A data processing method and system based on intelligent online course scheduling

Abstract

The present invention relates to the field of data processing technology, and in particular to a data processing method and system based on intelligent online class scheduling. The method comprises the following steps: constructing a course classification matrix for a school's academic affairs system, and using this to perform data table structure analysis, obtaining an associated data table structure, constructing an associated database, extracting student academic affairs data, and generating associated academic affairs data nodes, while combining student academic affairs data to perform adaptive class scheduling linked list data generation, allocating teachers and classroom resources based on the adaptive class scheduling linked list data, obtaining teacher allocation classroom data and student allocation classroom data, and performing associated mapping to obtain classroom personnel allocation data, obtaining student course selection data, thereby optimizing classroom personnel allocation data, and then combining the adaptive class scheduling linked list data to perform intelligent class scheduling, and obtaining an intelligent class scheduling plan. The present invention uses data processing to achieve intelligent class scheduling, thereby improving resource utilization and saving time costs.

Description

Technical Field

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

[0002] The traditional offline scheduling method can no longer meet the growing personalized needs and efficient management goals. In this context, the data processing method based on intelligent online scheduling aims to use technical means such as data analysis and algorithm optimization to achieve efficient configuration of course resources and meet students' personalized needs. At present, the intelligent online scheduling system mainly relies on the following technologies: constraint satisfaction problem solving technology, which transforms the scheduling problem into a constraint satisfaction problem, and uses intelligent algorithms to search for feasible scheduling plans that meet all constraints. However, the current intelligent online scheduling system still has the following shortcomings: lack of adaptability. Most of the existing scheduling systems schedule classes based on pre-set rules and constraints, and lack the ability to adapt to dynamically changing environments. For example, when new teachers or students are added, the system often cannot adjust the scheduling plan in time, resulting in the current intelligent online scheduling system being unable to give full play to its due effectiveness. For example, due to the lack of adaptability, the system often needs to make a lot of modifications and adjustments when facing emergencies or changes in demand, which increases the workload of scheduling. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a data processing method and system based on intelligent online class scheduling to solve at least one of the above technical problems.

[0004] A data processing method based on intelligent online course scheduling, comprising the following steps:

[0005] Step S1: constructing a course classification matrix for the school's teaching affairs system to obtain a course classification matrix; analyzing the data table structure according to the course classification matrix to obtain a related data table structure; constructing a teaching affairs database for the related data table structure to obtain a related database;

[0006] Step S2: extracting student data from the school's teaching affairs system to obtain student teaching affairs data; generating associated nodes for the associated database to obtain associated teaching affairs data nodes; analyzing student class scheduling data for the student teaching affairs data based on the associated teaching affairs data nodes to obtain adaptive class scheduling linked list data;

[0007] Step S3: Based on the adaptive class scheduling linked list data, the associated database is assigned to classroom resources and teachers, and teacher allocation classroom data is obtained; based on the teacher allocation classroom data, the adaptive class scheduling linked list data is assigned to courses and students, and student allocation classroom data is obtained; mapping and associating the teacher allocation classroom data and the student allocation classroom data, and obtaining classroom personnel allocation data;

[0008] Step S4: Acquire student active selection data from the school's academic affairs system to obtain student course selection data; optimize the adaptive class scheduling linked list data and classroom personnel allocation data based on the student course selection data to obtain optimized classroom personnel allocation data; perform intelligent class scheduling based on the adaptive class scheduling linked list data and the optimized classroom personnel allocation data to obtain an intelligent class scheduling plan.

[0009] The present invention clearly shows the correlation and hierarchical relationship between courses by constructing a course classification matrix, and provides a reliable basis for the design of the database. On this basis, the data table structure is analyzed and an associated database is constructed, which realizes the systematic management of the teaching data, lays a solid data foundation for the subsequent intelligent class scheduling, extracts student data from the teaching system, and generates associated teaching data nodes in combination with the associated database, and constructs the correlation between students, courses, and majors. On this basis, the student teaching data and credit requirements are analyzed, and the adaptive class scheduling linked list data is generated, which realizes the accurate grasp of the personalized needs of students, and provides a flexible data structure for the subsequent intelligent class scheduling. Based on the adaptive class scheduling linked list data, the teacher, classroom resources and courses are intelligently matched, and the teacher allocation classroom data and the student allocation classroom data are generated, and finally the mapping association is generated. Classroom personnel allocation data. This step realizes the optimal configuration of teaching resources, meets the student's course selection needs and teacher teaching arrangements to the greatest extent, allows students to actively participate in course selection, and further optimizes the class scheduling plan. By obtaining the student's course selection data, the system can adjust the arrangement according to the student's preference. The staffing optimization process can balance student preferences and resource allocation, ensuring that the class scheduling plan meets student needs while making reasonable use of teacher and classroom resources. Ultimately, the generation of intelligent class scheduling plans can greatly simplify the academic management process, provide schools and students with an efficient, personalized, and convenient class scheduling experience, improve the adaptability of class scheduling when there are new staff, and reduce modifications and adjustments to the system when faced with emergencies or changes in demand, thereby reducing the workload of class scheduling.

[0010] Preferably, the present invention further provides a data processing system based on intelligent online class scheduling, which is used to execute the data processing method based on intelligent online class scheduling as described above. The data processing system based on intelligent online class scheduling includes:

[0011] The associated database construction module is used to construct a course classification matrix for the school's teaching affairs system to obtain a course classification matrix; perform data table structure analysis based on the course classification matrix to obtain an associated data table structure; and construct a teaching affairs database based on the associated data table structure to obtain an associated database;

[0012] The module for generating an adaptive class scheduling linked list is used to extract student data from the school's teaching affairs system to obtain student teaching affairs data; generate associated nodes for the associated database to obtain associated teaching affairs data nodes; perform student class scheduling data analysis on the student teaching affairs data based on the associated teaching affairs data nodes to obtain adaptive class scheduling linked list data;

[0013] The classroom personnel allocation module is used to allocate classroom resources to teachers on the associated database based on the adaptive class scheduling linked list data, and obtain teacher allocation classroom data; allocate courses to students on the adaptive class scheduling linked list data based on the teacher allocation classroom data, and obtain student allocation classroom data; map and associate the teacher allocation classroom data and the student allocation classroom data, and obtain classroom personnel allocation data;

[0014] The intelligent class scheduling module is used to obtain student active selection data from the school's academic affairs system to obtain student course selection data; optimize the adaptive class scheduling linked list data and classroom personnel allocation data based on the student course selection data to obtain optimized classroom personnel allocation data; perform intelligent class scheduling based on the adaptive class scheduling linked list data and the optimized classroom personnel allocation data to obtain an intelligent class scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0016] Figure 1 A schematic diagram of the steps of a data processing method based on intelligent online class scheduling according to the present invention;

[0017] Figure 2 for Figure 1 Detailed step flow diagram of step S2;

[0018] Figure 3 for Figure 2 Detailed step flow chart of step S25.

[0019] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0022] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0023] To achieve this, please refer to Figures 1 to 3 The present invention provides a data processing method based on intelligent online course scheduling, comprising the following steps:

[0024] Step S1: constructing a course classification matrix for the school's teaching affairs system to obtain a course classification matrix; analyzing the data table structure according to the course classification matrix to obtain a related data table structure; constructing a teaching affairs database for the related data table structure to obtain a related database;

[0025] Step S2: extracting student data from the school's teaching affairs system to obtain student teaching affairs data; generating associated nodes for the associated database to obtain associated teaching affairs data nodes; analyzing student class scheduling data for the student teaching affairs data based on the associated teaching affairs data nodes to obtain adaptive class scheduling linked list data;

[0026] Step S3: Based on the adaptive class scheduling linked list data, the associated database is assigned to classroom resources and teachers, and teacher allocation classroom data is obtained; based on the teacher allocation classroom data, the adaptive class scheduling linked list data is assigned to courses and students, and student allocation classroom data is obtained; mapping and associating the teacher allocation classroom data and the student allocation classroom data, and obtaining classroom personnel allocation data;

[0027] Step S4: Acquire student active selection data from the school's academic affairs system to obtain student course selection data; optimize the adaptive class scheduling linked list data and classroom personnel allocation data based on the student course selection data to obtain optimized classroom personnel allocation data; perform intelligent class scheduling based on the adaptive class scheduling linked list data and the optimized classroom personnel allocation data to obtain an intelligent class scheduling plan.

