An intelligent course scheduling method and device based on big data technology

Abstract

The present invention relates to the field of educational technology and provides an intelligent course scheduling method and device based on big data technology, including: rounding down the course start time to generate a standardized course schedule; calculating the status information of each course node in the standardized course schedule to obtain the class scheduling node; calculating the number of classes corresponding to each course based on the course information of the class scheduling node, the target number of classes for each grade, and the course preference of each grade; splitting the class scheduling number for each course into a first class scheduling number and a second class scheduling number; after the first class scheduling number is batch-scheduled to the standardized course schedule, the second class scheduling number is batch-scheduled to the standardized course schedule. The performance of already opened courses is analyzed and evaluated through big data, and feasible course scheduling suggestions are provided based on the course performance of other centers in the same region to improve the class consumption efficiency of store members.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and in particular to an intelligent course scheduling method and device based on big data technology. Background Art

[0002] With the new semester, the issue of class scheduling arises. Class scheduling is the process of arranging the current class's courses within the class time according to certain rules in the new semester.

[0003] In traditional technology, the scheduling method includes: offline scheduling combined with parents' elective courses, which may lead to inaccurate scheduling and occupation of center resources. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes an intelligent course scheduling method and device based on big data technology.

[0005] In order to achieve the above object of the present invention, the present invention is implemented by the following technologies:

[0006] In one aspect, the present invention provides an intelligent course scheduling method based on big data technology, comprising the steps of:

[0007] Round down the class start time to generate a standardized timetable;

[0008] Calculate the status information of each course node in the standardized course schedule to obtain the course scheduling node for the closed class;

[0009] Calculate the number of scheduled classes for each course based on the course information of the scheduled node, the target number of scheduled classes for each grade, and the course preference for each grade;

[0010] Split the number of classes for each course into the first number of classes and the second number of classes;

[0011] After the first number of scheduled classes are batch-scheduled into the standardized timetable, the second number of scheduled classes are batch-scheduled into the standardized timetable.

[0012] Further preferably, before calculating the number of scheduled classes corresponding to each course based on the course information of the scheduled class node, the target number of scheduled classes for each grade, and the course preference for each grade, the method further includes the following steps:

[0013] Calculate the status information of each course scheduling node, and close the course of the corresponding course scheduling node based on the status information.

[0014] Further preferably, after calculating the status information of each scheduling node and closing the course of the corresponding scheduling node based on the status information, the method further includes the following steps:

[0015] Count the attendance information of each grade in the historical class schedule after the class is closed, and calculate the number of scheduled classes for each grade;

[0016] Calculate the number of newly scheduled classes for each grade based on the attendance information of different courses at each grade;

[0017] The target number of scheduled classes for each academic level includes the preset number of scheduled classes and the newly added number of scheduled classes.

[0018] Further preferably, before calculating the number of scheduled classes corresponding to each course based on the course information of the scheduled class node, the target number of scheduled classes for each grade, and the course preference for each grade, the method further includes the following steps:

[0019] According to the course attendance distribution of each academic level within a preset time period, the course preference of each academic level is obtained.

[0020] Further preferably, the calculating of the number of scheduled classes corresponding to each course according to the course information of the scheduling node of the closed class, the target number of scheduled classes for each grade, and the course preference for each grade comprises the steps of:

[0021] According to the distribution of course attendance within the preset time end of each academic level, the target number of scheduled classes is converted into the corresponding number of scheduled classes for each course.

[0022] Further preferably, rounding down the course start time to generate a standardized course schedule includes the following steps:

[0023] Take the preset time as a scheduling node time, round down the class start time, and generate a standardized timetable;

[0024] The preset time includes the preset class time of a course.

[0025] More preferably, the method further comprises the steps of:

[0026] Generate course priority based on the attendance information of the corresponding courses of the current idle nodes;

[0027] Schedule the courses in a cyclic manner according to the course priorities;

[0028] The currently idle nodes include nodes that match the idle classroom time, idle classroom type, and idle teacher resources of the course.

[0029] More preferably, the method further comprises the steps of:

[0030] When the required scheduling node for the course is a continuous scheduling node, determining whether the standardized timetable has a continuous scheduling node;

[0031] When the continuous course scheduling node exists, based on the attendance information of the course, the first n / 2 courses in reverse order of the course are selected for course scheduling;

[0032] Where n is the number of classes required for each course.

[0033] More preferably, the method further comprises the steps of:

[0034] Displays the historical timetable with class closing nodes and the standardized timetable with new class opening nodes.

