CN111125432B - Video matching method and training rapid matching system based on same - Google Patents
Video matching method and training rapid matching system based on same Download PDFInfo
- Publication number
- CN111125432B CN111125432B CN201911358722.3A CN201911358722A CN111125432B CN 111125432 B CN111125432 B CN 111125432B CN 201911358722 A CN201911358722 A CN 201911358722A CN 111125432 B CN111125432 B CN 111125432B
- Authority
- CN
- China
- Prior art keywords
- training
- user
- video
- training video
- keyword
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012549 training Methods 0.000 title claims abstract description 161
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000010801 machine learning Methods 0.000 claims abstract description 15
- 238000003860 storage Methods 0.000 claims abstract description 13
- 230000006399 behavior Effects 0.000 claims abstract description 9
- 238000004891 communication Methods 0.000 claims abstract description 5
- 230000006870 function Effects 0.000 claims description 34
- 238000001228 spectrum Methods 0.000 claims description 28
- 238000004422 calculation algorithm Methods 0.000 claims description 5
- 238000013135 deep learning Methods 0.000 claims description 3
- 230000002787 reinforcement Effects 0.000 claims description 3
- 238000007637 random forest analysis Methods 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 abstract description 11
- 238000012360 testing method Methods 0.000 description 7
- 239000003245 coal Substances 0.000 description 3
- 239000000463 material Substances 0.000 description 2
- 238000005728 strengthening Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
- G06F16/735—Filtering based on additional data, e.g. user or group profiles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/75—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
- G06Q50/2057—Career enhancement or continuing education service
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Educational Technology (AREA)
- Educational Administration (AREA)
- Artificial Intelligence (AREA)
- Marketing (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Business, Economics & Management (AREA)
- Primary Health Care (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Databases & Information Systems (AREA)
- Multimedia (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Marine Sciences & Fisheries (AREA)
- Medical Informatics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
- Computational Linguistics (AREA)
- Mining & Mineral Resources (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The invention provides a rapid matching system for safety production training and a video matching method, wherein the system comprises a user side and a cloud end, the user side is in communication connection with the cloud end, the cloud end comprises an ID generating unit, a storage unit and a control unit, and the ID generating unit and the storage unit are respectively connected with the control unit; the ID generating unit acquires user initial information from a user side and generates a user ID and a password; the storage unit stores identity information of each user ID of the system, a behavior log of each user ID, training videos and training curriculum schedules, wherein the behavior log comprises time length for the user ID to watch each training video; the control unit monitors whether the learning duration of the user meets the requirement, matches training videos to the user by adopting a machine learning method, and plays the training videos at the user side. The rapid matching system for the safe production training can monitor the training time of the user, and can also match training videos for different users according to the time of learning each training video by different users.
Description
Technical Field
The invention relates to the field of computers, in particular to a rapid matching system for safety production training and a video matching method.
Background
The coal mine safety production responsibility is heavier than that of Taishan mountain. The national institutes of security committee decides about further strengthening the work of security production training (security committee 2012 10) puts forward that "the lack of training is a major potential safety hazard", requires the realization of 100% security training of the whole staff, emphasizes the comprehensive strengthening of security training infrastructure of each enterprise, and is a solid advance of security training content standardization, mode diversification, management informatization, method modernization and supervision daily, in an effort to implement full-coverage, multi-means, high-quality security training. Because the requirements on personnel with different posts or working contents are inconsistent in the training process, how to systematically and intelligently supervise and recommend the training conditions of various post personnel is a problem to be solved.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a safe production training rapid matching system and a video matching method.
In order to achieve the above purpose, the invention provides a rapid matching system for safety production training, which comprises a user side and a cloud end, wherein the user side is in communication connection with the cloud end, the cloud end comprises a I D generation unit, a storage unit and a control unit, and the I D generation unit and the storage unit are respectively connected with the control unit;
the I D generating unit acquires user initial information from a user side and generates a user I D and a password;
the storage unit stores identity information of each user I D of the system, a behavior log of each user I D, training videos, training curriculum schedules and training examination results, wherein the behavior log comprises time periods for the user I D to watch each training video;
the control unit monitors whether the learning duration of the user meets the requirement, matches the training video to the user by adopting a machine learning method, and plays the training video at the user side.
