CN116843525A - Intelligent automatic course arrangement method, system, equipment and storage medium - Google Patents
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Abstract
The application discloses an intelligent automatic course arranging method, a system, equipment and a storage medium, wherein the method comprises the steps of distributing a plurality of first templates to a plurality of computers, and randomly designating classrooms and time for each first template to obtain a plurality of second templates; carrying out hard constraint condition inspection on each piece of to-be-class data in the plurality of second templates, and calculating a satisfaction rate of each piece of to-be-class data meeting the hard constraint condition by adopting a soft constraint mathematical model to obtain a plurality of satisfaction rate values corresponding to each piece of to-be-class data; selecting a highest satisfaction rate value from the plurality of satisfaction rate values, marking the successful course arrangement mark on the course arrangement data corresponding to the highest satisfaction rate value which is larger than or equal to the expected satisfaction rate value, and deleting the course arrangement data which are marked with the successful course arrangement mark in the course arrangement data set; and adopting the data set to be processed after deleting the data set to be processed after the successful processing mark is processed in the data set to be processed to process the next iteration. The application can improve the course arrangement efficiency and quality.
Description
Technical Field
The application relates to the technical field of automatic course arrangement, in particular to an intelligent automatic course arrangement method, system, equipment and storage medium.
Background
Along with the continuous expansion of universities and colleges, the requirement on hardware resources of the universities and colleges is higher, meanwhile, in order to improve the teaching quality, the satisfaction degree of teachers and students on educational administration work is met, the course arrangement work becomes increasingly heavy, and the course arrangement difficulty is increased. Industry proposes various types of automatic course arrangement methods to improve the course arrangement efficiency and quality. For example, conventional automatic course arrangement algorithms have the following drawbacks: firstly, iteration is not provided for a random specific number of course arrangement results from the parallel angle; secondly, the expected parameters are not configured on a plurality of dimensions of soft constraint, and the expected parameter setting capability of user intervention is not provided; thirdly, after a plurality of iterations, records which are not exhausted are processed. Therefore, the efficiency and quality of course arrangement are poor.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the application provides an intelligent automatic course arrangement method, system, equipment and storage medium, which can improve the course arrangement efficiency and quality.
In a first aspect, an embodiment of the present application provides an intelligent automatic course arrangement method, where the intelligent automatic course arrangement method includes:
acquiring a to-be-class-arranged data set, and presetting a plurality of first templates containing the to-be-class-arranged data set;
distributing the plurality of first templates to a plurality of computers, and randomly designating classrooms and time for each first template to obtain a plurality of second templates;
carrying out hard constraint condition inspection on each piece of to-be-class data in the plurality of second templates, and calculating a satisfaction rate of each piece of to-be-class data meeting the hard constraint condition by adopting a soft constraint mathematical model to obtain a plurality of satisfaction rate values corresponding to each piece of to-be-class data;
selecting a highest satisfaction rate value from the plurality of satisfaction rate values, marking the to-be-processed course data corresponding to the highest satisfaction rate value which is larger than or equal to the expected satisfaction rate, and deleting the to-be-processed course data marked with the to-be-processed course marking in the to-be-processed course data set;
and carrying out the next iteration by adopting the to-be-processed course data set after deleting the to-be-processed course data with the successful course processing mark in the to-be-processed course data set, and completing course processing if the iteration times reach the preset convergence parameters and all to-be-processed course processing data are successfully processed.
Compared with the prior art, the first aspect of the application has the following beneficial effects:
according to the method, firstly, hard constraint condition examination is carried out on each piece of to-be-class-arranged data in a plurality of second templates, so that class arrangement conflicts can be reduced; then, calculating the satisfaction rate of each piece of to-be-processed class data meeting the hard constraint condition by adopting a soft constraint mathematical model to obtain a plurality of satisfaction rate values corresponding to each piece of to-be-processed class data, selecting a highest satisfaction rate value from the plurality of satisfaction rate values, and selecting the highest satisfaction rate value to improve class processing quality; the method can improve the course arrangement efficiency through simultaneous iteration of a plurality of computers.
According to some embodiments of the application, assigning the plurality of first templates to a plurality of computers comprises:
the number of the first templates is compared with the number of the computers, and quotient and remainder are obtained;
and distributing the first templates corresponding to the quotient to each computer, and distributing the first templates corresponding to the remainder to the last computer.
