New energy vehicle charging pile space layout method and operation management platform

Abstract

The invention relates to the technical field of new energy vehicles, and provides a new energy vehicle charging pile space layout method and an operation management platform, and the method comprises the steps: obtaining the position attribute of an existing charging pile in a target region; according to a first clustering result of the position attributes of the existing charging piles, determining a hot spot area; determining candidate sites based on the hot spot area and a preset site selection strategy; creating a virtual charging pile for each candidate construction site, and collecting user demand data based on each virtual charging pile; and screening out a target charging pile from the virtual charging piles based on the user demand data, and generating a charging pile layout plan according to the target charging pile. On the basis of the method, a reasonable charging pile layout plan can be obtained in the new charging pile construction preparation stage, and effective suggestions are provided for site selection of the charging piles.

Description

[0001] Technical Field The present application relates to the technical field of new energy vehicles, and in particular to a method for spatial layout of new energy vehicle charging piles, an operation management platform and an electronic device. Background Art

[0002] With the development of urban construction, the construction of healthy cities has become an important guiding principle for urban construction planning. Healthy cities emphasize reducing dependence on traditional fuel vehicles and reducing air pollution and greenhouse gas emissions. As an essential infrastructure for new energy vehicles, the popularization and convenience of charging piles can encourage more residents to choose electric vehicles as a means of transportation, thereby reducing the negative impact on the environment and promoting green travel. At the same time, as an important supporting facility for the new energy vehicle industry, the technological innovation and industrial development of new energy vehicle charging piles play an important role in promoting the optimization and upgrading of the urban economic structure. The popularization and reasonable layout of new energy vehicle charging piles can provide more convenient charging services, reduce the "range anxiety" of electric vehicle users, and improve their travel experience and quality of life.

[0003] Therefore, under the guidance of healthy cities, how to reasonably plan the spatial layout of charging piles has become a key research topic in the industry. Summary of the Invention

[0004] In order to reasonably plan the spatial layout of charging piles and reduce the charging pressure in hot spots, the embodiments of the present application provide a method for spatial layout of new energy vehicle charging piles, an operation management platform and an electronic device.

[0005] The method for spatial layout of new energy vehicle charging piles provided by the present application is applied to an operation management platform for new energy vehicle charging piles, and includes the steps of: obtaining the location attributes of existing charging piles in a target area; determining a hot spot area according to a first clustering result of the location attributes of each of the existing charging piles; determining candidate site construction points based on the hot spot area and a preset site selection strategy; creating virtual charging piles for each of the candidate site construction points, and collecting user demand data based on each of the virtual charging piles; screening out target charging piles from each of the virtual charging piles based on the user demand data, and generating a charging pile layout plan according to the target charging piles.

[0006] Based on the above technical solution, the layout of existing charging piles in the target area is fully considered, and the hot spots are directly determined based on the existing charging piles, providing an accurate and reliable data analysis basis for determining the candidate site for construction, so as to ensure that the layout plan of charging piles can effectively relieve the charging pressure of users and ensure the investment return to a certain extent, promoting the healthy development of the industry. Further, by creating virtual charging piles at each candidate site for construction to collect the user demand data of each virtual charging pile, the target charging piles with actual user demand can be further screened out, thus avoiding misjudgment caused by inaccurate or incomplete data in the early stage. It can be seen that based on the above technical solution, not only can a charging pile layout plan that meets the actual user demand be obtained, but also the data basis used in the charging pile layout planning process is real-time, reliable and highly accurate. Compared with the prediction analysis based on various big data including vehicles and users in the prior art, the data is more timely and accessible, and the solution is more feasible.

[0007] In one implementation, the target area completely covers and is larger than the site selection planning area.

[0008] Based on the above technical solution, by reasonably expanding the site selection planning area, more comprehensive data on relevant existing charging piles can be obtained, so as to provide a more accurate data basis for the charging pile layout at the edge of the site selection planning area.

[0009] In one implementation, the obtaining of the position attributes of the existing charging piles in the target area includes: determining the geographical location information of the existing charging piles; querying the map to obtain the position area corresponding to the geographical location information as the first feature information; if the position area is a road parking space, obtaining other feature information within a preset range from the existing charging pile as the second feature information; if the position area is a non-road parking space, obtaining other feature information within a preset range from the position area as the second feature information; generating the position attributes of the existing charging piles based on the first feature information and the second feature information.

