A method, apparatus, and storage medium for recommending private rooms based on adaptive genetic algorithms.

Abstract

This invention discloses a method, apparatus, and storage medium for recommending private rooms based on an adaptive genetic algorithm, comprising the following steps: mapping the distribution locations of each private room within a venue to a two-dimensional distribution map, wherein the two-dimensional distribution map is divided into two or more regions, each region including two or more private rooms; establishing an adaptive genetic algorithm and setting the parameters of the adaptive genetic algorithm; and recommending private rooms through the adaptive genetic algorithm to achieve a balanced distribution of people in each region within the venue. This invention recommends private rooms to customers using an adaptive genetic algorithm, thus dispersing customers within the venue, thereby avoiding contact between customers during off-peak hours and minimizing cross-contact during peak hours.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to a method, apparatus, and storage medium for recommending private rooms based on an adaptive genetic algorithm. Background Technology

[0002] Currently, most management systems used in KTV entertainment venues only have basic functions such as reservations and payments, and do not have functions such as personnel allocation and management. Summary of the Invention

[0003] In view of the above problems, this application provides a box recommendation method based on an adaptive genetic algorithm.

[0004] To achieve the above objectives, the inventors provide a box recommendation method based on an adaptive genetic algorithm, comprising the following steps:

[0005] The distribution of each private room in the venue is mapped into a two-dimensional distribution map, which is divided into two or more regions, each region including two or more private rooms;

[0006] Establish an adaptive genetic algorithm and set its parameters;

[0007] The adaptive genetic algorithm is used to recommend private rooms to achieve a balanced distribution of people in all areas of the venue. This includes the following steps:

[0008] Upon receiving a private room reservation, the usage information of the private room is read, and the usage information and the two-dimensional distribution map are input into the adaptive genetic algorithm;

[0009] The adaptive genetic algorithm calculates the area with the lowest personnel density in the venue based on the usage of the private rooms and the two-dimensional distribution map; and recommends the best private room obtained by searching within the area with the lowest personnel density.

[0010] In some technical solutions, the area records the distance between each of the private rooms and other private rooms within the area.

[0011] In some technical solutions, establishing an adaptive genetic algorithm includes the following steps:

[0012] Each of the aforementioned compartments is converted into chromosomes using a chromosome encoding strategy;

[0013] An initial population is formed, wherein the individuals in the initial population include the chromosome and other randomly generated chromosomes, and the number of individuals in the initial population is greater than or equal to three times the number of boxes;

[0014] Set the fitness function f i And selection operators.

[0015] In some technical solutions, the adaptive genetic algorithm calculates the area with the lowest personnel density in the venue based on the usage of the private rooms and the two-dimensional distribution map; and recommends the best private room obtained by searching within the area with the lowest personnel density, including the following steps:

[0016] Calculate the area with the lowest population density within the two-dimensional distribution map using the regional density calculation formula;

[0017] The formula for calculating the density of the region is: In the formula: n is the number of private rooms already occupied in this area, and m is the total number of private rooms in this area;

[0018] Calculate the fitness of each chromosome in the population;

[0019] Selection operations are performed by selecting operators;

[0020] Implement adaptive crossover and mutation strategies;

[0021] Repeat the above steps until the termination condition is met, then stop the iteration to obtain the optimal private room.

[0022] In some technical solutions, the fitness function In the formula: Let be the objective function. In the formula P represents the sum of distances from the current private room i to several already occupied private rooms j in this area, where P is the penalty function.

[0023] In some technical solutions, the selection operator uses a roulette wheel betting strategy with partial elite retention for the selection operation;

[0024] The probability of individual i being selected in the roulette wheel game.

[0025] In the formula, popsize is the population size; f i Let be the fitness value of the i-th individual.

[0026] In some technical solutions, the adaptive crossover and mutation strategies are determined using an adaptive value selection method, where the adaptive crossover value is P. c The mutation strategy takes the value Pm;

[0027]

[0028]

[0029] In the formula: P cmax With P cminf' and f' represent the upper and lower limits of the crossover probability range during genetic operations, respectively, and f' is the fitness value of the higher individual among the two individuals undergoing the crossover operation. avg f is the average fitness value of the population per generation. max P represents the maximum fitness value in the current population, where μ is a constant; mmax With P mmin , where represents the upper and lower limits of the mutation probability range, and f is the fitness value of the mutated individual.

