A middle school teaching overall planning and scheduling system and method based on multi-objective optimization

Abstract

The application discloses a middle school teaching overall arrangement scheduling system and method based on multi-target optimization, relates to the technical field of education informatization scheduling, and comprises a course overall arrangement terminal, a classroom overall arrangement port, an algorithm control model and a visual control platform. The complete middle school teaching overall arrangement scheduling system is constructed, the course overall arrangement terminal collects and manages the course basic data of all grades and all classes in the school, including the course name, the course type, the weekly class hours, the continuous class demand, the combined class demand and the cross-grade concurrent class information. The application effectively solves the problems of low scheduling efficiency, frequent conflicts, and the fact that soft requirements cannot be considered in the middle school teaching overall arrangement scheduling, and the scheduling time is compressed from the traditional 7 to 14 days to the minute level. The middle school teaching resource data and the corresponding analysis results are managed, visualized and stored, which is helpful for realizing the middle school teaching resource management through the internet cloud management and control and improving the intelligent level of the middle school teaching resource management.

Description

Technical Field

[0001] This invention relates to the field of educational informatization scheduling technology, specifically to a multi-objective optimization-based junior high school teaching coordination and scheduling system and method. Background Technology

[0002] With the deepening of educational informatization, the teaching management of junior high schools is becoming increasingly complex. Junior high school involves collaborative teaching across multiple grades, classes, subjects, and dozens of teachers, requiring the allocation of hundreds of teaching tasks, teachers, classrooms, and time slots each week, making resource coordination extremely difficult. Simultaneously, it must also meet several implicit rules, such as balanced course distribution, restrictions on continuous teacher instruction, and a balance between work and rest for students.

[0003] The scheduling problem is essentially a multi-objective combinatorial optimization problem under multiple constraints. Since 1976, it has been proven to be an NP-complete problem, and its difficulty increases exponentially with the problem size. The overall scheduling of junior high school teaching requires handling multiple constraints simultaneously: hard constraints include the inability to schedule the same teacher, class, and classroom at the same time, and classroom capacity must meet student requirements; soft constraints include teacher time slot preferences, subject time slot distribution, daily course balance for classes, reasonable subject date intervals, and balanced utilization of classroom resources, etc. These constraints are intertwined, making the solution extremely difficult.

[0004] The current technology for coordinating and scheduling junior high school teaching mainly relies on the following methods: First, traditional manual scheduling. This method, where academic staff manually create schedules based on experience, has significant drawbacks: low efficiency (a complete schedule takes 7 to 14 days); frequent conflicts (it's difficult to thoroughly check all combinations); complex rules (dozens of rules that are difficult to fully consider); neglect of soft needs; and rigid adaptability (temporary schedule changes can have far-reaching consequences).

[0005] Second, there are scheduling software programs based on simple rules. Some software programs use rule engines or backtracking methods to achieve automatic scheduling, but they can only handle simple deterministic rules and are unable to cope with complex multi-constraint and multi-objective optimization needs; they can usually only generate a single solution and cannot provide multiple optimization solutions to choose from; and their ability to handle soft constraints is limited.

[0006] Third, research on heuristic algorithms such as genetic algorithms. Some researchers have designed three-dimensional coding structures and constructed fitness functions that integrate subject-specific lesson order quality and arrangement gradients for scheduling optimization, but shortcomings remain: most systems use weighted summation to transform multiple objectives into a single objective, with weight coefficients relying on prior knowledge, making it difficult to accurately reflect the true trade-offs between objectives; they lack a complete and systematic architecture, failing to integrate data collection, resource scheduling, algorithm optimization, and visualization control into an organic whole; they lack system designs tailored to the specific needs of junior high school teaching (such as cross-grade teaching, combined classes, and consecutive class requirements); and they lack scalability, making it difficult to integrate with third-party algorithms or external systems such as academic management systems and electronic class boards, resulting in information silos. Furthermore, existing systems generally lack robust encrypted storage, encrypted transmission, and access control mechanisms for data security.

[0007] Chinese Patent Publication No. CN 119831790 B discloses a smart campus teaching space management system, including a data acquisition module for acquiring teaching space data, teaching plan data, resource usage data, and reservation request data; a data processing module for preprocessing the acquired data to obtain teaching space parameter data, teaching plan parameter data, resource usage parameter data, and reservation parameter data; weighted fusion of the teaching space parameter data and resource usage parameter data to obtain spatial feature data; a resource management module for constructing a teaching space resource library based on the spatial feature data; an intelligent scheduling module for initial allocation of teaching spaces based on the teaching plan parameter data and acquiring available space data; a reservation management module for secondary allocation of teaching spaces; and a communication module for sending reservation messages to users. This provides an efficient and flexible solution for smart campus management.

