CN111914068A - Method for extracting knowledge points of test questions - Google Patents
Method for extracting knowledge points of test questions Download PDFInfo
- Publication number
- CN111914068A CN111914068A CN202010850830.9A CN202010850830A CN111914068A CN 111914068 A CN111914068 A CN 111914068A CN 202010850830 A CN202010850830 A CN 202010850830A CN 111914068 A CN111914068 A CN 111914068A
- Authority
- CN
- China
- Prior art keywords
- data
- test question
- knowledge point
- knowledge
- test
- 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.)
- Pending
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 131
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000009795 derivation Methods 0.000 claims description 27
- 238000012545 processing Methods 0.000 claims description 15
- 230000009467 reduction Effects 0.000 claims description 10
- 238000003062 neural network model Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 description 10
- 238000013528 artificial neural network Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000013527 convolutional neural network Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013144 data compression Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000000306 recurrent effect Effects 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
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
-
- 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
-
- G06T5/70—
-
- G06T5/90—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
Abstract
The invention relates to a method for extracting test question knowledge points, which comprises the following steps: the server receives the test question content data, and analyzes the test question content data according to the knowledge deduction model to obtain test question answer data and first test question condition data; determining whether the answer data of the test questions comprises the answer data of the intermediate step; when the answer data of the test questions comprises the answer data of the intermediate step, determining second test question condition data corresponding to the intermediate step data in the first test question condition data; inquiring the knowledge point data matched with the second test question condition data in a knowledge point database; obtaining target data according to the knowledge point data and the test question content data; and when the answer data of the test questions does not comprise the answer data of the intermediate step, inquiring the knowledge point data matched with the first test question condition data in the knowledge point database, and obtaining target data according to the knowledge point data and the test question content data.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method for extracting test question knowledge points.
Background
With the rapid and stable development of social economy and the strong support of the nation on education informatization, particularly the fund support on basic education informatization, the construction pace of the education informatization is accelerated. Since education stepped on the stage of software market, along with the change of internet technology and the gradual popularization, and the national emphasis on education and training industry and the increase of investment, education software occupies one third of the whole software market, and forms a three-day-down situation with office software and financial software.
Knowledge extraction refers to the process of identifying, discovering and extracting concepts, types, facts and their associated relationships, constraint rules, and steps, rules for problem solving from digital resources. At present, most of the existing test question knowledge points are extracted by manual problem solving and analysis according to the problems. The extraction method of the knowledge points is time-consuming, labor-consuming and low in efficiency.
Disclosure of Invention
The invention aims to provide a method for extracting knowledge points of test questions, which aims to solve the defects of the prior art, obtains answers of each step in the problem solving and derivation conditions corresponding to the answers of each step through model analysis of the test questions, and determines all knowledge points related to an entity according to the derivation conditions corresponding to the answers of each step, so that the extracted knowledge points have higher accuracy, better uniformity and more comprehensive knowledge points.
In order to achieve the purpose, the invention provides a method for extracting test question knowledge points, which comprises the following steps:
the server obtains test question content data according to the image data to be identified, and analyzes the test question content data according to a knowledge derivation model to obtain test question answer data and first test question condition data;
determining whether the answer data of the test questions comprises answer data of intermediate steps;
when the answer data of the test questions comprises answer data of an intermediate step, determining second test question condition data corresponding to the intermediate step data in the first test question condition data;
inquiring the knowledge point data matched with the second test question condition data in a knowledge point database;
obtaining target data according to the knowledge point data and the test question content data;
and when the answer data of the test questions does not comprise the answer data of the intermediate step, inquiring the knowledge point data matched with the first test question condition data in a knowledge point database, and obtaining target data according to the knowledge point data and the test question content data.
Preferably, before the server receives the test question content data, the method further includes:
the user terminal acquires image data to be identified according to an acquisition instruction input by a user;
the user terminal performs noise reduction processing on the image data to be identified to obtain noise-reduced image data to be identified and sends the noise-reduced image data to a server;
and the server identifies the image data to be identified according to the neural network model to obtain test question content data.
Further preferably, the step of acquiring, by the user terminal according to the acquisition instruction input by the user, the image data to be identified specifically includes:
the user terminal acquires original image data according to the acquisition instruction input by the user and displays the original image data;
and receiving a region selection instruction input by the user according to the original image data, and obtaining the image data to be identified according to the region selection instruction.