The rapid matching system for the safe production training can monitor the training time of the user, and can also match training videos for different users according to the time of learning each training video by different users.
Preferred embodiments of the present application: the system further comprises a certificate generation unit which generates a certificate according to the level of the current user I D, the time period of watching each training video by the user I D, training examination results and certificate generation requirements, and the control unit monitors whether the certificate is in the validity period according to the time period of the validity period of the certificate. The method can automatically generate the certificate for the user needing the certificate and intelligently monitor the validity period of the certificate.
Preferred embodiments of the present application: the machine learning method comprises the following steps:
the training schedule corresponding to the class of the user I D is subjected to deep learning by the time period for the user I D to watch each training video and the training examination result, so that a course which is not learned and/or needs reinforcement learning is obtained, and the course is pushed to the user I D.
Preferred embodiments of the present application: the training videos include a on-demand training video and an optional training video, and the newly added training video is matched into the on-demand training video or the optional training video through a machine learning algorithm.
The application also provides a video matching method based on machine learning, which comprises the following steps:
establishing a mandatory training video database and an optional training video database, wherein each mandatory sample in the mandatory training video database comprises mandatory feature variables and mandatory training videos; each selectable sample in the selectable training content database comprises a selectable frequency characteristic variable and a selectable training video;
extracting an alternative sample set from the alternative training video database, and extracting an alternative sample set from the alternative training video database;
constructing a first classifier function by using the sampled optional sample set, and constructing a second classifier function by using the sampled optional sample set;
extracting characteristic variables of training videos to be matched, and inputting the characteristic variables into a first classifier function and a second classifier function as input variables to obtain the similarity with a mandatory sample set and the similarity with an optional sample set;
if the similarity between the training video to be matched and the necessary sample set reaches the necessary specified proportion value, matching the training video to be matched into a necessary training video database; and if the similarity with the optional sample set reaches an optional specified proportion value, matching the training video to be matched into an optional training video database, and if the optional specified proportion value and the optional specified proportion value reach, matching the training video to be matched into an optional training video database.
The video matching method is simple, and training videos to be matched can be quickly matched to the optional training video database or the optional training video database.
The method has the preferable scheme that: the classifier function compares the video through the sound spectrum and the image recognition degree, and comprises the following specific steps:
obtaining a keyword A in a sample set; acquiring a frequency spectrum of a keyword A;
acquiring a keyword B of a training video to be matched, and acquiring a frequency spectrum of the keyword B;
calculating the similarity C=a of the two frequency spectrums and the difference +b of the center frequency points, wherein the difference of the center frequency points is the difference of the center frequency points of the frequency spectrums of the keyword A and the frequency spectrums of the keyword B, and the difference of the frequency spectrums is the difference of the frequency spectrums of the keyword A and the frequency spectrums of the keyword B, wherein 0.5< a <1, 0< B <0.5 and a+b=1;
if the similarity C is larger than a threshold value, acquiring information of a shot object in a training video picture to be matched, acquiring information of a reference object in a sample set, and calculating the similarity D of the two objects;
obtaining overall similarity
When the classifier function is a first classifier function, the keyword A is an essential feature variable; when the classifier function is a second classifier function, the keyword A is an optional feature variable; the keyword B is a characteristic variable of the training video to be matched.
The beneficial effects of the invention are as follows: the invention realizes supervision of training time length of users participating in safe production training, can also match training videos for different users according to time length of learning each training video by different users, can automatically generate certificates for users needing certificates, intelligently monitor valid period of the certificates, and also realize automatic matching classification of the training videos.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic block diagram of a safe production training quick match system.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, unless otherwise specified and defined, it should be noted that the terms "mounted," "connected," and "coupled" are to be construed broadly, and may be, for example, mechanical or electrical, or may be in communication with each other between two elements, directly or indirectly through intermediaries, as would be understood by those skilled in the art, in view of the specific meaning of the terms described above.
