Course schedule automatic generation method, device, equipment, storage medium and program product
By automatically identifying and adapting to heterogeneous academic affairs system interfaces through a multi-agent collaborative mechanism, and combining constraint satisfaction problem-solving algorithms to generate timetables, the problem of data integration difficulties in university academic affairs systems has been solved, achieving efficient and fair automatic timetable generation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHENGDOU HUAQIYUN TECH CO LTD
- Filing Date
- 2026-05-06
- Publication Date
- 2026-07-31
AI Technical Summary
The heterogeneous academic affairs systems of universities and educational institutions are difficult to integrate with in a low-intrusion and adaptive manner, resulting in poor fairness and low efficiency in timetable generation.
A multi-agent collaborative mechanism is adopted, which coordinates the agents to receive requests, perceive and adapt to the agent recognition system and generate mapping rules, combines a knowledge base and a large language model to extract data, and uses the timetable generation agent to generate timetables based on a constraint satisfaction problem solving algorithm.
It enables automatic interface adaptation when the system structure changes, reduces integration and maintenance costs, improves the robustness and fairness of timetable generation, reduces manual intervention, and improves scheduling efficiency and intelligence.
Smart Images
Figure CN122492141A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of scheduling data processing technology, and in particular to methods, apparatus, equipment, storage media and program products for automatic timetable generation. Background Technology
[0002] Currently, universities and educational institutions generally have multiple heterogeneous academic affairs systems, such as course scheduling systems, course selection systems, and grade management systems. These systems are often built by different vendors at different times, with varying data formats, interface protocols (such as RESTful, SOAP, and web pages without interfaces), and update frequencies. Traditional data integration methods mainly fall into two categories: one is to write interfaces point-to-point, which has high maintenance costs and poor scalability; the other is to use ETL tools for periodic data extraction, but this cannot cope with changes in the fields or structures of the source systems, and the integration channel becomes invalid once an academic affairs system is upgraded.
[0003] Furthermore, timetable creation itself involves complex constraint satisfaction problems, and manual timetable creation is time-consuming and prone to conflicts. Therefore, there is an urgent need for a method that can achieve low-intrusion, adaptive data integration with heterogeneous academic affairs systems and intelligently generate timetables. Summary of the Invention
[0004] The main purpose of this application is to provide a method, apparatus, device, storage medium, and program product for automatically generating timetables, aiming to solve the technical problem of poor fairness in the automatic generation of timetables in related technologies.
[0005] Firstly, to achieve the above objectives, this application provides a method for automatically generating timetables, characterized in that the method includes: The timetable generation request is obtained by coordinating the intelligent agent; Based on the timetable generation request, the perceptual adaptation agent is invoked to determine the data source connection information of the target academic affairs system, obtain the data required for timetable generation from the target academic affairs system, and load the scheduling constraint rules from the knowledge base. After the coordinating agent integrates the scheduling constraints and the data required for timetable generation, it calls the timetable generation agent to generate the target timetable based on the constraint satisfaction problem solving algorithm.
[0006] In one embodiment, the step of determining the access address or data source connection information of the target academic affairs system based on the timetable generation request includes: The sensory adaptation agent receives a list of access addresses of the target academic affairs system and identifies the system type and interface characteristics by probing requests. If the identification is successful, the pre-stored mapping rules in the knowledge base are loaded and data extraction is performed; If identification fails or the data mapping extraction count follows a preset rule, then: The timetable-related pages or data interfaces of the target academic affairs system are traversed to collect field samples and their context information. The field samples are semantically parsed based on a large language model, and the semantics of the source fields are mapped to a predefined unified intermediate data model. Field mapping scripts are automatically generated based on mapping relationships, and these field mapping scripts are stored in the knowledge base as mapping rules. Return to the pre-stored mapping rules in the loaded knowledge base and perform data extraction.
[0007] In one embodiment, the step of loading pre-stored mapping rules in the knowledge base and performing data extraction includes: In response to the data structure required for generating the timetable, the coordinating agent adds corresponding data sources; each data source corresponds to a data structure. The data source is assigned to the perceptual adaptive agent by performing the following steps: For each data source, the agent extracts necessary fields through querying, combines them with pre-stored mapping rules, and performs field mapping fusion processing to complete data extraction.
[0008] In one embodiment, the step of automatically generating a field mapping script based on the mapping relationship and storing the field mapping script as a mapping rule in the knowledge base includes: The automatically generated data extraction script is subjected to sandbox verification. Field coverage and mapping accuracy are calculated by replaying historical data or testing sample data. When the preset verification conditions are met, the data extraction script is stored as a mapping rule in the shared knowledge base and managed in a versioned manner.
[0009] In one embodiment, the constraint rules include hard constraint rules and soft constraint rules. The step of integrating the scheduling constraint rules and the data required for timetable generation through the coordinating agent, and then calling the timetable generation agent to generate the target timetable based on the constraint satisfaction problem solving algorithm includes: The system generates an initial feasible solution by using a greedy algorithm to sort courses by their scarcity based on the timetable generation intelligence. The initial solution was optimized using constraint propagation and local search to obtain multiple candidate timetables; The candidate timetables are scored using multiple objectives, and the timetable with the highest comprehensive score is selected as the output to obtain the target timetable.
[0010] Secondly, to achieve the above solution, this application further provides an automatic timetable generation device, the device comprising: The request acquisition module obtains the timetable generation request through the coordinating agent; The adaptation module, based on the timetable generation request, calls the perception and adaptation agent to determine the data source connection information of the target academic affairs system, obtains the data required for timetable generation from the target academic affairs system, and loads the scheduling constraint rules from the knowledge base; The timetable generation module integrates the scheduling constraints and the data required for timetable generation through the coordinating agent, and then calls the timetable generation agent to generate the target timetable based on the constraint satisfaction problem solving algorithm.
[0011] Thirdly, to achieve the above solution, this application further provides an automatic timetable generation device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the above-described automatic timetable generation method.
[0012] Fourthly, to achieve the above solution, this application further provides a storage medium, which is a computer-readable storage medium, and stores a computer program thereon. When the computer program is executed by a processor, it implements the steps of the above-mentioned automatic timetable generation method.
[0013] Fifthly, to achieve the above solution, this application further provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the above-described automatic timetable generation method.
[0014] One or more technical solutions proposed in this application have at least the following technical effects: This application integrates the adaptive data integration and timetable generation process of heterogeneous academic affairs systems by introducing a multi-agent collaborative mechanism. Compared with traditional point-to-point interfaces or fixed ETL methods, it can automatically complete semantic parsing and mapping rule generation when the system structure or fields change, thereby significantly improving the robustness and scalability of data integration. At the same time, by modeling the timetable problem as a constraint satisfaction problem and combining a multi-stage optimization strategy of greedy initialization, constraint propagation and local search, it achieves comprehensive processing of hard and soft constraints, improving the fairness and overall quality of timetable allocation while ensuring no conflicts, reducing the cost of manual intervention, and improving the efficiency and intelligence level of timetable scheduling. Attached Figure Description
[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0016] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating the automatic timetable generation method in this application embodiment.
[0018] Figure 2 This is a schematic diagram of the module connections for the automatic timetable generation device.
[0019] Figure 3 A schematic diagram of the equipment structure for automatically generating timetables.
[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.
[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.
[0023] The main solution of this application embodiment is as follows: First, the coordinating agent receives the timetable generation request and triggers the perceptual adaptation agent to perform interface detection and type identification on the target academic affairs system. It combines the existing mapping rules in the knowledge base or parses the semantics of the fields through a large language model to construct a unified intermediate data model and complete the adaptive extraction and fusion of heterogeneous data. Then, the coordinating agent integrates the data required for scheduling with the preset hard and soft constraint rules. Finally, the timetable generation agent is called to model the scheduling problem as a constraint satisfaction problem. An initial solution is generated using a greedy strategy, and iterative optimization is performed through constraint propagation and local search. The candidate timetables are scored in multiple ways, and the target timetable that satisfies the constraints and is the best overall is output.
[0024] Specifically, this application provides a method for automatically generating timetables, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the automatic timetable generation method of this application.
[0025] In this embodiment, the method for automatically generating timetables includes steps S10 to S30: Step S10: Obtain the timetable generation request by coordinating the intelligent agent.
[0026] Step S20: Based on the timetable generation request, invoke the perceptual adaptation agent to determine the data source connection information of the target academic affairs system, obtain the data required for timetable generation from the target academic affairs system, and load the scheduling constraint rules from the knowledge base.
[0027] Step S30: After the coordinating agent integrates the information of the scheduling constraint rules and the data required for timetable generation, it calls the timetable generation agent to generate the target timetable based on the constraint satisfaction problem solving algorithm.