[0030] In some technical solutions, the chromosome encoding strategy uses binary numbers for chromosome encoding, and the length of the chromosome is the minimum binary length that can represent the number of boxes, and out-of-bounds chromosomes are filtered out.

[0031] To solve the above-mentioned technical problems, the present invention also provides another technical solution:

[0032] A box recommendation device based on an adaptive genetic algorithm includes:

[0033] The mapping module is used to map the distribution location of each private room in the venue into a two-dimensional distribution map, wherein the two-dimensional distribution map is divided into two or more regions, and each region includes two or more private rooms;

[0034] The preset module is used to establish the adaptive genetic algorithm and set the parameters of the adaptive genetic algorithm;

[0035] The recommendation module is used to recommend private rooms using the adaptive genetic algorithm to ensure a balanced distribution of people in each area of ​​the venue. When a private room reservation is received, the recommendation module reads the room usage information and inputs the room usage information and the two-dimensional distribution map into the adaptive genetic algorithm.

[0036] The adaptive genetic algorithm calculates the area with the lowest personnel density in the venue based on the usage of the private rooms and the two-dimensional distribution map; and recommends the best private room obtained by searching within the area with the lowest personnel density.

[0037] To solve the above-mentioned technical problems, the present invention also provides another technical solution:

[0038] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the box recommendation method based on an adaptive genetic algorithm as described in any of the above.

[0039] Unlike existing technologies, the above-mentioned technical solution maps the distribution of each private room within the venue to a two-dimensional distribution map and establishes an adaptive genetic algorithm. The private rooms are transformed into chromosomes, serving as individuals in the genetic algorithm's population. The adaptive genetic algorithm recommends private rooms to customers, ensuring that customers are dispersed throughout the venue. Therefore, contact between customers can be avoided during off-peak hours, and cross-contact can be minimized during peak hours. This technical solution improves the user experience within the venue and ensures a high degree of balance in the use of areas and private rooms while maintaining low personnel density, significantly reducing the risk of user contact infection.

[0040] The above description of the invention is merely an overview of the technical solution of this application. In order to enable those skilled in the art to better understand the technical solution of this application and to implement it based on the description and drawings, and to make the above-mentioned objectives and other objectives, features and advantages of this application easier to understand, the following description is provided in conjunction with the specific embodiments and drawings of this application. Attached Figure Description

[0041] The accompanying drawings are only used to illustrate the principles, implementation methods, applications, features, and effects of specific embodiments of the present invention and other related contents, and should not be considered as limitations on this application.

[0042] In the accompanying drawings of the instruction manual:

[0043] Figure 1 A flowchart illustrating the compartment recommendation method based on adaptive genetic algorithm as described in the specific implementation;

[0044] Figure 2 The flowchart for recommending private rooms using an adaptive genetic algorithm is shown in the specific implementation method.

[0045] Figure 3 A detailed flowchart illustrating the process of obtaining the optimal private room in the specific implementation method;

[0046] Figure 4 This is a block diagram of the compartment recommendation device based on adaptive genetic algorithm described in a specific implementation.

[0047] Figure 5 A schematic diagram of the computer-readable storage medium described in a specific embodiment;

[0048] The reference numerals used in the above figures are explained as follows:

[0049] 400. A box recommendation device based on an adaptive genetic algorithm;

[0050] 401. Mapping Module; 402. Preset Module; 403. Recommendation Module;

[0051] 500. Computer-readable storage medium; Detailed Implementation

[0052] To illustrate the possible application scenarios, technical principles, implementable specific solutions, and achievable objectives and effects of this application in detail, the following description, in conjunction with the listed specific embodiments and accompanying drawings, provides a detailed explanation. The embodiments described herein are merely illustrative of the technical solutions of this application and are therefore intended to limit the scope of protection of this application.

[0053] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The term "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment, nor does it specifically limit its independence or connection with other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0054] Unless otherwise defined, the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the use of related terms herein is merely for the purpose of describing particular embodiments and is not intended to limit this application.

[0055] In the description of this application, the term "and / or" is used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and A and B exist simultaneously. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" logical relationship.

[0056] In this application, terms such as “first” and “second” are used only to distinguish one entity or operation from another, and do not necessarily require or imply any actual quantity, hierarchy or order relationship between these entities or operations.

[0057] Without further limitations, the use of terms such as “comprising,” “including,” “having,” or other similar open-ended expressions in this application is intended to cover non-exclusive inclusion, which does not exclude the presence of additional elements in a process, method, or product that includes the stated elements, such that a process, method, or product that includes a list of elements may include not only those defined elements but also other elements not expressly listed, or elements inherent to such a process, method, or product.

[0058] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than," "less than," and "exceeding" are understood to exclude the stated number; expressions such as "above," "below," and "within" are understood to include the stated number. Furthermore, in the description of the embodiments in this application, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times," unless otherwise explicitly specified.

[0059] In the description of the embodiments of this application, the space-related expressions used, such as "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "vertical," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiments or drawings. They are only for the purpose of describing the specific embodiments of this application or for the reader's understanding, and do not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0060] Unless otherwise expressly specified or limited, the terms "installation," "connection," "linking," "fixing," and "setting," as used in the description of the embodiments of this application, should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two components or the interaction between two components. For those skilled in the art to which this application pertains, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0061] Please see Figures 1 to 5 This embodiment provides a method, apparatus, and storage medium for recommending private rooms based on an adaptive genetic algorithm. This method, apparatus, and storage medium can be applied to indoor venues with multiple private rooms, such as KTVs, foot massage parlors, restaurants, and bars. The method recommends the best private room to customers, thereby balancing the density of people in different areas of the venue, ensuring minimal overlap in routes between customers in different rooms, maintaining greater social distancing among customers, and reducing cross-contact.

[0062] like Figure 1 As shown, the box recommendation method based on adaptive genetic algorithm includes the following steps:

[0063] S101. Map the distribution location of each private room in the venue into a two-dimensional distribution map, wherein the two-dimensional distribution map is divided into two or more regions, and each region includes two or more private rooms;

[0064] S102. Establish an adaptive genetic algorithm and set the parameters of the adaptive genetic algorithm;

[0065] S103. Recommend private rooms using the adaptive genetic algorithm to ensure a balanced distribution of people in each area of ​​the venue.

[0066] like Figure 2 As shown, the step of recommending private rooms using the adaptive genetic algorithm to achieve a balanced distribution of people in each area of ​​the venue includes the following steps:

[0067] S201. Upon receiving a private room reservation, read the private room usage information and input the private room usage information and the two-dimensional distribution map into the adaptive genetic algorithm.

[0068] S202. The adaptive genetic algorithm calculates the area with the lowest personnel density in the venue based on the usage of the private rooms and the two-dimensional distribution map; and recommends the best private room obtained by searching within the area with the lowest personnel density.

[0069] In step S101, the spatial structure of the venue and the distribution of the private rooms within the venue are first mapped into a two-dimensional distribution map for easy calculation. Then, the two-dimensional distribution map is divided into two or more regions. Each region records the distance between each private room and other private rooms within that region. The region is defined as KM, where KM = {P1, P2, ..., P...} i , ..., P n}, wherein the P i The distance between each private room in the i-th region is recorded.

[0070] The Where, x mn Indicates the Pth i Within a given area, the distance between the nth box and the mth box.

[0071] In step S102, the parameters set include the population size, maximum number of iterations, and elite rate of the adaptive genetic algorithm.

[0072] In step S103, the core of the adaptive genetic algorithm for recommending private rooms is customer decentralization. That is, the adaptive genetic algorithm calculates a reasonable private room allocation scheme to balance the density of people in different areas of the room, ensure that customers in different private rooms have less overlapping routes, avoid contact between customers during off-peak hours, and minimize cross-contact during peak hours, so that customers in the venue have a greater social distance and reduce the risk of contact infection.

[0073] In this embodiment, all customers visiting the venue will be automatically recommended private rooms using the aforementioned adaptive genetic algorithm. The room recommendation process utilizes an adaptive genetic algorithm to intelligently allocate private rooms based on their occupancy status (i.e., whether they are currently in use).

[0074] In one embodiment, establishing the adaptive genetic algorithm includes the following steps:

[0075] Each of the aforementioned compartments is converted into chromosomes using a chromosome encoding strategy;

[0076] An initial population is formed, wherein the individuals in the initial population include the chromosome and other randomly generated chromosomes, and the number of individuals in the initial population is greater than or equal to three times the number of boxes;

[0077] Set the fitness function f i And selection operators.

[0078] In this embodiment, a genetic algorithm is used to solve the private room recommendation problem. Therefore, an encoding process is needed to convert the actual situation parameters of the private rooms within the venue into objects or parameters such as genes and chromosomes in the genetic algorithm. This allows the genetic algorithm to interpret and process the actual situation involved in the private room recommendation. Given that the research object in this embodiment is private room information in enclosed venues, and each private room has its own unique number, the chromosome encoding strategy in this embodiment preferentially selects a binary encoding method that is simple to encode and decode and whose crossover and mutation operations are easy to implement. The chromosome encoding strategy converts the decimal private room number into binary code. The length of the chromosome can be adaptively adjusted according to the number of private rooms in the venue. The length of the chromosome is taken as the minimum binary length that can represent the number of private rooms; therefore, out-of-bounds chromosomes generated during the encoding process are also filtered out.

[0079] When forming the initial population, n data strings are randomly generated, each data string being called an individual or chromosome. The n individuals generated are called a population. During computation, the randomly generated individuals are used as the initial values ​​for iteration. When performing the adaptive genetic algorithm, it is necessary to comprehensively weigh the settings of the population parameters. In this implementation, the population size is three times the number of private rooms in the venue.

[0080] In this embodiment, the overall optimization objective of the adaptive genetic algorithm is to find the minimum probability of customer contact within the venue; therefore, the fitness function is set accordingly. In the formula: Let be the objective function. In the formula P represents the sum of distances from the current private room i to several already occupied private rooms j in this area, where P is the penalty function.

[0081] In this embodiment, The smaller the value, the greater the distance between the current room and other occupied rooms, and the lower the probability of contact. As the value exceeding the maximum room number increases, The larger the value, the better.

[0082] The selection operator employs a roulette wheel selection strategy that combines partial elite retention with selection operations.

[0083] The probability of individual i being selected in the roulette wheel game.

[0084] In the formula, popsize is the population size; f i Let be the fitness value of the i-th individual.

[0085] In this embodiment, in order to ensure the genetic diversity of the population and at the same time improve the convergence efficiency of the algorithm, a selection operation is performed by using a combination of roulette wheel betting and partial elite retention.

[0086] like Figure 3 As shown, the adaptive genetic algorithm calculates the area with the lowest personnel density in the venue based on the usage of the private rooms and the two-dimensional distribution map; and recommends the best private room obtained by searching within the area with the lowest personnel density, including the following steps:

[0087] S301. Calculate the area with the lowest population density in the two-dimensional distribution map according to the area density calculation formula;

[0088] S302. Calculate the fitness of each chromosome in the population;

[0089] S303, Perform a selection operation using a selection operator;

[0090] S304. Execute adaptive crossover and mutation strategies;

[0091] S305. Determine if the termination condition is met. If not, repeat steps S301-S304. If yes, jump to S306 to obtain the optimal box (i.e., repeat the above steps until the termination condition is met and stop iterating to obtain the optimal box).

[0092] During the evolutionary iteration process, the algorithm needs to calculate the density of each region in the two-dimensional distribution map. Therefore, in step S301, it is necessary to determine a region with the lowest density for searching.

[0093] The formula for calculating regional density is: In the formula: n is the number of private rooms already occupied in this area, and m is the total number of private rooms in this area.

[0094] In step S302, the fitness function f from the above embodiments is used. i Calculate the fitness of each chromosome in the population. In step S303, the selection operation is performed using the roulette wheel selection strategy with partial elite retention described in the above embodiments.

[0095] In step S304, the adaptive value selection method can provide the optimal crossover probability and mutation probability relative to a certain solution. Therefore, in this embodiment, the value of the adaptive crossover is P. c The values ​​of Pm and the mutation strategy are determined by an adaptive value selection method.

[0096]

[0097]

[0098] In the formula: P cmax With P cmin f' and f' represent the upper and lower limits of the crossover probability range during genetic operations, respectively, and f' is the fitness value of the higher individual among the two individuals undergoing the crossover operation. avg f is the average fitness value of the population per generation. max P represents the maximum fitness value in the current population, where μ is a constant; mmax With P mmin , where represents the upper and lower limits of the mutation probability range, and f is the fitness value of the mutated individual.

[0099] like Figure 4 As shown, in one embodiment, a box recommendation device 400 based on an adaptive genetic algorithm is provided. This box recommendation device 400 based on an adaptive genetic algorithm includes:

[0100] The mapping module 401 is used to map the distribution location of each private room in the venue into a two-dimensional distribution map, wherein the two-dimensional distribution map is divided into two or more regions, and each region includes two or more private rooms;

[0101] The preset module 402 is used to establish an adaptive genetic algorithm and set the parameters of the adaptive genetic algorithm;

[0102] Recommendation module 403 is used to recommend private rooms through the adaptive genetic algorithm to ensure a balanced distribution of people in each area of ​​the venue; when the recommendation module receives a private room reservation, it reads the private room usage information and inputs the private room usage information and the two-dimensional distribution map into the adaptive genetic algorithm;

[0103] The adaptive genetic algorithm calculates the area with the lowest personnel density in the venue based on the usage of the private rooms and the two-dimensional distribution map; and recommends the best private room obtained by searching within the area with the lowest personnel density.

[0104] like Figure 5 As shown, in another embodiment, a computer-readable storage medium 500 is provided, on which a computer program is stored, which, when executed by a processor, implements the box recommendation method based on an adaptive genetic algorithm as described in any of the above embodiments.

[0105] The aforementioned room recommendation method based on adaptive genetic algorithms can comprehensively manage the occupancy and vacancy status of all rooms within the venue. Through this adaptive genetic algorithm, it rationally allocates rooms to newly arrived customers, distributing them across different rooms and balancing the density of people in different areas of the venue. This ensures minimal overlap in routes between customers in different rooms. This implementation method avoids contact between customers during off-peak hours and minimizes cross-contact during peak hours, thus maintaining greater social distancing and reducing the risk of contact infection. Therefore, it significantly reduces the risk of contact infection for customers within the venue.

[0106] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not limit the scope of patent protection of this application. Any technical solutions that are based on the essential concept of this application and utilize the content described in the text and drawings of this application, resulting in equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this application.

Claims

1. A method for recommending private rooms based on an adaptive genetic algorithm, characterized in that, Includes the following steps: The distribution of each private room in the venue is mapped into a two-dimensional distribution map, which is divided into two or more regions, each region including two or more private rooms; Establish an adaptive genetic algorithm and set its parameters; The adaptive genetic algorithm is used to recommend private rooms to achieve a balanced distribution of people in all areas of the venue. This includes the following steps: Upon receiving a private room reservation, the usage information of the private room is read, and the usage information and the two-dimensional distribution map are input into the adaptive genetic algorithm; The adaptive genetic algorithm calculates the area with the lowest personnel density in the venue based on the usage of the private rooms and the two-dimensional distribution map; and recommends the best private room obtained by searching within the area with the lowest personnel density. The establishment of the adaptive genetic algorithm includes the following steps: Each of the aforementioned compartments is converted into chromosomes using a chromosome encoding strategy; An initial population is formed, wherein the individuals in the initial population include the chromosome and other randomly generated chromosomes, and the number of individuals in the initial population is greater than or equal to three times the number of boxes; Set the fitness function and selection operators; The adaptive genetic algorithm calculates the area with the lowest personnel density within the venue based on the usage of the private rooms and the two-dimensional distribution map; and recommends the best private room obtained by searching within the area with the lowest personnel density, including the following steps: Calculate the area with the lowest population density within the two-dimensional distribution map using the regional density calculation formula; The formula for calculating the density of the region is: In the formula: n is the number of private rooms already occupied in this area, and m is the total number of private rooms in this area; Calculate the fitness of each chromosome in the population; Selection operations are performed by selecting operators; Implement adaptive crossover and mutation strategies; Repeat the above steps until the termination condition is met, then stop the iteration to obtain the optimal private room.

2. The compartment recommendation method based on adaptive genetic algorithm according to claim 1, characterized in that, The area records the distances between each of the VIP boxes and other VIP boxes within the area.

3. The compartment recommendation method based on adaptive genetic algorithm according to claim 1, characterized in that, The fitness function In the formula: G is the objective function, In the formula This represents the sum of the distances from the current private room X to several already occupied private rooms J in this area. For the penalty function, 。 4. The compartment recommendation method based on adaptive genetic algorithm according to claim 1, characterized in that, The selection operator employs a roulette wheel selection strategy that combines partial elite retention with selection operations. The individual in the roulette game Probability of being selected ; In the formula Population size; For the first The fitness value of each individual.

5. The compartment recommendation method based on adaptive genetic algorithm according to claim 1, characterized in that, The adaptive crossover and mutation strategies are determined using an adaptive value selection method, where the adaptive crossover value is... The mutation strategy takes the value Pm; ； ； In the formula: and These represent the upper and lower limits of the crossover probability range during genetic operations. The fitness value of the higher of the two individuals to be crossovered. The average fitness value of the population per generation. This represents the maximum fitness value in the current population. It is a constant; and These represent the upper and lower limits of the range of mutation probability values. This represents the fitness value of the mutated individual.

6. The compartment recommendation method based on adaptive genetic algorithm according to claim 1, characterized in that, The chromosome encoding strategy uses binary numbers for chromosome encoding. The length of the chromosome is the minimum binary length that can represent the number of compartments, and out-of-bounds chromosomes are filtered out.

7. A box recommendation device based on an adaptive genetic algorithm, characterized in that, include: The mapping module is used to map the distribution location of each private room in the venue into a two-dimensional distribution map, wherein the two-dimensional distribution map is divided into two or more regions, and each region includes two or more private rooms; The preset module is used to establish the adaptive genetic algorithm and set the parameters of the adaptive genetic algorithm; The recommendation module is used to recommend private rooms using the adaptive genetic algorithm to ensure a balanced distribution of people in each area of ​​the venue. When a private room reservation is received, the recommendation module reads the room usage information and inputs the room usage information and the two-dimensional distribution map into the adaptive genetic algorithm. The adaptive genetic algorithm calculates the area with the lowest personnel density in the venue based on the usage of the private rooms and the two-dimensional distribution map. And by searching within the area with the lowest population density, the best private room is recommended; The establishment of the adaptive genetic algorithm includes the following steps: Each of the aforementioned compartments is converted into chromosomes using a chromosome encoding strategy; An initial population is formed, wherein the individuals in the initial population include the chromosome and other randomly generated chromosomes, and the number of individuals in the initial population is greater than or equal to three times the number of boxes; Set the fitness function and selection operators; The adaptive genetic algorithm calculates the area with the lowest personnel density within the venue based on the usage of the private rooms and the two-dimensional distribution map; and recommends the best private room obtained by searching within the area with the lowest personnel density, including the following steps: Calculate the area with the lowest population density within the two-dimensional distribution map using the regional density calculation formula; The formula for calculating the density of the region is: In the formula: n is the number of private rooms already occupied in this area, and m is the total number of private rooms in this area; Calculate the fitness of each chromosome in the population; Selection operations are performed by selecting operators; Implement adaptive crossover and mutation strategies; Repeat the above steps until the termination condition is met, then stop the iteration to obtain the optimal private room.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the box recommendation method based on the adaptive genetic algorithm as described in any one of claims 1 to 6.

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