CN113901053A - Teaching material index management system based on big data - Google Patents

Teaching material index management system based on big data Download PDF

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CN113901053A
CN113901053A CN202111184840.4A CN202111184840A CN113901053A CN 113901053 A CN113901053 A CN 113901053A CN 202111184840 A CN202111184840 A CN 202111184840A CN 113901053 A CN113901053 A CN 113901053A
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teaching material
basic information
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management system
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李乐
张运华
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2228—Indexing structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/242—Query formulation
    • G06F16/243—Natural language query formulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G06Q50/205—Education administration or guidance

Abstract

The invention provides a teaching material index management system based on big data, which comprises: the system comprises a first acquisition module, a second acquisition module and an index module; the first acquisition module is used for acquiring basic information of the target teaching material, wherein the basic information comprises the name, the field, the author and the facing group of the target teaching material; the second acquisition module is used for acquiring target words appearing in the target teaching material and constructing feature vectors of the target words based on the basic information of the target teaching material and the target words; the index module is used for searching matched semantic interpretation information from the big database by using a search engine based on big data technology aiming at the acquired feature vector of the target word and outputting a semantic interpretation result corresponding to the target word. The method is helpful for the reader to understand specific key or difficult words in the target teaching material, and improves the reading experience of the reader.

Description

Teaching material index management system based on big data
Technical Field
The invention relates to the technical field of intelligent interaction, in particular to a teaching material index management system based on big data.
Background
At present, for key or difficult phrases in book teaching materials (e.g., index phrases in the teaching materials), such phrases may be chinese or english, and have different meanings in specific fields or teaching materials, and general translation software cannot accurately interpret such phrases, so that a reader is prone to have a situation of being unable to understand or understanding a deviation in a reading process, which affects reading experience.
Disclosure of Invention
Aiming at the technical problem that readers are prone to have incomprehensible or comprehension deviation in the reading process, the invention aims to provide a teaching material index management system based on big data.
The purpose of the invention is realized by adopting the following technical scheme:
the invention discloses a teaching material index management system based on big data, which comprises: the system comprises a first acquisition module, a second acquisition module and an index module;
the first acquisition module is used for acquiring basic information of the target teaching material, wherein the basic information comprises the name, the field, the author, the group-oriented property and the like of the target teaching material;
the second acquisition module is used for acquiring target words appearing in the target teaching material and constructing feature vectors of the target words based on the basic information of the target teaching material and the target words;
the index module is used for searching matched semantic interpretation information from the big database by using a search engine based on big data technology aiming at the acquired feature vector of the target word and outputting a semantic interpretation result corresponding to the target word.
In one embodiment, the display device further comprises a display module;
the display module is used for displaying the semantic interpretation result corresponding to the target word.
In one embodiment, the first obtaining module includes a first input unit;
the first input unit is used for acquiring at least one item of basic information of a target teaching material input by a user, matching the corresponding target teaching material from the teaching material database according to the basic information of the target teaching material input by the user, and acquiring complete basic information according to the matched target teaching material.
In one embodiment, the first acquiring module further includes a first shooting unit;
the first shooting unit is used for collecting cover images of the target teaching materials, summarizing and matching the corresponding target teaching materials from the teaching material database based on the obtained cover images, and obtaining complete basic information of the target teaching materials according to the matched target teaching materials.
In one embodiment, the second obtaining module includes a second input unit and a vector extracting unit;
the second input unit is used for acquiring target words appearing in the target teaching materials input by the user;
the vector extraction unit is used for constructing a feature vector of the target word according to the obtained target word and basic information of the target teaching material, wherein the feature vector of the target word comprises the target word and the basic information of the target teaching material, and the basic information of the target teaching material comprises at least one of the field of the teaching material, the author of the teaching material and the audience group of the teaching material.
In one embodiment, the second acquiring module further includes a second shooting unit;
the second shooting unit is used for collecting target word pictures in the target teaching materials and carrying out text recognition based on the obtained target word pictures to obtain corresponding target words.
In one embodiment, the index module comprises a search unit and an output unit;
