Intelligent online course arrangement method and system based on data processing

Abstract

The invention discloses an intelligent online course arrangement method and system based on data processing, and relates to the technical field of online course arrangement, the system comprises a data tracking end, a course complementing end and a multi-course collaboration end, and the data tracking end is used for collecting basic information of students in real time and arranging the students to fill in learning interest questionnaires; the course complementing end is used for tracking whether students of corresponding classes have rejection mentality or not in real time and detecting the completion condition of learning courses in real time through online course learning content, and the multi-course collaboration end is used for formulating an optimal course arrangement optimization scheme of multi-course collaboration according to an interest scheme of questionnaire survey. According to the intelligent online course arrangement method and system based on data processing, the number of students in each class can be tracked in real time, chaos of the students in the online class is avoided, the requirements of different students are met, the situation that the individual students have courses missing or incomplete is prevented, and when the individual students have courses missing or incomplete, the course arrangement efficiency is improved. And multi-class collaboration can be realized for students.

Description

Technical Field

[0001] The present invention relates to the technical field of online course scheduling, and in particular to an intelligent online course scheduling method and system based on data processing. Background Art

[0002] Online scheduling refers to the process of arranging and managing school courses using computer software systems. With the development of educational informatization, the construction of digital campuses has gradually become a trend, and the online scheduling system, as an important component of it, plays a vital role. The online scheduling system uses automation technology to greatly simplify the tedious process of traditional manual scheduling. The traditional scheduling method is inefficient and error-prone, and requires a lot of time and effort.

[0003] At present, with the rapid development of science and technology, the application of intelligent online course scheduling is becoming more and more extensive, and there are also some shortcomings: 1. When scheduling intelligent online courses, due to the different personal circumstances of students, each student has different interests. When scheduling online courses for students, it is impossible to automatically match the learning plan according to the students' personal circumstances in time. At the same time, the number of students in each class cannot be tracked in real time, which may cause confusion among students in online classes and cannot ensure the normal progress of learning courses; 2. Due to the different interests of students, it is impossible to detect in real time whether the students are interested in listening to the class at the current moment, resulting in the scheduled courses being unable to meet the needs of individual students, resulting in individual students missing or incomplete courses; 3. When scheduling online courses, due to the different interests of students, the way they are interested in listening to the class is also different. When individual students miss or incomplete courses, it is impossible to achieve multi-course collaboration for students and make up for the gaps in interesting courses.

[0004] Therefore, an intelligent online course scheduling method and system based on data processing is proposed to solve the above problems. Summary of the invention

[0005] The main purpose of the present invention is to provide an intelligent online course scheduling method and system based on data processing to solve the problems raised in the above background.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: an intelligent online course scheduling method and system based on data processing, including a data tracking terminal, a course supplement terminal and a multi-course collaboration terminal;

[0007] The data tracking terminal is used to collect basic information of students in real time, arrange students to fill in learning interest questionnaires, automatically match students' learning plans according to the results of the learning interest questionnaires, and self-track the learning plans in real time, integrate courses for students with similar plans, set the number of students in each class, automatically sort students, and track the classes of sorted students to ensure that the learning courses of students of the same quality proceed normally;

[0008] The course supplement terminal is used to track in real time whether students in the corresponding class have a rejection mentality, and to detect the completion of the course in real time through the online course learning content, to determine whether students have missed or incomplete lessons, and to retrieve the students' active interest values ​​in real time during the lessons, and to retrieve the missed or incomplete lessons in real time for the missed or incomplete lessons records;

[0009] The multi-course collaboration terminal is used to formulate the best scheduling optimization plan for multi-course collaboration according to the interest scheme of the questionnaire survey, to schedule courses according to the interests of each student, and to formulate a course learning plan based on the students' interests, so as to integrate multiple courses and teach them in a complementary manner, so as to achieve the effect of filling the gaps of interesting multi-course collaboration.

[0010] The data tracking terminal includes a data collection module, a questionnaire survey module and a course integration module;

[0011] The data collection module includes an information collection unit and an interest recording unit;

[0012] The information collection unit is used to collect basic information of trainees in real time through a data collector;

[0013] The interest recording unit is used to automatically match learning plans with the same interest according to the interest course values ​​of the interest questionnaire, and formulate a course plan for each interest.

