Site selection and layout method, device, equipment and storage medium for charging facility construction

Abstract

The present invention discloses a method, apparatus, device, and storage medium for site selection and layout of charging facility construction. The method comprises: obtaining layer data from an electronic map and performing gridding processing on the layer data to obtain at least one grid area; determining charging performance information for each grid area based on charging-related information of existing charging facilities in each grid area and regional attribute information of the corresponding grid area; and determining the site selection and layout results for each grid area relative to the charging facilities based on the charging performance information of each grid area, in combination with a preset site selection analysis model and layout optimization model. The above technical solution achieves scientific and rational site selection and layout of charging facilities, meets user charging needs, improves user satisfaction, and thereby promotes full resource utilization and improves resource utilization efficiency.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of big data technology, and in particular to a site selection and layout method, apparatus, equipment, and storage medium for charging facility construction. Background Art

[0002] With the progress of society and the improvement of economic level, people are paying more and more attention to the requirements of energy conservation and emission reduction. Traditional automobile exhaust emissions are the main factor of urban air pollution. The development of electric vehicles and new energy vehicles is of great significance to improving the urban environment. Building a complete and scientific charging infrastructure site layout is the basic premise and important guarantee for the widespread application of electric vehicles.

[0003] However, the existing site selection and planning analysis for electric vehicle charging facilities does not take into account power factors such as power data demand and substation capacity, nor does it consider factors affecting the operating status of charging stations. This leads to unclear site selection for charging facilities, and after the site is determined, there are problems such as insufficient research and inaccurate quantification. The scientific and reasonable site selection and layout of charging facility construction is an issue that needs to be urgently addressed. Summary of the Invention

[0004] The embodiments of the present invention provide a method, apparatus, device, and storage medium for site selection and layout of charging facility construction, so as to achieve scientific and rational site selection and layout of charging facility construction, meet charging needs, and improve user satisfaction.

[0005] In a first aspect, an embodiment of the present invention provides a method for site selection and layout of charging facility construction, comprising:

[0006] Acquiring layer data in the electronic map, and performing grid processing on the layer data to obtain at least one grid area;

[0007] Determining charging performance information of each grid area based on charging-related information of existing charging facilities in each grid area and regional attribute information of the corresponding grid area;

[0008] Based on the charging performance information of each grid area, combined with a preset site selection analysis model and a layout optimization model, the site selection and layout results of each grid area relative to the charging facilities are determined.

[0009] In a second aspect, an embodiment of the present invention further provides a device for site selection and layout of charging facility construction, including:

[0010] An area acquisition module is used to acquire layer data in the electronic map and perform grid processing on the layer data to obtain at least one grid area;

[0011] a data determination module, configured to determine charging performance information of each of the grid areas based on charging-related information of existing charging facilities in each of the grid areas and regional attribute information of the corresponding grid area;

[0012] The site selection determination module is used to determine the site selection and layout results of each grid area relative to the charging facilities based on the charging performance information of each grid area in combination with a preset site selection analysis model and a layout optimization model.

[0013] In a third aspect, an embodiment of the present invention further provides a computer device, comprising:

[0014] one or more processors;

[0015] a storage device for storing one or more programs,

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the site selection and layout method for charging facility construction as described in any one of the embodiments of the present invention.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a site selection and layout method for charging facility construction as described in any one of the embodiments of the present invention.

[0018] The technical solution of the embodiment of the present invention obtains at least one grid area by obtaining layer data from an electronic map and performing grid processing on the layer data; determines the charging performance information of each grid area based on the charging-related information of the existing charging facilities in each grid area and the regional attribute information of the corresponding grid area; and determines the location and layout results of each grid area relative to the charging facilities based on the charging performance information of each grid area, combined with a preset site selection analysis model and layout optimization model. The above technical solution, based on the charging performance information of each grid area and the method of combining the site selection analysis model and layout optimization model, solves the problems of unclear site selection and lack of in-depth research in the site selection and layout of charging facilities in the prior art, realizes the scientific and rational location and layout of charging facilities, meets the charging needs of users, improves user satisfaction, and promotes the full utilization of resources and improves resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A design flow chart of a method for site selection and layout of charging facility construction provided in Example 1 of the present invention;

[0020] Figure 2 A flowchart of a method for site selection and layout of charging facility construction provided in the second embodiment of the present invention;

[0021] Figure 3 This is a rendering of the implementation effect of the site selection analysis model in the site selection and layout method for charging facility construction provided in the second embodiment of the present invention;

[0022] Figure 4 This is a diagram showing the implementation effect of the layout optimization model in the site selection and layout method for charging facility construction provided in the second embodiment of the present invention;

[0023] Figure 5 This is a diagram showing the effect of calculating the charging facility operation status score in the charging facility construction site selection and layout method provided in the second embodiment of the present invention;

[0024] Figure 6 This is a structural diagram of a device for site selection and layout of charging facility construction provided in a third embodiment of the present invention;

[0025] Figure 7 This is a structural diagram of a computer device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0027] Example 1

[0028] Figure 1 This is a design flow chart of a method for site selection and layout of charging facility construction provided in the first embodiment of the present invention. This embodiment is applicable to the site selection and layout of charging facility construction, such as the site selection and layout of electric vehicle charging stations / charging piles. The method can be executed by a site selection and layout device for charging facility construction, which can be implemented in hardware and / or software. Figure 1 The method provided in the embodiment of the present invention specifically includes the following steps:

[0029] S110: Acquire layer data in the electronic map, and perform grid processing on the layer data to obtain at least one grid area.

[0030] Among them, electronic maps can be considered as digital maps that are stored and consulted in digital form, which can be used to find various places, various locations, and driving routes. For example, electronic maps can be Baidu maps, Gaode maps, or Sogou maps. Electronic maps can also be considered to be composed of one or more layer data superimposed on each other, and each layer data can represent a part of geographical or traffic information. Layer data can include standard maps, satellite maps, and traffic maps. Grid processing can be considered as a method of dividing layer data into one or more grid areas according to a grid standard. The grid standard can be to divide layer data into preset kilometers, to divide layer data into administrative areas, or to divide layer data according to longitude and latitude. Among them, the grid area can be the grid area obtained after dividing the layer data according to the grid standard. Each grid area can contain different information, such as regional location information, traffic information, and charging facilities information.