[0035] An intelligent course scheduling device based on big data technology, comprising:

[0036] The standardization module is used to round down the course start time to generate a standardized timetable;

[0037] A first calculation module is used to calculate the status information of each course node in the standardized course schedule to obtain the course scheduling node of the closed class;

[0038] The second calculation module is used to calculate the number of scheduled classes corresponding to each course based on the course information of the scheduling node of the closed class, the target number of scheduled classes for each grade, and the course preference of each grade;

[0039] A splitting module is used to split the number of scheduled classes for each course into the first number of scheduled classes and the second number of scheduled classes;

[0040] The course scheduling module is used to schedule the second course scheduling quantity in batches to the standardized course schedule after the first course scheduling quantity is batched to the standardized course schedule.

[0041] The intelligent course scheduling method and device based on big data technology provided by the present invention has at least one of the following beneficial effects:

[0042] 1) The present invention directly inputs database data into the standardized timetable, and batch calculation of big data is more efficient than conventional program calculations, and more accurate and convenient than manually extracted data.

[0043] 2) The present invention achieves standardized input and output, making it easier to understand and troubleshoot problems (data visualization).

[0044] 3) In the present invention, when scheduling classes based on big data technology, more reasonable data results can be obtained by referring to the data performance of other centers in the same region. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The preferred embodiments will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present invention.

[0046] Figure 1This is a flow chart of an embodiment of an intelligent course scheduling method based on big data technology in the present invention;

[0047] Figure 2 This is the intended meaning of the present invention;

[0048] Figure 3 This is a flow chart of another embodiment of an intelligent course scheduling method based on big data technology in the present invention;

[0049] Figure 4 It is a structural diagram of an embodiment of an intelligent class scheduling device based on big data technology in the present invention. DETAILED DESCRIPTION

[0050] In the following description, specific details such as specific system structures and technologies are provided for illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present application with unnecessary details.

[0051] It will be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections.

[0052] To simplify the drawings, only the parts relevant to the present invention are schematically shown in each figure. They do not represent the actual structure of the product. Furthermore, to simplify the drawings and facilitate understanding, in some figures, only one of the components with the same structure or function is schematically depicted or labeled. As used herein, "one" not only means "only one" but also "more than one."

[0053] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0054] In addition, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific embodiments of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings and other embodiments can be obtained based on these drawings without inventive work.

[0056] Example 1

[0057] The present invention provides an embodiment of an intelligent course scheduling method based on big data technology. Figure 1 As shown, the steps include:

[0058] S100 rounds down the course start time to generate a standardized timetable.

[0059] Specifically, class start times are rounded down to form a standardized timetable. For example, class start times are standardized to every 45 minutes to avoid overlapping classes that occupy the center's capacity (the center's capacity includes classrooms and teachers) due to non-standardized times.

[0060] S200 calculates the status information of each course node in the standardized course timetable to obtain the course scheduling node for the closed class.

[0061] For example, after the timetable is standardized, the timetable includes scheduled courses and free timetables; each box on the timetable is a course node.

[0062] Specifically, the performance of each schedule node (including the following elements: classroom, day of the week, time, and course name) in recent weeks is calculated. Nodes with less than 4 attendees for three consecutive weeks are selected, and the course information in the nodes is cleared to close the courses.

[0063] S300 calculates the number of scheduled classes corresponding to each course based on the course information of the scheduled node of the class, the target number of scheduled classes for each grade, and the course preference for each grade.

[0064] Specifically, after closing the course, calculate the number of classes required to achieve the set target frequency.

[0065] Among them, if the number of attendees in the last week exceeded 8, it will be calculated based on the actual number of attendees. If it is less than 8, it will be calculated as if it could reach 8. In this case, the additional number of classes required for each academic level will be calculated.

[0066] In addition, the attendance distribution of courses for each grade in the computing center in the last three weeks is used as course preference. According to the course distribution ratio, the number of courses required for the grade is converted into the number of classes required for each course.

[0067] S400 divides the number of scheduled classes for each course into the first number of scheduled classes and the second number of scheduled classes.

[0068] S500 After the first number of scheduled classes are batch-scheduled into the standardized timetable, the second number of scheduled classes are batch-scheduled into the standardized timetable.

[0069] Specifically, if the courses are filled directly, it will be impossible to determine the priority of each course, as the priorities are essentially equal. The logic of splitting the courses into upper and lower levels in this embodiment solves the above problem.

