Method, device and equipment for determining site selection of power conversion cabinet and medium

Abstract

The invention relates to a method, a device and equipment for determining the site selection of a battery changing cabinet, and a medium. The method comprises the following steps: generating an initial power conversion cabinet site selection population corresponding to a target area, wherein the initial power conversion cabinet site selection population comprises a plurality of initial power conversion cabinet site selection schemes; performing non-dominated grading on each initial power conversion cabinet site selection scheme in the initial power conversion cabinet site selection population to obtain a plurality of non-dominated layers; determining the total congestion degree of each initial power conversion cabinet site selection scheme of each non-dominated layer, wherein the total congestion degree represents the spatial distribution density of each initial power conversion cabinet site selection scheme in the non-dominated layer; and according to the total congestion degree and the non-dominated level of each initial power conversion cabinet site selection scheme, iteration of the initial power conversion cabinet site selection population is carried out, a target power conversion cabinet site selection population corresponding to the target area is generated, and the target power conversion cabinet site selection population comprises a plurality of target power conversion cabinet site selection schemes. By adopting the method, the accuracy of determining the mounting position of the battery changing cabinet can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, equipment and medium for determining the location of a battery swap cabinet. Background Art

[0002] Today, electric vehicles are experiencing a golden age of rapid development, thanks to their significant advantages, such as environmental protection and energy conservation. As a key facility for recharging electric vehicle energy, the rationality of the location of battery swap cabinets directly affects the convenience of electric vehicle use and the operator's operating efficiency.

[0003] Existing site selection methods have obvious limitations. For example, some methods focus on minimizing costs and are committed to reducing construction and operating costs; while other methods focus on maximizing user satisfaction and strive 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 an urgent problem to be solved at this stage. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, device, equipment and medium for determining the site selection of a battery swap cabinet that can improve the accuracy of the installation location of the battery swap cabinet in response to the above technical problems.

[0006] A method for determining the location of a battery swap cabinet comprises: generating an initial battery swap cabinet location population corresponding to a target area, the initial battery swap cabinet location population including multiple initial battery swap cabinet location plans, one initial battery swap cabinet location plan corresponding to a location plan for a battery swap cabinet position in the target area; performing non-dominated level division on each initial battery swap cabinet location plan in the initial battery swap cabinet location population to obtain multiple non-dominated layers, each non-dominated layer including at least one initial battery swap cabinet location plan, the initial battery swap cabinet location plans in the same non-dominated layer having the same non-dominated level, and the non-dominated level characterizing the differences between each initial battery swap cabinet location plan and other initial battery swap cabinet location plans The excellent level of the scheme indicator; determine the total congestion of each initial battery swap cabinet site selection scheme in each non-dominated layer, and the total congestion represents the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer; according to the total congestion and non-dominated level of each initial battery swap cabinet site selection scheme, determine multiple iterative battery swap cabinet site selection schemes from the initial battery swap cabinet site selection population; perform cross-processing iteration and mutation processing iteration on the multiple iterative battery swap cabinet site selection schemes until each iterative battery swap cabinet site selection scheme meets the preset conditions, and obtain the target battery swap cabinet site selection population corresponding to the target area, and the target battery swap cabinet site selection population includes multiple target battery swap cabinet site selection schemes.

[0007] A device for determining the location of a battery swap cabinet, the device comprising: an initial population generation module for generating an initial battery swap cabinet location population corresponding to a target area, the initial battery swap cabinet location population comprising a plurality of initial battery swap cabinet location plans, one initial battery swap cabinet location plan corresponding to a location plan for a battery swap cabinet position within the target area; a non-dominated layer division module for performing non-dominated level division on each initial battery swap cabinet location plan in the initial battery swap cabinet location population to obtain a plurality of non-dominated layers, each non-dominated layer comprising at least one initial battery swap cabinet location plan, the non-dominated levels of the initial battery swap cabinet location plans within the same non-dominated layer being the same, and the non-dominated levels characterizing the differences between each initial battery swap cabinet location plan and other initial battery swap cabinet location plans The excellent level of the scheme indicator; the total congestion determination module is used to determine the total congestion of each initial battery swap cabinet site selection scheme in each non-dominated layer, and the total congestion characterizes the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer; the iteration module is used to determine multiple iterative battery swap cabinet site selection schemes from the initial battery swap cabinet site selection population according to the total congestion and non-dominated level of each initial battery swap cabinet site selection scheme; the iteration module is used to perform cross-processing iteration and mutation processing iteration on multiple iterative battery swap cabinet site selection schemes until each iterative battery swap cabinet site selection scheme meets the preset conditions, and obtain the target battery swap cabinet site selection population corresponding to the target area, and the target battery swap cabinet site selection population includes multiple target battery swap cabinet site selection schemes.

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

[0009] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the methods described above.

[0010] The above-mentioned method, device, equipment and medium for determining the site selection of battery swap cabinets generate an initial battery swap cabinet site selection population corresponding to the target area, and the initial battery swap cabinet site selection population includes multiple initial battery swap cabinet site selection schemes; each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population is divided into non-dominated levels to obtain multiple non-dominated layers, and each non-dominated layer includes 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 level represents 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; the total congestion of each initial battery swap cabinet site selection scheme in each non-dominated layer is determined, and the total congestion represents the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer; according to the total congestion and non-dominated level of each initial battery swap cabinet site selection scheme, the initial battery swap cabinet site selection population is iterated to generate a target battery swap cabinet site selection population corresponding to the target area, and the target battery swap cabinet site selection population includes multiple target battery swap cabinet site selection schemes. Therefore, the determination of the target battery swap cabinet site selection plan is based on the non-dominated level division of the initial battery swap cabinet site selection population, as well as the congestion calculation and iteration, so that the target battery swap cabinet site selection plan can integrate the excellent levels among multiple plan indicators, which can improve the accuracy of the target battery swap cabinet site selection plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is an application scenario diagram of a method for determining a location for a battery swap cabinet in one embodiment; Figure 2 A flowchart of a method for determining a location for a battery swap cabinet in one embodiment is shown; Figure 3 This is a structural block diagram of a device for determining a location for a battery swap cabinet in one embodiment; Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0013] The method for determining the location of the battery swap cabinet provided in this application can be applied to Figure 1In the application environment shown. 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 an initial battery swap cabinet site selection population corresponding to the target area based on the user instructions, and divides each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population into non-dominated levels to obtain multiple non-dominated layers. Furthermore, the server 104 can determine the total congestion of each initial battery swap cabinet site selection scheme in each non-dominated layer, and iterate the initial battery swap cabinet site selection population according to the total congestion and 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. The terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0014] In one embodiment, Figure 2 As shown, a method for determining the location of a power exchange cabinet is provided. Figure 1 The following steps are used as an example to illustrate the server in the example: Step 202: Generate an initial battery swap cabinet site selection population corresponding to the target area. The initial battery swap cabinet site selection population includes multiple initial battery swap cabinet site selection plans.

[0015] In this embodiment, the server can create a population containing multiple potential site selection plans for the corresponding target area based on user instructions, namely, the initial battery swap cabinet site selection population. Each individual in the population represents a possible initial battery swap cabinet site selection plan. The initial battery swap cabinet site selection population includes multiple initial battery swap cabinet site selection plans.

[0016] In this embodiment, an initial battery swap cabinet site selection plan corresponds to a site selection plan for a battery swap cabinet location within the target area. Specifically, if it is necessary to install battery swap cabinets at 100 battery swap cabinet locations within the target area, the initial battery swap cabinet site selection population includes 100 initial battery swap cabinet site selection plans corresponding to these 100 battery swap cabinet locations.

[0017] Among them, each initial battery swap cabinet site selection plan may include multiple plan indicators, and multiple plan indicators together constitute the initial battery swap cabinet site selection plan.

[0018] Step 204: perform non-dominated level classification on each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population to obtain multiple non-dominated layers.

[0019] Each non-dominated layer can include at least one initial battery swap cabinet site selection plan. Initial battery swap cabinet site selection plans within the same non-dominated layer have the same non-dominated level. The non-dominated level indicates how well each initial battery swap cabinet site selection plan compares to other initial battery swap cabinet site selection plans.