[0031] In an embodiment of the present invention, the layer data of the electronic map can be obtained by acquiring the application programming interface (API) of the electronic map, and the obtained layer data can be gridded to obtain one or more grid areas, which facilitates subsequent information analysis based on the grid areas.

[0032] S120 . Determine charging performance information of each grid area based on charging-related information of existing charging facilities in each grid area and regional attribute information of the corresponding grid area.

[0033] Among them, the charging-related information can be information related to the existing charging facilities in each grid area, such as the charging station archive, the 96-point power curve of the charging pile, the daily charging capacity of the charging pile, and other information; and the information that is not related to the existing charging facilities in each grid area is the regional attribute information, for example, the regional attribute information can be the parking lot location, point of interest (POI), road network structure and electric vehicle ownership in each grid area; of course, in actual applications, if the information such as the daily charging capacity of the charging pile and the 96-point power curve of the charging pile is missing and abnormal, the information such as the daily charging capacity of the charging pile and the 96-point power curve of the charging pile can be identified and deleted through the isolation forest algorithm, and then the original missing values ​​and the missing values ​​generated after removing the outliers can be supplemented using the Lagrange Interpolation Polynomial method.

[0034] Among them, the charging performance information can be considered as key charging information in each grid area, for example, it can be information such as charging demand, power supply capacity and charging facility operation capacity in each grid area.

[0035] In an embodiment of the present invention, the existing charging facility information in each grid area can be obtained based on each grid area, and the charging-related information in each grid area can be obtained. Correspondingly, the regional attribute information of each grid area can also be obtained, so that the charging performance information of each grid area can be obtained based on the charging-related information of the existing charging facilities in each grid area and the corresponding regional attribute information, so as to facilitate subsequent processing based on the charging performance information.

[0036] S130. Determine the location and layout results of each grid area relative to the charging facilities based on the charging performance information of each grid area and in combination with a preset location analysis model and layout optimization model.

[0037] The site selection analysis model may be a classification model constructed using a machine learning classification algorithm, which can be used to predict whether each grid area needs new charging facilities, while the layout optimization model may be a regression model constructed using a machine learning regression algorithm, which can be used to predict the specific number of charging facilities that will be relocated in and out. The site selection and layout results may include information on the results of the construction, relocation, and relocation of charging facilities in each grid area.

[0038] In an embodiment of the present invention, after obtaining the charging performance information of each grid area, the site selection and layout results of the corresponding charging facilities in each grid area can be obtained based on the charging performance information in each grid area, combined with a preset site selection analysis model and layout optimization model, so as to obtain specific results of the construction, relocation and removal of charging facilities in each grid area.

[0039] The technical solution of the embodiment of the present invention obtains at least one grid area by obtaining layer data from an electronic map and performing grid processing on the layer data; determines the charging performance information of each grid area based on the charging-related information of the existing charging facilities in each grid area and the regional attribute information of the corresponding grid area; and determines the location and layout results of each grid area relative to the charging facilities based on the charging performance information of each grid area, combined with a preset site selection analysis model and layout optimization model. The above technical solution, based on the charging performance information of each grid area and the method of combining the site selection analysis model and layout optimization model, solves the problems of unclear site selection and lack of in-depth research in the site selection and layout of charging facilities in the prior art, realizes the scientific and rational location and layout of charging facilities, meets the charging needs of users, improves user satisfaction, and promotes the full utilization of resources and improves resource utilization efficiency.

[0040] Example 2

[0041] Figure 2 This is a flowchart of a method for site selection and layout of charging facility construction provided in the second embodiment of the present invention. This embodiment of the present invention is a specific embodiment of the above invention. Figure 2 The method provided in the embodiment of the present invention specifically includes the following steps:

[0042] S201: Acquire layer data in an electronic map, and divide the layer data into at least one unit grid area in units of 1 kilometer.

[0043] In the embodiment of the present invention, the API interface of the electronic map may be called first, and then the layer data in the electronic map may be obtained, and the layer data may be divided into one or more unit grid areas according to a unit of 1 kilometer.

[0044] S202 : Clustering each unit grid area according to the vehicle travel data and vehicle charging power consumption data associated with each unit grid area.

[0045] Vehicle travel data can be considered as grid area travel popularity, which can be obtained by analyzing taxi order origin and destination data and driving navigation origin and destination data. In practice, if grid area travel popularity is missing, the missing value can be filled using the average of the grid area travel popularity for the current day and the previous day. Vehicle charging power consumption data can be obtained by analyzing the 96-point power curve of the charging station and the daily charging capacity of the charging station.

[0046] It should be noted that after the electronic map is divided into multiple unit grid areas, the number of unit grid areas is large and the area of ​​the unit grid areas is small, which is not conducive to the subsequent analysis of the grid areas. Therefore, it is necessary to cluster the unit grid areas.

[0047] In an embodiment of the present invention, each unit grid area can be clustered based on the vehicle travel data and vehicle charging power consumption data associated with each unit grid area. Specifically, a clustering algorithm can be used for clustering, for example, a K-means clustering algorithm (KMeans) can be used to cluster each unit grid area.

[0048] The specific clustering process can be as follows: First, K unit grid areas are selected from the unit grid area set composed of each unit grid area as the initial cluster centers. The K value can be determined using the CH index (CalinsKi-Harabasz, CH index). Then, the distances between all unit grid areas and the K cluster centers are calculated, and each unit grid area is assigned to the cluster closest to it. The cluster center and the unit grid areas assigned to it represent a cluster. After each unit grid area is assigned, the cluster center is recalculated based on the existing unit grid areas in the cluster. This process is repeated until a termination condition is met. The termination condition can be that no unit grid areas are reassigned to different clusters, no cluster centers change again, or the sum of squared errors reaches a local minimum. After the termination condition is met, the clustering results are output.

[0049] S203: Acquire multiple types of grid areas formed after clustering processing.

[0050] In an embodiment of the present invention, after clustering processing is performed using a clustering algorithm, several unit grid areas with similar distances can be clustered into a new grid area, thereby obtaining one or more new types of grid areas after clustering processing of all unit grid areas, which facilitates subsequent analysis of the grid areas.

[0051] It should be noted that after obtaining the multiple types of grid areas formed after clustering processing, the charging performance information of each grid area can be determined based on the charging association information of the existing charging facilities in each grid area and the regional attribute information of the corresponding grid area.

[0052] Optionally, based on the above embodiment, the charging performance information includes: charging demand and charging capacity matching information of the grid area, and charging facility operation status information.