According to some embodiments of the application, the hard constraint is that the class, teacher, course, and location do not conflict at the same time.
According to some embodiments of the application, the soft constraint mathematical model is constructed by:
wherein ,representing each piece of data to be class>Satisfaction rate of->、/>、/>、/> and />Representing weight parameters->Representing the actual number of students, and->Indicating the number of people accommodated in classroom and->Indicating a value selected when the number of class weekly courses meets a first preset value,/->Indicating a selected value when the number of course arrangement per week of the teacher satisfies a second preset value,/->Indicating whenSelecting a value when the number of course arrangement per week meets a third preset value, and +.>And (3) representing a value selected when the average number of course arrangement per day of time per week meets a fourth preset value.
According to some embodiments of the application, when each piece of data to be lesson-lined in the plurality of second templates is subjected to hard constraint checking, the intelligent automatic lesson-lined method further comprises:
and if the to-be-class-arranged data in the second templates do not meet the hard constraint condition, setting the meeting rate corresponding to the to-be-class-arranged data to zero and carrying out class-arrangement conflict record.
According to some embodiments of the application, after selecting a satisfaction rate highest value from the plurality of satisfaction rate values, the intelligent automatic course arrangement method further comprises:
if the highest satisfaction rate value selected from the plurality of satisfaction rate values is smaller than the expected satisfaction rate, marking the to-be-processed lesson data corresponding to the highest satisfaction rate value smaller than the expected satisfaction rate with a non-processed lesson mark.
According to some embodiments of the application, when the iteration number reaches a preset convergence parameter, the intelligent automatic course arrangement method further includes:
when the iteration times reach preset convergence parameters, checking whether there is data to be lessons to be lessoned which are recorded without lessons or lesson conflict;
and if the data to be processed with the non-processed or processed conflict records exist, processing the data to be processed with the non-processed or processed conflict records by adopting a greedy algorithm.
In a second aspect, the embodiment of the application further provides an intelligent automatic course arrangement system, which comprises:
the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring a to-be-processed course data set and presetting a plurality of first templates containing the to-be-processed course data set;
the template distribution unit is used for distributing the plurality of first templates to a plurality of computers, and randomly designating classrooms and time for each first template to obtain a plurality of second templates;
the constraint checking unit is used for checking the hard constraint condition of each piece of to-be-class data in the plurality of second templates, calculating the satisfaction rate of each piece of to-be-class data meeting the hard constraint condition by adopting a soft constraint mathematical model, and obtaining a plurality of satisfaction rate values corresponding to each piece of to-be-class data;
the data course arrangement unit is used for selecting a highest satisfaction rate value from the plurality of satisfaction rate values, marking the course arrangement data corresponding to the highest satisfaction rate value which is larger than or equal to the expected satisfaction rate, and deleting the course arrangement data marked with the successful course arrangement mark in the course arrangement data set;
and the course arrangement completion unit is used for carrying out the next iteration by adopting the course arrangement data set after deleting the course arrangement data with the successful course arrangement mark in the course arrangement data set, and completing course arrangement if the iteration number reaches the preset convergence parameter and all the course arrangement data are successful.
In a third aspect, the embodiment of the present application further provides an intelligent automatic course arrangement device, including at least one control processor and a memory for communication connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform an intelligent automatic course arrangement method as described above.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform an intelligent automatic course arrangement method as described above.
It is to be understood that the advantages of the second to fourth aspects compared with the related art are the same as those of the first aspect compared with the related art, and reference may be made to the related description in the first aspect, which is not repeated herein.
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The foregoing and/or additional aspects and advantages of the application will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of an intelligent automatic course arrangement method according to an embodiment of the application;
FIG. 2 is a schematic diagram illustrating steps of an intelligent automatic course arrangement method according to another embodiment of the present application;
FIG. 3 is a block diagram of an intelligent automatic course arrangement system according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application.
In the description of the present application, the description of first, second, etc. is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present application, it should be understood that the direction or positional relationship indicated with respect to the description of the orientation, such as up, down, etc., is based on the direction or positional relationship shown in the drawings, is merely for convenience of describing the present application and simplifying the description, and does not indicate or imply that the apparatus or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application.