[0070] In this embodiment, the schedule has many blank spaces. The number of scheduled classes is converted to blank spaces for batch processing. The number of classes to be offered is split in half, with the upper and lower halves. If the number is an odd number, the upper half has one less class than the lower half. The upper half of the schedule is scheduled first, starting with the class with the smallest number of courses. The lower half of the schedule is then scheduled, also starting with the class with the smallest number of courses. This rule is used to set the order for each class schedule.

[0071] In this embodiment, the present invention directly inputs database data into a standardized curriculum schedule, and batch calculations of big data are more efficient than conventional program operations and more accurate and convenient than manually excerpted data. The present invention achieves standardized input and output, making it easier to understand and troubleshoot problems (data visualization). At the same time, when scheduling courses based on big data technology, referring to the data performance of other centers in the same region can produce more reasonable data results.

[0072] Example 2

[0073] Based on the above embodiment, the parts in this embodiment that are the same as those in the above embodiment will not be repeated one by one. This embodiment provides an intelligent class scheduling method based on big data technology, such as Figure 3 As shown, specifically including:

[0074] Before calculating the number of scheduled classes corresponding to each course based on the course information of the scheduled class node, the target number of scheduled classes for each grade, and the course preference for each grade in step S300, the following steps are further included:

[0075] According to the course attendance distribution of each academic level within a preset time period, the course preference of each academic level is obtained.

[0076] Before calculating the number of scheduled classes corresponding to each course based on the course information of the scheduled class node, the target number of scheduled classes for each grade, and the course preference for each grade in step S300, the following steps are further included:

[0077] Calculate the status information of each course scheduling node, and close the course of the corresponding course scheduling node based on the status information.

[0078] After calculating the status information of each course scheduling node and closing the course of the corresponding course scheduling node based on the status information, the method further includes the following steps:

[0079] The attendance information of each grade in the historical class schedule after the class is closed is counted to calculate the preset number of scheduled classes for each grade; the number of newly scheduled classes for each grade is calculated based on the attendance information of different courses in each grade; wherein the target number of scheduled classes for each grade includes the preset number of scheduled classes and the newly scheduled number of scheduled classes.

[0080] The method of calculating the number of scheduled classes corresponding to each course based on the course information of the scheduled class node, the target number of scheduled classes for each grade, and the course preference for each grade includes the following steps:

[0081] According to the distribution of course attendance within the preset time end of each academic level, the target number of scheduled classes is converted into the corresponding number of scheduled classes for each course.

[0082] The process of rounding down the class start time to generate a standardized class schedule includes the following steps:

[0083] The preset time is used as a scheduling node time, and the class start time is rounded down to generate a standardized class timetable; wherein the preset time includes the preset class time of a course.

[0084] In this embodiment, the steps are further included: generating a course priority based on the attendance information of the course corresponding to the current idle node; and cyclically scheduling the course according to the course priority; wherein the current idle node includes a node that meets the idle classroom time, idle classroom type and idle teacher resources of the course.

[0085] Among them, the attendance information of the courses corresponding to the idle nodes is: the attendance performance data of other central stores in the same province at the same time node.

[0086] In this embodiment, the following steps are further included: when the required scheduling node for the course is a continuous scheduling node, determining whether the standardized timetable has a continuous scheduling node; when the continuous scheduling node exists, taking the first n / 2 courses in reverse order of the course based on the attendance information of the course for scheduling; wherein n is the number of scheduled courses required for each course.

[0087] In this embodiment, the step of displaying the historical timetable with the closing nodes marked and the standardized timetable with the newly added opening nodes marked is also included.

[0088] For example, in this embodiment, in order to provide recommendations for offline course openings based on the store's recent performance data, this embodiment uses big data to analyze and evaluate the performance of existing courses, and provides feasible course scheduling recommendations based on the performance of courses in other centers in the same region.

[0089] For example: Through big data analysis, valuable suggestions are provided for the class schedule of the central store, such as which poorly performing courses need to be closed and when a new course should be scheduled, so as to improve the class consumption efficiency of store members.

[0090] The first step is to count the attendance of the center's previous class schedules, analyze the health of the center's class schedule, and determine the number of classes required for each academic level.

[0091] The second step is to standardize the class timetable and standardize the course start time to a time point every 45 minutes to avoid overlapping of courses occupying the center's capacity (the center's capacity includes classrooms and teachers) due to non-standard time.

[0092] The third step is to calculate the performance of each schedule node (including the following elements: classroom, day of the week, time, and course name) in recent weeks. Nodes with less than 4 attendees for three consecutive weeks are selected, and the course information in the nodes is cleared to close the courses.

