A method, system, device and storage medium for optimizing deployment of public charging stations

Abstract

The present invention provides a method, system, device and storage medium for optimizing the deployment of public charging stations. The method is to obtain the planning information of public charging stations and the information related to the deployment of charging stations in the target area, obtain the first charging station planning information of each subordinate area according to the planning information of the public charging stations, the number of electric vehicles in the target area and each subordinate area, obtain the corresponding second charging station planning information in combination with the population density and per capita gross domestic product of each subordinate sub-area, obtain the corresponding charging station location distribution information and charging gun allocation information based on the clustering results of the point of interest information of each subordinate sub-area, and update and optimize the charging station location distribution information and charging gun allocation information based on the charging distance hierarchical evaluation results obtained according to the charging station location distribution information. The present invention effectively improves the scientificity and effectiveness of the deployment of public charging stations by disassembling the planning objectives in a hierarchical manner, and can also better meet the convenience needs of users for actual charging.

Description

Technical Field

[0001] The present invention relates to the technical field of public charging station deployment planning, and in particular to a public charging station deployment optimization method, system, device and storage medium. Background Art

[0002] With the popularity of electric vehicles, the construction of charging infrastructure has become one of the important factors in promoting the development of the electric vehicle industry. The deployment and site selection of public charging stations directly affects the charging convenience, charging efficiency and charging cost of electric vehicle users. The rationality of the deployment method is of great significance to promoting the widespread application of electric vehicles.

[0003] Existing charging station deployment methods mainly include quantitative analysis methods based on statistical data such as traffic flow and economic level, and modeling analysis methods that use geographic information system (GIS) technology for spatial analysis and model construction. However, existing methods often ignore the actual needs and preferences of potential users and are difficult to fully reflect the complexity of charging behavior. Although the introduction of POI (Point of Interest) information can optimize the deployment decision of charging stations to a certain extent, the existing charging station site selection and charging demand analysis methods based on POI information are still in the development stage. At present, they only involve the site selection of charging piles or charging stations within a specific small range based on POI information and charging behavior data. There is a lack of complex and large-scale regional level charging station deployment optimization research, and there is no top-down hierarchical disassembly planning target for charging stations and charging guns that comprehensively considers multiple factors such as electric vehicle ownership, POI information, population density and per capita GDP. Different deployment design of charging stations and charging guns is difficult to adapt to the differences in geographical distribution, population distribution and economic development level in different regions, and cannot guarantee the scientificity and rationality of the deployment planning of charging stations and charging guns at all levels, and cannot effectively meet the development needs of electric vehicles and user convenience needs in different regions. Summary of the invention

[0004] The purpose of the present invention is to provide a public charging station deployment optimization method, which comprehensively considers multiple factors such as the number of electric vehicles, POI information, population density and per capita GDP, and decomposes the public charging station planning objectives from top to bottom to adapt to the differences in geographical distribution, population distribution and economic development level of different regions, effectively ensure the scientificity and rationality of the deployment planning of charging stations and charging guns in various levels, effectively meet the electric vehicle development needs and user convenience needs of different regions, and provide strong support for promoting the sustainable development of the electric vehicle industry.

[0005] In order to achieve the above objectives, a public charging station deployment optimization method, system, computer device and storage medium are provided.

[0006] In a first aspect, an embodiment of the present invention provides a method for optimizing the deployment of public charging stations, the method comprising the following steps:

[0007] Acquire public charging station planning information and charging station deployment related information of the target area; the public charging station planning information includes the target charging station demand and the target charging gun demand; the charging station deployment related information includes the number of electric vehicles in the target area, the number of electric vehicles in each subordinate area within the target area, the population density of the subordinate sub-areas of each subordinate area, the per capita gross domestic product and point of interest information;

[0008] According to the public charging station planning information, the number of electric vehicles in the target area, and the number of electric vehicles in each subordinate area within the target area, obtaining first charging station planning information corresponding to each subordinate area;

[0009] According to the first charging station planning information of each subordinate area and the population density and per capita gross domestic product of each subordinate sub-area, the second charging station planning information of each subordinate sub-area is obtained;

[0010] According to the second charging station planning information of each subordinate sub-area, the corresponding point of interest information is clustered according to the location data based on the K-means clustering algorithm, and the charging station location distribution information and charging gun allocation information of each subordinate sub-area are obtained according to the corresponding clustering results;

[0011] According to the charging station location distribution information of each subordinate sub-area, a charging distance hierarchical evaluation result is obtained, and according to the charging distance hierarchical evaluation result, the charging station location distribution information and charging gun allocation information of each subordinate sub-area are updated and optimized; the charging distance hierarchical evaluation result includes the charging distance of the charging station and the charging distance of the subordinate sub-area.

[0012] Furthermore, the first charging station planning information includes the number of charging stations allocated in the subordinate area and the number of charging guns allocated in the subordinate area;

[0013] The step of obtaining the public charging station planning information corresponding to each subordinate area according to the public charging station planning information, the electric vehicle ownership of the target area, and the electric vehicle ownership of each subordinate area within the target area comprises:

[0014] According to the number of electric vehicles in the target area and the number of electric vehicles in each subordinate area within the target area, obtaining the proportion of the number of vehicles in each subordinate area;

[0015] The number of charging stations allocated to the corresponding subordinate regions is obtained according to the product of the proportion of automobile ownership in each subordinate region and the demand for the target charging station;

[0016] The corresponding number of charging guns allocated to the subordinate area is obtained based on the product of the proportion of car ownership in each subordinate area and the target demand for charging guns.

[0017] Furthermore, the second charging station planning information includes the number of charging stations allocated in the subordinate sub-areas and the number of charging guns allocated in the subordinate sub-areas;

[0018] The step of obtaining the second charging station planning information of each subordinate sub-region according to the first charging station planning information of each subordinate region and the population density and per capita GDP of each subordinate sub-region includes:

[0019] According to the population density and per capita GDP of each subordinate sub-region, the comprehensive proportion of each subordinate sub-region is obtained;

[0020] The corresponding number of charging stations allocated in the subordinate sub-regions is obtained according to the product of the comprehensive proportion of each subordinate sub-region and the corresponding number of charging stations allocated in the subordinate sub-region;

[0021] The corresponding number of charging guns allocated in the subordinate sub-region is obtained according to the product of the comprehensive proportion of each subordinate sub-region and the corresponding number of charging guns allocated in the subordinate sub-region.

