Battery replacement cabinet site selection determination method, device, equipment and medium

Abstract

The application relates to a battery swap cabinet site selection determination method, device, equipment and medium. The method comprises the following steps: generating an initial battery swap cabinet site selection population corresponding to a target area, the initial battery swap cabinet site selection population comprising a plurality of initial battery swap cabinet site selection schemes; performing non-dominated level division on each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population to obtain a plurality of non-dominated layers; determining the total crowding degree of each initial battery swap cabinet site selection scheme in each non-dominated layer, wherein the total crowding degree represents the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer; and performing iteration on the initial battery swap cabinet site selection population according to the total crowding degree and the non-dominated level of each initial battery swap cabinet site selection scheme to generate a target battery swap cabinet site selection population corresponding to the target area, wherein the target battery swap cabinet site selection population comprises a plurality of target battery swap cabinet site selection schemes. The method can improve the accuracy of determining the installation position of the battery swap cabinet.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a battery swap cabinet site selection method and device, equipment and medium. BACKGROUND

[0002] In today's era, electric vehicles are in a golden period of rapid development due to their environmental protection, energy saving and other advantages. The rationality of the site selection of the battery swap cabinet, as a key facility for electric vehicle energy supply, directly affects the convenience of use of electric vehicles and the operation efficiency of operators.

[0003] The existing site selection methods have obvious limitations. For example, some methods focus on minimizing cost as the core target, aiming to reduce construction and operation costs; while other methods focus on maximizing user satisfaction, striving to provide users with the best experience when using battery swap services.

[0004] How to more accurately determine the installation location of the battery swap cabinet is a problem to be solved at the present stage. SUMMARY

[0005] Therefore, it is necessary to provide a battery swap cabinet site selection method, device, equipment and medium capable of improving the accuracy of the installation location of the battery swap cabinet.

[0006] A battery swap cabinet site selection method comprises: generating an initial battery swap cabinet site selection population corresponding to a target area, the initial battery swap cabinet site selection population comprising a plurality of initial battery swap cabinet site selection schemes, and each initial battery swap cabinet site selection scheme corresponding to a site selection scheme of a battery swap cabinet position in the target area; performing non-dominated level division on each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population to obtain a plurality of non-dominated layers, each non-dominated layer comprising at least one initial battery swap cabinet site selection scheme, the initial battery swap cabinet site selection schemes in the same non-dominated layer having the same non-dominated level, and the non-dominated level representing the excellent level of the scheme indicators of each initial battery swap cabinet site selection scheme and other initial battery swap cabinet site selection schemes; determining the total crowding degree of each initial battery swap cabinet site selection scheme in each non-dominated layer, and the total crowding degree representing the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer; determining a plurality of iteration battery swap cabinet site selection schemes from the initial battery swap cabinet site selection population according to the total crowding degree and the non-dominated level of each initial battery swap cabinet site selection scheme; performing cross processing iteration and mutation processing iteration on the plurality of iteration battery swap cabinet site selection schemes until each iteration battery swap cabinet site selection scheme meets a preset condition, to obtain a target battery swap cabinet site selection population corresponding to the target area, and the target battery swap cabinet site selection population comprising a plurality of target battery swap cabinet site selection schemes.

[0007] The device comprises an initial population generation module for generating an initial battery swap cabinet site selection population corresponding to a target area, the initial battery swap cabinet site selection population comprising a plurality of initial battery swap cabinet site selection schemes, and one initial battery swap cabinet site selection scheme corresponding to a site selection scheme of one battery swap cabinet position in the target area; a non-dominated layer division module for performing non-dominated level division on each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population to obtain a plurality of non-dominated layers, each non-dominated layer comprising at least one initial battery swap cabinet site selection scheme, the initial battery swap cabinet site selection schemes in the same non-dominated layer having the same non-dominated level, and the non-dominated level representing the excellent level of each initial battery swap cabinet site selection scheme and other initial battery swap cabinet site selection schemes; a total crowding degree determination module for determining the total crowding degree of each initial battery swap cabinet site selection scheme in each non-dominated layer, and the total crowding degree representing the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer; an iteration module for determining a plurality of iteration battery swap cabinet site selection schemes from the initial battery swap cabinet site selection population according to the total crowding degree and the non-dominated level of each initial battery swap cabinet site selection scheme; and the iteration module is configured to perform cross processing iteration and mutation processing iteration on the plurality of iteration battery swap cabinet site selection schemes until each iteration battery swap cabinet site selection scheme meets a preset condition, to obtain a target battery swap cabinet site selection population corresponding to the target area, and the target battery swap cabinet site selection population comprising a plurality of target battery swap cabinet site selection schemes.

[0008] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of any of the methods when executing the computer program.

[0009] A computer readable storage medium stores a computer program, and the computer program implements the steps of any of the methods when executed by a processor.

[0010] The aforementioned method, apparatus, equipment, and medium for determining the location of battery swapping cabinets involve generating an initial battery swapping cabinet location population corresponding to a target area, which includes multiple initial battery swapping cabinet location schemes; classifying each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population into multiple non-dominated layers, each non-dominated layer including at least one initial battery swapping cabinet location scheme, with the initial battery swapping cabinet location schemes within the same non-dominated layer having the same non-dominated level, and the non-dominated level characterizing the excellence level of each initial battery swapping cabinet location scheme compared to other initial battery swapping cabinet location schemes; determining the total congestion degree of each initial battery swapping cabinet location scheme in each non-dominated layer, with the total congestion degree characterizing the spatial distribution density of each initial battery swapping cabinet location scheme within the non-dominated layer; and iterating the initial battery swapping cabinet location population based on the total congestion degree and non-dominated level of each initial battery swapping cabinet location scheme to generate a target battery swapping cabinet location population corresponding to the target area, which includes multiple target battery swapping cabinet location schemes. Therefore, the determination of the target battery swapping station location scheme is based on the non-dominated level classification of the initial battery swapping station location population, and the calculation and iteration of congestion degree. This allows the target battery swapping station location scheme to integrate the good and bad levels of multiple scheme indicators, which can improve the accuracy of the target battery swapping station location scheme determination. Attached Figure Description

[0011] Figure 1 This is an application scenario diagram of the battery swapping cabinet location determination method in one embodiment;

[0012] Figure 2 This is a flowchart illustrating the method for determining the location of a battery swapping cabinet in one embodiment;

[0013] Figure 3 This is a structural block diagram of the battery swapping cabinet location determination device in one embodiment;

[0014] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0016] The battery swapping cabinet location determination method provided in this application can be applied to, for example... Figure 1The application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The user can generate and send user instructions through the terminal 102, so that the server 104 generates the initial battery swap cabinet site selection population corresponding to the target area based on the user instructions, and performs non-dominated level division on each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population, to obtain multiple non-dominated layers. Further, the server 104 can determine the total congestion degree of each initial battery swap cabinet site selection scheme in each non-dominated layer, and according to the total congestion degree of each initial battery swap cabinet site selection scheme and the non-dominated level, iterate the initial battery swap cabinet site selection population to generate the target battery swap cabinet site selection population corresponding to the target area. Among them, the terminal 102 can be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, and the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0017] In one embodiment, as shown in Figure 2 , a battery swap cabinet site selection determination method is provided, which is applied to the server in Figure 1 for example, including the following steps:

[0018] Step 202, generating an initial battery swap cabinet site selection population corresponding to the target area, the initial battery swap cabinet site selection population including multiple initial battery swap cabinet site selection schemes.

[0019] In this embodiment, the server can create a population containing multiple potential site selection schemes corresponding to the target area based on the user instructions, that is, the initial battery swap cabinet site selection population, each individual in the population represents a possible initial battery swap cabinet site selection scheme, and the initial battery swap cabinet site selection population includes multiple initial battery swap cabinet site selection schemes.

[0020] In this embodiment, an initial battery swap cabinet site selection scheme corresponds to a site selection scheme of a battery swap cabinet position in the target area. Specifically, if it is required to install a battery swap cabinet at 100 battery swap cabinet positions in the target area, then the initial battery swap cabinet site selection population includes 100 initial battery swap cabinet site selection schemes corresponding to the 100 battery swap cabinet positions.

[0021] Among them, each initial battery swap cabinet site selection scheme can include multiple scheme indicators, and the multiple scheme indicators together constitute the initial battery swap cabinet site selection scheme.

[0022] Step 204, performing non-dominated level division on each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population to obtain multiple non-dominated layers.