[0020] In this embodiment, after generating the initial battery swap cabinet site selection population, the server can combine the various scheme indicators of the initial battery swap cabinet site selection scheme, compare the scheme indicators between the initial battery swap cabinet site selection schemes, and determine the excellent level of the initial battery swap cabinet site selection scheme corresponding to each scheme indicator. Based on the comparison results, the non-dominated level of each initial battery swap cabinet site selection scheme is determined, and then the non-dominated layer is divided based on the non-dominated level, and the initial battery swap cabinet site selection schemes with the same dominance level are divided into the same non-dominated layer to obtain multiple non-dominated layers.

[0021] Step 206: Determine the total congestion of each initial battery swap cabinet site selection plan on each non-dominated layer.

[0022] Among them, the total congestion represents the spatial distribution density of each initial battery swap cabinet site selection plan in the non-dominated layer.

[0023] In this embodiment, after the server divides the initial battery swap cabinet site selection population, it can further calculate the total congestion of each initial battery swap cabinet site selection plan based on each plan indicator.

[0024] Step 208: Determine multiple iterative battery swap cabinet site selection plans from the initial battery swap cabinet site selection population based on the total congestion and non-dominated level of each initial battery swap cabinet site selection plan.

[0025] In this embodiment, the server can determine the iterative battery swap cabinet site selection plan for iteration from the population based on the total congestion and non-dominated level of each initial battery swap cabinet site selection plan, and perform subsequent iterative processing.

[0026] Step 210, perform cross-processing iteration and variation processing iteration on multiple iterative battery swap cabinet site selection plans until each iterative battery swap cabinet site selection plan meets the preset conditions, and obtain the target battery swap cabinet site selection population corresponding to the target area, and the target battery swap cabinet site selection population includes multiple target battery swap cabinet site selection plans.

[0027] Among them, meeting the preset conditions means that the various scheme indicators of each iterative battery swap cabinet site selection scheme obtained after crossover and mutation iteration are optimal, that is, the Pareto optimal solution (that is, the various scheme indicators of each target battery swap cabinet site selection scheme are not inferior to the corresponding scheme indicators of other target battery swap cabinet site selection schemes, and are better than other target battery swap cabinet site selection schemes in at least one scheme indicator).

[0028] In this embodiment, the server may iterate the initial battery swap cabinet site selection population through a genetic algorithm to obtain a target battery swap cabinet site selection population corresponding to the target area.

[0029] In this embodiment, the target battery swap cabinet site selection population may include multiple target battery swap cabinet site selection plans, and multiple target battery swap cabinet site selection plans may be used as the final battery swap cabinet site selection plans for the target area.

[0030] In the above-mentioned method for determining the location of a battery swap cabinet, an initial battery swap cabinet location population corresponding to the target area is generated, and the initial battery swap cabinet location population includes multiple initial battery swap cabinet location plans; each initial battery swap cabinet location plan in the initial battery swap cabinet location population is divided into non-dominated levels to obtain multiple non-dominated layers, and each non-dominated layer includes at least one initial battery swap cabinet location plan. The non-dominated levels of the initial battery swap cabinet location plans in the same non-dominated layer are the same, and the non-dominated levels represent the excellent level of the solution indicators of each initial battery swap cabinet location plan compared with other initial battery swap cabinet location plans; determine each non-dominated level. The total congestion of each initial battery swap cabinet site selection scheme in the distribution layer, the total congestion characterizes the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer; according to the total congestion and non-dominated level of each initial battery swap cabinet site selection scheme, multiple iterative battery swap cabinet site selection schemes are determined from the initial battery swap cabinet site selection population; the iterative battery swap cabinet site selection schemes are subjected to cross-processing iteration and variation processing iteration until each iterative battery swap cabinet site selection scheme meets the preset conditions, and the target battery swap cabinet site selection population corresponding to the target area is obtained, and the target battery swap cabinet site selection population includes multiple target battery swap cabinet site selection schemes. Therefore, 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 congestion calculation and iteration, so that the target battery swap cabinet site selection scheme can integrate the excellent levels among the indicators of multiple schemes, which can improve the accuracy of the determination of the target battery swap cabinet site selection scheme.

[0031] In one embodiment, the scheme indicators of each initial battery swap cabinet site selection plan may include the installation location indicator of the battery swap cabinet and the user satisfaction indicator.

[0032] Among them, the installation location indicator refers to the installation geographical location of the battery swap cabinet determined by the initial battery swap cabinet site selection plan.

[0033] In this embodiment, geographic location is a key factor in selecting a battery swap cabinet site, typically expressed accurately 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 using a GPS device or a map API to ensure the accuracy of the site selection plan.

[0034] In this embodiment, the user satisfaction index is a comprehensive index that is affected by multiple factors, such as the distance between the battery swap cabinet and the user, the convenience of surrounding transportation, the service quality of the battery swap cabinet, etc. The calculation of user satisfaction can be done by using a weighted average method to quantify the influence of each factor. For example, the user satisfaction index can be calculated using the following formula: S=w 1 *D+w 2 *T+w 3 *Q in, S represents the user satisfaction index, D Represents the distance factor, T Indicates transportation convenience. Q Indicates the quality of service, w 1 、 w 2 、 w 3 Represent the weight of each factor.

[0035] In this embodiment, the user satisfaction index generally ranges from 0 to 1. For example, a value above 0.85 indicates a high user satisfaction, and a value below 0.5 indicates a low user satisfaction.

[0036] In one embodiment, the scheme indicators of the initial battery exchange cabinet site selection plan may also include cost indicators, which may specifically include construction costs and operating costs.

[0037] Specifically, construction costs may include the purchase, installation, and land lease of the battery swap cabinet, and are typically measured in tens of thousands of yuan. For example, the construction cost of a battery swap cabinet might be 5 million yuan. Construction costs can be determined based on market research, supplier quotes, and historical data on battery swap cabinet installations.

[0038] Furthermore, operating costs can include maintenance, power consumption, and personnel management expenses for the battery swap cabinet, typically measured in 10,000 yuan per year. For example, the annual operating cost of a battery swap cabinet might be 500,000 yuan. Operating costs can be determined based on historical operating data and industry standards.

[0039] In this embodiment, generating an initial battery swap cabinet site selection population corresponding to the target area can include: determining the population size; determining multiple battery swap cabinet installation locations based on the location information and population size of the target area, and obtaining multiple installation location indicators; determining corresponding user satisfaction indicators based on each installation location indicator; obtaining multiple initial battery swap cabinet site selection plans based on each installation location indicator and the corresponding user satisfaction indicator to obtain the initial battery swap cabinet site selection population corresponding to the target area.

[0040] Specifically, the server can determine the size of the population based on actual application needs. As mentioned above, if it is necessary to install battery swap cabinets at 100 locations in the target area, the population size can be set to 100 individuals.

[0041] Furthermore, the server can randomly generate installation location indicators, construction costs, operating costs, and user satisfaction indicators for each individual to obtain initial battery swap cabinet site selection plans. For example, the server can randomly generate 100 longitude and latitude coordinates (i.e., installation location indicators) based on the location information of the target area, with each coordinate corresponding to a battery swap cabinet site selection plan.

[0042] Furthermore, the server can determine the distance between the battery swap cabinet and the user, the convenience of surrounding transportation, the service quality of the battery swap cabinet, etc. based on various latitude and longitude coordinates, and then determine the user satisfaction index.

[0043] Similarly, the server can determine construction costs, operating costs, etc. based on historical data and combined with information such as latitude and longitude coordinates.

[0044] In this embodiment, after generating the initial battery swap cabinet site population, the server can also perform individual feasibility verification. This means that each generated individual (initial battery swap cabinet site plan) is feasibility verified to ensure that it meets actual conditions. For example, it checks whether the installation location indicators are within the available area and whether the construction and operating costs are within the budget.

[0045] In this embodiment, the server can record each initial battery swap cabinet site selection plan using a population data structure. Specifically, the server can record the detailed information of each individual in the population data structure to facilitate 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.

[0046] Through the above steps, an initial battery swap cabinet site selection population containing multiple potential site selection options can be created, laying the foundation for the subsequent optimization process.

[0047] In a specific embodiment, it is necessary to plan the site selection plan for 100 battery swap cabinets for a city (target area). First, the server can determine the population size to be 100 individuals. Then, the server uses a random number generator to generate 100 longitude and latitude coordinates, each coordinate corresponding to a site selection plan for a battery swap cabinet. Next, the server verifies the feasibility of each generated coordinate to ensure that it is within the available area of the city and that the construction cost and operating cost are within the 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 created an initial battery swap cabinet site selection population containing 100 potential site selection plans, providing basic data for subsequent data processing.