[0008] In summary, existing junior high school teaching coordination and scheduling technologies generally suffer from problems such as low scheduling efficiency, frequent conflicts, inability to meet soft needs, and lack of data security. They are unable to meet the current management needs of junior high schools for intelligence, precision, and security, and are difficult to monitor and handle each link in real time, thus causing many inconveniences.

[0009] Therefore, this invention requires the design of a multi-objective optimization-based junior high school teaching coordination and scheduling system and method to solve the above-mentioned problems. Summary of the Invention:

[0010] The purpose of this invention is to provide a multi-objective optimization-based junior high school teaching coordination and scheduling system and method to solve the problems mentioned in the background art.

[0011] To address the above problems, the present invention provides a technical solution: A multi-objective optimization-based junior high school teaching coordination and scheduling system is disclosed. The system includes a course coordination terminal, a classroom coordination port, an algorithm control model, and a visualization control platform. The visualization control platform maintains real-time data communication and sharing with the course coordination terminal, the classroom coordination port, and the algorithm control model via the Internet. Staff members can log in to the visualization control platform to access the coordination and scheduling system and view the real-time operating data of the course coordination terminal, the classroom coordination port, and the algorithm control model.

[0012] In a preferred embodiment of the present invention, the course coordination terminal is used to collect, store and manage basic course data for all grades and classes in the school, including course name, course type, weekly class hours, consecutive class requirements, combined class requirements and cross-grade teaching information. The classroom management portal is used to collect, store, and manage basic data on all types of classroom resources across the school, including classroom type, classroom capacity, classroom location, available time slots, and equipment configuration information. The algorithm control model is used to receive data uploaded by the course coordination terminal and the classroom coordination port, and automatically solves and optimizes the course scheduling scheme based on a multi-objective optimization algorithm; The visualization control platform is used to provide a unified visual human-computer interaction interface to display real-time data on timetable arrangement results, classroom occupancy status, algorithm running progress, and various optimization indicators.

[0013] In a preferred embodiment of the present invention, the course coordination terminal includes a course coordination unit, a course adjustment control unit, and a course change data synchronization unit. The course coordination unit and the course adjustment control unit are bidirectionally connected, and the output end of the course adjustment control unit is connected to the input end of the course change data synchronization unit. The course coordination unit is used to input, edit, and delete basic course information for each grade and class, and to set course priorities and scheduling order. The class rescheduling control unit is used to receive class rescheduling requests submitted by teachers and to perform conflict detection and feasibility assessment on the class rescheduling requests; The course change data synchronization unit is used to automatically synchronize the changed data to the classroom coordination port and the algorithm control model when course information changes.

[0014] In a preferred embodiment of the present invention, the classroom coordination port includes a classroom distribution location coordination unit, a classroom scheduling information coordination unit, and a classroom data synchronization unit. The outputs of the classroom distribution location coordination unit and the classroom scheduling information coordination unit are both communicatively connected to the input of the classroom data synchronization unit. The classroom distribution location coordination unit is used to input and manage the geographical location information of all types of classrooms in the school; The classroom scheduling information coordination unit is used to record and manage the scheduling occupancy of each classroom during the current semester; The classroom data synchronization unit is used to automatically synchronize the changed data to the course coordination terminal and the algorithm control model when classroom scheduling information changes.

[0015] In a preferred embodiment of the present invention, the algorithm control model includes an algorithm control unit, a model generation unit, and an external algorithm access unit, wherein the algorithm control unit and the model generation unit are bidirectionally connected. The algorithm control unit is used to configure and manage the operating parameters of the multi-objective optimization algorithm, including population size, number of iterations, crossover probability, mutation probability, and weight coefficients of various soft constraints. The model generation unit is used to construct a multi-dimensional constraint matrix for the constraint satisfaction problem based on the data provided by the course coordination terminal and the classroom coordination port, run CSP pre-sorting to generate an initial timetable population, and then perform iterative optimization through an improved genetic algorithm to generate a Pareto optimal timetable scheme set that satisfies hard constraints and balances multiple optimization objectives. The external algorithm access unit is used to provide a standardized algorithm interface, support the access and replacement of third-party optimization algorithms, and realize the pluggable expansion of the algorithm module.

[0016] In a preferred embodiment of the present invention, the visualization control platform includes a visualization display unit, a visualization operation unit, and a platform adaptation unit. The output end of the visualization display unit is communicatively connected to the input end of the visualization operation unit, and the visualization operation unit and the platform adaptation unit are bidirectionally communicatively connected. The visualization unit is used to display the timetable arrangement results, classroom occupancy rate, teacher class hour distribution, and achievement of various optimization indicators in the form of heat maps, statistical charts, and three-dimensional timetable views; The visual operation unit is used to provide an interface for reviewing, confirming, rejecting, and manually adjusting timetable plans; The platform adaptation unit is used to adapt to different types of terminal devices.