[0023] Each non-dominated layer can include at least one initial battery swap cabinet site selection scheme, and the non-dominated ranks of the initial battery swap cabinet site selection schemes in the same non-dominated layer are the same. The non-dominated rank represents the excellent ranks of each initial battery swap cabinet site selection scheme and other initial battery swap cabinet site selection schemes in terms of the scheme indicators.

[0024] In this embodiment, after generating the initial battery swap cabinet site selection population, the server can compare the scheme indicators of the initial battery swap cabinet site selection schemes, determine the excellent ranks of the initial battery swap cabinet site selection schemes in terms of the scheme indicators, determine the non-dominated ranks of the initial battery swap cabinet site selection schemes based on the comparison results, and then divide the non-dominated layers based on the non-dominated ranks, divide the initial battery swap cabinet site selection schemes with the same non-dominated ranks into the same non-dominated layer, and obtain the multiple non-dominated layers.

[0025] In step 206, the total crowding degrees of the initial battery swap cabinet site selection schemes in each non-dominated layer are determined.

[0026] The total crowding degree represents the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer.

[0027] In this embodiment, after dividing the initial battery swap cabinet site selection population, the server can further calculate the total crowding degrees of the initial battery swap cabinet site selection schemes based on the scheme indicators.

[0028] In step 208, the multiple iteration battery swap cabinet site selection schemes are determined from the initial battery swap cabinet site selection population based on the total crowding degrees and the non-dominated ranks of the initial battery swap cabinet site selection schemes.

[0029] In this embodiment, the server can determine the iteration battery swap cabinet site selection schemes for iteration from the population based on the total crowding degrees and the non-dominated ranks of the initial battery swap cabinet site selection schemes, and perform subsequent iteration processing.

[0030] In step 210, the multiple iteration battery swap cabinet site selection schemes are subjected to cross processing iteration and mutation processing iteration until the iteration battery swap cabinet site selection schemes meet the preset conditions, and a target battery swap cabinet site selection population corresponding to the target region is obtained. The target battery swap cabinet site selection population includes multiple target battery swap cabinet site selection schemes.

[0031] The preset conditions refer to the fact that each scheme indicator of each iteration battery swap cabinet site selection scheme obtained after cross processing and mutation processing iteration is optimal, that is, a Pareto optimal solution (that is, each scheme indicator of each target battery swap cabinet site selection scheme is not worse than the scheme indicators of other target battery swap cabinet site selection schemes, and is better than other target battery swap cabinet site selection schemes in at least one scheme indicator).

[0032] In this embodiment, the server can use a genetic algorithm to iterate the initial battery swap cabinet site selection population to obtain the target battery swap cabinet site selection population corresponding to the target area.

[0033] In this embodiment, the target battery swap cabinet site selection population can include multiple target battery swap cabinet site selection schemes, which can be used as the final battery swap cabinet site selection scheme for the target area.

[0034] In the above battery swap cabinet site selection determination method, an initial battery swap cabinet site selection population corresponding to a target area is generated, and the initial battery swap cabinet site selection population includes multiple initial battery swap cabinet site selection schemes. Non-dominated level division is performed on each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population to obtain multiple non-dominated layers, each non-dominated layer including at least one initial battery swap cabinet site selection scheme. The non-dominated levels of the initial battery swap cabinet site selection schemes in the same non-dominated layer are the same, and the non-dominated levels represent the superior levels of the scheme indicators of each initial battery swap cabinet site selection scheme and other initial battery swap cabinet site selection schemes. The total crowding degree of each initial battery swap cabinet site selection scheme in each non-dominated layer is determined, and the total crowding degree represents the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer. According to the total crowding degree and the non-dominated level of each initial battery swap cabinet site selection scheme, multiple iteration battery swap cabinet site selection schemes are determined from the initial battery swap cabinet site selection population. The iteration battery swap cabinet site selection schemes are subjected to cross processing iteration and mutation processing iteration until each iteration battery swap cabinet site selection scheme meets a preset condition, and a target battery swap cabinet site selection population corresponding to the target area is obtained, which includes multiple target battery swap cabinet site selection schemes. Thus, the determination of the target battery swap cabinet site selection scheme is based on the non-dominated level division of the initial battery swap cabinet site selection population and the crowding degree calculation and iteration, so that the target battery swap cabinet site selection scheme can comprehensively consider the superior levels between multiple scheme indicators, and the accuracy of the target battery swap cabinet site selection scheme determination can be improved.

[0035] In one embodiment, the scheme indicators of each initial battery swap cabinet site selection scheme can include an installation location indicator of the battery swap cabinet and a user satisfaction indicator.

[0036] The installation location indicator refers to the geographic location of the battery swap cabinet determined by the initial battery swap cabinet site selection scheme.

[0037] In this embodiment, the geographic location is one of the key factors for battery swap cabinet site selection, and is usually accurately represented using latitude and longitude coordinates. For example, the geographic location of Beijing can be represented as (39.9042, 116.4074). These coordinates can be obtained through a GPS device or a map API to ensure the accuracy of the site selection scheme.

[0038] In this embodiment, the user satisfaction index is a comprehensive index affected by multiple factors, such as the distance between the battery swap cabinet and the user, the convenience of the surrounding traffic, the service quality of the battery swap cabinet, etc. The calculation of the user satisfaction can adopt the weighted average method to quantify the influence degree of each factor. For example, the user satisfaction index can be calculated by the following formula:

[0039] S = w 1 *D + w 2 *T + w 3 *Q

[0040] wherein, S represents the user satisfaction index, D represents the distance factor, T represents the traffic convenience, Q represents the service quality, w 1 , w 2 , w 3 respectively represent the weight of each factor.

[0041] In this embodiment, the range of the user satisfaction index is usually between 0 and 1, for example, 0.85 or above represents a higher user satisfaction, and 0.5 or below represents a lower user satisfaction.

[0042] In one of the embodiments, the scheme index of the initial battery swap cabinet site selection scheme can also include a cost index, which can specifically include construction cost and operation cost.

[0043] Specifically, the construction cost can include the purchase, installation, land rental, etc. of the battery swap cabinet, usually in units of ten thousand yuan. For example, the construction cost of a battery swap cabinet can be 500 thousand yuan. The construction cost can be determined based on market research, supplier quotes and historical data of battery swap cabinet installation.

[0044] Further, the operation cost can include the maintenance, power consumption, personnel management, etc. of the battery swap cabinet, usually in units of ten thousand yuan / year. For example, the annual operation cost of a battery swap cabinet can be 50 thousand yuan. The operation cost can be determined based on historical operation data and industry standards.

[0045] In this embodiment, generating an initial battery swapping cabinet site selection population for the corresponding target area may include: determining the population size; determining multiple proposed installation locations for battery swapping cabinets based on the location information of the target area and the population size, and obtaining multiple installation location indicators; determining corresponding user satisfaction indicators based on each installation location indicator; and obtaining multiple initial battery swapping cabinet site selection schemes based on each installation location indicator and the corresponding user satisfaction indicators, so as to obtain the initial battery swapping cabinet site selection population for the corresponding target area.

[0046] Specifically, the server can determine the population size based on actual application needs. As mentioned earlier, if battery swapping cabinets need to be installed at 100 locations within the target area, the population size can be set to 100 individuals.

[0047] Furthermore, the server can randomly generate installation location indicators, construction costs, operating costs, and user satisfaction indicators for each individual to obtain initial battery swapping cabinet site selection schemes. For example, the server can randomly generate 100 latitude and longitude coordinates (i.e., installation location indicators) based on the location information of the target area, with each coordinate corresponding to a battery swapping cabinet site selection scheme.

[0048] Furthermore, the server can determine the distance between the battery swapping station and the user, the convenience of surrounding transportation, and the service quality of the battery swapping station based on various latitude and longitude coordinates, thereby determining user satisfaction indicators.

[0049] Similarly, servers can determine construction costs, operating costs, etc., based on historical data and information such as latitude and longitude coordinates.

[0050] In this embodiment, after generating the initial battery swapping cabinet site selection population, the server can also perform individual feasibility verification. That is, the feasibility of each generated individual (initial battery swapping cabinet site selection scheme) is verified to ensure that it meets the actual conditions. For example, it checks whether the installation location indicators are within the available area, and whether the construction cost and operating cost are within the budget.

[0051] In this embodiment, the server can record the initial battery swapping cabinet location schemes using a population data structure. Specifically, the server can record detailed information about each individual in the population data structure for subsequent optimization operations. For example, a two-dimensional array or database table can be used to store each individual's installation location indicators, construction costs, operating costs, and user satisfaction indicators.

