Internet-based educational resource management system and method, and storage medium

Abstract

The invention belongs to the technical field of educational resource management, and particularly relates to an educational resource management system and method based on the Internet and a storage medium, and the system comprises a platform end, a user end and a supervision end. The system integrates, classifies and stores various education resources through the resource management sharing module and updates the resources, the user management module manages user information of teachers, students, parents and administrators and carries out identity verification and authority control on all users, and the learning recommendation module can carry out targeted learning planning. The system helps students to learn and improve scores more efficiently, monitors the learning communication behaviors of the students through a behavior monitoring and analysis module, judges the learning performance of the corresponding students in a detection period, and sends a judgment result to the corresponding parents, thereby strengthening the subsequent learning supervision of the corresponding students in time, and improving the learning efficiency of the students. The subsequent learning efficiency and learning effect are ensured, and effective management of the education platform is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of educational resource management, and in particular to an Internet-based educational resource management system, method and storage medium. Background Art

[0002] Traditional education management methods have problems such as time and space limitations, uneven resource allocation, and low management efficiency. With the development of modern educational technology, Internet-based education management systems have gradually become an indispensable part of the education industry, which is conducive to maximizing the use of educational resources, improving education quality, and reducing education costs. The Chinese invention patent with publication number CN111626687A discloses an online intelligent education resource management system. The technical solution of this invention can quickly understand the shortcomings of students' learning and the deficiencies of teachers' teaching, locate problems more accurately and quickly, and thus improve the overall teaching quality and efficiency. However, in actual application, the above-mentioned technical solution is difficult to effectively manage educational resources and users while making targeted learning plan recommendations and accurately evaluating students' learning performance. It is also unable to reasonably analyze the operation and maintenance management performance of the education platform and provide timely warning feedback, and the degree of intelligence is low. In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to provide an Internet-based education resource management system, method and storage medium, which solves the problems that the prior art is difficult to achieve targeted learning plan recommendations and accurately evaluate students' learning performance, and is unable to reasonably analyze the operation and maintenance management performance of the education platform and provide timely warning feedback, and has a low level of intelligence.

[0004] To achieve the above object, the present invention provides the following technical solutions: The Internet-based education resource management system includes a platform end, a user end, and a supervisory end. The user end is used for students, parents, and teachers to log in to the education platform, and the supervisory end is used to supervise the education platform. The platform end includes a resource management and sharing module, a user management module, a learning recommendation module, an online learning module, an interactive communication module, and a behavior monitoring and analysis module. The resource management sharing module is used to integrate, classify and store various educational resources, including courseware, video tutorials, question banks and online courses, and supports the upload, download, edit and review functions of educational resources; The user management module is used to manage the user information of teachers, students, parents and administrators, including user registration, login, permission allocation and role management, and supports identity authentication and permission control for all users; The learning recommendation module uses intelligent algorithms to provide students with personalized learning resource recommendations and learning plans based on their learning history, interest preferences, and performance data, and sends the recommendation analysis information to the corresponding students and their parents; After students log in to the education platform, they study courses through the online learning module, and conduct online communication and learning discussions among students through the interactive communication module; the behavior monitoring and analysis module monitors students' learning and communication behaviors, sets detection cycles, determines the learning performance of the corresponding students during the detection cycle, and sends the judgment results to the corresponding parents.

[0005] Furthermore, the specific judgment process of the behavior monitoring and analysis module is as follows: The learning monitoring coefficient and interaction monitoring coefficient of the corresponding students within the detection period are obtained, and the corresponding preset weight coefficients are assigned to the learning monitoring coefficient and the interaction monitoring coefficient. The learning monitoring coefficient and the interaction monitoring coefficient are respectively multiplied by the corresponding preset weight coefficients, and the two sets of multiplication results are summed up to obtain the behavior monitoring value, and the behavior monitoring value is numerically compared with the preset behavior monitoring threshold. If the behavior monitoring value exceeds the preset behavior monitoring threshold, a learning performance abnormality signal is generated; if the behavior monitoring value does not exceed the preset behavior monitoring threshold, a learning performance qualified signal is generated.