As shown in fig. 1, the invention provides a rapid matching system for safety production training, which comprises a user side and a cloud end, wherein the user side is in communication connection with the cloud end, the cloud end comprises a I D generation unit, a storage unit and a control unit, and the I D generation unit and the storage unit are respectively connected with the control unit.
The I D generating unit obtains user initial information from the user terminal and generates the user I D and the password. The user I D has at least one level, each level corresponding to a different training curriculum.
The storage unit stores identity information of each user I D of the system, a behavior log of each user I D, a training video, and a training curriculum, wherein the behavior log includes a duration of time that each training video was viewed by the user I D.
The control unit monitors whether the learning duration of the user meets the requirement according to the sum of the time durations of watching the training videos of the user in the behavior log, matches the training videos with the user by adopting a machine learning method, and plays the training videos at the user side. The machine learning method comprises the following steps:
the user can take a training test at the user end, the training test results are stored in the storage unit, the training test results comprise the content scores of all the parts of the test, the wrong problem distribution conditions and the like, the time for the user I D to watch each training video and the training curriculum schedule corresponding to the class to which the user I D belongs are subjected to deep learning, so that a curriculum which is not learned and/or needs reinforcement learning is obtained, and the curriculum is pushed to the user I D.
The training quick matching system further includes a certificate generation unit that generates a certificate according to the level of the current user I D, the length of time the user I D views each training video, training test results, and a certificate generation requirement. If the training test result is not qualified, the certificate is not allowed to be generated. The control unit monitors whether the certificate is in the validity period according to the validity period duration of the certificate.
Such as: the level is the coal mine special operation level, and then the coal mine special operation personnel should be trained according to a specified training outline before taking qualification tests. Wherein the time for initial training is not less than ninety hours. The expiration of the validity period of the special operation card needs to be delayed for changing the card, and the licensor should participate in special training at least twenty-four times before the expiration of the validity period. The control unit monitors whether the training time of the user of the special job level meets the requirement, can push training videos matched with the user to the special job level, and can monitor whether the special job operation certificate is in the validity period.
The training video of the cloud can be updated at any time, the training video comprises a mandatory training video and an optional training video, and the newly added training video is matched into the mandatory training video or the optional training video through a machine learning algorithm.
The invention also provides a video matching method based on machine learning, which comprises the following steps:
establishing a mandatory training video database and an optional training video database, wherein each mandatory sample in the mandatory training video database comprises mandatory feature variables and mandatory training videos; each selectable sample in the selectable training content database includes a selectable frequency characteristic variable and a selectable training video.
The method comprises the steps of extracting an alternative sample set from an alternative training video database, and extracting an alternative sample set from an alternative training video database.
A first classifier function is constructed using the sampled set of candidate samples and a second classifier function is constructed using the sampled set of candidate samples.
And extracting characteristic variables of training videos to be matched, and inputting the characteristic variables into the first classifier function and the second classifier function as input variables to obtain the similarity with the alternative sample set and the similarity with the alternative sample set.
The classifier function compares the video through the sound spectrum and the image recognition degree, and specifically comprises the following steps:
obtaining a keyword A in a sample set, namely a vocabulary with high repetition probability, wherein the keyword A can be a single word or a set of a plurality of words; and acquiring the frequency spectrum of the keyword A.
And obtaining a keyword B of the training video to be matched, and obtaining the frequency spectrum of the keyword B.
Calculating the difference of the similarity C=a of the two frequency spectrums and the difference of the center frequency points +b of the two frequency spectrums, wherein the difference of the center frequency points is the difference of the center frequency points of the frequency spectrums of the keyword A and the frequency spectrums of the keyword B, the difference of the frequency spectrums is the difference of the frequency spectrums of the keyword A and the frequency spectrums of the keyword B, 0.5< a <1, 0< B <0.5, and a+b=1.
