Site selection method and device of charging station, electronic equipment and readable storage medium

Abstract

This disclosure provides a method, apparatus, electronic device, and readable storage medium for selecting charging stations, relating to the field of data processing technology, particularly spatiotemporal big data or charging station technology. The specific implementation scheme is as follows: The spatial distribution information of charging capacity within the planned area is determined based on relevant data from existing charging stations; the spatial distribution information of charging demand within the planned area is determined based on the parking locations of electric vehicles within the planned area; and the site selection for the charging station to be built within the planned area is determined based on the spatial distribution information of charging capacity and charging demand. In this scheme, the site selection for the charging station is obtained by analyzing the spatial distribution information of charging capacity and charging demand within the planned area, ensuring the rationality of the charging station site selection and thus improving the utilization effect of the charging station.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more particularly to the field of spatiotemporal big data or charging station technology. Specifically, this disclosure relates to a method, apparatus, electronic device and readable storage medium for selecting a charging station. Background Technology

[0002] Electric vehicles are increasingly being chosen by users due to their energy-saving and environmentally friendly advantages. Charging stations, as supporting facilities for charging electric vehicles, have a significant impact on the popularization of electric vehicles.

[0003] When planning the construction of charging stations, the rationality of the site selection directly affects the effectiveness of the charging stations. Therefore, how to select a reasonable site for charging stations has become an important technical issue in the construction planning process. Summary of the Invention

[0004] To address at least one of the aforementioned deficiencies, this disclosure provides a method, apparatus, electronic device, and readable storage medium for selecting a charging station.

[0005] According to a first aspect of this disclosure, a method for selecting the location of a charging station is provided, the method comprising:

[0006] The spatial distribution information of charging capacity in the planned area is determined based on the relevant data of the existing charging stations in the planned area.

[0007] The spatial distribution information of charging demand within the planned area is determined based on the parking locations of electric vehicles within the planned area.

[0008] Based on the spatial distribution information of charging capacity and charging demand, the site selection of charging stations to be built in the planned area is determined.

[0009] According to a second aspect of this disclosure, a site selection device for a charging station is provided, the device comprising:

[0010] The charging capacity spatial distribution module is used to determine the spatial distribution information of charging capacity in the area to be planned based on the relevant data of the existing charging stations in the area to be planned.

[0011] The charging demand spatial distribution module is used to determine the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles within the planned area.

[0012] The charging station site selection module is used to determine the site selection of charging stations to be built within the planned area based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand.

[0013] According to a third aspect of this disclosure, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to at least one of the aforementioned processors; wherein,

[0016] The memory stores instructions that can be executed by at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the location method for the charging station.

[0017] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause a computer to execute the above-described charging station site selection method.

[0018] According to a fifth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method for selecting the location of a charging station.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0020] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0021] Figure 1 This is a flowchart illustrating a method for selecting a charging station location according to an embodiment of this disclosure;

[0022] Figure 2 This is a flowchart illustrating another method for selecting the location of a charging station provided in an embodiment of this disclosure;

[0023] Figure 3 This is a schematic diagram of the structure of a site selection device for a charging station provided in an embodiment of this disclosure;

[0024] Figure 4 This is a schematic diagram of the structure of another charging station site selection device provided in an embodiment of this disclosure;

[0025] Figure 5 This is a block diagram of an electronic device used to implement the site selection method for a charging station according to embodiments of the present disclosure. Detailed Implementation

[0026] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0027] Figure 1 The diagram illustrates a flow chart of a charging station site selection method according to an embodiment of this disclosure, as shown below. Figure 1 As shown, the method can mainly include:

[0028] Step S110: Determine the spatial distribution information of charging capacity within the planned area based on the relevant data of existing charging stations in the planned area;

[0029] Step S120: Determine the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles within the planned area;

[0030] Step S130: Based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand, determine the location of the charging station to be built in the planning area.

[0031] The areas to be planned are those requiring the construction of charging stations. The areas to be planned also include charging stations that have already been built and are in use.

