A second-class activity management method and system based on an extreme value search algorithm

Abstract

This invention relates to the field of information processing technology and discloses a method and system for managing extracurricular activities based on an extreme value search algorithm. The method includes: acquiring extracurricular activity parameters; generating a set of activity plans based on the acquired parameters; configuring conflict indicators and conflict weight coefficients corresponding to the extracurricular activity parameters; constructing an activity plan optimization model based on the extracurricular activity parameters, the set of activity plans, the conflict indicators, and the conflict weight coefficients; solving the optimization model using an extreme value search algorithm to obtain a target activity plan; and outputting the target activity plan. This invention can quantify the conflict value of activity plans through conflict indicators and conflict weight coefficients, and select the most suitable activity plan from the set of activity plans using an extreme value search algorithm, thus solving the problems of unreasonable resource allocation and low automation in existing extracurricular activity management systems.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and in particular to a method and system for managing extracurricular activities based on an extreme value search algorithm. Background Technology

[0002] Traditional extracurricular activities can only be coordinated and planned offline by instructors. However, with the rapid development of internet technology, some extracurricular activities have already migrated from offline operations to online platforms that can be operated digitally.

[0003] However, the management of extracurricular activities involves many aspects. Existing online systems or platforms only have basic modules for activity publishing, registration, and check-in, covering limited scenarios and failing to support the management of the entire process and all scenarios of extracurricular activities. This results in incomplete system functionality and insufficient scenario adaptability. Furthermore, most online systems or platforms simply transplant offline content online, achieving only preliminary digitization without considering conflicts that may arise during extracurricular activities. These conflicts include time conflicts between activity organizers and venues, conflicts between the number of participants and venue size, and conflicts between activity level and budget. This not only results in insufficient intelligence and a lack of personalized services but also issues such as data incoordination, weak statistical analysis capabilities, and low efficiency. Moreover, these conflicts also negatively impact the user experience.

[0004] The existing technology still has the problem of insufficient compatibility between the extracurricular activity management system and extracurricular activities. Therefore, the existing technology needs to be improved. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a second classroom activity management method based on an extreme value search algorithm to address the shortcomings of existing second classroom activity management systems and their insufficient adaptability to second classroom activities.

[0006] The technical solution adopted by this invention to solve the technical problem is as follows: In a first aspect, the present invention provides a method for managing extracurricular activities based on an extreme value search algorithm, comprising: Obtain parameters for extracurricular activities, and generate a set of activity plans based on the obtained parameters. Configure conflict indicators and conflict weight coefficients corresponding to the parameters of the second classroom activity; An activity plan optimization model is constructed based on the second classroom activity parameters, the activity plan set, the conflict index, and the conflict weight coefficient. The optimization model of the activity scheme is solved using the extreme value search algorithm to obtain the target activity scheme; Output the target activity plan.

[0007] In one implementation, the second classroom activity parameters include any one or more combinations of activity requirements, activity participants, activity venue parameters, and activity scale parameters.

[0008] In one implementation, the conflict index and conflict weight coefficient corresponding to the second classroom activity parameters include: Configure the activity personnel conflict index and activity personnel conflict weight coefficient corresponding to the activity personnel parameters; Configure the activity venue conflict index and activity venue conflict weight coefficient corresponding to the activity venue parameters; Configure the activity scale conflict index and activity scale conflict weight coefficient corresponding to the activity scale parameter.

[0009] In one implementation, constructing an activity plan optimization model based on the second classroom activity parameters, the activity plan set, the conflict index, and the conflict weight coefficient includes: Based on the parameters of the second classroom activity, determine the constraints of the activity plan optimization model; Based on the conflict index and the conflict weight coefficient, calculate the conflict value of each activity scheme in the activity scheme set; An activity scheme optimization model is constructed based on the constraints and the objective function, with the goal of minimizing the conflict value.

[0010] In one implementation, the step of using an extreme value search algorithm to solve the activity scheme optimization model to obtain the target activity scheme includes: Determine the initial scheme variables, initial convergence variables, and initial convergence coefficients of the activity scheme optimization model; Based on the initial scheme variables, initial convergence variables, and initial convergence coefficients, calculate the initial solution of the activity scheme optimization model; Based on the extreme value search algorithm and the initial solution, the activity scheme optimization model is iterated a predetermined number of times to obtain the final solution of the activity scheme optimization model; Output the target activity plan corresponding to the final solution.

[0011] In one implementation, the step of performing a predetermined number of iterative solutions on the activity scheme optimization model based on the extreme value search algorithm and the initial solution to obtain the final solution of the activity scheme optimization model includes: At the beginning of each iteration, the scheme variables and convergence variables for the current iteration are calculated based on the scheme variables corresponding to the target solution of the previous iteration and the step size of the current iteration; wherein, the target solution of the first iteration is the initial solution. Based on the scheme variables and convergence variables of this iteration, and the initial convergence coefficients, calculate the new solution corresponding to this iteration; Choose whether to use the new solution to replace the target solution based on preset acceptance conditions; Output the final solution of the activity scheme optimization model; wherein the final solution is the target solution corresponding to the last iteration.

