Dual-objective optimization method for kitchen waste transfer station location and service platform

Abstract

The application relates to a kitchen garbage transfer station optimization site selection method and a service platform considering double targets, the method comprising the following steps: randomly encoding candidate transfer station candidate points to generate a first site selection configuration coding array, the first site selection configuration coding array comprising a plurality of first codes; performing non-dominated solution sorting and genetic evolution operation on the first codes based on a preset improved NSGA-II algorithm to generate a hybrid site selection configuration coding array; performing new population iteration on the hybrid site selection configuration coding array by using an elite reservation strategy corresponding to the improved NSGA-II algorithm to generate a second site selection configuration coding array with a plurality of second codes; determining the fitness of the second codes, performing non-dominated solution sorting, genetic evolution operation and new population iteration processing based on the elite reservation strategy on the second codes in the second site selection configuration coding array according to the fitness to generate a target site selection configuration coding array, and obtaining target codes from the target site selection configuration coding array to obtain a site selection result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of garbage classification, in particular to a kitchen garbage transfer station optimization site selection method and service platform considering double targets. BACKGROUND

[0002] With the implementation of garbage classification system, more and more scholars begin to focus on the research of garbage transfer station under the background of garbage classification, but most of the research focuses on the site selection problem of a single type of garbage transfer station or the configuration problem of internal processing equipment; meanwhile, in the related art, the reconstruction of garbage transfer station in garbage classification is subject to land resources, funds and NIMBY effect, the existing garbage transfer station reconstruction site selection scheme only models and solves the multi-objective selection of various processing facilities for municipal solid waste, and does not research the site selection problem of kitchen garbage transfer station.

[0003] In the related art, the site selection of kitchen garbage transfer station has the following challenges: first, kitchen garbage transfer station is a typical NIMBY facility, kitchen garbage is perishable and smelly, and the odor, leachate and noise generated by the operation of the transfer station will affect the environment and cause complaints from surrounding residents, and if it is too far from the kitchen garbage generation point of the residents, the cost of cleaning and transporting will increase; second, land resources are scarce, and it is difficult to have a site for building a garbage transfer station, especially under the garbage classification system, the reconstruction of municipal solid waste classification collection and transportation system requires the construction of a large number of classified garbage transfer stations; third, the construction cost of kitchen garbage transfer station is high, in order to minimize the impact on the surrounding environment and improve the operation efficiency of the transfer station, certain intelligent equipment will be invested, such as intelligent tank, intelligent deodorization equipment, sewage treatment equipment and various environmental monitoring equipment, etc., but the investment cost will increase.

[0004] At present, the reconstruction site selection scheme of kitchen garbage transfer station in the related art has the problems of high site selection cost and construction cost, high collection and transportation cost of the reconstructed garbage transfer station, and no effective solution has been proposed. SUMMARY

[0005] The embodiments of the present application provide a kitchen garbage transfer station optimization site selection method and service platform considering double targets, as well as a device and a storage medium, to at least solve the problem that the reconstruction site selection scheme of kitchen garbage transfer station in the related art has high site selection cost and construction cost, high collection and transportation cost of the reconstructed garbage transfer station, and no effective solution has been proposed.

[0006] In a first aspect, an embodiment of the present application provides a method for optimizing site selection of a kitchen waste transfer station considering double targets, comprising: randomly encoding candidate points of a candidate transfer station to generate a first site selection configuration encoding array, wherein the first site selection configuration encoding array comprises a plurality of first encodings, the first encoding is used to represent a site selection result of configuring the candidate point of the transfer station as a target transfer station, the target transfer station is associated with a site selection cost parameter and an environmental negative effect parameter, the site selection cost parameter is determined according to a garbage collection cost parameter and a garbage transfer cost parameter corresponding to the target transfer station; based on a preset improved NSGA-II algorithm, performing non-dominated solution sorting and genetic evolution operation on the first encoding to generate a hybrid site selection configuration encoding array, wherein the genetic evolution operation adopts a dynamic crossover probability and a dynamic mutation probability; using an elite retention strategy corresponding to the improved NSGA-II algorithm, performing new population iteration on the hybrid site selection configuration encoding array to generate a second site selection configuration encoding array with a plurality of second encodings, wherein the elite retention strategy introduces a mechanism for filling the second encoding through crossover and mutation operations, the second encoding is used to represent a site selection result of currently configuring the candidate point of the transfer station as the target transfer station; determining a fitness corresponding to the second encoding, performing non-dominated solution sorting, genetic evolution operation and new population iteration processing based on the elite retention strategy on the second encoding in the second site selection configuration encoding array according to the fitness to generate a target site selection configuration encoding array, and obtaining a target encoding from the target site selection configuration encoding array to obtain a site selection result, wherein the site selection result comprises the target encoding, and the fitness is determined according to the site selection cost parameter and the environmental negative effect parameter associated with the target transfer station corresponding to the second encoding.

[0007] In a second aspect, an embodiment of the present application provides a device for optimizing site selection of a kitchen waste transfer station considering double targets, comprising:

[0008] The encoding module is configured to randomly encode candidate points of a candidate transfer station to generate a first site selection configuration encoding array, wherein the first site selection configuration encoding array comprises a plurality of first encodings, the first encoding is used to represent a site selection result of configuring the candidate point of the transfer station as a target transfer station, the target transfer station is associated with a site selection cost parameter and an environmental negative effect parameter, the site selection cost parameter is determined according to a garbage collection cost parameter and a garbage transfer cost parameter corresponding to the target transfer station.

[0009] The operation module is configured to perform non-dominated solution sorting and genetic evolution operation on the first encoding based on a preset improved NSGA-II algorithm to generate a hybrid site selection configuration encoding array, wherein the genetic evolution operation adopts a dynamic crossover probability and a dynamic mutation probability.

[0010] The generating module is configured to generate a second site configuration coding array with a plurality of second codes by using an elite preservation strategy corresponding to the improved NSGA-II algorithm to perform new population iteration on the mixed site configuration coding array, wherein the elite preservation strategy introduces a mechanism for filling the second codes by using a crossover and mutation operation, and the second codes are used to represent a site selection result of currently configuring the candidate point of the transfer station as the target transfer station.

[0011] The processing module is configured to determine a fitness corresponding to the second code, perform non-dominated solution sorting, genetic evolution operation and new population iteration processing based on the elite preservation strategy on the second code in the second site configuration coding array according to the fitness, generate a target site configuration coding array, and obtain a target code from the target site configuration coding array to obtain a site selection result, wherein the site selection result includes the target code, and the fitness is determined according to a site selection cost parameter and an environmental negative effect parameter associated with the target transfer station corresponding to the second code.

[0012] In a third aspect, an embodiment of the present application provides a service platform, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method for optimizing site selection of a kitchen waste transfer station considering double targets according to the first aspect when executing the computer program.

[0013] In a fourth aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon, and the program is executable by a processor to implement the steps of the method for optimizing site selection of a kitchen waste transfer station considering double targets according to the first aspect.

[0014] Compared with the related art, the kitchen waste transfer station optimization site selection method and service platform considering double targets, the device and the storage medium provided by the embodiments of the present application, by randomly encoding the candidate transfer station candidate points, a first site selection configuration code array is generated, the first site selection configuration code array includes a plurality of first codes; based on the preset improved NSGA-Ⅱ algorithm, the first codes are sorted and genetically evolved, and a hybrid site selection configuration code array is generated; using the elite retention strategy corresponding to the improved NSGA-Ⅱ algorithm, the hybrid site selection configuration code array is iterated to generate a second site selection configuration code array with a plurality of second codes; the fitness of the second code is determined, and the second code in the second site selection configuration code array is sorted, genetically evolved, and iterated based on the elite retention strategy according to the fitness, to generate a target site selection configuration code array, and obtain a target code from the target site selection configuration code array, to obtain a site selection result including the target code; by introducing the fast non-dominated sorting algorithm, using the elite retention strategy that fills the second code through the cross mutation operation, and using the crowding degree and crowding degree comparison operator, the calculation complexity of the algorithm is reduced, the target code is quickly solved, and the basic framework based on the NSGA-Ⅱ algorithm is used, the cross and mutation probability, which is originally a fixed value, is set to a dynamic change value that decreases according to the logsig function as the number of iterations increases, to increase the convergence of the population in the iteration process, so that the population searches in the direction of the global optimal solution, so that the configuration cost of the target transfer station selected is minimized and the total environmental negative effect is minimized, solving the reconstruction site selection scheme of the kitchen waste transfer station, and solving the problems of high site selection cost and construction cost, and high waste transfer and transfer cost of the reconstructed waste transfer station.

[0015] The details of one or more embodiments of the present application are presented in the following drawings and description to make other features, objects and advantages of the present application more apparent. BRIEF DESCRIPTION OF DRAWINGS

[0016] The drawings described herein are intended to provide further understanding of the present application, and form a part of the present application. The schematic embodiments of the present application and the description thereof are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a hardware structure block diagram of a terminal of the kitchen waste transfer station optimization site selection method considering double targets according to the embodiments of the present application;

[0018] Figure 2 is a flowchart of the kitchen waste transfer station optimization site selection method considering double targets according to the embodiments of the present application;

[0019] Figure 3The flowchart of the improved NASG-II algorithm used in the preferred embodiment of the present application;

[0020] Figure 4 The structure block diagram of the kitchen garbage transfer station optimal site selection device considering double targets according to the embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the present application more clear and understandable, the present application is described and explained below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present application. In addition, it can be understood that although the efforts made in this development process can be complex and lengthy, some design, manufacture or production changes made on the basis of the technical content disclosed in the present application are only routine technical means for those of ordinary skill in the art related to the content disclosed in the present application, and should not be understood as insufficient disclosure of the content disclosed in the present application.