If the similarity C is larger than a set threshold value, acquiring information of the shot objects in the training video pictures to be matched, acquiring information of the reference objects in the sample set, and calculating the similarity D of the two objects, wherein an existing algorithm, such as an image comparison algorithm, is adopted for the similarity D.
Obtaining overall similarity
Here, when the classifier function is the first classifier function, the sample set is a mandatory sample set, and the keyword a is a mandatory feature variable; when the classifier function is a second classifier function, the sample set is an optional sample set, and the keyword A is an optional characteristic variable; the keyword B is a characteristic variable of the training video to be matched.
Preferably, the first classifier function and the second classifier function are random forest model functions.
If the similarity between the training video to be matched and the necessary sample set reaches the necessary specified proportion value, matching the training video to be matched into a necessary training video database; and if the similarity with the optional sample set reaches an optional specified proportion value, matching the training content to be matched into an optional training video database, and if the optional specified proportion value and the optional specified proportion value reach, matching the training video to be matched into an optional training video database.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (7)
1. The video matching method based on machine learning is characterized by comprising the following steps:
establishing a mandatory training video database and an optional training video database, wherein each mandatory sample in the mandatory training video database comprises mandatory feature variables and mandatory training videos; each selectable sample in the selectable training content database comprises a selectable frequency characteristic variable and a selectable training video;
extracting an alternative sample set from the alternative training video database, and extracting an alternative sample set from the alternative training video database;
constructing a first classifier function by using the sampled optional sample set, and constructing a second classifier function by using the sampled optional sample set;
extracting characteristic variables of training videos to be matched, and inputting the characteristic variables into a first classifier function and a second classifier function as input variables to obtain the similarity with a mandatory sample set and the similarity with an optional sample set;
the classifier function compares the video through the sound spectrum and the image recognition degree, and comprises the following specific steps:
obtaining a keyword A in a sample set; acquiring a frequency spectrum of a keyword A;
acquiring a keyword B of a training video to be matched, and acquiring a frequency spectrum of the keyword B;
calculating the similarity C=a of the two frequency spectrums and the difference +b of the center frequency points, wherein the difference of the center frequency points is the difference of the center frequency points of the frequency spectrums of the keyword A and the frequency spectrums of the keyword B, and the difference of the frequency spectrums is the difference of the frequency spectrums of the keyword A and the frequency spectrums of the keyword B, wherein 0.5< a <1, 0< B <0.5 and a+b=1;
if the similarity C is larger than a threshold value, acquiring information of a shot object in a training video picture to be matched, acquiring information of a reference object in a sample set, and calculating the similarity D of the two objects;
acquiring overall similarity e=c×d;
when the classifier function is a first classifier function, the keyword A is an essential feature variable; when the classifier function is a second classifier function, the keyword A is an optional feature variable; the keyword B is a characteristic variable of the training video to be matched;
if the similarity between the training video to be matched and the necessary sample set reaches the necessary specified proportion value, matching the training video to be matched into a necessary training video database; and if the similarity with the optional sample set reaches an optional specified proportion value, matching the training video to be matched into an optional training video database, and if the optional specified proportion value and the optional specified proportion value reach, matching the training video to be matched into an optional training video database.
2. The machine learning based video matching method of claim 1, wherein the first classifier function and the second classifier function are both random forest model functions.
3. The training rapid matching system based on the machine learning-based video matching method of claim 1 is characterized by comprising a user side and a cloud end, wherein the user side is in communication connection with the cloud end, the cloud end comprises an ID generating unit, a storage unit and a control unit, and the ID generating unit and the storage unit are respectively connected with the control unit;
the ID generation unit acquires user initial information from a user side and generates a user ID and a password;
the storage unit stores identity information of each user ID of the system, a behavior log of each user ID, training videos, training curriculum schedules and training examination results, wherein the behavior log comprises time length for watching each training video by the user ID;
the control unit monitors whether the learning duration of the user meets the requirement, matches the training video to the user by adopting a machine learning method, and plays the training video at the user side.
4. The training quick matching system according to claim 3, further comprising a certificate generation unit that generates a certificate according to a level of a current user ID, a time period for which the user ID views each training video, a training examination result, and a certificate generation requirement.