In the description of the present application, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present application can be determined reasonably by a person skilled in the art in combination with the specific content of the technical solution.
As the scale of universities is continuously enlarged, the requirements on hardware resources of the schools are higher, and meanwhile, in order to improve the teaching quality, the satisfaction degree of teachers and students on educational administration work is met, the course arrangement work becomes increasingly heavy, and the course arrangement difficulty is increased. Industry proposes various types of automatic course arrangement methods to improve the course arrangement efficiency and quality. For example, conventional automatic course arrangement algorithms have the following drawbacks: firstly, iteration is not provided for a random specific number of course arrangement results from the parallel angle; secondly, the expected parameters are not configured on a plurality of dimensions of soft constraint, and the expected parameter setting capability of user intervention is not provided; thirdly, after a plurality of iterations, records which are not exhausted are processed. Therefore, the efficiency and quality of course arrangement are poor.
In order to solve the problems, the application firstly carries out hard constraint condition inspection on each piece of data to be class arranged in a plurality of second templates, so that class arrangement conflict can be reduced; then, calculating the satisfaction rate of each piece of to-be-processed class data meeting the hard constraint condition by adopting a soft constraint mathematical model to obtain a plurality of satisfaction rate values corresponding to each piece of to-be-processed class data, selecting a highest satisfaction rate value from the plurality of satisfaction rate values, and selecting the highest satisfaction rate value to improve class processing quality; the method can improve the course arrangement efficiency through simultaneous iteration of a plurality of computers.
Before proceeding to further detailed description of the disclosed embodiments, the terms and terms involved in the disclosed embodiments are described, which are applicable to the following explanation:
greedy algorithm, which is a simpler, faster design technique for solving some of the problems of optimizing. The greedy algorithm is characterized in that the greedy algorithm is performed step by step, and is usually used for carrying out optimal selection according to a certain optimization measure based on the current situation, various possible overall situations are not considered, and a great amount of time which is needed to be consumed for finding the optimal solution and exhausting all possible situations is saved. The greedy algorithm adopts a top-down method to make successive greedy selections in an iterative manner, and each time greedy selection is made, the required problem is reduced to a sub-problem with smaller scale, and an optimal solution of the problem can be obtained through greedy selection of each step.
Referring to fig. 1, an embodiment of the present application provides an intelligent automatic course arrangement method, which includes, but is not limited to, steps S100 to S500, wherein:
step S100, acquiring a to-be-class-arranged data set, and presetting a plurality of first templates containing the to-be-class-arranged data set;
step S200, distributing a plurality of first templates to a plurality of computers, and randomly designating classrooms and time for each first template to obtain a plurality of second templates;
step S300, carrying out hard constraint condition inspection on each piece of to-be-class data in a plurality of second templates, and calculating a satisfaction rate of each piece of to-be-class data meeting the hard constraint condition by adopting a soft constraint mathematical model to obtain a plurality of satisfaction rate values corresponding to each piece of to-be-class data;
step S400, selecting a highest satisfaction rate value from a plurality of satisfaction rate values, marking the successful course arrangement mark on the course arrangement data corresponding to the highest satisfaction rate value which is greater than or equal to the expected satisfaction rate, and deleting the course arrangement data which are marked with the successful course arrangement mark in the course arrangement data set;
and S500, performing the next iteration by adopting the to-be-processed course data set after deleting the to-be-processed course data set with the successful course processing mark in the to-be-processed course data set, and completing course processing if the iteration number reaches the preset convergence parameter and all the to-be-processed course processing data are successfully processed.