[0052] By following the steps above, an initial battery swapping station location population containing multiple potential location options can be created, laying the foundation for the subsequent optimization process.

[0053] In one specific embodiment, it is necessary to plan the location schemes for 100 battery swapping stations in a city (target area). First, the server determines the population size to be 100 individuals. Then, the server uses a random number generator to generate 100 latitude and longitude coordinates, each coordinate corresponding to a battery swapping station location scheme. Next, the server verifies the feasibility of each generated coordinate, ensuring that it is located within the usable area of ​​the city and that the construction and operating costs are within budget. Finally, the server records the detailed information of each individual in a two-dimensional array for subsequent optimization operations. In this way, the server successfully creates an initial battery swapping station location population containing 100 potential location schemes, providing basic data for subsequent data processing.

[0054] In one embodiment, the non-dominated levels of each initial battery swapping station location scheme in the initial battery swapping station location population are divided to obtain multiple non-dominated layers. This may include: determining the initial non-dominated level of each initial battery swapping station location scheme; comparing the scheme indicators of any two initial battery swapping station location schemes to determine the dominance relationship between the two initial battery swapping station location schemes, whereby the dominance relationship characterizes the superiority or inferiority of any two initial battery swapping station location schemes in multiple scheme indicators; determining the non-dominated level of each corresponding initial battery swapping station location scheme based on the dominance relationship; and dividing each initial battery swapping station location scheme into multiple non-dominated layers based on each non-dominated level.

[0055] Specifically, the server can perform a non-dominated ranking operation on each individual in the population to determine its non-dominated level. The main purpose of non-dominated ranking is to stratify the individuals in the population according to their performance on different objectives (program indicators), ensuring that there is no dominance relationship between individuals in each stratum.

[0056] In this embodiment, the dominance relationship characterizes the superiority or inferiority of any two initial battery swapping station location schemes across multiple scheme indicators. Specifically, in the battery swapping station location problem, it is assumed that there are two objectives: minimizing construction costs and maximizing user satisfaction. For two individuals... A and B ,if A The construction cost is less than or equal to B The construction cost, and A User satisfaction greater than or equal to B User satisfaction, and at least on one objective A Superior B So let's say A Dominate B That is, if C A ≤ C B and S A≥ S B and( C A < C B or S A > S B )but A Dominate B .in, C A and C B Each represents an individual A and B Construction costs, S A and S B Each represents an individual A and B User satisfaction.

[0057] Furthermore, the server can initialize the non-dominated level of all individuals to 1, indicating that they all belong to the first non-dominated layer.

[0058] Furthermore, the server can compare individuals, that is, for every individual in the population. A The server compares it with all other individuals in the population. B Compare and judge A Whether or not B Domination. If A quilt B Domination will A The non-dominant level increases by 1.

[0059] Furthermore, the server can stratify individuals in the population based on their non-dominance level. Individuals with a non-dominance level of 1 belong to the first non-dominance level, individuals with a non-dominance level of 2 belong to the second non-dominance level, and so on.

[0060] In this embodiment, the server repeats the above process for each individual until all individuals are assigned to the corresponding non-dominated layer.

[0061] In one specific embodiment, such as a population comprising individuals A, B and C .individual A The construction cost was 4 million yuan, and the user satisfaction rate was 0.8; individual B The construction cost was 4.5 million yuan, and the user satisfaction rate was 0.85; individual C The construction cost was 5 million yuan, and the user satisfaction rate was 0.9. First, the server initialized the non-dominance level of all individuals to 1. Then, the server compared the individuals...A and B ,Discover A The construction cost is less than B And user satisfaction is also lower than B ,therefore A Do not dominate B , B Nor dominion A Next, the server compares the individual... A and C ,Discover A The construction cost is less than C And user satisfaction is also lower than C ,therefore A Do not dominate C , C Nor dominion A Finally, the server compares individual... B and C ,Discover B The construction cost is less than C Furthermore, user satisfaction is also lower than C, therefore B Do not dominate C , C Nor dominion B Since no individual is dominated by other individuals, all individuals belong to the first non-dominated layer. In this way, the server successfully performed non-dominated sorting and stratification of the individuals in the population.

[0062] As mentioned above, each initial battery swapping cabinet site selection scheme can include multiple scheme indicators, namely installation location indicators, user satisfaction indicators, and cost indicators.

[0063] In one embodiment, determining the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer may include: determining target scheme indicators from multiple scheme indicators; sorting the target scheme indicators of the initial battery swapping cabinet location schemes in the same non-dominated layer to obtain a sorting result of the initial battery swapping cabinet location schemes in the same non-dominated layer according to the target scheme indicators; determining the individual congestion of each initial battery swapping cabinet location scheme corresponding to the target scheme indicator based on the sorting result; re-determining target scheme indicators from the remaining scheme indicators, and continuing to sort and determine the individual congestion until all scheme indicators are determined to obtain the individual congestion of each initial battery swapping cabinet location scheme corresponding to each scheme indicator; and determining the total congestion of each initial battery swapping cabinet location scheme based on the individual congestion of each initial battery swapping cabinet location scheme corresponding to multiple scheme indicators.

[0064] In this embodiment, the server can calculate the crowding of individuals in each non-dominated layer and measure the distribution density of individuals in each target space (i.e., each scheme indicator) to maintain population diversity.

[0065] In this embodiment, the server can use each scheme indicator as the target scheme indicator, and sort multiple initial battery swapping cabinet location schemes within the same dominance layer and calculate the individual congestion degree to obtain the individual congestion degree of each scheme indicator corresponding to each initial battery swapping cabinet location scheme.

[0066] Furthermore, the server adds up the individual congestion levels of each initial battery swapping cabinet location scheme based on the individual congestion levels of multiple scheme indicators corresponding to each initial battery swapping cabinet location scheme, and obtains the total congestion level of each initial battery swapping cabinet location scheme.

[0067] In one embodiment, determining the individual congestion degree of the target scheme index corresponding to each initial battery swapping cabinet location scheme based on the ranking results may include: determining, according to the ranking results, the boundary battery swapping cabinet location schemes and non-boundary battery swapping cabinet location schemes corresponding to the target scheme index among the initial battery swapping cabinet location schemes within the same non-dominated layer, and determining the adjacent battery swapping cabinet location schemes corresponding to each non-boundary battery swapping cabinet location scheme; determining that the individual congestion degree of the target scheme index corresponding to the boundary battery swapping cabinet location scheme is infinite; and obtaining the individual congestion degree of the target scheme index corresponding to each non-boundary battery swapping cabinet location scheme based on the target scheme index of the adjacent battery swapping cabinet location schemes of each non-boundary battery swapping cabinet location scheme.

[0068] In one embodiment, before determining the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer, the method may further include: normalizing the scheme indicators of each initial battery swapping cabinet location scheme in each non-dominated layer to obtain normalized initial battery swapping cabinet location schemes.

[0069] In this embodiment, the server normalizes each scheme indicator to eliminate dimensional differences between indicators from different schemes. The normalization expression is as follows:

[0070]

[0071] in, Represents an individual x In the i The value on each target, and They represent the first in the population. i The minimum and maximum values ​​of each objective (project indicator). Normalized objectives (project indicators). The range is between [0, 1].

[0072] In this embodiment, determining the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer may include: determining the total congestion of each initial battery swapping cabinet location scheme after normalizing the scheme indicators of each non-dominated layer.

[0073] In this embodiment, for each scheme metric, the server can rank the individuals within the non-dominated layer. The ranking is based on the normalized value of that scheme metric. After sorting, the position of each individual on the indicator of the scheme is determined.

[0074] Furthermore, for the sorted individuals, the congestion of the boundary individuals (i.e., the first and last individuals in the sorting result, also known as the boundary battery swapping cabinet location scheme) is set to infinity. This ensures that these individuals are preferentially retained in subsequent selection processes. The mathematical expression is:

[0075]

[0076]

[0077] in, and These represent the first and last individuals after sorting.

[0078] Furthermore, for the intermediate individuals after sorting (i.e., removing the first and last individuals from the sorting result, also known as the non-boundary battery swapping station location scheme), its congestion degree is determined by calculating the difference in normalized values ​​of its adjacent individuals (adjacent battery swapping station location schemes of the non-boundary battery swapping station location scheme) on the scheme's index. The specific formula is:

[0079]

[0080] in, Indicates the sorted order of the first... j Individual, m Indicates the number of indicators in the plan. and They represent The adjacent individuals in the first i The normalized value for each objective. The higher the crowding value, the sparser the distribution of that individual in the corresponding scheme indicator.