[0006] Furthermore, the behavior monitoring and analysis module is connected to the learning monitoring module and the interaction monitoring module in communication, and the learning monitoring module monitors the online learning process of the corresponding student, obtains the learning monitoring coefficient of the corresponding student in the detection period through analysis, and sends the learning monitoring coefficient to the behavior monitoring and analysis module; The interaction monitoring module monitors the interactive communication process of the corresponding students, obtains the interaction monitoring coefficient of the corresponding students in the detection period through analysis, and sends the interaction monitoring coefficient to the behavior monitoring and analysis module.

[0007] Furthermore, the specific analysis process of the learning monitoring module is as follows: The learning plan completion progress data of the corresponding students within the detection period is collected and marked as the learning plan completion value, and the online learning process of the corresponding students is monitored through the camera. If the corresponding students do not appear in the monitoring area, it is determined that the corresponding students are in a learning disengagement state; If the corresponding student appears in the monitoring area, the corresponding student's behavior is captured to identify his abnormal learning behavior, and the duration of the corresponding student's learning disengagement and the duration of abnormal learning behavior during the detection period are summed up to obtain the non-optimal learning duration; The total online learning time of the corresponding students during the detection period is obtained, and the ratio of the non-optimal learning time to the total online learning time of the corresponding students is calculated to obtain the learning inferior table value, and the learning inferior table value is numerically calculated with the corresponding student's learning plan completion value to obtain the learning monitoring coefficient.

[0008] Furthermore, the specific analysis process of the interactive monitoring module is as follows: The total duration of online communication and interaction of the corresponding students during the detection period is collected and marked as the total online interaction time. All interaction records of the corresponding students are scanned to identify interaction sentences that are not related to learning. The number of occurrences of interaction sentences that are not related to learning corresponding to the corresponding students during the detection period is marked as the poor interaction frequency. The interaction monitoring coefficient is calculated by ratio of the poor interaction frequency to the total online interaction time of the corresponding students.

[0009] Furthermore, the platform also includes an operation and maintenance monitoring and evaluation module, which is used to set an evaluation period, conduct a comprehensive analysis of the operation and maintenance performance of the education platform during the evaluation period, generate an operation and maintenance qualification signal or an operation and maintenance abnormality signal through the analysis, and send the operation and maintenance abnormality signal to the supervision end.

[0010] Furthermore, the specific analysis process of the platform operation and maintenance monitoring and evaluation module is as follows: The decline in the number of users of the education platform during the evaluation period is obtained and marked as the user number decline detection value, and the number of users whose online time exceeds the preset online time threshold during the evaluation period is collected and marked as the sticky user detection value, and the user number decline detection value and the sticky user detection value are numerically compared with the preset user number decline detection threshold and the preset sticky user detection threshold respectively. If the user number decline detection value exceeds the preset user number decline detection threshold or the sticky user detection value does not exceed the preset sticky user detection threshold, an operation and maintenance abnormality signal is generated; If the user number drop detection value does not exceed the preset user number drop detection threshold and the sticky user detection value exceeds the preset sticky user detection threshold, all user evaluation information for the education platform during the evaluation period is collected, the number of negative review information for the education platform is marked as the platform negative review detection value, and the difference between the operating investment amount and the return amount for the education platform during the evaluation period is marked as the operating profit value; The platform negative review detection value and the operating profit value are numerically compared with the preset platform negative review detection threshold and the preset operating profit threshold respectively. If the platform negative review detection value exceeds the preset platform negative review detection threshold or the operating profit value does not exceed the preset operating profit threshold, an operation and maintenance abnormality signal is generated.