[0022] Furthermore, the step of obtaining the comprehensive proportion of each subordinate sub-region according to the population density and per capita GDP of each subordinate sub-region includes:

[0023] According to the population density and per capita GDP of each subordinate sub-region, the corresponding weighted comprehensive value is obtained;

[0024] The weighted comprehensive values ​​of all subordinate sub-regions within the same subordinate region are accumulated to obtain the corresponding subordinate region comprehensive cumulative value;

[0025] According to the ratio of the weighted comprehensive value of each subordinate sub-region and the corresponding subordinate region comprehensive cumulative value, the comprehensive proportion of each subordinate sub-region is obtained; the comprehensive proportion is expressed as:

[0026]

[0027] In the formula,

[0028]

[0029] in, Indicates In the subordinate area The comprehensive proportion of the subordinate sub-regions; Indicates The collection of sub-regions under each sub-region; , and Respectively represent In the subordinate area Population density, per capita GDP and weighted composite value of each sub-region; Represents the weight coefficient.

[0030] Furthermore, the step of obtaining the charging station location distribution information and charging gun allocation information of each subordinate sub-area according to the corresponding clustering result includes:

[0031] The centroid positions of the clusters in the clustering results are used as the target construction positions of the charging stations in the charging station location distribution information;

[0032] According to the ratio of the number of points of interest within each cluster to the total number of points of interest in the entire subordinate sub-area, the charging gun allocation ratio of the corresponding charging station target construction location is obtained, and according to the charging gun allocation ratio and the corresponding number of charging gun allocations in the subordinate sub-area, the charging gun allocation information is obtained; the charging gun allocation information includes the number of charging gun allocations at each charging station target construction location.

[0033] Further, the step of obtaining a charging distance hierarchical evaluation result according to the charging station location distribution information of each subordinate sub-area, and updating and optimizing the charging station location distribution information and charging gun allocation information of each subordinate sub-area according to the charging distance hierarchical evaluation result includes:

[0034] Obtaining a corresponding charging distance of a charging station according to an average value of distances between target construction locations of each charging station in the charging station location distribution information and each point of interest in the corresponding cluster;

[0035] According to the average charging distance of all charging stations in the same subordinate sub-region, the corresponding subordinate sub-region charging distance is obtained;

[0036] Calculate the distance deviation between the charging distance of each charging station and the charging distance of the corresponding subordinate sub-area, and compare and analyze the distance deviation with the upper and lower deviation thresholds;

[0037] The cluster whose distance deviation value is greater than the upper deviation threshold is used as a charging station cluster to be added, and the charging station cluster to be added is divided into at least two cluster subclusters based on the K-means clustering algorithm, and the centroid position of the corresponding cluster subclusters is used as the target construction position of the new charging station;

[0038] The cluster whose distance deviation value is less than the deviation lower limit threshold is taken as the charging station cluster to be reduced, the charging station cluster to be reduced is merged with the cluster to be merged to obtain a corresponding merged cluster, and the charging station target construction position of the merged cluster is re-determined based on the K-means clustering algorithm; the cluster to be merged is a cluster adjacent to the charging station cluster to be reduced and the distance deviation is within a preset range;

[0039] The charging gun allocation information is updated and optimized according to the updated charging station location distribution information of the subordinate sub-areas.

[0040] Furthermore, the method further comprises:

[0041] The average power of the public charging guns in the target area and the operational charging capacity of each charging station in each subordinate sub-area are periodically obtained, and the unit power load density of each charging station is obtained according to the operational charging capacity, the corresponding charging gun allocation information and the average power of the public charging guns. For the charging stations whose unit power load density exceeds the preset mean deviation range, the number of charging guns and the power of the charging guns are optimized and adjusted.

[0042] In a second aspect, an embodiment of the present invention provides a public charging station deployment optimization system, the system comprising:

[0043] An information acquisition module is used to acquire the public charging station planning information and charging station deployment related information of the target area; the public charging station planning information includes the target charging station demand and the target charging gun demand; the charging station deployment related information includes the number of electric vehicles in the target area, the number of electric vehicles in each subordinate area in the target area, the population density of the subordinate sub-areas of each subordinate area, the per capita GDP and the point of interest information;

[0044] A first planning module, configured to obtain first charging station planning information corresponding to each subordinate area according to the public charging station planning information, the number of electric vehicles in the target area, and the number of electric vehicles in each subordinate area within the target area;

[0045] The second planning module is used to obtain the second charging station planning information of each subordinate sub-region according to the first charging station planning information of each subordinate region and the population density and per capita gross domestic product of each subordinate sub-region;

[0046] A deployment analysis module is used to perform cluster analysis on the corresponding point of interest information according to the location data based on the second charging station planning information of each subordinate sub-area based on the K-means clustering algorithm, and obtain the charging station location distribution information and charging gun allocation information of each subordinate sub-area based on the corresponding clustering results;

[0047] The deployment optimization module is used to obtain a hierarchical evaluation result of charging distances according to the location distribution information of charging stations in each subordinate sub-area, and update and optimize the location distribution information of charging stations and the charging gun allocation information of each subordinate sub-area according to the hierarchical evaluation result of charging distances; the hierarchical evaluation result of charging distances includes the charging distance of the charging station and the charging distance of the subordinate sub-area.

[0048] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0049] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0050] The above-mentioned present invention provides a public charging station deployment optimization method, system, computer device and storage medium, by obtaining the public charging station planning information of the target area including the target charging station demand and the target charging gun demand and the charging station deployment related information including the electric vehicle ownership of the target area, the electric vehicle ownership of each subordinate area in the target area, the population density of the subordinate sub-areas of each subordinate area, the per capita GDP and the point of interest information, according to the public charging station planning information, the electric vehicle ownership of the target area, the electric vehicle ownership of each subordinate area in the target area, the first charging station planning information corresponding to each subordinate area is obtained, and according to the first charging station planning information of each subordinate area and the After obtaining the second charging station planning information of each subordinate sub-region according to the population density and per capita GDP of each subordinate sub-region, based on the second charging station planning information of each subordinate sub-region, the corresponding point of interest information is clustered and analyzed according to the location data based on the K-means clustering algorithm, and the charging station location distribution information and charging gun allocation information of each subordinate sub-region are obtained according to the corresponding clustering results, and the charging distance hierarchical evaluation results including the charging distance of the charging station and the charging distance of the subordinate sub-region are obtained according to the charging station location distribution information of each subordinate sub-region, and a technical solution is provided for updating and optimizing the charging station location distribution information and charging gun allocation information of each subordinate sub-region according to the charging distance hierarchical evaluation results. Compared with the existing technology, this public charging station deployment optimization method comprehensively considers multiple factors such as the number of electric vehicles, POI information, population density and per capita GDP, and decomposes the public charging station planning goals from top to bottom to adapt to the differences in geographical distribution, population distribution and economic development level of different regions, effectively ensuring the scientificity and rationality of the deployment planning of charging stations and charging guns at all levels, and effectively meeting the electric vehicle development needs and user convenience needs of different regions, providing strong support for promoting the sustainable development of the electric vehicle industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of a method for optimizing the deployment of public charging stations in an embodiment of the present invention;

[0052] Figure 2 is another flow chart of the method for optimizing the deployment of public charging stations in an embodiment of the present invention;

[0053] Figure 3 is a structural diagram of a public charging station deployment optimization system according to an embodiment of the present invention;

[0054] Figure 4 is another structural schematic diagram of the public charging station deployment optimization system in an embodiment of the present invention;

[0055] Figure 5It is a diagram of the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical scheme and beneficial effects of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] In one embodiment, Figure 1 As shown, a method for optimizing the deployment of public charging stations is provided, comprising the following steps:

[0058] S11. Obtain the public charging station planning information and charging station deployment related information of the target area; the public charging station planning information includes the target charging station demand and the target charging gun demand, and the target charging station demand can be understood as the expected number of charging stations to be deployed and constructed, and the target charging gun demand can be understood as the total number of preset charging guns; the charging station deployment related information includes the number of electric vehicles in the target area, the number of electric vehicles in each subordinate area within the target area, the population density of the subordinate sub-areas of each subordinate area, the per capita GDP and information on points of interest, points of interest The information includes the name of the point of interest, its subordinate sub-areas and location data, and the location data can preferably be set to longitude and latitude values; it should be noted that there is a top-down hierarchical relationship between the target area, subordinate areas and subordinate sub-areas, and the corresponding specific area range can be determined according to actual application needs. For example, if it is necessary to plan the deployment of public charging stations in a certain province, the corresponding target area can be understood as the provincial range, and the subordinate areas within the target area can be understood as the municipal range within the provincial range, and the subordinate sub-areas of each subordinate area can be understood as the county-level area within the municipal range.

[0059] S12. Obtain the first charging station planning information corresponding to each subordinate area based on the public charging station planning information, the number of electric vehicles in the target area, and the number of electric vehicles in each subordinate area within the target area; wherein the first charging station planning information can be understood as the deployment target of the next-level area obtained by decomposing the public charging station planning information of the target area based on the proportion of electric vehicles in each subordinate area, taking into account that the number of electric vehicles is a key factor that directly affects the number of charging stations and charging guns deployed, that is, the first charging station planning information includes the number of charging stations allocated in the subordinate area and the number of charging guns allocated in the subordinate area. Specifically, the step of obtaining the public charging station planning information corresponding to each subordinate area based on the public charging station planning information, the number of electric vehicles in the target area, and the number of electric vehicles in each subordinate area within the target area includes:

[0060] According to the number of electric vehicles in the target area and the number of electric vehicles in each subordinate area within the target area, the proportion of the number of vehicles in each subordinate area is obtained; wherein the proportion of the number of vehicles is expressed as:

[0061]

[0062] in, and Respectively represent the target area The number of electric vehicles and the proportion of vehicles in each subordinate region; It represents the number of electric vehicles in the target area, which can be understood as the sum of the number of electric vehicles in all subordinate areas within the target area.

[0063] The corresponding number of charging stations allocated to the subordinate regions is obtained according to the product of the proportion of automobile ownership in each subordinate region and the demand for the target charging station; wherein the number of charging stations allocated to the subordinate regions can be expressed as:

[0064]

[0065] in, and Respectively represent the target area The percentage of cars in each subordinate region and the number of charging stations allocated in each subordinate region; Indicates the target charging station demand in the target area.

[0066] The corresponding number of charging guns allocated to the subordinate area is obtained according to the product of the proportion of car ownership in each subordinate area and the target demand for charging guns; wherein the number of charging guns allocated to the subordinate area is expressed as:

[0067]

[0068] in, and Respectively represent the target area The percentage of cars in each subordinate region and the number of charging guns allocated in each subordinate region; Indicates the target charging gun demand in the target area.

[0069] Through the above method steps, the public charging station planning information of the target area can be decomposed into each subordinate area of ​​the next level to obtain the first charging station planning information of each subordinate area within the target area. Then, the following method can be used to combine the differences in geographical distribution, population distribution and economic development level of the subordinate sub-areas in each subordinate area to perform adaptive target decomposition, so as to ensure the scientificity and rationality of the deployment planning of charging stations and charging guns in each level of area.

[0070] S13. Obtain the second charging station planning information of each subordinate sub-region based on the first charging station planning information of each subordinate region and the population density and per capita GDP of each subordinate sub-region; wherein the second charging station planning information can be understood as the deployment requirements that meet the use requirements of the sub-regions obtained by further subdividing the deployment targets of the subordinate regions within the target region by sub-regions, that is, the second charging station planning information includes the number of charging stations allocated in the subordinate sub-regions and the number of charging guns allocated in the subordinate sub-regions. Specifically, the steps of obtaining the second charging station planning information of each subordinate sub-region based on the first charging station planning information of each subordinate region and the population density and per capita GDP of each subordinate sub-region include:

[0071] According to the population density and per capita GDP of each subordinate sub-region, the comprehensive proportion of each subordinate sub-region is obtained; wherein, the population density of the subordinate sub-region can be obtained based on the total population and the land area in the region; the per capita GDP can be understood as the per capita GDP value of the corresponding region. The corresponding comprehensive proportion can be understood as the proportion of the number of deployed charging stations and the number of allocated charging guns in the region obtained by considering the population density and per capita GDP of the subordinate sub-region at the same time. Specifically, the step of obtaining the comprehensive proportion of each subordinate sub-region according to the population density and per capita GDP of each subordinate sub-region includes:

[0072] According to the population density and per capita GDP of each subordinate sub-region, the corresponding weighted comprehensive value is obtained; the weighted comprehensive value can be understood as a comprehensive impact indicator obtained by weighted average of population density and per capita GDP while considering the impact of population density and economic level on the deployment of regional charging stations, expressed as:

[0073]

[0074] In the formula,

[0075]

[0076] in, , , , and Respectively represent In the subordinate area Population, land area, population density, per capita GDP and weighted comprehensive value of each sub-region; It represents the weight coefficient, which can be set according to the importance of the two factors in the actual subordinate sub-area on the deployment number of charging stations and charging guns, and is not specifically limited here.

[0077] The weighted comprehensive values ​​of all subordinate sub-regions in the same subordinate region are accumulated to obtain the corresponding subordinate region comprehensive cumulative value; wherein, the subordinate region comprehensive cumulative value can be expressed as , Indicates The collection of sub-regions under each sub-region; Indicates In the subordinate area The weighted comprehensive value of the subordinate sub-regions.

[0078] According to the ratio of the weighted comprehensive value of each subordinate sub-region and the corresponding subordinate region comprehensive cumulative value, the comprehensive proportion of each subordinate sub-region is obtained; the comprehensive proportion is expressed as:

[0079]

[0080] in, Indicates In the subordinate area The comprehensive proportion of the subordinate sub-regions; Indicates The collection of sub-regions under each sub-region; Indicates In the subordinate area The weighted comprehensive value of the subordinate sub-regions.

[0081] The corresponding number of charging stations allocated in the subordinate sub-region is obtained according to the product of the comprehensive proportion of each subordinate sub-region and the number of charging stations allocated in the corresponding subordinate region; wherein the number of charging stations allocated in the subordinate sub-region can be expressed as:

[0082]

[0083] In the formula, Indicates the target area The number of charging stations allocated to each subordinate area; and Respectively represent the target area The first of the subordinate regions The comprehensive proportion of each subordinate sub-region and the number of charging stations allocated to each subordinate sub-region.

[0084] The corresponding number of charging guns allocated in the subordinate sub-region is obtained by multiplying the comprehensive proportion of each subordinate sub-region by the number of charging guns allocated in the corresponding subordinate sub-region; wherein the number of charging guns allocated in the subordinate sub-region can be expressed as:

[0085]

[0086] In the formula, Indicates the target area The number of charging guns allocated to each subordinate area; and Respectively represent the target area The first of the subordinate regions The comprehensive proportion of each subordinate sub-area and the number of charging guns allocated to the subordinate sub-areas.

[0087] After obtaining the number of charging stations and charging guns allocated in all subordinate sub-areas of each subordinate area within the target area through the above method steps, the charging station construction location of each subordinate sub-area can be analyzed based on the usage of public charging stations in real life and people's driving habits, so as to meet the charging convenience needs of electric vehicle users in each subordinate sub-area as much as possible and improve the user experience.

[0088] S14. Based on the second charging station planning information of each subordinate sub-area, the corresponding point of interest information is clustered and analyzed according to the location data based on the K-means clustering algorithm, and the charging station location distribution information and charging gun allocation information of each subordinate sub-area are obtained according to the corresponding clustering results; wherein, the point of interest information can be understood as location information related to people's travel habits, which belongs to places with relatively concentrated flow of people. Planning and building public charging stations around such places can better meet the charging needs of electric vehicles. The point of interest information in this embodiment may include location classifications such as catering, shopping, transportation facilities, hotel accommodation, science and education culture, tourist attractions, automobile-related services, life services and medical care. In actual applications, all point of interest information of each subordinate sub-area can be queried based on the known map interface for scientific analysis of the site selection of public charging stations in the region, and the specific content of each point of interest information is shown in Figure 1.

[0089] Table 1 Schematic diagram of the content of interest point information

[0090]

[0091] In practical applications, the K-means clustering algorithm is used to perform spatial clustering on the location data (e.g., longitude and latitude data) of the point of interest information in all subordinate sub-areas in the target area, and a clustering cluster equal to the number of charging stations allocated in the subordinate sub-areas in the corresponding second charging station planning information is obtained, and based on the obtained clustering results, the charging station location distribution information and charging gun allocation information of the corresponding subordinate sub-areas are obtained through the following method steps. Specifically, the step of obtaining the charging station location distribution information and charging gun allocation information of each subordinate sub-area according to the corresponding clustering results includes:

[0092] The centroid positions of each cluster in the clustering result are respectively used as target construction positions of each charging station in the charging station location distribution information; that is, the centroid positions of all clusters in each subordinate sub-region are determined as a charging station site selection position in the sub-region.

[0093] According to the ratio of the number of points of interest in each cluster to the total number of points of interest in the entire subordinate sub-region, the corresponding charging gun allocation ratio of the charging station target construction location is obtained, and according to the charging gun allocation ratio and the corresponding number of charging guns allocated in the subordinate sub-region, the charging gun allocation information is obtained; wherein, the charging gun allocation information includes the number of charging guns allocated at each charging station target construction location, and the number of charging guns allocated at each charging station target construction location (charging station) is positively correlated with the number of points of interest around the charging station. The specific number of charging guns allocated at each charging station target construction location can be expressed as:

[0094]

[0095] In the formula, and Respectively represent In the subordinate area The first of the subordinate sub-regions The number of charging guns assigned to each cluster (charging station) and the number of points of interest within the cluster; Indicates In the subordinate area The total number of points of interest in the subordinate sub-areas; Indicates the target area The first of the subordinate regions The number of charging guns allocated to each subordinate sub-area.

[0096] By clustering based on the location information of points of interest as mentioned above, the spatial distribution of public charging stations in each subordinate sub-area is obtained, and then the proportion of the points of interest contained in each cluster in the subordinate sub-area is used as the basis for allocating the number of public charging guns at the charging station. This can ensure that the site selection of charging stations in each sub-area effectively meets the usage habits of public charging stations and people's travel habits, and improve the rationality and scientificity of the site selection of charging stations and the allocation of charging guns.

[0097] S15. Obtain a hierarchical evaluation result of charging distances based on the location distribution information of charging stations in each subordinate sub-area, and update and optimize the location distribution information of charging stations and the charging gun allocation information in each subordinate sub-area based on the hierarchical evaluation result of charging distances; wherein, the hierarchical evaluation result of charging distances can be understood as taking into account the convenience needs of the public charging station locations expected by electric vehicle users in actual applications and their destinations (such as restaurants, shopping malls, service areas, tourist attractions, etc. where users can charge their cars while parking and resting) and selecting the distance between the point of interest and the public charging station as the evaluation basis, and performing a convenience evaluation on the charging station selection locations determined based on cluster analysis; correspondingly, the hierarchical evaluation result of charging distances includes the charging distance of the charging station and the charging distance of the subordinate sub-areas.

