Network teaching system and network teaching method thereof

Abstract

The invention relates to a network teaching system and a network teaching method thereof. The network teaching system comprises a teacher terminal used for uploading a calligraphy teaching video and a standard copybook; the student terminal comprises a touch screen and a camera, the touch screen is used for writing input, and the camera is used for capturing the writing process in real time; the stroke sequence recognition module accurately detects errors in the writing process by comparing the writing sequence of the students with the stroke sequence of the standard copybook, when the stroke sequence errors are detected, the system prompts the students to adjust the writing sequence through immediate feedback, and the pressure induction feedback module detects pressure changes of the students during writing through the touch screen. The handwriting thickness is analyzed through an algorithm, whether the writing strength accords with the preset reasonable range or not is judged, and when the writing strength deviation of a student is too large, the system reminds the student to adjust the strength through a visual or sound signal so as to ensure the handwriting quality of writing.

Description

Technical Field

[0001] The present invention relates to the technical field of network teaching, and in particular to a network teaching system and a network teaching method thereof. Background Art

[0002] The background technology of online teaching system and its online teaching method is usually to meet the needs of remote learning and personalized learning in modern education. With the rapid development of information technology, online teaching has gradually become an important way of learning. Traditional teaching methods mainly rely on offline classes, and the interaction between students and teachers needs to be face-to-face. The learning efficiency is subject to geographical location and time arrangement. However, the emergence of online teaching systems breaks the limitations of time and space, allowing students to learn anytime and anywhere. Especially in the field of calligraphy teaching, traditional teaching relies on on-site demonstration by teachers and repeated practice by students, but this method lacks personalized real-time feedback, and it is difficult for students to accurately grasp key factors such as stroke order, writing strength and character proportion.

[0003] Although some existing online teaching systems provide basic teaching functions, they often lack accurate detection and feedback of the writing process in calligraphy teaching. For example, many systems cannot accurately identify errors in stroke order or deviations in writing strength, making it difficult for students to receive effective guidance during practice. At the same time, the detection of glyph proportions often relies on subjective judgment rather than real-time calibration through precise algorithms.

[0004] Therefore, the online teaching system and teaching methods for calligraphy teaching urgently need an intelligent system that can achieve real-time monitoring, feedback and adjustment to improve the effect and efficiency of calligraphy learning. Such a system not only needs to capture detailed data during the writing process, but also needs to provide targeted improvement suggestions through advanced algorithms to help students get the same guidance effect in remote learning as in face-to-face teaching. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In order to solve the above problems in the prior art, the present invention provides an online teaching system and an online teaching method thereof, which solve the problem that many systems cannot accurately identify stroke order errors or deviations in writing strength, resulting in students having difficulty in receiving effective guidance during the practice process. At the same time, the detection of glyph proportions often relies on subjective judgment rather than real-time calibration through precise algorithms.

[0007] (II) Technical solution

[0008] In order to achieve the above object, the main technical solutions adopted by the present invention are:

[0009] A network teaching system, comprising:

[0010] Teacher terminal: used to upload calligraphy teaching videos and standard copybooks;

[0011] Student terminal: contains a touch screen and a camera. The touch screen is used for writing input, and the camera is used to capture the writing process in real time.

[0012] Stroke order recognition module: records the student's writing order in real time and compares it with the standard copybook to detect stroke order errors. The algorithm is as follows:

[0013]

[0014] Among them, D i represents the difference between the stroke order of the student and the standard copybook. The algorithm accumulates the total number of errors of all strokes, E order ,When there are errors, the system gives immediate feedback and reminds students to adjust the writing order;

[0015] Pressure sensing feedback module: monitors writing force, determines the thickness of the handwriting and provides feedback. The algorithm is as follows:

[0016]

[0017] Where P represents the average writing pressure, P low and P high For a reasonable pressure range, when the pressure exceeds the normal range, the system will prompt through the change of handwriting thickness to help students adjust the pressure to ensure that the handwriting meets the requirements;

[0018] Font ratio detection module: used to determine whether the proportion of written fonts meets the standard. The algorithm is as follows:

[0019]

[0020] Among them, R std is the width-to-height ratio of the standard copybook, R stu The width-to-height ratio of the characters written by students, E ratio Indicates the difference in ratio between the two. When the ratio error E ratio When the set range is exceeded, the system will issue a reminder to instruct students to adjust the proportion of the font.

[0021] The pressure sensing feedback module includes a real-time error prompt module, which can immediately pop up an error prompt message on the screen when the real-time error prompt module recognizes that the stroke sequence is wrong or the writing force is abnormal.

