Improved voronoi diagram-based method for site selection and capacity determination of electric vehicle charging stations

Abstract

The present application relates to the technical field of charging station site selection, and particularly relates to a method for site selection and capacity determination of electric vehicle charging stations based on improved Voronoi diagram. The method comprises: in a plurality of node groups corresponding to a target road network, analyzing path lengths and congestion values of shortest travel paths between two different nodes included in each node group to obtain a plurality of first distances; and analyzing interval distances between each node group and a plurality of historical sites corresponding to the target road network to determine adjacent historical sites corresponding to each node group; correcting the first distances of the corresponding node groups according to site influence coefficients of the adjacent historical sites corresponding to each node group to obtain second distances of the corresponding node groups; generating a Voronoi diagram corresponding to the target road network according to the second distances, and generating site selection planning information based on vertices in the Voronoi diagram. The present application can output more accurate site selection planning information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging station site selection, and particularly relates to a trolley charging station site selection and capacity determination method based on an improved Voronoi diagram. BACKGROUND

[0002] At present, the importance of environmental protection is increasing, and the use rate of electric vehicles is also increasing because electric vehicles are mainly powered by on-board power sources and have less impact on the environment. Therefore, the demand for electric vehicle charging stations is also increasing.

[0003] In related technologies, the Euclidean distance is often used to divide the Voronoi diagram, and the spatial straight-line distance is used as the only standard for regional division. The service area of each charging station is simply defined as the geographical range closest to the station in terms of straight-line distance. However, in actual scenarios, the real service area of a charging station is not only affected by distance, but also by population density and related factors such as the construction of charging stations. Therefore, the Voronoi diagram constructed based solely on spatial distance cannot accurately guide the trolley charging station site selection and capacity determination.

[0004] That is, the accuracy of the trolley charging station site selection and planning information obtained based on the prior art is low. SUMMARY

[0005] In order to solve the technical problem of low accuracy of trolley charging station site selection and planning information obtained based on the prior art, the purpose of the present application is to provide a trolley charging station site selection and capacity determination method based on an improved Voronoi diagram. The technical solution adopted is as follows:

[0006] In a first aspect, an embodiment of the present application provides a trolley charging station site selection and capacity determination method based on an improved Voronoi diagram, which comprises:

[0007] In a plurality of node groups corresponding to the target road network, the path length and congestion value of the shortest travel path between two different nodes included in each node group are analyzed to obtain a plurality of first distances;

[0008] In the plurality of node groups, the interval distance between each node group and a plurality of historical sites corresponding to the target road network is analyzed to determine the adjacent historical sites corresponding to each node group, wherein the historical sites are used to indicate the charging stations that have been constructed in the target road network;

[0009] According to the site influence coefficient of the adjacent historical sites corresponding to each node group, the first distance of the corresponding node group is corrected to obtain the second distance of the corresponding node group, wherein the site influence coefficient is used to indicate the degree to which the historical sites affect the distance between adjacent different nodes;

[0010] According to the second distance, a Voronoi diagram corresponding to the target road network is generated, and site planning information is generated based on a vertex in the Voronoi diagram.

[0011] In one embodiment, the site influence coefficient of each historical site is obtained by:

[0012] In each historical period, a load difference between each historical site and its adjacent site is analyzed to obtain a plurality of load variation difference values corresponding to each historical site, the plurality of load variation difference values corresponding to each historical site corresponding to each historical period one by one, and the adjacent site is the historical site closest to the corresponding historical site in the plurality of historical sites.

[0013] In each historical period, the traffic volume of the target traffic path between each historical site and its adjacent site is analyzed to obtain a plurality of traffic density values corresponding to each historical site, the plurality of traffic density values corresponding to each historical site corresponding to each historical period one by one.

[0014] According to the plurality of load variation difference values and the plurality of traffic density values corresponding to each historical site, a site influence coefficient of each historical site is obtained.

[0015] In one embodiment, the site influence coefficient of each historical site is obtained according to the plurality of load variation difference values and the plurality of traffic density values corresponding to each historical site, including:

[0016] In each historical period, a ratio of the load variation difference value and the traffic density value corresponding to each historical site is calculated to obtain a plurality of load difference indexes corresponding to each historical site, the plurality of load difference indexes corresponding to each historical site corresponding to each historical period one by one.

[0017] According to the plurality of load difference indexes corresponding to each historical site and the sequence difference index corresponding to each historical site, a load turbulence degree of each historical site is obtained, wherein the sequence difference index is used to indicate a load data sequence difference between the historical site and its adjacent site.

[0018] The plurality of load turbulence degrees of the plurality of historical sites are analyzed to obtain a site influence coefficient of each historical site.

[0019] In one embodiment, the load turbulence degree of each historical site is obtained according to the plurality of load difference indexes corresponding to each historical site and the sequence difference index corresponding to each historical site, including:

[0020] An average absolute error of the plurality of load difference indexes corresponding to each historical site is calculated to obtain a load difference fluctuation degree corresponding to each historical site.

[0021] The ratio of the load difference fluctuation degree and the sequence difference index corresponding to each historical station is calculated to obtain the load disorder degree of each historical station.

[0022] In an embodiment, the multiple load disorder degrees of the multiple historical stations are analyzed to obtain a station influence coefficient of each historical station, including:

[0023] The demand coverage area of each historical station is obtained.