[0014] The questionnaire survey module includes a questionnaire survey unit and a result display unit;

[0015] The questionnaire survey unit is used to distribute interest questionnaires to students and guide them to complete the questionnaires;

[0016] The result display unit is used to automatically generate a schedule of students' interest courses based on the scoring results of the interest questionnaire.

[0017] The curriculum integration module includes a similar integration unit, a program tracking unit, a sequencing tracking unit and an integration detection unit;

[0018] The same type integration unit is used to sort the students with the same interest course schedule by Arabic numerals, and generate a unified data integration learning plan by integrating the interest course schedule;

[0019] The program tracking unit is used to track the teaching process of the data integrated learning program in real time through a data tracker and a timer;

[0020] The sorting tracking unit is used to track the students sorted by Arabic numerals, identify whether the sequence number exceeds the number of students in the class, and calculate the difference between the maximum sequence number and the number of students in the class. If the difference is ≤0, it is judged that the number of students in the class is normal; if the difference is >0, it is judged that the number of students in the class exceeds the limit and the student is automatically kicked out of the class.

[0021] The integrated detection unit is used to integrate the data of students with the same learning plan, and to detect the students' listening situation after the data integration. The detection includes the students' listening and checking-in records, the students' question records, the students' question-asking records, and the students' hand-raising frequency records. By setting the detection standard parameters, the difference between the detected value and the value is calculated. If the difference is >0, the student's learning course is judged to be normal. If the difference is ≤0, the student's learning course is judged to be abnormal.

[0022] The course supplement terminal includes a course detection module, a rejection psychology module and an abnormal self-tracking module;

[0023] The course detection module includes a course tracking unit and a detection completion unit;

[0024] The course tracking unit is used to track the real-time status of the learning course of the students in the corresponding class in real time through a data tracker and a timer;

[0025] The detection completion unit is used to calculate the student's mastery of the knowledge points at the current moment, where the student's completion of the knowledge points of the learning plan at the current moment is calculated by the following formula:

[0026]

[0027]

[0028] Among them, P represents the student's completion rate of the knowledge points at the current moment, g n It represents the probability that the students can correctly solve the knowledge point without fully mastering it, d n represents the probability of students answering wrong questions when they have fully mastered the knowledge points, Ω n represents the set of questions that test the completion of knowledge points among the M questions answered by the students, w represents any knowledge point in the knowledge point completion set, y(w) represents the score of the student on the knowledge point in the knowledge point answer information Y, β n It indicates the students' answers to the knowledge points. If the difference between the calculated P and the knowledge point completion rate at the current moment is ≥ 0, the knowledge point is considered to be completed. If the difference between the calculated P and the knowledge point completion rate at the current moment is < 0, the knowledge point is considered to be incomplete.

[0029] The repulsion psychology module includes an interest detection unit and a repulsion judgment unit;

[0030] The interest detection unit is used to detect the student's active interest value for the class according to the student's listening status. The detection method is as follows:

[0031] Step 1: Calculate the student's active interest value for the class at the current moment. The calculation formula for the student's active interest value for the class at the current moment is as follows:

[0032] M=∑V×E

[0033] Among them, M represents the current student's positive interest value in the knowledge points of the learning plan, V represents the current student's utility value of the knowledge points of the learning plan, and E represents the current student's expectation value of the knowledge points of the learning plan;

[0034] Step 2: Calculate the difference between the calculated positive interest value and the standard value to determine whether it meets the standard. If the calculated difference is ≥ 0, it is determined that the positive interest value meets the standard. If the calculated difference is < 0, it is determined that the positive interest value does not meet the standard.

[0035] The exclusion judgment unit is used to record the student courses whose positive interest values ​​do not meet the standards as invalid courses through a timer. Invalid courses are also called missed and incomplete courses.