Further preferably, the noise reduction processing includes adjusting the size of the image data to be recognized and gray scale processing.
Preferably, before the analyzing the test question content data according to the knowledge derivation model to obtain test question answer data and first test question condition data, the method further includes:
the knowledge derivation model is trained according to a plurality of derivation theorem data.
Preferably, the target data includes one or more knowledge point data.
Further preferably, when the knowledge point data is multiple, the obtaining of the target data according to the knowledge point data and the test question content data specifically includes:
determining whether there is duplicate knowledge point data in a plurality of knowledge point data;
deleting the repeated knowledge point data when the repeated knowledge point data exists in a plurality of knowledge point data;
and obtaining the target data according to the data of the knowledge point after the duplication removal and the test question content data.
According to the method for extracting the knowledge points of the test questions, provided by the embodiment of the invention, the answers of each step in the problem solving process and the derivation conditions corresponding to the answers of each step are obtained through analyzing the test questions through the model, and all the knowledge points related to the entity are determined according to the derivation conditions corresponding to the answers of each step, so that the extracted knowledge points are higher in accuracy, better in uniformity and more comprehensive in knowledge points.
Drawings
Fig. 1 is a flowchart of a method for extracting test question knowledge points according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
The method for extracting the knowledge points of the test questions is used for analyzing the test questions uploaded by the user and extracting the knowledge points in the test questions. The flow chart of the method is shown in figure 1, and the method comprises the following steps:
in particular, the user terminal may be understood as a smart device with a networking function, such as a smart phone. The user inputs an acquisition instruction to the user terminal. The acquisition instruction may be understood as an instruction to acquire a test question to be uploaded. The test questions to be uploaded can be shot by the camera device or can be manually input by the user in the user terminal. However, whether the test questions to be uploaded are obtained by taking a picture or manually input, the test questions to be uploaded are data in the form of pictures.
When the test questions to be uploaded are shot by the camera device, the user terminal needs to acquire the use permission of the camera, the microphone and other devices for the user. After the user terminal acquires the use authority of devices such as a camera and a microphone, the user terminal acquires original image data through the camera according to an acquisition instruction input by the user, and displays the original image data to the user. And inputting a region selection instruction according to the original image data by a user, and obtaining the image data to be identified according to the region selection instruction after the user terminal receives the region selection instruction. The original image data can be understood as an original photo taken by a user through the camera device. The image data to be identified can be understood as the test question part to be uploaded in the original photo after the user conducts region interception on the original photo. The process can be understood as a process of selecting the content in the shot photo by the user, and is beneficial to carrying out more efficient identification on the photo content subsequently.
Similarly, when the test questions to be uploaded are manually input by the user in the user terminal, the user can select the manually input contents. And during selection, inputting a region selection instruction according to the original image data by a user, and obtaining the image data to be identified according to the region selection instruction after the user terminal receives the region selection instruction.
102, the user terminal carries out noise reduction processing on image data to be recognized and sends the image data to be recognized after noise reduction to a server;
specifically, most of images acquired by the existing image capturing apparatus are high-resolution color images, and the data storage amount of the high-resolution color images is relatively large, which is not beneficial to subsequent image processing. Therefore, after the user terminal acquires the image data to be identified, the noise reduction processing is firstly carried out on the image data to be identified. Preferably, the noise reduction processing includes image resizing and gradation processing. The user terminal zooms the image data to be recognized into a preset size, and the channel is a single-channel gray image.
And then, the user terminal compresses the image data to be recognized after the noise reduction, and packages and sends the image data to be recognized to the server, so that the server can perform subsequent processing on the image data to be recognized after the noise reduction.
103, the server identifies the image data to be identified according to the neural network model to obtain test question content data;
specifically, a Neural Network (NN) is a data model simulating a human actual Neural Network, which is a complex Network system formed by a large number of simple processing units widely connected to each other, and can also be understood as a highly complex nonlinear dynamical learning system. The NN model has the advantage of self-learning function. For example, when image recognition is implemented, a number of different pictures and corresponding recognized features (recognition results) are input into the NN model, and the NN model obtains a series of new features for predicting output variables through a self-learning function.