[0024] According to the demand coverage area of each historical station, multiple adjacent stations corresponding to each historical station are determined, wherein the adjacent stations are historical stations whose demand coverage areas are adjacent to the demand coverage area of the corresponding historical station.

[0025] The load disorder degree difference between each historical station and its adjacent stations is analyzed to obtain a station influence coefficient of each historical station.

[0026] In an embodiment, the load disorder degree difference between each historical station and its adjacent stations is analyzed to obtain a station influence coefficient of each historical station, including:

[0027] The difference between the load disorder degree of each historical station and the load disorder degree of each adjacent station corresponding to the historical station is calculated to obtain multiple first difference indices corresponding to each historical station.

[0028] The difference in demand coverage area between each historical station and each adjacent station corresponding to the historical station is analyzed to obtain multiple second difference indices corresponding to each historical station.

[0029] The ratio of each first difference index corresponding to each historical station and the corresponding second difference index is calculated to obtain multiple adjacent difference indices corresponding to each historical station.

[0030] The sum of the multiple adjacent difference indices corresponding to each historical station is calculated to obtain a station influence coefficient of each historical station.

[0031] In an embodiment, the difference in demand coverage area between each historical station and each adjacent station corresponding to the historical station is analyzed to obtain multiple second difference indices corresponding to each historical station, including:

[0032] The intersection of the demand coverage area of a first historical station and the demand coverage area of a first adjacent station is determined as a target boundary point, wherein the first historical station is any one of the multiple historical stations, and the first adjacent station is any one of the multiple adjacent stations corresponding to the first historical station.

[0033] determining a distance between the first historical station and the target boundary point as a first target distance, and a distance between the first adjacent station and the target boundary point as a second target distance;

[0034] calculating a difference between the first target distance and the second target distance to obtain a second difference index of the first adjacent station corresponding to the first historical station.

[0035] In one embodiment, the step of obtaining the congestion value of the shortest travel path between two different nodes included in each node group comprises:

[0036] calculating a ratio between an average speed and a maximum speed of each road segment included in the shortest travel path between the first node and the second node to obtain a road segment smoothness value of each road segment, wherein the first node and the second node are two different nodes included in any one of the plurality of node groups;

[0037] analyzing the road segment smoothness value of each road segment to obtain the congestion value of the shortest travel path between the first node and the second node.

[0038] In one embodiment, the step of obtaining the path smoothness value of the shortest travel path between the first node and the second node according to the road segment smoothness value of each road segment comprises:

[0039] the step of analyzing the road segment smoothness value of each road segment to obtain the congestion value of the shortest travel path between the first node and the second node comprises:

[0040] calculating a road length proportion of each road segment in the shortest travel path between the first node and the second node to obtain a road weight of each road segment;

[0041] performing weighted calculation on the road segment smoothness value of the plurality of road segments according to the road weight of each road segment to obtain the path smoothness value of the shortest travel path between the first node and the second node;

[0042] obtaining the congestion value of the shortest travel path between the first node and the second node according to the path smoothness value of the shortest travel path between the first node and the second node, wherein a sum value of the path smoothness value and the corresponding congestion value is 1.

[0043] In one embodiment, the step of correcting the first distance of the corresponding node group according to the station influence coefficient of the corresponding adjacent historical station of each node group to obtain the second distance of the corresponding node group comprises:

[0044] The first distance of each node group is multiplied by a site influence coefficient of a corresponding adjacent historical site to obtain a second distance of each node group.

[0045] In a second aspect, another embodiment of the present application provides a device for improved Voronoi diagram-based electric vehicle charging station site selection and capacity determination, which comprises:

[0046] A node analysis module is configured to analyze, in a plurality of node groups corresponding to a target road network, a path length of a shortest travel path between two different nodes included in each node group and a congestion value to obtain a plurality of first distances.

[0047] A historical analysis module is configured to analyze, in the plurality of node groups, a separation distance between each node group and a plurality of historical sites corresponding to the target road network to determine a corresponding adjacent historical site for each node group, wherein the historical sites are used to indicate constructed charging stations in the target road network.

[0048] A distance correction module is configured to correct the first distance of each node group according to a site influence coefficient of the corresponding adjacent historical site for the node group to obtain a second distance of the corresponding node group, wherein the site influence coefficient is used to indicate a degree to which the historical site influences the distance between the adjacent different nodes.

[0049] An information generation module is configured to generate a Voronoi diagram corresponding to the target road network according to the plurality of second distances and generate site planning information based on vertices in the Voronoi diagram, wherein the site planning information is used to indicate positions of to-be-constructed charging stations in the target road network.

[0050] In a third aspect, another embodiment of the present application further provides an electronic device, which comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program is executed by the processor to implement the steps of the method in the first aspect.

[0051] In a fourth aspect, another embodiment of the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method in the first aspect.

[0052] The present application has the following advantages:

[0053] The present application determines the first distance between different nodes by analyzing the path length of the shortest travel path and the road congestion degree between different nodes in the target road network, that is, the travel distance between different nodes is quantified, and then the degree of influence of the nearest constructed charging station on different nodes is analyzed, the first distance is corrected to obtain the corresponding second distance, and the charging station site selection planning is carried out according to the second distance. Since the calculation of the second distance not only considers the length of the shortest travel path between different nodes, but also fully considers the road congestion and the influence of the constructed charging station on the length of the shortest travel path, the distance between different nodes in the charging scenario can be accurately represented, and the site selection planning information output according to the distance is more accurate and reliable. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0055] Figure 1 A schematic flow chart of a trolley charging station site selection and capacity determination method based on an improved Voronoi diagram provided by an embodiment of the present application;

[0056] Figure 2 A structural schematic diagram of a trolley charging station site selection and capacity determination device based on an improved Voronoi diagram provided by an embodiment of the present application;

[0057] Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object, the specific embodiments, structures, features and effects of a trolley charging station site selection and capacity determination method based on an improved Voronoi diagram according to the present application are described in detail as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0060] The application provides a specific scheme of a method for selecting and determining the capacity of a trolley charging station based on an improved Voronoi diagram.