[0012] In one implementation, the step of selecting whether to replace the target solution with the new solution based on preset acceptance conditions includes: Calculate the difference between the new solution and the target solution; If the difference between the new solution and the target solution is positive, the new solution is used to replace the target solution. If the difference between the new solution and the target solution is not positive, determine whether the new solution satisfies the preset acceptance function; If the new solution satisfies the preset acceptance function, the new solution is used to replace the target solution.

[0013] Secondly, the present invention provides a second-classroom activity management system based on an extreme value search algorithm, comprising: The data acquisition module is used to acquire the first-stage task data of the logistics and distribution task, and to build a collaborative transportation network model based on the first-stage task data. The data scheme acquisition module is used to acquire parameters of extracurricular activities and generate a set of activity schemes based on the acquired parameters. The activity plan conflict configuration module is used to configure the conflict indicators and conflict weight coefficients corresponding to the second classroom activity parameters; The activity plan optimization model construction module is used to construct an activity plan optimization model based on the second classroom activity parameters, the activity plan set, the conflict index, and the conflict weight coefficient. The activity plan optimization model solving module is used to solve the activity plan optimization model using an extreme value search algorithm to obtain the target activity plan. The result output module is used to output the target activity plan.

[0014] Thirdly, the present invention provides a terminal, comprising: a processor and a memory, wherein the memory stores a second classroom activity management program based on an extreme value search algorithm, and the second classroom activity management program based on the extreme value search algorithm, when executed by the processor, is used to implement the operation of the second classroom activity management method based on the extreme value search algorithm as described in the first aspect.

[0015] Fourthly, the present invention also provides a computer-readable storage medium storing a second classroom activity management program based on an extreme value search algorithm, wherein the second classroom activity management program based on the extreme value search algorithm, when executed by a processor, is used to implement the operation of the second classroom activity management method based on the extreme value search algorithm as described in the first aspect.

[0016] The present invention, by employing the above technical solution, has the following effects: This invention discloses a method and system for managing extracurricular activities based on an extreme value search algorithm. The method includes: acquiring extracurricular activity parameters; generating a set of activity plans based on the acquired parameters; configuring conflict indicators and conflict weight coefficients corresponding to the extracurricular activity parameters; constructing an activity plan optimization model based on the extracurricular activity parameters, the set of activity plans, the conflict indicators, and the conflict weight coefficients; solving the optimization model using an extreme value search algorithm to obtain a target activity plan; and outputting the target activity plan. This invention can quantify the conflict value of activity plans through conflict indicators and conflict weight coefficients, and select the most suitable activity plan from the set of activity plans using an extreme value search algorithm, thus solving the problems of unreasonable resource allocation and low automation in existing extracurricular activity management systems. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the extracurricular activity management method based on the extreme value search algorithm in this invention.

[0019] Figure 2 This is a graph of the extreme value search function in one implementation of the present invention. Figure 3 This is a flowchart of the activity scheme optimization model solution method in one implementation of the present invention.

[0020] Figure 4 This is a schematic diagram of the structure of a second classroom activity management system based on an extreme value search algorithm in one implementation of the present invention.

[0021] Figure 5 This is a functional schematic diagram of the terminal in one implementation of the present invention.

[0022] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0024] Exemplary methods Extracurricular activity management involves many aspects. Existing online systems or platforms only have basic modules for activity publishing, registration, and check-in, covering limited scenarios and unable to support the management of the entire process and all scenarios of extracurricular activities. They suffer from incomplete system functionality and insufficient scenario adaptability. Furthermore, most online systems or platforms simply transplant offline content online, achieving only preliminary digitization without considering conflicts that may arise during extracurricular activities. These conflicts include time conflicts between activity organizers and venues, conflicts between the number of participants and venue size, and conflicts between activity level and budget. This not only results in insufficient intelligence and a lack of personalized services but also suffers from data incoordination, weak statistical analysis capabilities, and low efficiency. Moreover, these conflicts also negatively impact the user experience.

[0025] The existing technology still has the problem of insufficient compatibility between the extracurricular activity management system and extracurricular activities. Therefore, the existing technology needs to be improved.

[0026] To address the above technical problems, this invention provides a method for managing extracurricular activities based on an extreme value search algorithm. The method includes: acquiring extracurricular activity parameters; generating an activity plan set based on the acquired parameters; configuring conflict indicators and conflict weight coefficients corresponding to the extracurricular activity parameters; constructing an activity plan optimization model based on the extracurricular activity parameters, the activity plan set, the conflict indicators, and the conflict weight coefficients; solving the activity plan optimization model using an extreme value search algorithm to obtain a target activity plan; and outputting the target activity plan. This invention can quantify the conflict value of activity plans through conflict indicators and conflict weight coefficients, and select the most suitable activity plan from the activity plan set using an extreme value search algorithm, thus solving the problems of unreasonable resource allocation and low automation in existing extracurricular activity management systems.

[0027] like Figure 1 As shown, this embodiment of the invention provides a method for managing extracurricular activities based on an extreme value search algorithm, including the following steps: Step S100: Obtain the parameters of the second classroom activities, and generate a set of activity plans based on the obtained parameters of the second classroom activities.

[0028] In this embodiment, the parameters for the second classroom activity include any one or more combinations of the following: activity requirements, participant parameters, venue parameters, and scale parameters.

[0029] The methods for obtaining parameters for extracurricular activities include, but are not limited to: obtaining them directly from the existing extracurricular activity management system, obtaining them by analyzing activity records of already conducted extracurricular activities, and having school administrators set them according to the actual situation.

[0030] In this embodiment, the activity requirements include defined parameters such as the activity theme, activity type, activity time, and activity organizer. For example, the activity theme is a textual description of the activity, which can be obtained through manual supplementation, extraction from the activity requirements document, or generation by a large language model. The activity type is a preset activity type, including but not limited to: academic, literacy, public welfare, and service types. The activity time includes the start and end times of the activity, which can be set through calendar selection or manual entry. The activity organizer includes but is not limited to: the school's Youth League Committee, the college's Youth League Committee, youth volunteers, and student organizations, and is generally obtained through the activity requirements document.

[0031] In this embodiment, the event personnel parameters mainly include the person in charge and the participants. The person in charge needs to be selected from the staff of the organizer, and the participants are generally selected by defining a range of eligible people, that is, by adding limiting conditions to screen from all people in the school.

[0032] In this embodiment, the activity venue parameters mainly include the location and size of the activity venue. The location of the activity venue includes, but is not limited to, specific classrooms, studios, outdoor venues, etc. The size of the activity venue is generally set according to the actual situation, such as large venues (more than 50 people) and small venues (less than 50 people).

[0033] In this embodiment, the activity scale parameters mainly include activity level, activity funding type, and activity budget. Activity level includes, but is not limited to, school level, college level, etc.; activity funding type includes, but is not limited to, school-level funding, college-level funding, self-raised funds, etc.; activity budget is generally entered manually, set manually, or extracted directly from the activity requirements document.

[0034] In this embodiment, the acquired extracurricular activity parameters are preprocessed and stored in a preset database. When the extracurricular activity management method based on the extreme value search algorithm is executed, the extracurricular activity parameters are directly called from the preset database to perform tasks such as activity plan optimization, activity plan adjustment, and activity initiation.

[0035] In this embodiment, an activity plan set is generated based on the acquired second classroom activity parameters. Specifically, the parameter types of the second activity parameters to be used in the activity plan are determined. The parameter types refer to detailed parameter branch types such as activity theme and activity type.

[0036] Each activity plan randomly selects one activity parameter from the parameter types of the second activity parameter that needs to be used, and generates an activity plan containing a set of activity parameters. For example: Activity Plan 1, theme text description, public welfare type, 10:00 am - 12:00 pm on May 12, youth volunteers, person in charge A, small, self-funded, 0 yuan, outdoor venue, 10 people, specific participants (randomly or designatedly selected from the defined range of participants).

[0037] Finally, we obtain a set of activity plans that include all extracurricular activity plans.

[0038] It should be noted that, generally, the activity requirements are the fixed requirements at the time of the activity initiation. In this case, the activity requirements of each plan in the generated set of activity plans must be consistent, but the activity personnel parameters, activity venue parameters, and activity scale parameters are randomly generated; for example, for a certain activity requirement, a set of activity plans with random activity personnel parameters, activity venue parameters, and activity scale parameters is generated.

[0039] In this embodiment, relevant information is read from the acquired data entity objects, activity members are extracted, activity venues are selected, and an activity model is instantiated. Based on this, the first set of activity planning schemes is generated. Then, the activity members are replaced one by one, and a new activity scheme is generated for each replacement, until all activity members are updated, completing the first round of activity scheme generation. Then, the second round of activity scheme generation begins. First, the activity venue is replaced in the last activity scheme generated in the first round to generate the first activity scheme of the new second round. Then, the activity members are replaced one by one, and a new activity scheme is generated for each replacement, until all activity members are replaced, ending the second round of activity scheme generation. Then, the same rules are followed for the third and fourth rounds to generate new activity schemes, until the number of activity schemes reaches the set maximum number of activity schemes. Each generated activity scheme is numbered sequentially starting from 1, and the number corresponds to the independent variable of each activity scheme. The maximum number of activity schemes in this embodiment This is set by the administrator in the system.

[0040] like Figure 1 As shown, this embodiment of the invention provides a method for managing extracurricular activities based on an extreme value search algorithm, including the following steps: Step S200: Configure the conflict index and conflict weight coefficient corresponding to the second classroom activity parameters.

[0041] Specifically, in one implementation of this embodiment, step S200 includes the following steps: Step S201: Configure the activity personnel conflict index and activity personnel conflict weight coefficient corresponding to the activity personnel parameters.