Preferably, in the embodiment of the present invention, a Convolutional Neural Network (CNN) model and a Recurrent Neural Network (RNN) model are used to identify the image data to be identified. The server decompresses the received image data compression packet to be recognized, releases the image data to be recognized, and recognizes character information on the image data to be recognized according to the CNN model and the RNN model to obtain test question content data in the image data to be recognized. The test question content data can be understood as the test questions uploaded by the user.
specifically, the knowledge derivation model is obtained by pre-training according to a plurality of derivation theorem data. The knowledge derivation model can be understood as an automatic problem solving model, and the derivation theoretic data can be understood as the existing theory, including the information of known syntactic logic, computational logic, formulas, theorems, axiom and problem solving methods and the like.
Because the knowledge derivation model is obtained by training a plurality of derivation theorem data, after the test question content data is input into the knowledge derivation model, the knowledge derivation model can analyze the test question content data according to the derivation theorem data to obtain the test question answers and the derivation conditions corresponding to the test question answers. The answer data of the test questions may be understood as the answer of the test questions including the solving process of each step. The first test question condition data may be understood as a derivation condition corresponding to the answer to the test question including each solving process.
In a specific example, the test question content data is: 2cos30 ° -2sin30 ° 3tan45 ° + |1-tan60 ° |, the answer data of the test questions obtained by the analysis of the knowledge derivation model is as follows:
the first test question condition data comprises
When a<0, | a | ═ a ═ 2a ", and" — "1 +3-1 ═ 1".
specifically, if the problem solving process includes a plurality of steps, the answer data to the test questions includes intermediate-step answer data. The intermediate step answer data can be understood as the answer of each step of solving the problem process.
In the specific example described above, the answer data of the intermediate step is
And
since each solving process may correspond to a knowledge point, if the knowledge points are extracted only according to the last solving step or the most complicated solving step in the answers, the obtained knowledge points are not comprehensive. When the answer data of the test questions includes the answer data of the middle step, it indicates that a plurality of steps are required for solution, and the test questions may include a plurality of knowledge points, the following steps 106 and 107 are performed, and then step 108 is performed. When the answer data of the test questions does not include the answer data of the intermediate step, it is indicated that the test questions can be answered by only one step, and the test questions only include one knowledge point, the following step 106' is executed, and then the step 108 is directly executed.
specifically, when the answer data of the test questions includes the answer data of the intermediate step, it is described that the test questions may include a plurality of knowledge points, and the server needs to extract each knowledge point. The server determines second test question condition data corresponding to each of the intermediate-step answer data among the first test question condition data according to the intermediate-step answer data. The second test question condition data can be understood as the derivation condition corresponding to the answer of the intermediate step.
In the specific example above, the intermediate step answer data
The corresponding second test question condition data is
"tan 45 ° -1" and
intermediate step answer data
The corresponding second test question condition data is' when a<At 0, | a | ═ a ".
specifically, the knowledge point database stores the correspondence between the test question condition data and the knowledge point data. That is, the server can find the knowledge point data corresponding to the test question condition data according to the test question condition data. And the server queries the knowledge point data matched with the second test question condition data in the knowledge point database, namely the knowledge points of the current test question.
In the above specific example, the second question condition data is stored in the knowledge point database
"tan 45 ° -1" and
the corresponding knowledge point data is the trigonometric function value of the special angle. Second test question Condition data "when a<When 0, | a | ═ a "corresponds to knowledge point data" defined as an absolute value ". The server according to the second test question condition data
“tan45°＝1”、
And when a<When 0, | a | ═ a "the knowledge point data queried for are" trigonometric function values for special angles "and" absolute value definitions ".
Step 106', inquiring the knowledge point data matched with the first test question condition data in a knowledge point database;
specifically, when the answer data of the test questions does not include the answer data of the intermediate step, the test questions only include one knowledge point, and the server only needs to query the knowledge point data matched with the first test question condition data in the knowledge point database according to the first test question condition data. And the server queries the knowledge point data matched with the first tested question condition data in the knowledge point database, namely the only knowledge point of the current tested question.
specifically, if the server obtains a plurality of knowledge point data according to the above steps, the server first determines whether there is duplicate data in the knowledge point data. When there is duplicate data in the knowledge point data, the duplicate knowledge point data is deleted, that is, the deduplication processing is performed on a plurality of knowledge point data. And then the server obtains target data according to the data of the knowledge point and the data of the test question content after the duplication removal. If the server obtains one knowledge point data according to the steps, the server directly obtains the target data according to the knowledge point data and the test question content data without going through a repetition step.
