Remote education monitoring method and system based on adaptive optimization algorithm

Abstract

The invention discloses a remote education monitoring method and system based on an adaptive optimization algorithm. The method comprises the following steps: acquiring parameter information of students, making a teaching plan, setting a teaching course, generating a teaching video, establishing a login account based on identity information of the students, and displaying the teaching video. Acquiring learning state information of the student in real time based on a visual detection technology, analyzing the concentration degree of the student based on the learning state information, and judging whether the concentration degree is greater than or equal to a set concentration degree threshold value; if the concentration degree is greater than or equal to the set concentration degree threshold value, continuing to play the teaching video, and scoring the student condition of the student; if the concentration degree is smaller than the set concentration degree threshold value, reminding information is generated, and popup reminding or sound alarming is carried out based on the reminding information; the body movement and facial expression of the student are monitored in real time through the visual detection technology, so that the learning state of the student is accurately analyzed, early warning is performed on the non-focused student according to the learning state of the student, and the effect of remote education is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distance education, and specifically relates to a distance education monitoring method and system based on an adaptive optimization algorithm. Background Art

[0002] Distance education refers to a teaching mode that uses communication media such as television or the Internet for teaching. It is a new concept generated after the application of modern information technology in education, which can break through the limitations of time and space, provide more learning opportunities, and reduce teaching costs.

[0003] In the existing distance education process for students, due to the lack of restriction of monitoring and management, it is not convenient to implement the attendance check-in of students at the remote end, and during the remote teaching process, it is not easy to understand the mastery of each student on the course knowledge points, resulting in poor teaching quality. Summary of the Invention

[0004] The object of the present invention is to propose a distance education monitoring method based on an adaptive optimization algorithm, including the following steps: S1. Obtain the parameter information of students, formulate a teaching plan based on the parameter information of students, and set teaching courses based on the teaching plan; S2. Upload the teaching courses to the teaching terminal, generate teaching videos, establish login accounts based on the identity information of students, log in to the teaching terminal according to the login accounts, and display the teaching videos; S3. Based on the visual detection technology, obtain the learning status information of students in real time, analyze the concentration of students based on the learning status information, and determine whether the concentration is greater than or equal to the set concentration threshold; S4. If it is greater than or equal to the set concentration threshold, continue to play the teaching video and score the student situation; S5. If it is less than the set concentration threshold, generate a reminder message and perform a pop-up reminder or sound alarm based on the reminder message.

[0005] Further, step S1 specifically includes: S101. Obtain the parameter information of students, where the parameter information of students includes the name, grade, age, gender, pre-enrollment educational level and the knowledge base already mastered of students; S102. Analyze and evaluate the comprehension ability, memory ability and logical thinking ability of students based on the entrance test and previous academic achievements; S103. Analyze the learning ability and time arrangement of students based on the parameter information of students, set personalized teaching goals for each student based on the learning ability and time arrangement, and formulate a matching teaching plan; S104, Provide corresponding learning resources based on the teaching plan, where the learning resources include textbooks and reference books; S105, Analyze the teaching objectives and learning progress based on the teaching plan and set up teaching courses.

[0006] Furthermore, step S2 uploads the teaching courses to the teaching terminal and generates teaching videos, specifically including: Obtain the teaching courses and analyze the teaching purposes of the teaching courses; Classify the teaching courses based on the teaching objectives to obtain courses of multiple categories; Store the courses of different categories separately, analyze the difficulty of the courses within the same category, sort the teaching courses according to the difficulty of the courses to obtain a teaching course list; Upload the teaching course lists of all categories separately to the terminal to obtain teaching videos of multiple categories; The teaching videos of different categories cannot be played simultaneously.

[0007] Furthermore, step S2 establishes a login account based on the student's identity information, logs in to the teaching terminal according to the login account, and displays the teaching videos, specifically including: Obtain the login interface, register on the login interface based on the student's identity information to obtain registration information; Analyze whether the registration information is successful; If it is successful, generate a registered account, set a login password based on the registered account, obtain the login status, and when the login status shows success, display the teaching videos; If it is not successful, generate exception information, where the exception information includes identity occupation, password not meeting requirements, or account error.