[0028] In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the steps of the data processing method based on intelligent online class scheduling of the present invention. In this example, the data processing method based on intelligent online class scheduling includes the following steps:

[0029] Step S1: constructing a course classification matrix for the school's teaching affairs system to obtain a course classification matrix; analyzing the data table structure according to the course classification matrix to obtain a related data table structure; constructing a teaching affairs database for the related data table structure to obtain a related database;

[0030] In an embodiment of the present invention, teacher resource data, teacher information, teaching information, scientific research projects, etc. and classroom resource data, classroom information, available time, etc. are obtained through the education system API or data export, and data cleaning and preprocessing are performed. Then, a course classification matrix is ​​constructed according to the school curriculum system, and teachers and classrooms are associated with course categories. Based on the data characteristics and the associated relationship analysis data table structure, the teacher information table, classroom information table, course information table, class scheduling information table, etc. are designed, and the association mode and field constraints between tables are determined. Finally, based on the designed associated data table structure, a suitable database management system is selected, a database and a data table are created, the processed teacher and classroom resource data are imported, and an associated database is constructed.

[0031] Step S2: extracting student data from the school's teaching affairs system to obtain student teaching affairs data; generating associated nodes for the associated database to obtain associated teaching affairs data nodes; analyzing student class scheduling data for the student teaching affairs data based on the associated teaching affairs data nodes to obtain adaptive class scheduling linked list data;

[0032] In an embodiment of the present invention, by obtaining student academic data, student status information, and course study information from the academic affairs system and preprocessing them, professional subject credit data is obtained by major division, and credit demand data of each major is extracted. At the same time, the associated academic affairs extraction data such as courses, teachers, and classrooms are extracted from the associated database and injected into the node to form an associated academic affairs data node network. Finally, based on the associated academic affairs data nodes and student major data and credit requirements, the student course requirements and teacher classroom resources are analyzed, the class scheduling data analysis is performed, and the results are stored in the adaptive class scheduling linked list data.

[0033] Step S3: Based on the adaptive class scheduling linked list data, the associated database is assigned to classroom resources and teachers, and teacher allocation classroom data is obtained; based on the teacher allocation classroom data, the adaptive class scheduling linked list data is assigned to courses and students, and student allocation classroom data is obtained; mapping and associating the teacher allocation classroom data and the student allocation classroom data, and obtaining classroom personnel allocation data;

[0034] In an embodiment of the present invention, based on the adaptive class scheduling linked list data, the teacher information is queried from the associated database to generate the teacher class scheduling associated data, and the course personnel data, teachers, students, time, and location are extracted. Then, according to the course personnel data and the classroom resource information in the associated database, the classroom is allocated to generate the teacher allocation classroom data. Then, according to the teacher allocation classroom data and the adaptive class scheduling linked list data, the students are allocated to the corresponding courses and classrooms to generate the student allocation classroom data. Finally, the teacher allocation classroom data and the student allocation classroom data are mapped and associated to generate the final classroom personnel allocation data, which includes the course schedule, teacher and student information of each classroom.

[0035] Step S4: Acquire student active selection data from the school's academic affairs system to obtain student course selection data; optimize the adaptive class scheduling linked list data and classroom personnel allocation data based on the student course selection data to obtain optimized classroom personnel allocation data; perform intelligent class scheduling based on the adaptive class scheduling linked list data and the optimized classroom personnel allocation data to obtain an intelligent class scheduling plan.

[0036] In an embodiment of the present invention, course selection is performed through the teaching affairs system to generate student course selection data. Then, students are assigned to the adaptive class scheduling linked list data based on these data. If the number of students in the course exceeds the capacity limit, adjustments are made to generate new personnel allocation data. Based on the new personnel allocation data, the classroom personnel allocation data is optimized to solve problems such as classroom capacity and teacher time conflicts. Finally, an intelligent class scheduling plan is generated based on the adaptive class scheduling linked list data and the optimized classroom personnel allocation data.

[0037] The present invention clearly shows the correlation and hierarchical relationship between courses by constructing a course classification matrix, and provides a reliable basis for the design of the database. On this basis, the data table structure is analyzed and an associated database is constructed, which realizes the systematic management of the teaching data, lays a solid data foundation for the subsequent intelligent class scheduling, extracts student data from the teaching system, and generates associated teaching data nodes in combination with the associated database, and constructs the correlation between students, courses, and majors. On this basis, the student teaching data and credit requirements are analyzed, and the adaptive class scheduling linked list data is generated, which realizes the accurate grasp of the personalized needs of students, and provides a flexible data structure for the subsequent intelligent class scheduling. Based on the adaptive class scheduling linked list data, the teacher, classroom resources and courses are intelligently matched, and the teacher allocation classroom data and the student allocation classroom data are generated, and finally the mapping association is generated. Classroom personnel allocation data. This step realizes the optimal configuration of teaching resources, meets the student's course selection needs and teacher teaching arrangements to the greatest extent, allows students to actively participate in course selection, and further optimizes the class scheduling plan. By obtaining the student's course selection data, the system can adjust the arrangement according to the student's preference. The staffing optimization process can balance student preferences and resource allocation, ensuring that the class scheduling plan meets student needs while making reasonable use of teacher and classroom resources. Ultimately, the generation of intelligent class scheduling plans can greatly simplify the academic management process, provide schools and students with an efficient, personalized, and convenient class scheduling experience, improve the adaptability of class scheduling when there are new staff, and reduce modifications and adjustments to the system when faced with emergencies or changes in demand, thereby reducing the workload of class scheduling.

[0038] Preferably, step S1 comprises the following steps:

[0039] Step S11: Acquire teacher resource data from the school's educational affairs system to obtain teacher resource data;

[0040] In an embodiment of the present invention, the teacher information data is obtained through the API interface or data export function provided by the teaching system, including basic information such as teacher number, name, department, title, contact information, etc. Then, the teacher's teaching information is obtained, such as the courses that can be taught, the teaching schedule, the historical teaching situation, etc. In order to ensure the integrity of the data, it is also necessary to collect other relevant information of the teacher, such as scientific research projects, administrative positions, etc. Finally, the obtained raw data is cleaned and preprocessed, such as removing duplicate data, processing missing values, etc., and the data is converted into a unified format for subsequent processing and analysis.

[0041] Step S12: Acquire classroom resource data from the school's educational affairs system to obtain classroom resource data;

[0042] In the embodiment of the present invention, the basic information of the classroom is obtained through the API interface or data export function of the teaching system, including the classroom number, the teaching building where it is located, the classroom type, the number of people it can accommodate, etc. Secondly, the available time information of the classroom is obtained, such as which time periods are occupied and which time periods are available for class scheduling. Finally, the obtained classroom resource data is cleaned and preprocessed, such as processing missing values ​​and unifying data formats.

[0043] Step S13: constructing a course classification matrix for the teacher resource data and the classroom resource data to obtain a course classification matrix;

[0044] In an embodiment of the present invention, all courses are classified according to the school's professional settings and curriculum system to establish a course category tree structure, for example, "literature" is used as a first-level classification, "Chinese literature" and "foreign literature" are used as second-level classifications, etc. Then, according to the "teachable courses" information in the teacher resource data, the teacher is associated with the course category, and a teacher-course matrix is ​​constructed. Each element in the matrix indicates whether the teacher can teach this type of course. Similarly, according to the "classroom type" information in the classroom resource data, the classroom is associated with the course category, and a classroom-course matrix is ​​constructed. Each element in the matrix indicates whether this type of classroom is suitable for this type of course. Finally, the teacher-course matrix and the classroom-course matrix are integrated.