[0098] In order to meet the charging convenience requirements as much as possible and effectively ensure that each charging station has a high utilization rate (rationality of spatial distribution) to avoid waste of resources, this embodiment preferably performs convenience and spatial distribution rationality analysis based on the charging distance hierarchical evaluation results at the same time, so as to ensure the scientificity and rationality of the charging station location distribution information and charging gun allocation information of each subordinate sub-area as much as possible, so that each charging station can be efficiently utilized and unnecessary deployment waste can be avoided. Specifically, the steps of obtaining the charging distance hierarchical evaluation results based on the charging station location distribution information of each subordinate sub-area, and updating and optimizing the charging station location distribution information and charging gun allocation information of each subordinate sub-area based on the charging distance hierarchical evaluation results include:

[0099] According to the average value of the distances between the target construction locations of each charging station in the charging station location distribution information and each point of interest in the corresponding cluster, the corresponding charging distance of the charging station is obtained; that is, the charging distance of each charging station is the average value of the distances between the location data and the centroid position of all points of interest in its corresponding cluster, so as to represent the convenience index of the corresponding charging station spatial location setting, and the smaller the value, the more convenient it is for users to charge.

[0100] According to the average value of charging distances of all charging stations in the same subordinate sub-region, the corresponding subordinate sub-region charging distance is obtained; wherein, the subordinate sub-region charging distance can be understood as the average value of the convenience index of all charging stations in its corresponding region.

[0101] Taking into account that in actual applications, there may be an imbalance in charging services due to unreasonable deployment locations of charging stations in subordinate sub-areas, which in turn makes it difficult to ensure a high utilization rate of charging resources, this embodiment preferably evaluates the convenience of charging station settings through a hierarchical analysis of charging distances, and adjusts the location distribution or quantity distribution of charging stations based on the identified clusters with excessively large or small local charging distances through the following method steps, thereby improving the rationality of the overall distribution of public charging stations and ensuring the balance of charging resource distribution.

[0102] The distance deviation values ​​between the charging distance of each charging station and the charging distance of the corresponding subordinate sub-area are calculated respectively, and the distance deviation values ​​are compared and analyzed with the upper deviation threshold and the lower deviation threshold respectively; wherein, the upper deviation threshold and the lower deviation threshold can be set according to the convenience required in the actual application, and are not specifically limited here.

[0103] The clustering cluster whose distance deviation value is greater than the upper limit threshold of the deviation is taken as the cluster of charging stations to be added, and the clustering cluster to be added is divided into at least two clustering sub-clusters based on the K-means clustering algorithm, and the centroid position of the corresponding clustering sub-cluster is taken as the target construction position of the new charging station; specifically, if the distance deviation value of a clustering cluster is greater than the upper limit threshold of the deviation, it is considered that the average charging distance of the clustering cluster is too large, there is a problem of insufficient resource allocation, and additional charging station settings need to be added. The K-means clustering algorithm can be used to further divide the clustering cluster into two or more sub-clusters, and the centroid position of each clustering sub-cluster is determined as the location of the new public charging station.

[0104] The clustering cluster whose distance deviation value is less than the deviation lower limit threshold is taken as the charging station cluster to be reduced, and the charging station cluster to be reduced is merged with the clustering cluster to be merged to obtain the corresponding merged cluster, and the charging station target construction position of the merged cluster is re-determined based on the K-means clustering algorithm; the clustering cluster to be merged is the clustering cluster adjacent to the charging station cluster to be reduced and the distance deviation is within the preset range, and the preset range can be determined according to the actual application requirements and is not specifically limited here; specifically, if the distance deviation value of a clustering cluster is less than the deviation lower limit threshold, it means that the average charging distance of the clustering cluster is too small, there is a problem of excess resource allocation, and it is necessary to merge redundant charging stations to reduce the waste of public charging station resources. The clustering cluster can be merged into other clustering clusters adjacent to it whose distance deviation values ​​are within the preset range, and the merged cluster is analyzed again using the K-means clustering algorithm to redetermine the new public charging station location.

[0105] According to the updated charging station location distribution information of the subordinate sub-area, the charging gun allocation information is updated and optimized; specifically, after the charging station location distribution information of the subordinate sub-area is optimized and updated through the above-mentioned method steps, the above-mentioned method based on the ratio of the number of points of interest within each cluster to the total number of points of interest in the entire subordinate sub-area as the charging gun allocation proportion of each charging station can be re-adopted to update the charging gun allocation information of the subordinate sub-area.

[0106] The above-mentioned method of updating and optimizing the charging station location distribution information and charging gun allocation information of each subordinate sub-area according to the hierarchical evaluation results of charging distance can not only effectively ensure the variability of the site selection of each charging station, but also effectively avoid the problem of uneven distribution of charging resources caused by the site selection of charging stations, thereby ensuring the overall service level of public charging stations and the utilization efficiency of distributed resources.

[0107] It should be noted that after determining the charging station location distribution information and charging gun allocation information of each subordinate sub-area through the above-mentioned method steps, in order to facilitate the intuitive display of the scientificity and rationality of the charging station deployment, the charging distance of each charging station and the charging distance of the subordinate sub-area can be calculated respectively according to the updated charging station location distribution information and charging gun allocation information. In addition, the corresponding subordinate area charging distance can be obtained according to the average value of the charging distances of all subordinate sub-areas in the same subordinate area, and the target area charging distance can be obtained according to the average value of the charging distances of all subordinate areas, so as to display the convenience and scientificity of the charging station deployment in the entire target area in a hierarchical manner.

[0108] According to an embodiment of the present invention, the public charging station planning information of the target area is decomposed based on the number of electric vehicles in the target area and the number of electric vehicles in each subordinate area within the target area to obtain the first charging station planning information of each subordinate area, and then the first charging station planning information of the subordinate area is decomposed based on the population density and per capita GDP of each subordinate sub-area to obtain the second charging station planning information of each subordinate sub-area. According to the second charging station planning information of each subordinate sub-area, the corresponding point of interest information is clustered according to the position data based on the K-means clustering algorithm to obtain the charging station location distribution information and charging gun allocation information of each subordinate sub-area, and the charging station location distribution information including the charging distance and the charging gun allocation information of each subordinate sub-area is obtained based on the charging station location distribution information of each subordinate sub-area. The hierarchical evaluation results of the charging distances in the subordinate sub-regions are used to update and optimize the public charging station deployment optimization plan for the charging station location distribution information and charging gun allocation information of each subordinate sub-region. Based on comprehensive consideration of multiple factors such as the number of electric vehicles, POI information, population density and per capita GDP, the public charging station planning goals are disassembled from top to bottom to adapt to the differences in geographical distribution, population distribution and economic development level in different regions. It can not only effectively ensure the scientificity and rationality of the deployment planning of charging stations and charging guns in various levels of regions, but also effectively meet the development needs of electric vehicles and user convenience needs in different regions, and also ensure the overall service level of public charging stations and the utilization efficiency of distributed resources, thereby providing strong support for promoting the sustainable development of the electric vehicle industry.

[0109] In addition, considering that the deployment scheme of public charging stations in the target area obtained by the above method steps does not take into account the load balancing factor, there may be load imbalance in some charging stations during the actual operation. In order to ensure the load balancing of each charging station in the same subordinate sub-area as much as possible, this embodiment preferably monitors and optimizes the unit power load density of each charging station by regularly collecting the average power of the public charging guns in the target area and the operating charging amount of each charging station. Specifically, Figure 2 As shown, the method also includes:

[0110] S16, periodically obtain the average power of the public charging guns in the target area and the operational charging amount of each charging station in each subordinate sub-area, obtain the unit power load density of each charging station according to the operational charging amount, the corresponding charging gun allocation information and the average power of the public charging gun, and optimize the number of charging guns and the power of charging guns for the charging stations whose unit power load density exceeds the preset mean deviation range; wherein the operational charging amount of the charging station can be understood as the actual charging amount completed by the charging station in each monitoring cycle; the specific process of optimizing the number of charging guns and the power of charging guns by monitoring the unit power load density of each charging station is as follows:

[0111] 1) First, based on the operating charging capacity and number of charging guns of each charging station, as well as the average power of public charging guns in the entire target area, the unit power load density of each charging station is obtained based on the following unit power load density calculation formula:

[0112]

[0113] in, , and Respectively represent the target area In the subordinate area The first of the subordinate sub-regions The operating charging capacity of each charging station, the number of charging guns allocated and the unit power load density; Indicates the average power of public charging guns in the target area.

[0114] 2) According to the unit power load density of all charging stations in the same subordinate sub-area, the average load density and load density standard deviation of the subordinate sub-area are obtained, and the upper and lower thresholds of the load density in the preset mean deviation range are obtained accordingly, which are expressed as:

[0115] =

[0116] =

[0117] in, and Respectively represent the target area In the subordinate area The average load density and load density standard deviation of charging stations in each sub-region; and Respectively represent the target area In the subordinate area The load density upper limit threshold and the load density lower limit threshold within the preset mean deviation range of the subordinate sub-areas.

[0118] 3) Compare and analyze the unit power load density of each charging station with the upper and lower load density thresholds of the preset mean deviation range of the corresponding subordinate sub-areas:

[0119] when When the charging station is considered to be a high-load station, it is necessary to increase the number of charging guns or configure a higher If the number of charging guns is allowed to be increased, the specific number of charging guns can be expressed as:

[0120]

[0121] in, Represents the adjustment coefficient, used for smoothing; represents the ceiling function; Indicates the target area In the subordinate area In the subordinate sub-areas Each charging station needs to increase the number of charging guns;

[0122] If the number of charging guns cannot be increased, the power configuration of the existing charging guns needs to be increased, and the increased power configuration of the charging gun can be expressed as:

[0123]

[0124] in, Indicates the target area In the subordinate area In the subordinate sub-areas The power configuration of each charging gun after the charging gun of each charging station is improved; Indicates the power regulation coefficient, which can be determined according to actual application requirements.

[0125] when When , the charging station is considered to be a low-load site, and the number of charging guns needs to be reduced or the power configuration needs to be lowered; if the number of charging guns is allowed to be reduced, the specific reduction in the number of charging guns can be expressed as:

[0126]

[0127] in, Represents the adjustment coefficient, used for smoothing; represents the ceiling function; Indicates the target area In the subordinate area In the subordinate sub-areas The number of charging guns that need to be reduced for each charging station; it should be noted that if the number of charging guns after the reduction is lower than the minimum operating requirement, the number of charging guns at the charging station will be maintained at the minimum operating requirement;

[0128] If it is not allowed to reduce the number of charging guns, the power configuration of the existing charging guns needs to be reduced, and the reduced power configuration of the single gun can be expressed as:

[0129]

[0130] in, Indicates the target area In the subordinate area In the subordinate sub-areas The power configuration of each charging gun after the charging gun of each charging station is reduced; Indicates the power regulation coefficient, which can be determined according to actual application requirements.

[0131] According to an embodiment of the present invention, the public charging station planning information of the target area is decomposed based on the number of electric vehicles in the target area and the number of electric vehicles in each subordinate area within the target area to obtain the first charging station planning information of each subordinate area, and then the first charging station planning information of the subordinate area is decomposed based on the population density and per capita GDP of each subordinate sub-area to obtain the second charging station planning information of each subordinate sub-area. According to the second charging station planning information of each subordinate sub-area, the corresponding point of interest information is clustered according to the position data based on the K-means clustering algorithm to obtain the charging station location distribution information and charging gun allocation information of each subordinate sub-area, and the charging station location distribution information including the charging distance and the subordinate area is obtained based on the charging station location distribution information of each subordinate sub-area. The hierarchical evaluation results of the charging distance of the sub-region update and optimize the charging station location distribution information and charging gun allocation information of each subordinate sub-region, as well as the public charging station deployment optimization plan that regularly monitors the unit load density of the charging station to dynamically adjust the number of charging guns allocated and the power configuration of each station. It can not only effectively ensure the scientificity and rationality of the deployment planning of charging stations and charging guns in various levels of regions, but also effectively meet the development needs of electric vehicles and user convenience needs in different regions, and ensure the overall service level and distributed resource utilization efficiency of public charging stations. It can also dynamically maintain the load balance of local areas during operation by adaptively adjusting the number and power of charging guns at each charging station, thereby providing strong support for promoting the sustainable development of the electric vehicle industry.

[0132] It should be noted that although the steps in the above flowchart are shown in sequence according to the arrows, 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.

[0133] In one embodiment, Figure 3 As shown, a public charging station deployment optimization system is provided, the system comprising:

[0134] Information acquisition module 1, used to acquire public charging station planning information and charging station deployment related information of the target area; the public charging station planning information includes the target charging station demand and the target charging gun demand; the charging station deployment related information includes the number of electric vehicles in the target area, the number of electric vehicles in each subordinate area in the target area, the population density of the subordinate sub-areas of each subordinate area, the per capita GDP and the point of interest information;

[0135] A first planning module 2, configured to obtain first charging station planning information corresponding to each subordinate area according to the public charging station planning information, the number of electric vehicles in the target area, and the number of electric vehicles in each subordinate area within the target area;

[0136] The second planning module 3 is used to obtain the second charging station planning information of each subordinate sub-region according to the first charging station planning information of each subordinate region and the population density and per capita GDP of each subordinate sub-region;

[0137] Deployment analysis module 4, used to perform cluster analysis on the corresponding point of interest information according to the location data based on the second charging station planning information of each subordinate sub-area based on the K-means clustering algorithm, and obtain the charging station location distribution information and charging gun allocation information of each subordinate sub-area according to the corresponding clustering results;

[0138] The deployment optimization module 5 is used to obtain a hierarchical evaluation result of charging distance according to the location distribution information of charging stations in each subordinate sub-area, and update and optimize the location distribution information of charging stations and the charging gun allocation information of each subordinate sub-area according to the hierarchical evaluation result of charging distance; the hierarchical evaluation result of charging distance includes the charging distance of the charging station and the charging distance of the subordinate sub-area.

[0139] In one embodiment, Figure 4 As shown, a public charging station deployment optimization system is provided, and the system also includes:

[0140] The load balancing module 6 is used to periodically obtain the average power of the public charging guns in the target area and the operational charging capacity of each charging station in each subordinate sub-area, and obtain the unit power load density of each charging station according to the operational charging capacity, the corresponding charging gun allocation information and the average power of the public charging gun, and optimize the number of charging guns and the power of the charging guns for the charging stations whose unit power load density exceeds the preset mean deviation range.

[0141] For the specific definition of the public charging station deployment optimization system, please refer to the definition of the public charging station deployment optimization method above, and the corresponding technical effects can also be obtained equivalently, which will not be repeated here. Each module in the above-mentioned public charging station deployment optimization system can be implemented in whole or in part through software, hardware and a combination thereof. 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.

[0142] Figure 5FIG. 1 shows an internal structure diagram of a computer device in an embodiment, and the computer device may specifically be a terminal or a server. Figure 5 As shown, the computer device includes a processor, a memory, a network interface, a display, a camera and an input device 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 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 through a network connection. When the computer program is executed by the processor, a public charging station deployment optimization method 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 covered on the display screen, or a button, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0143] It can be understood by those skilled in the art that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present invention, and does not constitute a limitation on the computer device to which the scheme of the present invention is applied. The specific computing device may include more or less components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0144] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps of the above method are implemented when the processor executes the computer program.

[0145] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0146] In summary, an embodiment of the present invention provides a method, system, computer device and storage medium for optimizing the deployment of public charging stations. The method for optimizing the deployment of public charging stations realizes decomposing the public charging station planning information of a target area based on the number of electric vehicles in the target area and the number of electric vehicles in each subordinate area within the target area to obtain the first charging station planning information of each subordinate area, and then decomposing the first charging station planning information of each subordinate area based on the population density and per capita GDP of each subordinate sub-area to obtain the second charging station planning information of each subordinate sub-area. According to the second charging station planning information of each subordinate sub-area, the corresponding point of interest information is clustered according to the location data based on the K-means clustering algorithm to obtain the charging station location distribution information and charging gun allocation information of each subordinate sub-area, and based on the information of each subordinate sub-area The charging station location distribution information is obtained, including the charging distance of the charging station and the charging distance of the subordinate sub-area. The hierarchical evaluation results of the charging distance are used to update and optimize the charging station location distribution information and the charging gun allocation information of each subordinate sub-area, as well as the technical solution of regularly monitoring the unit load density of the charging station to dynamically adjust the number of charging guns allocated at each station and the power configuration of the charging gun. This method can not only effectively ensure the scientificity and rationality of the deployment planning of charging stations and charging guns in various levels of regions, but also effectively meet the development needs of electric vehicles and user convenience needs in different regions, and can also ensure the overall service level and distributed resource utilization efficiency of public charging stations. It can also dynamically maintain the load balance of local areas during operation by adaptively adjusting the number and power of charging guns at each charging station, thereby providing strong support for promoting the sustainable development of the electric vehicle industry.

[0147] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not 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.

