CN111125432A - Rapid matching system and video matching method for safety production training - Google Patents
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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 side, the user side is in communication connection with the cloud side, the cloud side 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, a behavior log of each user ID, training videos and a training curriculum schedule of the system, wherein the behavior log comprises the time length of each training video watched by the user ID; the control unit monitors whether the learning time of the user meets the requirement or not, a machine learning method is adopted to match the training video for the user, and the user side plays the training video. The rapid matching system for the safety production training can monitor the training duration of the user, and can match the training videos for different users according to the duration of learning of each training video of 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 in Taishan mountain. The "decisions of the State Council of the State administration on further strengthening the training work of safety production" (2012/10) put forward "the training is not in place and is a major potential safety hazard", require to realize the whole person 100% safety training, and emphasize that each enterprise strengthens the safety training infrastructure comprehensively, and it is firm to promote the safety training content standardization, the mode diversification, the management informatization, the method modernization and the supervision daily normalization, and strives to implement the safety training with full coverage, multiple means and high quality. In the training process, the requirements for personnel in different posts or working contents are inconsistent, and how to intelligently supervise and suggest 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 rapid matching system for safety production training 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 side, wherein the user side is in communication connection with the cloud side, the cloud side comprises an ID generation unit, a storage unit and a control unit, and the ID generation 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, a behavior log of each user ID, training videos, training curriculum schedules and training examination results of the system, wherein the behavior log comprises the time length of each training video watched by the user ID;
the control unit monitors whether the learning duration of the user meets the requirement or not, a machine learning method is adopted to match the training video for the user, and the user side plays the training video.
The rapid matching system for the safety production training can monitor the training duration of the user, and can match the training videos for different users according to the duration of learning of each training video of different users.
Preferred embodiments of the present application: the system also comprises a certificate generating unit, wherein the certificate generating unit generates a certificate according to the level of the current user ID, the time length for watching each training video by the user ID, the training examination result and the certificate generating requirement, and the control unit monitors whether the certificate is in the valid period according to the time length of the valid period of the certificate. The certificate can be automatically generated for users needing the certificate, and the validity period of the certificate can be intelligently monitored.
Preferred embodiments of the present application: the machine learning method comprises the following steps:
and carrying out deep learning on the duration of watching each training video by the user ID, the training examination result and the training curriculum corresponding to the level of the user ID, obtaining the curriculum which is not learned and/or needs to be strengthened, and pushing the curriculum to the user ID.
Preferred embodiments of the present application: the training videos comprise mandatory training videos and optional training videos, and the newly added training videos are matched into the mandatory training videos or the optional training videos through a machine learning algorithm.
The application also provides a video matching method based on machine learning, and the training video matching method 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 a mandatory characteristic variable and a mandatory training video; each optional sample in the optional training content database comprises an optional frequency characteristic variable and an optional training video;
extracting a necessary sample set from a necessary training video database, and extracting an optional sample set from an optional training video database;
constructing a first classifier function by using a necessary sample set obtained by sampling, and constructing a second classifier function by using an optional sample set obtained by sampling;
extracting characteristic variables of the training video to be matched and inputting the characteristic variables serving as input variables into the first classifier function and the second classifier function to obtain the similarity with a necessary sample set and the similarity with an optional sample set;
if the similarity with the necessary sample set reaches a necessary specified proportion value, matching the training video to be matched with the 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 both reach, matching the training video to be matched into the optional training video database.
The video matching method is simple, and can quickly match the training video to be matched to the optional training video database or the optional training video database.
The preferred scheme of the method is as follows: the classifier function compares the videos through the sound frequency spectrum and the image recognition degree, and the method specifically comprises the following steps:
acquiring a keyword A in a sample set; acquiring a frequency spectrum of the 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 of the two frequency spectrums as a difference of central frequency points and B as a spectrum width difference, wherein the difference of the central frequency points is the difference of the frequency spectrum of the keyword A and the central frequency point of the frequency spectrum of the keyword B, the spectrum width difference is the spectrum width difference of the frequency spectrum of the keyword A and the frequency spectrum of the keyword B, 0.5< a <1, 0< B <0.5 and a + B as 1;
if the similarity C is larger than the threshold value, acquiring the information of the shot object in the training video picture to be matched, acquiring the reference object information in the 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 a necessary feature variable; when the classifier function is a second classifier function, the keyword A is an optional feature variable; and the keyword B is a characteristic variable of the training video to be matched.