[0045] Step S14: Analyze the data table structure according to the teacher resource data, classroom resource data and course classification matrix to obtain the associated data table structure;

[0046] In an embodiment of the present invention, the entity tables that need to be established in the database are determined according to the data features and association relationships, such as the teacher information table, classroom information table, course information table, and class scheduling information table. Then, it is determined which fields each data table needs to contain. For example, the teacher information table contains fields such as teacher number, name, department, and title. Next, according to the association relationship between the course classification matrix and the data table, the association method between the tables is determined. For example, a many-to-many association relationship can be established between the teacher information table and the course information table, indicating that one teacher can teach multiple courses, and one course can also be taught by multiple teachers. Finally, the primary key and foreign key of each table, as well as the constraints such as the data type and length of the field are determined to form a complete association data table structure.

[0047] Step S15: constructing an educational affairs database for the teacher resource data and classroom resource data based on the associated data table structure to obtain an associated database.

[0048] In the embodiment of the present invention, through the designed associated data table structure, a suitable database management system, such as MySQL, Oracle, is selected to create a database and a data table, and set constraints such as primary key, foreign key, data type, field length, etc. Then, the processed teacher resource data and classroom resource data are imported into the corresponding database table to ensure the integrity and consistency of the data.

[0049] The present invention obtains teacher resource data from the school's teaching system to provide basic data support for the subsequent construction of an intelligent class scheduling system, thereby ensuring the rationality and executability of the class scheduling results. The teacher resource data obtained includes each teacher's teaching subjects, time arrangements, and other relevant information, laying the foundation for subsequent analysis and processing; the classroom resource data of the school's teaching system is obtained to fully grasp the key information such as the type, capacity, and available time of the classroom, and to provide necessary data support for classroom allocation and optimization in the subsequent class scheduling process, thereby ensuring the operability of the class scheduling plan; the teacher resource data, classroom resource data, and course information are integrated to construct a course classification matrix, clearly showing the relationship between courses and teachers and classrooms, providing a clear logical framework for subsequent data table structure analysis and database construction, and improving the efficiency and accuracy of data organization; on the basis of the course classification matrix, the data table structure is analyzed to clarify the relationship between each table in the database, such as the corresponding relationship between teachers and courses, classrooms and courses, providing a design blueprint for building a database with a reasonable structure and clear relationships, and ensuring the integrity and consistency of the data. Based on the associated data table structure obtained through analysis, the teacher resource data and classroom resource data are integrated into the academic affairs database, and an associated database containing multi-dimensional information such as courses, teachers, and classrooms is constructed to achieve centralized management and efficient use of academic resources, providing a strong data support and analysis basis for the intelligent scheduling system.

[0050] Preferably, step S14 comprises the following steps:

[0051] Step S141: Analyze the teacher teaching status data according to the teacher resource data to obtain the teacher teaching status data;

[0052] In the embodiment of the present invention, the teaching schedule information of each teacher is extracted from the teacher resource data, such as which days of the week and which time periods have classes. Then, combined with the school calendar information, such as the start and end dates of the semester, holiday arrangements, etc., the teaching status of each teacher in each time period of the entire semester is calculated, such as "having classes", "no classes", "can be arranged", etc. In order to describe the teaching status of teachers in a more refined manner.

[0053] Step S142: mapping the course classification matrix to the teaching status according to the teacher's teaching status data to generate teaching status mapping data;

[0054] In an embodiment of the present invention, by reading the constructed course classification matrix, the matrix describes the association relationship between courses, teachers, and classrooms. Then, according to the teacher teaching status data, the teaching status of each teacher in each time period is mapped to the corresponding course classification matrix element. For example, if a teacher "has a class" in a certain time period, the course classification matrix element corresponding to the teacher is marked as "cannot be scheduled"; if the teacher "has no class" in this time period, it is marked as "can be scheduled". Through such a mapping operation, the teaching status mapping data is generated, which reflects which teachers can be arranged to teach which courses in different time periods.

[0055] Step S143: determining the information relationship constraint of the teaching state mapping data to obtain the teaching relationship constraint data;

[0056] In the embodiment of the present invention, by analyzing the teaching status mapping data, the information relationship constraints between the tables in the database are clarified. For example, each course needs to determine a main teacher; each teacher can only teach one course in the same time period; each classroom can only arrange one course in the same time period, etc. These information relationship constraints are expressed in the form of rules, such as logical expressions, decision tables, etc., to form teaching relationship constraint data.

[0057] Step S144: setting the primary and foreign keys of the teaching status mapping data according to the teaching relationship constraint data to obtain the teaching primary and foreign key data;

[0058] In the embodiment of the present invention, through the determined teaching relationship constraint data, the association relationship is established for each entity in the teaching status mapping data, and the primary and foreign keys are set to ensure the consistency and integrity of the data. For example, the teacher number is set as the primary and foreign key in the course information table, which is associated with the primary key of the teacher information table, indicating which teacher is teaching the course; the course number and classroom number are set as the primary and foreign keys in the class scheduling information table, which are associated with the primary keys of the course information table and the classroom information table, respectively, indicating in which classroom the course is taught.

[0059] Step S145: Determine the associated data table structure of the course classification matrix according to the teaching relationship constraint data and the teaching primary and foreign key data, and generate an associated data table structure.

[0060] In an embodiment of the present invention, by integrating teaching relationship constraint data and teaching primary and foreign key data, the structure of the associated data table is designed for teacher resource data and classroom resource data. For example, a teacher information table, a course information table, a classroom information table, a class scheduling information table, etc. can be designed, and the fields that need to be included in each table and the association relationship between the tables can be determined. In the design process, it is necessary to follow the database design principles, such as avoiding data redundancy, ensuring data consistency, etc. Finally, a complete design scheme for the associated data table structure is generated.

[0061] The present invention extracts the teaching status data of each teacher, such as the current teaching situation, the teaching time period and other key information, by conducting an in-depth analysis of the teacher resource data, so as to provide data support for considering the teacher's time conflict and workload balance in the subsequent course scheduling process; the teacher teaching status data obtained by the analysis is mapped to the course classification matrix, and the teaching status mapping data is generated to intuitively display the teacher resource availability of each course in different time periods, so as to provide a real-time and accurate decision-making basis for the course scheduling system. The teaching status mapping data is analyzed to clarify the information relationship constraints between the various tables in the database, such as the teaching relationship between teachers and courses, to avoid data redundancy and conflict, and to ensure the logical integrity and data consistency of the database. According to the teaching relationship constraint data, the primary and foreign keys are set for the teaching status mapping data, and the association relationship between the various tables in the database is clarified, for example, the teacher ID in the teacher information table is set as the foreign key in the course information table, to establish the logical connection between the data, and to improve the efficiency of data query and update. Taking into account the teaching relationship constraint data and teaching primary and foreign key data, the structure of the associated data table of teacher resource data and classroom resource data is determined, and the associated data table structure containing multi-dimensional information such as courses, teachers, and classrooms is constructed, providing a clear blueprint for the subsequent construction of the database and ensuring the rationality and efficiency of the database structure. The intelligent scheduling system can more accurately grasp the actual situation of teacher resources and accurately match them with course requirements, thereby generating a more reasonable and efficient scheduling plan.

[0062] Preferably, step S2 comprises the following steps:

[0063] Step S21: extracting student data according to the school's educational affairs system to obtain student educational affairs data;

[0064] Step S22: dividing the student teaching data into professional subject credits to obtain professional subject credit data; extracting professional credit requirements from the student teaching data to obtain professional credit requirement data;

[0065] Step S23: extracting related data from the related database to obtain related teaching affairs extraction data;

[0066] Step S24: Perform node injection on the extracted data of related teaching affairs to obtain the related teaching affairs data node;

[0067] Step S25: Analyze the student class scheduling data based on the professional subject credit data and the professional credit demand data based on the associated teaching affairs data node to obtain the adaptive class scheduling linked list data.