the searching unit is used for searching semantic interpretation information corresponding to the feature vector from the large database according to the acquired feature vector of the target word based on a search engine;
and the output unit is used for sorting out the semantic interpretation result corresponding to the target word according to the acquired semantic interpretation information.
In one embodiment, the method is applied to electronic edition teaching materials; the first acquisition module also comprises basic information of the target teaching material directly acquired from the electronic edition teaching material; the second acquisition module also comprises a step of taking the index words of the target teaching materials as target words.
The invention has the beneficial effects that: the basic information of the target teaching material is acquired through the first acquisition module, and the system records the basic information of the target teaching material; meanwhile, a target word needing semantic interpretation confirmation is obtained through the second obtaining module, a feature vector of the target word is jointly constructed based on basic information of a target teaching material corresponding to the obtained target word in a combined mode, finally, the indexing module searches in a large database based on the obtained feature vector to obtain semantic interpretation information matched with the feature vector, finally, the semantic interpretation result of the target word is sorted out according to the obtained semantic interpretation information, the obtained semantic interpretation result can accord with the characteristics of the current teaching material, the problem that inaccurate interpretation is easily caused by the polysemous word in a specific environment is effectively solved, a reader can understand specific key words or difficult words in the target teaching material, and reading experience of the reader is improved.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram of an exemplary framework of a big data-based textbook index management system according to the present invention.
Reference numerals:
the device comprises a first acquisition module 10, a second acquisition module 20, an index module 30 and a display module 40.
Detailed Description
The invention is further described in connection with the following application scenarios.
Referring to fig. 1, an embodiment of the present invention provides a big data-based textbook index management system, including: a first obtaining module 10, a second obtaining module 20 and an indexing module 30;
the first obtaining module 10 is configured to obtain basic information of a target teaching material, where the basic information includes a name, a field, an author, a group, and the like of the target teaching material;
the second obtaining module 20 is configured to obtain a target word appearing in the target teaching material, and construct a feature vector of the target word based on the basic information of the target teaching material and the target word;
the indexing module 30 is configured to, for the obtained feature vector of the target word, search matching semantic interpretation information from the big database by using a search engine based on a big data technology, and output a semantic interpretation result corresponding to the target word.
In the above embodiment, the first obtaining module 10 obtains the basic information of the target teaching material, and the system records the basic information of the target teaching material; meanwhile, a target word needing semantic interpretation confirmation is acquired through the second acquisition module 20, a feature vector of the target word is jointly constructed based on basic information of a target teaching material corresponding to the acquired target word in a united manner, finally, the indexing module 30 searches in a large database based on the acquired feature vector to acquire semantic interpretation information matched with the feature vector, and finally, a semantic interpretation result of the target word is sorted out according to the acquired semantic interpretation information, so that the acquired semantic interpretation result can accord with the characteristics of the current teaching material, the problem that inaccurate interpretation is easily caused by multi-meaning words in a specific environment is effectively solved, readers can understand specific key or difficult words in the target teaching material, and the reading experience of the readers is improved.
In one scenario, the teaching material index management system of the invention can complete the setting of the system and each functional module based on intelligent terminal equipment (such as an intelligent mobile phone, a tablet computer, and the intelligent terminal equipment of the whole machine).
In one embodiment, the display device further comprises a display module 40;
the display module 40 is used for displaying the semantic interpretation result corresponding to the target word.
After the semantic interpretation result corresponding to the target word is obtained through the indexing module 30, the semantic interpretation result is displayed through the display module 40, so that the user can conveniently look up the semantic interpretation result.
In one embodiment, the first obtaining module 10 includes a first input unit;
the first input unit is used for acquiring at least one item of basic information of a target teaching material input by a user, matching the corresponding target teaching material from the teaching material database according to the basic information of the target teaching material input by the user, and acquiring complete basic information according to the matched target teaching material.
In the above embodiment, the user can input the basic information of the target teaching material into the first acquiring module 10 by a manual input mode; meanwhile, the user can also manually input information such as the name, the field and the author of the target teaching material, and the first input unit matches the complete basic information of the target teaching material from the teaching material database for calling when the feature vector of the target word is subsequently constructed.
In one embodiment, the first acquiring module 10 further includes a first shooting unit;