[0048] In one embodiment, each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population is divided into non-dominated levels to obtain multiple non-dominated layers, which may include: determining the initial non-dominated level of each initial battery swap cabinet site selection scheme; comparing the scheme indicators of any two initial battery swap cabinet site selection schemes to determine the dominance relationship between any two initial battery swap cabinet site selection schemes, where the dominance relationship represents the advantages and disadvantages of any two initial battery swap cabinet site selection schemes in multiple scheme indicators; determining the non-dominated level corresponding to each initial battery swap cabinet site selection scheme based on the dominance relationship; and dividing each initial battery swap cabinet site selection scheme into non-dominated layers based on each non-dominated level to obtain multiple non-dominated layers.

[0049] Specifically, the server can perform a non-dominated sorting operation on each individual in the population to determine its non-dominated level. The main purpose of non-dominated sorting is to stratify individuals in the population according to their performance on different goals (scheme indicators), ensuring that there is no dominance relationship between individuals in each layer.

[0050] In this embodiment, the dominance relationship characterizes the advantages and disadvantages of any two initial battery swap cabinet site selection schemes in terms of multiple scheme indicators. Specifically, in the battery swap cabinet site selection problem, it is assumed that there are two goals: minimizing construction costs and maximizing user satisfaction. A and B ,if A The construction cost is less than or equal to B construction costs, and A User satisfaction is greater than or equal to B of user satisfaction, and at least on one goal A Better than B , then 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 Represents individuals A and B construction costs, SA and S B Represents individuals A and B user satisfaction.

[0051] Furthermore, the server may initialize the non-dominated levels of all individuals to 1, indicating that they all belong to the first non-dominated layer.

[0052] Furthermore, the server can compare individuals, that is, for each individual in the population A , the server compares it to all other individuals in the population B Compare and judge A Whether B Dominate. If A quilt B Domination, then A Increases the non-dominated level by 1.

[0053] Furthermore, the server can stratify individuals in the population according to their non-dominance levels: individuals with a non-dominance level of 1 belong to the first non-dominant layer, individuals with a non-dominance level of 2 belong to the second non-dominant layer, and so on.

[0054] In this embodiment, the server repeats the above process for each individual until all individuals are assigned to the corresponding non-dominated layer.

[0055] In one embodiment, if the population comprises individuals A, B and C .individual A The construction cost is RMB 4 million, and the user satisfaction is 0.8; B The construction cost is RMB 4.5 million, and the user satisfaction is 0.85; C The construction cost is RMB 5 million and the user satisfaction is 0.9. First, the server initializes the non-dominated level of all individuals to 1. Then, the server compares the individual A and B ,Discover A The construction cost is less than B , and user satisfaction is also less than B ,therefore A Do not dominate B , B Nor dominate A Next, the server compares the individual A and C ,Discover A The construction cost is less than C , and user satisfaction is also less than C ,therefore A Do not dominate C ,C Nor dominate A Finally, the server compares individual B and C ,Discover B The construction cost is less than C , and user satisfaction is also less than C, so B Do not dominate C , C Nor dominate B Since no individual is dominated by another individual, all individuals belong to the first non-dominated layer. In this way, the server successfully performs non-dominated sorting and stratification of the individuals in the population.

[0056] As mentioned above, each initial battery swap cabinet site selection plan may include multiple plan indicators, namely, installation location indicators, user satisfaction indicators and cost indicators.

[0057] In one embodiment, determining the total congestion of each initial battery swap cabinet site selection plan for each non-dominated layer may include: determining a target solution indicator from multiple solution indicators; sorting the target solution indicators of the initial battery swap cabinet site selection plans in the same non-dominated layer to obtain the sorting results of the initial battery swap cabinet site selection plans in the same non-dominated layer according to the target solution indicators; based on the sorting results, determining the individual congestion of each initial battery swap cabinet site selection plan corresponding to the target solution indicator; re-determining the target solution indicator from the remaining solution indicators, and continuing to sort and determine the individual congestion until all solution indicators are determined, and obtaining the individual congestion of each initial battery swap cabinet site selection plan corresponding to each solution indicator; determining the total congestion of each initial battery swap cabinet site selection plan based on the individual congestion of each initial battery swap cabinet site selection plan corresponding to multiple solution indicators.

[0058] In this embodiment, the server may calculate the crowding degree of individuals in each non-dominated layer and measure the distribution density of individuals in each target space (ie, each solution indicator) to maintain the diversity of the population.

[0059] In this embodiment, the server can use each scheme indicator as the target scheme indicator, and sort multiple initial battery swap cabinet site selection schemes within the same control layer and calculate the individual congestion to obtain the individual congestion of each initial battery swap cabinet site selection scheme corresponding to each scheme indicator.

[0060] Furthermore, the server adds up the individual congestion of multiple scheme indicators corresponding to each initial battery swap cabinet site selection scheme to obtain the total congestion of each initial battery swap cabinet site selection scheme.

[0061] In one embodiment, based on the sorting results, determining the individual congestion of each initial battery swap cabinet site selection plan corresponding to the target solution index can include: determining the boundary battery swap cabinet site selection plan and non-boundary battery swap cabinet site selection plan corresponding to the target solution index in each initial battery swap cabinet site selection plan within the same non-dominated layer according to the sorting results, and determining the adjacent battery swap cabinet site selection plan corresponding to each non-boundary battery swap cabinet site selection plan; determining that the individual congestion of the boundary battery swap cabinet site selection plan corresponding to the target solution index is infinite; and obtaining the individual congestion of each non-boundary battery swap cabinet site selection plan corresponding to the target solution index based on the target solution index of the adjacent battery swap cabinet site selection plan of each non-boundary battery swap cabinet site selection plan.

[0062] In one of the embodiments, before determining the total congestion of each initial battery exchange cabinet site selection plan in each non-dominated layer, the above method may also include: normalizing each plan indicator of the initial battery exchange cabinet site selection plan in each non-dominated layer to obtain the normalized initial battery exchange cabinet site selection plan.

[0063] In this embodiment, the server performs normalization processing on each solution indicator to eliminate the dimensional differences between different solution indicators. The normalization processing formula is: in, Represents an individual x In the i The value on the target, and Respectively represent the i The minimum and maximum values of the target (scheme indicator). The normalized target (scheme indicator) The range is between [0,1].

[0064] In this embodiment, determining the total congestion of each initial battery swap cabinet site selection plan of each non-dominated layer may include: determining the total congestion of each initial battery swap cabinet site selection plan after normalizing the plan indicators of each non-dominated layer.

[0065] In this embodiment, for each solution indicator, the server can sort the individuals in the non-dominated layer. The sorting is based on the normalized value of the solution indicator. After sorting, the position of each individual on the scheme indicator is determined.

[0066] Furthermore, for the sorted individuals, the congestion degree of the boundary individuals (i.e., the first and last individuals in the sorting results, also known as the boundary battery swap cabinet location plan) is set to infinity ( ), to ensure that these individuals are retained first in subsequent selection operations. The mathematical expression is: in, and Represent the first and last individuals after sorting, respectively.

[0067] Furthermore, for the middle individual after sorting (i.e., excluding the first and last individuals in the sorting results, also known as the non-boundary battery swap cabinet location plan), its congestion is determined by calculating the difference between the normalized values of its adjacent individuals (the adjacent battery swap cabinet location plans of the non-boundary battery swap cabinet location plan) on the plan indicator. The specific formula is: in, Indicates the sorted j Individuals, m represents the number of program indicators, and Respectively The adjacent individual in i The larger the crowding value is, the sparser the distribution of the individual in the corresponding scheme indicator is.

[0068] Furthermore, for each individual, its total congestion is the sum of its congestion in all scheme indicators. The calculation formula for the total congestion is: in, Represents an individual x In the i The degree of congestion on a target (project indicator).