[0042] In this embodiment, an activity conflict index corresponding to the activity personnel parameters is configured. Conflict weighting coefficient with event participants Specifically, based on the activity's circumstances, corresponding values ​​need to be configured for potential conflicts among the participants, including the activity conflict index. The value corresponding to each participant in the activity.

[0043] For example, the conflict index of event participants First, identify the participants in the activity. For each person with potential conflicts, if there is a time conflict, the score is 100; if there is a time conflict, the score is 50; if there is no conflict, the score is 0. Conflict weighting coefficient of participants Set it to 60%.

[0044] Step S202: Configure the activity site conflict index and activity site conflict weight coefficient corresponding to the activity site parameters.

[0045] In this embodiment, an activity venue conflict index corresponding to the activity venue parameters is configured. Conflict weighting factor with event venue The specific values ​​need to be configured according to the event's circumstances and the potential conflicts that may exist at the event venue.

[0046] For example, event venue conflict indicators If the venue is occupied, take 100; if the venue space is insufficient, take 70; if there are no other conflicts, take 0. Event venue conflict weighting coefficient Set to 30%.

[0047] Step S203: Configure the activity scale conflict index and activity scale conflict weight coefficient corresponding to the activity scale parameter.

[0048] In this embodiment, an activity scale conflict index corresponding to the activity scale parameter is configured. Conflict weighting coefficient with event size The specific values ​​need to be configured according to the event's circumstances and the potential conflicts arising from the scale of the event.

[0049] For example, event size conflict indicators If there is a conflict in a large event, take 100; if there is a conflict in a small event, take 30. Activity Scale Conflict Weighting Coefficient Set it to 10%.

[0050] like Figure 1 As shown, this embodiment of the invention provides a method for managing extracurricular activities based on an extreme value search algorithm, including the following steps: Step S300: Construct an activity plan optimization model based on the second classroom activity parameters, the activity plan set, the conflict index, and the conflict weight coefficient.

[0051] Specifically, in one implementation of this embodiment, step S300 includes the following steps: Step S301: Based on the second classroom activity parameters, determine the constraints of the activity plan optimization model.

[0052] In this embodiment, based on the activity parameters of the second classroom, the constraints of the activity plan optimization model are determined according to the actual activity situation, for example: When the activity type is academic, the activity venue cannot be an outdoor venue; The activity must start at least one hour before its end; The event organizer must correspond to the event level. When the organizer is the school's Youth League Committee, the event level can only be selected as school level. The maximum number of people that the event venue can accommodate must not be less than the maximum number of participants. Constraints corresponding to the actual activity situation and the parameters of the second classroom activities.

[0053] Step S302: Based on the conflict index and the conflict weight coefficient, calculate the conflict value of each activity scheme in the activity scheme set.

[0054] In this embodiment, based on the conflict index and the conflict weight coefficient, the conflict value of each activity plan in the activity plan set is calculated, and the corresponding formula is as follows: ; in, The conflict value of the current activity plan. This refers to the scheme variables of the current activity plan.

[0055] Step S303: With minimizing the conflict value as the objective function, construct an activity scheme optimization model based on the constraints and the objective function.

[0056] In this embodiment, minimizing the conflict value is the objective function, that is: .

[0057] In this embodiment, an activity scheme optimization model is constructed based on the constraints and objective function determined in step S301. The activity scheme optimization model is used to find the activity scheme with the lowest conflict value and take it as the optimal activity scheme.

[0058] like Figure 1 As shown, this embodiment of the invention provides a method for managing extracurricular activities based on an extreme value search algorithm, including the following steps: Step S400: Use the extreme value search algorithm to solve the activity scheme optimization model to obtain the target activity scheme.

[0059] In this embodiment, the extreme value search algorithm used is based on simulated annealing, with improvements made to the related logic and process to optimize the search algorithm. This extreme value search algorithm can be achieved through, for example... Figure 2 The extreme value search function curve shown in the figure illustrates this, with the X-axis representing the function variable. The Y-axis represents the function. The value of , the curve in the graph represents the function. The value depends on the function variable The changes.

[0060] First, the extreme value search algorithm randomly selects a variable in the function. The variable The corresponding function value is ,Right now ; Next, move the variable to a value of The position of the vector, Indicates moving forward. This indicates moving backward, and based on the new variable. Recalculate the corresponding function value ;make ; like Then it is believed It is superior to The solution was recorded. and The value, and at the same time The value assigned to the variable such variables The above stores a solution that is better than the previous one, and then the positions of the independent variables are moved. until the optimal solution is found; if This means It is worse than The usual solution is to discard it, but this will result in finding a local optimum rather than a global optimum; for example... Figure 2 As shown, based on the above scheme, this extreme value search method can find local minima. Then, move the variable forward. Then you will encounter them one after another. Therefore, it is impossible to escape the local minimum value. The peak bottom, but in fact the global minimum should be the minimum corresponding to the second peak bottom. In this scenario, a jump-out minimum value should be added. Peak bottom conditions: Right now In this case, we consider absorbing worse solutions with a certain probability as solutions to the function in order to escape local peaks. In this embodiment, the probability function is set as follows: ; in, This is a search variable representing the convergence of the search. The lower the value, the higher the convergence and the closer it is to the extreme value. when When the condition is met, a worse solution is accepted as the solution to the function, thus escaping the local extremum peak. , representing the critical probability value at a certain moment; After the search method escapes the local extreme peak, it continues to move the variable position. This continues until the bottom of the global minimum peak is found, as shown in the figure. .