[0008] Furthermore, step S3 specifically includes: Real-time monitor the student's body movements and facial expressions based on vision detection technology; Analyze the student's sitting posture based on the student's body movements; the student's sitting posture includes body movements and head movements; Analyze the student's learning concentration based on facial expressions, where the student's facial expressions include smiling, frowning, confusion, or surprise; Analyze the student's learning status information based on the student's sitting posture and learning concentration; Compare the student's learning status information with the set status information to obtain the concentration.

[0009] Furthermore, step S4 specifically includes: Obtain the video playback status information and analyze whether the student is concentrating on learning based on the student's body movements and facial expressions; If focusing on learning, continue playing the video, evaluate the learning status information based on the evaluation rules to obtain the learning efficiency and learning progress; Analyze the teaching quality based on the learning efficiency and learning progress, conduct a reflection rating on the teacher based on the teaching quality, and reverse-correct the teaching course list based on the reflection rating; If not focusing on learning, pause the playback of the teaching video.

[0010] Furthermore, step S5 specifically includes; Obtain the student's concentration. If the student's concentration is less than the set concentration threshold, generate a reminder message; Generate a selection pop-up window based on the reminder message. The selection pop-up window includes that the course difficulty is low, the course interest is low, the course playback speed is low, or the course is not understood; Set a time value and analyze whether the student clicks on the selection pop-up window; If not clicked, analyze whether the student is in front of the computer based on visual detection technology. If not in front of the computer, determine that the student has left and generate a sound alarm; If in front of the computer, determine that the student is distracted or not studying the video, generate learning status information, and push the learning status information to the teacher side; If clicked, generate a corresponding adjustment strategy according to the item clicked by the student.

[0011] The second aspect of the present invention proposes a distance education monitoring system based on an adaptive optimization algorithm, which is applied to the distance education monitoring method based on the adaptive optimization algorithm, and includes: A login unit, which logs in according to the student's registered account and login password, and analyzes whether the registered account and login password match; A visual camera, which continuously monitors the student's body movements and facial expressions; A learning analysis unit, which analyzes the student's learning status information according to the student's body movements and facial expressions, and analyzes the student's learning concentration according to the learning status information; A course selection unit, which selects a matching teaching course according to the student's parameter information; An early warning unit, which analyzes the student's learning status according to the student's concentration, analyzes whether the student leaves the computer or is distracted according to the learning status, and issues an alarm.

[0012] Due to the application of the above technical solutions, the present invention has the following advantages compared with the prior art: The present invention continuously monitors the student's body movements and facial expressions through visual detection technology, thereby accurately analyzing the student's learning status, warning students who are not focused according to the student's learning status, and improving the effect of distance education. Brief Description of the Drawings

[0013] Figure 1 It shows a schematic flowchart of the distance education monitoring method based on the adaptive optimization algorithm provided by the embodiment of the present invention; Figure 2 It shows a flowchart of the teaching course selection method of the distance education monitoring method based on the adaptive optimization algorithm provided by this embodiment. Detailed Embodiment

[0014] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0015] It should be noted that the terms "first", "second", etc. in the description and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0016] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0017] As Figure 1 - Figure 2 shown, the embodiment of the present invention provides a distance education monitoring method based on an adaptive optimization algorithm, including the following steps: S1. Obtain the parameter information of the student, formulate a teaching plan based on the parameter information of the student, and set teaching courses based on the teaching plan; S2. Upload the teaching courses to the teaching terminal, generate teaching videos, establish a login account based on the identity information of the student, log in to the teaching terminal according to the login account, and display the teaching videos; S3. Based on the visual detection technology, obtain the learning status information of the student in real time, analyze the concentration of the student based on the learning status information, and determine whether the concentration is greater than or equal to the set concentration threshold; S4. If it is greater than or equal to the set concentration threshold, continue to play the teaching video and grade the students' learning situations. S5. If it is less than the set concentration threshold, generate a reminder message and perform a pop-up reminder or a sound alarm based on the reminder message.

[0018] It should be noted that by recording the students' login times, learning times, course access records, homework submission situations, etc., the learning activity and participation of the students can be understood. For example, the system can count the total time spent by students on each course and the staying time in different chapters to judge the attention degree of students to different knowledge points.

[0019] Compare the actual learning progress of the students with the preset teaching progress, and timely discover the students with lagging or excessive progress. For the students with lagging progress, analyze the reasons and provide corresponding tutoring and support; for the students with excessive progress, additional expansion resources can be provided. For example, the system automatically generates a learning progress report for the students, showing the completed course content, tasks to be completed, and the comparison with the class average progress.