[0022] In the present application, the phrase "embodiment" means that the specific features, structures or characteristics described in combination with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase at various places in the specification does not necessarily mean the same embodiment, nor is it an independent or alternative embodiment that is not mutually exclusive with other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present application can be combined with other embodiments without conflict.

[0023] Unless otherwise defined, technical terms and scientific terms used in the present application shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terms "a", "an", "one", "this", and the like, as used in the present application, do not denote number restriction, but can denote singular or plural. The terms "include", "comprise", "have" and any variations thereof in the present application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device including a list of steps or modules (units) is not limited to the listed steps or units, but can further include steps or units not listed or can further include other steps or units inherent to such processes, methods, products or devices. The term "multiple stages" in the present application refers to more than or equal to two stages. The term "and / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that A exists alone, A and B exist together, and B exists alone. The terms "first", "second", "third" and the like in the present application only distinguish similar objects, and do not represent a specific order for the objects.

[0024] The method embodiment provided by the embodiment can be executed in a terminal, a computer or a similar computing device. Taking the case of running on a terminal, Figure 1 is a hardware structure block diagram of a terminal considering the double-target kitchen garbage transfer station optimization site selection method of the embodiment of the present application. As shown in Figure 1 , the terminal can include one or more (only one is shown in Figure 1 ) processor 102 (the processor 102 can include but is not limited to processing devices such as microprocessor MCU or programmable logic device FPGA) and memory 104 for storing data. Optionally, the above terminal can also include transmission equipment 106 for communication function and input and output equipment 108. Those skilled in the art can understand that Figure 1 The structure shown is only schematic, which does not limit the structure of the above terminal. For example, the terminal can include more or less components than those shown in Figure 1 , or have a different configuration from Figure 1 .

[0025] The memory 104 can be used to store computer programs, such as software programs of application software and modules, such as a computer program of the method for optimizing the location of a kitchen waste transfer station considering double targets according to the embodiments of the present application. The processor 102 can execute various functions and data processing, i.e., implement the above method, by running the computer program stored in the memory 104. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include memories remotely arranged with respect to the processor 102, which can be connected to the terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0026] The transmission device 106 is used to receive or send data via a network. Examples of the network include a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet in a wireless manner.

[0027] The embodiments of the present application provide a method for optimizing the location of a kitchen waste transfer station considering double targets, which is run on the terminal, Figure 2 The method for optimizing the location of a kitchen waste transfer station considering double targets according to the embodiments of the present application is shown in a flowchart as Figure 2 The flowchart includes the following steps:

[0028] In step S201, the candidate points of the transfer station are randomly coded to generate a first location configuration coding array, wherein the first location configuration coding array includes a plurality of first codes, and each first code is used to represent a location result of configuring a candidate point of the transfer station as a target transfer station, the target transfer station is associated with a location cost parameter and an environmental negative effect parameter, and the location cost parameter is determined according to a garbage collection cost parameter and a garbage transfer cost parameter corresponding to the target transfer station.

[0029] In the embodiment, before the site selection method of the application is implemented, target information of a plurality of transfer station candidate points to be improved as target transfer stations has been obtained, and the corresponding target information includes position information (longitude and latitude) of the transfer station candidate points, site size of the transfer station candidate points, number of tank bodies equipped by the transfer station candidate points, capacity of each tank body, and surrounding associated garbage collection points; after the target information of the transfer station candidate points is determined, site selection planning of the transfer station is performed according to two target functions set, i.e., minimum site selection cost of the transfer station and minimum total environmental negative effect, that is, a plurality of alternative transfer station candidate points are configured as target transfer stations to be reconstructed, and then the two target functions are calculated, and then the site selection scheme of the optimal target transfer station meeting the budget is selected; in the embodiment, the site selection cost parameters include but are not limited to collection and transportation cost in the garbage collection and transportation (collection and transportation) process, station construction cost, and operation cost of the target transfer station; the environmental negative effect parameters include but are not limited to adverse effects of odor, noise, and air pollutants generated by the target transfer station on the permanent residents around the target transfer station; in the embodiment, the transfer station candidate points are initialized, that is, random coding is performed, and at least one of the plurality of transfer station candidate points is randomly configured as a target transfer station; then, a modified non-dominated sorting genetic algorithm II (Nondominated Sorting Genetic Algorithm II, NSGA-II for short) with an elite strategy is used for solving until a site selection result meeting the requirements is obtained, that is, a scheme in which the transfer station candidate points are reconstructed as target transfer stations to minimize the values of the two target functions is obtained; in the embodiment, a first coding corresponds to a genetic coding individual, and the site selection result corresponding to the first coding includes configuring at least one transfer station candidate point as a target transfer station, that is, the first coding is not a coding result of reconstructing one transfer station candidate point as a target transfer station, but a coding result of reconstructing one or more of a plurality of transfer station candidate points as target transfer stations; at the same time, the first site selection configuration coding array corresponds to a population; in the embodiment, when initialized, the number of individuals of the initialized population is set as N, the parent population P corresponding to the first site selection configuration coding array is randomly generated, the evolution generation t is set as 0, and each parameter such as the maximum evolution generation, the population size, and the mutation probability is set. t

[0030] ​It should be noted that in the present embodiment, there are multiple transfer station candidate points, and the garbage of the garbage collection point can only be transported to a target transfer station (a selected transfer station candidate point) nearby, one target transfer station can accept garbage from multiple garbage collection points, and the relationship between the garbage collection point and the transfer station is "many-to-one"; the garbage of one target transfer station can be transported to multiple garbage disposal points, and one garbage disposal point can accept garbage from multiple target transfer stations, and the relationship between the target transfer station and the garbage disposal point is "many-to-many"; it can be understood that the kitchen waste recycling includes three important links of front-end collection, middle-end collection and transportation, and end disposal; the site selection method of the present embodiment is for the middle-end collection and transportation link of the kitchen waste, that is, the collection and transportation process from the front-end garbage collection point to the end garbage disposal point, and the collection and transportation link is divided into two sub-links of cleaning and transportation and transfer; the cleaning and transportation sub-link is responsible for transporting the garbage of the garbage collection point to the target transfer station, and the transfer sub-link is responsible for transporting the garbage of the target transfer station to the garbage disposal point; the kitchen waste collection point has multiple categories and is widely distributed, mainly including restaurants in commercial areas, snack shops in food streets, hotel restaurants, canteens, garbage classification kiosks (points) in various communities, and farmers markets, etc.; the garbage of the garbage collection point can only be transported to the target transfer station by small cleaning vehicles (such as platform vehicles, tricycles, and closed small trucks) due to narrow roads, and the target transfer station is mainly established to reduce the garbage collection and transportation cost, is located in the urban area near the garbage collection point, and is responsible for temporarily storing the garbage of the surrounding garbage collection points, and then the garbage is transported to the final garbage disposal point by large transfer vehicles (such as large tank trucks).

[0031] In step S202, the first encoding is sorted by non-dominated solution and genetic evolution operation based on the preset improved NSGA-Ⅱ algorithm, and a hybrid site selection configuration encoding array is generated, wherein the genetic evolution operation adopts dynamic crossover probability and dynamic mutation probability.

[0032] In the present embodiment, after generating the first site selection configuration encoding array including multiple first encodings, the multiple first encodings are sequentially sorted by non-dominated solution and genetic evolution operation; in the present embodiment, the parent population corresponding to the first site selection configuration encoding array is first sorted by non-dominated solution, that is, each individual (corresponding to a first encoding) in the parent population P t is classified and the crowding distance is calculated; then, the offspring population Q t is generated, that is, each individual in the parent population P t is genetically operated by using the genetic algorithm to generate the offspring population Q t , and then the parent population P t and the offspring population Q t are merged to obtain a new population R corresponding to the hybrid site selection configuration encoding arrayt .

[0033] In this embodiment, based on the basic framework of the NSGA-II algorithm, the crossover and mutation probabilities, which were originally fixed values, are set to dynamically change values ​​that decrease according to the logsig function as the number of iterations increases, thereby increasing the convergence of the population and allowing the population to search towards the global optimal solution.

[0034] In step S203, the elite retention strategy corresponding to the improved NSGA-Ⅱ algorithm is used to perform a new population iteration on the mixed location configuration code array to generate a second location configuration code array with multiple second codes. The elite retention strategy introduces a mechanism to fill the second codes through crossover mutation operations. The second codes are used to represent the current location selection result of configuring the transfer station candidate point as the target transfer station.