[0081] Furthermore, for each individual, their total crowding level is the sum of their crowding levels across all scenario indicators. The formula for calculating total crowding level is:

[0082]

[0083] in, Represents an individual x In the i Crowding on each target (program indicator).

[0084] In a specific implementation process, a non-dominated layer contains three individuals. A, Band C The goal is to minimize construction costs and maximize user satisfaction; therefore, the solution metrics include both construction cost and user satisfaction indicators. First, the server normalizes the construction cost and user satisfaction indicators. The normalized construction costs are as follows: C A =0.2 , C B =0.5 , C C = 0.8 The normalized user satisfaction indicators are as follows: S A =0.3 , S B =0.6 , S C =0.9 Then, the server sorts the construction costs, resulting in the following sorted order of individuals: A , B , C For boundary individuals A and C Its crowding level is set to ∞. For intermediate individuals... B Its congestion level is determined by calculating the difference in normalized values ​​of construction costs and user satisfaction indicators between its neighboring individuals. Assume... C A =0.2 , C C =0.8 , S A =0.3 , S C =0.9 Then the individual B The congestion level is:

[0085]

[0086] In one embodiment, based on the total congestion level and non-dominated level of each initial battery swapping station location scheme, multiple iterative battery swapping station location schemes are determined from the initial battery swapping station location population. This includes: determining the number of layer schemes for each initial battery swapping station location scheme within each non-dominated layer; determining the initial battery swapping station location scheme whose non-dominated level meets a level threshold as the first iterative battery swapping station location scheme; when the number of layer schemes within the non-dominated layer is greater than a preset number, determining the initial battery swapping station location scheme that meets a preset congestion level threshold as the second iterative battery swapping station location scheme based on the total congestion level of each initial battery swapping station location scheme within the non-dominated layer; performing priority probability selection on each initial battery swapping station location scheme in the initial battery swapping station location population after removing the first and second iterative battery swapping station location schemes based on the non-dominated level and total congestion level to determine the third iterative battery swapping station location scheme; and using the first, second, and third iterative battery swapping station location schemes as iterative battery swapping station location schemes.

[0087] The number of layer schemes refers to the number of initial battery swapping cabinet location schemes corresponding to each non-dominated layer. For a non-dominated layer with only a single initial battery swapping cabinet location scheme, its corresponding initial battery swapping cabinet location scheme can be directly determined as an iterative battery swapping cabinet location scheme. For a non-dominated layer with multiple initial battery swapping cabinet location schemes, the server can determine the iterative battery swapping cabinet location scheme based on the total congestion and non-dominated level of each initial battery swapping cabinet location scheme.

[0088] In this embodiment, individuals with lower non-dominance levels (i.e., individuals located in higher non-dominance layers) have higher priority. The level threshold is used to filter individuals with higher priority.

[0089] Specifically, the server can select superior individuals to enter the next generation based on their non-dominance level and total crowding, guiding the population towards the Pareto front and eventually to the Pareto optimum. The specific steps are as follows:

[0090] First, the server filters individuals based on their non-dominance level. Individuals with lower non-dominance levels (i.e., those at higher non-dominance levels) have higher priority. Specifically, individuals with a non-dominance level of 1 are selected first (i.e., determined as the first iteration of the battery swapping station location scheme), followed by individuals with level 2, and so on. This ensures that superior individuals in the population can preferentially enter the next generation. Meeting the level threshold can mean either a non-dominance level of 1 or 2, whichever is determined based on actual application requirements.

[0091] Furthermore, within the same non-dominated layer, if multiple individuals (more than 1 layer schemes) have the same non-dominated level, the server can determine the layer scheme number against a preset number and proceed with subsequent processing. For example, if the preset number is 1, and there are multiple individuals (more than 1) within the non-dominated layer, the server can further filter based on the individuals' total crowding. Individuals with higher total crowding are sparser in their corresponding scheme indicators, helping to maintain population diversity. Therefore, individuals with higher total crowding are prioritized for selection (i.e., the second-iteration battery swapping cabinet location scheme). The specific formula is:

[0092]

[0093] in, selection_priority(x) This indicates the probability of being selected. non_dominated_rank (x) Represents an individual x The non-dominant class, total_crowding_distance (x) Represents an individual x The total crowding level, α is a weighting coefficient used to balance the effects of non-dominated rank and crowding level.

[0094] For example, there are 2 individuals in a non-dominated layer. A and B Among them, individuals A Non-dominant level = 1, total crowding = 0.8, α = 0.5, selection_priority(A) = 1 + 0.5 × 0.8 = 1.4. Individual B Non-dominant level = 1, total crowding = 0.5, α = 0.5, selection_priority(B) = 1 + 0.5 × 0.5 = 1.25. The conclusion is... A Its priority (1.4) is higher than B (1.25), therefore A The probability of being selected is higher. A The site selection scheme for the second-generation battery swapping cabinet.

[0095] Furthermore, for the remaining individuals (the initial battery swapping station locations excluding the first and second iteration locations from the initial location population), a roulette wheel selection method is used to determine the third iteration location. The probability of roulette wheel selection is proportional to the individual's selection priority. Specifically, the individual... x The probability of being selected is:

[0096]

[0097] In this embodiment, the server selects a certain number of individuals (using the first iteration of the battery swapping cabinet location scheme, the second iteration of the battery swapping cabinet location scheme, and the third iteration of the battery swapping cabinet location scheme) from the current population through the above steps to generate the next generation population. The new generation population will be used for subsequent crossover and mutation operations to further optimize the individuals in the population.

[0098] In one specific embodiment, it includes five individuals. A, B, C, D and E The population, of which individuals A and B The non-dominant level is 1, and the individual C , D and E The non-dominant level is 2. Individual A The overall crowding level is 1.5, and the individual... B The overall crowding level is 1.2, and the individual... C The overall crowding level is 0.8, and the individual... D The overall crowding level is 0.6, and the individual... E The total congestion level is 0.4. First, the server filters individuals based on their non-dominance level. A and B And then, for the remaining individuals... C , D and E The server calculates their selection priority. Assume... α =0.1, then the individual C The selection priority is 2 + 0.1 * 0.8 = 2.08 for individuals. D The selection priority is 2 + 0.1 * 0.6 = 2.06 for individuals. E The selection priority is 2 + 0.1 * 0.4 = 2.04. Next, the server calculates the probability of each individual being selected using the roulette wheel selection method. C The probability of being selected is 2.08 / (2.08+2.06+2.04) = 0.347. D The probability of being selected is 2.06 / (2.08+2.06+2.04) = 0.343. E The probability of being selected is 2.04 / (2.08 + 2.06 + 2.04) = 0.34. Finally, the server selects individuals based on these probabilities to generate the next generation of the population. In this way, the server can select superior individuals to enter the next generation and gradually guide the population towards the Pareto front.

[0099] In this embodiment, the core objective of the server in determining the third-iteration battery swapping cabinet location scheme through priority probability selection is to maintain a fixed population size. For example, if the population has 5 individuals, and the next generation population size still needs to be maintained at 5, then the selection process must satisfy the following: priority is given to retaining elite individuals (those not with a dominance level of 1). A and B (Directly moving to the next generation), to replenish the missing individuals from the remaining individuals, it is necessary to... C , D , E Then select 3 more, resulting in 5 individuals, to obtain an iterative battery swapping cabinet location scheme.

[0100] In one embodiment, multiple iterative battery swapping cabinet location schemes are subjected to cross-processing and mutation processing iterations until each iterative battery swapping cabinet location scheme meets preset conditions, thereby obtaining a target battery swapping cabinet location population for the corresponding target area. This may include: performing cross-processing on the scheme indicators of any two iterative battery swapping cabinet location schemes to obtain a cross-processed location scheme; performing random mutation processing on the scheme indicators of each iterative battery swapping cabinet location scheme to obtain a mutated location scheme; using the cross-processed location scheme and the mutated location scheme as iterative battery swapping cabinet location schemes, and continuing to perform cross-processing and random mutation processing iterations until each iterative battery swapping cabinet location scheme meets preset conditions, thereby obtaining a target battery swapping cabinet location population for the corresponding target area.