[0022] The pressure sensing feedback module has the function of dynamically adjusting the force feedback threshold, which automatically adjusts the detection sensitivity and feedback threshold according to the different writing styles and personal preferences of students.

[0023] The stroke order recognition module has an advanced difficulty setting function. The system automatically adjusts the strictness of stroke order detection according to the students' learning progress and calligraphy level. As the students' abilities improve, the tolerance is gradually reduced and the accuracy of detection is enhanced to meet the teaching needs of different stages.

[0024] The glyph proportion detection module can capture the overall proportion of the student's handwritten glyphs in real time, and compare the glyph width and height data captured by the camera with the proportions in the standard copybook. If a proportion deviation is detected, the system will issue a prompt message to remind the student to adjust the structure and proportion of the written glyphs.

[0025] The stroke sequence recognition module includes a handwriting smoothing processing module, which optimizes the handwriting captured by the touch screen through an algorithm to make the handwriting smoother and more natural, and reduce the problem of handwriting breakage or unevenness caused by hardware sensitivity.

[0026] A network teaching method comprises the following steps:

[0027] S1: Teachers upload standard calligraphy copybooks and teaching videos through the teacher terminal;

[0028] S2: Students practice calligraphy through the touch screen, and the system monitors the students' writing process in real time through the camera;

[0029] S3: The stroke sequence recognition module compares the student's writing sequence with the sequence in the standard copybook. When an error in the sequence is detected, the system immediately prompts the student and displays the correct writing sequence on the screen;

[0030] S4: The pressure sensing feedback module adjusts the thickness of the handwriting according to the pressure changes when the student is writing. If the writing force does not meet the threshold range set by the system, the system prompts the student to adjust the force through visual or sound feedback.

[0031] The S1 includes the step of generating a calligraphy learning report, which is generated by monitoring writing order errors and writing force changes, and is presented to teachers and students for analyzing and optimizing calligraphy practice content.

[0032] The strictness of the stroke order recognition module and the threshold of the pressure sensing feedback module will be dynamically adjusted according to the learning progress and performance of the students to adapt to the requirements of the students at different learning stages and gradually increase the training difficulty.

[0033] (III) Beneficial effects

[0034] The beneficial effects of the present invention are as follows: through integrated modules such as stroke sequence recognition, pressure sensing feedback, and font proportion detection, the intelligence and personalization of calligraphy teaching are comprehensively improved. The system uploads standard copybooks and teaching videos through the teacher terminal, and students use the touch screen to practice writing. The camera captures the writing process in real time. The stroke sequence recognition module accurately detects errors in the writing process by comparing the student's writing sequence with the stroke sequence of the standard copybook. When a stroke sequence error is detected, the system prompts the student to adjust the writing sequence through instant feedback. The pressure sensing feedback module uses the touch screen to detect the pressure changes when the student writes, and analyzes the thickness of the handwriting through an algorithm to determine whether the writing strength is within a preset reasonable range. When the student's writing strength deviation is too large, the system Visual or sound signals remind students to adjust the strength to ensure the quality of handwriting. The font proportion detection module monitors the width-to-height ratio of students' handwriting in real time and compares it with the standard copybook. If a large deviation in the font proportion is detected, the system will prompt students to adjust the writing structure through the screen to help them improve the beauty of the font. The working principle of the system is based on the combination of intelligent algorithm analysis and real-time monitoring functions, ensuring that each module is personalized according to the students' real-time performance, dynamically adjusting the detection sensitivity and feedback threshold to adapt to the learning needs of different stages. By automatically generating calligraphy learning reports, teachers and students can deeply analyze learning progress and problems, and then optimize the practice content, improve the efficiency and effectiveness of calligraphy practice, and achieve the goal of gradual progress and teaching students in accordance with their aptitude. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the system flow of the present invention;

[0036] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0037] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0038] Please refer to Figure 1-2 As shown, a network teaching system and network teaching method of the present invention include a teacher terminal: used for uploading calligraphy teaching videos and standard copybooks;

[0039] Student terminal: contains a touch screen and a camera. The touch screen is used for writing input, and the camera is used to capture the writing process in real time.