[0053] The charging demand and charging capacity matching information of the grid area may be information on whether the charging demand and charging capacity of the grid area match; and the charging facility operation status information may be information on whether the operation status of the charging facility is good.

[0054] S204: Determine the existing charging facilities and corresponding charging-related information included in each grid area based on the location information of the installed charging facilities and the longitude and latitude of the grid area.

[0055] It should be noted that the latitude and longitude information can be used to associate the grid area with the corresponding charging facilities and charging facility related information, so that it can be used as a basis for determining whether there are charging facilities and corresponding charging related information in the grid area.

[0056] In an embodiment of the present invention, the location information of the installed charging facilities and the regional longitude and latitude of the grid area can be obtained first, and then it is determined whether the location information of the installed charging facilities is within the regional longitude and latitude. If the location information of the installed charging facilities is within the regional longitude and latitude, it can be considered that the charging facilities are within the grid area, and the charging facility-related information of the grid area can be obtained accordingly, and then the existing charging facilities included in each grid area and the corresponding charging-related information can be obtained.

[0057] S205 . For each grid area, based on the regional attribute information and the charging-related information of each existing charging facility, the charging demand and charging capacity matching information of the grid area is determined using a pre-built charging demand prediction basic model.

[0058] Among them, the charging demand prediction basic model can be a regression model constructed using a machine learning regression algorithm.

[0059] In an embodiment of the present invention, for each grid area, regional attribute information and charging-related information of existing charging facilities can be used as input data of the corresponding grid area, and the input data of each grid area can be input into a charging demand prediction basic model. The charging demand prediction basic model can output charging demand and charging capacity matching information of the corresponding grid area.

[0060] S206 : Based on the charging-related information of each existing charging facility, determine the operating status information of the charging facilities in the grid area through a pre-built operating status analysis model.

[0061] The operating status analysis model may be a model for calculating a comprehensive score of the operating status of the charging facility.

[0062] In an embodiment of the present invention, based on the charging-related information of each existing charging facility, a comprehensive score of the operating status of the charging facility can be calculated through a pre-built operating status analysis model, thereby determining the operating status information of the charging facilities in each grid area.

[0063] S207 . For each grid area, determine the operation level of each existing charging facility in the grid area based on the charging facility operation status information in the corresponding charging performance information.

[0064] In an embodiment of the present invention, within each grid area, the operating level of each existing charging facility in the corresponding grid area can be determined by determining whether the charging facility operating status information in the corresponding charging performance information meets a preset threshold. For example, determining whether the comprehensive score of the charging facility operating status meets a preset threshold condition: if the comprehensive score of the charging facility operating status exceeds the threshold condition of 80 points, the charging facility operating level can be level one; if the comprehensive score of the charging facility operating status exceeds the threshold condition of 60 points but does not exceed 80 points, the charging facility operating level can be level two; if the comprehensive score of the charging facility operating status is less than the threshold condition of 60 points, the charging facility operating level can be level three.

[0065] S208: If the charging facility operation status information does not meet the operation threshold condition, the layout optimization model is combined with each operation level to obtain the relocation result of the existing charging facilities in the grid area.

[0066] In an embodiment of the present invention, a determination can be made as to whether the operating status information of the charging facility satisfies the operating threshold conditions. If the operating status information of the charging facility does not meet the operating threshold conditions, the charging facility can be relocated from the corresponding grid area. Furthermore, the layout optimization model can be combined with each operating level to obtain the number of charging facilities that have been relocated from the grid area. For example, if the operating status score of the charging facility is 50 points and the operating threshold condition is 60 points, then the operating status score of the charging facility is lower than the operating threshold condition. It can be determined that the operating status information of the charging facility does not meet the operating threshold conditions, indicating that the operating status of the charging facility is unqualified, and the charging facility can be relocated from the corresponding grid area.

[0067] S209. Otherwise, determine whether new charging facilities are needed in the grid area based on the traffic flow heat data of the grid area, the surrounding business data, and the charging demand and charging capacity matching information in the corresponding charging performance information.

[0068] Traffic flow data can be considered as the travel popularity of the grid area. Surrounding business data can include businesses related to transportation, company distribution, shopping, residential, and other charging facilities within the grid area.

[0069] In this embodiment of the present invention, if the charging facility operating status information meets the operating threshold conditions, the charging facility can be considered to be in good operating condition, and a determination can be made as to whether new charging facilities are needed in the grid area. Specifically, the determination of whether new charging facilities are needed in the grid area can be made based on the grid area's traffic flow data, surrounding business data, and the matching of charging demand and charging capacity in the corresponding charging performance information.

[0070] S210. If new charging facilities are needed, the site selection analysis model is combined with each operation level to obtain the results of new construction / relocation of existing charging facilities in the grid area.

[0071] In an embodiment of the present invention, if it is determined that new charging facilities are needed in a grid area, the site selection analysis model can be combined with each operating level to obtain the results of the construction / relocation of existing charging facilities in the grid area. For example, if the operating levels are divided into two levels, B and C, charging stations with an operating level of B have a large charging demand and users queue up to charge. Therefore, it is necessary to increase charging facilities to provide better service for electric vehicle users. Therefore, the site selection analysis model can be used to predict the result of the need for new charging facilities. This can be compared with the actual results of charging facilities with an operating level of B to determine the results of the construction / relocation of existing charging facilities.

[0072] For example, the traffic flow heat data of the grid area, the surrounding business data, and the charging demand and charging capacity matching information in the corresponding charging performance information can be used as the input data of the site selection analysis model. The XGBoost algorithm (eXtreme Gradient Boosting, XGBoost) classification algorithm can be used to build the site selection analysis model, and whether new charging facilities are built in the grid area is used as the prediction variable; the model input data consists of two parts: positive examples and negative examples. In order to balance the sample data, the charging power, traffic flow heat and surrounding business data of 50 grid areas with charging power greater than 3000 kWh are randomly selected as positive examples; the charging power, traffic flow heat and surrounding business data of 50 grid areas with charging power less than 500 kWh are randomly selected as negative examples; 80% of the input data is used as training data, and the rest is used as test data. By continuously adjusting the model parameters, the training set is fitted to the greatest extent, and the trained model is used to test the test data. Figure 3 shown.

[0073] Figure 3 This is a diagram showing the implementation effect of the site selection analysis model in the site selection and layout method for charging facility construction provided in the second embodiment of the present invention.