[0036] The anomaly self-tracking module includes an anomaly retrieval unit and a course retrieval unit;

[0037] The abnormal retrieval unit is used to retrieve all invalid courses during the execution of the student's learning plan according to the positive interest value;

[0038] The course search unit is used to automatically generate an abnormal course completion table for the corresponding students from all invalid courses through data integration.

[0039] The multi-course collaboration terminal includes an interest retrieval module, a best solution module and a multi-course collaboration module;

[0040] The interest retrieval module is used to retrieve the interesting activities with the highest positive interest value corresponding to the students in the questionnaire survey plan according to the questionnaire survey plan corresponding to the students;

[0041] The optimal solution module includes interest-based units and optimal course scheduling units;

[0042] The interest targeting unit is used to discuss and design interesting courses according to the interesting activities corresponding to the highest positive interest value, and formulate an interesting learning plan corresponding to the positive interest value of the students;

[0043] The optimal class scheduling unit is used to automatically generate the best class scheduling optimization plan according to the interesting learning plan and the corresponding student's class schedule.

[0044] The multi-course collaboration module includes an interesting complementary unit, a multi-course collaboration unit and a course gap filling unit;

[0045] The interest-combining unit is used to integrate the interest of the highest positive interest value interest activities with the invalid courses, and to realize teaching in a targeted and interesting way. The integration means adopts the class matching of the positive interest value and the interest value, and the sequence matching of the highest positive interest value and the interest value according to the sequence. The interest integration is to integrate the various subjects for teaching. The specific unit adopts the method of multi-disciplinary joint teaching to integrate the interest.

[0046] The multi-course collaboration unit is used to collaborate with multiple course teachers to discuss corresponding missed course plans for students based on the highest positive interest value, so as to achieve multiple effects in one course;

[0047] The course filling unit is used to implement a plan for filling the missed courses of corresponding students through multi-course collaboration.

[0048] The intelligent online course scheduling method based on data processing includes the following steps:

[0049] Step 1: Configure the IP address information of the online class scheduling remote control area server;

[0050] Step 2: Enter the data tracking terminal, collect basic information of students in real time, fill out the learning interest questionnaire, automatically match the learning plan of students, and conduct real-time self-tracking of the learning plan, and integrate courses for students with similar plans;

[0051] Step 3: Enter the course supplement terminal to track in real time whether students in the corresponding class have rejection psychology, and through real-time detection of online course learning content, determine whether students have missed or incomplete courses, and search for incomplete courses for abnormalities;

[0052] Step 4: Enter the multi-course collaboration terminal, and formulate the best scheduling optimization plan for multi-course collaboration based on the interest plans in the questionnaire survey to achieve an interesting multi-course collaboration to fill in the gaps.

[0053] The present invention has the following beneficial effects:

[0054] 1. In the present invention, by setting a data tracking terminal, when the intelligent online scheduling system is operated, the basic information of students is collected in real time, and the students are arranged to fill in the learning interest questionnaire. According to the results of the learning interest questionnaire, the learning plan of the students is automatically matched. The learning plan can be automatically matched according to the personal situation of the students in time, and the learning plan can be self-tracked in real time, and the courses of students with similar plans can be integrated to ensure that the learning courses of students with the same quality proceed normally. The number of students in each class can be tracked in real time to avoid confusion among students in the online classroom and ensure the normal progress of the learning courses.

[0055] 2. In the present invention, by setting up a course supplement terminal, when the intelligent online course scheduling system is operated, by real-time tracking whether the students in the corresponding class have a rejection mentality, and real-time retrieval of the students' active interest values ​​when listening to the class, it is possible to detect in real time whether the students are interested in the class at the current moment, and perform real-time retrieval of missed or incomplete courses for missed or incomplete class records, so that the arranged courses can meet the needs of different students and prevent individual students from missing or missing courses.