[0028] Step S20 includes steps A10 to A70: Step A10: The perceptual adaptive intelligent agent receives the access address list of the target academic affairs system and identifies the system type and interface characteristics by probing requests.
[0029] Step A20: If the recognition is successful, load the pre-stored mapping rules in the knowledge base and perform data extraction.
[0030] Step A30: If the identification fails or the data mapping extraction count is subject to a preset rule, then the timetable-related pages or data interfaces of the target academic affairs system are traversed to collect field samples and their context information.
[0031] Step A40: Semantic parsing of the field samples is performed based on the large language model, and the semantics of the source fields are mapped to a predefined unified intermediate data model.
[0032] Step A50: Automatically generate a field mapping script based on the mapping relationship, and store the field mapping script as a mapping rule in the knowledge base.
[0033] Step A60: Return to execute the pre-stored mapping rules in the loaded knowledge base and perform data extraction.
[0034] Step A20 includes steps A21 to A22: Step A21: In response to the data structure required for generating the timetable, the corresponding data source is added through the coordinating agent; each data source corresponds to a data structure.
[0035] Step A22, the data source is allocated to the perceptual adaptation agent and the following steps are performed: For each data source, the agent extracts necessary fields through querying, combines them with pre-stored mapping rules, and performs field mapping fusion processing to complete data extraction.
[0036] Step A50 includes step A51: The automatically generated data extraction script is subjected to sandbox verification. Field coverage and mapping accuracy are calculated by replaying historical data or testing sample data. When the preset verification conditions are met, the data extraction script is stored as a mapping rule in the shared knowledge base and managed in a versioned manner.
[0037] Step S30 includes steps B10 to B30: Step B10: Using the timetable generation intelligence, a greedy algorithm is used to sort the courses by their scarcity to generate an initial feasible solution.
[0038] Step B20: The initial solution is optimized using constraint propagation and local search to obtain multiple candidate timetables.
[0039] Step B30: Perform multi-objective scoring on the candidate timetables, select the timetable with the highest comprehensive score as the output, and obtain the target timetable. This embodiment addresses the problems of difficulty in heterogeneous data integration in existing academic affairs systems, low automation in timetable arrangement, and inability to adapt to interface changes. It provides an agent-based method to achieve: Automatically identify the data structure and access methods of heterogeneous academic affairs systems, and dynamically generate data extraction mapping rules.
[0040] When changes occur in the source system, the mapping rules are automatically detected and corrected without manual intervention.
[0041] Based on a unified multidimensional constraint model, a timetable that satisfies hard constraints and optimizes soft constraints is automatically generated.
[0042] In this embodiment, multiple intelligent agents are deployed, namely a perception and adaptation intelligent agent, a timetable generation intelligent agent, and a coordination intelligent agent. The perception and adaptation intelligent agent receives the access address list of the target academic affairs system and identifies the system type and interface characteristics by probing requests.
[0043] If recognition is successful, the pre-stored mapping rules in the knowledge base are loaded, and data extraction is performed; if recognition fails or the number of consecutive failures of existing mapping rules exceeds a threshold, the adaptive recognition process is initiated. Use web crawlers or automated browser tools to traverse the relevant pages of the timetable and extract field samples; By combining a large language model to parse the semantics of the fields, the source fields are mapped to a unified intermediate data model (UIM). Automatically generate data extraction scripts (XPath, JSONPath, or regular expressions) and store them in a knowledge base.
[0044] Regularly (e.g., daily) validate the integrated systems. If missing fields or abnormal data formats are found, repeat the above steps to update the mapping rules.
[0045] Subsequently, the coordinating agent integrates the collected timetable data, including courses, teachers, classrooms, times, and semesters, to generate the necessary data, and loads constraint rules from the knowledge base. These constraint rules are divided into hard constraints and soft constraints.
[0046] For example, hard constraints include: a teacher can only teach one course at a time, a classroom can only host one course at a time, and the number of weekly class hours must meet the teaching plan.
[0047] Soft constraints: teacher time preference, student course dispersion, and classroom type matching.