[0074] First, the grid area's traffic flow heat data, surrounding business data, and the charging demand and charging capacity matching information in the corresponding charging performance information are used as input data. Then, 80% of these input data are used as a training set and 20% as a test set. The training set is used to train the site selection analysis model, and then the test set is used to test the site selection analysis model. The training set is further divided into multiple sampling sets, such as sampling set 1, sampling set 2,..., sampling set N. Voting is performed through the XGBoost algorithm, and finally the result of whether the grid area needs new charging facilities is output.

[0075] S211. Otherwise, the layout optimization model is combined with each operation level to obtain the relocation result of the existing charging facilities in the grid area.

[0076] In an embodiment of the present invention, if it is determined that new charging facilities are not needed in a grid area, the layout optimization model can be combined with each operation level to obtain the relocation results of the existing charging facilities in the grid area. For example, if the operation level is divided into two levels, B and C, for charging stations with an operation level of C, the utilization rate of their charging piles is low, and there are idle charging piles. It is necessary to reduce the charging piles. Therefore, the relocation results of the existing charging facilities can be output by combining the layout optimization model with each operation level. Exemplarily, the Gradient Boosting Decision Tree (GBDT) regression algorithm can be used to predict the reasonable number of charging facilities, and compare it with the actual number of charging facilities at the operation level C to determine the number of charging facilities to be relocated, such as Figure 4 shown.

[0077] Figure 4 This is a diagram showing the implementation effect of the layout optimization model in the site selection and layout method for charging facility construction provided in the second embodiment of the present invention.

[0078] First, the traffic flow heat data of the grid area, the surrounding business data, and the charging demand and charging capacity matching information in the corresponding charging performance information are used as input data. Then, 80% of these input data are used as the training set and 20% as the test set. The training set is used to train the layout optimization model, and then the test set is used to test the layout optimization model. The gradient boosting decision tree algorithm, that is, the regression tree, is used to fit the training set, and finally the result of the number of new charging facilities needed in the grid area is output.

[0079] S212: Using the out-migration results and / or the new construction / migration results as the location layout results of the grid area relative to the charging facilities.

[0080] In the embodiment of the present invention, after obtaining the out-migration result and / or the new construction / migration result, the out-migration result and / or the new construction / migration result is used as the location layout result of each grid area relative to the charging facility.

[0081] Furthermore, based on the above embodiment, the charging demand and charging capacity matching information of the grid area is determined by using a pre-built charging demand prediction basic model based on regional attribute information and charging related information of each existing charging facility, including:

[0082] a1. Extract vehicle travel data from the regional attribute information and historical charging amounts from each charging-related information based on at least one attribute dimension of season, holiday, weather, and temperature.

[0083] In an embodiment of the present invention, vehicle travel data in regional attribute information of a grid area and historical charging amounts in each charging-related information can be extracted according to one or more attribute dimensions of season, holiday, weather, and temperature.

[0084] b1. For each attribute dimension, the corresponding vehicle travel data and historical charging amounts are used as input data into the charging demand prediction basic model to obtain the charging demand and charging capacity information of the grid area under the attribute dimension.

[0085] Among them, the basic model for charging demand prediction can be a regression model constructed using gradient boosting regression and random forest regression algorithms; and the grid search and time series cross-validation methods can be used to optimize the hyperparameters of the basic model for charging demand prediction to find the optimal hyperparameter values, and the accuracy of the basic model for charging demand prediction can be improved through the integrated learning Stacking framework.

[0086] In an embodiment of the present invention, under each attribute dimension, the vehicle travel data and the historical charging amounts of the grid area under the corresponding attribute dimension can be input as input data into the charging demand prediction basic model. The charging demand prediction basic model can output the charging demand and charging capacity information of the corresponding attribute dimension, thereby obtaining the charging demand and charging capacity information of the grid area under each attribute dimension.

[0087] c1. Summarize the charging demand and charging capacity information under each attribute dimension as the charging demand and charging capacity information of the grid area.

[0088] In an embodiment of the present invention, the charging demand and charging capacity information of a grid area under each attribute dimension can be aggregated to obtain the charging demand and charging capacity information of the grid area. A specific aggregation method can be a method of accumulating the charging demand and charging capacity information under each attribute dimension, or a method of setting corresponding weights for the attribute dimensions, that is, a method of taking the weighted sum of the charging demand and charging capacity information for each attribute dimension, wherein the accumulated sum of the weights of each attribute dimension is 1.

[0089] Furthermore, based on the above embodiment, based on the charging-related information of each existing charging facility, the operating status information of the charging facilities in the grid area is determined by using a pre-built operating status analysis model, including:

[0090] a2. Obtain a preset multi-level indicator evaluation table.

[0091] Among them, the multi-level indicator evaluation table includes a first-level indicator column, a second-level indicator column and a third-level indicator column. Each first-level indicator item in the first-level indicator column corresponds to at least one second-level indicator item in the second-level indicator column, and each second-level indicator item in the second-level indicator column corresponds to at least one third-level indicator item in the third-level indicator column.

[0092] Among them, the multi-level indicator evaluation table can be an evaluation table used to evaluate the operating status of charging facilities. The multi-level indicator evaluation table can include a first-level indicator column, a second-level indicator column and a third-level indicator column. Each level of indicator column can include corresponding indicator items at each level. There can be multiple indicator items at each level, and each first-level indicator item in the first-level indicator column corresponds to one or more second-level indicator items in the second-level indicator column, and each second-level indicator item in the second-level indicator column corresponds to one or more third-level indicator items in the third-level indicator column.

[0093] In the embodiment of the present invention, a preset multi-level indicator evaluation table is obtained to evaluate and analyze the operating status of the charging station, thereby providing a basis for optimizing the layout of the charging facilities.

[0094] For example, the multi-level indicator evaluation table is shown in Table 1. The first-level indicator column is equivalent to the first-level indicator in Table 1, the second-level indicator column is equivalent to the second-level indicator in Table 1, and the third-level indicator column is equivalent to the third-level indicator in Table 1. The first-level indicator items may include operation level, service level and management level; the second-level indicator items may include operation capacity, economic benefits, work order processing, satisfaction, equipment operation and maintenance and repair; the third-level indicator items may include the number of DC charging piles, the number of AC charging piles, the average monthly load of the charging station, the average monthly number of charging times, the average monthly charging time, the average monthly charging power, the average monthly charging pile utilization rate, the average monthly charging The first-level indicator's operational level corresponds to the second-level indicators of operational capacity and economic benefits. The second-level indicator's operational capacity corresponds to the third-level indicators of the number of DC charging piles, the number of AC charging piles, the average monthly load of the charging station, the average monthly number of charges, the average monthly charging duration, the average monthly charging power, and the average monthly charging pile utilization rate. The specific multi-level indicator evaluation table is shown in Table 1.