[0093] The fourth step is to calculate the number of classes required to achieve the set target after closing the course.

[0094] If the number of attendees in the last week exceeded 8, the actual number of attendees will be used for calculation. If the number of attendees is less than 8, it will be calculated as if the number could reach 8. In this case, the number of additional classes required for each academic level will be calculated.

[0095] In addition, the computer center calculates the course attendance distribution of each academic level in the past three weeks as course preference. According to the course distribution ratio, the number of courses required for the academic level is converted into the number of classes required for each course.

[0096] The fifth step is to split the number of classes into two parts, upper and lower. If it is an odd number, the upper half will have one less class than the lower half.

[0097] For example, the courses for the first half of the class are scheduled first, starting with the courses with the least number of courses, and then the courses for the second half of the class are scheduled, also starting with the courses with the least number of courses. This rule is used to set the order of each class schedule.

[0098] In addition, classes are opened in a cycle in sequence. The number of attendees of the courses in other centers in the province where the reference center is located is combined with the current idle nodes of the center (the classroom meets the course requirements, the classroom is idle at that time, and there are idle teachers at that time). The idle nodes are sorted in reverse order according to the number of attendees and the top n (the top 3 with the largest number of people) are taken. n is the number of classes required for the course to be opened in a cycle.

[0099] like Figure 2As shown, if you encounter PSS, A3, or other courses that require two consecutive sessions, you need to additionally determine whether there are any free time nodes before and after the idle node, and select consecutive free nodes. At the same time, in order to avoid conflicts, you need to select a time every 90 minutes, and finally take the first n / 2 bits in reverse order.

[0100] Finally, the overall data table of the center's class schedule is presented, and the recommended new class schedule and the old class schedule are presented. The closing nodes of the classes are marked on the old class schedule, and the newly added opening nodes of the classes are marked on the new class schedule.

[0101] In this embodiment, the present invention directly inputs database data into a standardized curriculum schedule, and batch calculations of big data are more efficient than conventional program operations and more accurate and convenient than manually excerpted data. The present invention achieves standardized input and output, making it easier to understand and troubleshoot problems (data visualization). At the same time, when scheduling courses based on big data technology, referring to the data performance of other centers in the same region can produce more reasonable data results.

[0102] Example 3

[0103] Based on the above embodiment, the parts in this embodiment that are the same as those in the above embodiment will not be repeated one by one. This embodiment provides an intelligent class scheduling device based on big data technology, such as Figure 4 Shown, including:

[0104] The standardization module 301 is used to round down the course start time to generate a standardized course timetable.

[0105] The first calculation module 302 is used to calculate the status information of each course node in the standardized course schedule to obtain the course scheduling node of the closed class.

[0106] The second calculation module 303 is used to calculate the number of scheduled classes corresponding to each course based on the course information of the scheduling node of the closed class, the target number of scheduled classes for each grade, and the course preference of each grade.

[0107] The splitting module 304 is used to split the number of scheduled classes for each course into a first number of scheduled classes and a second number of scheduled classes.

[0108] The course scheduling module 305 is configured to batch schedule the second course scheduling quantity into the standardized course schedule after the first course scheduling quantity has been batch scheduled into the standardized course schedule.

[0109] The device provided in this embodiment can execute any of the methods of embodiment one and embodiment two. In this embodiment, the standardized curriculum schedule is directly input into the database data through the present invention, and the batch calculation of big data is more efficient than conventional program operations, and more accurate and convenient than manually excerpted data. The present invention realizes standardized input and output, making it easier to understand and troubleshoot problems (data visualization). At the same time, when scheduling courses based on big data technology, referring to the data performance of other centers in the same region, more reasonable data results can be obtained.

[0110] The system closes some inappropriate classes based on historical scheduling performance data, and then recommends better scheduling plans based on the current available time nodes, classrooms, teachers and other resources, and data performance of other centers to improve class efficiency.

[0111] The existing class scheduling plan of the offline center needs to improve the efficiency of users' class consumption. Only by improving the efficiency of class consumption can we increase renewal and expansion of new students, thereby enhancing the profitability of the center.

[0112] In order to help the center improve its class efficiency, we identify poorly performing class scheduling nodes and shut them down. We also combine membership size, preferences, data from other centers, the center's own transportation capacity, etc. to provide a better class scheduling plan, thereby improving the center's class efficiency.

[0113] Those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, only the division of the above-mentioned program modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program units or modules to complete all or part of the functions described above. The program modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one processing unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software program unit. In addition, the specific names of the program modules are only for the purpose of distinguishing each other and are not used to limit the scope of protection of this application.