[0011] Furthermore, if the platform negative review detection value does not exceed the preset platform negative review detection threshold and the operating profit value exceeds the preset operating profit threshold, the number of times the education platform is paralyzed during the evaluation period is collected and marked as the paralysis detection value, and the timing starts when the education platform is paralyzed until the education platform returns to normal operation, and the paralysis duration is obtained accordingly, and the sum of all the paralysis durations during the evaluation period is marked as the paralysis time measurement value; The paralysis duration is numerically compared with a preset paralysis duration threshold. If the paralysis duration exceeds the preset paralysis duration threshold, the corresponding paralysis duration is marked as the impact duration, and the number of impact durations in the evaluation period is marked as the paralysis risk frequency value; The operation abnormality coefficient of the education platform is obtained by numerically calculating the paralysis detection value, the paralysis time measurement value and the paralysis risk frequency value, and the operation abnormality coefficient of the education platform is numerically compared with the preset education platform operation abnormality coefficient threshold value. If the education platform operation abnormality coefficient exceeds the preset education platform operation abnormality coefficient threshold value, an operation and maintenance abnormality signal is generated; If the education platform operation abnormality coefficient does not exceed the preset education platform operation abnormality coefficient threshold, the number of times the education platform is attacked by network attacks and the number of times it fails to successfully defend against network attacks during the evaluation period are collected and marked as the education platform threat value and the education platform anti-anomaly value respectively, and the education platform threat value and the education platform anti-anomaly value are numerically calculated to obtain the education platform hidden danger coefficient; the education platform hidden danger coefficient is numerically compared with the preset education platform hidden danger coefficient threshold; if the education platform hidden danger coefficient exceeds the preset education platform hidden danger coefficient threshold, an operation and maintenance abnormality signal is generated; if the education platform hidden danger coefficient does not exceed the preset education platform hidden danger coefficient threshold, an operation and maintenance qualified signal is generated.

[0012] Furthermore, the present invention proposes an Internet-based educational resource management method, comprising the following steps: Step 1: Integrate, classify and store various educational resources, and manage user information of teachers, students, parents and administrators; Step 2: Based on students’ learning history, interest preferences and performance data, use intelligent algorithms to provide students with personalized learning resource recommendations and learning plans; Step 3: Students log in to the education platform to study courses, communicate online and discuss learning; Step 4: Monitor students' learning and communication behaviors, determine the corresponding students' learning performance during the test period, and send the determination results to the corresponding parents.

[0013] Furthermore, the present invention also proposes an Internet-based educational resource management storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the Internet-based educational resource management method described in claim 9 is implemented.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, the resource management sharing module integrates, classifies and stores various educational resources and updates the resources. The user management module manages user information and performs identity authentication and authority control on all users. The learning recommendation module can carry out targeted learning planning to help students learn more efficiently and improve their grades. The behavior monitoring and analysis module monitors students' learning and communication behaviors and accurately judges the learning performance of the corresponding students, so as to timely strengthen the subsequent learning supervision of the corresponding students, ensure their subsequent learning efficiency and learning effect, and realize effective management of the education platform. 2. In the present invention, the operation and maintenance performance of the education platform during the evaluation period is comprehensively analyzed through the operation and maintenance monitoring and evaluation module to generate an operation and maintenance qualification signal or an operation and maintenance abnormality signal. When the operation and maintenance abnormality signal is generated, the supervision end issues an early warning to remind the administrator to reasonably plan and adjust the platform management plan, strengthen the subsequent management of the education platform, ensure the subsequent normal and continuous operation of the education platform and improve user reputation, significantly reduce the management difficulty of the education platform, and have a high degree of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention; Figure 3 This is a flow chart of the method of Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: Figure 1 As shown, the Internet-based education resource management system proposed by the present invention includes a platform end, a user end and a supervision end. The user end is used for students, parents and teachers to log in to the education platform, and the supervision end is used for administrators to supervise the education platform. The platform end includes a resource management sharing module, a user management module, a learning recommendation module, an online learning module, an interactive communication module and a behavior monitoring and analysis module; The resource management sharing module is used to integrate, classify and store various educational resources, such as courseware, video tutorials, question banks and online courses, and supports functions such as uploading, downloading, editing and reviewing of educational resources to ensure the accuracy and timeliness of resources.