[0020] Regularly assign homework and conduct online tests, and evaluate the students' mastery of knowledge according to their answering situations. By analyzing the score distributions and wrong question types of the homework and tests, it can be understood which knowledge points the students have weak links in, providing a basis for subsequent teaching adjustments. For example, use an intelligent marking system to quickly correct objective questions, provide grading references for subjective questions, and generate a detailed score analysis report at the same time.

[0021] Learning achievement display: Require students to complete some comprehensive learning projects or works, such as papers, reports, practical projects, etc., to show their application ability and innovative thinking of knowledge. By evaluating these learning achievements, the learning effects of the students can be more comprehensively understood. For example, in some online design courses, students submit their design works, which are evaluated by teachers and classmates together.

[0022] According to the embodiments of the present invention, step S1 specifically includes: S101. Obtain the parameter information of the students. The parameter information of the students includes the students' names, grades, ages, genders, pre-admission educational levels, and the knowledge bases they have mastered. S102. Based on the entrance test and previous learning achievements, analyze and evaluate the students' comprehension ability, memory ability, and logical thinking ability. S103. Based on the parameter information of the students, analyze the students' learning abilities and time arrangements, and set personalized teaching goals for each student based on the learning abilities and time arrangements, and formulate a matching teaching plan. S104. Provide corresponding learning resources based on the teaching plan. The learning resources include textbooks and reference books. S105. Analyze teaching objectives and learning progress based on the teaching plan, and set teaching courses.

[0023] It should be noted that when students study online alone, their learning status is captured through a camera, such as whether they are focused or leave the screen for a long time, to help teachers remotely understand the students' situation and give timely reminders and guidance. For example, when a student studies through an online platform at home, the system can monitor their learning status in real time. If it is found that the student is distracted for a long time, a reminder can be automatically sent to the parents or teachers.

[0024] Classroom teaching: Install cameras in the classroom to comprehensively observe the classroom responses of the entire class of students. Based on this, teachers can adjust the teaching rhythm and methods, and can also analyze the learning performance of individual students to provide a basis for personalized teaching. For example, when a teacher explains knowledge points in class and finds that most students look confused through the visual monitoring system, the teaching progress can be slowed down in time and re-explained.

[0025] According to an embodiment of the present invention, step S2 uploads the teaching course to the teaching terminal and generates a teaching video, which specifically includes: Obtain the teaching course and analyze the teaching purpose of the teaching course; Classify the teaching course based on the teaching objectives to obtain courses of multiple categories; Separate and store the courses of different categories, analyze the difficulty of the courses within the same category, sort the teaching courses according to the difficulty of the courses to obtain a teaching course list; Separate and upload the teaching course lists of all categories to the terminal to obtain teaching videos of multiple categories; The teaching videos of different categories cannot be played simultaneously.

[0026] According to an embodiment of the present invention, step S2 establishes a login account based on the student's identity information, logs in to the teaching terminal according to the login account, and displays the teaching video, which specifically includes: Obtain the login interface, register on the login interface based on the student's identity information to obtain registration information; Analyze whether the registration information is successful; If successful, generate a registered account, set a login password based on the registered account, obtain the login status, and when the login status shows success, display the teaching video; If not successful, generate exception information, and the exception information includes identity occupation, password not meeting requirements or account error.

[0027] According to an embodiment of the present invention, step S3 specifically includes: Based on visual detection technology, real-time monitor the body movements and facial expressions of students; Analyze the sitting postures of students based on their body movements; the sitting postures of students include body movements and head movements; Analyze the learning concentration of students based on their facial expressions, where the facial expressions of students include smiling, frowning, confusion, or surprise; Analyze the learning status information of students based on their sitting postures and learning concentration; Compare the learning status information of students with the set status information to obtain the degree of concentration.

[0028] It should be noted that by visually analyzing the body movements of students, such as whether they have a proper sitting posture, lying on the table for a long time may mean fatigue or inattention; frequent small movements may imply that students have difficulty concentrating on the learning content. For example, if the system detects that a student frequently twirls a pen or shakes their legs, it can be judged that their attention is distracted and relevant data can be recorded.