[0048] Finally, the timetable generation agent generates candidate timetables based on the constraint satisfaction problem (CSP) solving algorithm: A greedy algorithm is used to sort courses by scarcity to generate an initial feasible solution. Constraint propagation and local search (tacit search or genetic algorithm) are then used to optimize the initial solution, resulting in multiple candidate timetables. The candidate timetables are scored with multiple objectives (such as teacher preference satisfaction rate and classroom utilization rate). The timetable with the highest comprehensive score is selected as the output. If no feasible solution satisfies all hard constraints, the coordinating agent is called to output the reason for the conflict (e.g., "Classroom A101 is needed by two courses at the same time on Tuesday during the 3rd period"), requesting manual adjustment of constraint priority or release of resources.
[0049] The generated timetable is displayed to academic administrators through a visual interface, which supports drag-and-drop adjustments; each manual modification record is stored in the knowledge base as a preference case, which can be used to adjust the soft constraint weights when scheduling subsequent classes.
[0050] Understandably, compared to existing technologies, the embodiments of this invention can automatically identify and adapt to interface changes in heterogeneous academic affairs systems, eliminating the need to write fixed web crawlers and significantly reducing integration and maintenance costs. Simultaneously, by combining hard and soft constraints in multi-objective optimization, it ensures that the generated timetables are conflict-free and meet the preferences of teachers and students as much as possible. Through the accumulation of manual adjustments using a knowledge base, the generated timetables can become increasingly consistent with actual usage habits. It does not require the source system to have an open API, can work automatically via the web, and is suitable for legacy systems.
[0051] To enable those skilled in the art to better understand the solutions of this embodiment, the following examples provide a detailed description of this embodiment: For example, this example uses a data view related to academic affairs in a university system, which includes the following data structure: jskcap: Teacher teaching ability view (including teaching class information, course name, teacher, time and location, etc.), xsxk: Student course selection view, zxxs: On-campus student view, zzjg: Organizational structure view, zzjs: Organizational teacher view.
[0052] Deploy a Python 3.10 environment on the server and use the LangChain framework to build three agents (a perception and adaptation agent, a timetable generation agent, and a coordination agent) that share a PostgreSQL knowledge base.
[0053] Add five data sources to the coordination agent management interface. Each data source corresponds to one of the views mentioned above and provides database connection information (JDBC URL, username, password) or REST API address.
[0054] The coordinating agent decomposes the docking task to the perception and adaptation agent.
[0055] The perceptual adaptive agent executes the following sub-processes for each data source: For the jskcap table agent, execute DESCRIBE jskcap or query the first 10 records to obtain the field list and sample values.
[0056] By analyzing field names (such as JXBH, KCMC, ZGH, XQJ, JSBH, XN, XQ) and value features (such as 20252-245-4500034-1764645748952 including year and semester number), the agent matches the fingerprint of the "Teaching Class Information Table" in the knowledge base and identifies that the table is used to store teaching class arrangements.
[0057] For the xsxk table: the fields JXBDM (teaching class code), CODE (student ID), XM (name), and KCDM (course code) were found, and it was identified as a student course selection relationship table.
[0058] For the zxxs table: the following data were found: badge (student ID), name (student name), college_code (college code), and graduate_status (graduation status), which are identified as basic information of enrolled students.
[0059] For the zzjg table: the name (organization name), code, and parent_code were found, which were identified as an organizational structure tree.
[0060] For the zzjs table: the badge (employee ID), name (personal name), and college_code (college) were found and identified as the teacher's basic information table.
[0061] The agent loads a pre-built Unified Intermediate Data Model (UIM) from the knowledge base. Its core fields include: For each data source, the agent automatically generates a field mapping script using a large language model. It outputs a Python mapping function, identifies the table representing student course selection relationships, and performs the corresponding mapping.
[0062] The automatically generated mapping scripts are validated in a sandbox (using historical data replay, with field coverage ≥99%) and then stored in the mapping_rule table of the knowledge base. Each record contains: Data source identifier (e.g., jskcap@mysql_host), mapping script (Python code), version number, last verification time. Subsequently, the perceptual adaptation agent automatically re-runs the mapping every morning at midnight and compares the extraction results with the previous day's results.
[0063] If a field is found to change from an integer to the string "Friday" in jskcap.XQJ, the agent automatically triggers an adaptive correction process: re-invokes the large language model to analyze the new field format and updates the mapping script.
[0064] The coordinating agent collects all mapped UIM data and fuses them: Link the teaching class information provided by jskcap with the teacher academy information provided by zzjs to improve teacher attributes.
[0065] The course selection list provided by xsxk is filtered against the student status provided by zxxs (excluding graduated students) to obtain the actual set of students who have selected courses.