[0018] The user management module is used to manage the user information of teachers, students, parents and administrators, including user registration, login, permission allocation and role management, and supports identity authentication and permission control for all users to ensure the security and stability of the system.

[0019] The learning recommendation module uses intelligent algorithms to provide students with personalized learning resource recommendations and learning plans based on their learning history, interest preferences, and performance data, and sends the recommended analysis information to the corresponding students and their parents, which can help students make targeted learning plans, help them learn more efficiently and improve their performance, and help optimize teaching plans and resource allocation. In addition, the operation process of the learning recommendation module is as follows: Step 1: Data collection and preprocessing: Learning history data: collect information such as students’ learning progress, completion time, number of repeated learning, etc. in different courses, chapters, and knowledge points; track students’ online learning behaviors, such as clicks, browsing, collections, sharing, etc.; Interest preference data: collect students' subject preferences, learning method preferences (such as video, graphics, audio, etc.) and learning topic preferences through questionnaires, interactive feedback, learning community activities, etc.; analyze students' stay time and interaction frequency on different learning resources to infer their interests; Performance data: Regularly collect students' test scores, homework scores, quiz scores, etc. to understand students' learning effects and mastery; analyze performance data to identify students' strengths and weaknesses; Other relevant data: including basic information about students (such as age, grade, gender, etc.), as well as possible learning disabilities or special needs; collecting students’ learning goals and expectations in order to provide more appropriate recommendations; Step 2: Application of intelligent algorithms: User portrait construction: Based on the collected data, build a detailed user portrait of the students, including learning style, interest preferences, knowledge mastery, etc., and continuously update and improve the user portrait to reflect the students' latest learning status and needs; Learning resource recommendation: Based on user portraits, we use collaborative filtering, content-based recommendation, deep learning and other algorithms to provide students with personalized learning resource recommendations; collaborative filtering algorithm: recommend learning resources they like by finding other students who are similar to the current student; content-based recommendation algorithm: match the content characteristics of learning resources (such as subject, difficulty, format, etc.) with students' interests and preferences; deep learning algorithm: use neural network models to train large amounts of data to more accurately predict students' preferences and needs; Learning path planning: Analyze students' learning history, current knowledge mastery, and learning goals, and plan an optimal learning path for students from the current state to the target state; the path planning algorithm considers factors such as the relevance of learning resources, difficulty gradient, and learning time to ensure that students can master knowledge step by step; dynamically adjust the learning path based on students' learning progress and feedback to adapt to students' changes.

[0020] After students log in to the education platform through the user terminal, they can study courses through the online learning module, and conduct online communication and learning discussions among students through the interactive communication module. Through interaction and cooperation, knowledge sharing and dissemination are promoted, which is conducive to improving learning efficiency and enthusiasm.

[0021] The behavior monitoring and analysis module monitors the learning and communication behaviors of students and sets a detection period, preferably, the detection period is thirty days; judges the learning performance of the corresponding students in the detection period and sends the judgment results to the corresponding parents, which can comprehensively analyze and accurately evaluate the online learning performance of the corresponding students in the detection period, and strengthen the subsequent learning supervision of the corresponding students when the abnormal learning performance signal of the corresponding students is generated, so as to ensure their subsequent learning efficiency and learning effect; the specific judgment process of the behavior monitoring and analysis module is as follows: The learning monitoring coefficient and the interaction monitoring coefficient of the corresponding student in the detection period are obtained, and the corresponding preset weight coefficients are assigned to the learning monitoring coefficient and the interaction monitoring coefficient, wherein the value of the assigned preset weight coefficient is a positive number; the learning monitoring coefficient and the interaction monitoring coefficient are respectively multiplied by the corresponding preset weight coefficients, and the two sets of product results are summed to obtain the behavior monitoring value; it should be noted that the larger the value of the behavior monitoring value, the worse the learning performance of the corresponding student in the detection period is overall; The behavior monitoring value is numerically compared with the preset behavior monitoring threshold. If the behavior monitoring value exceeds the preset behavior monitoring threshold, it indicates that the corresponding student's learning performance during the detection period is generally poor, and a learning performance abnormality signal is generated; if the behavior monitoring value does not exceed the preset behavior monitoring threshold, it indicates that the corresponding student's learning performance during the detection period is generally good, and a learning performance qualified signal is generated.