[0017] In a preferred embodiment of the present invention, the overall scheduling system further includes a subsystem access unit, which provides a unified data interface and communication protocol to connect with the school's existing academic affairs management system, intelligent marking system, home-school communication platform and electronic class sign system.

[0018] In a preferred embodiment of the present invention, the overall scheduling system further includes a data security center, which is used to encrypt the storage and transmission of sensitive data stored and transmitted within the system.

[0019] A method for coordinating and scheduling junior high school teaching based on multi-objective optimization includes the following specific steps: S1. Construct a unified scheduling system to collect basic course data from all grades and classes in the school, as well as basic data from all types of classroom resources in the school. Establish a basic database containing course information, classroom information, teacher information, and time slot information, and configure and manage the sequential dependencies between courses, combined class requirements, and cross-grade teaching information. S2. Based on the data collected in step S1, construct a multi-dimensional constraint matrix, model the scheduling problem as a constraint satisfaction problem, and run the CSP pre-scheduling algorithm to generate an initial timetable population that satisfies all hard constraints. The hard constraints include that the same teacher can undertake at most one teaching task in the same time slice, the same class can be arranged for at most one course in the same time slice, the same classroom can be assigned to at most one teaching task in the same time slice, and the classroom capacity must be greater than or equal to the number of students in the corresponding teaching task. S3. Using the initial timetable population generated in step S2 as input, the algorithm iteratively optimizes the timetable through a multi-objective optimization algorithm. Under the premise of satisfying all hard constraints, it collaboratively optimizes multiple soft constraint objectives to generate a Pareto optimal timetable scheme set that balances multiple optimization objectives. The timetable arrangement results and the achievement of various optimization indicators are displayed through a visual interface for staff to review and select.

[0020] The multi-objective optimization process in step S3 uses the following formula to calculate the comprehensive fitness value of each timetable scheme. .

[0021] The beneficial effects of this invention are as follows: By setting up a course coordination terminal, a classroom coordination port, an algorithm control model, and a visualization control platform, this invention constructs a complete junior high school teaching coordination and scheduling system. In actual operation, the course coordination terminal collects and manages basic course data for all grades and classes in the school, including course name, course type, weekly class hours, consecutive class requirements, combined class requirements, and cross-grade teaching information; the classroom coordination port collects and manages basic data for all types of classroom resources in the school, including classroom type, classroom capacity, classroom distribution location, available time slots, and equipment configuration information; the algorithm control model receives data uploaded from both terminals and constructs a multi-dimensional constraint matrix. The invention generates an initial timetable population through CSP pre-scheduling, and then iteratively optimizes it using an improved genetic algorithm. A weighted summation comprehensive evaluation or Pareto non-dominated multi-objective evaluation is employed to generate a timetable scheme that satisfies hard constraints and balances multiple optimization objectives. Automatic synchronization and consistency of data across terminals are ensured through course change data synchronization and classroom data synchronization units. An external algorithm access unit supports the access and replacement of third-party optimization algorithms. A subsystem access unit connects to the school's existing academic management system, electronic class boards, and other external systems. A data security center provides encrypted storage, encrypted transmission, and role-based access management for sensitive data. This invention effectively solves the pain points of low scheduling efficiency, frequent conflicts, and inability to accommodate soft requirements in junior high school teaching coordination and scheduling. Scheduling time is reduced from the traditional 7-14 days to minutes. It manages, visualizes, and stores junior high school teaching resource data and corresponding analysis results, facilitating the management of junior high school teaching resources through internet cloud control and improving the level of intelligence in junior high school teaching resource management. Attached image description:

[0022] For ease of explanation, the present invention will be described in detail below with reference to specific embodiments and accompanying drawings.

[0023] Figure 1 This is a flowchart illustrating the overall steps of a multi-objective optimization-based junior high school teaching coordination and scheduling system and method of the present invention. Figure 2 This is a topological diagram of the overall scheduling system structure of a junior high school teaching scheduling system and method based on multi-objective optimization, according to the present invention. Figure 3 This is an overall flowchart of a multi-objective optimization-based junior high school teaching coordination and scheduling system and method according to the present invention. Detailed implementation method:

[0024] like Figure 1 , Figure 2 and Figure 3 As shown, the specific implementation adopts the following technical solution: A multi-objective optimization-based junior high school teaching coordination and scheduling system is disclosed. The system includes a course coordination terminal, a classroom coordination port, an algorithm control model, and a visualization control platform. The visualization control platform maintains real-time data communication and sharing with the course coordination terminal, the classroom coordination port, and the algorithm control model via the Internet. Staff members can log in to the visualization control platform to access the coordination and scheduling system and view the real-time operating data of the course coordination terminal, the classroom coordination port, and the algorithm control model.