[0095] Table 1 Multi-level indicator evaluation table

[0096]

[0097] b2. Determine the third-level indicator weight and the third-level indicator score relative to each third-level indicator item based on each charging-related information.

[0098] It should be noted that by obtaining charging-related information, specific information about the corresponding third-level indicators in the multi-level indicator evaluation table can be obtained, facilitating the subsequent calculation of the scores and weights of each third-level indicator. For example, by obtaining charging-related information, the specific number of DC charging piles can be obtained.

[0099] In an embodiment of the present invention, the third-level indicator weights and the third-level indicator scores for each third-level indicator item can be determined based on the charging association information of each grid area. Specifically, the entropy weight method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method can be used to respectively calculate the third-level indicator weights and the third-level indicator scores for the third-level indicator items.

[0100] It should be noted that the calculation of the three-level indicator score of the three-level indicator item can be specifically calculated by the superior and inferior solution distance method, combined with the charging correlation information of each grid area. The process of calculating the three-level indicator weight of the three-level indicator item can be: first, the value of the three-level indicator item can be smoothed by the sigmoid function, and then the three-level indicator weight can be calculated by the entropy weight method; specifically, the proportion P of the indicator value of the j-th indicator under the i-th grid area can be calculated by formula (1) ij :

[0101]

[0102] Among them, r ij is the specific value of the jth indicator item in the three-level indicator items under the i-th grid area, and m is the number of grid areas.

[0103] Then, the entropy value e of the jth indicator item in the three-level indicator items can be calculated by formula (2): j :

[0104]

[0105] in,

[0106] Finally, the third-level indicator weight of the jth indicator item in the third-level indicator item can be calculated by formula (3):

[0107]

[0108] Among them, g j =1-e j (j=1, 2,…, n).

[0109] c2. Based on the third-level indicator weights and third-level indicator scores of each third-level indicator item, determine the second-level indicator scores of the corresponding second-level indicator items, and determine the second-level indicator weights of the corresponding second-level indicator items through the multi-scheme decision-making hierarchical analysis method.

[0110] In the embodiment of the present invention, since each secondary indicator item in the secondary indicator column corresponds to one or more tertiary indicator items in the tertiary indicator column, the secondary indicator score of the corresponding secondary indicator item can be calculated by combining the tertiary indicator weights and the tertiary indicator scores of the corresponding tertiary indicator items, thereby obtaining the secondary indicator scores of each secondary indicator item. Then, the secondary indicator weights of each secondary indicator item can be calculated by using the multi-scheme decision-making analytic hierarchy process (AHP). The multi-scheme decision-making analytic hierarchy process can be described as follows:

[0111] Step 1: Establish a hierarchical model. Generally, it is divided into three layers: the first layer is the goal layer, the third layer is the solution layer, and the second layer is the criterion layer or indicator layer.

[0112] It should be noted that, in actual applications, the specific content of each layer can be set according to specific circumstances.

[0113] Step 2: Construct a contrast matrix. Starting from the second level, use a pairwise comparison matrix and a scale of 1 to 9. If each factor in the upper level dominates or is influenced by all factors in the lower level, it is called a complete hierarchy. Otherwise, it is called an incomplete hierarchy. The specific values ​​of the elements in the contrast matrix can be calculated using the contrast matrix scaling method in Table 2. The specific method is shown in Table 2.

[0114] Table 2 Comparison of matrix scaling methods

[0115]

[0116] Assume that there are z factors in a layer, X={x1,x2,……,x z}, we need to compare the degree of their influence on a certain criterion (or goal) in the upper layer and determine the proportion of each factor in the layer relative to a certain criterion, that is, to rank the degree of influence of the z factors on a certain goal in the upper layer.

[0117] The above comparisons are made between two factors, using a scale of 1 to 9. Let a represent the comparison of the xth factor with respect to the yth factor, and A is called a pairwise comparison matrix.

[0118]

[0119] Step 3: Check the matrix consistency using formula (4).

[0120]

[0121] in, λ max is the maximum eigenvalue of the judgment matrix, CI is the consistency index, RI is the random consistency index, and CR is the random consistency ratio. If CR < 0.1, the judgment matrix is ​​considered to have passed the consistency test and the test is successful, and step 4 is performed. Otherwise, it does not have satisfactory consistency and needs to be reconstructed into a pairwise comparison matrix.

[0122] Step 4: Calculate the secondary indicator weights of the secondary indicator items.

[0123] Multiply each row of A and raise it to the kth power to get the vector in,

[0124] To W * , do normalization processing to get the secondary index weight vector, W (A) =(w1, w2, ..., w k ) T ,in,

[0125] d2. Based on the secondary indicator weights and secondary indicator scores of each secondary indicator item, determine the primary indicator scores of the corresponding primary indicator items, and determine the primary indicator weights of the corresponding primary indicator items through the multi-scheme decision-making hierarchical analysis method.

[0126] In the embodiment of the present invention, since each first-level indicator item in the first-level indicator column corresponds to one or more second-level indicator items in the second-level indicator column, the first-level indicator score of the corresponding first-level indicator item can be calculated by the weighted combination of the second-level indicator weights and the second-level indicator scores of the corresponding second-level indicator items, thereby obtaining the first-level indicator score of each first-level indicator item, and then the first-level indicator weight of each first-level indicator item can be calculated by the multi-scheme decision-making hierarchical analysis method.

[0127] e2. Perform weighted processing on the first-level indicator weights and first-level indicator scores of each first-level indicator item to obtain the charging facility operation status information of the grid area.

[0128] In an embodiment of the present invention, the first-level indicator weight and the first-level indicator score of each first-level indicator item can be weighted and processed to obtain the score of the operating status of the charging facilities in each grid area, and then obtain the charging facility operating status information of the grid area.

[0129] For example, Figure 5 This is a diagram showing the implementation effect of calculating the charging facility operation status score in the charging facility construction site selection and layout method provided in the second embodiment of the present invention.