[0061] It should be noted that in the simulated annealing algorithm, if the cooling rate is too slow, the performance of the obtained solution will be more satisfactory. However, if the algorithm speed is too slow, and the cooling rate is too fast, it is very likely that the global optimal solution will not be obtained in the end.

[0062] In this embodiment, during the high-temperature stage, the probability of accepting inferior solutions is high, which can avoid premature convergence and escape from local optima. Therefore, a relatively slow cooling rate needs to be maintained during this stage. During the low-temperature stage, the probability of accepting inferior solutions is low, and it is more inclined to accept superior solutions to achieve stable convergence. During this stage, a relatively fast cooling rate can be maintained to improve the efficiency of the extreme value search algorithm.

[0063] In standard operations, a fixed-step cooling method is typically used, such as reducing the temperature by 1 degree Celsius in each search round. Alternatively, a fixed cooling rate can be used, such as setting a constant cooling coefficient. ,make ,in However, both of these methods have shortcomings and cannot meet the requirement of slow cooling during high-temperature stages and rapid cooling during low-temperature stages. In the improved extreme value search algorithm of this embodiment, the cooling gradient is dynamically changed by dynamically defining the cooling coefficient, so as to better meet the cooling requirements of different stages.

[0064] In addition, for routine operations that typically use a fixed independent variable movement step size, such as Since the step size is constant, this approach is not conducive to exploring the global optimal interval under large perturbations. In the improved extreme value search algorithm of this embodiment, a dynamic step size strategy is introduced to achieve large-span exploration at high temperatures. (with larger values) to find the globally optimal interval, and at low temperatures focus on a fine-grained local search ( (If the value is relatively small), it will converge to the global optimum.

[0065] In this embodiment, the activity scheme optimization model is solved based on the improved extreme value search algorithm to obtain the target activity scheme. The flowchart of the corresponding activity scheme optimization model solution method is as follows. Figure 3 As shown, the activity plan set generated in the activity plan optimization model is first obtained. The process involves: setting an activity plan; specifying initial plan variables, initial convergence variables, and initial convergence coefficients, and obtaining the initial solution of the model (function); executing a preset number of iterations, in each iteration first obtaining the plan variables corresponding to the next plan, calculating the convergence variables, generating a new solution corresponding to the plan, and determining whether the difference between the new solution and the target solution is positive. If it is positive, the activity plan corresponding to the new solution is retained; if it is not positive but meets the preset acceptance function, the activity plan corresponding to the new solution is also retained; if it is not positive and does not meet the preset acceptance condition, the new solution is discarded; after executing the preset number of iterations, the target solution corresponding to the last iteration is output as the final solution of the activity plan optimization model, and the activity plan corresponding to the final solution is obtained.

[0066] Specifically, in one implementation of this embodiment, step S400 includes the following steps: Step S401: Determine the initial scheme variables, initial convergence variables, and initial convergence coefficients of the activity scheme optimization model.

[0067] In this embodiment, the convergence index of the activity scheme optimization model is determined. The convergence metric includes the initial convergence variable. and initial convergence coefficients ,and .

[0068] In this embodiment, the convergence metric changes with the number of iterations, and its formula is as follows: ; ; in, and For the first The convergence metric for the next iteration.

[0069] In this embodiment, the initial convergence variable is configured. Initial convergence coefficients .

[0070] In this embodiment, the initial scheme variables of the activity scheme optimization model are determined. And set the movement amount of the scheme variables. ;in, Let be a random integer variable. For the moving average, As a moving constant, is the step size for the independent variable.

[0071] Based on the determined initial plan variables and the movement amounts of the plan variables, the subsequent plan variables can be obtained as follows: ; in, That is to The calculated value discards the decimal part and retains only the integer part, discarding the decimal part.

[0072] In this embodiment, when the convergence variable is set Sometimes, ,along with As the value decreases, the value will change dynamically, eventually reaching... Exploration terminates before the value is less than 1, by using This dynamic variable's step size better meets the needs of large-scale exploration at high temperatures and focused local fine-tuning search at low temperatures, thereby finding the global optimal solution.

[0073] In this embodiment, the conventional fixed convergence coefficient mode is broken, and the convergence coefficient is made dynamic. During the initial high-temperature exploration phase, the initial convergence coefficient... The coefficient is set to a floating-point value very close to 1, and then slowly reduced. This results in a higher convergence coefficient in the initial stage, which better meets the need for slow cooling during exploration of high-temperature large disturbances. As the exploration progresses, the convergence coefficient becomes smaller and smaller, reaching a stage minimum in the low-temperature stage. The smaller convergence coefficient means that the cooling rate is getting faster and the cooling gradient is increasing. The process of searching for the global optimum is accelerating in the low-temperature stage, thereby improving the efficiency of the extreme value search algorithm.