[0069] In one specific implementation, a non-dominated layer consists of three individuals A, B and C , the goal is to minimize the construction cost and maximize user satisfaction, that is, the solution indicators include construction cost and user satisfaction index. First, the server normalizes the construction cost and user satisfaction index. The normalized construction costs are C A =0.2 , C B =0.5 , C C = 0.8 ; The normalized user satisfaction indicators are S A =0.3 , S B =0.6 , S C=0.9 Then, the server sorts the construction costs and obtains the sorted individual order as A 、 B 、 C For boundary individuals A and C , its crowding degree is set to ∞. B , its congestion is determined by calculating the difference between the normalized values of its adjacent individuals in construction cost and user satisfaction index. C A =0.2 , C C =0.8 , S A =0.3 , S C =0.9 , then the individual B The congestion degree is: In one embodiment, based on the total congestion and non-dominated level of each initial battery swap cabinet site selection plan, multiple iterative battery swap cabinet site selection plans are determined from the initial battery swap cabinet site selection population, including: determining the number of layer plans of the initial battery swap cabinet site selection plan in each non-dominated layer; determining the initial battery swap cabinet site selection plan whose non-dominated level meets the level threshold as the first iterative battery swap cabinet site selection plan; when the number of layer plans in the non-dominated layer is greater than the preset number, based on the total congestion of each initial battery swap cabinet site selection plan in the non-dominated layer, determining the initial battery swap cabinet site selection plan that meets the preset congestion threshold as the second iterative battery swap cabinet site selection plan; based on the non-dominated level and the total congestion, performing priority probability selection on each initial battery swap cabinet site selection plan in the initial battery swap cabinet site selection population excluding the first iterative battery swap cabinet site selection plan and the second iterative battery swap cabinet site selection plan to determine the third iterative battery swap cabinet site selection plan; and using the first iterative battery swap cabinet site selection plan, the second iterative battery swap cabinet site selection plan and the third iterative battery swap cabinet site selection plan as the iterative battery swap cabinet site selection plan.

[0070] The number of layer solutions refers to the number of initial battery swap cabinet site selection solutions corresponding to each non-dominated layer. For a non-dominated layer with only a single initial battery swap cabinet site selection solution, its corresponding initial battery swap cabinet site selection solution can be directly determined as the iterative battery swap cabinet site selection solution. For a non-dominated layer with multiple initial battery swap cabinet site selection solutions, the server can determine the iterative battery swap cabinet site selection solution based on the total congestion of each initial battery swap cabinet site selection solution and the non-dominated level.

[0071] In this embodiment, individuals with lower non-dominant ranks (ie, individuals in higher non-dominant layers) have higher priorities. The rank threshold is used to filter individuals with higher priorities.

[0072] Specifically, the server can select outstanding individuals to enter the next generation based on the individual's non-dominated level and total congestion, guiding the population to converge to the Pareto frontier and approach the Pareto optimal solution. The specific steps are as follows: First, the server screens individuals based on their non-dominance level. Individuals with lower non-dominance levels (i.e., individuals in higher non-dominance layers) are prioritized. Specifically, individuals with a non-dominance level of 1 are selected first (i.e., determined as the first iteration of the battery swap cabinet site selection plan), followed by individuals with a level of 2, and so on. This ensures that outstanding individuals in the population advance to the next generation first. Meeting the level threshold can mean a non-dominance level of 1 or 2, depending on actual application requirements.

[0073] Furthermore, in the same non-dominated layer, if there are multiple individuals (the number of layer solutions is greater than 1) with the same non-dominated level, the server can determine the number of layer solutions and the preset number, and perform subsequent processing. For example, if the preset number is 1, and there are multiple individuals (greater than 1) in the non-dominated layer, the server can further screen them based on the total congestion of the individuals. Individuals with higher total congestion are more sparsely distributed in the corresponding solution indicators, which can help maintain the diversity of the population. Therefore, individuals with higher total congestion are selected first (that is, the second iterative battery swap cabinet site selection plan). The specific formula is: in, selection_priority(x) represents the probability of being selected, non_dominated_rank (x) Represents an individual x The non-dominated level of total_crowding_distance (x) Represents an individual x The total congestion of , α is a weight coefficient used to balance the influence of non-dominated level and congestion.

[0074] For example, there are two individuals in a non-dominated layer A and B Among them, individual A Non-dominated level = 1, total crowding = 0.8, α = 0.5, selection_priority (A) = 1 + 0.5 × 0.8 = 1.4. B Non-dominated level = 1, total congestion = 0.5, α = 0.5, selection_priority (B) = 1 + 0.5 × 0.5 = 1.25. The conclusion is A has a higher priority (1.4) than B (1.25), so A The probability of being selected is higher. A Site selection plan for the second iteration of battery swap cabinets.

[0075] Furthermore, for the remaining individuals (the initial battery cabinet location plans excluding the first and second iteration battery cabinet location plans in the initial battery cabinet location population), a roulette wheel selection method is used to select and determine the third iteration battery cabinet location plan. The probability of roulette wheel selection is proportional to the individual's selection priority. Specifically, individual x The probability of being selected is: In this embodiment, the server selects a certain number of individuals from the current population (the first, second, and third iterations of the battery swap cabinet site selection plan) through the above steps to generate the next generation population. This new generation population is used in subsequent crossover and mutation operations to further optimize the individuals in the population.

[0076] In one embodiment, five individuals A, B, C, D and E A population in which individuals A and B The non-dominance level of individual C 、 D and E The non-dominance level of is 2. A The total crowding degree is 1.5, and the individual B The total crowding degree is 1.2, and the individual C The total crowding degree is 0.8, and the individual D The total crowding degree is 0.6, and the individual E The total congestion of is 0.4. First, the server selects individuals according to their non-dominated levels. A and B , and retain them directly to the next generation. Then, for the remaining individuals C 、 D and E , the servers calculate their selection priorities. Assuming α =0.1, then the individual C The selection priority of individual is 2+0.1*0.8=2.08. D The selection priority of individual is 2+0.1*0.6=2.06. E The selection priority is 2+0.1*0.4=2.04. Next, the server calculates the probability of each individual being selected according to the roulette selection method. C The probability of being selected is 2.08 / (2.08+2.06+2.04)=0.347, individual D The probability of being selected is 2.06 / (2.08+2.06+2.04)=0.343, individual EThe probability of being selected is 2.04 / (2.08 + 2.06 + 2.04) = 0.34. Finally, the server makes selections based on these probabilities to generate the next generation of the population. In this way, the server selects outstanding individuals for the next generation and gradually guides the population toward convergence to the Pareto frontier.

[0077] In this embodiment, the core goal of the server's priority probability selection to determine the third iteration of the battery swap cabinet location plan is to maintain a fixed population size. For example, if there are 5 individuals in the population, and the next generation population size still needs to be maintained at 5, then the selection process must meet the following requirements: prioritize the elite individuals (those with a non-dominated level of 1) A and B Directly into the next generation), to make up for the missing number from the remaining individuals, C 、 D 、 E Select 3 more individuals, that is, 5 individuals are obtained to obtain the iterative battery swap cabinet site selection plan.

[0078] In one embodiment, multiple iterative battery swap cabinet site selection plans are subjected to cross-processing iterations and mutation processing iterations until each iterative battery swap cabinet site selection plan meets preset conditions, and a target battery swap cabinet site selection population corresponding to the target area is obtained. This may include: cross-processing the plan indicators of any two iterative battery swap cabinet site selection plans to obtain a cross-site selection plan; performing random mutation processing on the plan indicators of each iterative battery swap cabinet site selection plan to obtain a mutated site selection plan; using the cross-site selection plan and the mutated site selection plan as the iterative battery swap cabinet site selection plan, and continuing to perform cross-processing and random mutation processing iterations until each iterative battery swap cabinet site selection plan meets the preset conditions, and a target battery swap cabinet site selection population corresponding to the target area is obtained.

[0079] In this embodiment, the server can perform crossover and mutation operations on the selected individuals to generate new individuals, and comprehensively explore the solution space to find a better Pareto optimal solution, that is, until each iterative battery swap cabinet site selection plan meets the preset conditions.

[0080] Specifically, the crossover operation generates a new candidate solution by exchanging some of the solution indicators of two individuals. In the battery swap cabinet location problem, each individual solution indicator can include installation location indicators, construction costs, operating costs, and user satisfaction indicators. The specific implementation process of the crossover operation is as follows: First, the server randomly selects two parent individuals and (Two iterative battery swap cabinet site selection schemes) for crossover operations.

[0081] Furthermore, the server determines the intersection point, that is, randomly selects one or more intersection points in the individual solution indicators, for example, selecting the installation location indicator as the intersection point.

[0082] Furthermore, the server exchanges the solution indicators of the two parent individuals at the intersection and generates two new child individuals. and For example, if the parent individual The installation position index is (39.9042, 116.4074), the parent individual The installation position index is (31.2304, 121.4737), then the installation position index is exchanged at the intersection to generate the offspring individual The installation position index is (39.9042, 121.4737), and the offspring individual The installation position index is (31.2304,116.4074).

[0083] Furthermore, the server can adjust genes such as construction cost, operation cost, and user satisfaction index of the offspring individuals according to the installation position index after crossover to ensure that the newly generated individuals are feasible.

[0084] In this embodiment, the mutation operation introduces new possibilities by randomly changing certain individual solution indicators. In the battery swap cabinet location problem, the specific implementation process of the mutation operation is as follows: First, the server randomly selects an individual from the current population P (Iterative battery swap cabinet site selection plan), used for mutation operations.

[0085] Furthermore, the server randomly selects one or more mutation points in the individual solution indicators, for example, construction cost is selected as a mutation point.

[0086] Furthermore, the server randomly changes the individual solution indicators at the mutation point, for example, randomly changing the construction cost from 5 million yuan to 4.8 million yuan or 5.2 million yuan.

[0087] Furthermore, the server adjusts genes such as the individual's operating cost and user satisfaction index according to the mutated construction cost to ensure that the newly generated individual is feasible.

[0088] 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 the subsequent iterative optimization process to gradually approach the Pareto frontier.

[0089] In a specific implementation, there are two parent individuals P 1 and P 2 ,in P 1The 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 2 The 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, exchanges the installation location indexes of the two parent individuals, and generates the child individual C 1 The installation position index is (39.9042, 121.4737), and the offspring individual C 2 The installation location index of is (31.2304, 116.4074). Then, the server adjusts the construction cost, operation cost and user satisfaction index of the offspring individual according to the cross-installation location index. For example, the offspring individual C 1 The construction cost is RMB 4.75 million, the operating cost is RMB 525,000 per year, and the user satisfaction index is 0.865; the offspring individual C 2 The construction cost is RMB 4.75 million, the operating cost is RMB 525,000 per year, and the user satisfaction index is 0.865. Next, the server selects an individual P 3 , its construction cost is 5 million yuan, operating cost is 500,000 yuan / year, and user satisfaction index is 0.85, and 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, after the mutation, the individual P 3 The operating cost is RMB 480,000 per year and the user satisfaction index is 0.84. Finally, the server will generate the new generation of individuals C 1 、 C 2 and the mutated individuals P 3 Added to the next generation population for subsequent iterative optimization.

[0090] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 2At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed 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 part of the sub-steps or stages of other steps.

[0091] In one embodiment, Figure 3 As shown, a battery swap cabinet site selection determination device is provided, including: an initial population generation module 100, a non-dominated layer division module 200, a total congestion determination module 300, an iterative battery swap cabinet site selection scheme determination module and an iteration module 500, wherein: The initial population generation module 100 is used to generate an initial battery swap cabinet site selection population corresponding to the target area. The initial battery swap cabinet site selection population includes multiple initial battery swap cabinet site selection plans.

[0092] The non-dominated layer division module 200 is used to perform non-dominated level division on each initial battery swap cabinet site selection plan in the initial battery swap cabinet site selection population to obtain multiple non-dominated layers. Each non-dominated layer includes at least one initial battery swap cabinet site selection plan. The non-dominated levels of the initial battery swap cabinet site selection plans in the same non-dominated layer are the same. The non-dominated level represents the excellent level of the plan indicators of each initial battery swap cabinet site selection plan compared with other initial battery swap cabinet site selection plans.

[0093] The total congestion determination module 300 is used to determine the total congestion of each initial battery swap cabinet site selection plan in each non-dominated layer. The total congestion represents the spatial distribution density of each initial battery swap cabinet site selection plan in the non-dominated layer.

[0094] The iterative battery swap cabinet site selection scheme determination module 400 is used to determine multiple iterative battery swap cabinet site selection schemes from the initial battery swap cabinet site selection population based on the total congestion and non-dominated level of each initial battery swap cabinet site selection scheme.

[0095] The iterative module 500 is used to perform cross-processing iterations and variation processing iterations on multiple iterative battery swap cabinet site selection plans until each iterative battery swap cabinet site selection plan meets the preset conditions, thereby obtaining an iteration of the initial battery swap cabinet site selection population and generating a target battery swap cabinet site selection population corresponding to the target area. The target battery swap cabinet site selection population includes multiple target battery swap cabinet site selection plans.

[0096] In one embodiment, the non-dominated layer partitioning module 200 may include: The initial non-dominated level determination submodule is used to determine the initial non-dominated level of each initial battery swap cabinet site selection plan.

[0097] The dominance relationship determination submodule is used to compare the scheme indicators of any two initial battery swap cabinet site selection schemes and determine the dominance relationship between any two initial battery swap cabinet site selection schemes. The dominance relationship represents the advantages and disadvantages of any two initial battery swap cabinet site selection schemes in multiple scheme indicators.

[0098] The non-dominance level determination submodule is used to determine the non-dominance level corresponding to each initial battery swap cabinet site selection plan based on the dominance relationship.

[0099] The non-dominated layer division submodule is used to divide each initial battery swap cabinet site selection plan into non-dominated layers based on each non-dominated level to obtain multiple non-dominated layers.

[0100] In one embodiment, each initial battery exchange cabinet site selection plan includes multiple plan indicators.

[0101] In this embodiment, the total congestion determination module 300 may include: The target program indicator determination submodule is used to determine the target program indicator from multiple program indicators.

[0102] The sorting submodule is used to sort the target scheme indicators of the initial battery swap cabinet site selection plan within the same non-dominated layer, and obtain the sorting results of the initial battery swap cabinet site selection plan within the same non-dominated layer according to the target scheme indicators.

[0103] The individual congestion determination submodule is used to determine the individual congestion of each initial battery swap cabinet site selection plan corresponding to the target plan indicator based on the sorting results.

[0104] 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 until all scheme indicators are determined, and obtain the individual congestion of each scheme indicator corresponding to each initial battery swap cabinet site selection scheme.

[0105] The total congestion determination submodule is used to determine the total congestion of each initial battery swap cabinet site selection plan based on the individual congestion of multiple plan indicators corresponding to each initial battery swap cabinet site selection plan.

[0106] In one embodiment, the individual congestion determination submodule may include: The first determination unit is used to determine the boundary battery swap cabinet site selection plan and non-boundary battery swap cabinet site selection plan corresponding to the target plan indicator in each initial battery swap cabinet site selection plan within the same non-dominated layer according to the sorting results, and to determine the adjacent battery swap cabinet site selection plan corresponding to each non-boundary battery swap cabinet site selection plan.

[0107] The second determination unit is used to determine that the individual congestion degree of the target scheme indicator corresponding to the boundary battery exchange cabinet site selection scheme is infinite.

[0108] The individual congestion determination unit is used to obtain the individual congestion of each non-boundary battery swap cabinet site selection plan corresponding to the target scheme indicator based on the target scheme indicator of the adjacent battery swap cabinet site selection plan of each non-boundary battery swap cabinet site selection plan.

[0109] In one embodiment, the above apparatus may further include: The normalization processing module is used to normalize the various scheme indicators of the initial battery swap cabinet site selection scheme in each non-dominated layer before determining the total congestion of each initial battery swap cabinet site selection scheme in each non-dominated layer to obtain the normalized initial battery swap cabinet site selection scheme.

[0110] In this embodiment, the total congestion determination submodule is used to determine the total congestion of each initial battery swap cabinet site selection plan after the plan indicators of each non-dominated layer are normalized.

[0111] In one embodiment, the iterative battery swap cabinet site selection scheme determination module 400 may include: The layer scheme number determination submodule is used to determine the number of layer schemes for the initial battery swap cabinet location selection plan in each non-dominated layer; The first iterative battery swap cabinet site selection plan determination submodule is used to determine the initial battery swap cabinet site selection plan whose non-dominance level meets the level threshold as the first iterative battery swap cabinet site selection plan.

[0112] The second iterative battery swap cabinet site selection plan determination submodule is used to determine the initial battery swap cabinet site selection plan that meets the preset congestion threshold as the second iterative battery swap cabinet site selection plan when the number of layer plans in the non-dominated layer is greater than the preset number, based on the total congestion of each initial battery swap cabinet site selection plan in the non-dominated layer.

[0113] The third iterative battery swap cabinet site selection plan determination submodule is used to prioritize the probability of selecting each initial battery swap cabinet site selection plan in the initial battery swap cabinet site selection population, excluding the first iterative battery swap cabinet site selection plan and the second iterative battery swap cabinet site selection plan, based on the non-dominated level and the total congestion, to determine the third iterative battery swap cabinet site selection plan.

[0114] The iterative battery swap cabinet site selection plan determination submodule is used to use the first iterative battery swap cabinet site selection plan, the second iterative battery swap cabinet site selection plan and the third iterative battery swap cabinet site selection plan as the iterative battery swap cabinet site selection plan.

[0115] In one embodiment, the iteration module 500 may include: The cross submodule is used to perform cross processing on the scheme indicators of any two iterative battery swap cabinet site selection schemes to obtain the cross site selection scheme.

[0116] The mutation submodule is used to perform random mutation processing on the scheme indicators of each iterative battery swap cabinet site selection scheme to obtain the mutated site selection scheme.

[0117] The iterative submodule is used to use the post-crossover site selection plan and the post-mutation site selection plan as the iterative battery swap cabinet site selection plan, and continue to perform crossover processing and random mutation processing iterations until each iterative battery swap cabinet site selection plan meets the preset conditions, and obtains the target battery swap cabinet site selection population corresponding to the target area.

[0118] In one embodiment, the scheme indicators of each initial battery swap cabinet site selection scheme may include the installation location indicator of the battery swap cabinet and the user satisfaction index.

[0119] In this embodiment, the initial population generation module 100 may include: The size determination submodule is used to determine the population size.

[0120] The installation location indicator determination submodule is used to determine the proposed installation locations of multiple battery swap cabinets based on the location information and population size of the target area, and obtain multiple installation location indicators.

[0121] The user satisfaction index determination submodule is used to determine corresponding user satisfaction indexes based on the installation location indexes.

[0122] The initial population generation submodule is used to obtain multiple initial battery swap cabinet site selection plans based on each installation location indicator and the corresponding user satisfaction indicator, so as to obtain the initial battery swap cabinet site selection population corresponding to the target area.

[0123] For the specific limitations of the battery swap cabinet site determination device, please refer to the limitations of the battery swap cabinet site determination method above, which will not be repeated here. The various modules in the above-mentioned battery swap cabinet site determination device can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0124] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as the initial battery swap cabinet site selection population, non-dominated level, total congestion, target battery swap cabinet site selection population, etc. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the site selection of a battery swap cabinet is implemented.

[0125] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for determining the location of a battery swap cabinet is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0126] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0127] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program: generating an initial battery swap cabinet site selection population corresponding to a target area, wherein the initial battery swap cabinet site selection population includes multiple 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 multiple non-dominated layers, wherein each non-dominated layer includes at least one initial battery swap cabinet site selection scheme, and the initial battery swap cabinet site selection schemes in the same non-dominated layer have the same non-dominated level, and the non-dominated level represents the difference between each initial battery swap cabinet site selection scheme and other initial The excellence level of the scheme indicators of the battery swap cabinet site selection scheme; determining the total congestion of each initial battery swap cabinet site selection scheme in each non-dominated layer, the total congestion characterizes the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer; according to the total congestion and non-dominated level of each initial battery swap cabinet site selection scheme, determining multiple iterative battery swap cabinet site selection schemes from the initial battery swap cabinet site selection population; performing cross-processing iteration and mutation processing iteration on the multiple iterative battery swap cabinet site selection schemes until each iterative battery swap cabinet site selection scheme meets the preset conditions, and obtaining the target battery swap cabinet site selection population corresponding to the target area, the target battery swap cabinet site selection population including multiple target battery swap cabinet site selection schemes.

[0128] In one embodiment, when the processor executes the computer program, it implements non-dominated level division of each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population to obtain multiple non-dominated layers, which may include: determining the initial non-dominated level of each initial battery swap cabinet site selection scheme; comparing the scheme indicators of any two initial battery swap cabinet site selection schemes to determine the dominance relationship between any two initial battery swap cabinet site selection schemes, where the dominance relationship represents the advantages and disadvantages of any two initial battery swap cabinet site selection schemes in multiple scheme indicators; determining the non-dominated level corresponding to each initial battery swap cabinet site selection scheme based on the dominance relationship; and dividing each initial battery swap cabinet site selection scheme into non-dominated layers based on each non-dominated level to obtain multiple non-dominated layers.

[0129] In one embodiment, when the processor executes the computer program, each initial battery swap cabinet site selection plan includes multiple scheme indicators; determining the total congestion of each initial battery swap cabinet site selection plan for each non-dominated layer may include: determining a target scheme indicator from multiple scheme indicators; sorting the target scheme indicators of the initial battery swap cabinet site selection plans in the same non-dominated layer to obtain the sorting results of the initial battery swap cabinet site selection plans in the same non-dominated layer according to the target scheme indicators; based on the sorting results, determining the individual congestion of each initial battery swap cabinet site selection plan corresponding to the target scheme indicator; 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, and obtaining the individual congestion of each initial battery swap cabinet site selection plan corresponding to each scheme indicator; determining the total congestion of each initial battery swap cabinet site selection plan based on the individual congestion of each initial battery swap cabinet site selection plan corresponding to multiple scheme indicators.

[0130] In one embodiment, when the processor executes the computer program, it determines the individual congestion of each initial battery swap cabinet site selection plan corresponding to the target solution indicator based on the sorting result, which may include: determining the boundary battery swap cabinet site selection plan and non-boundary battery swap cabinet site selection plan corresponding to the target solution indicator in each initial battery swap cabinet site selection plan within the same non-dominated layer according to the sorting result, and determining the adjacent battery swap cabinet site selection plan corresponding to each non-boundary battery swap cabinet site selection plan; determining that the individual congestion of the boundary battery swap cabinet site selection plan corresponding to the target solution indicator is infinite; and obtaining the individual congestion of each non-boundary battery swap cabinet site selection plan corresponding to the target solution indicator according to the target solution indicator of the adjacent battery swap cabinet site selection plan of each non-boundary battery swap cabinet site selection plan.

[0131] In one embodiment, before the processor executes the computer program to determine the total congestion of each initial battery swap cabinet site selection plan in each non-dominated layer, it can also implement the following steps: normalize the various plan indicators of the initial battery swap cabinet site selection plan in each non-dominated layer to obtain the normalized initial battery swap cabinet site selection plan.

[0132] In one embodiment, when the processor executes a computer program, determining the total congestion of each initial battery swap cabinet site selection plan for each non-dominated layer may include: determining the total congestion of each initial battery swap cabinet site selection plan after normalizing the plan indicators of each non-dominated layer.

[0133] In one embodiment, when the processor executes the computer program, it determines multiple iterative battery swap cabinet site selection plans from the initial battery swap cabinet site selection population based on the total congestion and non-dominated level of each initial battery swap cabinet site selection plan, which may include: determining the number of layer plans of the initial battery swap cabinet site selection plan in each non-dominated layer; determining the initial battery swap cabinet site selection plan whose non-dominated level meets the level threshold as the first iterative battery swap cabinet site selection plan; when the number of layer plans in the non-dominated layer is greater than the preset number, according to the total congestion of each initial battery swap cabinet site selection plan in the non-dominated layer, determining the initial battery swap cabinet site selection plan that meets the preset congestion threshold as the second iterative battery swap cabinet site selection plan; according to the non-dominated level and total congestion, performing priority probability selection on each initial battery swap cabinet site selection plan in the initial battery swap cabinet site selection population excluding the first iterative battery swap cabinet site selection plan and the second iterative battery swap cabinet site selection plan, and determining the third iterative battery swap cabinet site selection plan; using the first iterative battery swap cabinet site selection plan, the second iterative battery swap cabinet site selection plan and the third iterative battery swap cabinet site selection plan as the iterative battery swap cabinet site selection plan.