[0130] First, obtain a multi-level indicator evaluation table S301, then smooth the values ​​of the third-level indicator items through the sigmoid function S302, and then calculate the third-level indicator weights S303 through the entropy weight method; use the superior and inferior solution distance method to obtain the third-level indicator score S304; the second-level indicator score of the corresponding second-level indicator item can be calculated by the third-level indicator weights of each third-level indicator item and the weighted combination of the third-level indicator scores S305, and the second-level indicator weights of each second-level indicator item can be calculated through the multi-scheme decision-making hierarchical analysis method S306; the first-level indicator score of the corresponding first-level indicator item can be calculated by the second-level indicator weights of each second-level indicator item and the weighted combination of the second-level indicator scores S307, and then the first-level indicator weights of each first-level indicator item can be calculated through the multi-scheme decision-making hierarchical analysis method S308, finally, the first-level indicator weights and first-level indicator scores of each first-level indicator item are weighted and combined to obtain the comprehensive score of the charging facility operation status of the grid area S309.

[0131] Furthermore, based on the above embodiment, the layout optimization model is combined with each operation level to obtain the relocation results of existing charging facilities in the grid area, including:

[0132] a3. Based on each operation level, determine the list of charging facilities that need to be relocated in the grid area.

[0133] In an embodiment of the present invention, the relocation list of charging facilities that need to be relocated in each grid area can be determined based on the operating level of each charging facility. For example, if the operating levels are divided into two levels, B and C, charging stations with an operating level of C have low charging pile utilization and some charging piles are idle. Therefore, charging facilities with an operating level of C can be included in the relocation list of charging facilities that need to be relocated.

[0134] b3. Determine the number of charging facilities to be relocated in the grid area through the layout optimization model and the relocation list of charging facilities, and output it as the relocation result.

[0135] In an embodiment of the present invention, the number of charging facilities to be relocated can be predicted through a layout optimization model, and then combined with a list of charging facilities to be relocated, the number of charging facilities to be relocated in each grid area can be finally determined and output as a relocation result.

[0136] Accordingly, by combining the site selection analysis model with each operation level, the results of the construction / relocation of existing charging facilities in the grid area are obtained, including:

[0137] a4. Based on each operation level, determine the list of new charging facilities that need to be relocated / relocated in the grid area.

[0138] In an embodiment of the present invention, the operational level of each charging facility can be used to determine a list of newly built / relocated charging facilities within a grid area. For example, if the operational levels are divided into two levels, B and C, charging stations at operational level B have a high demand for charging and users often queue for charging, necessitating additional charging facilities. Therefore, charging facilities at operational level B can be included in the list of newly built / relocated charging facilities.

[0139] b4. Determine the number of charging facilities to be built / relocated in the grid area through the layout optimization model and the list of new / relocated charging facilities, and output it as the new / relocated result.

[0140] In an embodiment of the present invention, the number of charging facilities to be relocated can be first predicted through a layout optimization model, and then combined with a list of new charging facilities to be built / relocated, the number of new charging facilities to be built / relocated in each grid area can be finally determined, and can be output as a new construction / relocation result.

[0141] Example 3

[0142] Figure 6 This is a schematic diagram of the structure of a charging facility site selection and layout device, provided in Example 3 of the present invention. This device can execute the charging facility site selection method provided in any of the embodiments of the present invention, and possesses the corresponding functional modules and beneficial effects of the execution method. The device can be implemented in software and / or hardware, and specifically includes: an area acquisition module 401, a data determination module 402, and a site determination module 403.

[0143] The region acquisition module 401 is used to acquire layer data in the electronic map and perform grid processing on the layer data to obtain at least one grid region;

[0144] A data determination module 402 is configured to determine charging performance information of each grid area based on charging-related information of existing charging facilities in each grid area and regional attribute information of the corresponding grid area;

[0145] The site selection determination module 403 is used to determine the site selection and layout results of each grid area relative to the charging facilities based on the charging performance information of each grid area in combination with a preset site selection analysis model and a layout optimization model.

[0146] The technical solution of the embodiment of the present invention obtains layer data from an electronic map through a region acquisition module and performs gridding processing on the layer data to obtain at least one grid area; the data determination module determines the charging performance information of each grid area based on the charging-related information of the existing charging facilities in each grid area and the regional attribute information of the corresponding grid area; and the site determination module determines the site selection and layout results of each grid area relative to the charging facilities based on the charging performance information of each grid area, combined with a preset site selection analysis model and layout optimization model. The above technical solution, based on the charging performance information of each grid area and the method combining the site selection analysis model and layout optimization model, solves the problems of unclear site selection and lack of in-depth research in the site selection and layout of charging facilities in the prior art, realizes the scientific and rational site selection and layout of charging facilities, meets the charging needs of users, improves user satisfaction, and thus promotes the full utilization of resources and improves resource utilization efficiency.

[0147] Furthermore, based on the above embodiment of the invention, the region acquisition module 401 includes:

[0148] a division unit, configured to obtain layer data from the electronic map and divide the layer data into at least one unit grid area in units of 1 kilometer;

[0149] a clustering unit, configured to perform clustering processing on each of the unit grid areas according to the vehicle travel data and vehicle charging power consumption data associated with each of the unit grid areas;

[0150] The acquisition unit is used to obtain the multi-class grid area formed after clustering processing.

[0151] Furthermore, based on the above-mentioned embodiment of the invention, the charging performance information includes: charging demand and charging capacity matching information of the grid area, and charging facility operation status information;

[0152] Accordingly, based on the above embodiment of the invention, the data determination module 402 includes:

[0153] A first determining unit is configured to determine existing charging facilities and corresponding charging-related information included in each grid area based on location information of installed charging facilities and the longitude and latitude of the grid area;

[0154] a second determining unit configured to determine, for each grid area, charging demand and charging capacity matching information of the grid area based on the area attribute information and the charging-related information of each existing charging facility and using a pre-built charging demand prediction basic model;

[0155] The third determining unit is configured to determine the operating status information of the charging facilities in the grid area based on the charging-related information of each of the existing charging facilities and through a pre-built operating status analysis model.

[0156] Furthermore, based on the above embodiments of the invention, the second determining unit is specifically configured to:

[0157] Extracting the vehicle travel data in the regional attribute information and the historical charging amount in each of the charging-related information according to at least one attribute dimension of season, holiday, weather, and temperature;

[0158] For each attribute dimension, the corresponding vehicle travel data and each of the historical charging amounts are input as input data into the charging demand prediction basic model to obtain the charging demand and charging capacity information of the grid area under the attribute dimension;

[0159] The charging demand and charging capacity information under each attribute dimension is summarized as the charging demand and charging capacity information of the grid area.