[0010] Based on the above technical solution, by using the position area corresponding to the geographical location where the existing charging pile is located as the position attribute, it can be used as the basis for determining hot spots in subsequent analysis with position areas such as roads and parks, so as to more accurately analyze the charging pile layout in various position areas. Compared with only analyzing the coordinates of existing charging piles, the analysis scope can be effectively expanded.

[0011] In one implementation, the determination of the hot spots based on the first clustering result of the position attributes of each of the existing charging piles includes performing clustering analysis on the coordinates corresponding to each feature information in the position attributes of each of the existing charging piles based on the DBSCAN algorithm.

[0012] Based on the DBSCAN algorithm for clustering analysis, different possibilities of clustering can be obtained by adjusting the neighborhood radius and the minimum number of sample points.

[0013] In one implementation, the method further includes obtaining the noise points determined by the DBSCAN algorithm and determining the hot spot areas based on the noise points.

[0014] By determining the hot spot areas based on the noise points, it can help to a certain extent in exploring the potential areas that have not been effectively developed.

[0015] In one implementation, the determining of the candidate site for construction based on the hot spot areas and the preset site selection strategy includes: dividing all the optional sites for construction into multiple batches; for each batch of the optional sites for construction, constructing an input layer based on the coordinates of each of the optional sites for construction, the coordinates of each feature information in the existing charging pile location attributes in each of the hot spot areas, and other parameters used when determining the hot spot areas; calculating each of the input layers respectively based on the DBSCAN algorithm to obtain corresponding second clustering results; determining the hot spot areas to which each of the optional sites for construction belongs in the second clustering results, and if the hot spot area to which the optional site for construction belongs includes the hot spot area, determining the optional site for construction as a candidate site for construction.

[0016] Based on the above technical solutions, obtaining the corresponding second clustering results batch by batch can avoid the situation where in the case of a large number of optional sites for construction, the hot spot areas in the second clustering results deviate greatly from the first clustering results, thus deviating from the actual situation. At the same time, directly determining the hot spot areas to which the optional sites for construction belong according to the second clustering results, and then determining the candidate sites for construction according to the relationship between the hot spot areas and the hot spot areas in the first clustering results, can not only ensure that the candidate sites for construction are planned based on the real hot spot areas, but also can explore potential sites for construction.

[0017] In one implementation, the method for screening out the target charging piles from each of the virtual charging piles based on the user demand data further includes obtaining the predicted investment returns of each of the virtual charging piles and selecting the virtual charging piles with predicted investment returns meeting the investment expectations as the target charging piles.

[0018] In one implementation, the method for obtaining the predicted investment return of the virtual charging pile includes: obtaining the energy efficiency attributes and coordinates of all the existing charging piles in the hot spot area where the virtual charging pile is located; constructing a utilization prediction model based on the utilization rate and coordinates in the energy efficiency attributes of each existing charging pile; using the coordinates of the virtual charging pile as the input layer to calculate the utilization prediction model to obtain a utilization prediction value; constructing an energy consumption prediction model based on the equipment energy consumption and coordinates in the energy efficiency attributes of all the existing charging piles; using the coordinates of the virtual charging pile as the input layer to calculate the energy consumption prediction model to obtain an energy consumption prediction value; and estimating the investment return period of the virtual charging pile based on the utilization prediction value and the energy consumption prediction value.

[0019] Based on the above technical solution, by predicting the utilization rate and equipment energy consumption of the virtual charging pile, the prediction accuracy of the investment return can be improved, thereby providing a guarantee for the investment return of the target charging pile to a certain extent.

[0020] Based on the same inventive concept, an embodiment of the present application also provides a new energy vehicle charging pile operation management platform, and the platform is used to implement the above method.

[0021] In addition, an embodiment of the present application also provides an electronic device, and the electronic device includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the above method is implemented. Description of the Drawings

[0022] The drawings forming a part of the present application are used to provide a further understanding of the present application. The schematic embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application.

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 Show the flowchart of the new energy vehicle charging pile space layout method provided by the embodiment of the present application.

[0025] Figure 2 Show the flowchart of the method for obtaining the position attributes of the existing charging piles in the embodiment of the present application.

[0026] Figure 3 Show the flowchart of the method for determining the candidate site for construction in the embodiment of the present application.

[0027] Figure 4 The flowchart of the method for obtaining the predicted investment return of the virtual charging pile in the embodiment of the present application is shown. Specific implementation manners

[0028] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0029] In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" refers to two or more, and the "first", "second" and various numerical numbers are only for the convenience of description and do not limit the scope of the embodiments of the present application.