In this embodiment, in order to improve the course arrangement efficiency, the present embodiment presets a plurality of first templates including a to-be-arranged course data set by acquiring the to-be-arranged course data set, distributes the plurality of first templates to a plurality of computers, randomly designates classrooms and time for each first template to obtain a plurality of second templates, and can iterate the templates simultaneously through the plurality of computers; in order to reduce the course arrangement conflict, the embodiment performs hard constraint condition check on each piece of data to be course arranged in a plurality of second templates; in order to improve the course arrangement quality, the embodiment calculates the satisfaction rate by adopting a soft constraint mathematical model for each piece of course arrangement data meeting the hard constraint condition to obtain a plurality of satisfaction rate values corresponding to each piece of course arrangement data, selects the highest satisfaction rate value from the plurality of satisfaction rate values, marks the course arrangement data corresponding to the highest satisfaction rate value which is more than or equal to the expected satisfaction rate, and deletes the course arrangement data which marks the course arrangement data in the course arrangement data set; and (3) carrying out the next iteration by adopting the to-be-processed course data set after deleting the to-be-processed course data with the successful course processing mark in the to-be-processed course data set, and completing course processing if the iteration times reach the preset convergence parameters and all the to-be-processed course processing data are successfully processed.
In some embodiments, assigning the plurality of first templates to the plurality of computers comprises:
the number of the first templates is compared with the number of the computers, and quotient and remainder are obtained;
and distributing the first templates corresponding to the number of the quotient to each computer, and distributing the first templates corresponding to the number of the remainder to the last computer.
In this embodiment, by distributing a plurality of templates to a plurality of computers and performing iterative computation simultaneously by a plurality of computations, the course arrangement efficiency can be improved.
In some embodiments, the hard constraint is that no conflicts occur at the same time for a class, teacher, course, place.
In this embodiment, classes, teachers, courses, places do not collide at the same time when the classes are arranged, that is, the hard constraint condition includes that the same class cannot be used for teaching at two places at the same time, the same teacher cannot be used for teaching at two places at the same time, the same classroom cannot be used for constraining conditions for occurrence of collision, such as two different courses, etc. Therefore, these hard constraints must be met when scheduling lessons to avoid the occurrence of lesson scheduling conflicts, thereby improving the quality of lessons scheduling.
In some embodiments, the soft constraint mathematical model is constructed by:
wherein ,representing each piece of data to be class>Satisfaction rate of->、/>、/>、/> and />Representing weight parameters->Representing the actual number of students, and->Indicating the number of people accommodated in classroom and->Indicating a value selected when the number of class weekly courses meets a first preset value,/->Indicating a selected value when the number of course arrangement per week of the teacher satisfies a second preset value,/->Indicating a selected value when the number of course arrangement per week satisfies a third preset value,/->And (3) representing a value selected when the average number of course arrangement per day of time per week meets a fourth preset value.
In this embodiment, the soft constraint refers to meeting special requirements in five dimensions of class, teacher, course, time and place. The lessons are arranged by defining the soft constraint mathematical model with five dimensions, the configuration expected parameters in the five dimensions of the soft constraint are considered, and the expected parameter setting capability of user intervention is provided, so that the lesson arranging quality can be improved, the satisfaction degree of teachers and students on educational administration work is met, and meanwhile, the teaching quality is also improved.
In some embodiments, when performing hard constraint checking on each piece of data to be class-lined in the plurality of second templates, the intelligent automatic class-lined method further includes:
and if the to-be-class-arranged data in the plurality of second templates do not meet the hard constraint condition, setting the satisfaction rate corresponding to the to-be-class-arranged data to zero and carrying out class-arrangement conflict record.
In this embodiment, the satisfaction rate that does not satisfy the hard constraint condition is set to zero, and courses that do not satisfy the hard constraint condition are equivalent to the course arrangement conflict, so that the satisfaction rate does not need to be calculated, the calculated amount can be reduced, the course arrangement efficiency is improved, and the course arrangement quality is improved.
In some embodiments, after selecting the satisfaction rate highest value from the plurality of satisfaction rate values, the intelligent automatic course arrangement method further comprises:
if the highest satisfaction rate value selected from the plurality of satisfaction rate values is smaller than the expected satisfaction rate, marking the to-be-processed lesson data corresponding to the highest satisfaction rate value smaller than the expected satisfaction rate with a non-processed lesson mark.
In the embodiment, the to-be-class-arranged data smaller than the expected satisfaction rate is marked with the non-class-arranged mark, so that the class-arranging quality can be improved.
In some embodiments, when the iteration number reaches the preset convergence parameter, the intelligent automatic course arrangement method further includes:
when the iteration times reach preset convergence parameters, checking whether there is data to be lessons to be lessoned which are recorded without lessons or lesson conflict;
and if the data to be processed with the non-processed or processed conflict records exist, processing the data to be processed with the non-processed or processed conflict records by adopting a greedy algorithm.