[0061] The application provides a method for selecting and determining the capacity of a trolley charging station based on an improved Voronoi diagram. Figure 1 Fig. 1 shows a schematic flowchart of a method for selecting and determining the capacity of a trolley charging station based on an improved Voronoi diagram according to an embodiment of the application, which comprises the following steps:

[0062] In step S1, the path length and congestion value of the shortest travel path between two different nodes included in each node group in a plurality of node groups corresponding to a target road network are analyzed to obtain a plurality of first distances.

[0063] The plurality of first distances and the plurality of node groups correspond to each other, and the congestion value is used to indicate the road congestion degree of the corresponding shortest travel path.

[0064] The target road network can be understood as a road network corresponding to any region to be planned for trolley charging station site selection.

[0065] In the application, the target road network comprises a plurality of nodes and edges connecting the nodes, wherein the nodes represent road junctions or entrances of population gathering areas (such as commercial buildings, hotels, schools, residential areas, etc.) in the region indicated by the target road network, and the edges represent roads connecting the entrances of different road junctions or population gathering areas. The plurality of node groups can be understood as all node groups formed by the two-by-two combination of any different nodes in the plurality of nodes included in the target road network.

[0066] In applications, the relevant information (such as the positions of the nodes included and the lengths of the edges included) of the target road network can be obtained through various open source platforms (such as the OpenStreetMap platform), or the target road network can be manually drawn through the QGIS tool.

[0067] For example, the shortest travel path between two different nodes included in each node group can be obtained through a path planning algorithm (such as the Floyd-Warshall algorithm).

[0068] The path length of the shortest travel path between two different nodes included in each node group is used to represent the shortest travel distance between the entrances of two different road junctions or population gathering areas indicated by the two different nodes included in each node group. That is, the greater the path length, the greater the corresponding shortest travel distance.

[0069] The higher the congestion value, the more serious the road congestion degree of the corresponding shortest travel path.

[0070] In one embodiment, the step of obtaining the congestion value of the shortest travel path between two different nodes included in each node group comprises:

[0071] In the shortest travel path between the first node and the second node, the ratio of the average speed and the maximum speed of each road segment is calculated to obtain the road segment smoothness value of each road segment, wherein the first node and the second node are two different nodes included in any one of the plurality of node groups.

[0072] The road segment smoothness value of each road segment is analyzed to obtain the congestion value of the shortest travel path between the first node and the second node.

[0073] The average speed can be understood as the average value of a plurality of speed data monitored in a historical period corresponding to the road segment, wherein the speed data monitored in the historical period of each road segment can be obtained through a data opening platform of the traffic department, or through a corresponding data opening interface of each navigation program.

[0074] The maximum speed can be understood as the highest speed set by the traffic department for the corresponding road segment.

[0075] The higher the road smoothness value, the higher the average speed of the corresponding road segment in the historical period, that is, the higher the road traffic efficiency of the corresponding road segment in the historical period, and the lighter the degree of road congestion.

[0076] In this embodiment, by analyzing the speed of each road segment in the shortest travel path and calculating the ratio of the average speed and the maximum speed of each road segment, the road smoothness of each road segment is determined, and the congestion value of the shortest travel path is calculated by comprehensively considering the road segment smoothness value of each road segment. The accuracy of the calculated congestion value can be guaranteed, wherein the average speed of each road segment is standardized by taking the maximum speed of each road segment as the denominator, which can effectively balance the differences in traffic conditions between different road segments, making the evaluation of the road smoothness of each road segment more accurate and reliable.

[0077] Further, the step of analyzing the road segment smoothness value of each road segment to obtain the congestion value of the shortest travel path between the first node and the second node comprises:

[0078] The road length proportion of each road segment in the shortest travel path between the first node and the second node is calculated to obtain the road weight of each road segment.

[0079] weighting and calculating the link passable values of the plurality of road segments according to the road weights of each of the road segments to obtain a path passable value of the shortest passable path between the first node and the second node;

[0080] obtaining a congestion value of the shortest passable path between the first node and the second node according to the path passable value of the shortest passable path between the first node and the second node, wherein a sum value of the path passable value and the corresponding congestion value is 1.

[0081] wherein the road weight of each of the road segments is a ratio of a road length of each of the road segments and a path length of the corresponding shortest passable path.

[0082] Exemplarily, if the path passable value of the shortest passable path between the first node and the second node is set as Q, the congestion value of the shortest passable path between the first node and the second node is (1-Q).

[0083] In the above setting, when evaluating the passable degree of the shortest passable path between different nodes, not only the road passable degree of each of the road segments is considered, but also the link length of each of the road segments is further considered to accurately quantify the passable degree of the shortest passable path between different nodes from two aspects of link length and average vehicle speed, which can make the determined congestion value more accurate and reliable.