[0134] In one embodiment, when the processor executes the computer program, it implements cross-processing iterations and mutation processing iterations on multiple iterative battery swap cabinet site selection plans until each iterative battery swap cabinet site selection plan meets the preset conditions and obtains the target battery swap cabinet site selection population corresponding to the target area. It may include: cross-processing the plan indicators of any two iterative battery swap cabinet site selection plans to obtain a cross-site selection plan; performing random mutation processing on the plan indicators of each iterative battery swap cabinet site selection plan to obtain a mutated site selection plan; using the cross-site selection plan and the mutated site selection plan as the iterative battery swap cabinet site selection plan, and continuing to perform cross-processing and random mutation processing iterations until each iterative battery swap cabinet site selection plan meets the preset conditions and obtains the target battery swap cabinet site selection population corresponding to the target area.

[0135] In one embodiment, the scheme indicators of each initial battery swap cabinet site selection scheme include the installation location indicator of the battery swap cabinet and the user satisfaction index.

[0136] Specifically, when the processor executes the computer program, it generates the initial battery swap cabinet site selection population corresponding to the target area, which may include: determining the population size; determining multiple battery swap cabinet installation locations based on the location information and population size of the target area, and obtaining multiple installation location indicators; determining the corresponding user satisfaction indicators based on each installation location indicator; obtaining multiple initial battery swap cabinet site selection plans based on each installation location indicator and the corresponding user satisfaction indicator to obtain the initial battery swap cabinet site selection population corresponding to the target area.

[0137] 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, the following steps are implemented: generating an initial battery swap cabinet site selection population corresponding to a target area, the initial battery swap cabinet site selection population including multiple 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 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 being the same, and the non-dominated levels characterizing the differences between each initial battery swap cabinet site selection scheme and other initial battery swap cabinet site selection schemes. The excellence level of the scheme indicators of the site selection scheme; determine the total congestion of each initial battery swap cabinet site selection scheme in each non-dominated layer, and the total congestion characterizes the spatial distribution density of each initial battery swap cabinet site selection scheme in the non-dominated layer; according to the total congestion and non-dominated level of each initial battery swap cabinet site selection scheme, determine multiple iterative battery swap cabinet site selection schemes from the initial battery swap cabinet site selection population; perform cross-processing iteration and mutation processing iteration on the multiple iterative battery swap cabinet site selection schemes until each iterative battery swap cabinet site selection scheme meets the preset conditions, and obtain the target battery swap cabinet site selection population corresponding to the target area, and the target battery swap cabinet site selection population includes multiple target battery swap cabinet site selection schemes.

[0138] In one embodiment, when the computer program is executed by the processor, it implements non-dominated level division of each initial battery swap cabinet site selection scheme in the initial battery swap cabinet site selection population to obtain multiple non-dominated layers, which may include: determining the initial non-dominated level of each initial battery swap cabinet site selection scheme; comparing the scheme indicators of any two initial battery swap cabinet site selection schemes to determine the dominance relationship between any two initial battery swap cabinet site selection schemes, where the dominance relationship represents the advantages and disadvantages of any two initial battery swap cabinet site selection schemes in multiple scheme indicators; determining the non-dominated level corresponding to each initial battery swap cabinet site selection scheme based on the dominance relationship; and dividing each initial battery swap cabinet site selection scheme into non-dominated layers based on each non-dominated level to obtain multiple non-dominated layers.

[0139] In one embodiment, when the computer program is executed by the processor, each initial battery swap cabinet site selection plan includes multiple scheme indicators; determining the total congestion of each initial battery swap cabinet site selection plan for each non-dominated layer may include: determining a target scheme indicator from multiple scheme indicators; sorting the target scheme indicators of the initial battery swap cabinet site selection plans in the same non-dominated layer to obtain the sorting results of the initial battery swap cabinet site selection plans in the same non-dominated layer according to the target scheme indicators; based on the sorting results, determining the individual congestion of each initial battery swap cabinet site selection plan corresponding to the target scheme indicator; 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, and obtaining the individual congestion of each initial battery swap cabinet site selection plan corresponding to each scheme indicator; determining the total congestion of each initial battery swap cabinet site selection plan based on the individual congestion of each initial battery swap cabinet site selection plan corresponding to multiple scheme indicators.

[0140] In one embodiment, when the computer program is executed by the processor, it is implemented to determine the individual congestion of each initial battery swap cabinet site selection plan corresponding to the target solution indicator based on the sorting result, which may include: determining the boundary battery swap cabinet site selection plan and non-boundary battery swap cabinet site selection plan corresponding to the target solution indicator in each initial battery swap cabinet site selection plan within the same non-dominated layer according to the sorting result, and determining the adjacent battery swap cabinet site selection plan corresponding to each non-boundary battery swap cabinet site selection plan; determining that the individual congestion of the boundary battery swap cabinet site selection plan corresponding to the target solution indicator is infinite; and obtaining the individual congestion of each non-boundary battery swap cabinet site selection plan corresponding to the target solution indicator according to the target solution indicator of the adjacent battery swap cabinet site selection plan of each non-boundary battery swap cabinet site selection plan.

[0141] In one embodiment, when the computer program is executed by the processor, before determining the total congestion of each initial battery swap cabinet site selection plan in each non-dominated layer, the following steps can also be implemented: normalizing each plan indicator of the initial battery swap cabinet site selection plan in each non-dominated layer to obtain the normalized initial battery swap cabinet site selection plan.

[0142] In one embodiment, when the computer program is executed by the processor, it can determine the total congestion of each initial battery swap cabinet site selection plan of each non-dominated layer, which may include: determining the total congestion of each initial battery swap cabinet site selection plan after normalizing the plan indicators of each non-dominated layer.

[0143] In one embodiment, when the computer program is executed by the processor, it is implemented to determine multiple iterative battery swap cabinet site selection plans from the initial battery swap cabinet site selection population based on the total congestion and non-dominated level of each initial battery swap cabinet site selection plan, which may include: determining the number of layer plans of the initial battery swap cabinet site selection plan in each non-dominated layer; determining the initial battery swap cabinet site selection plan whose non-dominated level meets the level threshold as the first iterative battery swap cabinet site selection plan; when the number of layer plans in the non-dominated layer is greater than the preset number, according to the total congestion of each initial battery swap cabinet site selection plan in the non-dominated layer, determining the initial battery swap cabinet site selection plan that meets the preset congestion threshold as the second iterative battery swap cabinet site selection plan; according to the non-dominated level and total congestion, performing priority probability selection on each initial battery swap cabinet site selection plan in the initial battery swap cabinet site selection population excluding the first iterative battery swap cabinet site selection plan and the second iterative battery swap cabinet site selection plan, and determining the third iterative battery swap cabinet site selection plan; using the first iterative battery swap cabinet site selection plan, the second iterative battery swap cabinet site selection plan and the third iterative battery swap cabinet site selection plan as the iterative battery swap cabinet site selection plan.

[0144] In one embodiment, when the computer program is executed by the processor, it implements cross-processing iterations and mutation processing iterations on multiple iterative battery swap cabinet site selection plans until each iterative battery swap cabinet site selection plan meets the preset conditions and obtains the target battery swap cabinet site selection population corresponding to the target area. It may include: cross-processing the plan indicators of any two iterative battery swap cabinet site selection plans to obtain a cross-site selection plan; performing random mutation processing on the plan indicators of each iterative battery swap cabinet site selection plan to obtain a mutated site selection plan; using the cross-site selection plan and the mutated site selection plan as the iterative battery swap cabinet site selection plan, and continuing to perform cross-processing and random mutation processing iterations until each iterative battery swap cabinet site selection plan meets the preset conditions and obtains the target battery swap cabinet site selection population corresponding to the target area.

[0145] In one embodiment, the scheme indicators of each initial battery swap cabinet site selection scheme include the installation location indicator of the battery swap cabinet and the user satisfaction index.

[0146] Specifically, when the computer program is executed by the processor, it generates an initial battery swap cabinet site selection population corresponding to the target area, which may include: determining the population size; determining multiple battery swap cabinet installation locations based on the location information and population size of the target area, and obtaining multiple installation location indicators; determining corresponding user satisfaction indicators based on each installation location indicator; obtaining multiple initial battery swap cabinet site selection plans based on each installation location indicator and the corresponding user satisfaction indicator to obtain the initial battery swap cabinet site selection population corresponding to the target area.