[0025] Course data management S101. The course coordination terminal is used to collect, store and manage basic course data for all grades and classes in the school, including course name, course type, weekly class hours, consecutive class requirements, combined class requirements and cross-grade teaching information, and to coordinate and dynamically adjust the course arrangement scheme.

[0026] S102. The course coordination terminal includes a course coordination unit, a course adjustment control unit, and a course change data synchronization unit. The course coordination unit and the course adjustment control unit maintain a bidirectional communication connection, and the output end of the course adjustment control unit maintains a communication connection with the input end of the course change data synchronization unit.

[0027] S103, the overall scheduling system controls the input, editing, and deletion of basic course information for each grade and class in the course coordination unit, sets the priority and scheduling order of courses, and configures and manages the sequential dependencies between courses.

[0028] S104. The overall scheduling system control unit receives the class rescheduling application submitted by the teacher, performs conflict detection and feasibility assessment on the application, executes the class rescheduling operation after confirming its feasibility, and records the class rescheduling history log.

[0029] S105. When course information changes, the overall scheduling system control course change data synchronization unit automatically synchronizes the changed data to the classroom overall port and the algorithm control model to ensure the consistency of data across all terminals.

[0030] Classroom resource management S201. The classroom coordination port is used to collect, store and manage basic data of all types of classroom resources in the school, including classroom type, classroom capacity, classroom distribution location, available time period and equipment configuration information, and to uniformly schedule the allocation and use of classroom resources.

[0031] S202, The classroom coordination port includes a classroom distribution location coordination unit, a classroom scheduling information coordination unit, and a classroom data synchronization unit, and the outputs of the classroom distribution location coordination unit and the classroom scheduling information coordination unit are all connected to the input of the classroom data synchronization unit.

[0032] S203, the overall scheduling system controls the distribution of classrooms. The overall scheduling unit inputs and manages the geographical location information of all types of classrooms in the school, including the distribution of teaching buildings, floor locations, classroom numbers, and cross-campus travel time, providing spatial constraint data for teachers to teach across campuses.

[0033] S204, the overall scheduling system controls classroom scheduling information. The overall unit records and manages the scheduling and occupancy status of each classroom in the current semester, including the occupancy status, occupied classes and courses for each time period, and provides a classroom idle time query function.

[0034] S205. When classroom scheduling information changes, the overall scheduling system control classroom data synchronization unit automatically synchronizes the changed data to the course coordination terminal and the algorithm control model to ensure the consistency of data across all terminals.

[0035] Solving multi-objective optimization algorithms S301. The algorithm control model is used to receive data uploaded by the course coordination terminal and the classroom coordination port, and automatically solves and optimizes the course scheduling scheme based on the multi-objective optimization algorithm to generate a timetable scheme that meets hard constraints and takes into account multi-objective optimization.

[0036] S302. The algorithm control model includes an algorithm control unit, a model generation unit, and an external algorithm access unit. The output of the algorithm control unit is kept in communication with the input of the model generation unit, and the output of the external algorithm access unit is kept in communication with the input of the algorithm control unit.

[0037] S303, the overall scheduling system control algorithm control unit configures and manages the operating parameters of the multi-objective optimization algorithm, including population size, number of iterations, crossover probability, mutation probability and weight coefficients of various soft constraints, and controls the start, pause and termination of the algorithm.

[0038] S304. The overall scheduling system control model generation unit constructs a multi-dimensional constraint matrix for the constraint satisfaction problem based on the data provided by the course coordination terminal and the classroom coordination port, runs CSP pre-schedule to generate an initial timetable population, and then performs iterative optimization through an improved genetic algorithm to generate a Pareto optimal timetable scheme set that satisfies hard constraints and balances multiple optimization objectives.

[0039] S305. The overall scheduling system controls the external algorithm access unit to provide a standardized algorithm interface, supporting the access and replacement of third-party optimization algorithms, and realizing pluggable expansion of algorithm modules; the third-party optimization algorithms include any one or more combinations of genetic algorithms, simulated annealing algorithms, particle swarm optimization algorithms, ant colony optimization algorithms, tabu search algorithms, NSGA-II algorithms and NSGA-III algorithms.

[0040] S306, the control model generation unit of the overall scheduling system divides all constraints in the course scheduling problem into two levels: hard constraints and soft constraints when constructing the multi-dimensional constraint matrix. Hard constraints include: the same teacher can undertake at most one teaching task in the same time slot; the same class can be scheduled for at most one course in the same time slot; the same classroom can be assigned to at most one teaching task in the same time slot; the classroom capacity must be greater than or equal to the number of students in the corresponding teaching task; the dependencies between courses must be respected; teachers teaching across campuses can only teach in one campus in the same time slot. Soft constraints include: the satisfaction of teachers' preferences for teaching time slots; the balance of the daily distribution of subjects in the same class; the rationality of the interval of the same subject on different dates; and the balance of classroom resource utilization in different time slots.