[0068] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S2 in FIG. 1 , in this embodiment, step S2 includes the following steps:

[0069] Step S21: extracting student data according to the school's educational affairs system to obtain student educational affairs data;

[0070] In the embodiment of the present invention, the student status information of all students in school is obtained through the API interface or data export function provided by the educational system, including student number, name, year of admission, department, major, etc. Then, the course information of students is obtained, such as courses taken, course grades, credit points, etc. Finally, the obtained raw data is cleaned and preprocessed, such as removing duplicate data, processing missing values, etc., and the data is converted into a unified format.

[0071] Step S22: dividing the student teaching data into professional subject credits to obtain professional subject credit data; extracting professional credit requirements from the student teaching data to obtain professional credit requirement data;

[0072] In the embodiment of the present invention, students are classified according to their majors according to the "major" field in the student academic data to generate professional subject credit data. For example, all students of the "computer science and technology" major are classified into one category to form a student data set of the major. At the same time, according to the school's training program, the credit requirements of each major are extracted. For example, the "computer science and technology" major requires students to complete at least 150 credits upon graduation, and the credit requirements of each major are stored in the professional credit requirement data.

[0073] Step S23: extracting related data from the related database to obtain related teaching affairs extraction data;

[0074] In the embodiment of the present invention, key data related to class scheduling are extracted from the associated database according to predefined data extraction rules, such as course number, course name, lecturer, class time, classroom and other information in the course information table; teacher number, teacher name, department, and courses that can be taught in the teacher information table; classroom number, building, classroom type, and number of people in the classroom information table. These information are integrated together to form associated teaching affairs extraction data.

[0075] Step S24: Perform node injection on the extracted data of related teaching affairs to obtain the related teaching affairs data node;

[0076] In an embodiment of the present invention, each data item in the associated educational affairs extraction data is injected into the corresponding node according to a predefined node structure. For example, course information can be injected into a "course node", teacher information can be injected into a "teacher node", and classroom information can be injected into a "classroom node". At the same time, based on the association relationship between the data, a connection relationship between the nodes is established, for example, connecting the "course node" with the "teacher node" to indicate which teachers teach the course; connecting the "course node" with the "classroom node" to indicate which classrooms the course is taught in. Ultimately, a node network of associated educational affairs data containing multiple nodes and connection relationships is formed.

[0077] Step S25: Analyze the student class scheduling data based on the professional subject credit data and the professional credit demand data based on the associated teaching affairs data node to obtain the adaptive class scheduling linked list data.

[0078] In the embodiment of the present invention, the course requirements of students of each major are analyzed according to the professional subject credit data and the professional credit requirement data, such as which courses are required courses, which courses are elective courses, how many teaching classes are required for each major, etc. Then, according to the information in the associated teaching data node, such as the courses that teachers can teach, the available time of the classroom, etc., the student class scheduling data is analyzed to determine the course arrangement, teacher allocation, classroom allocation and other information of each major. Finally, the analysis results are stored in the adaptive class scheduling linked list data structure.

[0079] The present invention provides the intelligent class scheduling system with basic information of students, including student name, student number, major, courses taken, etc., by extracting student academic affairs data from the school academic affairs system, providing a data basis for subsequent personalized class scheduling. The student academic affairs data is processed in detail, student groups are divided according to majors, professional subject credit data is generated, and the credit requirements of each major are extracted, so as to provide data support for subsequent class scheduling according to professional requirements and to achieve balanced distribution of courses. Key data related to class scheduling, such as course information, teacher information, classroom information, etc., are extracted from the associated database to provide necessary data materials for the subsequent construction of associated academic affairs data nodes, ensuring the reliability and consistency of the data source. The associated academic affairs extracted data is injected into a pre-defined node structure, and an associated academic affairs data node is constructed to associate the originally scattered data to form an organic whole, which is convenient for subsequent data analysis and processing. Based on the associated academic data nodes, combined with professional subject credit data and professional credit demand data, the student class scheduling data is analyzed to generate adaptive class scheduling linked list data. This data structure can flexibly adapt to changes in students, courses, teachers, classrooms and other factors, laying the foundation for generating personalized and dynamic class scheduling plans. The intelligent class scheduling system can fully grasp student information, professional needs and academic resources, and provide strong data support for generating class scheduling plans that meet student needs and conform to teaching rules.

[0080] Preferably, step S25 comprises the following steps:

[0081] Step S251: constructing a dynamic sequence of teaching affairs disciplines for the associated teaching affairs data nodes to obtain a dynamic sequence of teaching affairs disciplines; performing a preliminary traversal of the dynamic sequence of teaching affairs disciplines by subject to obtain a subject traversal result node;

[0082] Step S252: Filter the teaching affairs discipline dynamic sequence based on the discipline traversal result node to obtain a filtered discipline dynamic sequence;

[0083] Step S253: Compare the professional credit demand data with the professional subject credit data. If the professional credit demand data is greater than or equal to the professional subject credit data, the professional credit demand data is marked as credit-qualified data; if the professional credit demand data is less than the professional subject credit data, the professional credit demand data is marked as credit-unqualified data.

[0084] Step S254: integrating the credit reaching data and the credit failing data to obtain credit statistical data;

[0085] Step S255: Based on the credit statistical data, the filtered subject dynamic sequence is processed by adaptive class scheduling linked list output. If the credit statistical data is credit achievement data, the filtered subject dynamic sequence is processed by class scheduling linked list generation to generate credit achievement processing data; if the credit statistical data is credit failure data, the filtered subject dynamic sequence is continuously traversed through nodes to generate credit failure processing data; the credit failure processing data and the credit failure processing data are integrated to obtain adaptive class scheduling linked list data.

[0086] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S25 in FIG. 1 , in this embodiment, step S25 includes the following steps:

[0087] Step S251: constructing a dynamic sequence of teaching affairs disciplines for the associated teaching affairs data nodes to obtain a dynamic sequence of teaching affairs disciplines; performing a preliminary traversal of the dynamic sequence of teaching affairs disciplines by subject to obtain a subject traversal result node;

[0088] In an embodiment of the present invention, the associated teaching data nodes are grouped according to the subject classification information in the course information node, for example, all the course nodes, teacher nodes, classroom nodes, etc. belonging to the "mathematics" subject are grouped together. Then, according to the prerequisite relationship between the courses and the order of study recommended in the teaching plan, the nodes in each subject are sorted to construct a dynamic sequence of teaching subjects. Next, the dynamic sequence of each subject is traversed once, and the basic information of each node, such as node type, node ID, associated nodes, etc., is recorded, and the traversal results are stored in the subject traversal result node.

[0089] Step S252: Filter the teaching affairs discipline dynamic sequence based on the discipline traversal result node to obtain a filtered discipline dynamic sequence;

[0090] In an embodiment of the present invention, the subject traversal result nodes are screened according to the course scheduling requirements and constraints, for example, nodes that do not meet the course scheduling conditions are filtered out, such as teacher nodes that have been fully scheduled, classroom nodes in unavailable time periods, etc. At the same time, according to the students' course selection situation, course nodes with insufficient number of students can be filtered out, or according to the teaching plan, course nodes that are not offered this semester can be filtered out. Finally, a dynamic sequence of filtered subjects is obtained, which only contains node information that meets the course scheduling conditions.

[0091] Step S253: Compare the professional credit demand data with the professional subject credit data. If the professional credit demand data is greater than or equal to the professional subject credit data, the professional credit demand data is marked as credit-qualified data; if the professional credit demand data is less than the professional subject credit data, the professional credit demand data is marked as credit-unqualified data.