[0035] In this embodiment, based on the elite retention strategy corresponding to the improved NSGA-Ⅱ algorithm, the next generation population is generated; that is, the new population R is sorted by fast non-dominated sorting. t Each individual (corresponding to a corresponding code) in the ensemble is graded and the crowding distance is calculated. Individuals with a crowding distance of the target value (for example, zero) are probabilistically deleted to reduce duplication and increase the diversity of the population. At the same time, if the corresponding codes are insufficient after deletion (the number of individuals is insufficient), crossover mutation is performed again based on the corresponding second code to maintain the population size, thereby achieving the new generation of parent population P corresponding to the second location configuration code array through the elite retention strategy. t+1 In this embodiment, in order to prevent the problem of insufficient number of second codes to be solved, a cross-mutation method is introduced into the inner loop to fill the second codes and quickly solve the optimal solution.

[0036] Step S204, determine the fitness corresponding to the second code, and according to the fitness, perform non-dominated solution sorting, genetic evolution operation and new population iterative processing based on the elite retention strategy on the second code in the second location configuration code array to generate a target location configuration code array, and obtain the target code from the target location configuration code array to obtain a location result, wherein the location result includes the target code, and the fitness is determined based on the location cost parameter and environmental negative effect parameter associated with the target transfer station corresponding to the second code.

[0037] In the embodiment, after implementing the non-dominated solution sorting, genetic evolution operation and population iteration based on the improved NSGA-Ⅱ algorithm on the first encoding, the fitness corresponding to each second encoding is calculated, that is, the site selection cost and environmental negative effect parameter associated with the site selection result corresponding to the second encoding are calculated, and it is determined whether the corresponding site selection cost and environmental negative effect parameter are minimum, so as to decide to continue to perform the non-dominated solution sorting, genetic evolution operation and population iteration based on the improved NSGA-Ⅱ algorithm on the current generated encoding, until the target site selection configuration encoding array is generated, and the site selection result including the target encoding is obtained.

[0038] Through the above steps S201 to S204, the first site selection configuration encoding array including a plurality of first encodings is generated by randomly encoding the candidate transfer station candidate points; the non-dominated solution sorting and genetic evolution operation are performed on the first encoding based on the preset improved NSGA-Ⅱ algorithm, and the hybrid site selection configuration encoding array is generated; the new population iteration is performed on the hybrid site selection configuration encoding array by using the elite reservation strategy corresponding to the improved NSGA-Ⅱ algorithm, and the second site selection configuration encoding array having a plurality of second encodings is generated; the fitness corresponding to the second encoding is determined, the second encoding in the second site selection configuration encoding array is sorted, genetically evolved and processed by the new population iteration based on the elite reservation strategy according to the fitness, the target site selection configuration encoding array is generated, and the target encoding is obtained from the target site selection configuration encoding array, and the site selection result including the target encoding is obtained; the calculation complexity of the algorithm is reduced, the target encoding is quickly solved by introducing the fast non-dominated sorting algorithm, the mechanism of the elite reservation strategy of filling the second encoding by using the crossover and mutation operation, and the crowding distance and crowding distance comparison operator, and the crossover and mutation probabilities which are originally fixed values are set to dynamic values decreasing according to the logsig function with the increase of the iteration number based on the basic framework of the NSGA-Ⅱ algorithm, so as to increase the convergence of the population in the iteration process, make the population search in the direction of the global optimal solution, minimize the configuration cost of the target transfer station and minimize the total environmental negative effect, and solve the reconstruction site selection scheme of the kitchen waste transfer station, which has the problems of high site selection cost and construction cost, and high collection and transportation cost of the waste transfer station after reconstruction.

[0039] It should be noted that the embodiments of the present application consider the method for site selection and optimal configuration of kitchen waste transfer station under the double target to realize the minimization of site selection cost and environmental negative effect double target, improve the traditional multi-objective genetic algorithm, increase the number and quality of optimal solution, and improve the solving speed as the target, and the site selection method of the embodiments of the present application is implemented through the following aspects: first, according to the characteristics of kitchen waste collection and transportation, a mixed integer programming model is established to depict the site selection and optimal configuration problem of the transfer station, and the target is to minimize the site selection cost of the transfer station (including construction cost, operation cost, cleaning and transportation cost and transfer cost) and environmental negative effect; second, in view of the problems that the traditional heuristic algorithm for solving multi-objective is difficult to converge in the solving process and is easy to fall into local optimum, an improved non-dominated sorting genetic algorithm (NSGA-II) with an elite strategy is adopted, that is, the crossover and mutation probabilities which are originally fixed values are set to dynamic decreasing values changing with the iteration number, the diversity of the population is increased, the population is searched in the direction of the global optimal solution, and the effectiveness of the improved algorithm is verified.

[0040] In some embodiments, based on the preset improved NSGA-II algorithm, the non-dominated solution sorting and genetic evolution operation are performed on the first encoding to generate a hybrid site selection and configuration encoding array, and the following steps are implemented:

[0041] In step 21, the non-dominated sorting algorithm is used to perform non-dominated sorting on all the first encodings, and the crowding degree of all the first encodings subjected to the non-dominated sorting is calculated to obtain the corresponding encoding parameters of each first encoding, wherein the encoding parameters include the non-dominated sorting parameters and the crowding degree.

[0042] In this embodiment, the non-dominated sorting algorithm corresponding to the improved NSGA-II algorithm is used to perform non-dominated layering on all the first encodings until all the first encodings are layered, so as to obtain the non-dominated sorting parameters corresponding to each first encoding, which represent to which layer of non-dominated layer the corresponding first encoding is allocated; then the corresponding crowding degree of the first encoding allocated to each layer of non-dominated layer is calculated, so that the corresponding encoding parameters of each first encoding are obtained, which provide screening parameters for selecting the optimal encoding (corresponding to the first non-dominated encoding) based on the crowding degree comparison operator.

[0043] It can be understood that the non-dominated solution sorting and crowding degree calculation involved in the embodiments of the present application are known to those skilled in the art, and the corresponding sorting and calculation are known technologies, that is, the non-dominated solution sorting and crowding degree calculation of the present application do not constitute a limitation that is not clear to the present application.

[0044] In step 22, the first non-dominated encoding is selected from all the first encodings according to the non-dominated sorting parameters and the crowding degree.

[0045] In the embodiment, after the fast non-dominant sorting and the crowdedness calculation are completed, all the first encodings have two attribute parameters, i.e., the non-dominant sorting parameter and the crowdedness. According to the two attributes, the crowdedness comparison operator can be defined, and based on the crowdedness comparison operator, the corresponding first non-dominant encoding can be screened out. Specifically, if the non-dominant sorting of the e-th first encoding is better than that of the g-th first encoding, or the e-th first encoding and the g-th first encoding have the same non-dominant sorting and the crowdedness of the e-th first encoding is greater than that of the g-th first encoding, the e-th first encoding is taken as the first non-dominant encoding. In this way, all the first non-dominant encodings are screened out from all the first encodings.

[0046] In step 23, the genetic evolution operation is performed on all the first encodings by using the preset genetic algorithm to generate a preset number of first sub-encodings. The genetic evolution operation includes one of tournament selection, crossover and mutation. The crossover corresponds to the dynamic crossover probability generated by the logsig function with the preset gradient decreasing. The mutation corresponds to the dynamic mutation probability generated by the logsig function with the preset gradient decreasing.

[0047] In the embodiment, the genetic algorithm is used to perform the genetic operation including selection, crossover and mutation on all the first encodings to generate the offspring population including a plurality of first sub-encodings. In the embodiment, the mutation and crossover probabilities are set to dynamically decrease with the change of the iteration number, thereby increasing the diversity of the population and enabling the population to search in the direction of the global optimal solution.

[0048] In step 24, all the first non-dominant encodings and all the first sub-encodings are merged to generate the hybrid site configuration encoding array.

[0049] Through the above steps 21 to 24, the non-dominant solution sorting and the genetic evolution operation are performed on the first encodings based on the improved NSGA-II algorithm to generate the hybrid site configuration encoding array. By using the crowdedness and the crowdedness comparison operator, the calculation complexity of the algorithm is reduced, the individuals (corresponding to the encodings) in the optimal front can be uniformly expanded to the entire Pareto domain, and the diversity of the population is ensured.

[0050] In some embodiments, the improved NSGA-II algorithm is used to perform the new population iteration on the hybrid site configuration encoding array by using the elite retention strategy to generate the second site configuration encoding array having a plurality of second encodings. The following steps are used to achieve this:

[0051] Step 31, using a non-dominant sorting algorithm, the intention coding corresponding to the hybrid site configuration coding array is non-dominantly sorted, and the crowdedness of all intention codings after non-dominant sorting is calculated to obtain the coding parameters corresponding to each intention coding.

[0052] In this embodiment, the non-dominant sorting algorithm corresponding to the improved NSGA-II algorithm is used to divide all intention codings corresponding to the hybrid site configuration coding array into non-dominant layers until all intention codings are divided into layers, so as to obtain the non-dominant sorting parameters corresponding to each intention coding, which represent which layer the corresponding intention coding is allocated to; then the corresponding crowdedness of intention codings allocated to each non-dominant layer is calculated, so as to obtain the coding parameters corresponding to each intention coding; in this embodiment, all individuals (corresponding to intention codings) in the population R t corresponding to the hybrid site configuration coding array are graded and the crowded distance (corresponding to crowdedness) is calculated by using the fast non-dominant sorting, so as to obtain the coding parameters including non-dominant sorting parameters and crowdedness corresponding to all intention codings.

[0053] Step 32, according to the non-dominant sorting parameters of the coding parameters of the intention coding, the first candidate coding is screened out from all intention codings, and the intention coding corresponding to the intention coding with the target crowdedness is deleted from all intention codings except the first candidate coding, so as to obtain the second candidate coding.

[0054] In this embodiment, after the coding parameters of all intention codings are determined, the intention codings are screened according to the non-dominant sorting parameters, and then the intention codings with the crowdedness equal to the target crowdedness (for example, the crowdedness is 0) are deleted from the remaining intention codings, so as to obtain the second candidate coding.

[0055] In this embodiment, the fast non-dominant sorting is used for screening and probabilistic deletion, so as to reduce the repetition of intention codings and increase the diversity of the population corresponding to the second site configuration coding array.

[0056] Step 33, it is judged whether the sum of the coding numbers of the first candidate coding and the second candidate coding is less than the preset coding number threshold, and in the case that the sum of the coding numbers of the first candidate coding and the second candidate coding is not less than the preset coding number threshold, it is determined that the second coding corresponding to the second site configuration coding array includes all first candidate codings and all second candidate codings.

[0057] Step 34, in the case that the sum of the encoding numbers of the first alternative encoding and the second alternative encoding is less than the preset encoding number threshold, the first alternative encoding and the second alternative encoding are crossed and mutated according to the corresponding dynamic probability by using the genetic algorithm to generate third alternative encodings with the encoding number being the preset encoding number threshold, and all the third alternative encodings are taken as the second encodings corresponding to the second site configuration encoding data.

[0058] In the embodiment, after the intention encoding of the target congestion degree is performed, if the number of the screened intention encodings exceeds the set number, all the screened intention encodings are taken as the second encodings corresponding to the second site configuration encoding data; if the number of the screened intention encodings is insufficient, the screened intention encodings are crossed and mutated in the inner loop to generate third alternative encodings with the encoding number satisfying the set number, and the second encodings corresponding to the second site configuration encoding data are obtained.

[0059] By using the non-dominated sorting algorithm in the above steps, the intention encodings corresponding to the mixed site configuration encoding data are non-dominated sorted, and the congestion degrees of all the intention encodings that have completed the non-dominated sorting are calculated to obtain the encoding parameters corresponding to each intention encoding; the first alternative encodings are screened from all the intention encodings according to the non-dominated sorting parameters of the encoding parameters corresponding to the intention encodings, and the intention encodings with the corresponding congestion degrees being the target congestion degree are deleted from all the intention encodings except the first alternative encodings to obtain the second alternative encodings; it is judged whether the sum of the encoding numbers of the first alternative encodings and the second alternative encodings is less than the preset encoding number threshold, and in the case that the sum of the encoding numbers of the first alternative encodings and the second alternative encodings is not less than the preset encoding number threshold, it is determined that the second encodings corresponding to the second site configuration encoding data include all the first alternative encodings and all the second alternative encodings; and in the case that the sum of the encoding numbers of the first alternative encodings and the second alternative encodings is less than the preset encoding number threshold, the first alternative encodings and the second alternative encodings are crossed and mutated according to the corresponding dynamic probability by using the genetic algorithm to generate third alternative encodings with the encoding number being the preset encoding number threshold, and all the third alternative encodings are taken as the second encodings corresponding to the second site configuration encoding data, so that the second site configuration encoding data with multiple second encodings is generated, that is, the next generation population is generated, by using the elite reservation strategy.

[0060] In some embodiments, the fitness corresponding to the second encodings is determined by the following steps:

[0061] Step 41, the target transfer stations corresponding to the second encodings are determined, and based on the position information of the target transfer stations, the garbage collection points and the garbage disposal points associated with each target transfer station are determined.

[0062] In the embodiment, the second encoding not only refers to the second encoding generated by processing the first encoding based on the improved NSGA-II algorithm, but also can be the relevant encoding generated by processing the second encoding in the second site configuration encoding array through non-dominated solution sorting, genetic evolution operation and new population iteration based on the elite reservation strategy. It can be understood that the fitness of the corresponding encoding is calculated to determine whether the corresponding encoding obtained at present is continued to be processed through non-dominated solution sorting, genetic evolution operation and new population iteration based on the elite reservation strategy. When the fitness of the corresponding encoding at present meets the expectation, the corresponding encoding is taken as the target encoding.

[0063] Step 42, after determining the total garbage collection cost of each target transfer station according to the garbage collection cost corresponding to each target transfer station and all garbage collection points associated therewith, determining the total garbage transfer cost of each target transfer station according to the transfer cost of the target transfer station and all garbage disposal points associated therewith, and calculating the site selection cost parameter of each target transfer station based on the construction cost, operation cost, total garbage collection cost and total garbage transfer cost of each target transfer station.

[0064] In the embodiment, because the garbage of the garbage collection point is limited to be transported to the nearest target transfer station, and the garbage of the target transfer station also needs to be transferred to a certain garbage disposal point, the garbage collection point that transports garbage to the target transfer station candidate point is also confirmed after the target transfer station candidate point is configured, so that the corresponding collection cost can be determined, and the garbage of the target transfer station is transferred to which garbage disposal point can also be determined, that is, the corresponding transfer cost of the target transfer station can be determined, and the construction cost and operation cost of the target transfer station are determined, so that the site selection cost parameter of the target transfer station candidate point configured as the target transfer station can be determined, that is, the corresponding target function of the target transfer station, that is, the total transfer station site selection cost, is determined.

[0065] Step 43, determining the number of residents around the garbage collection point associated with each target transfer station, the amount of garbage transported by the garbage collection point to the corresponding target transfer station, the arc length distance of the garbage collection point and the target transfer station on the same transport trajectory circle, the first negative effect influence degree corresponding to the garbage capacity, and the second negative effect influence degree corresponding to the distance, and calculating the environmental negative effect parameter of each target transfer station according to the number of residents, the amount of garbage, the arc length distance, the first negative effect influence degree and the second negative effect influence degree.

[0066] Step 44, taking the site selection cost parameter and the environmental negative effect parameter corresponding to each target transfer station as the sub-fitness corresponding to the target transfer station, and summing the sub-fitness of all target transfer stations corresponding to the second encoding to obtain the fitness corresponding to each second encoding.

[0067] Through the above steps 41 to 44, the corresponding fitness is calculated according to the two objective functions of the target transfer station, so as to provide data parameters for guiding whether the corresponding coding continues to perform non-dominated solution sorting, genetic evolution operation and new population iteration based on elite reservation strategy.

[0068] In some embodiments, the site selection cost parameter and the environmental negative effect parameter are calculated, including the following steps:

[0069] Step 51, the site selection cost parameter C is calculated according to the following formula:

[0070] .

[0071] Step 52, the environmental negative effect parameter U is calculated according to the following formula:

[0072] .

[0073] In this embodiment, when calculating the site selection cost parameter C and the environmental negative effect parameter U, the corresponding constraints include:

[0074] (1) The garbage of each garbage collection point can only be unloaded to one target transfer station, and the constraint formula is as follows:

[0075] .

[0076] (2) The garbage of each target transfer station must be transported to at least one garbage disposal point for disposal, and the constraint formula is as follows:

[0077] .

[0078] (3) The garbage of the garbage collection point will only be transported to the open target transfer station, and the constraint formula is as follows:

[0079] .

[0080] (4) Only the open target transfer station has garbage transported to the garbage disposal point, and the constraint formula is as follows:

[0081] .

[0082] (5) The distance of garbage collection (the distance between the garbage collection point and the target transfer station), the constraint formula is as follows:

[0083] ;

[0084] .

[0085] (6) The distance of transfer (the distance between the target transfer station and the garbage disposal point), the constraint formula is as follows:

[0086] ;

[0087] .

[0088] (7) The actual construction capacity of the target transfer station is greater than or equal to the minimum construction capacity of the transfer station candidate point and less than or equal to the maximum construction capacity of the transfer station candidate point, and the constraint expression is as follows: .

[0089] (8) The construction capacity of the target transfer station is greater than or equal to the total amount of garbage transported from the garbage collection point to the target transfer station, and the constraint expression is as follows: .

[0090] (9) The treatment capacity of the garbage treatment point is greater than or equal to the total garbage generation amount of the garbage collection point, and the constraint expression is as follows: .

[0091] (10) The material balance relationship of the garbage amount entering and exiting the target transfer station, and the constraint expression is as follows: .

[0092] (11) The garbage of the garbage collection point is preferentially transported to the nearest target transfer station for unloading, and the constraint expression is as follows:

[0093] .

[0094] (12) The garbage of the garbage collection point is preferentially transported to the nearest target transfer station for unloading, and the constraint expression is as follows:

[0095] .

[0096] (13) The garbage of the target transfer station is preferentially transported to the nearest garbage treatment point for unloading, and the constraint expression is as follows:

[0097] .

[0098] (14) The treatment capacity of the garbage treatment point is greater than the total amount transported from the target transfer station to the garbage treatment plant, and the constraint expression is as follows:

[0099] .

[0100] (15) The generation amount of the garbage collection point is equal to the amount transported to the garbage treatment point, and the constraint expression is as follows:

[0101] .

[0102] (16) The tank loading rate of the target transfer station must be greater than or equal to , and the constraint expression is as follows:

[0103] .

[0104] wherein, i represents the i-th garbage collection point, I represents the set of garbage collection points, i∈I={1, 2, …, m}, m represents the number of garbage collection points, m is a positive integer; j represents the j-th transfer station candidate point, j∈J; h represents the h-th transfer station candidate point, h∈J; J represents the set of transfer station candidate points, J={1, 2, …, n}, n represents the number of transfer station candidate points, n is a positive integer; k represents the k-th garbage disposal point, k∈K; r represents the r-th garbage disposal point, r∈K; K represents the set of garbage disposal points, K={1, 2, …, p}, p represents the number of garbage disposal points, p is a positive integer; C represents the site selection cost parameter; C max represents the maximum value of the site selection cost parameter; C1 represents the construction cost of the target transfer station; C2 represents the operation cost of the target transfer station; C3 represents the total garbage collection and transportation cost corresponding to one target transfer station; C4 represents the total garbage transfer cost corresponding to one target transfer station; c1 represents the unit construction cost of the j-th transfer station candidate point after being configured as a target transfer station; c2 represents the unit operation cost of each target transfer station; c3 represents the unit transportation cost of the garbage collection and transportation vehicle from the garbage collection point to the target transfer station; c4 represents the unit transportation cost of the garbage transfer vehicle from the target transfer station to the garbage disposal point; T represents the planned service life of the target transfer station; ; Q j represents the actual construction capacity corresponding to the j-th target transfer station, Q j =h j ×Q o ; h j represents the number of tank bodies stored under the actual construction capacity of the j-th target transfer station; Q o represents the capacity of a single tank body of the transfer station candidate point or the target transfer station;

[0105] ; ; p ij represents the amount of garbage transported from the i-th garbage collection point to the j-th transfer station candidate point per day; d ij represents the arc length distance of the i-th garbage collection point and the j-th transfer station candidate point on the same delivery trajectory circle; d ih represents the arc length distance of the i-th garbage collection point and the h-th transfer station candidate point on the same delivery trajectory circle; D jk represents the arc length distance of the j-th transfer station candidate point and the k-th garbage disposal point on the same delivery trajectory circle; D jr represents the arc length distance of the j-th transfer station candidate point and the r-th garbage disposal point on the same delivery trajectory circle; long ilongitude coordinate of the center point of the ith garbage collection point; lat i latitude coordinate of the center point of the ith garbage collection point; long i longitude coordinate of the center point of the jth transfer station candidate point; lat j latitude coordinate of the center point of the jth transfer station candidate point; long h longitude coordinate of the center point of the hth transfer station candidate point; lat h latitude coordinate of the center point of the hth transfer station candidate point; long k longitude coordinate of the center point of the kth garbage disposal point; lat k latitude coordinate of the center point of the kth garbage disposal point; long ij longitude difference between the ith garbage collection point and the jth transfer station candidate point; lat ij latitude difference between the ith garbage collection point and the jth transfer station candidate point; long ih longitude difference between the ith garbage collection point and the hth transfer station candidate point; lat ih latitude difference between the ith garbage collection point and the hth transfer station candidate point; long jk longitude difference between the jth transfer station candidate point and the kth garbage disposal point; lat ih latitude difference between the jth transfer station candidate point and the kth garbage disposal point; long jr longitude difference between the jth transfer station candidate point and the rth garbage disposal point; lat ih latitude difference between the jth transfer station candidate point and the rth garbage disposal point;

[0106] ; ; U represents an environmental negative effect parameter; H i a represents the first negative effect degree corresponding to the garbage capacity of the transfer station candidate point or the target transfer station; p jk b represents the second negative effect degree corresponding to the distance between the garbage collection point and the target transfer station or the transfer station candidate point; represents an arbitrary symbol, represents that there is any i belonging to I, represents that there is any j belonging to J, and represents that there is any k belonging to K; represents the maximum construction capacity of the jth transfer station candidate point.

[0107] In some embodiments, the target code is obtained from the target site configuration code array, and the target code is obtained by the following steps:

[0108] Step 61, determine the fitness corresponding to all third encodings of each target site selection configuration encoding, wherein the third encoding represents the second encoding corresponding to the target site selection configuration encoding.

[0109] Step 62, select the third encoding with the minimum fitness from all third encodings, to obtain the target encoding.

[0110] Through the determination of the fitness corresponding to all third encodings of each target site selection configuration encoding in the above steps, the third encoding represents the second encoding corresponding to the target site selection configuration encoding; and the third encoding with the minimum fitness is selected from all third encodings to obtain the target encoding, so that the optimal solution of the site selection result is determined.

[0111] Figure 3 The flowchart of the improved NASG-II algorithm used in the preferred embodiment of the present application is shown in FIG. 1. Figure 3 The flowchart includes the following steps:

[0112] Step S301, initialization, randomly generate a parent population P t , set the evolution generation t=0, and then execute step S302.

[0113] In the embodiment, the integer coding mode is selected, the population size is set to N, and various parameters such as the maximum evolution generation, the population size, the mutation probability (change rule-function value), etc. are set.

[0114] Step S302, parent population non-dominated solution sorting, and then execute step S303.

[0115] In the embodiment, the fast non-dominated sorting is used to classify each individual in P t and calculate the crowding distance.

[0116] Step S303, selection, crossover, and mutation, generate a child population Q t with N individuals, and then execute step S304.

[0117] In the embodiment, the genetic algorithm (selection, crossover, and mutation) is used to generate the child population Q t from P t .

[0118] Step S304, merge P t and Q t to obtain a new population R t , and then execute step S305.

[0119] Step S305, sort R tThe non-dominated solution ranking and crowding degree calculation are performed on the individuals of the population R, and then step S306 is performed.

[0120] Step S306, probabilistic deletion R t The individuals of the population R are ranked according to the crowding degree, and then step S307 is performed.

[0121] Step S307, judging whether the number of individuals in the population Rt after the individuals are deleted is greater than or equal to N? If yes, step S308 is performed, and if no, step S309 is performed.

[0122] Step S308, selecting N individuals as a new parent population by using an elite reservation strategy, and then step S310 is performed.

[0123] Step S309, selecting, crossing, and mutating to supplement the number of individuals, and then step S308 is performed.

[0124] Step S310, generating a new generation of parent population P t+1 , and then step S311 is performed.

[0125] Step S311, updating the crossing and mutation probabilities, and then step S312 is performed.

[0126] Step S312, judging whether the iteration number G is greater than or equal to the maximum number? If yes, the iteration is terminated, and if no, G+1 is performed, and then step S302 is performed.

[0127] The embodiment also provides a kitchen waste transfer station optimization site selection device considering double targets, which is used to implement the above-mentioned embodiments and preferred embodiments, and details have been described above. As used below, the terms "module", "unit", "sub-unit", and the like can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and is contemplated.

[0128] Figure 4 is a structural block diagram of a kitchen waste transfer station optimization site selection device considering double targets according to the embodiment of the present application, as shown in Figure 4 The device includes an encoding module 41, an operation module 42, a generation module 43, and a processing module 44, wherein,

[0129] The encoding module 41 is configured to randomly encode the candidate transfer station candidate points to generate a first site selection configuration encoding array, wherein the first site selection configuration encoding array includes a plurality of first encodings, and each first encoding is used to represent a site selection result of configuring a candidate transfer station candidate point as a target transfer station, and the target transfer station is associated with a site selection cost parameter and an environmental negative effect parameter, and the site selection cost parameter is determined according to a garbage collection cost parameter and a garbage transfer cost parameter corresponding to the target transfer station.

[0130] The operation module 42 is coupled to the encoding module 41 and is configured to perform non-dominated solution sorting and genetic evolution operation on the first encoding based on a preset improved NSGA-II algorithm to generate a hybrid site selection configuration encoding array, and the genetic evolution operation adopts a dynamic crossover probability and a dynamic mutation probability.

[0131] The generation module 43 is coupled to the operation module 42 and is configured to perform new population iteration on the hybrid site selection configuration encoding array by using an elite reservation strategy corresponding to the improved NSGA-II algorithm to generate a second site selection configuration encoding array including a plurality of second encodings, wherein the elite reservation strategy introduces a mechanism for supplementing the second encodings by crossover and mutation operation, and each second encoding is used to represent a site selection result of currently configuring a candidate transfer station candidate point as a target transfer station.

[0132] The processing module 44 is coupled to the generation module 43 and is configured to determine a fitness corresponding to each second encoding, perform non-dominated solution sorting, genetic evolution operation and new population iteration processing based on the elite reservation strategy on the second encodings in the second site selection configuration encoding array according to the fitness, generate a target site selection configuration encoding array, obtain a target encoding from the target site selection configuration encoding array, and obtain a site selection result including the target encoding, and the fitness is determined according to the site selection cost parameter and the environmental negative effect parameter associated with the target transfer station corresponding to the second encoding.

[0133] By the double-target kitchen waste transfer station optimization site selection device of the embodiments of the present application, the candidate transfer station candidate points are randomly coded to generate a first site selection configuration coding array, the first site selection configuration coding array includes a plurality of first codes; based on the preset improved NSGA-II algorithm, the first codes are sorted and genetically evolved to generate a hybrid site selection configuration coding array; the elite retention strategy corresponding to the improved NSGA-II algorithm is used to iterate the new population of the hybrid site selection configuration coding array to generate a second site selection configuration coding array with a plurality of second codes; the fitness of the second codes is determined, and the second codes in the second site selection configuration coding array are sorted, genetically evolved, and iterated in the new population based on the elite retention strategy according to the fitness to generate a target site selection configuration coding array, and the target codes are obtained from the target site selection configuration coding array to obtain a site selection result including the target codes; by introducing the fast non-dominated sorting algorithm, using the mechanism of the elite retention strategy of filling the second codes through crossover and mutation operations, and using the crowding degree and crowding degree comparison operator, the calculation complexity of the algorithm is reduced, the target codes are quickly solved, and the crossover and mutation probabilities, which are originally fixed values, are set to dynamic values that decrease according to the logsig function as the number of iterations increases, so as to increase the convergence of the population in the iteration process, make the population search in the direction of the global optimal solution, minimize the configuration cost of the selected target transfer station, and minimize the total environmental negative effect, solve the reconstruction site selection scheme of the kitchen waste transfer station, and solve the problems of high site selection cost and construction cost, and high waste transfer and transfer cost of the reconstructed waste transfer station.

[0134] In some embodiments, the operation module 42 further includes:

[0135] The first calculation unit is configured to sort all the first codes using the non-dominated sorting algorithm, calculate the crowding degree of all the first codes after the non-dominated sorting, and obtain the coding parameters corresponding to each first code, wherein the coding parameters include the non-dominated sorting parameters and the crowding degree.

[0136] The screening unit is coupled to the first calculation unit and is configured to screen the first non-dominated codes from all the first codes according to the non-dominated sorting parameters and the crowding degree.

[0137] The operation unit is coupled to the screening unit, and is configured to perform genetic evolution operation on all the first encodings by using a preset genetic algorithm to generate a preset number of first sub-encodings, the genetic evolution operation including one of tournament selection, crossover, and mutation, the crossover corresponding to a dynamic crossover probability generated by using a logsig function with a preset gradient decrease, and the mutation corresponding to a dynamic mutation probability generated by using the logsig function with the preset gradient decrease.

[0138] The generation unit is coupled to the operation unit, and is configured to combine all the first non-dominated encodings and all the first sub-encodings to generate a hybrid site configuration encoding array.

[0139] In some embodiments, the generation module 43 further includes:

[0140] The second calculation unit is configured to perform non-dominated sorting on the intended encodings corresponding to the hybrid site configuration encoding array by using a non-dominated sorting algorithm, and perform crowding degree calculation on all the intended encodings after the non-dominated sorting to obtain an encoding parameter corresponding to each intended encoding.

[0141] The processing unit is coupled to the second calculation unit, and is configured to screen a first candidate encoding from all the intended encodings according to a non-dominated sorting parameter of the encoding parameter corresponding to the intended encoding, and delete, from all the intended encodings except the first candidate encoding, an intended encoding corresponding to a crowding degree of the intended encoding being the target crowding degree to obtain a second candidate encoding.

[0142] The judgment unit is coupled to the processing unit, and is configured to judge whether a sum of the number of the first candidate encoding and the number of the second candidate encoding is less than a preset encoding number threshold, and in a case where it is judged that the sum of the number of the first candidate encoding and the number of the second candidate encoding is not less than the preset encoding number threshold, determine that the second encoding corresponding to the second site configuration encoding array includes all the first candidate encoding and all the second candidate encoding, and in a case where it is judged that the sum of the number of the first candidate encoding and the number of the second candidate encoding is less than the preset encoding number threshold, perform crossover and mutation on the first candidate encoding and the second candidate encoding by using the genetic algorithm according to corresponding dynamic probabilities to generate third candidate encodings with the number of the encoding being the preset encoding number threshold, and take all the third candidate encodings as the second encoding corresponding to the second site configuration encoding array.

[0143] In some embodiments, the processing module 44 is further configured to determine target intermediate transfer stations corresponding to the second encoding, and determine, based on location information of the target intermediate transfer stations, garbage collection points and garbage disposal points associated with each target intermediate transfer station; determine, after determining total garbage collection costs of each target intermediate transfer station corresponding to all garbage collection points associated therewith, total garbage transfer costs of each target intermediate transfer station corresponding to transfer costs of the target intermediate transfer station and all garbage disposal points associated therewith, and calculate site selection cost parameters of each target intermediate transfer station based on construction costs, operation costs, total garbage collection costs and total garbage transfer costs corresponding to each target intermediate transfer station; determine, for each target intermediate transfer station, number of residents around garbage collection points associated therewith, amount of garbage transported from the garbage collection points to the corresponding target intermediate transfer station, arc length distance of the garbage collection points and the target intermediate transfer station on the same transport trajectory circle, first negative effect influence degree corresponding to garbage capacity, and second negative effect influence degree corresponding to distance, and calculate environmental negative effect parameters of each target intermediate transfer station based on the number of residents, the amount of garbage, the arc length distance, the first negative effect influence degree and the second negative effect influence degree; take the site selection cost parameters and the environmental negative effect parameters corresponding to each target intermediate transfer station as sub-fitness corresponding to the corresponding target intermediate transfer station, and sum the sub-fitness of all target intermediate transfer stations corresponding to the second encoding to obtain fitness corresponding to each second encoding.

[0144] In some embodiments, the processing module 44 is further configured to calculate the site selection cost parameter C according to the following formula:

[0145] ;

[0146] and calculate the environmental negative effect parameter U according to the following formula:

[0147] .

[0148] When calculating the site selection cost parameter C and the environmental negative effect parameter U, the corresponding constraints include:

[0149] ;

[0150] wherein i represents an i-th garbage collection point, I represents a set of garbage collection points, i∈I={1, 2, …, m}, m represents a number of garbage collection points, m being a positive integer; j represents a j-th intermediate transfer station candidate point, j∈J; h represents an h-th intermediate transfer station candidate point, h∈J; J represents a set of intermediate transfer station candidate points, J={1, 2, …, n}, n represents a number of intermediate transfer station candidate points, n being a positive integer; k represents a k-th garbage disposal point, k∈K; r represents an r-th garbage disposal point, r∈K; K represents a set of garbage disposal points, K={1, 2, …, p}, p represents a number of garbage disposal points, p being a positive integer; C represents the site selection cost parameter; Cmax represents the maximum value of the site selection cost parameter; C1 represents the construction cost of a target transfer station; C2 represents the operation cost of a target transfer station; C3 represents the total cost of garbage collection and transportation corresponding to a target transfer station; C4 represents the total cost of garbage transfer corresponding to a target transfer station; c1 represents the unit construction cost corresponding to the jth transfer station candidate point configured as a target transfer station; c2 represents the unit operation cost corresponding to each target transfer station; c3 represents the unit transportation cost of a collection and transportation vehicle transporting garbage from a garbage collection point to a target transfer station; c4 represents the unit transportation cost of a transfer vehicle transporting garbage from a target transfer station to a garbage disposal point; T represents the planning service life of a target transfer station; ; Q j represents the actual construction capacity corresponding to the jth target transfer station, Q j = h j × Q o ; h j represents the number of tank bodies stored under the actual construction capacity of the jth target transfer station; Q o represents the capacity of a single tank body of a transfer station candidate point or a target transfer station; ; ; p ij represents the amount of garbage transported from the ith garbage collection point to the jth transfer station candidate point per day; d ij represents the arc length distance of the ith garbage collection point and the jth transfer station candidate point on the same delivery trajectory circle; d ih represents the arc length distance of the ith garbage collection point and the hth transfer station candidate point on the same delivery trajectory circle; D jk represents the arc length distance of the jth transfer station candidate point and the kth garbage disposal point on the same delivery trajectory circle; D jr represents the arc length distance of the jth transfer station candidate point and the rth garbage disposal point on the same delivery trajectory circle; long i represents the longitude coordinate of the center point of the ith garbage collection point; lat i represents the latitude coordinate of the center point of the ith garbage collection point; long i represents the longitude coordinate of the center point of the jth transfer station candidate point; lat j represents the latitude coordinate of the center point of the jth transfer station candidate point; long h represents the longitude coordinate of the center point of the hth transfer station candidate point; lat h represents the latitude coordinate of the center point of the hth transfer station candidate point; long k represents the longitude coordinate of the center point of the kth garbage disposal point; lat k represents the latitude coordinate of the center point of the kth garbage disposal point; ∆long ijrepresents the longitudinal difference between the ith garbage collection point and the jth transfer station candidate point; Δlat ij represents the longitudinal difference between the ith garbage collection point and the jth transfer station candidate point; Δlat ih represents the longitudinal difference between the ith garbage collection point and the jth transfer station candidate point; Δlat ih represents the longitudinal difference between the ith garbage collection point and the jth transfer station candidate point; Δlat jk represents the longitudinal difference between the ith garbage collection point and the jth transfer station candidate point; Δlat ih represents the longitudinal difference between the ith garbage collection point and the jth transfer station candidate point; Δlat jr represents the longitudinal difference between the ith garbage collection point and the jth transfer station candidate point; Δlat ih represents the longitudinal difference between the ith garbage collection point and the jth transfer station candidate point; Δlat ; ; U represents the environmental negative effect parameter; H i represents the number of residents around the garbage collection point; a represents the first negative effect influence degree corresponding to the garbage capacity of the transfer station candidate point or the target transfer station; p jk represents the amount of garbage transported by the jth target transfer station to the kth garbage disposal point per day; b represents the second negative effect influence degree corresponding to the distance between the garbage collection point and the target transfer station or the transfer station candidate point; represents an arbitrary symbol, represents that there exists any i belonging to I, represents that there exists any j belonging to J, and represents that there exists any k belonging to K; represents the maximum construction capacity of the jth transfer station candidate point; γ represents the transfer coefficient of each transfer station candidate point or target transfer station; S k represents the maximum amount of garbage disposed per day by the kth garbage disposal point; s jk represents the amount of garbage transported by the jth transfer station candidate point to the kth garbage disposal point after being configured as a target transfer station; q i represents the daily garbage production of the ith garbage collection point; q ij represents the amount of garbage transported by the ith garbage collection point to the jth target transfer station per day.

