image editing submodule is used for carrying out image editing processing on the source material to be edited according to the currently applicable video editing mode;
the sound editing submodule is used for carrying out sound editing processing on the source material to be edited according to the currently applicable video editing mode;
the time axis editing submodule is used for carrying out time axis editing processing on the source material to be edited according to the currently applicable video editing mode;
the editing quality evaluation submodule is used for carrying out corresponding quality evaluation processing on processing results of the image editing processing, the sound editing processing and the time axis editing processing;
and the video editing result determining submodule is used for determining an output result of the video editing operation according to the quality evaluation processing result.
5. The video editing system for artificial intelligence teaching of claim 4, wherein:
the image editing submodule comprises a color editing unit, a texture editing unit, a contour editing unit and a brightness editing unit; wherein,
the color editing unit, the texture editing unit, the contour editing unit and the brightness editing unit are used for respectively carrying out color editing processing, texture editing processing, contour editing processing and brightness editing processing on the source material to be edited;
or
The sound editing submodule comprises an intensity editing unit, a tone editing unit and a speed editing unit;
wherein,
the intensity editing unit, the tone editing unit and the speech speed editing unit are used for respectively carrying out intensity editing processing, tone editing processing and speech speed editing processing on the source material to be edited.
6. The video editing system for artificial intelligence teaching of claim 1, wherein: the video editing mode determining module is configured to, when determining the currently applicable video editing mode according to the teaching content data, further include: obtaining parameter values for ordering the video editing module based on the teaching content data, wherein the steps comprise:
step A1, calculating the effect value M of different video editing modules on the teaching content data processing according to the formula (1)i；
Wherein M isiRepresenting the judgment effect value of the ith video editing module on the teaching content data, m representing the total number of the video editing modules, η× EjRepresenting the processing action value of the ith video editing module on the jth material data in the teaching content data, η being the action effect value of data processing and being a randomly generated value between 0 and 1, n representing the total number of the material data in the teaching content data, EjThe proportion of the number of the jth material data in the total number of the material data in the teaching content data is represented;
a2, calculating a standard value M of the effect of the video editing module on the teaching content data processing according to a formula (2);
wherein M represents a standard value of the effect of the video editing module on the teaching content data processing, vhA maximum effect value, v, representing the effect of the historical video editing module on the processing of the teaching content datalThe minimum effect value represents the effect of the historical video editing module on the teaching content data processing;
a3, solving the sequencing parameter Si of the effect value of different video editing modules on the teaching content data judgment according to the formula (3);
sequencing the obtained Si values from large to small, wherein the corresponding sequence result is the sequencing result of the video editing modules, selecting a first video editing module sequenced at the first position in the sequencing result, and determining a video editing mode corresponding to the first video editing module;
and if the first video editing module cannot work, selecting the next video editing module according to the sorting result, and determining the video editing mode.
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