[0029] Track the rotation and tilt directions of students' heads to determine whether they are paying attention to learning materials, teachers, or the screen. Looking down for a long time or looking elsewhere may indicate that a student is absent-minded or not interested in the learning content. For example, when a teacher is explaining a courseware, if the system monitors that a student's head continuously deviates from the screen direction, it can remind the teacher to pay attention to the status of this student.

[0030] Identify students' expressions, such as smiling, frowning, confusion, surprise, etc., to understand their understanding degree and emotional state of the learning content. A confused expression may indicate that a student has difficulty understanding, while a smile may indicate recognition of knowledge or a sense of pleasure in learning. For example, during the explanation of an online course, if the system identifies that a large number of students show confused expressions with frowning, it can prompt the teacher to explain the relevant knowledge points in more detail.

[0031] Eye contact: Monitor the gaze direction and the duration of eye fixation of students to determine whether they are having effective eye contact with the teacher or the screen, reflecting their degree of concentration and attention to the learning content. For example, during a classroom interaction session, by analyzing the eye contact situation of students, teachers can understand the degree of students' participation in thinking about questions.

[0032] According to the embodiments of the present invention, step S4 specifically includes: Obtain the video playback status information, and analyze whether students are concentrating on learning based on their body movements and facial expressions; If students are concentrating on learning, continue to play the video, evaluate the learning status information based on the evaluation rules to obtain the learning efficiency and learning progress; Analyze the teaching quality based on the learning efficiency and learning progress, conduct a reflection rating on the teacher based on the teaching quality, and reverse-correct the teaching course list based on the reflection rating; If students are not concentrating on learning, pause the playback of the teaching video.

[0033] It should be noted that multiple high-definition cameras are arranged in the classroom or learning environment to ensure that the faces, upper bodies, and limb movements of students can be comprehensively and clearly captured. These cameras need to have good picture quality, low latency, and sufficient shooting angles and ranges. For example, in the scenario of distance education, when students use the cameras of their own devices, the system needs to optimize the compatibility of the cameras of different devices to ensure stable and clear images are captured.

[0034] Image recognition algorithm: Use advanced computer vision algorithms to process and analyze the captured images and video streams. It includes face recognition technology to determine the identities of students, pose estimation algorithms to analyze the body postures of students, facial expression recognition algorithms to recognize facial expressions, and eye movement tracking algorithms to monitor eye activities, etc. For example, convolutional neural networks based on deep learning can be used to train facial expression recognition models to improve the accuracy of recognition.

[0035] According to the embodiment of the present invention, step S5 specifically includes: Obtain the concentration of the student. If the concentration of the student is less than the set concentration threshold, generate a reminder message; Generate a selection pop-up window based on the reminder message. The selection pop-up window includes that the course difficulty is low, the course interest is low, the course playback speed is low, or the course is not understood; Set a time value and analyze whether the student clicks on the selection pop-up window; If not clicked, based on visual detection technology, analyze whether the student is in front of the computer. If not in front of the computer, determine that the student has left and generate an audible alarm; If in front of the computer, determine that the student is distracted or not studying the video, generate learning status information, and push the learning status information to the teacher side; If clicked, generate a corresponding adjustment strategy according to the item clicked by the student.

[0036] It should be noted that According to the embodiment of the present invention, it also includes: regularly assigning homework and conducting online tests, and evaluating the student's mastery of knowledge according to the student's answering situation. By analyzing the score distribution of homework and tests, the types of wrong questions, etc., it is possible to understand which knowledge points the student has weak links in, providing a basis for subsequent teaching adjustments. For example, use an intelligent marking system to quickly correct objective questions, provide scoring references for subjective questions, and generate a detailed score analysis report at the same time.

[0037] Display of learning achievements: Require students to complete some comprehensive learning projects or works, such as papers, reports, practical projects, etc., to demonstrate their application ability of knowledge and innovative thinking. By evaluating these learning achievements, the learning effects of students can be more comprehensively understood. For example, in some online design courses, students submit their design works, which are evaluated by teachers and classmates together.