And finally, the server stores the target data to a test question database for later use. The target data may be understood as data including test questions and test question knowledge point information. In the above specific example, the target data is: "2 cos30 ° -2sin30 ° +3tan45 ° + |1-tan60 ° |, consider trigonometric function values and absolute value definitions for a particular angle".
According to the method for extracting the knowledge points of the test questions, provided by the embodiment of the invention, the answers of each step in the problem solving process and the derivation conditions corresponding to the answers of each step are obtained through analyzing the test questions through the model, and all the knowledge points related to the entity are determined according to the derivation conditions corresponding to the answers of each step, so that the extracted knowledge points are higher in accuracy, better in uniformity and more comprehensive in knowledge points.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a user terminal, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A method for extracting test question knowledge points is characterized by comprising the following steps:
the server obtains test question content data according to the image data to be identified, and analyzes the test question content data according to a knowledge derivation model to obtain test question answer data and first test question condition data;
determining whether the answer data of the test questions comprises answer data of intermediate steps;
when the answer data of the test questions comprises answer data of an intermediate step, determining second test question condition data corresponding to the intermediate step data in the first test question condition data;
inquiring the knowledge point data matched with the second test question condition data in a knowledge point database;
obtaining target data according to the knowledge point data and the test question content data;
and when the answer data of the test questions does not comprise the answer data of the intermediate step, inquiring the knowledge point data matched with the first test question condition data in a knowledge point database, and obtaining target data according to the knowledge point data and the test question content data.
2. The method for extracting examination question knowledge points according to claim 1, wherein before the server receives the examination question content data, the method further comprises:
the user terminal acquires image data to be identified according to an acquisition instruction input by a user;
the user terminal performs noise reduction processing on the image data to be identified to obtain noise-reduced image data to be identified and sends the noise-reduced image data to a server;
and the server identifies the image data to be identified according to the neural network model to obtain test question content data.
3. The method for extracting examination question knowledge points according to claim 2, wherein the step of acquiring the image data to be recognized by the user terminal according to the acquisition instruction input by the user specifically comprises:
the user terminal acquires original image data according to the acquisition instruction input by the user and displays the original image data;
and receiving a region selection instruction input by the user according to the original image data, and obtaining the image data to be identified according to the region selection instruction.
4. The method for extracting examination question knowledge points according to claim 2, wherein the noise reduction processing includes adjusting the size of image data to be recognized and gradation processing.
5. The method for extracting test question knowledge points according to claim 1, wherein before the analyzing the test question content data according to the knowledge derivation model to obtain the test question answer data and the first test question condition data, the method further comprises:
the knowledge derivation model is trained according to a plurality of derivation theorem data.
6. The method for extracting examination question knowledge points according to claim 1, wherein the target data includes one or more knowledge point data.
7. The method for extracting test question knowledge points according to claim 6, wherein when there are a plurality of knowledge point data, the obtaining of target data according to the knowledge point data and the test question content data specifically comprises:
determining whether there is duplicate knowledge point data in a plurality of knowledge point data;
deleting the repeated knowledge point data when the repeated knowledge point data exists in a plurality of knowledge point data;
and obtaining the target data according to the data of the knowledge point after the duplication removal and the test question content data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010850830.9A CN111914068A (en) | 2020-08-21 | 2020-08-21 | Method for extracting knowledge points of test questions |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010850830.9A CN111914068A (en) | 2020-08-21 | 2020-08-21 | Method for extracting knowledge points of test questions |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111914068A true CN111914068A (en) | 2020-11-10 |