[0040] Stroke order recognition module: records the student's writing order in real time and compares it with the standard copybook to detect stroke order errors. The algorithm is as follows:

[0041]

[0042] Among them, Di represents the difference between the stroke order of the student and the standard copybook. The algorithm accumulates the total number of errors of all strokes, E order ,When there are errors, the system gives immediate feedback and reminds students to adjust the writing order;

[0043] Pressure sensing feedback module: monitors writing force, determines the thickness of the handwriting and provides feedback. The algorithm is as follows:

[0044]

[0045] Where P represents the average writing pressure, P low and P high For a reasonable pressure range, when the pressure exceeds the normal range, the system will prompt through the change of handwriting thickness to help students adjust the pressure to ensure that the handwriting meets the requirements;

[0046] Font ratio detection module: used to determine whether the proportion of written fonts meets the standard. The algorithm is as follows:

[0047]

[0048] Among them, R std is the width-to-height ratio of the standard copybook, R stu The width-to-height ratio of the characters written by students, E ratio Indicates the proportional difference between the two. When the proportional error E ratioWhen the set range is exceeded, the system issues a reminder to instruct students to adjust the proportion of the glyphs. In the actual implementation process, the intelligence and personalization of calligraphy teaching are comprehensively improved through integrated modules such as stroke order recognition, pressure sensing feedback, and glyph proportion detection. The system uploads standard copybooks and teaching videos through the teacher's terminal, and students use the touch screen to practice writing. The camera captures the writing process in real time. The stroke order recognition module accurately detects errors in the writing process by comparing the student's writing order with the stroke order of the standard copybook. When a stroke order error is detected, the system prompts students to adjust the writing order through instant feedback. The pressure sensing feedback module uses the touch screen to detect changes in student pressure when writing, and uses algorithms to analyze the thickness of the handwriting to determine whether the writing strength is within the preset reasonable range. When the student's writing strength deviation is too large, the system Visual or sound signals remind students to adjust the strength to ensure the quality of handwriting. The font proportion detection module monitors the width-to-height ratio of students' handwriting in real time and compares it with the standard copybook. If a large deviation in the font proportion is detected, the system will prompt students to adjust the writing structure through the screen to help them improve the beauty of the font. The working principle of the system is based on the combination of intelligent algorithm analysis and real-time monitoring functions, ensuring that each module is personalized according to the students' real-time performance, dynamically adjusting the detection sensitivity and feedback threshold to adapt to the learning needs of different stages. By automatically generating calligraphy learning reports, teachers and students can deeply analyze learning progress and problems, and then optimize the practice content, improve the efficiency and effectiveness of calligraphy practice, and achieve the goal of gradual progress and teaching students in accordance with their aptitude.

[0049] Optionally, the pressure sensing feedback module includes a real-time error prompt module, which can immediately pop up an error prompt message on the screen when it identifies an error in the stroke order or abnormal writing force. In the actual implementation process, the system first monitors the pressure applied by the student during the writing process, and detects the change in the thickness of the handwriting through the touch screen. When the pressure sensing feedback module identifies that the student's writing force is abnormal, it immediately transmits the information to the real-time error prompt module. The real-time error prompt module immediately pops up a prompt message on the screen to inform the student that the force is not within the set range or the stroke order is wrong. Through the real-time error prompt function, the system can quickly identify and feedback the student's wrong behavior, reminding the student to correct the stroke order and writing force in time. This function greatly reduces the repeated errors of students in practice, improves learning efficiency, and helps students master the correct writing method faster. This instant prompt can prevent the accumulation of errors, enhance the accuracy and instant correction ability of learning, and optimize the learning effect.

[0050] Optionally, the pressure sensing feedback module has the function of dynamically adjusting the force feedback threshold, which automatically adjusts the detection sensitivity and feedback threshold according to the different writing styles and personal preferences of students. In the actual implementation process, the system first detects the writing style and strength of the students, analyzes their personal writing habits and handwriting characteristics, and the pressure sensing feedback module automatically adjusts the feedback threshold according to the writing styles of different students, adapts the sensitivity and reasonable pressure detection range. When the students are writing, the system monitors their handwriting strength in real time based on the adjusted threshold and provides appropriate feedback as needed. The dynamic adjustment of the force feedback threshold function allows the system to adjust the sensitivity according to the individual differences of different students, enhancing the personalized teaching experience. The system adapts to the writing style of each student, avoids overly strict or loose detection, and enhances the learning experience. Students can practice writing within a framework that suits their own style, thereby improving their comfort and confidence in learning.

[0051] Optionally, the stroke sequence recognition module has an advanced difficulty setting function. The system automatically adjusts the strictness of the stroke sequence detection according to the student's learning progress and calligraphy level. As the student's ability improves, the tolerance is gradually reduced and the accuracy of the detection is enhanced to meet the teaching needs of different stages. In the actual implementation process, the system regularly evaluates the student's learning progress and calligraphy level, and adjusts the difficulty by detecting the accuracy of their stroke sequence. As the student's learning ability improves, the system automatically reduces the error tolerance range in the stroke sequence detection and gradually increases the strictness of the detection. When the system detects an error, it will immediately prompt and display the correct stroke sequence to help students gradually adapt to more difficult calligraphy exercises. Through the advanced difficulty setting function, the system can gradually increase the strictness of the stroke sequence detection according to the student's learning progress, ensuring that students can accept adaptive challenges at each stage of learning. This function improves the gradualness of calligraphy teaching, ensuring that students gradually improve their skill level, achieve higher writing accuracy, and effectively improve learning outcomes.