[0092] In the embodiment of the present invention, by reading the professional credit requirement data and professional subject credit data, the completed credits and graduation requirement credits of each major are compared one by one. If the completed credits of a major meet the graduation requirements, the credit requirement data of the major is marked as "credits meeting the standard data"; conversely, if the completed credits of a major do not meet the graduation requirements, the credit requirement data of the major is marked as "credits not meeting the standard data".

[0093] Step S254: integrating the credit reaching data and the credit failing data to obtain credit statistical data;

[0094] In the embodiment of the present invention, the credit requirement data of all majors are summarized, the number of people who "meet the credit requirement" and "do not meet the credit requirement" for each major is counted, and the corresponding ratios are calculated to form credit statistical data.

[0095] Step S255: Based on the credit statistical data, the filtered subject dynamic sequence is processed by adaptive class scheduling linked list output. If the credit statistical data is credit achievement data, the filtered subject dynamic sequence is processed by class scheduling linked list generation to generate credit achievement processing data; if the credit statistical data is credit failure data, the filtered subject dynamic sequence is continuously traversed through nodes to generate credit failure processing data; the credit failure processing data and the credit failure processing data are integrated to obtain adaptive class scheduling linked list data.

[0096] In an embodiment of the present invention, an adaptive class scheduling linked list output process is performed on the filtered subject dynamic sequence according to the credit statistical data. For students who have reached the credit standard, a class scheduling linked list is generated according to the conventional class scheduling logic, such as course priority, teacher schedule, etc., and the result is stored in the credit standard processing data. For students who have not reached the credit standard, they are given priority to take courses that do not meet the credit requirements, and according to the prerequisite relationship between courses, and the available time of teachers and classrooms, continuous traversal node operations are performed to generate credit non-standard processing data. Finally, the credit standard processing data and the credit non-standard processing data are integrated to form a complete adaptive class scheduling linked list data.

[0097] The present invention constructs the associated teaching data nodes into a dynamic sequence according to the subject logic, performs a preliminary traversal of the subjects, generates the subject traversal result nodes, and provides a data basis for subsequent personalized course scheduling for different subjects. The teaching subject dynamic sequence is filtered based on the subject traversal result nodes, and the node information irrelevant to the current course scheduling demand is removed, the data structure is streamlined, and the efficiency of the subsequent course scheduling algorithm is improved. The professional credit demand data is compared with the professional subject credit data to determine the current credit completion status of the students, and mark them, so as to provide a basis for subsequent differentiated course scheduling according to the credit status of the students. The credit standard data and the credit non-standard data are integrated to form a comprehensive credit statistical data, which provides data support for taking into account the overall credit progress of the students in the subsequent course scheduling process. The key to generating an adaptive course scheduling linked list. According to the different situations of the credit statistical data, the system dynamically adjusts the course scheduling linked list. For students who meet the credit standard, a corresponding course scheduling linked list is generated; for students who do not meet the credit standard, the nodes are continuously traversed to ensure that they complete the study of all necessary subjects. This adaptive mechanism enables the course scheduling to meet the credit requirements of different students and provide personalized and flexible learning paths. By dynamically processing the subject sequence, evaluating the credit situation, and adaptively adjusting the course scheduling list, the system can provide students with a course schedule that meets their credit progress and professional requirements. This method improves the accuracy and personalization of course scheduling, ensuring that students' learning experience is more efficient and effective.

[0098] Preferably, step S3 comprises the following steps:

[0099] Step S31: Based on the adaptive class scheduling linked list data, the associated database is processed to obtain the teacher class scheduling associated data;

[0100] In the embodiment of the present invention, the adaptive class scheduling linked list data is read, which contains the course information that each student needs to take and the order of courses. Then, according to the course information, the teacher information of each course, such as teacher number, name, department, etc., is queried from the associated database. The teacher information is associated with the adaptive class scheduling linked list data to generate teacher class scheduling associated data.

[0101] Step S32: extracting course personnel data from teacher class schedule related data to obtain course personnel data;

[0102] In the embodiment of the present invention, by traversing the teacher schedule associated data, the teacher and student information of each course is extracted and summarized and counted. For example, the number of teachers, the number of students, the class time, the class location and other information of each course are counted. This information is stored in the course personnel data.

[0103] Step S33: Allocate classroom resources to teachers in the associated database according to the course personnel data to obtain teacher allocation classroom data;

[0104] In the embodiment of the present invention, according to the teaching time, number of students and other information of each course in the course personnel data, the classroom resources that meet the conditions are queried in the associated database, such as the number of people that the classroom can accommodate, the available time period, etc. Then, according to certain allocation rules, such as giving priority to classrooms that are closer to the teaching teacher's office, giving priority to classrooms with better equipment and facilities, etc., the classroom resources are allocated to each course, and the teacher allocation classroom data is generated, which contains information such as the courses, classrooms, number of students and other information taught by each teacher in each time period.

[0105] Step S34: allocating courses to students based on the teacher-allocated classroom data to obtain student-allocated classroom data;

[0106] In the embodiment of the present invention, the classroom allocation of each course is obtained by reading the teacher allocation classroom data. Then, according to the course selection information of each student in the adaptive class scheduling linked list data, the students are allocated to the corresponding courses and classrooms, and the student allocation classroom data is generated. In the allocation process, the classroom capacity limit needs to be considered.

[0107] Step S35: Map and associate the teacher allocation classroom data and the student allocation classroom data to obtain classroom personnel allocation data.

[0108] In an embodiment of the present invention, by integrating teacher allocation classroom data and student allocation classroom data, final classroom personnel allocation data is generated, which includes information such as course schedule, lecturers, and students in each classroom in each time period.

[0109] The present invention associates teacher class scheduling data with an adaptive class scheduling linked list. By processing an associated database, the system can determine the teacher resources required for each course and match them with the courses in the class scheduling linked list. This association processing ensures the corresponding relationship between teacher resources and course arrangements, laying the foundation for subsequent classroom allocation and student allocation. Course personnel data are extracted from the teacher class scheduling associated data. The course personnel data contains information such as the number and type of teachers required for each course. By extracting these data, the system can understand the demand for teacher resources in course teaching and provide guidance for teachers to allocate classroom resources. According to the course personnel data, combined with the classroom resource information in the associated database, appropriate classroom resources are allocated to each teacher, and teacher allocation classroom data is generated to ensure the rational use of classroom resources and avoid conflicts in time and space. Based on the teacher allocation classroom data, the student information in the adaptive class scheduling linked list data is allocated to the corresponding classroom, and the student allocation classroom data is generated to achieve accurate matching of students with courses, teachers, and classrooms, so that students can query the class schedule and class location conveniently. The teacher-classroom allocation data and the student-classroom allocation data are mapped and associated to generate the final classroom personnel allocation data, which completely records the course schedule, teaching teachers and student information of each classroom in different time periods, providing comprehensive data support for academic management and student learning.

[0110] Preferably, step S4 comprises the following steps:

[0111] Step S41: Acquire student active selection data from the school's teaching affairs system to obtain student course selection data;

[0112] In the embodiment of the present invention, students are allowed to freely choose courses within a specified time. After logging into the academic affairs system, students can browse the course list and view detailed information of the courses, such as course name, lecturer, class time, class location, etc., and select corresponding courses according to their interests and needs. After the students complete the course selection operation, the system records the students' course selection information, including student number, name, selected courses, etc., and generates student course selection data.

[0113] Step S42: performing new personnel allocation on the adaptive class scheduling linked list data according to the student course selection data to obtain new personnel allocation data;

[0114] In an embodiment of the present invention, the course information selected by each student is obtained by reading the student course selection data. Then, according to the course information, the student is assigned to the corresponding course node in the adaptive course scheduling linked list data. If the number of students on a certain course node reaches the upper limit of the classroom, it is necessary to assign some students to the same course of other time periods or other teachers according to certain rules, such as giving priority to students with fewer selected courses, and updating the adaptive course scheduling linked list data to generate new personnel allocation data.