[0038] A second aspect of the present invention proposes a distance education monitoring system based on an adaptive optimization algorithm, which is applied to the distance education monitoring method based on the adaptive optimization algorithm, and includes: A login unit, which logs in according to the student's registered account and login password, and analyzes whether the registered account and login password match; A vision camera, which monitors the student's body movements and facial expressions in real time; A learning analysis unit, which analyzes the student's learning status information according to the student's body movements and facial expressions, and analyzes the student's learning concentration according to the learning status information; A course selection unit, which selects a matching teaching course according to the student's parameter information; An early warning unit, which analyzes the student's learning status according to the student's concentration, analyzes whether the student leaves the computer or is absent-minded according to the learning status, and issues an alarm.

[0039] In summary, the present invention uses vision detection technology to monitor the student's body movements and facial expressions in real time, so as to accurately analyze the student's learning status, and warns the inattentive students according to the student's learning status, improving the effect of distance education.

[0040] Those skilled in the art can understand that, for the sake of convenience of description, an example is given in which the number of memories and processors is both set to one. In an actual terminal or server, there may be multiple processors and memories. The memory may also be referred to as a storage medium or a storage device, etc., and the embodiments of the present application do not limit this.

[0041] It should be understood that in the embodiments of the present application, the processor may be a central processing unit (Central Processing Unit, abbreviated as CPU), and the processor may also be other general-purpose processors, digital signal processors (Digital Signal Processing, abbreviated as DSP), application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), field-programmable gate arrays (Field-Programmable Gate Array, abbreviated as FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may also adopt a general-purpose microprocessor, a graphics processing unit (GPU) or one or more integrated circuits to execute relevant programs to implement the functions required to be executed in the embodiments of the present application.

[0042] The processor may also be an integrated circuit chip with the ability to process signals. In the implementation process, each step of this application can be completed by the integrated logic circuit in the hardware of the processor or instructions in the form of software. The above-mentioned processor can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of this application. The steps of the method disclosed in combination with the embodiments of this application can be directly embodied as being executed and completed by the hardware decoding processor, or completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the functions required to be executed by the units included in the method, device and storage medium of the embodiments of this application.

[0043] It should also be understood that the memory mentioned in the embodiments of this application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache.

[0044] By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM).

[0045] The memory may also be a Compact Disc Read-Only Memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing the desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory may exist independently and be connected to the processor via a bus. The memory may also be integrated with the processor. The memory may store a program. When the program stored in the memory is executed by the processor, the processor is used to execute each step of the determination method in the above embodiments of the present application.

[0046] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, the memory (storage module) is integrated in the processor. It should be noted that the memory described herein is intended to include, but not limited to, these and any other suitable types of memory.

[0047] It should be understood that the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the preceding and following associated objects.

[0048] In the implementation process, each step of the above method may be completed by the integrated logic circuit in the hardware of the processor or the instructions in the form of software. The steps of the method disclosed in combination with the embodiments of the present application may be directly embodied as being executed by the hardware processor, or executed by the combination of the hardware and software modules in the processor. The software module may be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. This storage medium is located in the memory. The processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0049] Those of ordinary skill in the art can realize that the various illustrative logical blocks (ILB) and steps described in combination with the embodiments disclosed herein can be implemented in electronic hardware, or in the combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0050] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer-programmed program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a processor, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a computer network, or other programmable devices.

[0051] This embodiment also provides a computer-readable storage medium storing a computer program that causes a computer to execute to implement the above-mentioned distance education monitoring method based on an adaptive optimization algorithm.

[0052] It should be noted that the computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber) or wirelessly (such as infrared, wireless, microwave, etc.), or from one website, computer, server, or data center to a mobile phone processor by wire. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive), etc.