[0030] The features, structures or characteristics in the present application can be combined in one or more embodiments in any suitable manner. In various embodiments of the present application, the order of the numbers of the processes does not mean the order of execution, and the order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0031] Some optional features in the embodiments of the present application can, in some scenarios, be implemented independently without relying on other features, solve the corresponding technical problems, achieve the corresponding effects, and can also be combined with other features according to requirements in some scenarios.

[0032] In the present application, unless otherwise specified, the same or similar parts between various embodiments can be referred to each other. In various embodiments of the present application, if there is no special specification and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referred to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships. The embodiments of the present application do not constitute a limitation to the protection scope of the present application.

[0033] The embodiments of the present application will be described in detail below with reference to the drawings.

[0034] A method for spatial layout of a new energy vehicle charging pile provided by an embodiment of the present application is applied to a new energy vehicle charging pile operation management platform. Please refer to Figure 1 , and the method specifically includes the following steps.

[0035] S101, obtain the position attributes of the existing charging piles in the target area.

[0036] Specifically, the target area is determined according to the site selection planning area for the to-be-built charging station. In one example, the target area is the site selection planning area; in another example, the target area is determined by expanding a preset distance outward from the site selection planning area. Therefore, the target area completely covers and is larger than the site selection planning area. The preset distance can be adjusted according to the actual situation. The site selection planning area is the area where the charging station is planned to be built.

[0037] The location attribute is used to indicate the characteristic information related to the geographical location of the existing charging pile, including but not limited to road signs and area names.

[0038] In one example, please refer to Figure 2 , and the method for obtaining the location attribute of the existing charging pile includes the following steps.

[0039] S201, determine the geographical location information of the existing charging pile.

[0040] The geographical location information includes location coordinates.

[0041] S202, query the map to obtain the location area corresponding to the geographical location information as the first characteristic information.

[0042] The corresponding location area is the location area where the existing charging pile is located, such as a park, a building, a road parking space, an office building, a residential building, etc.

[0043] S203, if the location area is a road parking space, obtain other characteristic information within a preset range from the existing charging pile as the second characteristic information.

[0044] The preset range can be set as a fixed value or dynamically adjusted according to the number of other characteristic information obtained to ensure that the number of other characteristic information obtained is not less than the preset threshold.

[0045] S204, if the location area is a non-road parking space, obtain other characteristic information within a preset range from the location area as the second characteristic information.

[0046] S205, generate the location attribute of the existing charging pile based on the first characteristic information and the second characteristic information.

[0047] Based on this, the characteristic information included in the location attribute can be made more accurate and comprehensive, providing an effective data basis for subsequent planning and analysis.

[0048] For example, assume that charging pile A is installed in the parking lot of park P. Park P is located at the intersection of main roads R1 and R2 and is adjacent to library L. When determining the location attributes of charging pile A, first, the map can be queried based on the coordinate information of charging pile A to first determine the park P where A is located as the first feature information. Then, query the road and other area information within 20 meters near park P, namely R1, R2, and L, as the second feature information. In this way, the location attributes of charging pile A are generated as: P, R1, R2, L.

[0049] It can be understood that the relevant information of existing charging piles can be obtained from public service platforms, such as various map applications, charging pile query applications, and relevant data in the operation and management system. It should be noted that in order to obtain accurate information on existing charging piles in the target area, the data obtained from each channel can be integrated and cross-verified to obtain complete and accurate data.

[0050] In one implementation, the working status of each existing charging pile, such as charging or idle, can also be obtained through a public service platform. Based on this, the utilization rates of each existing charging pile within a preset statistical period can be respectively counted, including but not limited to the daily average utilization rate, the daily maximum utilization rate, etc.

[0051] In another implementation, the equipment energy consumption of each existing charging pile can be further determined by combining the total power consumption and charging power consumption of each existing charging pile.

[0052] Generate utility attributes based on the above utilization rate and equipment energy consumption data.

[0053] S102. Determine the hot spots according to the first clustering result of the location attributes of each existing charging pile.