[0056] 3. In the present invention, by setting a multi-course collaborative terminal, when the intelligent online scheduling system is operated, the best scheduling optimization plan for multi-course collaboration is formulated according to the interest scheme of the questionnaire survey, and targeted interest scheduling is carried out for each student. A course learning plan is formulated in combination with what the students are interested in, and multiple courses are integrated for teaching in a complementary manner. When individual students miss or have incomplete courses, multi-course collaboration can be achieved for the students, achieving the effect of filling the gaps with interesting multi-course collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of the process architecture of the intelligent online course scheduling method and system based on data processing of the present invention;

[0058] Figure 2 It is a schematic diagram of the architecture of the data tracking end of the intelligent online course scheduling method and system based on data processing of the present invention;

[0059] Figure 3 A schematic diagram of the structure of the course supplement end of the intelligent online course scheduling method and system based on data processing of the present invention;

[0060] Figure 4 The present invention is a schematic diagram of the architecture of a multi-course collaborative terminal of an intelligent online course scheduling method and system based on data processing. DETAILED DESCRIPTION

[0061] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0062] Embodiment 1

[0063] Please refer to Figure 1-2 As shown: an intelligent online course scheduling method and system based on data processing, including a data tracking terminal, a course supplement terminal and a multi-course collaboration terminal;

[0064] The data tracking terminal is used to collect basic information of students in real time and arrange students to fill out a learning interest questionnaire. According to the results of the learning interest questionnaire, it automatically matches the students' learning plans and tracks the learning plans in real time. It integrates courses for students with similar plans, sets the number of students in each class, automatically sorts the students, and tracks the classes of sorted students to ensure that the learning courses of students of the same quality proceed normally.

[0065] The course supplement terminal is used to track in real time whether the students in the corresponding class have a rejection mentality, and to detect the completion of the course in real time through the online course learning content, to determine whether the students have missed or incomplete lessons, and to retrieve the students' active interest values ​​in real time during the lessons, and to retrieve the missed or incomplete courses in real time for the missed or incomplete lesson records;

[0066] The multi-course collaboration terminal is used to develop the best scheduling optimization plan for multi-course collaboration based on the interest scheme of the questionnaire survey, schedule courses according to the targeted interests of each student, and develop course learning plans based on what the students are interested in. It integrates multiple courses into a complementary teaching style to achieve the effect of filling the gaps in interesting multi-course collaboration.

[0067] The data tracking end includes data collection module, questionnaire survey module and course integration module;

[0068] The data collection module includes an information collection unit and an interest recording unit;

[0069] The information collection unit is used to collect the basic information of the trainees in real time through the data collection instrument;

[0070] The interest recording unit is used to automatically match learning plans with the same interest according to the interest course values ​​of the interest questionnaire, and to develop course plans for each interest.

[0071] The questionnaire survey module includes a questionnaire survey unit and a result display unit;

[0072] The questionnaire survey unit is used to distribute interest questionnaires to students and guide them to complete the questionnaires;

[0073] The result display unit is used to automatically generate a schedule of students' interest courses based on the scoring results of the interest questionnaire.

[0074] The curriculum integration module includes similar integration unit, program tracking unit, sequence tracking unit and integration detection unit;

[0075] The same type integration unit is used to sort the students with the same interest course schedule by Arabic numerals, and generate a unified data integration learning plan by integrating the interest course schedule data;

[0076] The program tracking unit is used to track the teaching process of the data integrated learning program in real time through a data tracker and a timer;

[0077] The sorting tracking unit is used to track the serial numbers of students sorted by Arabic numerals, identify whether the serial number exceeds the number of students in the class, and calculate the difference between the maximum serial number and the number of students in the class. If the difference is ≤0, the number of students in the class is normal; if the difference is >0, the number of students in the class is exceeded and they are automatically kicked out of the class;

[0078] The integrated detection unit is used to integrate the data of students with the same learning plan, and to detect the students' listening situation after the data integration. The detection includes the students' listening and checking-in records, the students' question records, the students' being asked questions records and the students' hand-raising frequency records. By setting the detection standard parameters, the difference between the detected value and the value is calculated. If the difference is >0, the student's learning course is judged to be normal. If the difference is ≤0, the student's learning course is judged to be abnormal.

[0079] By collecting students' basic information in real time and arranging students to fill out learning interest questionnaires, the system automatically matches students' learning plans based on the results of the learning interest questionnaires. It can automatically match learning plans according to students' personal circumstances in a timely manner, track learning plans in real time, and integrate courses for students with similar plans to ensure that learning courses for students of the same quality proceed normally. It can track the number of students in each class in real time to avoid confusion among students in online classes and ensure the normal progress of learning courses.