the first shooting unit is used for collecting a cover image of the target teaching material, matching the corresponding target teaching material from the teaching material database based on the obtained cover image, and obtaining complete basic information of the target teaching material according to the matched target teaching material.
Meanwhile, aiming at the paper edition teaching materials, a user can shoot a cover image of the target teaching material through the intelligent terminal equipment, the first shooting unit matches corresponding teaching material information from the teaching material database according to the obtained cover image, and the acquisition of basic information of the target teaching material is automatically completed according to the matched teaching material information.
In a scene, when a user reads a teaching material, only a cover image of the teaching material needs to be shot, the basic information of the target teaching material can be automatically input, and the convenience degree of the user is improved.
In one embodiment, the first shooting unit collects a cover image of a target teaching material, and matches a corresponding target teaching material from a teaching material database based on the obtained cover image, and the method specifically includes:
firstly, preprocessing a cover image of an acquired target teaching material;
carrying out edge detection and segmentation processing on the pre-processed cover image to obtain a cover area image;
performing edge detection and character recognition on the obtained cover area image to obtain corresponding character recognition information as text characteristics;
performing feature extraction on the obtained cover area image to obtain corresponding image feature information as image features;
and fusing the obtained text features and the image features to obtain cover features of the target teaching materials, and matching the corresponding target teaching materials from the teaching material database based on the obtained cover features.
Basic information and a cover image corresponding to the teaching materials are prestored in the teaching material database, and cover characteristics obtained based on the unified method provide a basis for completing matching of the target teaching materials based on the cover image of the target teaching materials.
The matching of the corresponding target teaching materials based on the cover features can be completed based on a neural network model or a deep learning network, and the application is not particularly limited herein.
Aiming at the problems that a user is easily influenced by the cover material and light of a teaching material in the process of shooting a cover image of a target teaching material, the shot cover image is easily reflected or has insufficient definition, and the accuracy of matching the target teaching material according to the cover image in the follow-up process is influenced; in one embodiment, in the first shooting unit, for a cover image of an acquired target teaching material, preprocessing is performed first to improve a display effect of the acquired cover image, where preprocessing the cover image specifically includes:
converting the acquired cover image from an RGB color space to an HSV color space;
based on the acquired brightness component, firstly, detecting a reflective point existing in the cover image, wherein the adopted reflective point detection function is as follows:
Figure BDA0003298740540000041
in the formula, V (x, y) represents the lightness component value of the pixel point (x, y), med (V) represents the lightness component median value of each pixel point in the cover image, VT1Denotes a set first lightness component threshold value, where VT1∈[0.22，0.35]，
Figure BDA0003298740540000051
Representing the neighborhood region centered around the pixel point (x, y)
Figure BDA0003298740540000052
The average value of brightness components of the inner pixel points, wherein
Figure BDA0003298740540000053
A 3 × 3 or 5 × 5 neighborhood range may be taken; vT2Represents a set second brightness component threshold, where VT2∈[0.1，0.15]；
Marking the pixel points which accord with the detection function as reflection points to obtain a reflection point set phiFAnd non-reflective dot sets
Figure BDA0003298740540000054
And carrying out brightness adjustment processing on the obtained reflection points, wherein the adopted brightness adjustment function is as follows:
Figure BDA0003298740540000055
wherein V' (x, y) represents a luminance component value of the luminance-adjusted reflection point (x, y),
Figure BDA0003298740540000056
expressing the minimum value of the brightness component of each pixel point in the neighborhood range of the reflecting point (x, y), mean (V) expressing the average value of the brightness component of each pixel point in the cover image, beta expressing the set inhibition factor, wherein the beta belongs to [0.05, 0.1 ]]；
After the brightness adjustment processing is sequentially carried out on the reflecting points in the reflecting point set, the brightness component V after the primary processing is obtained1；
And aiming at the brightness component after one-time processing, performing brightness enhancement processing of N iterations, wherein the brightness enhancement processing function is as follows:
Figure BDA0003298740540000057
in the formula (I), the compound is shown in the specification,
Figure BDA0003298740540000058
representing the brightness component values of the pixel points (x, y) after the t +1 th iteration processing,
Figure BDA0003298740540000059
representing the neighborhood range with the pixel point (x, y) as the center after the t-th iteration processing
Figure BDA00032987405400000510
Average value of luminance components, V, of inner pixelsZIndicates a set standard luminance component value, where VZ∈[0.7，0.8]And beta represents a set step adjustment function, wherein beta is equal to 0.06, 0.07]S (x, y) represents a saturation component value of the pixel point (x, y), wherein
Figure BDA00032987405400000511
N denotes the total number of iterations, N ∈ [2, 5 ]]When t +1 is equal to N, then output
Figure BDA00032987405400000512
As a secondarily processed luminance component V2；
Based on the resulting luminance component V2And reconstructing, and converting from the HSV color space to the RGB color space again to obtain the preprocessed cover image.