[0052] Optionally, the glyph proportion detection module can capture the overall proportion of the student's written glyph in real time, and compare the glyph width and height data captured by the camera with the proportion in the standard copybook. If a proportion deviation is detected, the system will issue a prompt message to remind the student to adjust the structure and proportion of the written glyph. In the actual implementation process, when the student writes on the touch screen, the system captures the width and height ratio of the written glyph through the camera in real time. The system compares the captured glyph data with the proportion in the standard copybook to detect whether there is a significant proportion deviation. Once the system detects that the glyph width and height ratio deviation exceeds the set range, it will immediately issue a prompt message through the screen to guide the student to make adjustments. The glyph proportion detection module can effectively correct the errors in the glyph structure and proportion during the student's writing process through real-time capture and comparison functions. This module helps students maintain the correct writing proportion during practice to ensure the beauty and accuracy of the glyphs. Through timely feedback, the system can help students continuously adjust and optimize the writing structure and improve the overall calligraphy level.

[0053] Optionally, the stroke sequence recognition module includes a handwriting smoothing processing module, which optimizes the handwriting captured by the touch screen through an algorithm to make the handwriting more fluent and natural, and reduce the problem of handwriting breakage or unevenness caused by hardware sensitivity. In the actual implementation process, when the student is writing, the touch screen captures the handwriting data of the writing, and the system analyzes the continuity of the handwriting through an algorithm. The handwriting smoothing processing module processes the handwriting in the writing process smoothly through the optimization algorithm, reducing problems such as broken pen and uneven handwriting. The optimized handwriting is displayed on the screen, and the student can see a writing effect that is closer to nature. The handwriting smoothing processing module effectively improves the natural fluency of the writing process and reduces the handwriting interruption or unevenness caused by the touch screen hardware. Through algorithm optimization, students can practice writing more smoothly, and the handwriting display effect is better, which helps to enhance students' writing confidence and the natural sense of calligraphy practice. This function ensures that the system provides a more stable writing experience at the technical support level.

[0054] A network teaching method includes the following steps:

[0055] S1: Teachers upload standard calligraphy copybooks and teaching videos through the teacher terminal;

[0056] S2: Students practice calligraphy through the touch screen, and the system monitors the students' writing process in real time through the camera;

[0057] S3: The stroke sequence recognition module compares the student's writing sequence with the sequence in the standard copybook. When an error in the sequence is detected, the system immediately prompts the student and displays the correct writing sequence on the screen;

[0058] S4: The pressure sensing feedback module adjusts the thickness of the handwriting according to the pressure changes when the student is writing. If the writing force does not meet the threshold range set by the system, the system prompts the student to adjust the force through visual or sound feedback.

[0059] Optionally, S1 includes the step of generating a calligraphy learning report, which is generated by monitoring the errors in the writing order and the changes in writing strength, and is displayed to teachers and students for analyzing and optimizing the calligraphy practice content. In the actual implementation process, the online teaching method can achieve all-round writing guidance through a complete calligraphy teaching process. Teachers can easily upload teaching materials, the system automatically monitors the students' writing process, and provides instant feedback through multiple modules. This method not only effectively corrects students' writing errors, but also provides detailed guidance through multiple dimensions such as writing strength and font proportion, comprehensively improving the personalization and accuracy of calligraphy teaching, and helping students quickly improve their calligraphy skills.

[0060] Optionally, the strictness of the stroke order recognition module and the threshold of the pressure sensing feedback module will be dynamically adjusted according to the students' learning progress and performance to adapt to the requirements of students at different learning stages and gradually increase the difficulty of training. In the actual implementation process, the system regularly evaluates the students' learning performance, records their writing progress and error frequency, and dynamically adjusts the strictness of the stroke order detection and the threshold of the pressure sensing feedback module. As the students' learning ability improves, the system gradually increases the strictness of the detection and reduces the error tolerance to ensure that students can practice calligraphy accurately at a higher level of difficulty. Through the dynamic adjustment function based on the students' learning progress, the system can adapt to the needs of students at different learning stages and gradually increase the detection difficulty and feedback sensitivity. This function ensures that students can accept adaptive challenges at all stages and help them make continuous progress. The flexibility of the system not only improves students' learning outcomes, but also optimizes the experience of personalized teaching, making it more targeted and practical.