[0080] Embodiment 2

[0081] Please refer to Figure 3 As shown: Based on the first embodiment, the course supplement terminal includes a course detection module, a rejection psychology module and an abnormal self-tracking module;

[0082] The course detection module includes a course tracking unit and a detection completion unit;

[0083] The course tracking unit is used to track the real-time status of the learning course of the students in the corresponding class in real time through a data tracker and a timer;

[0084] The detection completion unit is used to calculate the student's mastery of the current knowledge point, where the student's completion of the knowledge points in the current learning plan is calculated as follows:

[0085]

[0086]

[0087] Among them, P represents the student's completion rate of the knowledge points at the current moment, g n It represents the probability that the students can correctly solve the knowledge point without fully mastering it, dn represents the probability of students answering wrong questions when they have fully mastered the knowledge points, Ω n represents the set of questions that test the completion of knowledge points among the M questions answered by the students, w represents any knowledge point in the knowledge point completion set, y(w) represents the score of the student on the knowledge point in the knowledge point answer information Y, β n It indicates the students' answers to the knowledge points. If the difference between the calculated P and the knowledge point completion rate at the current moment is ≥ 0, the knowledge point is considered to be completed. If the difference between the calculated P and the knowledge point completion rate at the current moment is < 0, the knowledge point is considered to be incomplete.

[0088] The rejection psychology module includes an interest detection unit and a rejection judgment unit;

[0089] The interest detection unit is used to detect the student's active interest value in the class according to the student's listening status. The detection method is as follows:

[0090] Step 1: Calculate the student's active interest value for the class at the current moment. The calculation formula for the student's active interest value for the class at the current moment is as follows:

[0091] M=∑V×E

[0092] Among them, M represents the current student's positive interest value in the knowledge points of the learning plan, V represents the current student's utility value of the knowledge points of the learning plan, and E represents the current student's expectation value of the knowledge points of the learning plan;

[0093] Step 2: Calculate the difference between the calculated positive interest value and the standard value to determine whether it meets the standard. If the calculated difference is ≥ 0, it is determined that the positive interest value meets the standard. If the calculated difference is < 0, it is determined that the positive interest value does not meet the standard.

[0094] The exclusion judgment unit is used to record the courses of students whose positive interest values ​​do not meet the standards as invalid courses through a timer. Invalid courses are also called missed and incomplete courses.

[0095] The anomaly self-tracking module includes an anomaly retrieval unit and a course retrieval unit;

[0096] The abnormal retrieval unit is used to retrieve all invalid courses in the process of executing the student's learning plan according to the positive interest value;

[0097] The course retrieval unit is used to automatically generate an abnormal course completion table for the corresponding students from all invalid courses through data integration.

[0098] By real-time tracking whether students in the corresponding class have a rejection mentality, and real-time retrieving students' active interest values ​​when listening to lectures, it is possible to detect in real time whether students are interested in listening to lectures at the current moment, and to search for missed or incomplete lecture records in real time, so that the scheduled courses can meet the needs of different students and prevent individual students from missing or incomplete courses.

[0099] Embodiment 3

[0100] Please refer to Figure 4 As shown: Based on the first embodiment, the multi-course collaboration terminal includes an interest retrieval module, a best solution module and a multi-course collaboration module;

[0101] The interest retrieval module is used to retrieve the interesting activities with the highest positive interest value corresponding to the students in the questionnaire survey plan according to the questionnaire survey plan of the corresponding students;

[0102] The best solution module includes interest-based units and best course scheduling units;

[0103] The interest-based unit is used to discuss and design interesting courses based on the interesting activities corresponding to the highest positive interest value, and formulate interesting learning plans corresponding to the students' positive interest values;

[0104] The best class scheduling unit is used to automatically generate the best class scheduling optimization plan based on the interesting learning plan and the corresponding student's class schedule.