[0054] In one implementation, classification statistics can be directly performed on each feature information in the location attributes of each existing charging pile to obtain the first clustering result, and hot spots with the number of charging piles greater than N or the top M in terms of the number of charging piles are selected from the first clustering result. For example, if the number of charging piles with the main road R1 included in the location attributes is greater than N, then R1 can be determined as a hot spot. Among them, the values of N and M can be set as empirical values or can be set according to the number requirements of hot spots. For example, if the number of hot spots should be no less than 5, then the sizes of N and M can be adjusted to meet the corresponding requirements. Based on this, classification statistics can be performed using the feature information recorded in the location attributes as the clustering dimension to obtain the number of charging piles in the areas indicated by each feature information.

[0055] However, during the early-stage data collection process, problems may occur, such as differences in the recording methods of the same feature information and inaccurate grasp of the collection granularity of the early-stage feature information. For example, if charging pile A is set in the parking lot of park P, the finally recorded information may be park P or the parking lot AP of park A. Therefore, the problem of inaccurate first clustering results may occur.

[0056] In order to solve the problem of inaccurate first clustering results caused by early-stage data collection, in another implementation, an input layer can be constructed based on the coordinates corresponding to each feature information in the location attributes of each existing charging pile, and clustering analysis can be performed based on a clustering algorithm to obtain hot spots.

[0057] In an example, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is used. After obtaining the hot spots output by the algorithm, it is possible to further determine whether to adjust the neighborhood radius and the minimum number of samples in the input layer according to whether the number of hot spots output by the algorithm or the corresponding coordinate range meets the preset requirements, so as to finally obtain hot spots that meet the preset requirements.

[0058] Specifically, the preset requirements may include one or more of the following: the number of hot spots is not less than the first threshold, the number of hot spots is not greater than the second threshold, the type of feature information included in the hot spots does not exceed the third threshold, or the coordinate range of the hot spots does not exceed the fourth threshold. The magnitudes of the thresholds can be set according to actual needs. When the output hot spots do not meet the preset requirements, the neighborhood radius and / or the minimum number of samples can be adjusted to reconstruct the input layer and perform calculations. This cycle continues until the output hot spots meet the preset requirements. It should be noted that it is possible to further obtain the charging piles identified as noise points during the calculation process of the clustering algorithm to provide a data analysis basis for subsequent planning.

[0059] In some embodiments of the present application, the hot spots can be determined by combining the above two methods. For example, the existing charging piles with the location attribute of road parking spaces can be clustered based on the first embodiment to obtain the hot spots determined based on the roads, so as to know the distribution of the existing charging piles along each road. At the same time, the existing charging piles with the location attribute of non-road parking spaces are clustered based on the second embodiment to obtain the hot spots determined by the density of the charging pile areas. It should be noted that the hot spots obtained based on the second embodiment are used to indicate the aggregation of the existing charging piles, corresponding to the coordinate range, so it may be a certain feature information or a set of multiple feature information. For example, the coordinate range indicated by the first clustering result can be the range of Park P or the range including Park P and Library L. In this way, the hot spots determined by combining the two methods can include hot roads and dense areas.

[0060] In some embodiments, in order to ensure the utilization rate of the charging piles, based on the above embodiments, the utility attribute of the charging piles can be further combined to finally determine the hot spots. That is, after obtaining the first clustering result of the location attributes of each existing charging pile, the average utilization rate of each category is calculated based on the utilization rate of the existing charging piles in each category, and the hot spots are determined by excluding the categories with an average utilization rate less than the threshold. That is to say, the hot spots include the clustering areas with a large number and a high utilization rate. In this way, the problem of idle charging piles caused by building charging piles in the clustering areas with a low utilization rate can be avoided in advance.

[0061] S103. Determine the candidate site for construction based on the hot spots and the preset site selection strategy.

[0062] In implementation, the optional sites for building charging stations within the site selection planning area can be determined in advance. The method for determining the optional sites can include determining the areas with the geographical conditions for building stations, such as parking lots, road parking spaces, and areas where parking can be planned, within the site selection planning area based on the map data, and conducting on-site inspections on these locations to screen out the areas that meet other conditions for building charging stations as the optional sites.

[0063] The preset site selection strategy is used to define the positional relationship between the candidate site for construction and the hot spots.

[0064] In one implementation, the preset site selection strategy includes determining candidate site construction locations based on map data. In one example, the optional site construction locations within the hot spot area can be determined as candidate site construction locations. In another example, the optional site construction locations with a driving distance to the hot spot area not exceeding a preset range and the traffic conditions of the driving route meeting the requirements can be further determined as candidate site construction locations. Specifically, the traffic conditions of the driving route can be determined according to the average congestion duration of the driving route per day. For example, if the average congestion duration does not exceed 2 hours, candidate site construction locations near the hot spot area and easy to reach can be selected, so as to have the ability to share the charging pressure within the hot spot area.