[0147] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may 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 many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double 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.

[0148] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0149] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for determining the location of a battery swap cabinet, characterized in that: include: Generate an initial battery swap cabinet site selection population corresponding to the target area, wherein the initial battery swap cabinet site selection population includes multiple initial battery swap cabinet site selection plans, and one initial battery swap cabinet site selection plan corresponds to a site selection plan for a battery swap cabinet location in the target area; Performing a non-dominated level classification 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, and the initial battery swap cabinet site selection schemes in the same non-dominated layer have the same non-dominated level, and the non-dominated level represents the superiority level of the scheme indicators of each initial battery swap cabinet site selection scheme compared with other initial battery swap cabinet site selection schemes; Determine the total congestion of each initial battery swap cabinet site selection plan in each non-dominated layer, where the total congestion represents the spatial distribution density of each initial battery swap cabinet site selection plan in the non-dominated layer; Determining a plurality of iterative battery swap cabinet site selection schemes from the initial battery swap cabinet site selection population according to the total congestion and non-dominated level of each of the initial battery swap cabinet site selection schemes; The multiple iterative battery swap cabinet site selection plans are subjected to cross-processing iteration and variation processing iteration until each of the iterative battery swap cabinet site selection plans meets the preset conditions, thereby obtaining a target battery swap cabinet site selection population corresponding to the target area, wherein the target battery swap cabinet site selection population includes multiple target battery swap cabinet site selection plans.

2. The method according to claim 1, wherein The 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, including: Determining the initial non-dominated level of each of the initial battery swap cabinet site selection plans; Compare the scheme indicators of any two initial battery swap cabinet site selection schemes to determine the dominance relationship between the any two initial battery swap cabinet site selection schemes, where the dominance relationship represents the advantages and disadvantages of the any two initial battery swap cabinet site selection schemes in terms of multiple scheme indicators; Determining a non-dominant level corresponding to each of the initial battery swap cabinet site selection plans based on the dominance relationship; Based on each of the non-dominated levels, each of the initial battery swap cabinet site selection plans is divided into non-dominated layers to obtain multiple non-dominated layers.

3. The method according to claim 1, wherein Each initial battery swap cabinet site selection plan includes multiple plan indicators; the total congestion degree of each initial battery swap cabinet site selection plan on each non-dominated layer is determined, including: determining a target program indicator from among the plurality of program indicators; Sorting the target solution indicators of the initial battery swap cabinet site selection schemes within the same non-dominated layer to obtain the sorting results of the initial battery swap cabinet site selection schemes within the same non-dominated layer according to the target solution indicators; Based on the ranking results, determine the individual congestion degree of each initial battery swap cabinet site selection plan corresponding to the target plan indicator; Continue to determine the target scheme indicators from the remaining scheme indicators, and continue to sort and determine the individual congestion until all scheme indicators are determined and completed, and obtain the individual congestion of each initial battery swap cabinet site selection scheme corresponding to each scheme indicator; According to the individual congestion of multiple scheme indicators corresponding to each initial battery swap cabinet site selection scheme, the total congestion of each initial battery swap cabinet site selection scheme is determined.

4. The method according to claim 3, wherein Determining the individual congestion degree of each initial battery swap cabinet site selection plan corresponding to the target plan indicator based on the ranking result includes: According to the ranking results, determine the boundary battery swap cabinet site selection scheme and the non-boundary battery swap cabinet site selection scheme corresponding to the target scheme indicator in each initial battery swap cabinet site selection scheme within the same non-dominated layer, and determine the adjacent battery swap cabinet site selection scheme corresponding to each non-boundary battery swap cabinet site selection scheme; Determine that the individual congestion degree of the target solution indicator of the boundary battery swap cabinet site selection solution is infinite; According to the target scheme indicators of the adjacent battery swap cabinet site selection plans of each non-boundary battery swap cabinet site selection plan, the individual congestion degree of each non-boundary battery swap cabinet site selection plan corresponding to the target scheme indicator is obtained.

5. The method according to claim 1, wherein Determining a plurality of iterative battery swap cabinet site selection schemes from the initial battery swap cabinet site selection population according to the total congestion and non-dominated level of each of the initial battery swap cabinet site selection schemes includes: Determine the number of layer plans for the initial battery swap cabinet location plan in each non-dominated layer; Determine the initial battery swap cabinet site selection plan whose non-dominated level meets the level threshold as the first iterative battery swap cabinet site selection plan; When the number of layer solutions in the non-dominated layer is greater than the preset number, the initial battery swap cabinet site selection plan that meets the preset congestion threshold is determined as the second iterative battery swap cabinet site selection plan based on the total congestion of each initial battery swap cabinet site selection plan in the non-dominated layer; According to the non-dominated level and the total congestion, the initial battery swap cabinet site selection plans excluding the first iterative battery swap cabinet site selection plan and the second iterative battery swap cabinet site selection plan in the initial battery swap cabinet site selection population are prioritized and probabilistically selected to determine the third iterative battery swap cabinet site selection plan; The first iterative battery swap cabinet site selection plan, the second iterative battery swap cabinet site selection plan and the third iterative battery swap cabinet site selection plan are used as iterative battery swap cabinet site selection plans.

6. The method according to claim 1, wherein The iterative cross-processing and variation processing iterations are performed on the multiple iterative battery swap cabinet site selection schemes until each of the iterative battery swap cabinet site selection schemes meets a preset condition, thereby obtaining a target battery swap cabinet site selection population corresponding to the target area, including: Perform cross-processing on the scheme indicators of any two iterative battery swap cabinet site selection schemes to obtain the cross-processed site selection scheme; Performing random variation processing on the scheme indicators of each iterative battery swap cabinet site selection scheme to obtain a mutated site selection scheme; The post-crossing site selection plan and the post-mutation site selection plan are used as the iterative battery swap cabinet site selection plan, and the cross processing and random mutation processing iterations are continued until each of the iterative battery swap cabinet site selection plans meets the preset conditions, and the target battery swap cabinet site selection population corresponding to the target area is obtained.

7. The method according to claim 1, wherein The scheme indicators of each of the initial battery swap cabinet site selection schemes include the installation location indicator of the battery swap cabinet and the user satisfaction indicator; The generating of the initial battery swap cabinet site selection population corresponding to the target area includes: Determine population size; Determine multiple locations where multiple battery swap cabinets are to be installed based on the location information of the target area and the population size, and obtain multiple installation location indicators; Determining corresponding user satisfaction indicators based on the installation location indicators; Based on each of the installation location indicators and the corresponding user satisfaction indicators, multiple initial battery swap cabinet site selection plans are obtained to obtain an initial battery swap cabinet site selection population corresponding to the target area.

8. A device for determining the location of a battery swap cabinet, characterized in that: The device comprises: An initial population generation module is used to generate an initial battery swap cabinet site selection population corresponding to the target area, wherein the initial battery swap cabinet site selection population includes multiple initial battery swap cabinet site selection plans, and one initial battery swap cabinet site selection plan corresponds to a site selection plan for a battery swap cabinet location in the target area; A non-dominated layer classification module is used to perform non-dominated level classification 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 includes at least one initial battery swap cabinet site selection scheme, and the initial battery swap cabinet site selection schemes in the same non-dominated layer have the same non-dominated level. The non-dominated level represents the superiority level of the scheme indicators of each initial battery swap cabinet site selection scheme compared with other initial battery swap cabinet site selection schemes; A total congestion determination module is used to determine the total congestion of each initial battery swap cabinet site selection plan on each non-dominated layer, where the total congestion represents the spatial distribution density of each initial battery swap cabinet site selection plan on the non-dominated layer; An iterative battery swap cabinet site selection scheme determination module, configured to determine a plurality of iterative battery swap cabinet site selection schemes from the initial battery swap cabinet site selection population according to the total congestion and non-dominated level of each of the initial battery swap cabinet site selection schemes; An iterative module is used to perform cross-processing iteration and variation processing iteration on the multiple iterative battery swap cabinet site selection plans until each iterative battery swap cabinet site selection plan meets the preset conditions, and obtains a target battery swap cabinet site selection population corresponding to the target area, wherein the target battery swap cabinet site selection population includes multiple target battery swap cabinet site selection plans.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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