[0115] Step S43: optimizing the classroom personnel allocation data based on the newly added personnel allocation data to obtain optimized classroom personnel allocation data;

[0116] In an embodiment of the present invention, the use of each classroom in each time period is re-evaluated based on the newly added personnel allocation data, such as whether the number of students exceeds the upper limit of the classroom capacity, whether there is a conflict in the teacher's teaching schedule, etc. If there is a conflict or unreasonable situation, it is necessary to optimize the personnel allocation, such as adjusting the course selection results of some students, allocating classroom resources, etc. Finally, the optimized classroom personnel allocation data is generated, which meets the constraints such as classroom capacity limit and teacher time arrangement.

[0117] Step S44: Perform intelligent class scheduling based on the adaptive class scheduling linked list data and the optimized classroom personnel allocation data to obtain an intelligent class scheduling plan.

[0118] In an embodiment of the present invention, the final intelligent scheduling plan is generated based on the scheduled time, teaching teachers, students and other information of each course node in the adaptive scheduling linked list data, and the available time period information of each classroom in the optimized classroom personnel allocation data.

[0119] The present invention obtains students' active course selection data through this step. By collecting students' course selection information from the school's academic affairs system, the system can understand students' preferences and needs. Students' course selection data reflects students' interest and choice in specific courses, providing an important reference for subsequent course scheduling optimization. The course scheduling plan is dynamically adjusted according to students' course selection. The system incorporates the courses selected by students into the adaptive course scheduling linked list through the new personnel allocation process to ensure that the course scheduling plan reflects the students' latest choices. This real-time update mechanism enhances the flexibility and responsiveness of the course scheduling system. The system optimizes classroom personnel allocation data. Based on the new personnel allocation, the system recalculates and adjusts the arrangement of personnel in the classroom to ensure a balance between classroom capacity and course selection. This step optimizes the utilization of classroom resources, makes classroom arrangements more reasonable, and meets students' course selection. Output the final intelligent course scheduling plan. By integrating the adaptive course scheduling linked list and the optimized classroom personnel allocation data, the system generates a complete and optimized course scheduling result. The plan takes into account students' course selection, while taking into account classroom capacity and teaching resources, providing schools and students with an efficient and personalized course scheduling experience. Allow students to actively participate in course selection and adjust the class schedule in real time based on their choices. Through these steps, the system can dynamically respond to students' needs and ensure that the class schedule meets students' preferences while maintaining a balanced classroom schedule and efficient use of teaching resources. This approach enhances student autonomy and improves user satisfaction with the class schedule system.

[0120] Preferably, step S42 includes the following steps:

[0121] Step S421: injecting new data into the adaptive class scheduling linked list data based on the student course selection data to obtain new class scheduling linked list data;

[0122] In an embodiment of the present invention, by reading the student course selection data, traversing each course selection record, the student's course selection information is inserted into the corresponding course node in the adaptive course scheduling linked list data. For example, if a student selects the course "Advanced Mathematics", the student's student number, name and other information are inserted into the student list of the "Advanced Mathematics" course node. If the course node does not exist in the adaptive course scheduling linked list data, it is necessary to first create the course node and set the basic information of the course, such as the lecturer, class time, etc., and then insert the student information into the node. Finally, the adaptive course scheduling linked list data containing the newly added course selection information is obtained.

[0123] Step S422: Analyze the data of newly assigned new personnel based on the newly added class scheduling linked list data to obtain the data of newly assigned new personnel in the classroom;

[0124] In the embodiment of the present invention, by traversing each course node in the newly added course scheduling linked list data, the number of newly added students that can be accommodated in each classroom in each time period is counted, and classroom allocation newly added personnel data is generated. For example, assuming that a classroom can arrange the "Advanced Mathematics" course in the first and second classes on Monday morning, the number of people that can be accommodated in the classroom is 100, and currently 80 students have chosen this course, then the number of newly added students that can be accommodated in the classroom in this time period is 20.

[0125] Step S423: Calculate the course new personnel accommodation data for the teacher's class schedule association data according to the classroom allocation new personnel data to obtain the course new personnel accommodation data;

[0126] In the embodiment of the present invention, by allocating the newly added personnel data according to the classroom, combined with the teaching time arrangement of each teacher in the teacher scheduling associated data, the number of newly added students that can be accommodated in each time period for each course is calculated, and the newly added personnel accommodation data for the course is generated. For example, assuming that the "Advanced Mathematics" course has two teaching classes in the first and second classes on Monday morning, respectively taught by teacher A and teacher B, the class taught by teacher A is full, and the class taught by teacher B has 10 vacancies, then the number of newly added students that can be accommodated in the course during the time period is 10.

[0127] Step S424: allocating new personnel to the classroom based on the new personnel accommodation data of the course to obtain the new personnel allocation data.

[0128] In the embodiment of the present invention, by allocating the newly added students to the corresponding courses and classrooms according to the newly added students' accommodation data and the newly added students' allocation data for the classrooms, the newly added students are allocated to the corresponding courses and classrooms, and the newly added students' allocation data is generated. In the allocation process, the course selection needs of students with smaller course capacity and fewer optional time periods need to be met first.

[0129] The present invention injects the course selection data of students into the adaptive course scheduling linked list. By adding the courses selected by students to the course scheduling linked list, the system can update the course scheduling plan in real time to reflect the latest preferences of students. This injection mechanism ensures the dynamic and responsiveness of the course scheduling system, so that the course scheduling can quickly adapt to the changes in students' needs. Analyze the allocable capacity of new personnel in the classroom. By calculating the data of new personnel allocated to the classroom, the system can determine the number of additional students that can be accommodated in each classroom. This analysis takes into account the classroom capacity and course selection, and provides guidance for subsequent teacher course scheduling. The system calculates the number of new personnel that can be accommodated in the course. By evaluating the teacher's course scheduling related data, the acceptable number of new students for each course is determined. This step ensures the quality of course teaching, avoids overcrowding, and maintains a good teaching environment. Allocate new personnel according to classroom and course capacity. By matching the data of new personnel allocated to the classroom with the course capacity, the system can allocate an appropriate number of new students to each course. This allocation method balances the student's course selection and the available capacity of the classroom, ensuring the smooth progress of teaching. The system can efficiently handle the student's active course selection and adjust the course scheduling plan in real time. By analyzing classroom capacity and course occupancy, the system can intelligently allocate additional staff to meet student needs while maintaining a balance between classroom and course schedules. This dynamic approach enhances the flexibility of the scheduling system.

[0130] Preferably, step S424 includes the following steps:

[0131] Step S4241: Compare the data of newly added students in the course and the data of newly added students in the classroom to obtain the comparison data of the number of students assigned;

[0132] In an embodiment of the present invention, list A is formed by exporting the "course new personnel accommodation data" of all courses from the course management system database. Then, the "classroom allocation new personnel data" of the classrooms corresponding to all courses are exported from the classroom allocation system database to form list B. Ensure that the course information in list A and list B corresponds one to one. Next, compare list A and list B line by line. Using the course ID as the key field, compare the "course new personnel accommodation data" and "classroom allocation new personnel data" corresponding to the same course ID. The comparison results can be recorded in a new list C, which contains the course ID, the course new personnel accommodation data, the classroom allocation new personnel data, and the difference between the two.