[0151] In some embodiments, the processing module 44 is further configured to determine the fitness corresponding to all third encodings of each target site configuration encoding, wherein the third encoding represents the second encoding corresponding to the target site configuration encoding; among all third encodings, the third encoding with the minimum fitness is selected to obtain the target encoding.

[0152] The embodiment also provides a service platform, comprising a memory and a processor, the memory storing a computer program, and the processor being configured to execute the computer program to perform the steps in any of the above method embodiments.

[0153] Optionally, the service platform can further comprise a transmission device connected with the processor and an input / output device connected with the processor.

[0154] Optionally, in the embodiment, the processor can be configured to perform the following steps through the computer program:

[0155] S1, randomly encoding the alternative transfer station candidate points to generate a first site selection configuration encoding array, the first site selection configuration encoding array comprising a plurality of first encodings, the first encoding being used to represent a site selection result of configuring a transfer station candidate point as a target transfer station, the target transfer station being associated with a site selection cost parameter and an environmental negative effect parameter, the site selection cost parameter being determined according to a garbage collection cost parameter and a garbage transfer cost parameter corresponding to the target transfer station.

[0156] S2, performing non-dominated solution sorting and genetic evolution operation on the first encoding based on a preset improved NSGA-II algorithm to generate a hybrid site selection configuration encoding array, the genetic evolution operation adopting a dynamic crossover probability and a dynamic mutation probability.

[0157] S3, performing new population iteration on the hybrid site selection configuration encoding array by using an elite reservation strategy corresponding to the improved NSGA-II algorithm to generate a second site selection configuration encoding array with a plurality of second encodings, the elite reservation strategy introducing a mechanism for filling the second encoding through crossover and mutation operation, the second encoding being used to represent a site selection result of currently configuring the transfer station candidate point as the target transfer station.

[0158] S4, determining the fitness corresponding to the second encoding, performing non-dominated solution sorting, genetic evolution operation and new population iteration processing based on the elite reservation strategy on the second encoding in the second site selection configuration encoding array according to the fitness to generate a target site selection configuration encoding array, and obtaining a target encoding from the target site selection configuration encoding array to obtain the site selection result, the site selection result comprising the target encoding, and the fitness being determined according to the site selection cost parameter and the environmental negative effect parameter associated with the target transfer station corresponding to the second encoding.

[0159] It should be noted that the specific examples in the embodiment can refer to the examples described in the above embodiments and optional implementation manners, which will not be described here again.

[0160] In addition, in combination with the double-target considering kitchen waste transfer station optimization site selection method in the above embodiments, an embodiment of the present application can provide a storage medium for implementation. The storage medium has a computer program stored thereon; the computer program is executed by a processor to implement any one of the double-target considering kitchen waste transfer station optimization site selection methods in the above embodiments.

[0161] Those skilled in the art should understand that each technical feature of the above embodiments can be combined arbitrarily, and in order to make the description simple, each technical feature in the above embodiments is not described in all possible combinations, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0162] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be pointed out that for ordinary skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the patent protection scope of the present application should be subject to the appended claims.

Claims

1. A dual-objective method for optimizing the site selection of a food waste transfer station, characterized in that: include: Randomly encoding the candidate transfer station points to generate a first site selection configuration code array, wherein the first site selection configuration code array includes a plurality of first codes, each of the first codes being used to represent a site selection result of configuring the candidate transfer station point as a target transfer station, the target transfer station being associated with a site selection cost parameter and a negative environmental effect parameter, the site selection cost parameter being determined based on a garbage removal cost parameter and a garbage transfer cost parameter corresponding to the target transfer station; Based on a preset improved NSGA-II algorithm, non-dominated solution sorting and genetic evolution operations are performed on the first code to generate a mixed location configuration code array, wherein the genetic evolution operation adopts dynamic crossover probability and dynamic mutation probability; Using the elite retention strategy corresponding to the improved NSGA-II algorithm, a new population iteration is performed on the mixed location configuration code array to generate a second location configuration code array having multiple second codes, wherein the elite retention strategy introduces a mechanism for completing the second codes through a crossover mutation operation, and the second codes are used to represent the current location selection result of configuring the transfer station candidate point as the target transfer station; Determining the fitness corresponding to the second code, performing non-dominated solution sorting, genetic evolution operations, and new population iterative processing based on an elite retention strategy on the second code in the second location configuration code array based on the fitness, generating a target location configuration code array, and obtaining a target code from the target location configuration code array to obtain a location selection result, wherein the location selection result includes the target code, and the fitness is determined based on a location cost parameter and an environmental negative effect parameter associated with the target transfer station corresponding to the second code; Based on the preset improved NSGA-II algorithm, the first coding is subjected to non-dominated solution sorting and genetic evolution operations to generate a mixed location configuration coding array, including: Performing non-dominated sorting on all the first codes using a non-dominated sorting algorithm, and calculating congestion for all the first codes that have completed the non-dominated sorting, to obtain coding parameters corresponding to each first code, wherein the coding parameters include a non-dominated sorting parameter and a congestion degree; screening out a first non-dominated code from all the first codes according to the non-dominated sorting parameter and the congestion degree; Using a preset genetic algorithm, performing a genetic evolution operation on all the first codes to generate a preset number of first sub-codes, the genetic evolution operation comprising one of the following: tournament selection, crossover, and mutation, wherein the crossover probability corresponding to the crossover adopts a dynamic crossover probability generated by a preset logsig function with a decreasing gradient, and the mutation probability corresponding to the mutation adopts a dynamic mutation probability generated by a preset logsig function with a decreasing gradient; Merging all of the first non-dominated codes and all of the first sub-codes to generate a mixed location configuration code array; Determining the fitness corresponding to the second encoding includes: Determining the target transfer station corresponding to the second code, and based on the location information of the target transfer station, determining the garbage collection point and garbage disposal point associated with each target transfer station; After determining the total garbage removal cost corresponding to each target transfer station based on the removal costs corresponding to each target transfer station and all the garbage collection points associated therewith, determining the total garbage transfer cost corresponding to each target transfer station based on the transfer costs of the target transfer station and all the garbage processing points associated therewith, and calculating the site selection cost parameter of each target transfer station based on the construction cost, operation cost, total garbage removal cost and total garbage transfer cost corresponding to each target transfer station; Determine the number of residents around the garbage collection point associated with each of the target transfer stations, the amount of garbage transported from the garbage collection point to the corresponding target transfer station, the arc length distance between the garbage collection point and the target transfer station on the same transport trajectory circle, the first negative effect influence degree corresponding to the garbage capacity, and the second negative effect influence degree corresponding to the distance, and calculate the environmental negative effect parameter of each of the target transfer stations based on the number of residents, the amount of garbage, the arc length distance, the first negative effect influence degree, and the second negative effect influence degree; The site selection cost parameter and the negative environmental effect parameter corresponding to each target transfer station are used as the sub-fitness corresponding to the corresponding target transfer station, and the sub-fitnesses of all the target transfer stations corresponding to the second code are summed to obtain the fitness corresponding to each second code.

2. The method according to claim 1, characterized in that Using the elite retention strategy corresponding to the improved NSGA-II algorithm, a new population iteration is performed on the mixed location configuration code array to generate a second location configuration code array having multiple second codes, including: Using a non-dominated sorting algorithm, non-dominated sorting is performed on the intention codes corresponding to the mixed location configuration coding array, and congestion calculation is performed on all the intention codes that have completed the non-dominated sorting to obtain a coding parameter corresponding to each intention code; screening a first candidate code from all the intended codes according to the non-dominated order parameter of the coding parameter corresponding to the intended code, and deleting, from all the intended codes other than the first candidate code, the intended code corresponding to the intended code whose congestion degree is the target congestion degree, to obtain a second candidate code; Determine whether the sum of the code numbers of the first alternative code and the second alternative code is less than a preset code number threshold, and if it is determined that the sum of the code numbers of the first alternative code and the second alternative code is not less than the preset code number threshold, determine that the second code corresponding to the second address configuration code array includes all the first alternative codes and all the second alternative codes.

3. The method according to claim 2, characterized in that When it is determined that the sum of the code numbers of the first alternative code and the second alternative code is less than a preset code number threshold, the method further includes: using the genetic algorithm to cross and mutate the first alternative code and the second alternative code according to the corresponding dynamic probability to generate a third alternative code with a code number that is the preset code number threshold, and using all the third alternative codes as the second code corresponding to the second location configuration code array.