In this embodiment, after multiple rounds of simultaneous iteration by the computer, there may be some data to be processed, which is recorded without course processing or course processing conflict, and the data to be processed is terminated by adopting a greedy algorithm, so that each greedy algorithm makes greedy selection, the required problem is simplified into a sub-problem with smaller scale, and an optimal solution of the problem can be obtained through greedy selection of each step. Therefore, the embodiment adopts the greedy algorithm to carry out ending, which not only can ensure that the remaining data to be processed with the processes of not processing the classes or having the processes of processing the classes conflict record can complete the processes of processing the classes, and the course arrangement result of the data to be arranged can be guaranteed to obtain an optimal solution, and the course arrangement quality is further improved through a greedy algorithm.
For ease of understanding by those skilled in the art, a set of preferred embodiments are provided below:
the conventional automatic course arrangement algorithm has the following disadvantages: firstly, iteration is not provided for a random specific number of course arrangement results from the parallel angle; secondly, the expected parameters are not configured on a plurality of dimensions of soft constraint, and the expected parameter setting capability of user intervention is not provided; thirdly, after a plurality of iterations, records which are not exhausted are processed. Therefore, the automatic course arrangement efficiency and quality are poor.
For the above problems, referring to fig. 2, in this embodiment, by defining a five-dimensional soft constraint mathematical model and adopting a convergeable greedy algorithm, firstly, satisfaction of soft constraint is improved, secondly, time efficiency of the algorithm is improved, and thirdly, by matching with the greedy algorithm, ending is performed, and finally, quality requirements of automatic course arrangement are improved. The method comprises the following specific steps:
1. initializing parameters.
And acquiring a class waiting data set S, wherein the record in each piece of class waiting data in the class waiting data set S comprises class, teacher, course and other attributes. Defining a first template number M of the to-be-duplicated class data set S (namely, duplicating M to-be-duplicated class data sets S), wherein M is set as a default value 20; defining the number C of available computers, wherein the C is set as a default value 4; a preset convergence parameter N, N is defined and a default value 50 is set.
The soft constraint mathematical model has five weight parameters、/>、/>、/> and />The sum of the five weight parameters is 1, default values of the five weight parameters are 0.2,0.2,0.2,0.2 and 0.2 according to actual conditions, and the return value is the actual satisfaction rate. The expected satisfaction rate of the satisfaction rate is defined as R, and the default value of the expected satisfaction rate is set to 80%.
It should be noted that, the default values of the present embodiment may be changed according to actual situations, and the present embodiment is not limited specifically.
2. The random model performs random allocation.
According to the first template number M and the available computer number C, calculating the template number to be completed by each computer, placing the remainder to the last computer, then distributing instructions to each computer, completing the random assignment of classrooms and time of the data set S to be class in each template, and obtaining a plurality of second templates.