In the above embodiment, a technical scheme for preprocessing a cover image is provided, where the cover image is first converted from an RGB color space to an HSV color space, and then a reflection point existing in the cover image is first detected based on an obtained brightness component V, where a detection function for detecting the reflection point is particularly provided, and brightness suppression is performed on the reflection point based on the detected reflection point, so as to eliminate the problem of unclear image due to reflection of the cover of a teaching material, and a processing function for performing brightness suppression specifically on the reflection point is provided, so that brightness of the reflection point can be effectively suppressed; the method is characterized in that overall brightness adjustment processing is further performed on the whole image aiming at the cover image with the inhibited reflection points, wherein based on an iterative brightness adjustment mode, the overall brightness level of the image can be improved, meanwhile, the distortion condition caused by sudden brightness change can be effectively inhibited, the overall definition of the image is effectively improved, meanwhile, when the brightness is improved, the technical scheme of adjustment based on the saturation characteristic of pixel points is improved, the detail characteristics in the image can be effectively highlighted, and the overall display effect of the image is improved. And a foundation is laid for further identifying specific cover basic information according to the cover image.
In one embodiment, the second obtaining module 20 includes a second input unit and a vector extracting unit;
the second input unit is used for acquiring target words appearing in the target teaching materials input by the user;
the vector extraction unit is used for constructing a feature vector of the target word according to the obtained target word and basic information of the target teaching material, wherein the feature vector of the target word comprises the target word and at least one of the field of the teaching material, the author of the teaching material and the audience group of the teaching material.
After the user inputs the basic information of the target teaching material, when the user needs to perform semantic interpretation on the formulated key words or difficult words in the reading process, the target words are input into the system through the second input unit, the system automatically completes construction of the feature vectors according to the target words and the basic information of the corresponding teaching material, and a foundation is laid for performing semantic interpretation search further based on the feature vectors.
In one scenario, the feature vector of the target word may contain multidimensional feature information, such as { chinese word; english words; the name of the target teaching material; the field of teaching materials; a teaching material author; textbook audience group (such as { cell; mobile communication technology; mobile communication; higher than a certain level; none }); aiming at the obtained feature vector containing the multi-dimensional information, when explanation searching is carried out according to target words subsequently, the explanation matched with the target words can be searched based on different characteristics of the field, the author writing style, audience groups and the like corresponding to specific target teaching materials, and the adaptability and the reliability of semantic explanation for the target words are improved.
In one embodiment, the second acquiring module 20 further includes a second shooting unit;
the second shooting unit is used for collecting target word pictures in the target teaching materials and carrying out text recognition based on the obtained target word pictures to obtain corresponding target words.
The target word can be obtained by shooting and word-taking, and the specific shooting and word-taking can be realized by adopting the existing technical scheme in the field, and the application is not limited specifically.
In one embodiment, the indexing module 30 includes a search unit and an output unit;
the searching unit is used for searching semantic interpretation information corresponding to the feature vector from the large database according to the acquired feature vector of the target word based on a search engine;
and the output unit is used for sorting out the semantic interpretation result corresponding to the target word according to the acquired semantic interpretation information.
The large database records basic information and corresponding text information of different literary works (including teaching materials, periodicals, papers, books and the like) in advance, and aims at standard explanation marks containing key words in the text information; the text information of the literature comprises an index table corresponding to different key words, and the standard explanation comprises official semantic explanation or expert semantic explanation of the key words in the corresponding literature.
The searching unit searches from the large database based on the obtained feature vector of the target word, and obtains semantic interpretation information for the target word matched with the target vector (for example, searching is performed according to the feature vector to obtain a plurality of interpretations about the target word, wherein the interpretations are arranged according to the matching degree from high to low to form interpreted information), and the output unit can select to screen out the information with the highest matching degree according to the obtained semantic interpretation information, or output the corresponding semantic interpretation result in a list manner.
In one embodiment, the method is applied to electronic edition teaching materials; the first obtaining module 10 further obtains basic information of the target teaching material directly from the electronic edition teaching material; the second obtaining module 20 further includes using the index words (keywords) of the target textbook as the target words.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
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 (8)