[0101] In this embodiment, the server can perform crossover and mutation operations on the selected individuals to generate new individuals, and fully explore the solution space to find a better Pareto optimal solution, that is, until each iterative battery swapping cabinet location scheme meets the preset conditions.

[0102] Specifically, the cross-operation generates new candidate solutions by exchanging some of the scheme indicators of two individual solutions. In the battery swapping cabinet site selection problem, the scheme indicators of each individual solution can include installation location indicators, construction costs, operating costs, and user satisfaction indicators. The specific implementation process of the cross-operation is as follows:

[0103] First, the server randomly selects two parent individuals. and (Two iterative battery swapping cabinet location schemes) are used for cross-operation.

[0104] Furthermore, the server determines the intersection points, that is, it randomly selects one or more intersection points from the individual's scheme indicators. For example, the installation location indicator is selected as the intersection point.

[0105] Furthermore, at the intersection, the server exchanges the scheme indicators of the two parent individuals and generates two new child individuals. and For example, if the parent individual The installation location index is (39.9042, 116.4074), parent individual If the installation location index is (31.2304, 121.4737), then the installation location indexes are swapped at the intersection to generate offspring individuals. The installation location index is (39.9042, 121.4737), and the offspring individual... The installation location index is (31.2304, 116.4074).

[0106] Furthermore, the server can adjust the genes of the offspring individuals, such as construction cost, operating cost, and user satisfaction, based on the installation location indicators after crossover, to ensure that the newly generated individuals are feasible.

[0107] In this embodiment, the mutation operation introduces new possibilities by randomly changing certain scheme indicators of an individual. The specific implementation process of the mutation operation in the battery swapping cabinet location problem is as follows:

[0108] First, the server randomly selects an individual from the current population. P (Iterative battery swapping cabinet location scheme) is used for mutation operations.

[0109] Furthermore, the server randomly selects one or more mutation points from the individual's scheme indicators. For example, construction cost may be selected as a mutation point.

[0110] Furthermore, at mutation points, the server randomly alters the individual's scheme indicators. For example, it randomly changes the construction cost from 5 million yuan to 4.8 million yuan or 5.2 million yuan.

[0111] Furthermore, the server adjusts the individual's operating costs and user satisfaction metrics based on the mutated construction costs to ensure that the newly generated individuals are feasible.

[0112] In this embodiment, the server generates a certain number of new individuals through crossover and mutation operations, and adds them to the next generation population. These new individuals will be used in subsequent iterative optimization processes to gradually approach the Pareto front.

[0113] In a specific implementation process, there are two parent individuals. P 1 and P 2 ,in P 1 The installation location index is (39.9042, 116.4074), the construction cost is 5 million yuan, the operating cost is 500,000 yuan / year, and the user satisfaction index is 0.85. P 2The installation location index is (31.2304, 121.4737), the construction cost is 4.5 million yuan, the operating cost is 550,000 yuan / year, and the user satisfaction index is 0.88. First, the server selects the installation location index as the intersection point, swaps the installation location indices of two parent individuals, and generates a child individual. C 1 The installation location index is (39.9042, 121.4737), and the offspring individual... C 2 The installation location metrics are (31.2304, 116.4074). Then, based on the crossover installation location metrics, the server adjusts the construction cost, operating cost, and user satisfaction metrics of the offspring instances. For example, the offspring instances... C 1 The construction cost was 4.75 million yuan, the operating cost was 525,000 yuan per year, and the user satisfaction index was 0.865; offspring individuals C 2 The construction cost was 4.75 million yuan, the operating cost was 525,000 yuan per year, and the user satisfaction index was 0.865. Next, the server will select an individual... P 3 Its construction cost is 5 million yuan, its operating cost is 500,000 yuan per year, and its user satisfaction index is 0.85. A mutation operation is performed. The server selects the construction cost as the mutation point and randomly changes it to 4.8 million yuan. Then, the server adjusts the individual's operating cost and user satisfaction index based on the mutated construction cost. For example, the mutated individual... P 3 The operating cost is 480,000 yuan per year, and the user satisfaction index is 0.84. Finally, the server will generate new offspring individuals. C 1 , C 2 and the mutated individuals P 3 It is added to the next generation of the population for subsequent iterative optimization.

[0114] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0115] In one embodiment, such as Figure 3 As shown, a device for determining the location of a battery swapping station is provided, comprising: an initial population generation module 100, a non-dominated layer partitioning module 200, a total congestion determination module 300, an iterative battery swapping station location determination module, and an iteration module 500, wherein:

[0116] The initial population generation module 100 is used to generate the initial battery swapping cabinet location population for the corresponding target area. The initial battery swapping cabinet location population includes multiple initial battery swapping cabinet location schemes.

[0117] The non-dominated layer division module 200 is used to divide the non-dominated levels of each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population to obtain multiple non-dominated layers. Each non-dominated layer includes at least one initial battery swapping cabinet location scheme. The non-dominated levels of the initial battery swapping cabinet location schemes within the same non-dominated layer are the same. The non-dominated level represents the excellence level of each initial battery swapping cabinet location scheme compared to other initial battery swapping cabinet location schemes.

[0118] The total congestion determination module 300 is used to determine the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer. The total congestion characterizes the spatial distribution density of each initial battery swapping cabinet location scheme in the non-dominated layer.

[0119] The iterative battery swapping cabinet location determination module 400 is used to determine multiple iterative battery swapping cabinet location schemes from the initial battery swapping cabinet location population based on the total congestion and non-dominated level of each initial battery swapping cabinet location scheme.

[0120] The iteration module 500 is used to perform cross-processing iteration and mutation processing iteration on multiple iterative battery swapping cabinet location schemes until each iterative battery swapping cabinet location scheme meets the preset conditions, thereby obtaining the iteration of the initial battery swapping cabinet location population and generating the target battery swapping cabinet location population for the corresponding target area. The target battery swapping cabinet location population includes multiple target battery swapping cabinet location schemes.

[0121] In one embodiment, the non-dominated layer partitioning module 200 may include:

[0122] The initial non-dominated level determination submodule is used to determine the initial non-dominated level of each initial battery swapping cabinet location scheme.

[0123] The dominance relationship determination submodule is used to compare the scheme indicators of any two initial battery swapping cabinet location schemes and determine the dominance relationship between the two initial battery swapping cabinet location schemes. The dominance relationship characterizes the superiority or inferiority of any two initial battery swapping cabinet location schemes in multiple scheme indicators.

[0124] The non-dominance level determination submodule is used to determine the non-dominance level of each initial battery swapping cabinet location scheme based on the dominance relationship.

[0125] The non-dominated layer partitioning submodule is used to partition the non-dominated layers of each initial battery swapping cabinet location scheme based on each non-dominated level, resulting in multiple non-dominated layers.

[0126] In one embodiment, each initial battery swapping cabinet location scheme includes multiple scheme indicators.

[0127] In this embodiment, the total congestion determination module 300 may include:

[0128] The target scheme indicator determination submodule is used to determine the target scheme indicator from multiple scheme indicators.

[0129] The sorting submodule is used to sort the target scheme indicators of the initial battery swapping cabinet location schemes within the same non-dominated layer, and obtain the sorting results of the initial battery swapping cabinet location schemes within the same non-dominated layer according to the target scheme indicators.

[0130] The Individual Congestion Determination Submodule is used to determine the individual congestion of each initial battery swapping cabinet location scheme corresponding to the target scheme index based on the sorting results.

[0131] The loop submodule is used to re-determine the target scheme indicators from the remaining scheme indicators, and continue to sort and determine the individual congestion degree until all scheme indicators are determined, and obtain the individual congestion degree of each scheme indicator corresponding to each initial battery swapping cabinet location scheme.

[0132] The total congestion determination submodule is used to determine the total congestion of each initial battery swapping cabinet location scheme based on the individual congestion of multiple scheme indicators corresponding to each initial battery swapping cabinet location scheme.

[0133] In one embodiment, the individual crowding determination submodule may include:

[0134] The first determining unit is used to determine, based on the sorting results, the boundary battery swapping cabinet location scheme and the non-boundary battery swapping cabinet location scheme corresponding to the target scheme index among the initial battery swapping cabinet location schemes within the same non-dominated layer, and to determine the adjacent battery swapping cabinet location schemes corresponding to each non-boundary battery swapping cabinet location scheme.

[0135] The second determining unit is used to determine that the individual congestion degree of the target scheme index corresponding to the boundary battery swapping cabinet site selection scheme is infinite.