The invention has the beneficial effects that: the invention realizes the supervision of the training time length of the users participating in the safety production training, simultaneously can match the training videos for different users according to the time length of the different users for learning each training video, can automatically generate the certificate for the users needing the certificate, intelligently monitors the validity period of the certificate, and also realizes the automatic matching and 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.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a functional block diagram of a rapid matching system for safety production training.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
As shown in fig. 1, the invention provides a rapid matching system for safety production training, which comprises a user side and a cloud side, wherein the user side is in communication connection with the cloud side, the cloud side comprises an ID generation unit, a storage unit and a control unit, and the ID generation 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 user ID has at least one level, each level corresponding to a different training curriculum.
The storage unit stores identity information of each user ID of the system, a behavior log of each user ID, training videos and a training curriculum schedule, wherein the behavior log comprises the time length for each training video watched by the user ID.
And the control unit monitors whether the learning time of the user meets the requirement or not according to the total time of the user watching each training video in the behavior log, matches the training video to the user by adopting a machine learning method, and plays the training video 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 result is stored in the storage unit and comprises the score of each part of test content, the distribution condition of wrong questions and the like, the time length of each training video watched by the user ID, the training test result and the training curriculum table corresponding to the level to which the user ID belongs are subjected to deep learning, the curriculum which is not learned and/or needs to be strengthened is obtained, and the curriculum is pushed to the user ID.
The training quick matching system further comprises a certificate generating unit, wherein the certificate generating unit generates a certificate according to the level of the current user ID, the time length of each training video watched by the user ID, the training examination result and the certificate generating requirement. And if the training examination result is unqualified, the certificate is not allowed to be generated. The control unit monitors whether the certificate is in the valid period according to the certificate valid period duration.
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 examinations. Wherein the time of the initial training is not less than ninety school hours. The expiration of the validity period of the special operation certificate requires a delay for certificate exchange, and a licensee should participate in special training at not less than twenty-four times before the validity period expires. The control unit monitors whether the training time of the user at the special operation level meets the requirements or not, can also push a training video matched with the user, and can also monitor whether the operation certificate of the special operation is in the valid period or not.
The training videos at the cloud end can be updated at any time, the training videos comprise necessary training videos and optional training videos, and the newly added training videos are matched into the necessary training videos or the optional training videos through a machine learning algorithm.
The invention also provides a video matching method based on machine learning, and the training video matching method 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 a mandatory characteristic variable and a mandatory training video; each selectable sample in the selectable training content database includes a selectable frequency feature variable and a selectable training video.
And extracting a necessary sample set from the necessary training video database, and extracting an optional sample set from the optional training video database.
And 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.
And extracting characteristic variables of the training video to be matched and inputting the characteristic variables serving as input variables into the first classifier function and the second classifier function to obtain the similarity with the necessary sample set and the similarity with the optional sample set.
The classifier function compares the videos through the sound frequency spectrum and the image recognition degree, and the method specifically comprises the following steps:
acquiring a keyword A in a sample set, namely a vocabulary with high repetition probability, wherein the vocabulary can be a single word or a set of a plurality of words; and acquiring the frequency spectrum of the keyword A.
And acquiring a keyword B of the training video to be matched and acquiring a frequency spectrum of the keyword B.
And calculating the similarity C of the two frequency spectrums, namely a difference of central frequency points and B spectrum width difference, wherein the difference of the central frequency points is the difference of the frequency spectrum of the keyword A and the central frequency point of the frequency spectrum of the keyword B, the spectrum width difference is the spectrum width difference of the frequency spectrum of the keyword A and the frequency spectrum of the keyword B, 0.5< a <1, 0< B <0.5 and a + B is 1.