[0074] Step S402: Calculate the initial solution of the activity scheme optimization model based on the initial scheme variables, initial convergence variables, and initial convergence coefficients.

[0075] In this embodiment, the initial solution of the activity scheme optimization model is calculated based on the initial scheme variables, initial convergence variables, and initial convergence coefficients, using the following formula: ; in, and It is an integer, representing the first... A set of activity plans.

[0076] Seek The function value is used as the initial solution. .

[0077] In this embodiment, the total number of activity plans The value of the initial plan variable can be configured directly, or it can be set directly to the total number of activity plans. Half of that, which is 450.

[0078] Step S403: Based on the extreme value search algorithm and the initial solution, perform a preset number of iterative solutions on the activity scheme optimization model to obtain the final solution of the activity scheme optimization model.

[0079] In this embodiment, the extreme value search algorithm and the initial solution are used. The process involves iteratively solving the activity plan optimization model a predetermined number of times. For example, the first iteration corresponds to the plan variables. and convergent variables The corresponding solution is .

[0080] Step S4031: At the beginning of each iteration, calculate the scheme variables and convergence variables for the current iteration based on the scheme variables corresponding to the target solution of the previous iteration and the step size of the current iteration; wherein, the target solution of the first iteration is the initial solution.

[0081] In this embodiment, at the beginning of each iteration, based on the scheme variables corresponding to the target solution of the previous iteration and the step size of the current iteration, we have: ; The scheme variables for this iteration are: ; In this embodiment, , , ;when hour, , .

[0082] The convergence variables for this iteration are: ; .

[0083] Step S4032: Based on the scheme variables and convergence variables of this iteration and the initial convergence coefficients, calculate the new solution corresponding to this iteration.

[0084] In this embodiment, the scheme variables are based on the current iteration. and convergent variables The initial convergence coefficients Calculate the new solution corresponding to this iteration. .

[0085] Step S4033: Select whether to use the new solution to replace the target solution according to the preset acceptance conditions; It should be noted that the preset acceptance conditions include: the difference between the new solution and the target solution is positive, or the new solution satisfies the preset acceptance function.

[0086] This embodiment defines the preset accept function as follows: ; in, It is a random variable, and .

[0087] In this embodiment, the preset acceptance function is used to determine whether to accept the relevant activity plan corresponding to the new solution when the new solution is not a better solution than the current solution. Specifically, when... A value greater than 0 indicates that a worse solution will be accepted as the current solution.

[0088] In this embodiment, selecting whether to use the new solution to replace the target solution based on preset acceptance conditions includes the following steps: Step a: Calculate the difference between the new solution and the target solution.

[0089] In this embodiment, the difference between the new solution and the target solution is directly calculated. .

[0090] Step b: If the difference between the new solution and the target solution is positive, replace the target solution with the new solution.

[0091] In this embodiment, if Then, the new solution is used to replace the target solution, that is, the solution is considered to be... It is a superior solution The solution is then stored. and the plan Set as the current solution.

[0092] Step c: If the difference between the new solution and the target solution is not positive, determine whether the new solution satisfies the preset acceptance function.

[0093] In this embodiment, if Then the plan is considered It is a better solution If the solution is the same as or worse than the solution, should the solution be changed? Further evaluation is needed before setting this as the current solution.

[0094] Step d: If the new solution satisfies the preset acceptance function, the new solution is used to replace the target solution.

[0095] In this embodiment, a further judgment is made based on the preset acceptance function defined in the preset acceptance conditions. If If the new solution satisfies the preset acceptance function, then the new solution is used to replace the target solution, i.e., the solution is... Set as the current solution; if If the objective solution is not found, then the new solution and alternative solutions are discarded. .

[0096] Step S4034: Output the final solution of the activity scheme optimization model; wherein the final solution is the target solution corresponding to the last iteration.

[0097] In this embodiment, the final solution of the activity scheme optimization model is output. The final solution is the target solution corresponding to the last iteration.

[0098] It should be noted that the iterative solution process for the activity scheme optimization model in this application involves a preset number of iterations, and the preset number of iterations is related to the total number of schemes in the activity scheme set. Correspondingly, excluding the number of iterations. Reaching the preset number of times In addition, other conditions can be used to determine whether to end the iterative solution process.

[0099] In this embodiment, the initial solution may or may not be an activity from the set of activity solutions. Each iteration of the solution process is the process of calculating the conflict value for each activity solution. The goal of the activity solution optimization model is to find the activity solution with the lowest conflict value.

[0100] For example, set a convergence variable. Minimum of convergent variable ,when When the time is reached, the iterative solution process ends.

[0101] In this embodiment, a minimum value for the convergence variable is set. It can be seen that during the predetermined number of iterations, the convergence coefficient... The value of will continuously decrease, thus converging the variable. The value also varies with the convergence coefficient. Gradient descent, when the variable converges... When the time is reached, the iterative solution process ends.

[0102] Step S401: Output the target activity plan corresponding to the final solution.