[0084] In one example, the process of analyzing the path length and the congestion value of the shortest passable path between two different nodes included in each node group to obtain a plurality of first distances can be:

[0085] normalizing the path length of the shortest passable path between two different nodes included in each node group to obtain a path normalized value of the shortest passable path between two different nodes included in each node group;

[0086] normalizing the congestion value of the shortest passable path between two different nodes included in each node group to obtain a congestion normalized value of the shortest passable path between two different nodes included in each node group;

[0087] weighting and calculating the path normalized value and the congestion normalized value of the shortest passable path between two different nodes included in each node group to obtain the plurality of first distances.

[0088] In the example, the normalization process can be completed by using the maximum-minimum normalization algorithm, the weight of the path length and the weight of the congestion value corresponding to different node groups remain consistent, and the weight of the path length and the weight of the congestion value can be set based on experience values, for example, the weight of the path length is set to 0.7, and the weight of the congestion value is set to 0.3 (the sum of the weight of the path length and the weight of the congestion value is 1).

[0089] In step S2, the interval distance between each node group and the plurality of historical sites corresponding to the target road network is analyzed to determine the adjacent historical site corresponding to each node group.

[0090] The historical site is used to indicate the charging station that has been constructed in the target road network, and the interval distance between the adjacent historical site and the corresponding node group is smaller than the interval distance between other historical sites and the corresponding node group.

[0091] In the present application, the interval distance between the node group and the historical site can be understood as the sum of the distances between two different nodes in the node group and the historical site.

[0092] In step S3, the first distance of the corresponding node group is corrected according to the site influence coefficient of the adjacent historical site corresponding to each node group to obtain the second distance of the corresponding node group.

[0093] The site influence coefficient is used to indicate the degree to which the historical site influences the distance between adjacent different nodes.

[0094] In the process, the nearest historical site to each node group is identified, and the influence degree of each node group on the corresponding adjacent historical site is analyzed, and the first distance of each node group is corrected accordingly, so that the quantification of the distance between different nodes in the charging scenario is more accurate.

[0095] Specifically, the first distance of the corresponding node group is corrected according to the site influence coefficient of the adjacent historical site corresponding to each node group to obtain the second distance of the corresponding node group, including:

[0096] The product of the first distance of each node group and the site influence coefficient of the adjacent historical site corresponding thereto is calculated to obtain the second distance of each node group.

[0097] For example, if the second distance of the node group composed of the i th node and the j th node in the target road network is set to d ij, the site influence coefficient of the adjacent historical site corresponding to the node group is set to a ij, and the first distance of the node group is set to d ij 0, then the second distance of the node group can be represented as:

[0098]

[0099] wherein, ​a path length representing a shortest travel path between an i-th node and a j-th node, a congestion value representing a shortest travel path between an i-th node and a j-th node, a site influence coefficient representing a neighboring historical site corresponding to a node group composed of an i-th node and a j-th node, and weights of the path length and the congestion value, respectively.

[0100] In step S4, a Voronoi diagram corresponding to the target road network is generated according to the plurality of second distances, and site planning information is generated based on vertices in the Voronoi diagram.

[0101] The site planning information is used to indicate a location of a charging station to be constructed in the target road network.

[0102] Exemplarily, the plurality of second distances can be processed by an incremental construction method, a divide-and-conquer method, or a Fortune algorithm to generate the Voronoi diagram corresponding to the target road network. The Voronoi diagram includes a plurality of vertices, each of which corresponds to a coverage area in which each node is closest to the corresponding vertex (compared to a distance from each node to other vertices outside the vertex corresponding to the coverage area).

[0103] The process of generating the site planning information based on the vertices in the Voronoi diagram can be as follows:

[0104] The vertices in the Voronoi diagram are all determined as virtual candidate sites to obtain a plurality of virtual candidate sites;

[0105] The nodes included in the coverage area corresponding to each virtual candidate site are all determined as associated nodes to obtain a plurality of associated nodes of each virtual candidate site;

[0106] A convex hull corresponding to the plurality of associated nodes of each virtual candidate site in the target road network is obtained to obtain a plurality of convex hull regions;

[0107] Among the plurality of convex hull regions, a convex hull region including a historical site is excluded to obtain a target convex hull region;

[0108] The target convex hull region is taken as a region in which a charging station is to be constructed, and the site planning information is generated.

[0109] The location of the charging station to be constructed in the target road network can be a center point position of the corresponding target convex hull region, or a position in the corresponding target convex hull region at which a land price cost of constructing a charging station is the lowest, or a position in the corresponding target convex hull region at which a constructible area (i.e., a capacity of the charging station after construction) is the largest.

[0110] The present application determines the first distance between different nodes in the target road network by analyzing the path length of the shortest travel path between different nodes in the target road network and the road congestion degree, that is, the travel distance between different nodes is quantified, and then the influence of the nearest constructed charging station on different nodes is analyzed, the first distance is corrected to obtain the corresponding second distance, and the charging station site planning is carried out according to the second distance. Since the calculation of the second distance not only considers the length of the shortest travel path between different nodes, but also fully considers the road congestion and the influence of the constructed charging station on the length of the shortest travel path, the distance between different nodes in the charging scenario can be accurately represented, and the site planning information output according to the distance is more accurate and reliable.

[0111] In one embodiment, the step of obtaining the station influence coefficient of each historical station includes:

[0112] In each historical period, the load difference between each historical station and its adjacent station is analyzed to obtain a plurality of load change difference values corresponding to each historical station. The plurality of load change difference values corresponding to each historical station correspond one-to-one to the plurality of historical periods. The adjacent station is the historical station closest to the corresponding historical station among the plurality of historical stations.