And if the similarity C is larger than a set threshold value, acquiring the information of the shot object in the training video picture to be matched, acquiring the reference object information in the sample set, and calculating the similarity D of the two objects, wherein the similarity D algorithm only adopts the existing algorithm, such as an image comparison algorithm.
Obtaining overall similarity
Here, when the classifier function is the first classifier function, the sample set is a necessary sample set, and the keyword a is a necessary 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 feature variable; and 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 both random forest model functions.
If the similarity with the necessary sample set reaches a necessary specified proportion value, matching the training video to be matched with the 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 both reach, matching the training video to be matched into the optional training video database.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean 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 invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. 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 invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (8)
1. A rapid matching system for safety production training is characterized by comprising a user side and a cloud side, wherein the user side is in communication connection with the cloud side, the cloud side comprises an ID generation unit, a storage unit and a control unit, and the ID generation 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, a behavior log of each user ID, training videos, training curriculum schedules and training examination results of the system, wherein the behavior log comprises the time length of each training video watched by the user ID;
the control unit monitors whether the learning duration of the user meets the requirement or not, a machine learning method is adopted to match the training video for the user, and the user side plays the training video.
2. The system for rapid matching of safety production training as claimed in claim 1, further comprising a certificate generation unit for generating a certificate according to the level of the current user ID, the duration of each training video viewed by the user ID, the training examination result and the certificate generation requirement.
3. The rapid matching system for safety production training as claimed in claim 2, wherein the control unit monitors whether the certificate is within the validity period according to the validity period of the certificate.
4. The rapid matching system for safety production training as claimed in claim 1, wherein the machine learning method is:
and carrying out deep learning on the duration of watching each training video by the user ID, the training examination result and the training curriculum corresponding to the level of the user ID, obtaining the curriculum which is not learned and/or needs to be strengthened, and pushing the curriculum to the user ID.
5. The rapid matching system for safety production training as claimed in claim 1, wherein the training videos include a mandatory training video and an optional training video, and the newly added training video is matched as the mandatory training video or the optional training video through a machine learning algorithm.
6. A video matching method based on machine learning is characterized in that the training video matching method 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 a mandatory characteristic variable and a mandatory training video; each optional sample in the optional training content database comprises an optional frequency characteristic variable and an optional training video;
extracting a necessary sample set from a necessary training video database, and extracting an optional sample set from an optional training video database;
constructing a first classifier function by using a necessary sample set obtained by sampling, and constructing a second classifier function by using an optional sample set obtained by sampling;
extracting characteristic variables of the training video to be matched and inputting the characteristic variables serving as input variables into the first classifier function and the second classifier function to obtain the similarity with a necessary sample set and the similarity with an optional sample set;
if the similarity with the necessary sample set reaches a necessary specified proportion value, matching the training video to be matched with the 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 both reach, matching the training video to be matched into the optional training video database.
7. The spatial data matching method of claim 6, wherein the classifier function compares the video by sound spectrum and image recognition, and comprises the steps of:
acquiring a keyword A in a sample set; acquiring a frequency spectrum of the 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 of the two frequency spectrums as a difference of central frequency points and B as a spectrum width difference, wherein the difference of the central frequency points is the difference of the frequency spectrum of the keyword A and the central frequency point of the frequency spectrum of the keyword B, the spectrum width difference is the spectrum width difference of the frequency spectrum of the keyword A and the frequency spectrum of the keyword B, 0.5< a <1, 0< B <0.5 and a + B as 1;
if the similarity C is larger than the threshold value, acquiring the information of the shot object in the training video picture to be matched, acquiring the reference object information in the 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 a necessary feature variable; when the classifier function is a second classifier function, the keyword A is an optional feature variable; and the keyword B is a characteristic variable of the training video to be matched.
8. The spatial data matching method of claim 6, wherein the first classifier function and the second classifier function are both random forest model functions.
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