[0022] It should be noted that the behavior monitoring and analysis module is connected to the learning monitoring module and the interaction monitoring module in communication. The learning monitoring module monitors the online learning process of the corresponding students, obtains the learning monitoring coefficient of the corresponding students in the detection period through analysis, and sends the learning monitoring coefficient to the behavior monitoring and analysis module, which can not only accurately feedback the online learning status of the corresponding students in the detection period, but also provide data support for the analysis process of the behavior monitoring and analysis module to ensure the accuracy of its analysis results; the specific analysis process of the learning monitoring module is as follows: The learning plan completion progress data of the corresponding students within the detection period is collected and marked as the learning plan completion value, and the online learning process of the corresponding students is monitored through the camera. If the corresponding students do not appear in the monitoring area, it is determined that the corresponding students are in a learning disengagement state; If the corresponding student appears in the monitoring area, the corresponding student's behavior is captured to identify his abnormal learning behavior (including drowsiness, deviation of vision, etc.), and the duration of the corresponding student's learning disengagement and the duration of abnormal learning behavior during the detection period are summed to calculate the non-optimal learning time; The total online learning time of the corresponding students during the detection period is obtained, and the ratio of the non-excellent learning time to the total online learning time of the corresponding students is calculated to obtain the learning inferior value. The learning inferior value W and the corresponding student's learning plan completion value L are numerically calculated using the formula Xaq=e1×W+e2 / (L+1) to obtain the learning monitoring coefficient Xaq; wherein, e1 and e2 are preset proportional coefficients, e2>e1>0.824; and, the larger the value of the learning monitoring coefficient Xaq, the less serious the corresponding student's online learning is during the detection period.

[0023] The interactive monitoring module monitors the interactive communication process of the corresponding students, obtains the interactive monitoring coefficient of the corresponding students in the detection period through analysis, and sends the interactive monitoring coefficient to the behavior monitoring analysis module, which can not only accurately feedback the online interactive status of the corresponding students in the detection period, but also provide data support for the analysis process of the behavior monitoring analysis module, further ensuring the accuracy of its analysis results; the specific analysis process of the interactive monitoring module is as follows: The total duration of online communication and interaction of the corresponding students during the detection period is collected and marked as the total online interaction time, and all interaction records of the corresponding students are scanned to identify interaction sentences that are not related to learning, and the number of occurrences of interaction sentences that are not related to learning corresponding to the corresponding students during the detection period is marked as the poor interaction frequency; the interaction monitoring coefficient is calculated by ratio of the poor interaction frequency to the total online interaction time of the corresponding students. It should be noted that the larger the value of the interaction monitoring coefficient, the worse the interaction performance of the corresponding student during the detection period.

[0024] Embodiment 2: Figure 2As shown, the difference between this embodiment and the first embodiment is that the platform end further includes an operation and maintenance monitoring and evaluation module, which is used to set an evaluation period, preferably, the evaluation period is forty-five days; the operation and maintenance performance of the education platform during the evaluation period is comprehensively analyzed, and an operation and maintenance qualified signal or an operation and maintenance abnormal signal is generated through the analysis; The abnormal operation and maintenance signals are sent to the supervision end. When the supervision end receives the abnormal operation and maintenance signals, it issues a corresponding warning to remind the administrator to reasonably plan and adjust the platform management plan, strengthen the subsequent management of the education platform, ensure the normal and continuous operation of the education platform, and improve user reputation. It significantly reduces the difficulty of managing the education platform and has a high degree of intelligence. The specific analysis process of the platform operation and maintenance monitoring and evaluation module is as follows: The decline in the number of users of the education platform during the evaluation period is obtained and marked as the user number decline detection value, and the number of users whose online time exceeds the preset online time threshold during the evaluation period is collected and marked as the sticky user detection value, and the user number decline detection value and the sticky user detection value are numerically compared with the preset user number decline detection threshold and the preset sticky user detection threshold respectively. If the user number decline detection value exceeds the preset user number decline detection threshold or the sticky user detection value does not exceed the preset sticky user detection threshold, indicating that the maintenance status for user maintenance is not good, an operation maintenance abnormality signal is generated; If the user number drop detection value does not exceed the preset user number drop detection threshold and the sticky user detection value exceeds the preset sticky user detection threshold, all user evaluation information on the education platform during the evaluation period is collected, and the number of negative review information on the education platform is marked as the platform negative review detection value, wherein the larger the value of the platform negative review detection value, the worse the user reputation of the education platform; and the difference between the operating investment amount and the return amount obtained for the education platform during the evaluation period is marked as the operating profit value; wherein, the smaller the value of the operating profit value, the worse the operating condition of the education platform; The platform negative review detection value and the operating profit value are numerically compared with the preset platform negative review detection threshold and the preset operating profit threshold respectively. If the platform negative review detection value exceeds the preset platform negative review detection threshold or the operating profit value does not exceed the preset operating profit threshold, indicating that the operating performance of the education platform is generally poor, an operation and maintenance abnormality signal is generated.

[0025] Furthermore, if the platform negative review detection value does not exceed the preset platform negative review detection threshold and the operating profit value exceeds the preset operating profit threshold, the number of times the education platform is paralyzed during the evaluation period is collected and marked as the paralysis detection value, and the timing starts when the education platform is paralyzed until the education platform returns to normal operation, and the paralysis duration is obtained accordingly, and the sum of all the paralysis durations during the evaluation period is marked as the paralysis duration measurement value; The paralysis duration is numerically compared with a preset paralysis duration threshold. If the paralysis duration exceeds the preset paralysis duration threshold, the corresponding paralysis duration is marked as the impact duration, and the number of impact durations in the evaluation period is marked as the paralysis risk frequency value; The paralysis detection value N, the paralysis measurement value S and the paralysis risk frequency value P are numerically calculated by the formula Quy=(cq1×N+cq3×P) / 2+cq2×S to obtain the education platform operation coefficient Quy; wherein cq1, cq2 and cq3 are preset proportional coefficients, cq3>cq1>cq2>0; and the larger the value of the education platform operation coefficient Quy is, the more unstable the operation of the education platform is during the evaluation period; Compare the education platform operation variance coefficient Quy with the preset education platform operation variance coefficient threshold. If the education platform operation variance coefficient Quy exceeds the preset education platform operation variance coefficient threshold, it indicates that the operation of the education platform is unstable during the evaluation period, and an operation and maintenance abnormality signal is generated; If the education platform operation abnormality coefficient Quy does not exceed the preset education platform operation abnormality coefficient threshold, indicating that the operation of the education platform is relatively stable during the evaluation period, the number of times the education platform is attacked by the network during the evaluation period and the number of times it fails to successfully defend against the network attack are collected and marked as the education platform threat value and the education platform anti-anomaly value respectively; The education platform threat value T and the education platform anomaly prevention value H are numerically calculated by the formula Rmg=k1×T+k2×H to obtain the education platform hidden danger coefficient Rmg; wherein k1 and k2 are preset proportional coefficients, k2>k1>0; and the larger the value of the education platform hidden danger coefficient Rmg, the greater the security risks existing in the education platform during the evaluation period; The education platform hidden danger coefficient Rmg is numerically compared with the preset education platform hidden danger coefficient threshold. If the education platform hidden danger coefficient Rmg exceeds the preset education platform hidden danger coefficient threshold, it indicates that the safety hazards of the education platform during the evaluation period are relatively large, and an operation and maintenance abnormality signal is generated; if the education platform hidden danger coefficient Rmg does not exceed the preset education platform hidden danger coefficient threshold, it indicates that the operation and maintenance management performance of the education platform during the evaluation period is generally good, and an operation and maintenance qualified signal is generated.