[0065] In another implementation, please refer to Figure 3 , the method for determining candidate site construction locations based on the hot spot area and the preset site selection strategy includes the following steps.

[0066] S301, Divide all optional site construction locations into multiple batches.

[0067] Among them, the quantity of each batch is not greater than the preset quantity. In one example, the preset quantity can be 1.

[0068] S302, For each batch of optional site construction locations, construct an input layer based on the coordinates of each optional site construction location, the coordinates of each feature information in the location attributes of the existing charging piles in each hot spot area, and other parameters used when determining the hot spot area.

[0069] Specifically, the other parameters are determined according to the clustering algorithm. In one example, they include the neighborhood radius and the minimum number of samples.

[0070] S303, Calculate each input layer respectively based on the clustering algorithm to obtain the corresponding second clustering results.

[0071] It should be noted that the clustering algorithm adopted in this step is the same as the clustering algorithm corresponding to the first clustering result.

[0072] S304, Determine the hot spot area to which each optional site construction location belongs in the second clustering result. If the hot spot area to which it belongs contains the hot spot area determined according to the first clustering result, determine this optional site construction location as a candidate site construction location.

[0073] Among them, containing the hot spot area determined according to the first clustering result means that the coordinate range of the hot spot area to which the optional site construction location belongs is the same as that of at least one hot spot area determined based on the first clustering result, or covers at least one hot spot area.

[0074] Specifically, when the clustering algorithm, neighborhood radius, and minimum number of samples remain unchanged, the output result after clustering analysis of the input layer constructed based on the existing charging piles corresponding to each hot spot area is consistent with the first clustering result. Thus, if the coordinate range of the hot spot area to which the optional site to be built belongs is the same as that of a certain hot spot area in the first clustering result, it indicates that the optional site to be built is located within this hot spot area, and then the optional site to be built can be determined as a candidate site; if the hot spot area to which the optional site to be built belongs covers at least one hot spot area in the first clustering result, it means that the optional site to be built is relatively close to the hot spot area and is included in this hot spot area, thereby expanding the scope of the hot spot area, and then the optional site to be built can also be determined as a candidate site. In this way, candidate sites can be quickly determined based on the same clustering algorithm, and potential areas can also be mined.

[0075] For example, the first clustering result includes hot spot areas Z1 and Z2. When a batch of optional sites to be built is introduced for clustering, and Z1 and Z2 are covered by Z3 in the second clustering result, it indicates that the optional site to be built is located between Z1 and Z2 and has the opportunity to share the charging demands of the two hot spot areas. Therefore, it belongs to a potential area.

[0076] It should be noted that in other embodiments of the present application, different second clustering results can be obtained by dynamically adjusting the preset quantity, and whether to use the optional site as a candidate site can be determined based on the clustering situation of the optional site under each second clustering result. In one example, corresponding second clustering results can be obtained by different preset quantities, and whether the optional site can be used as a candidate site can be determined based on the distance between the optional site and the existing charging pile with the highest utilization rate in the hot spot area to which it belongs under each second clustering result, as well as the utilization rate of the nearest existing charging pile.

[0077] For example, the preset quantity can be set to 1 and 3 respectively. The batch corresponding to the optional site D1 is batch B1 that only includes D1, and batch B2 that includes D1, D2, and D3, where D2 and D3 are randomly selected from other optional sites or can be obtained based on permutation and combination. It can be understood that when the optional sites for each batch are selected based on permutation and combination, the number of batches including D1 is related to the total number of optional sites, so that more second clustering results can be obtained to explore more possibilities.

[0078] Assume that in the second clustering result corresponding to batch B1, D1 is identified as a noise point, therefore, D1 cannot be used as a candidate site to be built, while in the second clustering result corresponding to batch B2, D1 is included in the hot spot area Z4, and the hot spot area Z4 covers Z5 in the first clustering result. It can be seen that due to the addition of D2 and D3, the cluster distribution is changed. Based on this, D1 can be determined as a candidate area. However, in order to prevent the preset number from being set too large, resulting in the hot spot area output in the second clustering result expanding larger than the hot spot area in the first clustering result, and deviating from the actual demand situation, it is possible to further determine whether D1 is used as a candidate site to be built by checking whether the distance between D1 and the existing charging pile with the highest utilization rate in the hot spot area or the utilization rate of the nearest existing charging pile meets the preset conditions.