5. The training quick match system of claim 4, wherein the control unit monitors whether a credential is within a validity period based on a credential validity period duration.
6. The training quick match system of claim 3, wherein the machine learning method is:
and performing deep learning on the time length of watching each training video by the user ID and the training curriculum schedule of training examination results corresponding to the class to which the user ID belongs, obtaining a curriculum which is not learned and/or needs reinforcement learning, and pushing the curriculum to the user ID.
7. The training quick match system of claim 3, wherein the training videos comprise a pickpocket training video and a pickpocket training video, the newly added training video being matched to the pickpocket training video or the pickpocket training video by a machine learning algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911358722.3A CN111125432B (en) | 2019-12-25 | 2019-12-25 | Video matching method and training rapid matching system based on same |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911358722.3A CN111125432B (en) | 2019-12-25 | 2019-12-25 | Video matching method and training rapid matching system based on same |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111125432A CN111125432A (en) | 2020-05-08 |
| CN111125432B true CN111125432B (en) | 2023-07-11 |
Family
ID=70502352
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911358722.3A Active CN111125432B (en) | 2019-12-25 | 2019-12-25 | Video matching method and training rapid matching system based on same |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111125432B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001312295A (en) * | 2000-04-27 | 2001-11-09 | Sharp Corp | Device, method, and system for voice recognition and program recording medium |
| JP2010128633A (en) * | 2008-11-26 | 2010-06-10 | Denso It Laboratory Inc | Information presentation device, method and program |
| CN102200967A (en) * | 2011-03-30 | 2011-09-28 | 中国人民解放军军事医学科学院放射与辐射医学研究所 | Method and system for processing text based on DNA sequences |
| CN105466710A (en) * | 2015-11-23 | 2016-04-06 | 电子科技大学 | Local mean value decomposition end point effect improvement method based on frequency domain similarity |
| CN106935248A (en) * | 2017-02-14 | 2017-07-07 | 广州孩教圈信息科技股份有限公司 | A kind of voice similarity detection method and device |
| CN106991628A (en) * | 2017-03-31 | 2017-07-28 | 河北天英软件科技有限公司 | A kind of the online training method of examination and system |
| CN108845578A (en) * | 2018-07-19 | 2018-11-20 | 上海工程技术大学 | A kind of removable video monitoring system of voice-control |
| CN109785859A (en) * | 2019-01-31 | 2019-05-21 | 平安科技（深圳）有限公司 | The method, apparatus and computer equipment of management music based on speech analysis |
Family Cites Families (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080082417A1 (en) * | 2006-07-31 | 2008-04-03 | Publicover Mark W | Advertising and fulfillment system |
| US8412525B2 (en) * | 2009-04-30 | 2013-04-02 | Microsoft Corporation | Noise robust speech classifier ensemble |
| JP5288617B2 (en) * | 2009-05-14 | 2013-09-11 | 株式会社エヌ・ティ・ティ・データ | Document management system, document management method, and computer program |
| TWI471854B (en) * | 2012-10-19 | 2015-02-01 | Ind Tech Res Inst | Guided speaker adaptive speech synthesis system and method and computer program product |
| US10546326B2 (en) * | 2013-09-26 | 2020-01-28 | Mark W. Publicover | Providing targeted content based on a user's preferences |
| CN104679779B (en) * | 2013-11-29 | 2019-02-01 | 华为技术有限公司 | The method and apparatus of visual classification |
| CN107169898A (en) * | 2017-05-12 | 2017-09-15 | 广州市和佳电子科技有限公司 | It is a kind of based on intelligent glasses be terminal training system and implementation method |
| CN107818528A (en) * | 2017-09-28 | 2018-03-20 | 珠海市领创智能物联网研究院有限公司 | A kind of staffs training system based on Internet of Things |
| CN108648123A (en) * | 2018-07-13 | 2018-10-12 | 江苏开放大学（江苏城市职业学院） | A method of its management network teaching process of the network teaching platform and utilization based on big data |