[0160] Furthermore, based on the above embodiments of the invention, the third determining unit is specifically configured to:

[0161] Obtaining a preset multi-level indicator evaluation table, wherein the multi-level indicator evaluation table includes a primary indicator column, a secondary indicator column, and a tertiary indicator column, each primary indicator item in the primary indicator column corresponds to at least one secondary indicator item in the secondary indicator column, and each secondary indicator item in the secondary indicator column corresponds to at least one tertiary indicator item in the tertiary indicator column;

[0162] Determining, based on each of the charging-related information, a third-level indicator weight and a third-level indicator score relative to each of the third-level indicator items;

[0163] Determining the secondary indicator scores of the corresponding secondary indicator items based on the third-level indicator weights and the third-level indicator scores of the third-level indicator items, and determining the secondary indicator weights of the corresponding secondary indicator items through a multi-scheme decision-making hierarchical analysis method;

[0164] Determine the first-level indicator score of the corresponding first-level indicator item based on the second-level indicator weight and the second-level indicator score of each second-level indicator item, and determine the first-level indicator weight of the corresponding first-level indicator item through a multi-scheme decision-making hierarchical analysis method;

[0165] The first-level indicator weight and the first-level indicator score of each of the first-level indicator items are weighted to obtain the charging facility operation status information of the grid area.

[0166] Furthermore, based on the above embodiment of the invention, the site selection determination module 403 includes:

[0167] a level unit, configured to determine, for each grid area, an operation level of each existing charging facility in the grid area based on the charging facility operation status information in the corresponding charging performance information;

[0168] a fourth determining unit configured to, if the charging facility operation status information does not meet the operation threshold condition, determine, using the layout optimization model in combination with each of the operation levels, a relocation result for existing charging facilities in the grid area; otherwise, determine whether new charging facilities are needed in the grid area based on traffic flow popularity data, surrounding business data, and charging demand and charging capacity matching information in the corresponding charging performance information;

[0169] an obtaining unit configured to obtain, if new charging facilities are required, a result of new construction / relocation of existing charging facilities in the grid area by using the site selection analysis model in combination with each of the operation levels; otherwise, a result of relocation of existing charging facilities in the grid area by using the layout optimization model in combination with each of the operation levels;

[0170] A result determination unit is configured to use the out-migration result and / or the new construction / migration result as a location selection and layout result of the grid area relative to the charging facility.

[0171] Furthermore, based on the above embodiments of the invention, the obtaining unit is specifically configured to:

[0172] Determining a relocation list of charging facilities that need to be relocated in the grid area based on each of the operation levels;

[0173] Determining the number of charging facilities to be relocated in the grid area using the layout optimization model and the charging facility relocation list, and outputting the result as a relocation result;

[0174] Accordingly, the site selection analysis model is combined with each of the operation levels to obtain the results of the construction / relocation of existing charging facilities in the grid area, including:

[0175] Determining a list of newly built / relocated charging facilities that need to be relocated in the grid area based on each of the operation levels;

[0176] The number of charging facilities to be newly built / relocated in the grid area is determined by using the layout optimization model and the list of newly built / relocated charging facilities, and is output as a result of the newly built / relocated charging facilities.

[0177] Example 4

[0178] Figure 7 This is a schematic diagram of the structure of a computer device provided by the fourth embodiment of the present invention. Figure 7A block diagram of a computer device 712 suitable for implementing embodiments of the present invention is shown. Figure 7 The computer device 712 shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present invention. The device 712 is a typical computing device for implementing the site selection and layout method for charging facility construction.

[0179] like Figure 7 As shown, computer device 712 is implemented as a general-purpose computing device. Components of computer device 712 may include, but are not limited to, one or more processors 716, storage device 728, and a bus 718 that connects various system components (including storage device 728 and processor 716).

[0180] Bus 718 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0181] The computer device 712 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 712, including volatile and non-volatile media, removable and non-removable media.

[0182] The storage device 728 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 730 and / or cache memory 732. The computer device 712 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 734 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 Not shown, often called a "hard drive"). Although Figure 7Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a Compact Disc-Read Only Memory (CD-ROM), a Digital Video Disc-Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 718 via one or more data medium interfaces. Storage device 728 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0183] A program 736 having a set (at least one) of program modules 726 may be stored, for example, in a storage device 728. Such program modules 726 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 726 generally implement the functions and / or methods of the embodiments described herein.

[0184] The computer device 712 may also communicate with one or more external devices 714 (e.g., a keyboard, a pointing device, a camera, a display 724, etc.), one or more devices that enable a user to interact with the computer device 712, and / or any device that enables the computer device 712 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 722. Furthermore, the computer device 712 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 720. As shown, the network adapter 720 communicates with the other modules of the computer device 712 via a bus 718. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in conjunction with the computer device 712, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk arrays (Redundant Arrays of Independent Disks, RAID) systems, tape drives, and data backup storage systems.

[0185] The processor 716 executes various functional applications and data processing by running the programs stored in the storage device 728, such as implementing the site selection and layout method for charging facility construction provided in the above embodiment of the present invention.

[0186] Example 5

[0187] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When executed by a processing device, the program implements the method for site selection and layout of charging facility construction, as described in an embodiment of the present invention. The computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0188] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0189] The computer-readable medium may be included in the computer device, or may exist independently without being incorporated into the computer device.

[0190] The computer-readable medium carries one or more programs. When the one or more programs are executed by the computer device, the computer device is caused to: obtain layer data in an electronic map, and perform gridding processing on the layer data to obtain at least one grid area;

[0191] Determining charging performance information of each grid area based on charging-related information of existing charging facilities in each grid area and regional attribute information of the corresponding grid area;

[0192] Based on the charging performance information of each grid area, combined with a preset site selection analysis model and a layout optimization model, the site selection and layout results of each grid area relative to the charging facilities are determined.

[0193] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0194] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0195] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.