[0079] Based on this, the preset number can be dynamically adjusted to obtain the hot spots to which each optional site belongs under different preset numbers, and the candidate sites can be determined in combination with the utilization rate of the relevant existing charging piles in the hot spots to tap into more demand potential areas.

[0080] S104, creating a virtual charging pile for each candidate site construction site, and collecting user demand data based on each virtual charging pile.

[0081] In one implementation, a virtual charging pile can be created at each candidate site on each charging pile query software, platform, and map query software, so as to determine the number of times each virtual charging pile is queried based on the user access data of each software platform, thereby determining the user demand data for each virtual charging pile.

[0082] Specifically, the corresponding virtual sites can be created on the maps of various query software according to the geographic coordinates of the candidate sites. The virtual charging piles can be visible to the user or invisible to the user. When visible to the user, they can be presented in the form of non-idle charging piles to avoid causing trouble to the user. After the creation of the virtual charging piles is completed, the corresponding user demand data can be determined by including the number of virtual charging piles in the query results pushed to the user according to the user's query conditions through the collection software.

[0083] For example, when a user queries the charging piles near address A through the software, the software queries based on the real charging pile information and the virtual charging pile information, and pushes the charging pile information near A to the user. If the pushed information contains the virtual charging pile, it is recorded as user demand data. In this way, the software can directly accumulate the user demand data of the virtual charging pile while determining the query results, and can provide corresponding query records. Compared with the method of matching candidate sites based on query records, the authenticity and accuracy of the data can be better guaranteed.

[0084] S105. Screen out the target charging piles from each virtual charging pile based on the user demand data.

[0085] In one implementation, the virtual charging piles whose user demand data meet the preset conditions can be directly screened out from each virtual charging pile and determined as the target charging piles. Among them, the preset conditions include that the proportion of the user demand quantity in the total user demand quantity is not less than the preset proportion threshold, or the user demand quantity is not less than the preset demand threshold. The preset proportion threshold and the preset demand threshold can be determined according to the minimum proportion and the minimum user demand quantity among the existing charging piles whose investment return rate in the target area meets the investment expectation, so as to ensure to a certain extent that the expected investment return rate of the target charging piles can meet the expectation and avoid risks. Among them, the investment return rate of the existing charging piles can be calculated based on the utilization rate and related costs, and the related costs include the equipment energy consumption in the energy efficiency attribute.

[0086] In other embodiments of the present application, the target charging piles can be selected based on the predicted investment returns of each virtual charging pile, and those with predicted investment returns meeting the investment expectation are used as the target charging piles. Please refer to Figure 4 , and the method for obtaining the predicted investment return of the virtual charging pile specifically includes the following steps.

[0087] S401. Obtain the energy efficiency attributes and coordinates of all existing charging piles in the hot spot area where the virtual charging pile is located.

[0088] Among them, the coordinates of the existing charging piles are the installation position coordinates of the existing charging piles.

[0089] S402. Based on the utilization rate and coordinates in the energy efficiency attribute of each existing charging pile, construct a utilization rate prediction model.

[0090] Among them, the utilization rate prediction model is used to predict the utilization rate of the charging pile at a certain coordinate position based on the mapping relationship between the coordinate and the utilization rate. Specifically, modeling methods such as linear regression, logistic regression, decision tree, random forest, and neural network can be selected and the model can be trained based on the utilization rate and coordinates in the energy efficiency attribute of the existing charging piles in the hot spot area to obtain the utilization rate prediction model.

[0091] It should be noted that in order to improve the prediction accuracy, the utilization rate prediction model is constructed based on the hot spot area, that is, one hot spot area corresponds to one utilization rate prediction model, which is used to predict the utilization rate of the virtual charging pile belonging to the hot spot area.

[0092] S403. Use the coordinates of the virtual charging pile as the input layer, calculate through the utilization rate prediction model, and obtain the utilization rate prediction value.

[0093] S404. Based on the equipment energy consumption and coordinates in the energy efficiency attribute of all existing charging piles, construct an energy consumption prediction model.