4. The method according to claim 1, wherein The site selection cost parameter C is calculated according to the following formula: ； The negative environmental effect parameter U is calculated according to the following formula: ； When calculating the site selection cost parameter C and the negative environmental effect parameter U, the corresponding constraints include: ； Where i represents the i-th garbage collection point, I represents the set of garbage collection points, i∈I={1, 2, …, m}, m represents the number of garbage collection points, and m is a positive integer; j represents the j-th transfer station candidate point, j∈J; h represents the h-th transfer station candidate point, h∈J; J represents the set of transfer station candidate points, J={1, 2, …, n}, n represents the number of transfer station candidate points, and n is a positive integer; k represents the k-th garbage disposal point, k∈K; r represents the r-th garbage disposal point, r∈K; K represents the set of garbage disposal points, K={1, 2, …, p}, p represents the number of garbage disposal points, and p is a positive integer; C represents the location cost parameter; C max represents the maximum value of the site selection cost parameter; C1 represents the construction cost of the target transfer station; C2 represents the operating cost of the target transfer station; C3 represents the total garbage collection and transportation cost corresponding to a target transfer station; C4 represents the total garbage transfer cost corresponding to a target transfer station; c1 represents the unit construction cost corresponding to the jth transfer station candidate point after being configured as the target transfer station; c2 represents the unit operating cost corresponding to each target transfer station; c3 represents the unit transportation cost of the garbage collection vehicle transporting garbage from the garbage collection point to the target transfer station; c4 represents the unit transportation cost of the transfer vehicle transporting garbage from the target transfer station to the garbage treatment point; T represents the planned service life of the target transfer station; ;Q j represents the actual construction capacity corresponding to the j-th target transfer station, Q j =h j ×Q o ;h j represents the number of tanks stored at the actual construction capacity of the j-th target transfer station; Q o Indicates the capacity of a single tank at a candidate transfer station or a target transfer station; ； ； p ij represents the amount of garbage transported from the i-th garbage collection point to the j-th transfer station candidate point every day; d ij represents the arc length distance between the i-th garbage collection point and the j-th transfer station candidate point on the same transportation trajectory circle; d ih represents the arc length distance between the i-th garbage collection point and the h-th transfer station candidate point on the same transportation trajectory circle; D jk represents the arc length distance between the jth transfer station candidate point and the kth garbage disposal point on the same transportation trajectory circle; D jr represents the arc length distance between the jth transfer station candidate point and the rth garbage disposal point on the same transportation trajectory circle; long i Indicates the longitude coordinate of the center point of the i-th garbage collection point; lat i Indicates the latitude coordinate of the center point of the i-th garbage collection point; long j Indicates the longitude coordinate of the center point of the j-th transfer station candidate point; lat j Indicates the latitude coordinate of the center point of the j-th transfer station candidate point; long h Indicates the longitude coordinate of the center point of the h-th transfer station candidate point; lat h Indicates the latitude coordinate of the center point of the h-th transfer station candidate point; long k Indicates the longitude coordinate of the center point of the kth garbage disposal point; lat k Indicates the latitude coordinate of the center point of the kth garbage disposal point; ∆long ij represents the longitude difference between the i-th garbage collection point and the j-th transfer station candidate point; ∆lat ij Indicates the latitude difference between the i-th garbage collection point and the j-th transfer station candidate point; ∆long ih represents the longitude difference between the i-th garbage collection point and the h-th transfer station candidate point; ∆lat ih Indicates the latitude difference between the i-th garbage collection point and the h-th transfer station candidate point; ∆long jk Indicates the longitude difference between the jth transfer station candidate point and the kth garbage disposal point; ∆lat jk Indicates the latitude difference between the jth transfer station candidate point and the kth garbage disposal point; ∆long jr represents the longitude difference between the jth transfer station candidate point and the rth garbage disposal point; ∆lat jr represents the latitude difference between the jth transfer station candidate point and the rth garbage disposal point; ； ； U represents the environmental negative benefit parameter; H i represents the number of residents around the garbage collection point; a represents the first negative effect impact degree corresponding to the garbage capacity of the transfer station candidate point or target transfer station; p jk represents the amount of garbage transported from the jth target transfer station to the kth garbage disposal point every day; b represents the second negative effect influence degree corresponding to the distance between the garbage collection point and the target transfer station or the transfer station candidate point; ∀ represents an arbitrary symbol, ∀i∈I means that there is any i belonging to I, ∀j∈J means that there is any j belonging to J, and ∀k∈K means that there is any k belonging to K; represents the maximum construction capacity of the jth transfer station candidate point; γ represents the transfer coefficient of each transfer station candidate point or target transfer station; S k represents the maximum amount of garbage processed by the kth garbage disposal point every day; s jk represents the amount of garbage transported to the kth garbage disposal point after the jth transfer station candidate point is configured as the target transfer station; q i represents the daily garbage production of the i-th garbage collection point; q ij It represents the amount of garbage transported from the i-th garbage collection point to the j-th target transfer station every day.

5. The method according to claim 1, wherein Obtaining a target code from the target location configuration code array includes: Determining the fitness corresponding to all third codes of each target location configuration code, wherein the third code represents the second code corresponding to the target location configuration code; Among all the third codes, the third code with the smallest fitness is selected to obtain the target code.

6. A dual-objective kitchen waste transfer station optimization site selection device, characterized in that: include: an encoding module, configured to randomly encode candidate transfer station points to generate a first location configuration code array, wherein the first location configuration code array includes a plurality of first codes, each of the first codes being used to represent a location selection result of configuring the transfer station point as a target transfer station, the target transfer station being associated with a location cost parameter and an environmental negative effect parameter, the location cost parameter being determined based on a garbage removal cost parameter and a garbage transfer cost parameter corresponding to the target transfer station; An operation module is configured to perform non-dominated solution sorting and genetic evolution operations on the first codes based on a preset improved NSGA-II algorithm to generate a mixed location configuration code array, wherein the genetic evolution operation uses a dynamic crossover probability and a dynamic mutation probability. The operation module is further configured to perform non-dominated sorting on all the first codes using a non-dominated sorting algorithm, and perform congestion calculation on all the first codes that have completed the non-dominated sorting to obtain coding parameters corresponding to each first code, wherein the coding parameters include a non-dominated sorting parameter and a congestion degree; select a first non-dominated code from all the first codes based on the non-dominated sorting parameter and the congestion degree; perform genetic evolution operations on all the first codes using a preset genetic algorithm to generate a preset number of first sub-codes, wherein the genetic evolution operations include one of the following: tournament selection, crossover, and mutation, wherein the crossover probability corresponding to the crossover uses a dynamic crossover probability generated by a preset gradient-decreasing logsig function, and the mutation probability corresponding to the mutation uses a dynamic mutation probability generated by a preset gradient-decreasing logsig function; and merge all the first non-dominated codes and all the first sub-codes to generate a mixed location configuration code array. a generation module, configured to perform a new population iteration on the mixed location configuration code array using an elite retention strategy corresponding to the improved NSGA-II algorithm, to generate a second location configuration code array having a plurality of second codes, wherein the elite retention strategy introduces a mechanism for completing the second codes through a crossover mutation operation, and the second codes are used to represent the current location selection result of configuring the transfer station candidate point as the target transfer station; A processing module is used to determine the fitness corresponding to the second code, and according to the fitness, perform non-dominated solution sorting, genetic evolution operation and new population iterative processing based on the elite retention strategy on the second code in the second location configuration code array to generate a target location configuration code array, and obtain the target code from the target location configuration code array to obtain a location result, wherein the location result includes the target code, and the fitness is determined according to the location cost parameter and environmental negative effect parameter associated with the target transfer station corresponding to the second code; the processing module is also used to determine the target transfer station corresponding to the second code, and determine the garbage collection point and garbage treatment point associated with each target transfer station based on the location information of the target transfer station; after determining the total garbage removal cost corresponding to each target transfer station based on the removal cost corresponding to each target transfer station and all the garbage collection points associated with it, determine the location cost corresponding to each target transfer station based on the transfer cost of the target transfer station and all the garbage treatment points associated with it. The total cost of garbage transfer, and the site selection cost parameter of each target transfer station is calculated based on the construction cost, operation cost, total garbage collection cost and total garbage transfer cost corresponding to each target transfer station; the number of residents around the garbage collection point associated with each target transfer station, the amount of garbage transported from the garbage collection point to the corresponding target transfer station, the arc length distance between the garbage collection point and the target transfer station on the same transport trajectory circle, the first negative effect influence degree corresponding to the garbage capacity, the second negative effect influence degree corresponding to the distance are determined, and the environmental negative effect parameter of each target transfer station is calculated based on the number of residents, the amount of garbage, the arc length distance, the first negative effect influence degree and the second negative effect influence degree; the site selection cost parameter and the environmental negative effect parameter corresponding to each target transfer station are used as the sub-fitness corresponding to the corresponding target transfer station, and the sub-fitness of all the target transfer stations corresponding to the second code are summed to obtain the fitness corresponding to each second code.

7. A service platform comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps of the method for optimizing the site selection of a kitchen waste transfer station considering dual objectives as described in any one of claims 1 to 5.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the site selection of a kitchen waste transfer station considering dual objectives as described in any one of claims 1 to 5 is implemented.

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