[0148] The above-mentioned embodiments only express several preferred implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principle of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for optimizing the deployment of public charging stations, characterized in that: The method comprises the following steps: Acquire public charging station planning information and charging station deployment related information of the target area; the public charging station planning information includes the target charging station demand and the target charging gun demand; the charging station deployment related information includes the number of electric vehicles in the target area, the number of electric vehicles in each subordinate area within the target area, the population density of the subordinate sub-areas of each subordinate area, the per capita gross domestic product and point of interest information; According to the public charging station planning information, the number of electric vehicles in the target area, and the number of electric vehicles in each subordinate area within the target area, first charging station planning information corresponding to each subordinate area is obtained; the first charging station planning information includes the number of charging stations allocated in the subordinate area and the number of charging guns allocated in the subordinate area; According to the first charging station planning information of each subordinate area and the population density and per capita gross domestic product of each subordinate sub-area, the second charging station planning information of each subordinate sub-area is obtained; the second charging station planning information includes the number of charging stations allocated in the subordinate sub-area and the number of charging guns allocated in the subordinate sub-area; According to the second charging station planning information of each subordinate sub-area, the corresponding point of interest information is clustered according to the location data based on the K-means clustering algorithm, and the charging station location distribution information and charging gun allocation information of each subordinate sub-area are obtained according to the corresponding clustering results; According to the charging station location distribution information of each subordinate sub-area, a charging distance hierarchical evaluation result is obtained, and according to the charging distance hierarchical evaluation result, the charging station location distribution information and charging gun allocation information of each subordinate sub-area are updated and optimized; the charging distance hierarchical evaluation result includes the charging distance of the charging station and the charging distance of the subordinate sub-area; The step of obtaining first charging station planning information corresponding to each subordinate area according to the public charging station planning information, the number of electric vehicles in the target area, and the number of electric vehicles in each subordinate area within the target area comprises: According to the number of electric vehicles in the target area and the number of electric vehicles in each subordinate area within the target area, obtaining the proportion of the number of vehicles in each subordinate area; The number of charging stations allocated to the corresponding subordinate regions is obtained according to the product of the proportion of automobile ownership in each subordinate region and the demand for the target charging station; The number of charging guns allocated to the corresponding subordinate area is obtained according to the product of the proportion of automobile ownership in each subordinate area and the target demand for charging guns; The step of obtaining the second charging station planning information of each subordinate sub-region according to the first charging station planning information of each subordinate region and the population density and per capita GDP of each subordinate sub-region includes: According to the population density and per capita GDP of each subordinate sub-region, the comprehensive proportion of each subordinate sub-region is obtained; The corresponding number of charging stations allocated in the subordinate sub-regions is obtained according to the product of the comprehensive proportion of each subordinate sub-region and the corresponding number of charging stations allocated in the subordinate sub-regions; The corresponding number of charging guns allocated in the subordinate sub-region is obtained according to the product of the comprehensive proportion of each subordinate sub-region and the corresponding number of charging guns allocated in the subordinate sub-region; The step of obtaining the charging station location distribution information and charging gun allocation information of each subordinate sub-area according to the corresponding clustering result includes: The centroid positions of the clusters in the clustering results are used as the target construction positions of the charging stations in the charging station location distribution information; According to the ratio of the number of points of interest within each cluster to the total number of points of interest in the entire subordinate sub-area, the charging gun allocation ratio of the corresponding charging station target construction location is obtained, and according to the charging gun allocation ratio and the corresponding number of charging gun allocations in the subordinate sub-area, the charging gun allocation information is obtained; the charging gun allocation information includes the number of charging gun allocations at each charging station target construction location.

2. The method for optimizing the deployment of public charging stations according to claim 1, characterized in that: The step of obtaining the comprehensive proportion of each subordinate sub-region according to the population density and per capita GDP of each subordinate sub-region comprises: According to the population density and per capita GDP of each subordinate sub-region, the corresponding weighted comprehensive value is obtained; The weighted comprehensive values ​​of all subordinate sub-regions within the same subordinate region are accumulated to obtain the corresponding subordinate region comprehensive cumulative value; According to the ratio of the weighted comprehensive value of each subordinate sub-region and the corresponding subordinate region comprehensive cumulative value, the comprehensive proportion of each subordinate sub-region is obtained; the comprehensive proportion is expressed as: In the formula, in, Indicates In the subordinate area The comprehensive proportion of the subordinate sub-regions; Indicates The collection of sub-regions under each sub-region; , and Respectively represent In the subordinate area Population density, per capita GDP and weighted composite value of each sub-region; Represents the weight coefficient.

3. The method for optimizing the deployment of public charging stations according to claim 1, characterized in that: The step of obtaining a charging distance hierarchical evaluation result according to the charging station location distribution information of each subordinate sub-area, and updating and optimizing the charging station location distribution information and charging gun allocation information of each subordinate sub-area according to the charging distance hierarchical evaluation result includes: Obtaining a corresponding charging distance of a charging station according to an average value of distances between target construction locations of each charging station in the charging station location distribution information and each point of interest in the corresponding cluster; According to the average charging distance of all charging stations in the same subordinate sub-region, the corresponding subordinate sub-region charging distance is obtained; Calculate the distance deviation between the charging distance of each charging station and the charging distance of the corresponding subordinate sub-area, and compare and analyze the distance deviation with the upper and lower deviation thresholds; The cluster whose distance deviation value is greater than the upper deviation threshold is used as a charging station cluster to be added, and the charging station cluster to be added is divided into at least two cluster subclusters based on the K-means clustering algorithm, and the centroid position of the corresponding cluster subclusters is used as the target construction position of the new charging station; The cluster whose distance deviation value is less than the deviation lower limit threshold is taken as the charging station cluster to be reduced, the charging station cluster to be reduced is merged with the cluster to be merged to obtain a corresponding merged cluster, and the charging station target construction position of the merged cluster is re-determined based on the K-means clustering algorithm; the cluster to be merged is a cluster adjacent to the charging station cluster to be reduced and the distance deviation is within a preset range; The charging gun allocation information is updated and optimized according to the updated charging station location distribution information of the subordinate sub-areas.

4. The method for optimizing the deployment of public charging stations according to claim 1, characterized in that: The method further comprises: The average power of the public charging guns in the target area and the operational charging capacity of each charging station in each subordinate sub-area are periodically obtained, and the unit power load density of each charging station is obtained according to the operational charging capacity, the corresponding charging gun allocation information and the average power of the public charging guns. For the charging stations whose unit power load density exceeds the preset mean deviation range, the number of charging guns and the power of the charging guns are optimized and adjusted.

5. A public charging station deployment optimization system, characterized in that: The method for optimizing the deployment of public charging stations according to claim 1 is applied, wherein the system comprises: An information acquisition module is used to acquire the public charging station planning information and charging station deployment related information of the target area; the public charging station planning information includes the target charging station demand and the target charging gun demand; the charging station deployment related information includes the number of electric vehicles in the target area, the number of electric vehicles in each subordinate area in the target area, the population density of the subordinate sub-areas of each subordinate area, the per capita GDP and the point of interest information; A first planning module, configured to obtain first charging station planning information corresponding to each subordinate area according to the public charging station planning information, the number of electric vehicles in the target area, and the number of electric vehicles in each subordinate area within the target area; The second planning module is used to obtain the second charging station planning information of each subordinate sub-region according to the first charging station planning information of each subordinate region and the population density and per capita gross domestic product of each subordinate sub-region; A deployment analysis module is used to perform cluster analysis on the corresponding point of interest information according to the location data based on the second charging station planning information of each subordinate sub-area based on the K-means clustering algorithm, and obtain the charging station location distribution information and charging gun allocation information of each subordinate sub-area based on the corresponding clustering results; The deployment optimization module is used to obtain a hierarchical evaluation result of charging distances according to the location distribution information of charging stations in each subordinate sub-area, and update and optimize the location distribution information of charging stations and the charging gun allocation information of each subordinate sub-area according to the hierarchical evaluation result of charging distances; the hierarchical evaluation result of charging distances includes the charging distance of the charging station and the charging distance of the subordinate sub-area.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. 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 4 are implemented.

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