[0094] Specifically, under different environmental conditions and installation conditions, even for charging piles of the same model, there may be differences in their device energy consumption. Therefore, constructing an energy consumption prediction model based on the device energy consumption and coordinates in the energy efficiency attributes of all existing charging piles can discover the impact of geographical location on device energy consumption, and thus more accurately predict the device energy consumption of virtual charging piles. In implementation, modeling methods such as linear regression, logistic regression, decision tree, random forest, neural network, etc. can be selected and used to train the model based on the device energy consumption and coordinates in the energy efficiency attributes of existing charging piles within the target area to obtain the energy consumption prediction model.

[0095] It should be noted that in other variable embodiments of the present application, other characteristic parameters, such as device power, line length, etc., can also be introduced in the construction of the device energy consumption prediction model to improve the prediction accuracy.

[0096] S405, using the coordinates of the virtual charging pile as the input layer, calculate the energy consumption prediction model to obtain the energy consumption prediction value.

[0097] S406, estimate the investment return period of the virtual charging pile based on the utilization prediction value and the energy consumption prediction value.

[0098] In specific implementation, the charging service fee income of the virtual charging pile can be estimated based on the utilization prediction value, and the energy consumption prediction value of the device can be added when calculating the operating cost. Then, combined with various other incomes, such as government subsidies, advertising income, etc., the annual income can be calculated, and combined with other various costs, the annual operating cost and the initial investment cost can be calculated, and finally the investment return period can be calculated. It can be understood that based on the embodiments of the present application, the utilization prediction value and the energy consumption prediction value of each virtual charging pile can be obtained to be used for calculating investment income and costs respectively. The specific calculation method can be set according to actual needs, and the present application is not limited thereto.

[0099] It should be noted that in other embodiments of the present application, the virtual charging piles that meet the relevant requirements can be determined by combining the user demand data and the predicted investment returns of each virtual charging pile as the target charging piles.

[0100] S106, generate a charging pile layout plan according to the target charging piles.

[0101] Specifically, the candidate site corresponding to the target charging pile can be determined as the target site for investing in newly built charging piles.

[0102] In implementation, the specific number of piles to be built at each target site can be further planned. In one example, the number of piles to be built can be determined according to the service saturation degree in the hotspot area where the target site belongs. Among them, the saturation degree can be determined according to the daily longest service duration of the existing charging piles, the longest service duration of the nearest existing charging pile to the target site, the total utilization rate of each existing charging pile during peak hours, etc., and the number of piles to be built is determined according to the saturation degree. The higher the saturation degree, the more piles are planned to be built.

[0103] In another example, the number of piles to be built can also be planned based on the predicted utilization rate of the target site. Specifically, the total number of charging piles planned to be built can be determined first, and then the distribution weights are set according to the predicted utilization rate of each target site, so as to finally determine the number of piles to be built at each target site. Among them, the higher the predicted utilization rate, the greater the weight, and the more piles are planned to be built. The predicted utilization rate of the target site is the predicted utilization rate of the corresponding virtual charging pile.

[0104] Based on the above technical solution, the location attributes and utility attributes of the existing charging piles in the target area can be obtained through the public query platform, and the layout plan of the newly built charging piles can be generated based on this, which can ensure the rationality and practicability of the layout plan. At the same time, since the data of the existing charging piles can be directly obtained in real time based on the public query platform, the authenticity and timeliness of the data are ensured, providing a good data basis for the planning analysis and ensuring the accuracy and effectiveness of the analysis process.

[0105] Furthermore, by determining the candidate sites based on the hotspot area, it can be ensured that the finally determined target sites have a certain user demand, which can not only relieve the charging pressure of regional users, but also ensure the investment income of the investors, thus helping to relieve the charging anxiety of users and optimize the industrial income to meet the requirements of healthy city construction.

[0106] In other embodiments of the present application, the investment potential areas can also be mined based on the noise points in the first clustering result. Specifically, the charging demand of the area can be determined according to the utilization rate of the existing charging piles corresponding to the noise points. If the utilization rate of the existing charging pile is higher than the average level, it can be determined that the area where the existing charging pile is located is the potential area. In this way, the candidate sites can be selected from the optional sites within the preset distance range from the existing charging piles. In this way, the early occupation in the potential area can be realized.

[0107] In addition, an embodiment of the present application further provides an electronic device, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, it implements the method in any implementation manner in the embodiments of the present application. Among them, the processor may adopt a general-purpose central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), a graphics processing unit (GPU), or one or more integrated circuits, and is used to execute relevant programs to implement the method in any implementation manner in the embodiments of the present application.