[0026] Embodiment 3: Figure 3 As shown, the difference between this embodiment and the first and second embodiments is that the Internet-based educational resource management method proposed by the present invention includes the following steps: Step 1: Integrate, classify and store various educational resources, and manage user information of teachers, students, parents and administrators; Step 2: Based on students’ learning history, interest preferences and performance data, use intelligent algorithms to provide students with personalized learning resource recommendations and learning plans; Step 3: Students log in to the education platform to study courses, communicate online and discuss learning; Step 4: Monitor students' learning and communication behaviors, determine the corresponding students' learning performance during the test period, and send the determination results to the corresponding parents.

[0027] The present invention also proposes an Internet-based educational resource management storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned Internet-based educational resource management method is implemented. A person of ordinary skill in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0028] The working principle of the present invention is as follows: when in use, various educational resources are integrated, classified and stored through the resource management sharing module and the resources are updated; the user management module manages the user information of teachers, students, parents and administrators and performs identity authentication and authority control on all users; the learning recommendation module uses intelligent algorithms based on information such as students' learning history, interest preferences and performance data to provide students with personalized learning resource recommendations and learning plans, and can carry out targeted learning plans to help students learn more efficiently and improve their grades, which is conducive to optimizing teaching plans and resource allocation; and course learning and online communication discussions are carried out through online learning modules and interactive communication modules, which is conducive to improving learning efficiency and learning enthusiasm; the behavior monitoring and analysis module monitors students' learning and communication behaviors, judges the learning performance of the corresponding students during the detection period and sends the judgment results to the corresponding parents, so as to timely strengthen the subsequent learning supervision of the corresponding students to ensure their subsequent learning efficiency and learning effects, and has a high degree of intelligence.

[0029] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An Internet-based educational resource management system, characterized in that: It includes the platform side, user side and supervision side. The user side is used for students, parents and teachers to log in to the education platform. The supervision side is used to supervise the education platform. The platform side includes resource management and sharing module, user management module, learning recommendation module, online learning module, interactive communication module and behavior monitoring and analysis module. The resource management sharing module is used to integrate, classify and store various educational resources, and supports the upload, download, edit and review functions of educational resources; the user management module is used to manage the user information of teachers, students, parents and administrators, and supports identity authentication and permission control for all users; The learning recommendation module uses intelligent algorithms to provide students with personalized learning resource recommendations and learning plans based on their learning history, interest preferences and academic performance data, and sends the recommendation analysis information to the corresponding students and their parents; after students log in to the education platform, they study courses through the online learning module, and conduct online communication and learning discussions among students through the interactive communication module; the behavior monitoring and analysis module monitors students' learning communication behaviors, sets a detection cycle, determines the corresponding students' learning performance during the detection cycle, and sends the judgment results to the corresponding parents.

2. The Internet-based educational resource management system according to claim 1, characterized in that: The specific judgment process of the behavior monitoring and analysis module is as follows: The learning monitoring coefficient and interaction monitoring coefficient of the corresponding students during the detection period are obtained, and the behavior monitoring value is obtained through calculation based on the learning monitoring coefficient and the interaction monitoring coefficient. If the behavior monitoring value exceeds the preset behavior monitoring threshold, an abnormal learning performance signal is generated; otherwise, a qualified learning performance signal is generated.