[0105] The multi-course collaboration module includes interesting complementary units, multi-course collaboration units and course gap-filling units;

[0106] The fun complementary unit is used to integrate the fun activities with the highest positive interest value with the ineffective courses, and to realize teaching in a targeted and interesting way. The integration means is to match the positive interest value with the interest value in class, and to match the highest positive interest value with the interest value in sequence. Fun integration is to integrate various subjects for teaching. Specific units use the method of multi-disciplinary co-teaching to integrate fun.

[0107] The multi-course collaboration unit is used to coordinate with multiple course teachers to discuss the corresponding missed course plan for students based on the highest positive interest value, so as to achieve multiple effects in one course;

[0108] The course filling unit is used to implement the corresponding students' missed course filling execution plan through multi-course collaboration.

[0109] According to the interest schemes from the questionnaire survey, we develop the best scheduling optimization plan for multiple courses, schedule courses according to each student's interests, and develop course learning plans based on the students' interests. We integrate multiple courses and teach them in a complementary way. When individual students miss or have incomplete courses, we can achieve multi-course coordination for the students and realize the effect of filling the gaps with interesting multi-course coordination.

[0110] In the present invention, an intelligent online course scheduling method and system based on data processing, when the system is in operation, first configures the IP address information of the remote control area server of the online course scheduling; by collecting the basic information of the students in real time, and arranging the students to fill in the learning interest questionnaire, according to the results of the learning interest questionnaire, automatically matching the learning plan of the students, and real-time self-tracking of the learning plan, integrating the courses for students with similar plans, and setting the number of students in each class, automatically sorting the students, and tracking the classes of the sorted students, ensuring that the learning courses of students with the same quality proceed normally, when the intelligent online course scheduling system is in operation, by collecting the basic information of the students in real time, and arranging the students to fill in the learning interest questionnaire, according to the results of the learning interest questionnaire, automatically matching the learning plan of the students, it is possible to automatically match the learning plan according to the personal situation of the students in time, and real-time self-tracking of the learning plan, and integrating the courses for students with similar plans, ensuring that the learning courses of students with the same quality proceed normally, and can track the number of students in each class in real time, avoid confusion among students in the online classroom, and ensure the normal progress of the learning course; by real-time tracking whether the students in the corresponding class have a rejection mentality, and real-time detection of the completion of the learning course through the online course learning content, it is judged whether the students listen Whether there are omissions or incomplete classes, and real-time retrieval of students' active interest values ​​during class, real-time retrieval of missed or incomplete courses for missed or incomplete class records. When operating the intelligent online class scheduling system, by real-time tracking whether the students in the corresponding class have rejection psychology, and real-time retrieval of students' active interest values ​​during class, it can detect in real time whether the students are interested in the class at the current moment, and real-time retrieval of missed or incomplete courses for missed or incomplete class records, so that the scheduled courses can meet the needs of different students and prevent individual students from missing or incomplete courses; formulate multi-course collaborative The best class scheduling optimization plan is developed, and classes are scheduled according to the interests of each student. Course learning plans are formulated based on the students' interests. Multiple courses are integrated and taught in a complementary manner to achieve the effect of filling the gaps in an interesting way. When the intelligent online class scheduling system is operated, the best class scheduling optimization plan for multiple courses is developed based on the interest plans of the questionnaire survey. Classes are scheduled according to the interests of each student, and course learning plans are formulated based on the students' interests. Multiple courses are integrated and taught in a complementary manner. When individual students miss or have incomplete courses, multiple courses can be coordinated for the students to achieve the effect of filling the gaps in an interesting way.

[0111] The intelligent online course scheduling method based on data processing includes the following steps:

[0112] Step 1: Configure the IP address information of the online class scheduling remote control area server;

[0113] Step 2: Enter the data tracking terminal, collect basic information of students in real time, fill out the learning interest questionnaire, automatically match the learning plan of students, and conduct real-time self-tracking of the learning plan, and integrate courses for students with similar plans;

[0114] Step 3: Enter the course supplement terminal to track in real time whether students in the corresponding class have rejection psychology, and through real-time detection of online course learning content, determine whether students have missed or incomplete courses, and search for incomplete courses for abnormalities;

[0115] Step 4: Enter the multi-course collaboration terminal, and formulate the best scheduling optimization plan for multi-course collaboration based on the interest plans in the questionnaire survey to achieve an interesting multi-course collaboration to fill in the gaps.