[0113] In each historical period, the traffic flow of the target travel path between each historical station and its adjacent station is analyzed to obtain a plurality of traffic density values corresponding to each historical station. The plurality of traffic density values corresponding to each historical station correspond one-to-one to the plurality of historical periods.

[0114] According to the plurality of load change difference values and the plurality of traffic density values corresponding to each historical station, the station influence coefficient of each historical station is obtained.

[0115] During the use of the charging station, the factors affecting the user's going to the charging station for charging are complex, such as the location of the charging station, the maximum capacity of the charging station that can be simultaneously charged, the electricity price provided by the corresponding operator of the charging station, the parking fee of the charging station, the proportion of the plurality of charging positions of the charging station being abnormally occupied by non-charging vehicles, and the number of vehicles in the residential area adjacent to the charging station. This makes it difficult to evaluate the influence of the historical station on the charging demand of the surrounding area from the user side.

[0116] To solve the above problem, the embodiment proposes to evaluate the influence of the historical station on the charging demand of the surrounding area from the station side by analyzing the load difference and traffic flow between adjacent stations, which can bypass the problem of many associated factors involved from the user side and ensure the accuracy of the calculated station influence coefficient.

[0117] The load variation difference is used to represent the absolute value of the difference between the load variation value of the corresponding historical station in the corresponding historical period and the load variation value of the adjacent station (the historical station with the shortest path between the corresponding historical station and the adjacent station) in the corresponding historical period. The load variation value is the absolute value of the difference between the load amount (i.e., the number of electric vehicles being charged) of the corresponding historical station at the start time of the corresponding historical period and the load amount at the end time. In the application, the current, voltage and other data of the charging pile in the historical station can be collected by the built-in sensor, and uploaded to the edge gateway through the MQTT / OCPP protocol to determine the load amount of each historical station at each time.

[0118] The traffic density value is used to represent the average value of the traffic density data of the f target reachable paths between the corresponding historical station and the adjacent station in the corresponding historical period, wherein the f target reachable paths are the first f reachable paths with the shortest path between the corresponding historical station and the adjacent station among the multiple reachable paths, f is an integer greater than 1 (for example, f can be equal to 5), and the traffic density data is the total number of vehicles passing through each road segment per hour in the multiple road segments (a connection between two nodes is a road segment) included in the target reachable path.

[0119] Further, the site influence coefficient of each historical station is obtained according to the multiple load variation difference values and the multiple traffic density values corresponding to each historical station, comprising:

[0120] In each historical period, the ratio of the load variation difference value and the traffic density value corresponding to each historical station is calculated to obtain multiple load difference indexes corresponding to each historical station, and the multiple load difference indexes corresponding to each historical station correspond one-to-one to the multiple historical periods;

[0121] The load turbulence degree of each historical station is obtained according to the multiple load difference indexes corresponding to each historical station and the sequence difference index corresponding to each historical station, wherein the sequence difference index is used to indicate the sequence difference of the load data between the historical station and its adjacent station.

[0122] The site influence coefficient of each historical station is obtained by analyzing the multiple load turbulence degrees of the multiple historical stations.

[0123] The multiple historical periods can be obtained by dividing the historical time period based on a preset period length. For example, if the preset period length is set to 1 hour and the historical time period is 7 days, 168 historical periods can be obtained.

[0124] The load data sequence can be understood as a sequence of multiple load variation values of the corresponding historical station in multiple historical time periods. Correspondingly, the sequence difference index can be understood as a dynamic time warping (DTW) distance between the load data sequence of the corresponding historical station and the load data sequence of the adjacent station.

[0125] The load difference index is used to represent the difference in actual charging load between the corresponding historical station and the adjacent station in the corresponding historical time period.

[0126] For example, the load difference index of the ith historical station in the multiple historical stations in the tth historical time period of the multiple historical time periods can be represented as:

[0127]

[0128] wherein, represents the load variation difference value of the ith historical station in the tth historical time period, represents the traffic density value of the ith historical station in the tth historical time period, represents a normalization function (such as a max-min normalization function).

[0129] It should be understood that the larger the load variation difference value, the greater the difference in load variation between the corresponding historical station and the adjacent station in the corresponding historical time period, and the larger the corresponding load difference index.

[0130] Similarly, the larger the traffic density value, the more vehicles passing between the corresponding historical station and the adjacent station in the corresponding historical time period, indicating that the corresponding historical station and its adjacent station have a higher degree of cooperation in providing charging services for the surrounding area, and the smaller the corresponding load difference index.

[0131] Further, the load disorder degree of each historical station is obtained according to the multiple load difference indexes corresponding to each historical station and the sequence difference index corresponding to each historical station, including:

[0132] calculating the mean absolute error of the multiple load difference indexes corresponding to each historical station to obtain the load difference fluctuation degree corresponding to each historical station;

[0133] calculating the ratio of the load difference fluctuation degree corresponding to each historical station to the sequence difference index to obtain the load disorder degree of each historical station.

[0134] For example, the load disorder degree of the ith historical station in the multiple historical stations can be represented as:

[0135] ​​

[0136] wherein, represents the mean value of the plurality of load difference indexes of the i-th historical site, represents the load difference index of the i-th historical site in the k-th historical period in the plurality of historical periods, and K represents the total number of the plurality of historical periods, represents the load difference fluctuation value corresponding to the i-th historical site, represents the sequence difference index of the i-th historical site, represents the load data sequence of the i-th historical site, represents the load data sequence of the adjacent site of the i-th historical site.

[0137] In the flow, the calculation based on the mean absolute error is used to determine the fluctuation of the load difference between the historical site and its adjacent site, and the calculation of the sequence difference index is combined to accurately realize the quantitative representation of the actual load of the historical site.

[0138] It should be noted that in actual application, due to the significant difference in the maximum load capacity (i.e. the maximum number of vehicles that can be charged simultaneously) of different historical sites, it is difficult to uniformly quantify the actual load of different historical sites. Based on this, the present application proposes to calculate the load disorder degree to combine the sequence difference and load difference fluctuation of the load data sequence between the historical site and its adjacent site, thereby accurately realizing the uniform quantification of the actual load of historical sites with different capacities. Even for two historical sites with significant capacity difference and adjacent to each other, the corresponding output load disorder degree can still be kept at a low value.

[0139] Further, the plurality of load disorder degrees of the plurality of historical sites are analyzed to obtain a site influence coefficient of each historical site, comprising:

[0140] obtaining a demand coverage area of each historical site;

[0141] determining a plurality of adjacent sites corresponding to each historical site according to the demand coverage area of each historical site, wherein the adjacent site is a historical site whose demand coverage area and the demand coverage area of the corresponding historical site are adjacent;

[0142] analyzing the load disorder degree difference between each historical site and its adjacent site to obtain a site influence coefficient of each historical site.

[0143] The demand coverage area of the historical site satisfies the following condition: the distance from any node in the demand coverage area to the corresponding historical site is less than or equal to the distance to other historical sites.

[0144] In the application, after the positions of the plurality of historical stations are determined, the distance from any node in the demand coverage area to the corresponding historical station can be less than the distance to other historical stations, which can be used as a basis for regional division, so as to divide the target road network (for example, using a Voronoi Diagram algorithm) and obtain the demand coverage area of each historical station.

[0145] The demand coverage areas are connected, which means that the demand coverage areas of different historical stations have overlapping parts.

[0146] In one embodiment, the analysis of the difference in load turbulence between each historical station and its adjacent station obtains a station influence coefficient of each historical station, including:

[0147] The difference between the load turbulence of each historical station and the load turbulence of each adjacent station corresponding to the historical station is calculated to obtain a plurality of first difference indexes corresponding to each historical station;

[0148] The difference between the demand coverage area of each historical station and the demand coverage area of each adjacent station corresponding to the historical station is analyzed to obtain a plurality of second difference indexes corresponding to each historical station;

[0149] The ratio of each first difference index corresponding to each historical station to the corresponding second difference index is calculated to obtain a plurality of adjacent difference indexes corresponding to each historical station;

[0150] The sum of the plurality of adjacent difference indexes corresponding to each historical station is calculated to obtain a station influence coefficient of each historical station.

[0151] It is to be explained that, in an ideal case, the plurality of charging stations arranged in the target area should be able to evenly and sufficiently cover the charging demand in each area, so the actual load between the plurality of constructed charging stations should tend to be consistent, in particular, the actual load of adjacent charging stations should tend to be consistent. If the actual load difference of the plurality of charging stations adjacent to the demand coverage area is large (i.e., the difference of the load disorder degree is large), it indicates that the charging demand balance in the area where the charging station is located is not ideal, so the existence of the corresponding charging station has a strong influence on the distance calculation between the adjacent nodes and other nodes, and the mapping distance between different nodes needs to be adaptively increased (which is intended to reflect the distance between different nodes in terms of charging demand, not the real physical distance between different nodes), so that more vertices (i.e., more charging stations) can be generated in the corresponding area when generating the Voronoi diagram later, thereby better balancing the charging demand in the corresponding area. Correspondingly, if the actual load difference of adjacent charging stations is small, it indicates that the charging demand in the area corresponding to the charging station can be effectively balanced, so the existence of the corresponding charging station has a weak influence on the distance calculation between the adjacent nodes and other nodes, and the mapping distance between different nodes needs to be adaptively reduced, so that fewer vertices can be generated in the corresponding area when generating the Voronoi diagram later.

[0152] Specifically, the difference between the demand coverage area of each historical station and each adjacent station corresponding thereto is analyzed to obtain a plurality of second difference indexes corresponding to each historical station, including:

[0153] The intersection of the demand coverage area of the first historical station and the demand coverage area of the first adjacent station is determined as the target boundary point, wherein the first historical station is any one of the plurality of historical stations, and the first adjacent station is any one of the plurality of adjacent stations corresponding to the first historical station.

[0154] The distance between the first historical station and the target boundary point is determined as the first target distance, and the distance between the first adjacent station and the target boundary point is determined as the second target distance.

[0155] The difference between the first target distance and the second target distance is calculated to obtain the second difference index of the first adjacent station corresponding to the first historical station.

[0156] It is to be explained that when there are a plurality of intersections of the demand coverage area of the first historical station and the demand coverage area of the first adjacent station, one of the nodes can be selected as the target boundary point of the first historical station and the first adjacent station.

[0157] Exemplarily, the station influence coefficient of the i-th historical station in the plurality of historical stations can be represented as: ​

[0158]

[0159] wherein, represents the load turbulence degree of the i-th historical site, represents the load turbulence degree of the j-th adjacent site corresponding to the i-th historical site, represents the distance between the i-th historical site and a target point (the intersection of the demand coverage area of the i-th historical site and the demand coverage area of the j-th adjacent site of the i-th historical site), represents the distance between the j-th adjacent site corresponding to the i-th historical site and the target point, and J represents the number of sites of the plurality of adjacent sites corresponding to the i-th historical site.

[0160] The present application provides a device for selecting the location and capacity of a trolley charging station based on an improved Voronoi diagram. Figure 2 which shows a structure diagram of a device 200 for selecting the location and capacity of a trolley charging station based on an improved Voronoi diagram according to an embodiment of the present application, the device comprising:

[0161] a node analysis module 201 configured to analyze the path length and congestion value of the shortest travel path between two different nodes included in each node group in a plurality of node groups corresponding to a target road network, to obtain a plurality of first distances, wherein the plurality of first distances and the plurality of node groups correspond to each other one by one, and the congestion value is used to indicate the road congestion degree of the corresponding shortest travel path;

[0162] a historical analysis module 202 configured to analyze the interval distance between each node group and a plurality of historical sites corresponding to the target road network in the plurality of node groups, to determine the adjacent historical sites corresponding to each node group, wherein the historical sites are used to indicate the charging stations that have been constructed in the target road network, and the interval distance between the adjacent historical sites and the corresponding node group is smaller than the interval distance between other historical sites and the corresponding node group;

[0163] a distance correction module 203 configured to correct the first distance of each node group according to the site influence coefficient of the adjacent historical sites corresponding to the node group, to obtain the second distance of the corresponding node group, wherein the site influence coefficient is used to indicate the degree to which the historical site influences the distance between different adjacent nodes;

[0164] an information generation module 204 configured to generate a Voronoi diagram corresponding to the target road network according to a plurality of second distances, and generate location planning information based on the vertices in the Voronoi diagram, wherein the location planning information is used to indicate the position of the charging station to be constructed in the target road network.

[0165] It should be noted that the apparatus provided in the above examples is only used for example by dividing the above function modules, and in actual application, the above functions can be completed by different function modules according to needs, that is, the internal structure of the computer device is divided into different function modules to complete all or part of the functions described above. In addition, the improved Voronoi diagram-based trolley charging station site selection and capacity determination device and the improved Voronoi diagram-based trolley charging station site selection and capacity determination method provided in the above examples belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0166] The embodiment of the present application also provides an electronic device. Please refer to Figure 3 The electronic device can include a processor 301, a memory 302, and a program 3021 stored in the memory 302 and executable on the processor 301.

[0167] The program 3021, when executed by the processor 301, can implement Figure 1 Any step in the corresponding method embodiment and achieve the same beneficial effects, which will not be repeated here.

[0168] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiment methods can be completed by program instructions related to hardware, and the program can be stored in a readable medium.

[0169] The embodiment of the present application also provides a readable storage medium, and the readable storage medium stores a computer program, and the computer program is executable by the processor to implement any step in the above-mentioned Figure 1 The corresponding method embodiment, and the same technical effects can be achieved, to avoid repetition, which will not be repeated here.

[0170] The computer readable storage medium of the embodiment of the present application can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: electrical connections having one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0171] Computer readable signal media can include a propagated data signal with computer readable program code embodied therein. For example, a propagated signal can be an electromagnetic signal, an optical signal, and / or any suitable combination thereof. Computer readable program code embodied on a computer readable medium can be read and executed by a computer to cause the computer to perform various operations as described herein.

[0172] A computer readable medium storing the program code can be transmitted or received over any suitable medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination thereof.

[0173] The computer program code for carrying out operations of the present application can be written in any suitable programming language such as object oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0174] The embodiments of the present application also provide a computer program product, which, when running on a computer, causes the computer to execute the above related steps to realize the method for selecting and determining the capacity of the electric vehicle charging station based on the improved Voronoi diagram provided by the above embodiments.

[0175] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0176] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

Claims

1. A method for site selection and capacity determination of trolley charging stations based on an improved Voronoi diagram, characterized in that, The method includes: In the target road network, the path length and congestion value of the shortest travel path between two different nodes in each node group are analyzed to obtain multiple first distances. In the plurality of node groups, the distance between each node group and the plurality of historical stations corresponding to the target road network is analyzed to determine the neighboring historical stations corresponding to each node group, wherein the historical stations are used to indicate the charging stations that have been built in the target road network; Based on the site influence coefficient of the neighboring historical sites corresponding to each node group, the first distance of the corresponding node group is corrected to obtain the second distance of the corresponding node group, wherein the site influence coefficient is used to indicate the degree to which the historical site affects the distance between different neighboring nodes; A Voronoi diagram corresponding to the target road network is generated based on multiple second distances, and location planning information is generated based on the vertices in the Voronoi diagram. The steps for obtaining the site influence coefficient for each historical site include: In each historical period, the load difference between each historical station and its neighboring stations is analyzed to obtain multiple load variation differences corresponding to each historical station. The multiple load variation differences corresponding to each historical station correspond one-to-one with the multiple historical periods. The neighboring stations are the historical stations that are closest to the corresponding historical station among the multiple historical stations. The load variation difference is used to represent: the absolute difference between the load variation value of the corresponding historical station in the corresponding historical period and the load variation value of its neighboring stations in the corresponding historical period. The load variation value is the absolute difference between the load amount of the corresponding historical station at the beginning time and the load amount at the end time of the corresponding historical period. The load amount is used to represent the number of electric vehicles charging in the historical station at the corresponding time. In each historical period, the traffic flow of the target passage route between each historical station and its adjacent stations is analyzed to obtain multiple traffic density values ​​corresponding to each historical station. The multiple traffic density values ​​corresponding to each historical station correspond one-to-one with the multiple historical periods. The site impact coefficient for each historical site is obtained based on multiple load variation differences and multiple traffic density values ​​corresponding to each historical site. The step of obtaining the site impact coefficient for each historical site based on multiple load variation differences and multiple traffic density values ​​corresponding to each historical site includes: In each historical period, the ratio of the load variation difference to the traffic density value corresponding to each historical station is calculated to obtain multiple load difference indices corresponding to each historical station. The multiple load difference indices corresponding to each historical station correspond one-to-one with the multiple historical periods. The load disorder of each historical site is obtained by using multiple load difference indices and sequence difference indices corresponding to each historical site. The sequence difference index is used to indicate the load data sequence difference between the historical site and its neighboring sites. The load disorder of multiple historical sites was analyzed to obtain the site impact coefficient for each historical site.

2. The method for site selection and capacity determination of trolley charging stations based on improved Voronoi diagrams according to claim 1, characterized in that, The process of obtaining the load disorder degree of each historical site based on multiple load difference indices and the sequence difference index corresponding to each historical site includes: Calculate the average absolute error of multiple load difference indices corresponding to each historical site to obtain the load difference volatility corresponding to each historical site; Calculate the ratio of load variation volatility to sequence variation index for each historical site to obtain the load disorder of each historical site.

3. The method for site selection and capacity determination of trolley charging stations based on improved Voronoi diagrams according to claim 1, characterized in that, The analysis of multiple load disturbance degrees at multiple historical sites yields a site impact coefficient for each historical site, including: Obtain the required coverage area for each of the aforementioned historical sites; Based on the required coverage area of ​​each historical site, determine multiple adjacent sites corresponding to each historical site, wherein the adjacent sites are historical sites whose required coverage areas and the required coverage areas of the corresponding historical sites are adjacent. The load disorder difference between each historical site and its neighboring sites is analyzed to obtain the site influence coefficient of each historical site.

4. The method for site selection and capacity determination of trolley charging stations based on improved Voronoi diagrams according to claim 3, characterized in that, The analysis of the load disorder differences between each historical site and its neighboring sites yields the site influence coefficient for each historical site, including: Calculate the difference between the load disorder of each historical site and the load disorder of each of its adjacent sites to obtain multiple first difference indices for each historical site. Analyze the differences in demand coverage areas between each historical site and each of its adjacent sites to obtain multiple second difference indices for each historical site; Calculate the ratio of each first difference index to the corresponding second difference index for each historical site to obtain multiple adjacency difference indices for each historical site. The sum of multiple adjacency difference indices corresponding to each historical site is calculated to obtain the site influence coefficient of each historical site.

5. The method for site selection and capacity determination of trolley charging stations based on improved Voronoi diagrams according to claim 4, characterized in that, The analysis of the differences in demand coverage areas between each historical site and its corresponding neighboring sites yields multiple second difference indices for each historical site, including: The intersection of the demand coverage area of ​​the first historical site and the demand coverage area of ​​the first adjacent site is determined as the target boundary point, wherein the first historical site is any one of the plurality of historical sites, and the first adjacent site is any one of the plurality of adjacent sites corresponding to the first historical site. The distance between the first historical station and the target boundary point is determined as the first target distance, and the distance between the first adjacent station and the target boundary point is determined as the second target distance; Calculate the difference between the first target distance and the second target distance to obtain the second difference index of the first neighboring station corresponding to the first historical station.

6. The method for site selection and capacity determination of trolley charging stations based on improved Voronoi diagrams according to claim 1, characterized in that, The steps for obtaining the congestion value of the shortest path between two different nodes in each node group include: In the shortest path between the first node and the second node, the ratio of the average speed to the maximum speed of each road segment is calculated to obtain the road segment smoothness value of each road segment. The first node and the second node are two different nodes included in any one of the multiple node groups. Analyze the traffic flow value of each road segment to obtain the congestion value of the shortest travel path between the first node and the second node.

7. The method for site selection and capacity determination of trolley charging stations based on improved Voronoi diagrams according to claim 6, characterized in that, The analysis of the traffic flow value of each road segment to obtain the congestion value of the shortest travel path between the first node and the second node includes: Calculate the proportion of road length of each road segment in the shortest travel path between the first node and the second node to obtain the road weight of each road segment; Based on the road weight of each road segment, the road accessibility values ​​of the multiple road segments are weighted and calculated to obtain the path accessibility value of the shortest travel path between the first node and the second node. Based on the path smoothness value of the shortest path between the first node and the second node, the congestion value of the shortest path between the first node and the second node is obtained, wherein the sum of the path smoothness value and the corresponding congestion value is 1.

8. The method for site selection and capacity determination of trolley charging stations based on improved Voronoi diagrams according to claim 1, characterized in that, The step of correcting the first distance of a corresponding node group based on the site influence coefficient of its neighboring historical sites to obtain the second distance of the corresponding node group includes: The second distance of each node group is obtained by multiplying the first distance of each node group by the site influence coefficient of its corresponding neighboring historical sites.

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