[0103] In this embodiment, the final solution is output. Corresponding target activity plan .

[0104] like Figure 1 As shown, this embodiment of the invention provides a method for managing extracurricular activities based on an extreme value search algorithm, including the following steps: Step S500: Output the target activity plan.

[0105] In this embodiment, the target activity plan is output, along with the corresponding second-classroom activity parameters.

[0106] In one implementation of this embodiment, the output activity plan can be adjusted manually according to the actual situation, such as adding or removing members, changing dates, or changing venues, in order to better meet actual needs.

[0107] In one implementation of this embodiment, after the activity ends, the activity record is uploaded to the Second Classroom Activity Management System. The Second Classroom Activity Management System compares the output activity plan with the uploaded activity record, which can assign extracurricular activity credits to the participants or award relevant prizes to students with excellent performance. The data is then pushed to the campus teaching service platform to achieve efficient data collaboration and improve the scientific and intelligent nature of data decision analysis.

[0108] This embodiment achieves the following technical effects through the above technical solution: An improved extreme value search algorithm is used to solve the activity scheme optimization model, breaking away from the conventional fixed-step cooling. By dynamically defining the cooling coefficient, the cooling gradient is dynamically changed to better meet the cooling needs at different stages. For routine operations that typically use a fixed variable movement step size, such as The constant step size is not conducive to exploring the global optimal range under large perturbations. In the improved extreme value search algorithm of this embodiment, a dynamic step size strategy is introduced to achieve large-span exploration at high temperatures. (with larger values) to find the globally optimal interval, and at low temperatures focus on a fine-grained local search ( (with smaller values), thus converging to the global optimum; The extreme value search method effectively solves the problems of unreasonable resource allocation and low automation in the existing system, realizing precise management of extracurricular activities, efficient organization of activities, and scientific decision analysis, and powerfully promotes the digital and intelligent transformation of extracurricular activity planning and management in universities; It can quantify the conflict value of activity plans through conflict indicators and conflict weight coefficients, and select the most suitable activity plan from the set of activity plans by combining extreme value search algorithm, thus solving the problems of unreasonable resource allocation and low degree of automation in the existing extracurricular activity management system.

[0109] Exemplary device Based on the above embodiments, the present invention also provides a second-classroom activity management system based on an extreme value search algorithm, such as... Figure 4 As shown, the extracurricular activity management system based on the extreme value search algorithm includes: Data scheme acquisition module 21 is used to acquire parameters of extracurricular activities and generate a set of activity schemes based on the acquired parameters. The activity plan conflict configuration module 22 is used to configure the conflict indicators and conflict weight coefficients corresponding to the second classroom activity parameters; The activity plan optimization model construction module 23 is used to construct an activity plan optimization model based on the second classroom activity parameters, the activity plan set, the conflict index, and the conflict weight coefficient. The activity plan optimization model solving module 24 is used to solve the activity plan optimization model using an extreme value search algorithm to obtain the target activity plan. The result output module 25 is used to output the target activity plan.

[0110] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 5 As shown.

[0111] The terminal includes: a processor, a memory, an interface, a display screen, and a communication module connected via a system bus; wherein, the processor of the terminal provides computing and control capabilities; the memory of the terminal includes a computer-readable storage medium and internal memory; the computer-readable storage medium stores an operating system and computer programs; the internal memory provides an environment for the operation of the operating system and computer programs in the computer-readable storage medium; the interface is used to connect to external devices; the display screen is used to display relevant information; and the communication module is used to communicate with a cloud server or other devices.

[0112] When executed by a processor, this computer program is used to implement a second-classroom activity management method based on an extreme value search algorithm.

[0113] It will be understood by those skilled in the art that Figure 5 The schematic diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] In one embodiment, a terminal is provided, comprising: a processor and a memory, the memory storing a second classroom activity management program based on an extreme value search algorithm, the second classroom activity management program based on the extreme value search algorithm being executed by the processor to implement the operation of the second classroom activity management method based on the extreme value search algorithm as described above.

[0115] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a second classroom activity management program based on an extreme value search algorithm, which, when executed by a processor, is used to implement the operation of the second classroom activity management method based on the extreme value search algorithm described above.

[0116] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, database, or other media used in the embodiments provided by this invention can include both non-volatile and volatile memory.

[0117] In summary, this invention provides a method and system for managing extracurricular activities based on an extreme value search algorithm. The method includes: acquiring extracurricular activity parameters; generating a set of activity plans based on the acquired parameters; configuring conflict indicators and conflict weight coefficients corresponding to the extracurricular activity parameters; constructing an activity plan optimization model based on the extracurricular activity parameters, the set of activity plans, the conflict indicators, and the conflict weight coefficients; solving the optimization model using an extreme value search algorithm to obtain a target activity plan; and outputting the target activity plan. This invention can quantify the conflict value of activity plans through conflict indicators and conflict weight coefficients, and select the most suitable activity plan from the set of activity plans using an extreme value search algorithm, thus solving the problems of unreasonable resource allocation and low automation in existing extracurricular activity management systems.