[0116] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. Intelligent online course scheduling system based on data processing, characterized by: The system includes a data tracking terminal, a course supplement terminal and a multi-course collaboration terminal; The data tracking terminal is used to collect basic information of students in real time, arrange students to fill in learning interest questionnaires, automatically match students' learning plans according to the results of the learning interest questionnaires, and self-track the learning plans in real time, integrate courses for students with similar plans, set the number of students in each class, automatically sort students, and track the classes of sorted students to ensure that the learning courses of students of the same quality proceed normally; The course supplement terminal is used to track in real time whether students in the corresponding class have a rejection mentality, and to detect the completion of the course in real time through the online course learning content, to determine whether students have missed or incomplete lessons, and to retrieve the students' active interest values ​​in real time during the lessons, and to retrieve the missed or incomplete lessons in real time for the missed or incomplete lessons records; The multi-course collaboration terminal is used to formulate the best scheduling optimization plan for multi-course collaboration according to the interest scheme of the questionnaire survey, to schedule courses according to the interests of each student, and to formulate a course learning plan based on the students' interests, so as to integrate multiple courses and teach them in a complementary manner, so as to achieve the effect of filling the gaps of interesting multi-course collaboration.

2. The system according to claim 1, characterized in that: The data tracking terminal includes a data collection module, a questionnaire survey module and a course integration module; The data collection module includes an information collection unit and an interest recording unit; The information collection unit is used to collect basic information of trainees in real time through a data collector; The interest recording unit is used to automatically match learning plans with the same interest according to the interest course values ​​of the interest questionnaire, and formulate a course plan for each interest.

3. The system according to claim 2, characterized in that: The questionnaire survey module includes a questionnaire survey unit and a result display unit; The questionnaire survey unit is used to distribute interest questionnaires to students and guide them to complete the questionnaires; The result display unit is used to automatically generate a schedule of students' interest courses based on the scoring results of the interest questionnaire.

4. The system according to claim 3, characterized in that: The curriculum integration module includes a similar integration unit, a program tracking unit, a sequencing tracking unit and an integration detection unit; The same type integration unit is used to sort the students with the same interest course schedule by Arabic numerals, and generate a unified data integration learning plan by integrating the interest course schedule; The program tracking unit is used to track the teaching process of the data integrated learning program in real time through a data tracker and a timer; The sorting tracking unit is used to track the students sorted by Arabic numerals, identify whether the sequence number exceeds the number of students in the class, and calculate the difference between the maximum sequence number and the number of students in the class. If the difference is ≤0, it is judged that the number of students in the class is normal; if the difference is >0, it is judged that the number of students in the class exceeds the limit and the student is automatically kicked out of the class. The integrated detection unit is used to integrate the data of students with the same learning plan, and to detect the students' listening situation after the data integration. The detection includes the students' listening and checking-in records, the students' question records, the students' question-asking records, and the students' hand-raising frequency records. By setting the detection standard parameters, the difference between the detected value and the value is calculated. If the difference is >0, the student's learning course is judged to be normal. If the difference is ≤0, the student's learning course is judged to be abnormal.

5. The system according to claim 1, characterized in that: The course supplement terminal includes a course detection module, a rejection psychology module and an abnormal self-tracking module; The course detection module includes a course tracking unit and a detection completion unit; The course tracking unit is used to track the real-time status of the learning course of the students in the corresponding class in real time through a data tracker and a timer; The detection completion unit is used to calculate the student's mastery of the knowledge points at the current moment, where the student's completion of the knowledge points of the learning plan at the current moment is calculated by the following formula: Among them, P represents the student's completion rate of the knowledge points at the current moment, g n It represents the probability that the students can correctly solve the knowledge point without fully mastering it, d n represents the probability of students answering wrong questions when they have fully mastered the knowledge points, Ω n represents the set of questions that test the completion of knowledge points among the M questions answered by the students, w represents any knowledge point in the knowledge point completion set, y(w) represents the score of the student on the knowledge point in the knowledge point answer information Y, β n It indicates the students' answers to the knowledge points. If the difference between the calculated P and the knowledge point completion rate at the current moment is ≥ 0, the knowledge point is considered to be completed. If the difference between the calculated P and the knowledge point completion rate at the current moment is < 0, the knowledge point is considered to be incomplete.