[0108] The processor may also be an integrated circuit electronic device with the ability to process signals. During the implementation process, each step of the method in any implementation manner in the embodiments of the present application may be completed by the integrated logic circuit in the hardware of the processor or the instruction in the form of software.

[0109] The above-mentioned processor may also be a general-purpose processor, a digital signal processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application may be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor.

[0110] The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory. The processor reads the information in the memory and combines its hardware to complete the functions required to be executed by the units included in the data processing device in the embodiments of the present application, or executes the method in any implementation manner in the embodiments of the present application.

[0111] Those skilled in the art can understand that all or part of the steps in the above implementation methods can be completed by instructing relevant hardware through a program. This program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0112] The above are all preferred embodiments of this application. The protection scope of this application is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. A method for spatial layout of new energy vehicle charging piles, characterized in that: The method is applied to the new energy vehicle charging pile operation management platform, comprising the steps of: Get the location attributes of existing charging piles in the target area; Determining a hotspot area according to a first clustering result of the location attributes of each of the existing charging piles; Determine candidate sites based on the hotspot areas and the preset site selection strategy; Creating a virtual charging pile for each of the candidate sites, and collecting user demand data based on each of the virtual charging piles; A target charging pile is selected from each of the virtual charging piles based on the user demand data, and a charging pile layout plan is generated according to the target charging piles.

2. The method according to claim 1, characterized in that The target area is completely covered and larger than the site planning area.

3. The method according to claim 1, characterized in that The method of obtaining the location attributes of the existing charging piles in the target area includes: Determine the geographic location information of the existing charging pile; Querying a map to obtain a location area corresponding to the geographical location information as first feature information; If the location area is a road parking space, other characteristic information of the distance from the existing charging pile within a preset range is obtained as the second characteristic information; If the location area is an off-road parking space, obtaining other characteristic information within a preset range of distance from the location area as second characteristic information; The location attribute of the existing charging pile is generated based on the first feature information and the second feature information.

4. The method according to claim 1, characterized in that: Determining the hotspot area according to the first clustering result of the location attributes of each of the existing charging piles includes performing cluster analysis on the coordinates corresponding to each feature information in the location attributes of each of the existing charging piles based on a DBSCAN algorithm.

5. The method according to claim 4, characterized in that The method further includes obtaining noise points determined by a DBSCAN algorithm, and determining hotspot areas based on the noise points.

6. The method according to claim 4, characterized in that The determining of candidate sites based on the hotspot area and the preset site selection strategy includes: Divide all optional sites into multiple lots; For each batch of the optional sites, an input layer is constructed based on the coordinates of each of the optional sites, the coordinates of each feature information in the location attributes of the existing charging piles in each of the hot spots, and other parameters used in determining the hot spots; Based on the DBSCAN algorithm, each of the input layers is calculated to obtain corresponding second clustering results; The hotspot area to which each of the optional sites belongs in the second clustering result is determined, and if the hotspot area to which the optional sites belong includes the hotspot area, the optional sites are determined to be candidate sites.

7. The method according to claim 1, characterized in that The method for selecting a target charging pile from the virtual charging piles based on the user demand data further includes obtaining a predicted investment return of each virtual charging pile, and selecting the virtual charging pile whose predicted investment return meets the investment expectation as the target charging pile.

8. The method according to claim 7, characterized in that The method for obtaining the predicted investment return of the virtual charging pile includes: Obtaining energy efficiency attributes and coordinates of all existing charging piles in the hotspot area to which the virtual charging pile belongs; Based on the utilization rate and coordinates in the energy efficiency attributes of each of the existing charging piles, a utilization rate prediction model is constructed; Taking the coordinates of the virtual charging pile as the input layer, the utilization rate prediction model is calculated to obtain a utilization rate prediction value; Based on the equipment energy consumption and coordinates in the energy efficiency attributes of all the existing charging piles, an energy consumption prediction model is constructed; Taking the coordinates of the virtual charging pile as the input layer, the energy consumption prediction model is calculated to obtain an energy consumption prediction value; The investment payback period of the virtual charging pile is estimated based on the utilization rate prediction value and the energy consumption prediction value.

9. A new energy vehicle charging pile operation and management platform, characterized in that: The platform is used to implement the method according to any one of claims 1 to 8.

10. An electronic device, characterized in that: The method comprises a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction implements the method according to any one of claims 1 to 8 when executed by the processor.

Images