3. And constructing a hard constraint mathematical model and a soft constraint mathematical model.
And according to the plurality of second templates, checking whether each piece of the to-be-processed class data in the to-be-processed class data set S meets the hard constraint condition, and if the to-be-processed class data does not meet the hard constraint condition, setting the actual meeting rate corresponding to the to-be-processed class data to 0 and performing class-processing conflict record. And for the data to be class-arranged meeting the hard constraint condition, calculating the actual meeting rate of the data to be class-arranged according to the soft constraint mathematical model. Wherein, the hard constraint means that the class, teacher, course and place cannot conflict at the same time, the soft constraint means that the class, teacher, course, time and place have special requirements in five dimensions, and a soft constraint mathematical model is constructed by the following formula:
wherein ,representing each piece of data to be class>Satisfaction rate of->、/>、/>、/> and />Representing weight parameters->Representing the actual number of students, and->Indicating the number of people accommodated in classroom and->Representing a value selected when the number of class weekly courses meets a first preset value, in particular +.>The first preset value of (2) is [1,0.5,0 ]]The value 1 is given when the weekly course number of the class is less than 8, the value 0.5 is given when the weekly course number of the class is more than 8 and less than 16, and the value 0 is given when the weekly course number of the class is more than or equal to 16; />Indicating a selection of a pick when the number of teacher's weekly course arrangement meets a second preset valueValue, in particular, < >>The second preset value of (2) is [1,0.5,0 ]]The value 1 is given when the number of the lessons of the teacher per week is smaller than 10, the value 0.5 is given when the number of the lessons of the teacher per week is larger than 10 and smaller than 20, and the value 0 is given when the number of the lessons of the teacher per week is larger than or equal to 20; />Indicating a value selected when the number of course arrangement per week satisfies a third preset value, in particular,/->The third preset value of (2) is [1,0.5,0 ]]The method comprises the steps that 1 is taken when the number of course arrangement per week is smaller than 12, 0.5 is taken when the number of course arrangement per week is larger than 12 and smaller than 24, and 0 is taken when the number of course arrangement per week is larger than or equal to 24; />Indicating a value selected when the average number of course arrangement per day per time week satisfies a fourth preset value, in particular,/->The fourth preset value of (2) is [1,0.5,0 ]]The value 1 is given when the average number of course arrangement per day of time is equal, the value 0.5 is given when the average error of the average number of course arrangement per day of time is less than 30%, and the value 0 is given when the average error of the average number of course arrangement per day of time is greater than or equal to 30%.
And calculating the satisfaction rate of the to-be-class data meeting the hard constraint condition in each template by adopting the soft constraint mathematical model. Because each template is a copied class data set S to be arranged, each template has the class data to be arranged, each class data to be arranged corresponds to a plurality of satisfaction values, the highest satisfaction value is selected from the plurality of satisfaction values, all class data records which satisfy the hard constraint condition and have the highest satisfaction rate in all templates are combined, the class data to be arranged with the highest satisfaction value being greater than or equal to the expected satisfaction rate R is marked with a successful class arranging sign, and the class data record to be arranged is removed from the class data set S to be arranged; if the highest satisfaction rate value selected from the plurality of satisfaction rate values is smaller than the expected satisfaction rate, marking the to-be-processed lesson data corresponding to the highest satisfaction rate value smaller than the expected satisfaction rate with a non-processed lesson mark.
And continuously iterating the process, checking whether the iteration times reach the preset convergence parameter N, and if the iteration times do not reach the preset convergence parameter N, continuing to iterate simultaneously by a plurality of computers.
4. And ending the model.
When the iteration times reach preset convergence parameters, checking whether there is data to be lessons to be lessoned which are recorded without lessons or lesson conflict; and if the data to be processed with the non-processed or processed conflict records exist, adopting a greedy algorithm as a ending model to process the processed data to be processed with the non-processed or processed conflict records. Specific:
after the iteration times reach the preset convergence parameter N, checking whether the to-be-processed course data set S has to-be-processed course data records with non-processed course or processed course conflict records, and calling a greedy algorithm for the to-be-processed course data records with non-processed course or processed course conflict records to process course processing again.
5. Results are compared.
And prompting the user of successful course arrangement records and non-course arrangement records, and calculating the quality rate according to the successful course arrangement records. Wherein, the calculation formula of the mass rate is as follows:
wherein ,representing the quality rate->Indicating all successful course arrangement records +.>Indicating the corresponding satisfaction rate of each successful course arrangement record,/->Representing the number of datasets to be class.
In this embodiment, the method of this embodiment is compared with other automatic course arrangement algorithms. Through the operation of tens of universities, the quality rate of the method and the quality rate of other automatic course arrangement algorithms are obtained through a quality rate calculation formula. The results show that compared with other automatic course arrangement algorithms, the course arrangement quality of the method of the embodiment is improved by about 15% and the course arrangement time is shortened by about 1/5 under the same environmental conditions and scale. By implementing the method, the school course arrangement satisfaction degree is improved, the management level of universities is improved, and the acceptance degree of teachers and students on educational administration work is improved.
Referring to fig. 3, the embodiment of the present application further provides an intelligent automatic course arrangement system, which includes a data acquisition unit 100, a template allocation unit 200, a constraint checking unit 300, a data course arrangement unit 400, and a course arrangement completion unit 500, wherein:
a data acquisition unit 100, configured to acquire a to-be-class data set, and preset a plurality of first templates including the to-be-class data set;
a template allocation unit 200 for allocating a plurality of first templates to a plurality of computers, and randomly designating classrooms and time for each first template to obtain a plurality of second templates;
the constraint checking unit 300 is configured to perform hard constraint condition checking on each piece of to-be-class data in the plurality of second templates, and calculate a satisfaction rate for each piece of to-be-class data that satisfies the hard constraint condition by using a soft constraint mathematical model, so as to obtain a plurality of satisfaction rate values corresponding to each piece of to-be-class data;
the data course arrangement unit 400 is configured to select a highest satisfaction rate value from the plurality of satisfaction rate values, and to sign a successful course arrangement for the course arrangement data corresponding to the highest satisfaction rate value greater than or equal to the expected satisfaction rate value, and to delete the course arrangement data to which the successful course arrangement sign is signed in the course arrangement data set;
the course arrangement completion unit 500 is configured to perform the next iteration by using the course arrangement data set after deleting the course arrangement data set on which the successful course arrangement mark is applied in the course arrangement data set, and if the iteration number reaches the preset convergence parameter and all the course arrangement data are successful, completing course arrangement.
It should be noted that, since an intelligent automatic course arrangement system in the present embodiment and the above-mentioned intelligent automatic course arrangement method are based on the same inventive concept, the corresponding content in the method embodiment is also applicable to the system embodiment, and will not be described in detail herein.
Referring to fig. 4, the embodiment of the application further provides an intelligent automatic course arrangement device, which includes:
at least one memory;
at least one processor;
at least one program;
the program is stored in the memory, and the processor executes at least one program to implement the intelligent automatic course arrangement method described above.
The electronic device can be any intelligent terminal including a mobile phone, a tablet personal computer, a personal digital assistant (Personal Digital Assistant, PDA), a vehicle-mounted computer and the like.
The electronic device according to the embodiment of the application is described in detail below.
Processor 1600, which may be implemented by a general-purpose central processing unit (Central Processing Unit, CPU), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc., is configured to execute related programs to implement the technical solutions provided by the embodiments of the present disclosure;
the Memory 1700 may be implemented in the form of Read Only Memory (ROM), static storage, dynamic storage, or random access Memory (Random Access Memory, RAM). Memory 1700 may store an operating system and other application programs, related program code is stored in memory 1700 when the technical solutions provided by the embodiments of the present disclosure are implemented in software or firmware, and the intelligent automatic course arrangement method for executing the embodiments of the present disclosure is invoked by processor 1600.
An input/output interface 1800 for implementing information input and output;
the communication interface 1900 is used for realizing communication interaction between the device and other devices, and can realize communication in a wired manner (such as USB, network cable, etc.), or can realize communication in a wireless manner (such as mobile network, WIFI, bluetooth, etc.);
bus 2000, which transfers information between the various components of the device (e.g., processor 1600, memory 1700, input/output interface 1800, and communication interface 1900);
wherein processor 1600, memory 1700, input/output interface 1800, and communication interface 1900 enable communication connections within the device between each other via bus 2000.
The disclosed embodiments also provide a storage medium that is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the above-described intelligent automatic course arrangement method.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present disclosure are for more clearly describing the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure, and as those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present disclosure are equally applicable to similar technical problems.
It will be appreciated by those skilled in the art that the technical solutions shown in the figures do not limit the embodiments of the present disclosure, and may include more or fewer steps than shown, or may combine certain steps, or different steps.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including multiple instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing a program. The embodiments of the present application have been described in detail with reference to the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present application.
Claims (10)
1. An intelligent automatic course arrangement method is characterized by comprising the following steps:
acquiring a to-be-class-arranged data set, and presetting a plurality of first templates containing the to-be-class-arranged data set;
distributing the plurality of first templates to a plurality of computers, and randomly designating classrooms and time for each first template to obtain a plurality of second templates;
carrying out hard constraint condition inspection on each piece of to-be-class data in the plurality of second templates, and calculating a satisfaction rate of each piece of to-be-class data meeting the hard constraint condition by adopting a soft constraint mathematical model to obtain a plurality of satisfaction rate values corresponding to each piece of to-be-class data;
selecting a highest satisfaction rate value from the plurality of satisfaction rate values, marking the to-be-processed course data corresponding to the highest satisfaction rate value which is larger than or equal to the expected satisfaction rate, and deleting the to-be-processed course data marked with the to-be-processed course marking in the to-be-processed course data set;
and carrying out the next iteration by adopting the to-be-processed course data set after deleting the to-be-processed course data with the successful course processing mark in the to-be-processed course data set, and completing course processing if the iteration times reach the preset convergence parameters and all to-be-processed course processing data are successfully processed.
2. The intelligent automatic course arrangement method of claim 1, wherein assigning the plurality of first templates to a plurality of computers comprises:
the number of the first templates is compared with the number of the computers, and quotient and remainder are obtained;
and distributing the first templates corresponding to the quotient to each computer, and distributing the first templates corresponding to the remainder to the last computer.
3. The intelligent automatic course arrangement method according to claim 1, wherein the hard constraint is that no conflict occurs at the same time for a class, teacher, course, and place.
4. The intelligent automatic course arrangement method of claim 1, wherein the soft constraint mathematical model is constructed by:
，
wherein ,representing each piece of data to be class>Satisfaction rate of->、/>、/>、/> and />Representing weight parameters->Representing the actual number of students, and->Indicating the number of people accommodated in classroom and->Indicating a value selected when the number of class weekly courses meets a first preset value,/->Indicating a selected value when the number of course arrangement per week of the teacher satisfies a second preset value,/->Indicating a selected value when the number of course arrangement per week satisfies a third preset value,/->And (3) representing a value selected when the average number of course arrangement per day of time per week meets a fourth preset value.
5. The intelligent automatic course arrangement method according to claim 1, wherein when each piece of data to be course arranged in the plurality of second templates is subjected to a hard constraint check, the intelligent automatic course arrangement method further comprises:
and if the to-be-class-arranged data in the second templates do not meet the hard constraint condition, setting the meeting rate corresponding to the to-be-class-arranged data to zero and carrying out class-arrangement conflict record.
6. The intelligent automatic course arrangement of claim 1, further comprising, after selecting a satisfaction rate highest value from the plurality of satisfaction rate values:
if the highest satisfaction rate value selected from the plurality of satisfaction rate values is smaller than the expected satisfaction rate, marking the to-be-processed lesson data corresponding to the highest satisfaction rate value smaller than the expected satisfaction rate with a non-processed lesson mark.
7. The intelligent automatic course arrangement method according to claim 6, wherein when the iteration number reaches a preset convergence parameter, the intelligent automatic course arrangement method further comprises:
when the iteration times reach preset convergence parameters, checking whether there is data to be lessons to be lessoned which are recorded without lessons or lesson conflict;
and if the data to be processed with the non-processed or processed conflict records exist, processing the data to be processed with the non-processed or processed conflict records by adopting a greedy algorithm.
8. An intelligent automatic course arrangement system, characterized in that the intelligent automatic course arrangement system comprises:
the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring a to-be-processed course data set and presetting a plurality of first templates containing the to-be-processed course data set;
the template distribution unit is used for distributing the plurality of first templates to a plurality of computers, and randomly designating classrooms and time for each first template to obtain a plurality of second templates;
the constraint checking unit is used for checking the hard constraint condition of each piece of to-be-class data in the plurality of second templates, calculating the satisfaction rate of each piece of to-be-class data meeting the hard constraint condition by adopting a soft constraint mathematical model, and obtaining a plurality of satisfaction rate values corresponding to each piece of to-be-class data;
the data course arrangement unit is used for selecting a highest satisfaction rate value from the plurality of satisfaction rate values, marking the course arrangement data corresponding to the highest satisfaction rate value which is larger than or equal to the expected satisfaction rate, and deleting the course arrangement data marked with the successful course arrangement mark in the course arrangement data set;
and the course arrangement completion unit is used for carrying out the next iteration by adopting the course arrangement data set after deleting the course arrangement data with the successful course arrangement mark in the course arrangement data set, and completing course arrangement if the iteration number reaches the preset convergence parameter and all the course arrangement data are successful.
9. An intelligent automatic course arrangement comprising at least one control processor and a memory for communication connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the intelligent automatic lesson-ranking method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the intelligent automatic course arrangement method according to any one of claims 1 to 7.
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