[0118] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for managing extracurricular activities based on an extreme value search algorithm, characterized in that, include: Obtain parameters for extracurricular activities, and generate a set of activity plans based on the obtained parameters. Configure conflict indicators and conflict weight coefficients corresponding to the parameters of the second classroom activity; An activity plan optimization model is constructed based on the second classroom activity parameters, the activity plan set, the conflict index, and the conflict weight coefficient. The optimization model of the activity scheme is solved using an extreme value search algorithm to obtain the target activity scheme. Output the target activity plan.

2. The method for managing extracurricular activities based on the extreme value search algorithm according to claim 1, characterized in that, The second classroom activity parameters include any one or more combinations of the following: activity requirements, participant parameters, venue parameters, and scale parameters.

3. The method for managing extracurricular activities based on the extreme value search algorithm according to claim 2, characterized in that, The conflict indicators and conflict weight coefficients corresponding to the configuration and the second classroom activity parameters include: Configure the activity personnel conflict index and activity personnel conflict weight coefficient corresponding to the activity personnel parameters; Configure the activity venue conflict index and activity venue conflict weight coefficient corresponding to the activity venue parameters; Configure the activity scale conflict index and activity scale conflict weight coefficient corresponding to the activity scale parameter.

4. The method for managing extracurricular activities based on the extreme value search algorithm according to claim 1, characterized in that, The step of constructing an activity plan optimization model based on the second classroom activity parameters, the activity plan set, the conflict index, and the conflict weight coefficient includes: Based on the parameters of the second classroom activity, determine the constraints of the activity plan optimization model; Based on the conflict index and the conflict weight coefficient, calculate the conflict value of each activity scheme in the activity scheme set; An activity scheme optimization model is constructed based on the constraints and the objective function, with the goal of minimizing the conflict value.

5. The method for managing extracurricular activities based on the extreme value search algorithm according to claim 1, characterized in that, The step of using an extreme value search algorithm to solve the activity scheme optimization model to obtain the target activity scheme includes: Determine the initial scheme variables, initial convergence variables, and initial convergence coefficients of the activity scheme optimization model; Based on the initial scheme variables, initial convergence variables, and initial convergence coefficients, calculate the initial solution of the activity scheme optimization model; Based on the extreme value search algorithm and the initial solution, the activity scheme optimization model is iterated a predetermined number of times to obtain the final solution of the activity scheme optimization model; Output the target activity plan corresponding to the final solution.

6. The method for managing extracurricular activities based on the extreme value search algorithm according to claim 5, characterized in that, The process of iteratively solving the activity scheme optimization model a predetermined number of times based on the extreme value search algorithm and the initial solution to obtain the final solution of the activity scheme optimization model includes: At the beginning of each iteration, the scheme variables and convergence variables for the current iteration are calculated based on the scheme variables corresponding to the target solution of the previous iteration and the step size of the current iteration; wherein, the target solution of the first iteration is the initial solution. Based on the scheme variables and convergence variables of this iteration, and the initial convergence coefficients, calculate the new solution corresponding to this iteration; Choose whether to use the new solution to replace the target solution based on preset acceptance conditions; Output the final solution of the activity scheme optimization model; wherein the final solution is the target solution corresponding to the last iteration.

7. The method for managing extracurricular activities based on the extreme value search algorithm according to claim 6, characterized in that, The step of selecting whether to use the new solution to replace the target solution according to preset acceptance conditions includes: Calculate the difference between the new solution and the target solution; If the difference between the new solution and the target solution is positive, the new solution is used to replace the target solution. If the difference between the new solution and the target solution is not positive, determine whether the new solution satisfies the preset acceptance function; If the new solution satisfies the preset acceptance function, the new solution is used to replace the target solution.

8. A second-classroom activity management system based on an extreme value search algorithm, used to implement the second-classroom activity management method based on an extreme value search algorithm as described in any one of claims 1-7, characterized in that, include: The data scheme acquisition module is used to acquire parameters of extracurricular activities and generate a set of activity schemes based on the acquired parameters. The activity plan conflict configuration module is used to configure the conflict indicators and conflict weight coefficients corresponding to the second classroom activity parameters; The activity plan optimization model construction module is used to construct an activity plan optimization model based on the second classroom activity parameters, the activity plan set, the conflict index, and the conflict weight coefficient. The activity plan optimization model solving module is used to solve the activity plan optimization model using an extreme value search algorithm to obtain the target activity plan. The result output module is used to output the target activity plan.

9. A terminal, characterized in that, include: The processor and memory, wherein the memory stores a second classroom activity management program based on an extreme value search algorithm, and the second classroom activity management program based on the extreme value search algorithm, when executed by the processor, is used to implement the operation of the second classroom activity management method based on the extreme value search algorithm as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a second classroom activity management program based on an extreme value search algorithm. When executed by a processor, the second classroom activity management program based on an extreme value search algorithm is used to implement the operation of the second classroom activity management method based on an extreme value search algorithm as described in any one of claims 1-7.

Images