[0041] S307, When the control model generation unit of the overall scheduling system generates the initial timetable population by running the CSP pre-schedule, it encodes the chromosomes in the form of a three-dimensional matrix. It uses the grade or class number, weekday number, and day period as three-dimensional coordinates to locate each teaching task, and encodes information such as course name, teacher, and classroom type into gene blocks, and establishes a mapping relationship with the three-dimensional coordinate points -33.

[0042] S308, the overall scheduling system control improved genetic algorithm adopts a selection method that combines roulette wheel selection and sorting selection, partial matching crossover operator and displacement mutation operator during the iterative optimization process, and ensures that the fitness of the best individual in each generation does not degenerate through the elite retention strategy.

[0043] S309. The overall fitness value of each timetable scheme is calculated using the following formula in the multi-objective optimization process controlled by the overall scheduling system.

[0044] Where X represents a complete timetable arrangement scheme vector; This is a teacher time period preference satisfaction function, used to quantify teachers' satisfaction with teaching time periods (such as morning, afternoon, and weekday distribution). The larger the value, the more it conforms to the teacher's subjective wishes. This is a classroom resource balance utilization function used to evaluate the occupancy balance and overall utilization efficiency of different types of classrooms at different time periods. This is a function for the uniformity of course time distribution, used to measure the degree of dispersion of the same class or subject across different dates and periods, to avoid excessive concentration of courses; α, β, and γ are the weight coefficients of the three optimization objectives mentioned above. They are preset by academic affairs administrators according to actual management needs and can be dynamically adjusted before the algorithm runs to reflect the importance of different objectives.

[0045] S310, the overall scheduling system controls the population size to be 100 to 500, the number of iterations to be 500 to 5000, the crossover probability to be 0.6 to 0.9, and the mutation probability to be 0.01 to 0.1. The specific values ​​are dynamically adjusted by the algorithm control unit according to the school size and data complexity.

[0046] Visual control and platform interaction S401. The visualization control platform is used to provide a unified visualization human-computer interaction interface, displaying real-time data on timetable arrangement results, classroom occupancy status, algorithm running progress, and various optimization indicators, and supporting staff to perform scheme review, manual fine-tuning, and data export operations.

[0047] S402, The visualization control platform includes a visualization display unit, a visualization operation unit, and a platform adaptation unit, maintaining a communication connection between the output end of the visualization display unit and the input end of the visualization operation unit, and maintaining a bidirectional communication connection between the visualization operation unit and the platform adaptation unit.

[0048] S403, the overall scheduling system control visualization display unit displays the timetable arrangement results, classroom occupancy rate, teacher class hour distribution and the achievement of various optimization indicators in the form of heat map, statistical chart and three-dimensional timetable view.

[0049] S404, the overall scheduling system control visualization operation unit provides an operation interface for reviewing, confirming, rejecting, and manually fine-tuning timetable schemes, and supports drag-and-drop timetable adjustments, one-click retracement of historical versions, and comparison of multiple timetable versions.

[0050] S405, the overall scheduling system control platform adaptation unit, adapts to different types of terminal devices, including PCs, tablets and mobile devices, to ensure a consistent visual operation experience on different devices.

[0051] Subsystem Access and Data Security S501. The overall scheduling system also includes a subsystem access unit, which provides a unified data interface and communication protocol to connect with the school's existing academic affairs management system, intelligent marking system, home-school communication platform and electronic class card system, so as to realize data connection and business collaboration between multiple systems and break down information silos.

[0052] S502. The overall scheduling system also includes a data security center, which is used to encrypt the storage and transmission of sensitive data stored and transmitted within the system, implement a role-based access control mechanism, supervise and audit the data access of third-party service providers, and regularly scan and repair data security vulnerabilities.

[0053] A multi-objective optimization-based method for coordinating and scheduling junior high school teaching, used to implement the aforementioned multi-objective optimization-based junior high school teaching coordination and scheduling system, includes the following specific steps: S6011 collects basic course data from all grades and classes across the school, as well as basic data from all types of classroom resources, and establishes a basic database containing course information, classroom information, teacher information, and time slot information. It also configures and manages the sequential dependencies between courses, combined class requirements, and cross-grade teaching information.

[0054] S6012. Based on the data collected in step S6011, construct a multi-dimensional constraint matrix, model the scheduling problem as a constraint satisfaction problem, and run the CSP pre-scheduling algorithm to generate an initial timetable population that satisfies all hard constraints. The hard constraints include that the same teacher can undertake at most one teaching task in the same time slice, the same class can be arranged for at most one course in the same time slice, the same classroom can be assigned to at most one teaching task in the same time slice, and the classroom capacity must be greater than or equal to the number of students in the corresponding teaching task.