| CN109934747A (en) * | 2019-03-18 | 2019-06-25 | 重庆替比网络科技有限公司 | A kind of record for vocational training, archives supervisory systems |
| CN110188266A (en) * | 2019-05-10 | 2019-08-30 | 广州职赢未来信息科技有限公司 | Curriculum information method for pushing, system, readable storage medium storing program for executing and terminal device |
| CN110599810A (en) * | 2019-08-12 | 2019-12-20 | 苏州凌瑞智能技术有限公司 | Customized intelligent manufacturing training system and method based on intelligent service platform |
-
2019
- 2019-12-25 CN CN201911358722.3A patent/CN111125432B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001312295A (en) * | 2000-04-27 | 2001-11-09 | Sharp Corp | Device, method, and system for voice recognition and program recording medium |
| JP2010128633A (en) * | 2008-11-26 | 2010-06-10 | Denso It Laboratory Inc | Information presentation device, method and program |
| CN102200967A (en) * | 2011-03-30 | 2011-09-28 | 中国人民解放军军事医学科学院放射与辐射医学研究所 | Method and system for processing text based on DNA sequences |
| CN105466710A (en) * | 2015-11-23 | 2016-04-06 | 电子科技大学 | Local mean value decomposition end point effect improvement method based on frequency domain similarity |
| CN106935248A (en) * | 2017-02-14 | 2017-07-07 | 广州孩教圈信息科技股份有限公司 | A kind of voice similarity detection method and device |
| CN106991628A (en) * | 2017-03-31 | 2017-07-28 | 河北天英软件科技有限公司 | A kind of the online training method of examination and system |
| CN108845578A (en) * | 2018-07-19 | 2018-11-20 | 上海工程技术大学 | A kind of removable video monitoring system of voice-control |
| CN109785859A (en) * | 2019-01-31 | 2019-05-21 | 平安科技（深圳）有限公司 | The method, apparatus and computer equipment of management music based on speech analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111125432A (en) | 2020-05-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sekaran et al. | Research methods for business: A skill building approach | |
| CN108874832B (en) | Target comment determination method and device | |
| CN107733666A (en) | Conference implementation method and device and electronic equipment | |
| CN110458130B (en) | Person identification method, person identification device, electronic equipment and storage medium | |
| US9613077B2 (en) | Natural language management of online social network connections | |
| CN104636501A (en) | Multimedia network artificial instant translation system and multimedia network artificial instant translation method | |
| CN111726682B (en) | Video clip generation method, device, equipment and computer storage medium | |
| US9626651B2 (en) | Automated social network introductions for e-meetings | |
| DE102012218485B4 (en) | Method for an instant messaging system and instant messaging system | |
| CN110689903B (en) | Method, device, equipment and medium for evaluating intelligent sound box | |
| CN111177462B (en) | Video distribution timeliness determination method and device | |
| CN110856016A (en) | Video live broadcast monitoring method and device, electronic equipment and storage medium | |
| US20210011976A1 (en) | Automatically identifying and minimizing potentially indirect meanings in electronic communications | |
| CN110633357A (en) | Voice interaction method, device, equipment and medium | |
| CN111125432B (en) | Video matching method and training rapid matching system based on same | |
| US20170223133A1 (en) | Monitoring and maintaining social group cohesiveness | |
| CN107210001A (en) | Use the autonomous learning systems of video segment | |
| CN111918073A (en) | Management method and device of live broadcast room | |
| CN105373568A (en) | Method and device for automatically learning question answers | |
| WO2020034756A1 (en) | Method and apparatus for predicting target device, and electronic device and storage medium | |
| CN116681556A (en) | Intelligent remote teaching cloud platform system and method based on education big data | |
| Gazizullina et al. | Prediction of twitter message deletion | |
| DE102016213104A1 (en) | Token-based authentication with signed message | |
| CN114363103B (en) | Method, device and computer readable medium for processing conference information | |
| CN106411601A (en) | System and method for batch configuration of cloud terminal time |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |