CN116227968A - Network education effect inspection system based on real-time monitoring information feedback analysis - Google Patents
Network education effect inspection system based on real-time monitoring information feedback analysis Download PDFInfo
- Publication number
- CN116227968A CN116227968A CN202211603068.XA CN202211603068A CN116227968A CN 116227968 A CN116227968 A CN 116227968A CN 202211603068 A CN202211603068 A CN 202211603068A CN 116227968 A CN116227968 A CN 116227968A
- Authority
- CN
- China
- Prior art keywords
- information
- module
- unit
- state
- action
- 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
- 238000004458 analytical method Methods 0.000 title claims abstract description 67
- 238000012544 monitoring process Methods 0.000 title claims abstract description 52
- 230000000694 effects Effects 0.000 title claims abstract description 32
- 238000007689 inspection Methods 0.000 title claims abstract description 11
- 230000009471 action Effects 0.000 claims abstract description 92
- 238000011156 evaluation Methods 0.000 claims abstract description 61
- 238000006243 chemical reaction Methods 0.000 claims description 26
- 230000014509 gene expression Effects 0.000 claims description 6
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims 4
- 230000008859 change Effects 0.000 abstract description 2
- 238000000034 method Methods 0.000 description 9
- 230000006872 improvement Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000012258 culturing Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 235000008331 Pinus X rigitaeda Nutrition 0.000 description 1
- 235000011613 Pinus brutia Nutrition 0.000 description 1
- 241000018646 Pinus brutia Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/53—Querying
- G06F16/532—Query formulation, e.g. graphical querying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/761—Proximity, similarity or dissimilarity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/49—Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/174—Facial expression recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Multimedia (AREA)
- General Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Databases & Information Systems (AREA)
- Educational Technology (AREA)
- Human Computer Interaction (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Computing Systems (AREA)
- Social Psychology (AREA)
- Quality & Reliability (AREA)
- Psychiatry (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Artificial Intelligence (AREA)
- Primary Health Care (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The invention relates to the technical field of education effect inspection. The invention relates to a network education effect checking system based on real-time monitoring information feedback analysis. The system comprises a video monitoring unit, an information analysis unit, an information storage unit, a state retrieval unit, an information feedback unit and a comprehensive evaluation unit. The invention monitors the actions of the teacher and the students simultaneously in real time, continuously monitors the states of the students in the course of surfing the net, analyzes the states of the students according to the information stored in the action storage module, can be manually input by the teacher when the actions without storage appear, and can monitor the whole surfing the net course, and the teacher can receive the state information of the students in the course of surfing the net and in the course of surfing the net, thereby being convenient for the teacher to adjust own teaching in the course of surfing the net and after the course, and also being convenient for reminding the students to change own states, thereby improving the efficiency of listening the course.
Description
Technical Field
The invention relates to the technical field of education effect inspection, in particular to a network education effect inspection system based on real-time monitoring information feedback analysis.
Background
Education is a practice activity for improving comprehensive quality of people, and is an important link in society since ancient times as a means for culturing teenagers, a traditional education method is that a teacher and students are in a family, and face-to-face teaching knowledge is given, the teacher can adjust own teaching mode in real time according to the states of the students in classrooms, and at present, the teacher and the students cannot give lessons in a classroom, so that a method of on-line teaching is needed, but the on-line teaching teacher has no method for observing the states of the students, which greatly reduces the teaching effect of the students compared with the traditional teaching method, which is very unfavorable for culturing the teenagers, so that the states of the students when surfing the lessons need to be monitored in real time, and therefore, a network education effect checking system based on real-time monitoring information feedback analysis is provided.
Disclosure of Invention
The invention aims to provide a network education effect checking system based on real-time monitoring information feedback analysis, so as to solve the problems in the background technology.
In order to achieve the above object, a network education effect inspection system based on real-time monitoring information feedback analysis is provided, which comprises a video monitoring unit, an information analysis unit, an information storage unit, a state retrieval unit, an information feedback unit and a comprehensive evaluation unit;
the video monitoring unit monitors actions and expressions of a user;
the information analysis unit analyzes according to the monitoring information of the video monitoring unit and judges the monitoring information;
the state searching unit searches in the information storage unit according to the judgment information of the information analysis unit;
the information feedback unit feeds back the information of the state retrieval unit;
the comprehensive evaluation unit is used for comprehensively evaluating the lesson performance of teachers and students;
the information storage unit is used for storing the information.
As a further improvement of the technical scheme, the video monitoring unit comprises a video monitoring module and a picture ordering module;
the video monitoring module monitors actions and expressions of a user in real time and transmits information to the picture ordering module;
the picture sorting module sorts the pictures shot by the video monitoring module according to time sequence.
As a further improvement of the technical scheme, the information analysis unit comprises a picture analysis module and an action judgment module;
the picture analysis module analyzes the pictures screened by the picture sorting module, and transmits an analysis result to the action judging module, and the picture analysis module analyzes the pictures by adopting an LBP algorithm;
the action judging module judges the information of the picture analyzing module and transmits the judging result to the state retrieving unit.
As a further improvement of the present technical solution, the steps of the LBP algorithm are as follows:
(1) determining a circle center g0 in the images in the image information in the image ordering module;
(2) defining R, wherein R is the number of pixel points which are away from a circle center g0 in the image information transmitted by the image ordering module;
(3) defining P, wherein P is the number of pixel points formed inside a circle P after a circle is drawn by taking R as a circle center in an image in image information transmitted by the image ordering module;
(4) judging the pixel points according to the following judgment basis: dividing the pixel points formed in the step (3) in a multi-scale mode to form a plurality of 9 multiplied by 9 square grids, wherein the gray value of the surrounding square grids in the 9 multiplied by 9 square grids is larger than the gray value in the middle, and the square grids are marked as 1; the gray value of the surrounding grids in the 9X 9 grids is smaller than the gray value in the middle, the grids are marked as 0, a starting point is defined in the 9X 9 grids, the grids rotate according to the starting point, and binary codes are listed according to the grids passing through the rotation;
(5) converting binary codes into decimal numbers, wherein the decimal numbers are LBPs of the pixel areas, the LBPs are ranges of 0 lambda P, and the LBPs are local binary patterns;
(6) obtaining LBP of each pixel segmentation, establishing a histogram, wherein the type of each LBP is taken as a horizontal axis, the occurrence frequency of each LBP is taken as a vertical axis, and the histogram is the characteristic of the image.
As a further improvement of the technical scheme, the information storage unit comprises an action storage module, a grading storage module and a state storage module;
the action storage module stores shot action information;
the score storage module stores the used scores by the comprehensive evaluation unit;
the state storage module stores the state corresponding to the action information in the action storage module and stores the information fed back by the information feedback unit.
As a further improvement of the technical scheme, the state retrieval unit comprises an information conversion module and an information retrieval module;
the information conversion module converts the action information in the action judging module and transmits the converted information to the information retrieval module;
the information retrieval module retrieves the action information converted by the information conversion module in the action storage module, records the action information which is not recorded in the action storage module, and retrieves the action information by adopting an information judgment algorithm.
As a further improvement of the technical scheme, the information judgment algorithm formula is as follows:
wherein A is one of a plurality of pictures stored in the action storage module; b is a picture just converted by the information conversion module; JA, B is the similarity between one picture in the action storage module and the picture just converted by the information conversion module; a U B is the number of all elements of a picture in the action storage module and the picture just converted by the information conversion module; a and B are the same element number of a picture in the action storage module and the picture just converted by the information conversion module; k is a similarity threshold where the two pictures represent the same motion.
As a further improvement of the technical scheme, the information feedback unit comprises a state feedback module and a state input module;
the state feedback module feeds back the state of the user according to the action picture information retrieved by the information retrieval module and transmits the information to the state input module;
the state input module inputs the action information which is not in the action storage module.
As a further improvement of the technical scheme, the comprehensive evaluation unit comprises an evaluation analysis module and a state evaluation module;
the evaluation analysis module analyzes and evaluates the class listening state of the user according to the information of the state feedback module and the state input module, and transmits an evaluation result to the state evaluation module;
the state evaluation module adjusts the information of the evaluation analysis module and transmits the result to the grading storage module for storage.
Compared with the prior art, the invention has the beneficial effects that:
in the network education effect inspection system based on the real-time monitoring information feedback analysis, the actions of a teacher and a student are monitored simultaneously in real time, when the teacher is not in a teaching state, the evaluation and analysis of the student are stopped, the calculation force is greatly saved, the state of the student is continuously monitored in the online class process, the state of the student is analyzed according to the information stored in the action storage module, when the action without storage occurs, the teacher can perform manual input, the monitoring of the whole online class process is completed, and the teacher can receive the state information of the student in the class process and after the class, the teacher can conveniently adjust own teaching in the class process and after the class, the student is also conveniently reminded of changing own state, and the class listening efficiency is further improved.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a block flow diagram of a video monitoring unit according to the present invention;
FIG. 3 is a flow chart of an information analysis unit of the present invention;
FIG. 4 is a flow chart of an information storage unit of the present invention;
FIG. 5 is a flow chart of the state retrieval unit of the present invention;
FIG. 6 is a block flow diagram of an information feedback unit of the present invention;
fig. 7 is a flow chart of the comprehensive evaluation unit of the present invention.
The meaning of each reference sign in the figure is:
1. a video monitoring unit;
11. a video monitoring module; 12. a picture ordering module;
2. an information analysis unit;
21. a picture analysis module; 22. an action judging module;
3. an information storage unit;
31. an action storage module; 32. a scoring storage module; 33. a state storage module;
4. a state retrieval unit;
41. an information conversion module; 42. an information retrieval module;
5. an information feedback unit;
51. a state feedback module; 52. a state input module;
6. a comprehensive evaluation unit;
61. an evaluation analysis module; 62. and a state evaluation module.
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 can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Example 1
Referring to fig. 1 to 7, the present embodiment is directed to providing a network education effect inspection system based on real-time monitoring information feedback analysis, which includes a video monitoring unit 1, an information analysis unit 2, an information storage unit 3, a state retrieval unit 4, an information feedback unit 5, and a comprehensive evaluation unit 6;
the video monitoring unit 1 monitors actions and expressions of a user;
the information analysis unit 2 analyzes and judges the monitoring information of the video monitoring unit 1;
the state searching unit 4 searches in the information storage unit 3 according to the judgment information of the information analyzing unit 2;
the information feedback unit 5 feeds back the information of the state retrieval unit 4;
the comprehensive evaluation unit 6 performs comprehensive evaluation on the lessons of teachers and students;
the information storage unit 3 is used for storing the above information.
In order to monitor whether a student is learning according to actions of a teacher and the student when surfing the internet, the video monitoring unit 1 comprises a video monitoring module 11 and a picture ordering module 12;
the video monitoring module 11 monitors actions and expressions of a user in real time and transmits information to the picture ordering module 12, the video monitoring module 11 monitors teachers and students in real time by adopting cameras, the teachers and the students are monitored simultaneously, and education effects from two angles are checked and analyzed, so that the obtained conclusions are more fit with reality, and the education effects are convenient to evaluate according to analysis results in the later period;
the picture sorting module 12 sorts the pictures shot by the video monitoring module 11 and sorts the pictures according to time sequence, the picture sorting module 12 screens action information contained in all the pictures shot by the video monitoring module 11, deletes pictures with lower definition, and reduces the small consumption of later analysis and calculation of the pictures.
The information analysis unit 2 includes a picture analysis module 21 and an action judgment module 22;
the picture analysis module 21 analyzes the pictures screened by the picture sorting module 12 and transmits an analysis result to the action judging module 22, the picture analysis module 21 analyzes the pictures by adopting an LBP algorithm, and after the picture sorting module 12 screens out the pictures, the picture analysis module analyzes the pine blessing information in the pictures according to the pixel points in the pictures so as to obtain the states of students and teachers in class;
the action judging module 22 judges the information of the picture analyzing module 21 and transmits the judging result to the state retrieving unit 4, and the action judging module 22 analyzes whether the picture information in the picture analyzing module 21 is a picture of a portrait or not and excludes pictures which do not belong to the portrait.
The LBP algorithm is as follows:
(1) determining a circle center g0 in the image information in the image ordering module 12;
(2) defining R, wherein R is the number of pixel points which are away from the circle center g0 in the image information transmitted by the image ordering module 12;
(3) defining P, wherein P is the number of pixel points formed inside a circle P after a circle is drawn by taking R as a circle center in an image in the image information transmitted by the image ordering module 12;
(4) judging the pixel points according to the following judgment basis: dividing the pixel points formed in the step (3) in a multi-scale mode to form a plurality of 9 multiplied by 9 square grids, wherein the gray value of the surrounding square grids in the 9 multiplied by 9 square grids is larger than the gray value in the middle, and the square grids are marked as 1; the gray value of the surrounding grids in the 9X 9 grids is smaller than the gray value in the middle, the grids are marked as 0, a starting point is defined in the 9X 9 grids, the grids rotate according to the starting point, and binary codes are listed according to the grids passing through the rotation;
(5) converting binary codes into decimal numbers, wherein the decimal numbers are LBPs of the pixel areas, the LBPs are ranges of 0 lambda P, and the LBPs are local binary patterns;
(6) obtaining LBP of each pixel segmentation, establishing a histogram, wherein the type of each LBP is taken as a horizontal axis, the occurrence frequency of each LBP is taken as a vertical axis, and the histogram is the characteristic of the image.
The information storage unit 3 includes an action storage module 31, a score storage module 32, and a status storage module 33;
the action storage module 31 stores shot action information, and inputs common action information before the network education effect inspection system is put into use, so that states of a teacher and a student in online class can be judged according to the common action information, and action information capable of representing the states of the student and the teacher in online class is added in the use process of the network education effect inspection system;
the score storage module 32 stores the evaluation for the comprehensive evaluation unit 6, stores the evaluation after the comprehensive evaluation unit 6 evaluates the states of the students and the teacher, and updates the evaluation in real time according to the depth of the net lesson;
the state storage module 33 stores the state corresponding to the motion information in the motion storage module 31, stores the information fed back by the information feedback unit 5, stores the state information corresponding to the motion information, facilitates the obtaining of the states of the student and the teacher when the motion information is obtained, and stores the state information of the teacher and the student according to the feedback of the information feedback unit 5.
The state retrieval unit 4 includes an information conversion module 41 and an information retrieval module 42;
the information conversion module 41 converts the motion information in the motion judging module 22 and transmits the converted information to the information retrieval module 42, and the information conversion module 41 converts the motion information in the motion judging module 22 into motion picture information which is in the same form as that in the motion storage module 31, namely, is formed by simplified pixels, so that the information retrieval module 42 can conveniently retrieve the motion information in the motion storage module 31;
the information retrieval module 42 retrieves the action information in the action storage module 31 according to the action information converted by the information conversion module 41, and records the action information which is not recorded in the action storage module 31, and the information retrieval module 42 retrieves the action information by adopting an information judgment algorithm, so that the action stored in the action storage module 31 at first is not applicable to all actions of all students and teachers because the actions of people are different every day, and the stored information of the action storage module 31 needs to be increased according to the actual actions of the students and the teachers in the course of surfing the net, so that the accuracy of judging the states of the students and the teachers is improved.
The information judgment algorithm formula is as follows:
wherein A is one of the pictures stored in the motion storage module 31; b is a picture just converted by the information conversion module 41; JA, B is the similarity between one picture in the motion storage module 31 and the just converted picture by the information conversion module 41; a U B is the number of elements of one picture in the motion storage module 31 and the whole picture just converted by the information conversion module 41; a and B are the same number of elements as a picture in the action storage module 31 and the picture just converted by the information conversion module 41; k is a similarity threshold where the two pictures represent the same motion.
The information feedback unit 5 includes a state feedback module 51 and a state input module 52;
the state feedback module 51 feeds back the state of the user according to the action picture information retrieved by the information retrieval module 42 and transmits the information to the state input module 52, the state feedback module 51 feeds back the fruit bearing time retrieved by the information retrieval module 42 to a teacher and a student, the teacher and the student can adjust according to the prompt information and feed back the action information which is not retrieved to the teacher, and the teacher is convenient to supplement the information;
the state input module 52 inputs the motion information which is not contained in the motion storage module 31, so that a teacher can define the motion information by himself, the information stored in the motion storage module 31 and the state storage module 33 is improved, and the accuracy of the later judgment of the learning effect of the student is improved.
The comprehensive evaluation unit 6 includes an evaluation analysis module 61 and a status evaluation module 62;
the evaluation analysis module 61 analyzes and evaluates the class listening state of the user according to the information of the state feedback module 51 and the state input module 52, and transmits an evaluation result to the state evaluation module 62, the evaluation analysis module 61 comprehensively evaluates the class listening state of the student, and a teacher can evaluate the class listening effect of the student according to the evaluation of the evaluation analysis module 61 after class;
the state evaluation module 62 adjusts the information of the evaluation analysis module 61, transmits the result to the score storage module 32 for storage, and a teacher adjusts according to the comprehensive evaluation of the evaluation analysis module 61 to obtain an evaluation closer to the real situation, so that the teacher can adjust teaching according to the evaluation in the later period, and the teaching effect of students is improved.
Before the network education effect checking system is used, various actions made by students in class and states of the moving representatives are recorded into an action storage module 31 and a state storage module 33, corresponding evaluation information is recorded into a scoring storage module 32, when the network education effect checking system is used, a video monitoring module 11 monitors actions of teachers and students in real time, a picture sorting module 12 carries out screening treatment on pictures in the video monitoring module 11, the screened pictures are sorted according to time sequence, a picture analysis module 21 carries out analysis according to information of the picture sorting module 12, an action judging module 22 carries out further screening according to analysis results of the picture analysis module 21, the situation that calculation force is wasted in later period is avoided, an information conversion module 41 converts the pictures screened by the action judging module 22, the pictures are simplified, the mode stored in the state storage module 33 is the same as that of the pictures converted by the information conversion module 41, if the same actions are searched, a state feedback module 51 feeds back the states of the students and the teachers according to the time sequence, the students and the teacher can adjust the state information of the students according to the analysis results of the picture sorting module, the students can adjust the evaluation information of the students in the class, the students can conveniently store the evaluation information of the students in the class, and the students in the class, the state storage module is further adjusted according to the information of the students, the students can adjust the evaluation information is stored in the state storage module 61, and the final evaluation information is stored in the state storage module is convenient for the students can be adjusted after the students are in the state information is stored by the state information is adjusted, the information is adjusted by the information after the information conversion module 61 is adjusted, the evaluation module is adjusted, the state is adjusted by the information is adjusted, the quality is has the quality is converted is has a quality is has a quality, and has is has; if different actions are retrieved, the status feedback module 51 feeds back the actions to the teacher, the teacher inputs the status and the evaluation represented by the actions into the score storage module 32 and the status storage module 33 through the status input module 52, the evaluation analysis module 61 evaluates the status information of the status input module 52 and the status feedback module 51, the teacher adjusts the status information of the student through the status evaluation module 62, in the network education effect checking system, the teacher and the student are monitored in real time at the same time, the teacher stops evaluating and analyzing the student when not in a teaching status, the calculation force is greatly saved, the student status is continuously monitored in the course of the online lesson, the student status is analyzed according to the information stored in the action storage module 31, the teacher can manually input the status information in the whole online lesson course when no stored actions occur, the teacher can receive the status information of the student in the course of the online lesson and the lesson, the teacher can conveniently adjust own teaching after the lesson, the teacher can conveniently remind the student to change own status, and the efficiency of the student is further improved.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. The network education effect inspection system based on real-time monitoring information feedback analysis is characterized in that: the system comprises a video monitoring unit (1), an information analysis unit (2), an information storage unit (3), a state retrieval unit (4), an information feedback unit (5) and a comprehensive evaluation unit (6);
the video monitoring unit (1) monitors actions and expressions of a user;
the information analysis unit (2) analyzes and judges the monitoring information of the video monitoring unit (1);
the state searching unit (4) searches in the information storage unit (3) according to the judging information of the information analyzing unit (2);
the information feedback unit (5) feeds back the information of the state retrieval unit (4);
the comprehensive evaluation unit (6) performs comprehensive evaluation on the lessons of teachers and students;
the information storage unit (3) is used for storing the information.
2. The network education effect examination system according to the feedback analysis of the real-time monitoring information as recited in claim 1, wherein: the video monitoring unit (1) comprises a video monitoring module (11) and a picture ordering module (12);
the video monitoring module (11) monitors actions and expressions of a user in real time and transmits information to the picture ordering module (12);
the picture ordering module (12) sorts the pictures shot by the video monitoring module (11) according to time sequence.
3. The network education effect examination system according to the feedback analysis of the real-time monitoring information as recited in claim 2, wherein: the information analysis unit (2) comprises a picture analysis module (21) and an action judgment module (22);
the picture analysis module (21) analyzes the pictures screened by the picture sorting module (12) and transmits an analysis result to the action judging module (22), and the picture analysis module (21) analyzes the pictures by adopting an LBP algorithm;
the action judging module (22) judges the information of the picture analyzing module (21) and transmits the judging result to the state retrieving unit (4).
4. A network education effect checking system according to the real-time monitoring information feedback analysis as recited in claim 3, wherein: the LBP algorithm comprises the following steps:
(1) determining a circle center g0 in the image information in the image ordering module (12);
(2) defining R, wherein R is the number of pixel points which are away from the circle center g0 in the image information transmitted by the image ordering module (12);
(3) defining P, wherein P is the number of pixel points formed inside a circle P after a circle is drawn by taking R as a circle center in an image in image information transmitted by the image ordering module (12);
(4) judging the pixel points according to the following judgment basis: dividing the pixel points formed in the step (3) in a multi-scale mode to form a plurality of 9 multiplied by 9 square grids, wherein the gray value of the surrounding square grids in the 9 multiplied by 9 square grids is larger than the gray value in the middle, and the square grids are marked as 1; the gray value of the surrounding grids in the 9X 9 grids is smaller than the gray value in the middle, the grids are marked as 0, a starting point is defined in the 9X 9 grids, the grids rotate according to the starting point, and binary codes are listed according to the grids passing through the rotation;
(5) converting binary codes into decimal numbers, wherein the decimal numbers are LBPs of the pixel areas, the LBPs are ranges of 0 lambda P, and the LBPs are local binary patterns;
(6) obtaining LBP of each pixel segmentation, establishing a histogram, wherein the type of each LBP is taken as a horizontal axis, the occurrence frequency of each LBP is taken as a vertical axis, and the histogram is the characteristic of the image.
5. The network education effect examination system according to the feedback analysis of the real-time monitoring information as recited in claim 1, wherein: the information storage unit (3) comprises an action storage module (31), a grading storage module (32) and a state storage module (33);
the action storage module (31) stores shot action information;
the score storage module (32) stores the used scores by the comprehensive evaluation unit (6);
the state storage module (33) stores the state corresponding to the action information in the action storage module (31) and stores the information fed back by the information feedback unit (5).
6. The network education effect verification system according to the feedback analysis of the real-time monitoring information as recited in claim 5, wherein: the state retrieval unit (4) comprises an information conversion module (41) and an information retrieval module (42);
the information conversion module (41) converts the action information in the action judging module (22) and transmits the converted information to the information retrieval module (42);
the information retrieval module (42) retrieves the action information converted by the information conversion module (41) in the action storage module (31), and records the action information which is not recorded in the action storage module (31), and the information retrieval module (42) adopts an information judgment algorithm for retrieval.
7. The network education effect verification system according to the feedback analysis of the real-time monitoring information as recited in claim 6, wherein: the information judgment algorithm formula is as follows:
wherein A is one of a plurality of pictures stored in the action storage module (31); b is a picture just converted by the information conversion module (41); j (A, B) is the similarity between one picture in the action storage module (31) and the just-converted picture of the information conversion module (41); a U-B is the number of elements of one picture in the motion storage module (31) and the whole picture just converted by the information conversion module (41); a and B are the same element number of a picture in the action storage module (31) and the picture just converted by the information conversion module (41); k is a similarity threshold where the two pictures represent the same motion.
8. The network education effect verification system according to the feedback analysis of the real-time monitoring information as recited in claim 6, wherein: the information feedback unit (5) comprises a state feedback module (51) and a state input module (52);
the state feedback module (51) feeds back the state of the user according to the action picture information searched by the information searching module (42) and transmits the information to the state input module (52);
the state input module (52) inputs the motion information that is not present in the motion storage module (31).
9. The network education effect verification system according to the feedback analysis of the real-time monitoring information as recited in claim 8, wherein: the comprehensive evaluation unit (6) comprises an evaluation analysis module (61) and a state evaluation module (62);
the evaluation analysis module (61) analyzes and evaluates the class listening state of the user according to the information of the state feedback module (51) and the state input module (52), and transmits an evaluation result to the state evaluation module (62);
the state evaluation module (62) adjusts the information of the evaluation analysis module (61) and transmits the result to the score storage module (32) for storage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211603068.XA CN116227968A (en) | 2022-12-13 | 2022-12-13 | Network education effect inspection system based on real-time monitoring information feedback analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211603068.XA CN116227968A (en) | 2022-12-13 | 2022-12-13 | Network education effect inspection system based on real-time monitoring information feedback analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116227968A true CN116227968A (en) | 2023-06-06 |
Family
ID=86570499
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211603068.XA Pending CN116227968A (en) | 2022-12-13 | 2022-12-13 | Network education effect inspection system based on real-time monitoring information feedback analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116227968A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108154450A (en) * | 2016-12-06 | 2018-06-12 | 上海交通大学 | Digital studying intelligent monitor system |
| CN109359521A (en) * | 2018-09-05 | 2019-02-19 | 浙江工业大学 | The two-way assessment system of Classroom instruction quality based on deep learning |
| CN112862639A (en) * | 2021-01-07 | 2021-05-28 | 上海知到知识数字科技有限公司 | Online education method and online education platform based on big data analysis |
| CN112883867A (en) * | 2021-02-09 | 2021-06-01 | 广州汇才创智科技有限公司 | Student online learning evaluation method and system based on image emotion analysis |
| CN114677023A (en) * | 2022-03-31 | 2022-06-28 | 江苏鼎傲教育科技集团有限公司 | Cloud education management system and method based on big data |