[0061] The above shows and describes the basic principles, main features and advantages of the present invention, and the standard parts used in the present invention can be purchased from the market, and special-shaped parts can be customized according to the description in the specification and the drawings. The specific connection methods of each part adopt the conventional means such as mature bolts, rivets, welding, etc. in the prior art. The machinery, parts and equipment all adopt the conventional models in the prior art, and the circuit connection adopts the conventional connection method in the prior art, which will not be described in detail here.

[0062] The above are only embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the specification and drawings of the present invention, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A network teaching system, characterized in that: include: Teacher terminal: used to upload calligraphy teaching videos and standard copybooks; Student terminal: contains a touch screen and a camera. The touch screen is used for writing input, and the camera is used to capture the writing process in real time. Stroke order recognition module: records the student's writing order in real time and compares it with the standard copybook to detect stroke order errors. The algorithm is as follows: Among them, D i represents the difference between the stroke order of the student and the standard copybook. The algorithm accumulates the total number of errors of all strokes, E order ,When there are errors, the system gives immediate feedback and reminds students to adjust the writing order; Pressure sensing feedback module: monitors writing force, determines the thickness of the handwriting and provides feedback. The algorithm is as follows: Where P represents the average writing pressure, P low and P high For a reasonable pressure range, when the pressure exceeds the normal range, the system will prompt through the change of handwriting thickness to help students adjust the pressure to ensure that the handwriting meets the requirements; Font ratio detection module: used to determine whether the proportion of written fonts meets the standard. The algorithm is as follows: Among them, R std is the width-to-height ratio of the standard copybook, R stu The width-to-height ratio of the characters written by students, E ratio Indicates the proportional difference between the two. When the proportional error E ratio When the set range is exceeded, the system will issue a reminder to instruct students to adjust the proportion of the font.

2. A network teaching system according to claim 1, characterized in that: The pressure sensing feedback module includes a real-time error prompt module, which can immediately pop up an error prompt message on the screen when the real-time error prompt module recognizes that the stroke sequence is wrong or the writing force is abnormal.

3. The network teaching system according to claim 1, characterized in that: The pressure sensing feedback module has the function of dynamically adjusting the force feedback threshold, which automatically adjusts the detection sensitivity and feedback threshold according to the different writing styles and personal preferences of students.

4. The network teaching system according to claim 1, characterized in that: The stroke order recognition module has an advanced difficulty setting function. The system automatically adjusts the strictness of stroke order detection according to the students' learning progress and calligraphy level. As the students' abilities improve, the tolerance is gradually reduced and the accuracy of detection is enhanced to meet the teaching needs of different stages.

5. The network teaching system according to claim 1, characterized in that: The glyph proportion detection module can capture the overall proportion of the student's handwritten glyphs in real time, and compare the glyph width and height data captured by the camera with the proportions in the standard copybook. If a proportion deviation is detected, the system will issue a prompt message to remind the student to adjust the structure and proportion of the written glyphs.

6. The network teaching system according to claim 1, characterized in that: The stroke sequence recognition module includes a handwriting smoothing processing module, which optimizes the handwriting captured by the touch screen through an algorithm to make the handwriting smoother and more natural, and reduce the problem of handwriting breakage or unevenness caused by hardware sensitivity.

7. A network teaching method, characterized in that: The following steps are involved: S1: Teachers upload standard calligraphy copybooks and teaching videos through the teacher terminal; S2: Students practice calligraphy through the touch screen, and the system monitors the students' writing process in real time through the camera; S3: The stroke sequence recognition module compares the student's writing sequence with the sequence in the standard copybook. When an error in the sequence is detected, the system immediately prompts the student and displays the correct writing sequence on the screen; S4: The pressure sensing feedback module adjusts the thickness of the handwriting according to the pressure changes when the student is writing. If the writing force does not meet the threshold range set by the system, the system prompts the student to adjust the force through visual or sound feedback.

8. A network teaching method according to claim 7, characterized in that: The S1 includes the step of generating a calligraphy learning report, which is generated by monitoring writing order errors and writing force changes, and is presented to teachers and students for analyzing and optimizing calligraphy practice content.

9. A network teaching method according to claim 7, characterized in that: The strictness of the stroke order recognition module and the threshold of the pressure sensing feedback module will be dynamically adjusted according to the learning progress and performance of the students to adapt to the requirements of the students at different learning stages and gradually increase the training difficulty.

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