[0066] If a course has a class time and location listed in jskcap, but no students have enrolled in xsxk, it will be marked as a "teaching class awaiting enrollment" and will not be included in the scheduling process for the time being.
[0067] The merged data serves as the input for the timetable generation agent.
[0068] From the merged data above, the following information is obtained for the second semester of the 2025-2026 academic year (XN='2025-2026', XQ='2'): Number of classes: 138 (from jskcap) Number of teachers: 210 (from ZGH in zzjs and jskcap) Number of classrooms: Extracted from the JSBH field of jskcap and deduplicated, a total of 45 classrooms (e.g., 2903A4002X represents Comprehensive Teaching A402). Student course selection records: 3450 records for this semester were filtered from xsxk.
[0069] Constraint rules are loaded from a knowledge base, for example: Hard constraint: The same teacher cannot teach two courses at the same time (based on the fact that XQJ, QSXJ, and ZZXJ of the same ZGH in jskcap cannot overlap).
[0070] Soft constraint: Teachers prefer to schedule classes mainly in the morning.
[0071] Finally, using the OR-Tools CP-SAT solver, we define the variable x[c,t,r] to represent that class c is assigned to classroom r in time slice t. The time slice granularity is 7 days a week × 5 periods per day (periods 1-5), for a total of 35 time slices.
[0072] Hard constraint coding: Teachers do not repeat classes: For each teacher tch, in any time slice t, there can be at most one teaching class.
[0073] Classrooms do not overlap: For each classroom (room), at most one teaching class can be taught in any given time period (t).
[0074] The weekly class hours are fixed: each class has QSZ~JSZ (start week - end week) and SKCD (teaching length) in jskcap, and the total class hours must be arranged.
[0075] After the solver ran for 8 minutes, it returned a timetable with no hard constraint conflicts and a teacher preference satisfaction rate of 91%.
[0076] Academic staff viewed the generated timetable through a web interface and discovered that a certain teacher had been scheduled to teach courses across campuses on the same half-day. They manually rescheduled one of the courses to a different time. This adjustment record was stored in the knowledge base as a "preference case" for future learning of scheduling weights.
[0077] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the automatic timetable generation method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.
[0078] This application also provides an automatic timetable generation device; please refer to [reference needed]. Figure 2 The automatic timetable generation device includes: The request module 10 obtains the timetable generation request through the coordinating agent; Adaptation module 20, based on the timetable generation request, calls the perception and adaptation intelligent agent to determine the data source connection information of the target academic affairs system, obtains the data required for timetable generation from the target academic affairs system, and loads the scheduling constraint rules from the knowledge base; The timetable generation module 30 integrates the scheduling constraint rules and the data required for timetable generation through a coordinating agent, and then calls the timetable generation agent to generate the target timetable based on the constraint satisfaction problem solving algorithm.
[0079] The automatic timetable generation device provided in this application, employing the automatic timetable generation method in the above embodiments, can solve the technical problem of poor fairness in automatic timetable generation in related technologies. Compared with related technologies, the beneficial effects of the automatic timetable generation device provided in this application are the same as those of the automatic timetable generation method provided in the above embodiments, and other technical features in the automatic timetable generation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.
[0080] This application provides an automatic timetable generation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the automatic timetable generation method in the above embodiments.
[0081] The following is for reference. Figure 3 The diagram illustrates a structural schematic suitable for implementing the automatic timetable generation device of the embodiments of this application. The automatic timetable generation device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The timetable automatic generation device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.
[0082] like Figure 3As shown, the timetable automatic generation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 (ROM) or a program loaded from a storage device 1003 into a random access memory 1004 (RAM). The random access memory 1004 also stores various programs and data required for the operation of the timetable automatic generation device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 (I / O interface) is also connected to the bus 1005. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the timetable generator to communicate wirelessly or wiredly with other devices to exchange data. Although a timetable generator with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.
[0083] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.
[0084] The timetable automatic generation device provided in this application, employing the timetable automatic generation method in the above embodiments, can solve the technical problem of poor fairness in automatic timetable generation in related technologies. Compared with related technologies, the beneficial effects of the timetable automatic generation device provided in this application are the same as those of the timetable automatic generation method provided in the above embodiments, and other technical features in this timetable automatic generation device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.
[0085] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.
[0086] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0087] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the timetable automatic generation method in the above embodiments.
[0088] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.