[0196] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0197] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0198] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for site selection and layout of charging facility construction, characterized in that: include: Acquiring layer data in the electronic map, and performing grid processing on the layer data to obtain at least one grid area; Determining charging performance information of each grid area based on charging-related information of existing charging facilities in each grid area and regional attribute information of the corresponding grid area; Determining the location and layout of charging facilities in each grid area based on the charging performance information of each grid area and in combination with a preset location analysis model and a layout optimization model; The charging performance information includes: the charging demand and charging capacity matching information of the grid area, and the charging facility operation status information; Accordingly, determining the charging performance information of each grid area according to the charging association information of the existing charging facilities in each grid area and the regional attribute information of the corresponding grid area includes: Determine the existing charging facilities and corresponding charging-related information included in each grid area based on the location information of the installed charging facilities and the regional longitude and latitude of the grid area; For each grid area, based on the area attribute information and the charging-related information of each existing charging facility, a pre-built charging demand prediction basic model is used to determine the matching information between the charging demand and charging capacity of the grid area; Based on the charging-related information of each of the existing charging facilities, the operating status information of the charging facilities in the grid area is determined through a pre-built operating status analysis model.

2. The method according to claim 1, characterized in that The step of obtaining layer data from the electronic map and performing gridding processing on the layer data to obtain at least one grid area includes: Acquire layer data in an electronic map, and divide the layer data into at least one unit grid area in units of 1 kilometer; performing clustering processing on each of the unit grid areas according to the vehicle travel data and vehicle charging power consumption data associated with each of the unit grid areas; Get the multi-class grid area formed after clustering processing.

3. The method according to claim 1, characterized in that The determining of the charging demand and charging capacity matching information of the grid area based on the regional attribute information and the charging association information of each of the existing charging facilities by using a pre-built charging demand prediction basic model includes: Extracting the vehicle travel data in the regional attribute information and the historical charging amount in each of the charging-related information according to at least one attribute dimension of season, holiday, weather, and temperature; For each attribute dimension, the corresponding vehicle travel data and each of the historical charging amounts are input as input data into the charging demand prediction basic model to obtain the charging demand and charging capacity information of the grid area under the attribute dimension; The charging demand and charging capacity information under each attribute dimension is summarized as the charging demand and charging capacity information of the grid area.

4. The method according to claim 1, wherein The determining of the charging facility operation status information of the grid area based on the charging-related information of each existing charging facility by using a pre-built operation status analysis model includes: Obtaining a preset multi-level indicator evaluation table, wherein the multi-level indicator evaluation table includes a primary indicator column, a secondary indicator column, and a tertiary indicator column, each primary indicator item in the primary indicator column corresponds to at least one secondary indicator item in the secondary indicator column, and each secondary indicator item in the secondary indicator column corresponds to at least one tertiary indicator item in the tertiary indicator column; Determining, based on each of the charging-related information, a third-level indicator weight and a third-level indicator score relative to each of the third-level indicator items; Determining the secondary indicator scores of the corresponding secondary indicator items based on the third-level indicator weights and the third-level indicator scores of the third-level indicator items, and determining the secondary indicator weights of the corresponding secondary indicator items through a multi-scheme decision-making hierarchical analysis method; Determine the first-level indicator score of the corresponding first-level indicator item based on the second-level indicator weight and the second-level indicator score of each second-level indicator item, and determine the first-level indicator weight of the corresponding first-level indicator item through a multi-scheme decision-making hierarchical analysis method; The first-level indicator weight and the first-level indicator score of each of the first-level indicator items are weighted to obtain the charging facility operation status information of the grid area.

5. The method according to any one of claims 1 to 4, characterized in that Determining the location and layout results of the charging facilities in each grid area based on the charging performance information of each grid area in combination with a preset location analysis model and a layout optimization model includes: For each grid area, the operating level of each existing charging facility in the grid area is determined based on the charging facility operating status information in the corresponding charging performance information; if the charging facility operating status information does not meet the operating threshold condition, the layout optimization model is combined with each of the operating levels to obtain the relocation result of the existing charging facilities in the grid area; otherwise, based on the traffic flow heat data of the grid area, the surrounding business data, and the charging demand and charging capacity matching information in the corresponding charging performance information, it is determined whether the grid area needs to have new charging facilities; If new charging facilities are needed, the site selection analysis model is combined with each of the operation levels to obtain a result of new construction / relocation of existing charging facilities in the grid area; otherwise, the layout optimization model is combined with each of the operation levels to obtain a result of relocation of existing charging facilities in the grid area; The out-migration result and / or the new construction / migration result are used as the site selection and layout result of the grid area relative to the charging facilities.

6. The method according to claim 5, characterized in that The step of obtaining the relocation result of the existing charging facilities in the grid area by combining the layout optimization model with each of the operation levels includes: Determining a relocation list of charging facilities that need to be relocated in the grid area based on each of the operation levels; Determining the number of charging facilities to be relocated in the grid area using the layout optimization model and the charging facility relocation list, and outputting the result as a relocation result; Accordingly, the site selection analysis model is combined with each of the operation levels to obtain the results of the construction / relocation of existing charging facilities in the grid area, including: Determining a list of newly built / relocated charging facilities that need to be relocated in the grid area based on each of the operation levels; The number of charging facilities to be newly built / relocated in the grid area is determined by using the layout optimization model and the list of newly built / relocated charging facilities, and is output as a result of the newly built / relocated charging facilities.

7. A site selection and layout device for charging facility construction, characterized in that: include: An area acquisition module is used to acquire layer data in the electronic map and perform grid processing on the layer data to obtain at least one grid area; a data determination module, configured to determine charging performance information of each of the grid areas based on charging-related information of existing charging facilities in each of the grid areas and regional attribute information of the corresponding grid area; A site selection determination module, configured to determine the site selection and layout results of each grid area relative to the charging facilities based on the charging performance information of each grid area, combined with a preset site selection analysis model and a layout optimization model; The charging performance information includes: the charging demand and charging capacity matching information of the grid area, and the charging facility operation status information; The data determination module includes: a first determination unit for determining existing charging facilities and corresponding charging-related information included in each grid area based on location information of installed charging facilities and the longitude and latitude of the grid area; a second determining unit configured to determine, for each grid area, charging demand and charging capacity matching information of the grid area based on the area attribute information and the charging-related information of each existing charging facility and using a pre-built charging demand prediction basic model; The third determining unit is configured to determine the operating status information of the charging facilities in the grid area based on the charging-related information of each of the existing charging facilities and through a pre-built operating status analysis model.

8. A computer device, characterized in that: The computer device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the site selection and layout method for charging facility construction as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for site selection and layout of charging facility construction as described in any one of claims 1 to 6 is implemented.

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