[0133] Step S4242: When the allocation number comparison data shows that the course newly added personnel capacity data is greater than the classroom allocation newly added personnel data, the allocation number comparison data is marked as more newly added allocation number data; when the allocation number comparison data shows that the course newly added personnel capacity data is less than or equal to the classroom allocation newly added personnel data, the allocation number comparison data is marked as less newly added allocation number data;

[0134] In an embodiment of the present invention, after obtaining List C, it is necessary to judge and mark each row of data in List C. The basis for judgment is the size relationship between the "data for accommodating new personnel in the course" and the "data for newly added personnel in the classroom allocation", that is, to compare the size relationship between the data in the second column and the third column in List C. If the "data for accommodating new personnel in the course" is greater than the "data for newly added personnel in the classroom allocation", that is, the difference is a positive number, then the difference is marked as "more newly added allocation data". For example, if the course ID is 001 and the comparison data of the number of allocated people is 5, then 5 is marked as "more newly added allocation data". If the "data for accommodating new personnel in the course" is less than or equal to the "data for newly added personnel in the classroom allocation", that is, the difference is a negative number or zero, then the difference is marked as "less newly added allocation data". For example, if a course ID is 002, the data for accommodating new personnel in the course is 10, and the data for newly added personnel in the classroom allocation is 12, then record in List C: 002, 10, 12, -2. This "-2" is marked as "less newly added allocation data". By traversing all the data in list C and marking them according to the above rules, we can finally get a list containing "data of more newly assigned people" and "data of less newly assigned people".

[0135] Step S4243: performing new classroom processing on the multiple newly added allocation number data to obtain multiple newly added allocation processing data;

[0136] In an embodiment of the present invention, the course ID and the corresponding multiple newly assigned number of people are extracted from the record marked as "multiple newly assigned number of people data". Then, new classrooms are allocated for these courses based on the existing idle classroom resources of the school. The principle of allocation is to meet the number of new students as much as possible, and to consider factors such as the capacity and geographical location of the classroom. The allocation results need to be recorded, including the course ID, the number of the newly added classroom, and the number of students assigned to the classroom. For example, the course ID is 001, the multiple newly assigned number of people data is 5, and the school has an idle classroom numbered A01 with a capacity of 10 people, then these 5 students can be assigned to the A01 classroom. The record is: 001, A01, 5. This record is the "multiple newly assigned processing data".

[0137] Step S4244: adding classroom personnel to the less newly added allocation data to obtain less newly added allocation processing data;

[0138] In an embodiment of the present invention, all records marked as "small number of newly added allocation data" are extracted from the output result of step S4242, including the course ID and the corresponding remaining capacity. Then, the students who have not been fully allocated in the "large number of newly added allocation data" are searched, and these students are given priority to be arranged in classrooms with larger remaining capacity. In the arrangement process, the compatibility of courses needs to be considered. Whenever a student is assigned to a classroom with remaining capacity, the remaining capacity of the classroom needs to be updated, and the allocation is recorded in the "small number of newly added allocation data", including the course ID of the receiving student, the classroom ID of the receiving student, and the number of students allocated. For example, the remaining capacity of classroom D of course C is 5 people, and 3 students from course E need to be arranged. Then these 3 students are assigned to classroom D, and recorded in the "small number of newly added allocation data": course C, classroom D, -3 people, negative numbers indicate a reduction in remaining capacity. Repeat this process until the remaining capacity of all "small number of newly added allocation data" is filled, or all excess students have been allocated.

[0139] Step S4245: Perform data integration processing based on the large amount of newly added allocation processing data and the small amount of newly added allocation processing data to obtain the newly added personnel allocation data.

[0140] In an embodiment of the present invention, a new data structure is created, called "newly added personnel allocation data", which is used to store the final allocation results. Then, all records in the "more newly added allocation data" are directly added to the "newly added personnel allocation data". Next, each record in the "few newly added allocation data" is traversed, and the records of the corresponding courses and classrooms in the "newly added personnel allocation data" are found according to the course ID and classroom ID in the record. If found, the number of students recorded in the "few newly added allocation data" is added to the number of students recorded in the corresponding record in the "newly added personnel allocation data". If not found, the record in the "few newly added allocation data" is added as a new record to the "newly added personnel allocation data".

[0141] The present invention clarifies the relationship between the remaining capacity of each course and the number of people that can be accommodated in each classroom by comparing the data of newly added personnel in the course and the data of newly added personnel in the classroom allocation, so as to provide a basis for subsequent differentiated allocation. The system marks the allocation number data according to the comparison result. When the course can accommodate more people, it is marked as more newly added allocation number data; when the classroom can allocate more people, it is marked as less newly added allocation number data. This marking mechanism simplifies the subsequent processing flow and provides guidance for allocation strategies in different situations. For the case of more newly added allocation number data, that is, the case of sufficient course capacity, consider allocating new classroom resources to the course to accommodate more students, and generate more newly added allocation processing data to ensure that all students who choose the course can be arranged. For the case of less newly added allocation number data, that is, the case of insufficient course capacity, students are allocated to the course based on the existing classroom resources, and less newly added allocation processing data is generated to meet the students' selection needs as much as possible. The more newly added allocation processing data and the less newly added allocation processing data are integrated to generate the final newly added personnel allocation data, which contains the course and classroom allocation information of all newly added students, and provides complete data support for updating the course scheduling plan. The intelligent scheduling system can flexibly adjust the allocation strategy according to the actual situation of course capacity and classroom resources, meet students' course selection needs to the greatest extent, and improve the rationality of scheduling results and student satisfaction.

[0142] Preferably, the present invention further provides a data processing system based on intelligent online class scheduling, which is used to execute the data processing method based on intelligent online class scheduling as described above. The data processing system based on intelligent online class scheduling includes:

[0143] The associated database construction module is used to construct a course classification matrix for the school's teaching affairs system to obtain a course classification matrix; perform data table structure analysis based on the course classification matrix to obtain an associated data table structure; and construct a teaching affairs database based on the associated data table structure to obtain an associated database;

[0144] The module for generating an adaptive class scheduling linked list is used to extract student data from the school's teaching affairs system to obtain student teaching affairs data; generate associated nodes for the associated database to obtain associated teaching affairs data nodes; perform student class scheduling data analysis on the student teaching affairs data based on the associated teaching affairs data nodes to obtain adaptive class scheduling linked list data;

[0145] The classroom personnel allocation module is used to allocate classroom resources to teachers on the associated database based on the adaptive class scheduling linked list data, and obtain teacher allocation classroom data; allocate courses to students on the adaptive class scheduling linked list data based on the teacher allocation classroom data, and obtain student allocation classroom data; map and associate the teacher allocation classroom data and the student allocation classroom data, and obtain classroom personnel allocation data;

[0146] The intelligent class scheduling module is used to obtain student active selection data from the school's academic affairs system to obtain student course selection data; optimize the adaptive class scheduling linked list data and classroom personnel allocation data based on the student course selection data to obtain optimized classroom personnel allocation data; perform intelligent class scheduling based on the adaptive class scheduling linked list data and the optimized classroom personnel allocation data to obtain an intelligent class scheduling plan.

[0147] In summary, the present invention provides a data processing method and system based on intelligent online class scheduling. The data processing system based on intelligent online class scheduling consists of an associated database construction module, an adaptive class scheduling linked list generation module, a classroom personnel allocation module, and an intelligent class scheduling module. It can realize any data processing method based on intelligent online class scheduling described in the present invention, and is used to combine the operations between computer programs running on each module to realize any data processing method based on intelligent online class scheduling. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient data processing process based on intelligent online class scheduling, thereby simplifying the operation flow of the data processing system based on intelligent online class scheduling.