[0053] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A distance education monitoring method based on an adaptive optimization algorithm, characterized in that It includes the following steps: S1. Obtain the parameter information of the students, formulate a teaching plan based on the parameter information of the students, and set teaching courses based on the teaching plan; S2. Upload the teaching courses to the teaching terminal, generate teaching videos, establish a login account based on the identity information of the students, log in to the teaching terminal according to the login account, and display the teaching videos; S3. Obtain the learning status information of the students in real time based on visual detection technology, analyze the concentration of the students based on the learning status information, and determine whether the concentration is greater than or equal to the set concentration threshold; S4. If it is greater than or equal to the set concentration threshold, continue to play the teaching video and score the students' situation; S5. If it is less than the set concentration threshold, generate a reminder message and perform a pop-up reminder or sound alarm based on the reminder message; Step S2 uploads the teaching courses to the teaching terminal and generates teaching videos, specifically including: Obtain the teaching courses and analyze the teaching objectives of the teaching courses; Classify the teaching courses based on the teaching objectives to obtain courses of multiple categories; Store different categories of courses separately, analyze the difficulty of the courses within the same category, sort the teaching courses according to the difficulty of the courses to obtain a teaching course list; Upload the teaching course lists of all categories to the terminal separately to obtain teaching videos of multiple categories; The teaching videos of different categories cannot be played simultaneously; Step S4 specifically includes: Obtain the video playback status information, and analyze whether the students are concentrating on learning based on the body movements and facial expressions of the students; If concentrating on learning, continue to play the video, evaluate the learning status information based on the evaluation rules to obtain the learning efficiency and learning progress; Analyze the teaching quality based on the learning efficiency and learning progress, conduct a reflection rating on the teacher based on the teaching quality, and reverse-correct the teaching course list based on the reflection rating; If not concentrating on learning, pause the playback of the teaching video.

2. The distance education monitoring method based on the adaptive optimization algorithm according to claim 1, wherein Step S1 specifically includes: S101. Obtain the parameter information of the students, and the parameter information of the students includes the students' names, grades, ages, genders, pre-admission educational levels, and the knowledge bases already mastered; S102. Analyze and evaluate the students' comprehension ability, memory ability, and logical thinking ability based on the entrance test and previous academic achievements; S103. Analyze the learning ability and time arrangement of the students based on the parameter information of the students, set personalized teaching objectives for each student based on the learning ability and time arrangement, and formulate a matching teaching plan; S104. Provide corresponding learning resources based on the teaching plan, and the learning resources include textbooks and reference books; S105. Analyze the teaching objectives and learning progress based on the teaching plan and set teaching courses.

3. The distance education monitoring method based on the adaptive optimization algorithm according to claim 1, characterized in that, Step S2 establishes a login account based on the identity information of the students, logs in to the teaching terminal according to the login account, and displays the teaching videos, specifically including: Obtain the login interface, register on the login interface based on the identity information of the students to obtain registration information; Analyze whether the registration information is successful; If successful, generate a registered account, set a login password based on the registered account, obtain the login status, and when the login status shows success, display the teaching video; If it is not successful, an exception message is generated, including identity occupation, password not meeting requirements, or account error.

4. The distance education monitoring method based on the adaptive optimization algorithm according to claim 3, wherein Step S3 specifically includes: Real-time monitoring of students' body movements and facial expressions based on visual detection technology; Analyzing students' sitting postures based on their body movements; students' sitting postures include body movements and head movements; Analyzing students' learning concentration based on facial expressions, where the students' facial expressions include smiling, frowning, confusion, or surprise; Analyzing students' learning status information based on their sitting postures and learning concentration; Comparing the students' learning status information with the set status information to obtain the concentration level.

5. The distance education monitoring method based on an adaptive optimization algorithm according to claim 1, wherein, Step S5 specifically includes; Obtaining the students' concentration level. If the students' concentration level is less than the set concentration threshold, a reminder message is generated; Generating a selection pop-up window based on the reminder message, where the selection pop-up window includes lower course difficulty, lower course interest, lower course playback speed, or not understanding the course; Setting a time value and analyzing whether the students click on the selection pop-up window; If not clicked, analyzing whether the students are in front of the computer based on visual detection technology. If not in front of the computer, it is determined that the students have left, and a sound alarm is generated; If in front of the computer, it is determined that the students are distracted or not studying the video, generating learning status information, and pushing the learning status information to the teacher side; If clicked, generating a corresponding adjustment strategy according to the item clicked by the students.

6. A distance education monitoring system based on an adaptive optimization algorithm is applied to the distance education monitoring method based on the adaptive optimization algorithm according to any one of claims 1-5, and is characterized in that, Including: A login unit that logs in according to the students' registered accounts and login passwords and analyzes whether the registered accounts and login passwords match; A visual camera that real-time monitors students' body movements and facial expressions; A learning analysis unit that analyzes students' learning status information based on their body movements and facial expressions and analyzes students' learning concentration according to the learning status information; A course selection unit that selects matching teaching courses according to the students' parameter information; An early warning unit that analyzes the students' learning status according to the students' concentration level, analyzes whether the students leave the computer or are distracted according to the learning status, and issues an alarm.

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