[0055] S6013. Using the initial timetable population generated in step S6012 as input, the algorithm iteratively optimizes the timetable through a multi-objective optimization algorithm. Under the premise of satisfying all hard constraints, it collaboratively optimizes multiple soft constraint objectives to generate a Pareto optimal timetable scheme set that balances multiple optimization objectives. The timetable arrangement results and the achievement of various optimization indicators are displayed through a visual interface for staff to review and select.

[0056] Example (Based on the overall teaching scheduling of a junior high school) System Configuration School size: Three grades, 36 classes in total, with a total of approximately 1,600 students; There are 98 teachers, including 12 teachers who teach across different grade levels. Course categories: 15, including Chinese, Mathematics, English, Physics, Chemistry, Biology, Moral Education and Law, History, Geography, Music, Art, Physical Education, Information Technology, Labor Technology, and school-based courses; Classroom resources: 40 regular classrooms, 6 laboratories, 3 music classrooms, 3 art classrooms, 4 computer classrooms, and 1 gymnasium; Total weekly class hours: 35 class hours per class per week, totaling 1260 class hours per week for the whole school; Time slots: 5 days a week, 7 sessions a day, for a total of 35 time slots; Course coordination terminal: Input basic course data and configure 32 consecutive class requirements, 18 combined class requirements, and 24 cross-grade teaching information; Classroom management portal: Input classroom capacity, location, available time slots, and equipment configuration information; Algorithm control model: population size 300, number of iterations 2000, crossover probability 0.8, mutation probability 0.05; Fitness evaluation method: weighted summation comprehensive evaluation, α=0.35, β=0.30, γ=0.35; Visual control platform: Deployed on the school's data center server, supporting access from PC and mobile devices; Implementation data: This system was deployed in a junior high school, covering the teaching coordination and scheduling of 36 administrative classes in grades 7, 8, and 9.

[0057] Table: System Performance Test Data in the Examples

[0058] Specific implementation case: On February 20, 2026, a junior high school conducted a comprehensive scheduling of teaching for the second semester of the 2025-2026 academic year.

[0059] Work process: 08:00:00: Academic affairs administrators log in to the visual control platform and enter the overall scheduling system.

[0060] 08:05:00: The course coordination terminal loads the basic course data for this semester, including all course information for 36 classes, 15 subjects, and 98 teachers, and configures 32 consecutive class requirements and 18 combined class requirements.

[0061] 08:10:00: The classroom management portal loads resource data for all 55 classrooms / venues in the school, including classroom type, capacity, location, and available time slots.

[0062] 08:15:00: The algorithm control model starts, and the algorithm control unit is configured with a population size of 300, 2000 iterations, a crossover probability of 0.8, and a mutation probability of 0.05.

[0063] 08:16:00: The model generation unit constructs a multidimensional constraint matrix, which includes 98 sets of teacher constraints, 36 sets of class constraints, and 55 sets of classroom constraints, with a total of approximately 1,500 hard constraint items.

[0064] 08:18:00: The model generation unit runs the CSP pre-schedule algorithm to generate an initial timetable population with a population size of 300 and a hard constraint satisfaction rate of 92.5%.

[0065] 08:20:00: Improve the genetic algorithm to start iterative optimization, and adopt a selection method that combines roulette wheel selection and sorting selection, partial matching crossover operator and displacement mutation operator.

[0066] 08:25:00: The algorithm has iterated to the 1200th generation, and the fitness value tends to converge.

[0067] 08:28:00: The algorithm completes all 2000 iterations and generates the Pareto optimal timetable scheme set, which consists of 12 non-dominated schemes.

[0068] 08:30:00: The visualization unit displays the achievement of optimization indicators for each scheme in the form of a 3D timetable view and statistical charts.

[0069] 08:35:00: Academic affairs administrators compared 12 plans through a visual operation unit, and selected plan 7 as the final implementation plan after comprehensively considering the teacher preference satisfaction rate and the evenness of course distribution.

[0070] 08:40:00: Academic affairs management personnel manually reviewed Plan 7 and confirmed that all hard constraints were met, the teacher time slot preference satisfaction rate was 87.6%, and the average classroom resource utilization rate was 78.3%.

[0071] 08:45:00: Academic affairs administrators can export the final timetable with one click through the visual operation unit and push it to the electronic class card system and teachers' mobile devices simultaneously.