[0114] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0115] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. Exemplarily, the device embodiments described above are merely schematic. Exemplarily, the division of the modules or units is merely a logical function division. There may be other division methods in actual implementation. Exemplarily, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0117] The units described as separate components may or may not be physically separate, and the components shown as units 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0118] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0119] It should be noted that the above embodiments can be freely combined as needed. The above description is only a preferred embodiment of the present invention. It should be pointed out that those skilled in the art can make several improvements and modifications without departing from the principles of the present invention, and such improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An intelligent course scheduling method based on big data technology, characterized in that: Including steps: Round down the class start time to generate a standardized timetable; Calculate the status information of each course node in the standardized course schedule to obtain the class scheduling node, wherein the status information of the course node is determined based on the number of attendees within a preset time period; Calculating the number of scheduled courses corresponding to each course based on the course information of the class closing node, the target number of scheduled courses for each grade, and the course preference of each grade, including: counting the grade attendance information in the historical course schedule after the class closing to calculate the preset number of scheduled courses for each grade; calculating the number of newly scheduled courses for each grade based on the attendance information of different courses for each grade; wherein the target number of scheduled courses for each grade includes the preset number of scheduled courses and the newly scheduled number of scheduled courses; obtaining the course preference of each grade based on the attendance distribution of courses for each grade within a preset time period; and converting the target number of scheduled courses into the number of scheduled courses corresponding to each course according to the course distribution ratio; Splitting the number of scheduled classes corresponding to each course into a first number of scheduled classes and a second number of scheduled classes; After the first number of scheduled classes are batch-scheduled into the standardized timetable, the second number of scheduled classes are batch-scheduled into the standardized timetable.

2. The intelligent course scheduling method according to claim 1, characterized in that: Before calculating the number of scheduled classes corresponding to each course based on the course information of the class scheduling node, the target number of scheduled classes for each grade, and the course preference for each grade, the method further includes the following steps: Calculate the status information of each course scheduling node, and close the course of the corresponding course scheduling node based on the status information.

3. The intelligent course scheduling method according to claim 1 or 2, characterized in that: The process of rounding down the class start time to generate a standardized class schedule includes the following steps: Take the preset time as a scheduling node time, round down the class start time, and generate a standardized timetable; The preset time includes the preset class time of a course.

4. The intelligent course scheduling method according to claim 3, characterized in that: Also includes the steps: Generate course priority based on the attendance information of the corresponding courses of the current idle nodes; Schedule the courses in a cyclic manner according to the course priorities; The currently idle nodes include nodes that match the idle classroom time, idle classroom type, and idle teacher resources of the course.

5. The intelligent course scheduling method according to claim 4, characterized in that: Also includes the steps: When the required scheduling node for the course is a continuous scheduling node, determining whether the standardized timetable has a continuous scheduling node; When the continuous course scheduling node exists, based on the attendance information of the course, the first n / 2 courses in reverse order of the course are selected for course scheduling; Where n is the number of classes required for each course.

6. The intelligent course scheduling method according to claim 5, characterized in that: Also includes the steps: Displays the historical timetable with class closing nodes and the standardized timetable with new class opening nodes.

7. An intelligent course scheduling device based on big data technology, characterized in that: include: The standardization module is used to round down the course start time to generate a standardized timetable; A first calculation module is configured to calculate status information of each course node in the standardized course schedule to obtain a course scheduling node for a closed class, wherein the status information of the course node is determined based on the number of attendees within a preset time period; The second calculation module is used to calculate the number of scheduled courses corresponding to each course based on the course information of the class closing node, the target number of scheduled courses for each grade, and the course preference of each grade, including: counting the grade attendance information in the historical course schedule after the class closing to calculate the preset number of scheduled courses for each grade; calculating the number of newly scheduled courses for each grade based on the attendance information of different courses in each grade; wherein the target number of scheduled courses for each grade includes the preset number of scheduled courses and the newly scheduled number of scheduled courses; obtaining the course preference of each grade based on the attendance course distribution of each grade within a preset time period; and converting the target number of scheduled courses into the number of scheduled courses corresponding to each course according to the course distribution ratio; A splitting module, configured to split the number of scheduled classes corresponding to each course into a first number of scheduled classes and a second number of scheduled classes; The course scheduling module is used to schedule the second course scheduling quantity in batches to the standardized course schedule after the first course scheduling quantity is batched to the standardized course schedule.

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