CN114550180B - Intelligent identification and statistics method and system and intelligent desk lamp - Google Patents
Intelligent identification and statistics method and system and intelligent desk lamp Download PDFInfo
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- CN114550180B CN114550180B CN202210125503.6A CN202210125503A CN114550180B CN 114550180 B CN114550180 B CN 114550180B CN 202210125503 A CN202210125503 A CN 202210125503A CN 114550180 B CN114550180 B CN 114550180B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F21—LIGHTING
- F21V—FUNCTIONAL FEATURES OR DETAILS OF LIGHTING DEVICES OR SYSTEMS THEREOF; STRUCTURAL COMBINATIONS OF LIGHTING DEVICES WITH OTHER ARTICLES, NOT OTHERWISE PROVIDED FOR
- F21V33/00—Structural combinations of lighting devices with other articles, not otherwise provided for
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
- Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection
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Abstract
The invention provides an intelligent identification and statistics method and system and an intelligent desk lamp, comprising the following steps: collecting a picture of a target to be identified, and displaying the obtained picture on a display; performing minimum unit division on the display, and associating the minimum unit with the question information; storing the association relationship between the picture obtained in the step S1 and the minimum unit divided by the picture in a local area and/or reporting the association relationship to a server side; and sequentially carrying out image acquisition on a plurality of targets to be identified, and carrying out intelligent statistics on the acquired images. Therefore, the intelligent system distribution mode with strong expansibility and high accuracy among different targets to be identified is provided.
Description
Technical Field
The invention relates to the field of intelligent table lamps, in particular to an intelligent identification and statistics method and system and an intelligent table lamp.
Background
The current intelligent class is continuously developed, students can automatically correct the teaching and assisting homework and the like, but the current implementation mode is to input homework typesetting templates and correct answers in advance or input a coordinate system in advance to identify the appointed coordinates, but the method has the advantages of extremely complex preparation work in the early stage, extremely high maintenance cost and wide applicability, and usually only one set of system can automatically identify and correct one type of test paper or teaching and assisting, and has limited functions. In the prior art, the teaching auxiliary materials can be automatically segmented into the areas to be unified by an image recognition technology, the automatic recognition mode is complex in algorithm and low in recognition accuracy, and the intelligent unified recognition mode with high accuracy and high expansibility is needed.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides an intelligent identification and statistics method, which comprises the following steps:
s1, acquiring a picture of a target to be identified, and displaying the acquired picture on a display;
s2, carrying out minimum unit division on the display, and associating the minimum unit with the question information;
s3, storing the association relationship between the picture obtained in the step S1 and the minimum unit divided by the picture in a local area and/or reporting the association relationship to a server side;
s4, sequentially carrying out image acquisition on a plurality of targets to be identified, and carrying out intelligent statistics on the acquired images.
Preferably, the minimum unit division specifically includes: the touch screen comprises a manual picture frame, a picture frame enlarged or reduced through a cross cursor, an external mouse frame selection and a handwriting pen frame selection.
Preferably, the step S2 specifically includes:
s2-1, marking a minimum unit of a title on a display by a user, and recording the area information of the minimum unit;
s2-2, the area information of the minimum unit is associated and bound with topic information, and the topic information comprises a topic number and/or a score.
Preferably, the title information is marked in an editing area of the display in a preset editing mode.
Preferably, the intelligent system comprises the steps of:
s4-1, performing image processing on the acquired images of the plurality of targets to be identified to obtain target images;
s4-2, cutting the target image according to the acquired area information of the minimum units to acquire area pictures of a plurality of minimum units;
s4-3, extracting correction marks from the region pictures in the minimum unit in sequence;
s4-4, identifying correction marks, obtaining the error of each minimum unit, and recording the score of the minimum unit;
and S4-5, accumulating the score values of all the minimum units to obtain a final score.
Preferably, the intelligent system comprises the steps of:
s4-21, extracting correction marks of the collected targets to be identified, identifying the correction marks, and recording coordinate areas corresponding to the correction marks;
s4-22, judging a target minimum unit to which the correction trace belongs according to the coverage degree of the coordinate area of the correction trace and the area information of the minimum unit, so as to obtain a correction result of the target minimum unit;
s4-23, accumulating the score values of all the minimum units to obtain a final score.
Preferably, the step S1 further includes performing image processing on the acquired picture, where the image processing includes one of denoising, rectangular correction, calibration correction, resizing, or a combination thereof.
Preferably, the method further comprises the following steps: s5, generating templates according to the minimum unit division and the topic association information, and sharing the templates to other users for intelligent statistics.
The intelligent identification system comprises a memory, a processor and a computer program stored on the memory, wherein the computer program runs on the processor, and the intelligent identification system method is realized when the processor executes the computer program.
The intelligent desk lamp is characterized by comprising an illumination module, a camera, a bracket, a display and a base;
the display and the intelligent desk lamp are integrated or split;
the camera is used for shooting pictures of the targets to be identified for displaying and/or identifying;
the support is used for connecting the lighting module and the base.