[0072] Results: The entire scheduling process took 45 minutes, which is more than 200 times more efficient than the traditional manual scheduling method (7-14 days). The generated timetable met all hard constraints 100%, the teacher time slot preference satisfaction rate increased by about 15 percentage points compared to existing scheduling software, the classroom resource utilization rate increased by about 12 percentage points, and the course time distribution evenness reached 92.1%. It effectively avoided unreasonable arrangements such as excessive concentration of core subjects in the morning and consecutive physical education classes. The scheduling plan was officially implemented after being reviewed and confirmed by the academic affairs management personnel. The number of course rescheduling requests during the semester decreased by about 60% compared to the previous semester.

[0073] Specifically, in practical applications, multiple course coordination terminals and multiple classroom coordination ports can be deployed, corresponding to different grades or different campuses. Data interaction occurs via the internet with the algorithm control model and visual control platform. This invention constructs a complete junior high school teaching coordination and scheduling system by setting up course coordination terminals, classroom coordination ports, algorithm control models, and a visual control platform. In actual operation, the course coordination terminals collect and manage basic course data for all grades and classes across the school, including course names, course types, weekly class hours, consecutive class requirements, combined class requirements, and cross-grade teaching information. The classroom coordination ports collect and manage basic data for various classroom resources across the school, including classroom type, classroom capacity, classroom distribution location, available time slots, and equipment configuration information. The algorithm control model receives data uploaded from both terminals, constructs a multi-dimensional constraint matrix, runs CSP pre-schedule to generate an initial timetable population, and then improves upon this. Genetic algorithms are used for iterative optimization, employing weighted summation comprehensive evaluation or Pareto non-dominated multi-objective evaluation to generate timetable schemes that meet hard constraints and balance multiple optimization objectives. A visual control platform provides a unified, visual human-computer interaction interface, displaying real-time data on timetable arrangement results, classroom occupancy status, algorithm progress, and various optimization indicators. It supports staff in reviewing schemes, making manual adjustments, and exporting data. A timetable adjustment control unit enables conflict detection and feasibility assessment of timetable adjustment requests. Automatic synchronization and consistency of data across terminals are achieved through course change data synchronization and classroom data synchronization units. An external algorithm access unit supports the access and replacement of third-party optimization algorithms. A subsystem access unit connects to the school's existing academic management system, electronic class displays, and other external systems. A data security center ensures encrypted storage and transmission of sensitive data, as well as role-based access management. This invention effectively solves the pain points of low scheduling efficiency, frequent conflicts, and inability to meet soft needs in the overall scheduling of junior high school teaching. The scheduling time is reduced from the traditional 7 to 14 days to minutes. It manages, visualizes, and stores junior high school teaching resource data and corresponding analysis results, which helps to realize junior high school teaching resource management through Internet cloud management and improve the level of intelligence in junior high school teaching resource management.

[0074] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0075] In the embodiments provided in this application, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or equipment, and may be electrical, mechanical, or other forms.

[0076] The course coordination terminal, classroom coordination port, algorithm control model, and visualization control platform may or may not be physically separate. The components displayed as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program instructions, such as USB flash drives, portable hard drives, read-only storage servers, random access storage servers, magnetic disks, or optical disks.

[0078] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-objective optimization-based junior high school teaching coordination and scheduling system, characterized in that, The overall scheduling system includes a course scheduling terminal, a classroom scheduling port, an algorithm control model, and a visualization control platform. The visualization control platform maintains real-time data communication and sharing with the course scheduling terminal, the classroom scheduling port, and the algorithm control model via the Internet. Staff members can log in to the visualization control platform to access the overall scheduling system and view the real-time operating data of the course scheduling terminal, the classroom scheduling port, and the algorithm control model one by one.

2. The junior high school teaching coordination and scheduling system based on multi-objective optimization according to claim 1, characterized in that: The course coordination terminal is used to collect, store, and manage basic course data for all grades and classes across the school, including course name, course type, weekly class hours, consecutive class requirements, combined class requirements, and cross-grade teaching information. The classroom management portal is used to collect, store, and manage basic data on all types of classroom resources across the school, including classroom type, classroom capacity, classroom location, available time slots, and equipment configuration information. The algorithm control model is used to receive data uploaded by the course coordination terminal and the classroom coordination port, and automatically solves and optimizes the course scheduling scheme based on a multi-objective optimization algorithm; The visualization control platform is used to provide a unified visual human-computer interaction interface to display real-time data on timetable arrangement results, classroom occupancy status, algorithm running progress, and various optimization indicators.

3. The junior high school teaching coordination and scheduling system based on multi-objective optimization according to claim 1, characterized in that: The course coordination terminal includes a course coordination unit, a course adjustment control unit, and a course change data synchronization unit. The course coordination unit and the course adjustment control unit are bidirectionally connected, and the output of the course adjustment control unit is connected to the input of the course change data synchronization unit. The course coordination unit is used to input, edit, and delete basic course information for each grade and class, and to set course priorities and scheduling order. The class rescheduling control unit is used to receive class rescheduling requests submitted by teachers and to perform conflict detection and feasibility assessment on the class rescheduling requests; The course change data synchronization unit is used to automatically synchronize the changed data to the classroom coordination port and the algorithm control model when course information changes.