[0136] The individual congestion determination unit is used to obtain the individual congestion of each non-boundary battery swapping station location scheme based on the target scheme indicators of the adjacent battery swapping station location schemes.

[0137] In one embodiment, the above-mentioned apparatus may further include:

[0138] The normalization module is used to normalize the indicators of each initial battery swapping cabinet location scheme in each non-dominated layer before determining the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer, so as to obtain the normalized initial battery swapping cabinet location scheme.

[0139] In this embodiment, the total congestion determination submodule is used to determine the total congestion of each initial battery swapping cabinet location scheme after the scheme index of each non-dominated layer is normalized.

[0140] In one embodiment, the iterative battery swapping cabinet location determination module 400 may include:

[0141] The layer scheme number determination submodule is used to determine the number of layer schemes for the initial battery swapping cabinet location scheme in each non-dominant layer;

[0142] The first iteration battery swapping cabinet location scheme determination submodule is used to determine the initial battery swapping cabinet location scheme that meets the level threshold of the non-dominant level as the first iteration battery swapping cabinet location scheme.

[0143] The second iteration battery swapping cabinet location scheme determination submodule is used to determine the initial battery swapping cabinet location scheme that meets the preset congestion threshold as the second iteration battery swapping cabinet location scheme when the number of layer schemes in the non-dominated layer is greater than the preset number, based on the total congestion of each initial battery swapping cabinet location scheme in the non-dominated layer.

[0144] The third iteration battery swapping cabinet location scheme determination submodule is used to perform priority probability selection on each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population after removing the first iteration battery swapping cabinet location scheme and the second iteration battery swapping cabinet location scheme, based on the non-dominance level and the total congestion degree, to determine the third iteration battery swapping cabinet location scheme.

[0145] The iterative battery swapping cabinet location selection scheme determination submodule is used to take the first iterative battery swapping cabinet location selection scheme, the second iterative battery swapping cabinet location selection scheme, and the third iterative battery swapping cabinet location selection scheme as the iterative battery swapping cabinet location selection scheme.

[0146] In one embodiment, the iteration module 500 may include:

[0147] The cross-processing submodule is used to perform cross-processing of scheme indicators on any two iterative battery swapping cabinet location schemes to obtain the cross-processed location scheme.

[0148] The mutation submodule is used to perform random mutation processing on the scheme indicators of each iterative battery swapping cabinet location scheme to obtain the mutated location scheme.

[0149] The iterative submodule is used to take the cross-location scheme and the mutated location scheme as the iterative battery swapping cabinet location scheme, and continue to perform cross-processing and random mutation processing iteratively until each iterative battery swapping cabinet location scheme meets the preset conditions, and obtain the target battery swapping cabinet location population for the corresponding target area.

[0150] In one embodiment, the scheme indicators for each initial battery swapping cabinet location scheme may include the installation location indicators of the battery swapping cabinet and the user satisfaction indicators.

[0151] In this embodiment, the initial population generation module 100 may include:

[0152] The size determination submodule is used to determine the population size.

[0153] The installation location index determination submodule is used to determine the proposed installation locations of multiple battery swapping cabinets based on the location information and population size of the target area, and to obtain multiple installation location indices.

[0154] The User Satisfaction Index Determination Submodule is used to determine the corresponding user satisfaction indexes based on the indicators for each installation location.

[0155] The initial population generation submodule is used to obtain multiple initial battery swapping cabinet location schemes based on each installation location index and the corresponding user satisfaction index, so as to obtain the initial battery swapping cabinet location population for the corresponding target area.

[0156] Specific limitations regarding the battery swapping cabinet location determination device can be found in the limitations of the battery swapping cabinet location determination method described above, and will not be repeated here. Each module in the aforementioned battery swapping cabinet location determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0157] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data such as the initial battery swapping cabinet location population, non-dominance level, total congestion, and target battery swapping cabinet location population. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a battery swapping cabinet location determination method.

[0158] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for determining the location of a battery swapping cabinet. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0159] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0160] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: generating an initial battery swapping cabinet location population corresponding to a target area, the initial battery swapping cabinet location population including multiple initial battery swapping cabinet location schemes; dividing each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population into multiple non-dominated layers, each non-dominated layer including at least one initial battery swapping cabinet location scheme, the initial battery swapping cabinet location schemes within the same non-dominated layer having the same non-dominated level, the non-dominated level representing that each initial battery swapping cabinet location scheme is different from other initial battery swapping cabinet location schemes. The process involves: determining the excellence level of the battery swapping cabinet location scheme indicators; determining the total congestion degree of each initial battery swapping cabinet location scheme in each non-dominated layer, where the total congestion degree characterizes the spatial distribution density of each initial battery swapping cabinet location scheme within the non-dominated layer; determining multiple iterative battery swapping cabinet location schemes from the initial battery swapping cabinet location population based on the total congestion degree and non-dominated level of each initial battery swapping cabinet location scheme; performing cross-processing iteration and mutation processing iteration on the multiple iterative battery swapping cabinet location schemes until each iterative battery swapping cabinet location scheme meets the preset conditions, thereby obtaining the target battery swapping cabinet location population for the corresponding target area, which includes multiple target battery swapping cabinet location schemes.

[0161] In one embodiment, when the processor executes a computer program, it performs non-dominated level division on each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population to obtain multiple non-dominated layers. This may include: determining the initial non-dominated level of each initial battery swapping cabinet location scheme; comparing the scheme indicators of any two initial battery swapping cabinet location schemes to determine the dominance relationship between the two initial battery swapping cabinet location schemes, whereby the dominance relationship characterizes the superiority or inferiority of any two initial battery swapping cabinet location schemes in multiple scheme indicators; determining the non-dominated level of each corresponding initial battery swapping cabinet location scheme based on the dominance relationship; and dividing each initial battery swapping cabinet location scheme into multiple non-dominated layers based on each non-dominated level.

[0162] In one embodiment, when the processor executes a computer program, it implements each initial battery swapping cabinet location scheme, which includes multiple scheme indicators. Determining the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer may include: determining a target scheme indicator from multiple scheme indicators; sorting the target scheme indicators of the initial battery swapping cabinet location schemes in the same non-dominated layer to obtain a sorting result of the initial battery swapping cabinet location schemes in the same non-dominated layer according to the target scheme indicators; determining the individual congestion of each initial battery swapping cabinet location scheme corresponding to the target scheme indicator based on the sorting result; re-determining the target scheme indicator from the remaining scheme indicators, and continuing to sort and determine the individual congestion until all scheme indicators are determined, obtaining the individual congestion of each scheme indicator corresponding to each initial battery swapping cabinet location scheme; and determining the total congestion of each initial battery swapping cabinet location scheme based on the individual congestion of each initial battery swapping cabinet location scheme corresponding to multiple scheme indicators.

[0163] In one embodiment, when the processor executes a computer program, it determines the individual congestion degree of the target scheme index corresponding to each initial battery swapping cabinet location scheme based on the sorting results. This may include: determining, according to the sorting results, the boundary battery swapping cabinet location schemes and non-boundary battery swapping cabinet location schemes corresponding to the target scheme index among the initial battery swapping cabinet location schemes within the same non-dominated layer, and determining the adjacent battery swapping cabinet location schemes corresponding to each non-boundary battery swapping cabinet location scheme; determining that the individual congestion degree of the target scheme index corresponding to the boundary battery swapping cabinet location scheme is infinite; and obtaining the individual congestion degree of the target scheme index corresponding to each non-boundary battery swapping cabinet location scheme based on the target scheme index of the adjacent battery swapping cabinet location schemes of each non-boundary battery swapping cabinet location scheme.

[0164] In one embodiment, before the processor executes the computer program to determine the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer, the following steps may also be performed: normalize the scheme indicators of each initial battery swapping cabinet location scheme in each non-dominated layer to obtain normalized initial battery swapping cabinet location schemes.

[0165] In one embodiment, when the processor executes a computer program to determine the total congestion of each initial battery swapping cabinet location scheme for each non-dominated layer, it may include: determining the total congestion of each initial battery swapping cabinet location scheme after normalizing the scheme indices for each non-dominated layer.