3. The Internet-based educational resource management system according to claim 2, characterized in that: The behavior monitoring and analysis module is connected to the learning monitoring module and the interaction monitoring module in communication. The learning monitoring module monitors the online learning process of the corresponding students, obtains the learning monitoring coefficient of the corresponding students in the detection period through analysis, and sends the learning monitoring coefficient to the behavior monitoring and analysis module; The interaction monitoring module monitors the interactive communication process of the corresponding students, obtains the interaction monitoring coefficient of the corresponding students in the detection period through analysis, and sends the interaction monitoring coefficient to the behavior monitoring and analysis module.

4. The Internet-based educational resource management system according to claim 3, characterized in that: The specific analysis process of the learning monitoring module is as follows: The sum of the time that the corresponding students are in a state of learning disengagement and the time when abnormal learning behavior occurs during the detection period is calculated to obtain the non-optimal learning time; the ratio of the non-optimal learning time to the total online learning time of the corresponding students is calculated to obtain the learning poor index value; the learning poor index value is numerically calculated with the corresponding student's learning plan completion value to obtain the learning monitoring coefficient.

5. The Internet-based educational resource management system according to claim 3, characterized in that: The specific analysis process of the interactive monitoring module is as follows: The number of occurrences of interactive sentences not related to learning corresponding to the students in the detection period is marked as the poor interaction frequency, and the interaction monitoring coefficient is calculated by ratio of the poor interaction frequency to the total online interaction time of the corresponding students.

6. The Internet-based educational resource management system according to claim 1, characterized in that: The platform also includes an operation and maintenance monitoring and evaluation module, which is used to set the evaluation period, conduct a comprehensive analysis of the operation and maintenance performance of the education platform during the evaluation period, generate an operation and maintenance qualification signal or an operation and maintenance abnormality signal through analysis, and send the operation and maintenance abnormality signal to the supervision end.

7. The Internet-based educational resource management system according to claim 6, characterized in that: The specific analysis process of the platform operation and maintenance monitoring and evaluation module is as follows: If the user number drop detection value exceeds the preset user number drop detection threshold or the sticky user detection value does not exceed the preset sticky user detection threshold, an operation and maintenance abnormality signal is generated; if the user number drop detection value does not exceed the preset user number drop detection threshold and the sticky user detection value exceeds the preset sticky user detection threshold, the platform negative review detection value and the operation profit value are numerically compared with the preset platform negative review detection threshold and the preset operation profit threshold respectively; if the platform negative review detection value exceeds the preset platform negative review detection threshold or the operation profit value does not exceed the preset operation profit threshold, an operation and maintenance abnormality signal is generated.

8. The Internet-based educational resource management system according to claim 7, characterized in that: If the platform negative review detection value does not exceed the preset platform negative review detection threshold and the operation profit value exceeds the preset operation profit threshold, the education platform operation coefficient is obtained by numerically calculating the paralysis detection value, the paralysis time measurement value and the paralysis risk frequency value. If the education platform operation coefficient exceeds the preset education platform operation coefficient threshold, an operation maintenance abnormality signal is generated; If the education platform operation abnormality coefficient does not exceed the preset education platform operation abnormality coefficient threshold, the education platform threat value and the education platform anti-anomaly value are numerically calculated to obtain the education platform hidden danger coefficient. If the education platform hidden danger coefficient exceeds the preset education platform hidden danger coefficient threshold, an operation and maintenance abnormality signal is generated; if the education platform hidden danger coefficient does not exceed the preset education platform hidden danger coefficient threshold, an operation and maintenance qualified signal is generated.

9. An Internet-based educational resource management method, characterized in that: The method adopts the Internet-based educational resource management system as described in any one of claims 1-8.

10. An Internet-based educational resource management storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the Internet-based educational resource management method described in claim 9 is implemented.

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