6. The system according to claim 5, characterized in that: The repulsion psychology module includes an interest detection unit and a repulsion judgment unit; The interest detection unit is used to detect the student's active interest value for the class according to the student's listening status. The detection method is as follows: Step 1: Calculate the student's active interest value for the class at the current moment. The calculation formula for the student's active interest value for the class at the current moment is as follows: M=∑V×E Among them, M represents the current student's positive interest value in the knowledge points of the learning plan, V represents the current student's utility value of the knowledge points of the learning plan, and E represents the current student's expectation value of the knowledge points of the learning plan; Step 2: Calculate the difference between the calculated positive interest value and the standard value to determine whether it meets the standard. If the calculated difference is ≥ 0, it is determined that the positive interest value meets the standard. If the calculated difference is < 0, it is determined that the positive interest value does not meet the standard. The exclusion judgment unit is used to record the student courses whose positive interest values ​​do not meet the standards as invalid courses through a timer. Invalid courses are also called missed and incomplete courses.

7. The system according to claim 6, characterized in that: The anomaly self-tracking module includes an anomaly retrieval unit and a course retrieval unit; The abnormal retrieval unit is used to retrieve all invalid courses during the execution of the student's learning plan according to the positive interest value; The course search unit is used to automatically generate an abnormal course completion table for the corresponding students from all invalid courses through data integration.

8. The system according to claim 1, characterized in that: The multi-course collaboration terminal includes an interest retrieval module, a best solution module and a multi-course collaboration module; The interest retrieval module is used to retrieve the interesting activities with the highest positive interest value corresponding to the students in the questionnaire survey plan according to the questionnaire survey plan corresponding to the students; The optimal solution module includes interest-based units and optimal course scheduling units; The interest targeting unit is used to discuss and design interesting courses according to the interesting activities corresponding to the highest positive interest value, and formulate an interesting learning plan corresponding to the positive interest value of the students; The optimal class scheduling unit is used to automatically generate the best class scheduling optimization plan according to the interesting learning plan and the corresponding student's class schedule.

9. The system according to claim 8, characterized in that: The multi-course collaboration module includes an interesting complementary unit, a multi-course collaboration unit and a course gap filling unit; The interest-combining unit is used to integrate the interest of the highest positive interest value interest activities with the invalid courses, and to realize teaching in a targeted and interesting way. The integration means adopts the class matching of the positive interest value and the interest value, and the sequence matching of the highest positive interest value and the interest value according to the sequence. The interest integration is to integrate the various subjects for teaching. The specific unit adopts the method of multi-disciplinary joint teaching to integrate the interest. The multi-course collaboration unit is used to collaborate with multiple course teachers to discuss corresponding missed course plans for students based on the highest positive interest value, so as to achieve multiple effects in one course; The course filling unit is used to implement a plan for filling the missed courses of corresponding students through multi-course collaboration.

10. An intelligent online course scheduling method based on data processing, referring to the intelligent online course scheduling system based on data processing according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Configure the IP address information of the online class scheduling remote control area server; Step 2: Enter the data tracking terminal, collect basic information of students in real time, fill out the learning interest questionnaire, automatically match the learning plan of students, and conduct real-time self-tracking of the learning plan, and integrate courses for students with similar plans; Step 3: Enter the course supplement terminal to track in real time whether students in the corresponding class have rejection psychology, and through real-time detection of online course learning content, determine whether students have missed or incomplete courses, and search for incomplete courses for abnormalities; Step 4: Enter the multi-course collaboration terminal, and formulate the best scheduling optimization plan for multi-course collaboration based on the interest plans in the questionnaire survey to achieve an interesting multi-course collaboration to fill in the gaps.

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