[0166] In one embodiment, when the processor executes a computer program, it determines multiple iterative battery swapping cabinet location schemes from the initial battery swapping cabinet location population based on the total congestion level and non-dominated level of each initial battery swapping cabinet location scheme. This may include: determining the number of layer schemes for the initial battery swapping cabinet location schemes in each non-dominated layer; determining the initial battery swapping cabinet location schemes whose non-dominated levels meet a level threshold as the first iterative battery swapping cabinet location scheme; when the number of layer schemes in the non-dominated layer is greater than a preset number, determining the initial battery swapping cabinet location schemes that meet a preset congestion threshold as the second iterative battery swapping cabinet location scheme based on the total congestion level of each initial battery swapping cabinet location scheme in the non-dominated layer; performing priority probability selection on each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population after removing the first and second iterative battery swapping cabinet location schemes based on the non-dominated level and the total congestion level to determine the third iterative battery swapping cabinet location scheme; and using the first, second, and third iterative battery swapping cabinet location schemes as iterative battery swapping cabinet location schemes.

[0167] In one embodiment, when the processor executes a computer program, it performs cross-processing and mutation processing iterations on multiple iterative battery swapping cabinet location schemes until each iterative battery swapping cabinet location scheme meets preset conditions, thereby obtaining a target battery swapping cabinet location population for the corresponding target area. This may include: performing cross-processing on the scheme indicators of any two iterative battery swapping cabinet location schemes to obtain a cross-processed location scheme; performing random mutation processing on the scheme indicators of each iterative battery swapping cabinet location scheme to obtain a mutated location scheme; using the cross-processed location scheme and the mutated location scheme as iterative battery swapping cabinet location schemes, and continuing to perform cross-processing and random mutation processing iterations until each iterative battery swapping cabinet location scheme meets preset conditions, thereby obtaining a target battery swapping cabinet location population for the corresponding target area.

[0168] In one embodiment, the scheme indicators for each initial battery swapping cabinet location scheme include the installation location indicator of the battery swapping cabinet and the user satisfaction indicator.

[0169] Specifically, when the processor executes the computer program, it generates an initial battery swapping cabinet site selection population for the corresponding target area. This can include: determining the population size; determining multiple proposed installation locations for battery swapping cabinets based on the location information of the target area and the population size, obtaining multiple installation location indicators; determining corresponding user satisfaction indicators based on each installation location indicator; and obtaining multiple initial battery swapping cabinet site selection schemes based on each installation location indicator and the corresponding user satisfaction indicators, so as to obtain the initial battery swapping cabinet site selection population for the corresponding target area.

[0170] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it performs the following steps: generating an initial battery swapping cabinet location population corresponding to a target area, the initial battery swapping cabinet location population including multiple initial battery swapping cabinet location schemes; dividing each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population into multiple non-dominated layers, each non-dominated layer including at least one initial battery swapping cabinet location scheme, the initial battery swapping cabinet location schemes within the same non-dominated layer having the same non-dominated level, the non-dominated level representing that each initial battery swapping cabinet location scheme has the same non-dominated level as other initial battery swapping cabinet location schemes. The system assesses the excellence level of each initial battery swapping station location scheme; determines the total congestion level of each initial battery swapping station location scheme in each non-dominated layer, where the total congestion level characterizes the spatial distribution density of each initial battery swapping station location scheme within the non-dominated layer; based on the total congestion level and non-dominated level of each initial battery swapping station location scheme, it identifies multiple iterative battery swapping station location schemes from the initial battery swapping station location population; it performs cross-processing iteration and mutation processing iteration on the multiple iterative battery swapping station location schemes until each iterative battery swapping station location scheme meets the preset conditions, thereby obtaining the target battery swapping station location population for the corresponding target area, which includes multiple target battery swapping station location schemes.

[0171] In one embodiment, when a computer program is executed by a processor, it performs non-dominated level division on each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population to obtain multiple non-dominated layers. This may include: determining the initial non-dominated level of each initial battery swapping cabinet location scheme; comparing the scheme indicators of any two initial battery swapping cabinet location schemes to determine the dominance relationship between the two initial battery swapping cabinet location schemes, whereby the dominance relationship characterizes the superiority or inferiority of any two initial battery swapping cabinet location schemes in multiple scheme indicators; determining the non-dominated level of each corresponding initial battery swapping cabinet location scheme based on the dominance relationship; and dividing each initial battery swapping cabinet location scheme into multiple non-dominated layers based on each non-dominated level.

[0172] In one embodiment, when a computer program is executed by a processor, it implements each initial battery swapping cabinet location scheme including multiple scheme indicators; determining the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer may include: determining a target scheme indicator from multiple scheme indicators; sorting the target scheme indicators of the initial battery swapping cabinet location schemes in the same non-dominated layer to obtain a sorting result of the initial battery swapping cabinet location schemes in the same non-dominated layer according to the target scheme indicators; determining the individual congestion of each initial battery swapping cabinet location scheme corresponding to the target scheme indicator based on the sorting result; re-determining the target scheme indicator from the remaining scheme indicators, and continuing to sort and determine the individual congestion until all scheme indicators are determined, obtaining the individual congestion of each scheme indicator corresponding to each initial battery swapping cabinet location scheme; and determining the total congestion of each initial battery swapping cabinet location scheme based on the individual congestion of each initial battery swapping cabinet location scheme corresponding to multiple scheme indicators.

[0173] In one embodiment, when the computer program is executed by the processor, it implements the determination of the individual congestion degree of the target scheme index corresponding to each initial battery swapping cabinet location scheme based on the sorting result. This may include: determining, according to the sorting result, the boundary battery swapping cabinet location schemes and non-boundary battery swapping cabinet location schemes corresponding to the target scheme index among the initial battery swapping cabinet location schemes within the same non-dominated layer, and determining the adjacent battery swapping cabinet location schemes corresponding to each non-boundary battery swapping cabinet location scheme; determining that the individual congestion degree of the target scheme index corresponding to the boundary battery swapping cabinet location scheme is infinite; and obtaining the individual congestion degree of the target scheme index corresponding to each non-boundary battery swapping cabinet location scheme based on the target scheme index of the adjacent battery swapping cabinet location schemes of each non-boundary battery swapping cabinet location scheme.

[0174] In one embodiment, before the computer program is executed by the processor to determine the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer, the following steps may also be performed: normalizing the scheme indicators of each initial battery swapping cabinet location scheme in each non-dominated layer to obtain normalized initial battery swapping cabinet location schemes.

[0175] In one embodiment, when a computer program is executed by a processor to determine the total congestion of each initial battery swapping cabinet location scheme for each non-dominated layer, it may include: determining the total congestion of each initial battery swapping cabinet location scheme after normalizing the scheme indices for each non-dominated layer.

[0176] In one embodiment, when a computer program is executed by a processor, it determines multiple iterative battery swapping cabinet location schemes from the initial battery swapping cabinet location population based on the total congestion level and non-dominated level of each initial battery swapping cabinet location scheme. This may include: determining the number of layer schemes for the initial battery swapping cabinet location schemes in each non-dominated layer; determining the initial battery swapping cabinet location schemes whose non-dominated levels meet a level threshold as the first iterative battery swapping cabinet location scheme; when the number of layer schemes in the non-dominated layer is greater than a preset number, determining the initial battery swapping cabinet location schemes that meet a preset congestion threshold as the second iterative battery swapping cabinet location scheme based on the total congestion level of each initial battery swapping cabinet location scheme in the non-dominated layer; performing priority probability selection on each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population after removing the first and second iterative battery swapping cabinet location schemes based on the non-dominated level and the total congestion level to determine the third iterative battery swapping cabinet location scheme; and using the first, second, and third iterative battery swapping cabinet location schemes as iterative battery swapping cabinet location schemes.

[0177] In one embodiment, when the computer program is executed by the processor, it performs cross-processing and mutation processing iterations on multiple iterative battery swapping cabinet location schemes until each iterative battery swapping cabinet location scheme meets preset conditions, thereby obtaining a target battery swapping cabinet location population for the corresponding target area. This may include: performing cross-processing on the scheme indicators of any two iterative battery swapping cabinet location schemes to obtain a cross-processed location scheme; performing random mutation processing on the scheme indicators of each iterative battery swapping cabinet location scheme to obtain a mutated location scheme; using the cross-processed location scheme and the mutated location scheme as iterative battery swapping cabinet location schemes, and continuing to perform cross-processing and random mutation processing iterations until each iterative battery swapping cabinet location scheme meets preset conditions, thereby obtaining a target battery swapping cabinet location population for the corresponding target area.

[0178] In one embodiment, the scheme indicators for each initial battery swapping cabinet location scheme include the installation location indicator of the battery swapping cabinet and the user satisfaction indicator.