Family
ID=73279439
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010850830.9A Pending CN111914068A (en) | 2020-08-21 | 2020-08-21 | Method for extracting knowledge points of test questions |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111914068A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113673475A (en) * | 2021-09-01 | 2021-11-19 | 北京云蝶智学科技有限公司 | Answering method based on image information |
| CN113688262A (en) * | 2021-09-01 | 2021-11-23 | 北京云蝶智学科技有限公司 | Data classification method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103955525A (en) * | 2014-05-09 | 2014-07-30 | 北京奇虎科技有限公司 | Method and client for searching answer to test question |
| CN107343223A (en) * | 2017-07-07 | 2017-11-10 | 北京慕华信息科技有限公司 | The recognition methods of video segment and device |
| CN107967254A (en) * | 2017-10-31 | 2018-04-27 | 科大讯飞股份有限公司 | Knowledge point Forecasting Methodology and device, storage medium, electronic equipment |
| CN108763588A (en) * | 2018-06-19 | 2018-11-06 | 深圳习习网络科技有限公司 | A kind of knowledge point quantitative analysis method and device |
| CN110096589A (en) * | 2019-03-14 | 2019-08-06 | 杭州笔声智能科技有限公司 | A kind of examination question feedback and recommendation explanation method and system based on image recognition technology |
| CN110309300A (en) * | 2018-08-23 | 2019-10-08 | 北京慧经知行信息技术有限公司 | A method of identification natural sciences knowledge-ID |
| CN110659352A (en) * | 2019-10-10 | 2020-01-07 | 浙江蓝鸽科技有限公司 | Test question and test point identification method and system |
-
2020
- 2020-08-21 CN CN202010850830.9A patent/CN111914068A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103955525A (en) * | 2014-05-09 | 2014-07-30 | 北京奇虎科技有限公司 | Method and client for searching answer to test question |
| CN107343223A (en) * | 2017-07-07 | 2017-11-10 | 北京慕华信息科技有限公司 | The recognition methods of video segment and device |
| CN107967254A (en) * | 2017-10-31 | 2018-04-27 | 科大讯飞股份有限公司 | Knowledge point Forecasting Methodology and device, storage medium, electronic equipment |
| CN108763588A (en) * | 2018-06-19 | 2018-11-06 | 深圳习习网络科技有限公司 | A kind of knowledge point quantitative analysis method and device |
| CN110309300A (en) * | 2018-08-23 | 2019-10-08 | 北京慧经知行信息技术有限公司 | A method of identification natural sciences knowledge-ID |
| CN110096589A (en) * | 2019-03-14 | 2019-08-06 | 杭州笔声智能科技有限公司 | A kind of examination question feedback and recommendation explanation method and system based on image recognition technology |
| CN110659352A (en) * | 2019-10-10 | 2020-01-07 | 浙江蓝鸽科技有限公司 | Test question and test point identification method and system |
Non-Patent Citations (1)
| Title |
|---|
| 韩云凤;张雨强;杨兴兴;: "高中化学教科书课后习题难度预估方法研究", 化学教育, no. 11, 2 June 2016 (2016-06-02) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113673475A (en) * | 2021-09-01 | 2021-11-19 | 北京云蝶智学科技有限公司 | Answering method based on image information |
| CN113688262A (en) * | 2021-09-01 | 2021-11-23 | 北京云蝶智学科技有限公司 | Data classification method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109034069B (en) | Method and apparatus for generating information | |
| CN107978189B (en) | Intelligent exercise pushing method and system and terminal equipment | |
| CN109377494B (en) | Semantic segmentation method and device for image | |
| WO2020029608A1 (en) | Method and apparatus for detecting burr of electrode sheet | |
| CN110956037B (en) | Multimedia content repeated judgment method and device | |
| CN110807472B (en) | Image recognition method and device, electronic equipment and storage medium | |
| CN111914068A (en) | Method for extracting knowledge points of test questions | |
| CN113052144A (en) | Training method, device and equipment of living human face detection model and storage medium | |
| CN108399401B (en) | Method and device for detecting face image | |
| CN113033677A (en) | Video classification method and device, electronic equipment and storage medium | |
| CN110111311B (en) | Image quality evaluation method and device | |
| CN113128522B (en) | Target identification method, device, computer equipment and storage medium | |
| CN110349108B (en) | Method, apparatus, electronic device, and storage medium for processing image | |
| TWI803243B (en) | Method for expanding images, computer device and storage medium | |
| CN110765304A (en) | Image processing method, image processing device, electronic equipment and computer readable medium | |
| CN111860212B (en) | Super-division method, device, equipment and storage medium for face image | |
| CN112801960B (en) | Image processing method and device, storage medium and electronic equipment | |
| CN114387451A (en) | Training method, device and medium for abnormal image detection model | |
| CN112990892A (en) | Video information acquisition method and image processing system for teaching evaluation | |
| CN112307158A (en) | Information retrieval method, device, equipment and medium | |
| CN113688262A (en) | Data classification method | |
| CN113705706A (en) | Data classification method based on image recognition | |
| CN111460214A (en) | Classification model training method, audio classification method, device, medium and equipment | |
| CN112434717A (en) | Model training method and device | |
| CN116453023B (en) | Video abstraction system, method, electronic equipment and medium for 5G rich media information |
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 |