The beneficial effects are that:
the invention can effectively identify and unify teaching assistance, operation and the like through the method of self-calibrating on the display by the user, overcomes the problem of inaccurate image identification in the prior art, and has better expansibility and higher accuracy because the template coordinates are not required to be recorded.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an intelligent recognition and statistics method provided by an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the invention, fall within the scope of protection of the invention.
Example 1
Referring to fig. 1, an intelligent identification and statistics method includes the following steps:
s1, acquiring a picture of a target to be identified, and displaying the acquired picture on a display;
s2, carrying out minimum unit division on the display, and associating the minimum unit with the question information;
s3, storing the association relationship between the picture obtained in the step S1 and the minimum unit divided by the picture in a local area and/or reporting the association relationship to a server side;
s4, sequentially carrying out image acquisition on a plurality of targets to be identified, and carrying out intelligent statistics on the acquired images.
Preferably, all the pictures can be uniformly corrected after being collected, the pictures can be corrected while being collected, and the correction can be performed when the threshold value is collected.
Preferably, the minimum unit division specifically includes: the touch screen comprises a manual picture frame, a picture frame enlarged or reduced through a cross cursor, an external mouse frame selection and a handwriting pen frame selection.
Preferably, the step S2 specifically includes:
s2-1, marking a minimum unit of a title on a display by a user, and recording the area information of the minimum unit;
s2-2, the area information of the minimum unit is associated and bound with topic information, and the topic information comprises a topic number and/or a score.
Preferably, the title information is marked in an editing area of the display in a preset editing mode.
If the mark 1.1_5 is the first big problem and the first small problem, the score is 5 points.
Preferably, the intelligent system comprises the steps of:
s4-1, performing image processing on the acquired images of the plurality of targets to be identified to obtain target images;
s4-2, cutting the target image according to the acquired area information of the minimum units to acquire area pictures of a plurality of minimum units; at this time, the information of the picture has a scaling relationship with the size of the picture when the display is displayed, so that the coordinate area is obtained after the scaling of the coordinate area;
preferably, the area information is a rectangular area formed by the upper left corner coordinates and the lower right corner coordinates, a teacher can define a minimum unit for judging the right-wrong of the small questions, and at the moment, the corresponding area of each small question is selected in the display screen as the minimum unit;
s4-3, extracting correction marks from the region pictures in the minimum unit in sequence;
the teacher has already batched the homework in advance, draw the shape such as the hook fork, but do not differentiate and count homework data, here the correction of the teacher is discerned, the common practice is to withdraw the color (red) first, then remove noise and get and revise the trace after filtering.
S4-4, identifying correction marks, obtaining the error of each minimum unit, and recording the score of the minimum unit;
and S4-5, accumulating the score values of all the minimum units to obtain a final score.
Preferably, the intelligent system comprises the steps of:
s4-21, extracting correction marks of the collected targets to be identified, identifying the correction marks, and recording coordinate areas corresponding to the correction marks;
s4-22, judging a target minimum unit to which the correction trace belongs according to the coverage degree of the coordinate area of the correction trace and the area information of the minimum unit, so as to obtain a correction result of the target minimum unit;
s4-23, accumulating the score values of all the minimum units to obtain a final score.
Preferably, the step S1 further includes performing image processing on the acquired picture, where the image processing includes one of denoising, rectangular correction, calibration correction, resizing, or a combination thereof.
Preferably, the method further comprises the following steps: s5, generating templates according to the minimum unit division and the topic association information, and sharing the templates to other users for intelligent statistics.
Example two
The intelligent identification system comprises a memory, a processor and a computer program stored on the memory, wherein the computer program runs on the processor, and the intelligent identification system method is realized when the processor executes the computer program.
Preferably, the system can further count the wrong questions, generate a wrong question book or accurately push questions to students based on the current correction condition, thereby helping students to hold the wrong questions in the first to third directions.
Example III
The intelligent desk lamp comprises a lighting module, a camera, a bracket, a display and a base;
the display and the intelligent desk lamp are integrated or split;
when the intelligent desk lamp and the display are split, the display can be a display screen of the learning panel;
the camera is used for shooting pictures of the targets to be identified for displaying and/or identifying;
the support is used for connecting the lighting module and the base.
Preferably, the base is provided with a limiting device for limiting the object to be identified in the designated area. And limiting the target to be identified in the designated area, so that the acquired picture is the most standard, and the error is the smallest during calculation.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention, for example. The one or more modules/units may be a series of computer program instruction segments capable of performing a specific function for describing the execution of the computer program in the smart desk lamp.
Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (7)
1. An intelligent identification and statistics method is characterized in that: the method comprises the following steps:
s1, acquiring a picture of a target to be identified, and displaying the acquired picture on a display;
s2, carrying out minimum unit division on the display, and associating the minimum unit with the question information; the step S2 specifically includes:
s2-1, marking a minimum unit of a title on a display by a user, and recording the area information of the minimum unit;
s2-2, associating and binding the area information of the minimum unit with topic information, wherein the topic information comprises a topic number and a score;
s3, storing the association relationship between the picture obtained in the step S1 and the minimum unit divided by the picture in a local area and/or reporting the association relationship to a server side;
s4, sequentially carrying out image acquisition on a plurality of targets to be identified, and carrying out intelligent statistics on the acquired images;
step S4 further includes:
s4-1, performing image processing on the acquired images of the plurality of targets to be identified to obtain target images;
s4-2, cutting the target image according to the acquired area information of the minimum units to acquire area pictures of a plurality of minimum units;
s4-3, extracting correction marks from the region pictures in the minimum unit in sequence;
s4-4, identifying correction marks, obtaining the error of each minimum unit, and recording the score of the minimum unit;
s4-5, accumulating the score values of all the minimum units to obtain a final score;
specifically, step S4 further includes: s4-21, extracting correction marks of the collected targets to be identified, identifying the correction marks, and recording coordinate areas corresponding to the correction marks;
s4-22, judging a target minimum unit to which the correction trace belongs according to the coverage degree of the coordinate area of the correction trace and the area information of the minimum unit, so as to obtain a correction result of the target minimum unit;
s4-23, accumulating the score values of all the minimum units to obtain a final score.
2. The intelligent identification and differentiation method as described in claim 1, characterized in that: the minimum unit division specifically includes: the touch screen comprises a manual picture frame, a picture frame enlarged or reduced through a cross cursor, an external mouse frame selection and a handwriting pen frame selection.
3. The intelligent identification and differentiation method as described in claim 2, characterized in that: and marking the title information in an editing area of the display in a preset editing mode.
4. The intelligent identification and differentiation method as described in claim 1, characterized in that: the step S1 further includes performing image processing on the acquired picture, where the image processing includes one of denoising, rectangular correction, calibration correction, resizing, or a combination thereof.
5. An intelligent identification and differentiation method as described in any one of claims 1-4, characterized in that: the method also comprises the following steps: s5, generating templates according to the minimum unit division and the topic association information, and sharing the templates to other users for intelligent statistics.
6. An intelligent identification system is characterized in that: comprising a memory, a processor and a computer program stored on the memory, said computer program running on the processor, wherein the processor implements the smart identification unification method according to any one of claims 1-5 when executing said computer program.
7. An intelligent desk lamp adopting the intelligent identification and statistics method as set forth in any one of claims 1-5, wherein the intelligent desk lamp comprises a lighting module, a camera, a bracket, a display and a base;
the display and the intelligent desk lamp are integrated or split;
the camera is used for shooting pictures of the targets to be identified for displaying and/or identifying;
the support is used for connecting the lighting module and the base.
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| CN202210125503.6A CN114550180B (en) | 2022-02-10 | 2022-02-10 | Intelligent identification and statistics method and system and intelligent desk lamp |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US20130052631A1 (en) * | 2010-05-04 | 2013-02-28 | Moodeye Media And Technologies Pvt Ltd | Customizable electronic system for education |
| CN101901338A (en) * | 2010-07-09 | 2010-12-01 | 北京商纳科技有限公司 | Method and system for calculating scores of test paper |
| CN107423738B (en) * | 2017-08-02 | 2020-10-23 | 广东工业大学 | Test paper subject positioning method and device based on template matching |
| CN107506762B (en) * | 2017-09-01 | 2020-03-20 | 昆山中骏博研互联网科技有限公司 | Score automatic input method based on image analysis |
| CN108229361A (en) * | 2017-12-27 | 2018-06-29 | 北京摩数教育科技有限公司 | A kind of electronic paper marking method |
| CN109086336A (en) * | 2018-07-05 | 2018-12-25 | 深圳闳宸科技有限公司 | Paper date storage method, device and electronic equipment |
| CN110245640A (en) * | 2019-06-21 | 2019-09-17 | 深圳市采集科技有限公司 | For being automatically positioned the method and work correction method, system and storage medium of topic in work correction |
| CN110414563A (en) * | 2019-06-27 | 2019-11-05 | 深圳中兴网信科技有限公司 | Total marks of the examination statistical method, system and computer readable storage medium |
| CN112163529A (en) * | 2020-09-30 | 2021-01-01 | 珠海读书郎网络教育有限公司 | System and method for uniformly dividing test paper |
| CN113191344A (en) * | 2021-04-22 | 2021-07-30 | 读书郎教育科技有限公司 | Automatic scoring method for intelligent desk lamp and intelligent desk lamp |
| CN113569845A (en) * | 2021-07-13 | 2021-10-29 | 读书郎教育科技有限公司 | Intelligent method for assisting teacher in automatic correction and intelligent desk lamp |
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