4. The junior high school teaching coordination and scheduling system based on multi-objective optimization according to claim 1, characterized in that: The classroom coordination port includes a classroom distribution location coordination unit, a classroom scheduling information coordination unit, and a classroom data synchronization unit. The outputs of the classroom distribution location coordination unit and the classroom scheduling information coordination unit are both communicatively connected to the input of the classroom data synchronization unit. The classroom distribution location coordination unit is used to input and manage the geographical location information of all types of classrooms in the school; The classroom scheduling information coordination unit is used to record and manage the scheduling occupancy of each classroom during the current semester; The classroom data synchronization unit is used to automatically synchronize the changed data to the course coordination terminal and the algorithm control model when classroom scheduling information changes.

5. The junior high school teaching coordination and scheduling system based on multi-objective optimization according to claim 1, characterized in that: The algorithm control model includes an algorithm control unit, a model generation unit, and an external algorithm access unit, wherein the algorithm control unit and the model generation unit are bidirectionally connected. The algorithm control unit is used to configure and manage the operating parameters of the multi-objective optimization algorithm, including population size, number of iterations, crossover probability, mutation probability, and weight coefficients of various soft constraints. The model generation unit is used to construct a multi-dimensional constraint matrix for the constraint satisfaction problem based on the data provided by the course coordination terminal and the classroom coordination port, run CSP pre-sorting to generate an initial timetable population, and then perform iterative optimization through an improved genetic algorithm to generate a Pareto optimal timetable scheme set that satisfies hard constraints and balances multiple optimization objectives. The external algorithm access unit is used to provide a standardized algorithm interface, support the access and replacement of third-party optimization algorithms, and realize the pluggable expansion of the algorithm module.

6. The junior high school teaching coordination and scheduling system based on multi-objective optimization according to claim 1, characterized in that: The visualization control platform includes a visualization display unit, a visualization operation unit, and a platform adaptation unit. The output end of the visualization display unit is communicatively connected to the input end of the visualization operation unit, and the visualization operation unit and the platform adaptation unit are bidirectionally communicatively connected. The visualization unit is used to display the timetable arrangement results, classroom occupancy rate, teacher class hour distribution, and achievement of various optimization indicators in the form of heat maps, statistical charts, and three-dimensional timetable views; The visual operation unit is used to provide an interface for reviewing, confirming, rejecting, and manually adjusting timetable plans; The platform adaptation unit is used to adapt to different types of terminal devices.

7. The junior high school teaching coordination and scheduling system based on multi-objective optimization according to claim 6, characterized in that: The overall scheduling system also includes a subsystem access unit, which provides a unified data interface and communication protocol to connect with the school's existing academic affairs management system, intelligent marking system, home-school communication platform and electronic class card system.

8. The junior high school teaching coordination and scheduling system based on multi-objective optimization according to claim 1, characterized in that: The overall scheduling system also includes a data security center, which is used to encrypt the storage and transmission of sensitive data within the system.

9. A method for coordinating and scheduling junior high school teaching based on multi-objective optimization, used to implement a junior high school teaching coordination and scheduling system based on multi-objective optimization as described in any one of claims 1-8, characterized in that: The specific steps include the following: S1. Construct a unified scheduling system to collect basic course data from all grades and classes in the school, as well as basic data from all types of classroom resources in the school. Establish a basic database containing course information, classroom information, teacher information, and time slot information, and configure and manage the sequential dependencies between courses, combined class requirements, and cross-grade teaching information. S2. Based on the data collected in step S1, construct a multi-dimensional constraint matrix, model the scheduling problem as a constraint satisfaction problem, and run the CSP pre-scheduling algorithm to generate an initial timetable population that satisfies all hard constraints. The hard constraints include that the same teacher can undertake at most one teaching task in the same time slice, the same class can be arranged for at most one course in the same time slice, the same classroom can be assigned to at most one teaching task in the same time slice, and the classroom capacity must be greater than or equal to the number of students in the corresponding teaching task. S3. Using the initial timetable population generated in step S2 as input, the algorithm iteratively optimizes the timetable through a multi-objective optimization algorithm. Under the premise of satisfying all hard constraints, it collaboratively optimizes multiple soft constraint objectives to generate a Pareto optimal timetable scheme set that balances multiple optimization objectives. The timetable arrangement results and the achievement of various optimization indicators are displayed through a visual interface for staff to review and select.

10. The method for coordinating and scheduling junior high school teaching based on multi-objective optimization according to claim 9, characterized in that: The multi-objective optimization process in step S3 uses the following formula to calculate the comprehensive fitness value of each timetable scheme. .

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