[0089] The aforementioned computer-readable storage medium may be included in the timetable automatic generation device; or it may exist independently and not be assembled into the timetable automatic generation device.
[0090] Computer program code for performing the operations of this application can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, and conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0092] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.
[0093] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described automatic timetable generation method, which can solve the technical problem of poor fairness in automatic timetable generation in related technologies. Compared with related technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the automatic timetable generation method provided in the above embodiments, and will not be repeated here.
[0094] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described automatic timetable generation method.
[0095] The computer program product provided in this application can solve the technical problem of poor fairness in the automatic timetable generation in related technologies. Compared with related technologies, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the automatic timetable generation method provided in the above embodiments, and will not be repeated here.
[0096] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.
Claims
1. A method for automatically generating a schedule, characterized by, The method includes: The timetable generation request is obtained by coordinating the intelligent agent; Based on the timetable generation request, the perceptual adaptation agent is invoked to determine the data source connection information of the target academic affairs system, obtain the data required for timetable generation from the target academic affairs system, and load the scheduling constraint rules from the knowledge base. After the coordinating agent integrates the scheduling constraints and the data required for timetable generation, it calls the timetable generation agent to generate the target timetable based on the constraint satisfaction problem solving algorithm.
2. The method of automatically generating a schedule of claim 1, wherein, The step of determining the access address or data source connection information of the target academic affairs system based on the timetable generation request includes: The sensory adaptation agent receives a list of access addresses of the target academic affairs system and identifies the system type and interface characteristics by probing requests. If the identification is successful, the pre-stored mapping rules in the knowledge base are loaded and data extraction is performed; If identification fails or the data mapping extraction count follows a preset rule, then: The timetable-related pages or data interfaces of the target academic affairs system are traversed to collect field samples and their context information. The field samples are semantically parsed based on a large language model, and the semantics of the source fields are mapped to a predefined unified intermediate data model. Field mapping scripts are automatically generated based on mapping relationships, and these field mapping scripts are stored in the knowledge base as mapping rules. Return to the pre-stored mapping rules in the loaded knowledge base and perform data extraction.
3. The automatic timetable generation method as described in claim 2, characterized in that, The steps of loading pre-stored mapping rules from the knowledge base and performing data extraction include: In response to the data structure required for generating the timetable, the coordinating agent adds corresponding data sources; each data source corresponds to a data structure. The data source is assigned to the perceptual adaptive agent by performing the following steps: For each data source, the agent extracts necessary fields through querying, combines them with pre-stored mapping rules, and performs field mapping fusion processing to complete data extraction.
4. The automatic timetable generation method as described in claim 2, characterized in that, The step of automatically generating a field mapping script based on the mapping relationship and storing the field mapping script as a mapping rule in the knowledge base includes: The automatically generated data extraction script is subjected to sandbox verification. Field coverage and mapping accuracy are calculated by replaying historical data or testing sample data. When the preset verification conditions are met, the data extraction script is stored as a mapping rule in the shared knowledge base and managed in a versioned manner.
5. The automatic timetable generation method as described in claim 1, characterized in that, The constraint rules include hard constraint rules and soft constraint rules. The step of integrating the scheduling constraint rules and the data required for timetable generation through the coordinating agent, and then calling the timetable generation agent to generate the target timetable based on the constraint satisfaction problem solving algorithm includes: The system generates an initial feasible solution by using a greedy algorithm to sort courses by their scarcity based on the timetable generation intelligence. The initial solution was optimized using constraint propagation and local search to obtain multiple candidate timetables; The candidate timetables are scored using multiple objectives, and the timetable with the highest comprehensive score is selected as the output to obtain the target timetable.
6. A timetable automatic generation device, characterized in that, The device includes: The request acquisition module obtains the timetable generation request through the coordinating agent; The adaptation module, based on the timetable generation request, calls the perception and adaptation agent to determine the data source connection information of the target academic affairs system, obtains the data required for timetable generation from the target academic affairs system, and loads the scheduling constraint rules from the knowledge base; The timetable generation module integrates the scheduling constraints and the data required for timetable generation through the coordinating agent, and then calls the timetable generation agent to generate the target timetable based on the constraint satisfaction problem solving algorithm.
7. A timetable automatic generation device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the automatic timetable generation method as described in any one of claims 1 to 5.
8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the automatic timetable generation method as described in any one of claims 1 to 5.
9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the automatic timetable generation method as described in any one of claims 1 to 5.