1. A teaching material index management system based on big data is characterized by comprising: the system comprises a first acquisition module, a second acquisition module and an index module;
the first acquisition module is used for acquiring basic information of the target teaching material, wherein the basic information comprises the name, the field, the author and the facing group of the target teaching material;
the second acquisition module is used for acquiring target words appearing in the target teaching material and constructing feature vectors of the target words based on the basic information of the target teaching material and the target words;
the index module is used for searching matched semantic interpretation information from the big database by using a search engine based on big data technology aiming at the acquired feature vector of the target word and outputting a semantic interpretation result corresponding to the target word.
2. The textbook index management system based on big data as claimed in claim 1, further comprising a display module;
the display module is used for displaying the semantic interpretation result corresponding to the target word.
3. The big-data-based teaching material index management system as claimed in claim 1, wherein the first obtaining module comprises a first input unit;
the first input unit is used for acquiring at least one item of basic information of a target teaching material input by a user, matching the corresponding target teaching material from the teaching material database according to the basic information of the target teaching material input by the user, and acquiring complete basic information according to the matched target teaching material.
4. The big-data-based teaching material index management system as claimed in claim 3, wherein the first obtaining module further comprises a first shooting unit;
the first shooting unit is used for collecting cover images of the target teaching materials, summarizing and matching the corresponding target teaching materials from the teaching material database based on the obtained cover images, and obtaining complete basic information of the target teaching materials according to the matched target teaching materials.
5. The big-data-based teaching material index management system as claimed in claim 3, wherein the second obtaining module comprises a second input unit and a vector extraction unit;
the second input unit is used for acquiring target words appearing in the target teaching materials input by the user;
the vector extraction unit is used for constructing a feature vector of the target word according to the obtained target word and basic information of the target teaching material, wherein the feature vector of the target word comprises the target word and the basic information of the target teaching material, and the basic information of the target teaching material comprises at least one of the field of the teaching material, the author of the teaching material and the audience group of the teaching material.
6. The big-data-based teaching material index management system as claimed in claim 5, wherein the second obtaining module further comprises a second shooting unit;
the second shooting unit is used for collecting target word pictures in the target teaching materials and carrying out text recognition based on the obtained target word pictures to obtain corresponding target words.
7. The big data-based teaching material index management system as claimed in claim 5, wherein the index module comprises a search unit and an output unit;
the searching unit is used for searching semantic interpretation information corresponding to the feature vector from the large database according to the acquired feature vector of the target word based on a search engine;
and the output unit is used for sorting out the semantic interpretation result corresponding to the target word according to the acquired semantic interpretation information.
8. The big data-based teaching material index management system according to claim 1, wherein for electronic edition teaching materials;
the first acquisition module also comprises basic information of the target teaching material directly acquired from the electronic edition teaching material;
the second acquisition module also comprises a step of taking the index words of the target teaching materials as target words.
CN202111184840.4A 2021-10-12 2021-10-12 Teaching material index management system based on big data Pending CN113901053A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114913971A (en) * 2022-06-10 2022-08-16 奇医天下大数据科技(珠海横琴)有限公司 Electronic prescription service management system based on artificial intelligence

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114913971A (en) * 2022-06-10 2022-08-16 奇医天下大数据科技(珠海横琴)有限公司 Electronic prescription service management system based on artificial intelligence
CN114913971B (en) * 2022-06-10 2023-05-09 奇医天下大数据科技(珠海横琴)有限公司 Electronic prescription service management system based on artificial intelligence

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Applicant before: Zhang Yunhua

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