[0148] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A data processing method based on intelligent online course scheduling, characterized in that: The following steps are involved: Step S1: construct a course classification matrix for the school's educational affairs system to obtain a course classification matrix; According to the course classification matrix, the data table structure is analyzed to obtain the associated data table structure; the teaching affairs database is constructed based on the associated data table structure to obtain the associated database; Step S2: extracting student data from the school's teaching affairs system to obtain student teaching affairs data; generating associated nodes for the associated database to obtain associated teaching affairs data nodes; analyzing student class scheduling data for the student teaching affairs data based on the associated teaching affairs data nodes to obtain adaptive class scheduling linked list data; Step S3: Based on the adaptive class scheduling linked list data, the associated database is assigned to classroom resources and teachers, and teacher allocation classroom data is obtained; based on the teacher allocation classroom data, the adaptive class scheduling linked list data is assigned to courses and students, and student allocation classroom data is obtained; mapping and associating the teacher allocation classroom data and the student allocation classroom data, and obtaining classroom personnel allocation data; Step S4: acquiring student active selection data from the school's teaching affairs system to obtain student course selection data; optimizing the adaptive class scheduling linked list data and classroom personnel allocation data based on the student course selection data to obtain optimized classroom personnel allocation data; Intelligent class scheduling is performed according to the adaptive class scheduling chain table data and the optimized classroom personnel allocation data to obtain an intelligent class scheduling plan; wherein step S4 includes: Step S41: Acquire student active selection data from the school's teaching affairs system to obtain student course selection data; Step S42: performing new personnel allocation on the adaptive class scheduling linked list data according to the student course selection data to obtain new personnel allocation data; wherein step S42 includes: Step S421: injecting new data into the adaptive class scheduling linked list data based on the student course selection data to obtain new class scheduling linked list data; Step S422: Analyze the data of newly assigned new personnel based on the newly added class scheduling linked list data to obtain the data of newly assigned new personnel in the classroom; Step S423: Calculate the course new personnel accommodation data for the teacher's class schedule association data according to the classroom allocation new personnel data to obtain the course new personnel accommodation data; Step S424: performing new personnel allocation on the classroom allocation new personnel data based on the course new personnel accommodation data to obtain new personnel allocation data; wherein step S424 includes: Step S4241: Compare the data of newly added students in the course and the data of newly added students in the classroom to obtain the comparison data of the number of students assigned; Step S4242: When the allocation number comparison data shows that the course newly added personnel capacity data is greater than the classroom allocation newly added personnel data, the allocation number comparison data is marked as more newly added allocation number data; when the allocation number comparison data shows that the course newly added personnel capacity data is less than or equal to the classroom allocation newly added personnel data, the allocation number comparison data is marked as less newly added allocation number data; Step S4243: performing new classroom processing on the multiple newly added allocation number data to obtain multiple newly added allocation processing data; Step S4244: adding classroom personnel to the less newly added allocation data to obtain less newly added allocation processing data; Step S4245: performing data integration processing according to the large amount of newly added allocation processing data and the small amount of newly added allocation processing data to obtain the newly added personnel allocation data; Step S43: optimizing the classroom personnel allocation data based on the newly added personnel allocation data to obtain optimized classroom personnel allocation data; Step S44: Perform intelligent class scheduling based on the adaptive class scheduling linked list data and the optimized classroom personnel allocation data to obtain an intelligent class scheduling plan.

2. The data processing method based on intelligent online course scheduling according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire teacher resource data from the school's educational affairs system to obtain teacher resource data; Step S12: Acquire classroom resource data from the school's educational affairs system to obtain classroom resource data; Step S13: constructing a course classification matrix for the teacher resource data and the classroom resource data to obtain a course classification matrix; Step S14: Analyze the data table structure according to the teacher resource data, classroom resource data and course classification matrix to obtain the associated data table structure; Step S15: constructing an educational affairs database for the teacher resource data and classroom resource data based on the associated data table structure to obtain an associated database.

3. The data processing method based on intelligent online class scheduling according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: Analyze the teacher teaching status data according to the teacher resource data to obtain the teacher teaching status data; Step S142: mapping the course classification matrix to the teaching status according to the teacher's teaching status data to generate teaching status mapping data; Step S143: determining the information relationship constraint of the teaching state mapping data to obtain the teaching relationship constraint data; Step S144: setting the primary and foreign keys of the teaching status mapping data according to the teaching relationship constraint data to obtain the teaching primary and foreign key data; Step S145: Determine the associated data table structure of the course classification matrix according to the teaching relationship constraint data and the teaching primary and foreign key data, and generate an associated data table structure.

4. The data processing method based on intelligent online class scheduling according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: extracting student data according to the school's educational affairs system to obtain student educational affairs data; Step S22: dividing the student teaching data into professional subject credits to obtain professional subject credit data; extracting professional credit requirements from the student teaching data to obtain professional credit requirement data; Step S23: extracting related data from the related database to obtain related teaching affairs extraction data; Step S24: Perform node injection on the extracted data of related teaching affairs to obtain the related teaching affairs data node; Step S25: Analyze the student class scheduling data based on the professional subject credit data and the professional credit demand data based on the associated teaching affairs data node to obtain the adaptive class scheduling linked list data.

5. The data processing method based on intelligent online class scheduling according to claim 4 is characterized in that: Step S25 includes the following steps: Step S251: constructing a dynamic sequence of teaching affairs disciplines for the associated teaching affairs data nodes to obtain a dynamic sequence of teaching affairs disciplines; performing a preliminary traversal of the dynamic sequence of teaching affairs disciplines by subject to obtain a subject traversal result node; Step S252: Filter the teaching affairs discipline dynamic sequence based on the discipline traversal result node to obtain a filtered discipline dynamic sequence; Step S253: Compare the professional credit demand data with the professional subject credit data. If the professional credit demand data is greater than or equal to the professional subject credit data, the professional credit demand data is marked as credit-qualified data; if the professional credit demand data is less than the professional subject credit data, the professional credit demand data is marked as credit-unqualified data. Step S254: integrating the credit reaching data and the credit failing data to obtain credit statistical data; Step S255: Based on the credit statistical data, the filtered subject dynamic sequence is processed by adaptive class scheduling linked list output. If the credit statistical data is credit achievement data, the filtered subject dynamic sequence is processed by class scheduling linked list generation to generate credit achievement processing data; if the credit statistical data is credit failure data, the filtered subject dynamic sequence is continuously traversed through nodes to generate credit failure processing data; the credit failure processing data and the credit failure processing data are integrated to obtain adaptive class scheduling linked list data.

6. The data processing method based on intelligent online class scheduling according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Based on the adaptive class scheduling linked list data, the associated database is processed to obtain the teacher class scheduling associated data; Step S32: extracting course personnel data from teacher class schedule related data to obtain course personnel data; Step S33: Allocate classroom resources to teachers in the associated database according to the course personnel data, and obtain teacher allocation classroom data; Step S34: allocating courses to students based on the teacher-allocated classroom data to obtain student-allocated classroom data; Step S35: Map and associate the teacher allocation classroom data and the student allocation classroom data to obtain classroom personnel allocation data.

7. A data processing system based on intelligent online course scheduling, characterized in that: Used to execute the data processing method based on intelligent online course scheduling as claimed in claim 1, the data processing system based on intelligent online course scheduling includes: The associated database construction module is used to construct a course classification matrix for the school's teaching affairs system to obtain a course classification matrix; perform data table structure analysis based on the course classification matrix to obtain an associated data table structure; and construct a teaching affairs database based on the associated data table structure to obtain an associated database; The module for generating an adaptive class scheduling linked list is used to extract student data from the school's teaching affairs system to obtain student teaching affairs data; generate associated nodes for the associated database to obtain associated teaching affairs data nodes; perform student class scheduling data analysis on the student teaching affairs data based on the associated teaching affairs data nodes to obtain adaptive class scheduling linked list data; The classroom personnel allocation module is used to allocate classroom resources to teachers on the associated database based on the adaptive class scheduling linked list data, and obtain teacher allocation classroom data; allocate courses to students on the adaptive class scheduling linked list data based on the teacher allocation classroom data, and obtain student allocation classroom data; map and associate the teacher allocation classroom data and the student allocation classroom data, and obtain classroom personnel allocation data; The intelligent class scheduling module is used to obtain student active selection data from the school's academic affairs system to obtain student course selection data; optimize the adaptive class scheduling linked list data and classroom personnel allocation data based on the student course selection data to obtain optimized classroom personnel allocation data; perform intelligent class scheduling based on the adaptive class scheduling linked list data and the optimized classroom personnel allocation data to obtain an intelligent class scheduling plan.

Images