| CN114760442A (en) * | 2022-04-06 | 2022-07-15 | 英才(广州)在线教育科技有限公司 | Monitoring system for online education management |
| CN115034938A (en) * | 2022-06-23 | 2022-09-09 | 四川仕虹腾飞信息技术有限公司 | Experiment teaching monitoring and evaluating system and method |
-
2022
- 2022-12-13 CN CN202211603068.XA patent/CN116227968A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108154450A (en) * | 2016-12-06 | 2018-06-12 | 上海交通大学 | Digital studying intelligent monitor system |
| CN109359521A (en) * | 2018-09-05 | 2019-02-19 | 浙江工业大学 | The two-way assessment system of Classroom instruction quality based on deep learning |
| CN112862639A (en) * | 2021-01-07 | 2021-05-28 | 上海知到知识数字科技有限公司 | Online education method and online education platform based on big data analysis |
| CN112883867A (en) * | 2021-02-09 | 2021-06-01 | 广州汇才创智科技有限公司 | Student online learning evaluation method and system based on image emotion analysis |
| CN114677023A (en) * | 2022-03-31 | 2022-06-28 | 江苏鼎傲教育科技集团有限公司 | Cloud education management system and method based on big data |
| CN114760442A (en) * | 2022-04-06 | 2022-07-15 | 英才(广州)在线教育科技有限公司 | Monitoring system for online education management |
| CN115034938A (en) * | 2022-06-23 | 2022-09-09 | 四川仕虹腾飞信息技术有限公司 | Experiment teaching monitoring and evaluating system and method |
Non-Patent Citations (1)
| Title |
|---|
| 廖恩红;李伟林;蔡晓斌;: "视像行为识别的大数据分析与教学决策研究", 工程技术研究, no. 12 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111242049B (en) | Face recognition-based student online class learning state evaluation method and system | |
| CN110334610B (en) | Multi-dimensional classroom quantification system and method based on computer vision | |
| CN109359215B (en) | Video intelligent pushing method and system | |
| CN111915148B (en) | Classroom teaching evaluation method and system based on information technology | |
| CN109214664B (en) | Emotional behavior comprehensive analysis system based on artificial intelligence | |
| CN110782375B (en) | Online learning overall process dynamic analysis method and system based on data | |
| CN117150151B (en) | Wrong question analysis and test question recommendation system and method based on large language model | |
| CN111523444A (en) | Classroom behavior detection method based on improved Openpos model and facial micro-expressions | |
| CN111930925A (en) | Test question recommendation method and system based on online teaching platform | |
| CN114971425B (en) | Database information monitoring method, device, equipment and storage medium | |
| CN115081965B (en) | Big data analysis system of condition of learning and condition of learning server | |
| CN115797829A (en) | Online classroom learning state analysis method | |
| CN111178263B (en) | Real-time expression analysis method and device | |
| Wang et al. | Educational question mining at scale: Prediction, analysis and personalization | |
| CN117556965A (en) | Teaching course optimization method, system and storage medium based on intelligent operation platform | |
| CN114187640A (en) | Learning situation observation method, system, equipment and medium based on online classroom | |
| CN117076782B (en) | Course recommendation method and device for online learning platform, computer equipment and medium | |
| CN113989608A (en) | Student experiment classroom behavior identification method based on top vision | |
| CN112116841A (en) | Personalized remote education system and method based on deep learning | |
| CN116883208A (en) | Classroom quality assessment system based on images | |
| CN116227968A (en) | Network education effect inspection system based on real-time monitoring information feedback analysis | |
| CN111415283A (en) | Factor analysis method and device for effective online teaching | |
| CN115829234A (en) | Automatic supervision system based on classroom detection and working method thereof | |
| Jiang | [Retracted] Analysis of Students’ Role Perceptions and their Tendencies in Classroom Education Based on Visual Inspection | |
| CN109410155A (en) | A kind of College Students ' Education training system and method |
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 | ||
| CB02 | Change of applicant information |
Address after: Room 901, No. 11 Kehui Fourth Street, Huangpu District, Guangzhou City, Guangdong Province, 510663 Applicant after: Guangzhou Huahan Information Technology Co.,Ltd. Address before: Room 901, No. 11 Kehui Fourth Street, Huangpu District, Guangzhou City, Guangdong Province, 510663 Applicant before: Guangzhou Huahan Education Technology Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20230606 |
|
| RJ01 | Rejection of invention patent application after publication |