[0179] Specifically, when the computer program is executed by the processor, it generates an initial battery swapping cabinet site selection population for the corresponding target area. This can include: determining the population size; determining multiple proposed installation locations for battery swapping cabinets based on the location information of the target area and the population size, and obtaining multiple installation location indicators; determining corresponding user satisfaction indicators based on each installation location indicator; and obtaining multiple initial battery swapping cabinet site selection schemes based on each installation location indicator and the corresponding user satisfaction indicators, so as to obtain the initial battery swapping cabinet site selection population for the corresponding target area.

[0180] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0181] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0182] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for determining the location of a battery swapping cabinet, characterized in that, include: Generate an initial battery swapping cabinet location population corresponding to the target area. The initial battery swapping cabinet location population includes multiple initial battery swapping cabinet location schemes. Each initial battery swapping cabinet location scheme corresponds to a location scheme for a battery swapping cabinet within the target area. The initial battery swapping station location schemes in the initial battery swapping station location population are divided into multiple non-dominated layers by non-dominated level classification, including: determining the initial non-dominated level of each initial battery swapping station location scheme; comparing the scheme indicators of any two initial battery swapping station location schemes to determine the dominance relationship between the two schemes, wherein the dominance relationship characterizes the superiority or inferiority of the two schemes in multiple scheme indicators; determining the non-dominated level of each initial battery swapping station location scheme according to the dominance relationship; and dividing the initial battery swapping station location schemes into multiple non-dominated layers based on the non-dominated levels, wherein each non-dominated layer includes at least one initial battery swapping station location scheme, and the initial battery swapping station location schemes within the same non-dominated layer have the same non-dominated level, wherein the non-dominated level characterizes the superiority or inferiority of each initial battery swapping station location scheme compared to other initial battery swapping station location schemes. The total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer is determined, whereby the total congestion represents the spatial distribution density of each initial battery swapping cabinet location scheme within the non-dominated layer. Each initial battery swapping cabinet location scheme includes multiple scheme indicators. Determining the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer includes: determining target scheme indicators from the multiple scheme indicators; sorting the target scheme indicators of the initial battery swapping cabinet location schemes within the same non-dominated layer to obtain a sorting result of the initial battery swapping cabinet location schemes within the same non-dominated layer according to the target scheme indicators; determining the individual congestion of each initial battery swapping cabinet location scheme corresponding to the target scheme indicators based on the sorting result; re-determining target scheme indicators from the remaining scheme indicators, and continuing to sort and determine individual congestion until all scheme indicators are determined, obtaining the individual congestion of each scheme indicator corresponding to each initial battery swapping cabinet location scheme; and determining the total congestion of each initial battery swapping cabinet location scheme based on the individual congestion of each initial battery swapping cabinet location scheme corresponding to the multiple scheme indicators. Based on the total congestion and non-dominated level of each initial battery swapping cabinet location scheme, multiple iterative battery swapping cabinet location schemes are determined from the initial battery swapping cabinet location population, including: determining the number of layer schemes for each initial battery swapping cabinet location scheme within each non-dominated layer; determining the initial battery swapping cabinet location scheme that meets the level threshold as the first iterative battery swapping cabinet location scheme; when the number of layer schemes within the non-dominated layer is greater than a preset number, determining the initial battery swapping cabinet location scheme that meets the preset congestion threshold as the second iterative battery swapping cabinet location scheme based on the total congestion of each initial battery swapping cabinet location scheme within the non-dominated layer; based on the non-dominated level and the total congestion, performing priority probability selection on each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population after removing the first and second iterative battery swapping cabinet location schemes to determine the third iterative battery swapping cabinet location scheme; and using the first iterative battery swapping cabinet location scheme, the second iterative battery swapping cabinet location scheme, and the third iterative battery swapping cabinet location scheme as iterative battery swapping cabinet location schemes. The multiple iterative battery swapping cabinet location schemes are subjected to cross-processing iteration and mutation processing iteration until each of the iterative battery swapping cabinet location schemes meets the preset conditions, thereby obtaining the target battery swapping cabinet location population corresponding to the target area, wherein the target battery swapping cabinet location population includes multiple target battery swapping cabinet location schemes.

2. The method as described in claim 1, characterized in that, Before determining the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer, the process may further include: normalizing the scheme indicators of each initial battery swapping cabinet location scheme in each non-dominated layer to obtain normalized initial battery swapping cabinet location schemes.

3. The method as described in claim 1, characterized in that, The determination of the individual congestion level of each initial battery swapping cabinet location scheme corresponding to the target scheme index based on the ranking results includes: Based on the sorting results, the boundary battery swapping station location scheme and the non-boundary battery swapping station location scheme corresponding to the target scheme index are determined among the initial battery swapping station location schemes within the same non-dominated layer, and the adjacent battery swapping station location schemes corresponding to each of the non-boundary battery swapping station location schemes are determined. The individual congestion degree of the target scheme index corresponding to the boundary battery swapping cabinet site selection scheme is determined to be infinite; Based on the target scheme indicators of the adjacent battery swapping cabinet location schemes of each non-boundary battery swapping cabinet location scheme, the individual congestion degree of the target scheme indicator corresponding to each non-boundary battery swapping cabinet location scheme is obtained.

4. The method as described in claim 1, characterized in that, The process of performing cross-processing and mutation processing iterations on the multiple iterative battery swapping cabinet location schemes until each of the iterative battery swapping cabinet location schemes meets preset conditions, thereby obtaining a target battery swapping cabinet location population corresponding to the target area, includes: For any two iterative battery swapping cabinet location schemes, cross-process the scheme indicators to obtain the cross-processed location scheme; The scheme indicators of each of the iterative battery swapping cabinet location schemes are subjected to random variation processing to obtain the varied location schemes; The cross-location scheme and the mutated location scheme are used as the iterative battery swapping cabinet location schemes, and cross-processing and random mutation processing are continued until each of the iterative battery swapping cabinet location schemes meets the preset conditions, thereby obtaining the target battery swapping cabinet location population corresponding to the target area.

5. The method as described in claim 1, characterized in that, The scheme indicators for each initial battery swapping cabinet site selection scheme include the installation location indicator of the battery swapping cabinet and the user satisfaction indicator. The generation of the initial battery swapping cabinet site selection population corresponding to the target area includes: Determine population size; Based on the location information of the target area and the population size, multiple proposed installation locations for battery swapping cabinets are determined, resulting in multiple installation location indicators. Based on the installation location indicators, the corresponding user satisfaction indicators are determined. Based on the installation location indicators and the corresponding user satisfaction indicators, multiple initial battery swapping cabinet site selection schemes are obtained to obtain an initial battery swapping cabinet site selection population corresponding to the target area.

6. The method as described in claim 5, characterized in that, The initial battery swapping cabinet location plan also includes cost indicators.

7. A device for determining the location of a battery swapping cabinet, characterized in that, For performing the battery swapping cabinet location determination method as described in any one of claims 1 to 6; the apparatus includes: An initial population generation module is used to generate an initial battery swapping cabinet location population for a corresponding target area. The initial battery swapping cabinet location population includes multiple initial battery swapping cabinet location schemes, and each initial battery swapping cabinet location scheme corresponds to a location scheme for a battery swapping cabinet within the target area. The non-dominated layer division module is used to divide the non-dominated levels of each initial battery swapping cabinet location scheme in the initial battery swapping cabinet location population to obtain multiple non-dominated layers. Each non-dominated layer includes at least one initial battery swapping cabinet location scheme. The non-dominated levels of the initial battery swapping cabinet location schemes within the same non-dominated layer are the same. The non-dominated level represents the excellence level of each initial battery swapping cabinet location scheme compared with other initial battery swapping cabinet location schemes. The total congestion determination module is used to determine the total congestion of each initial battery swapping cabinet location scheme in each non-dominated layer. The total congestion characterizes the spatial distribution density of each initial battery swapping cabinet location scheme in the non-dominated layer. The iterative battery swapping station location scheme determination module is used to determine multiple iterative battery swapping station location schemes from the initial battery swapping station location population based on the total congestion degree and non-dominance level of each initial battery swapping station location scheme. An iterative module is used to perform cross-processing iteration and mutation processing iteration on the multiple iterative battery swapping cabinet location schemes until each of the iterative battery swapping cabinet location schemes meets the preset conditions, thereby obtaining a target battery swapping cabinet location population corresponding to the target area, wherein the target battery swapping cabinet location population includes multiple target battery swapping cabinet location schemes.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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