[0032] Data related to charging stations can include the location information of the charging stations, as well as data that may reflect the charging capacity of the charging stations, such as the number and type of charging piles in the charging station. The types of charging piles can include fast charging piles and slow charging piles, with fast charging piles having higher charging efficiency than slow charging piles.

[0033] The spatial distribution information of charging capacity is used to describe the ability of existing charging stations to provide charging services to different locations in the area to be planned. It can be modeled based on relevant data of charging stations, and the constructed model can describe the spatial distribution information of charging capacity.

[0034] As an example, an area adjacent to a charging station is defined as the charging station's service area. For areas outside the service area, due to their greater distance from the charging station, the station's service capacity is weaker compared to areas within the service area. Spatial distribution information of charging capacity reflects the relative strength of charging capacity both inside and outside the service area.

[0035] Spatial distribution information on charging demand is used to describe the degree of demand for charging of electric vehicles at various locations within the planned area. Since the charging time of electric vehicles is generally long, users tend to charge while parked. Therefore, a model is built based on the parking locations of electric vehicles to describe the spatial distribution information of charging demand.

[0036] In this embodiment of the disclosure, when planning the construction of charging stations in an area to be planned, the charging capacity of the area to be planned can be determined based on relevant data of the charging stations, and the charging demand of each location in the area to be planned can be analyzed in combination with the parking location of the users. Thus, the site selection of the charging station to be built can be comprehensively analyzed, making the determined site selection more reasonable.

[0037] The method provided in this disclosure determines the spatial distribution information of charging capacity within a planned area based on relevant data of existing charging stations; determines the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles; and determines the site selection for charging stations to be built within the planned area based on the spatial distribution information of charging capacity and charging demand. In this solution, the site selection for charging stations is obtained by analyzing the spatial distribution information of charging capacity and charging demand within the planned area, ensuring the rationality of the charging station site selection and thus improving the utilization effect of the charging stations.

[0038] In one optional embodiment of this disclosure, the parking location includes a parking location that is not charging and a parking location that is charging. The spatial distribution information of charging demand includes a first spatial distribution information of charging demand determined based on the parking location that is not charging, and a second spatial distribution information of charging demand determined based on the parking location that is charging.

[0039] In this embodiment of the disclosure, the parking location may include a parking location without charging and a parking location with charging. Specifically, the parking location may be a location where the electric vehicle's speed is zero. The parking location without charging may be a location where the electric vehicle's speed is zero and the remaining battery power of the electric vehicle has not increased. The parking location with charging may be a location where the electric vehicle's speed is zero and the remaining battery power of the electric vehicle has increased.

[0040] Both parked and non-charging locations and parked and charging locations can reflect the charging needs of electric vehicles. The first spatial distribution information of charging needs can be determined for parked and non-charging locations, and the second spatial distribution information of charging needs can be determined for parked and charging locations.

[0041] In one optional approach disclosed herein, the spatial distribution information of charging capacity within the planned area is determined based on relevant data of existing charging stations within the planned area, including:

[0042] For each existing charging station in the area to be planned, the spatial distribution information of the sub-charging capacity of the existing charging stations in the area to be planned is determined based on the relevant data of the existing charging stations.

[0043] By overlaying the spatial distribution information of each sub-charging capacity, the spatial distribution information of charging capacity within the area to be planned is obtained.

[0044] In this embodiment of the disclosure, the area to be planned may include multiple existing charging stations. The sub-charging capacity spatial distribution information is used to describe the charging capacity of a corresponding existing charging station at various locations within the area to be planned.

[0045] In this embodiment of the disclosure, the spatial distribution information of the sub-charging capacity of each existing charging station in the area to be planned can be determined based on the relevant data of the existing charging stations. Then, the spatial distribution information of each sub-charging capacity is superimposed to obtain the spatial distribution information of the charging capacity in the area to be planned.

[0046] In one optional approach disclosed herein, determining the spatial distribution information of sub-charging capacity of existing charging stations within the planned area based on relevant data of existing charging stations includes:

[0047] Based on the relevant data of existing charging stations, a Gaussian distribution model of sub-charging capacity is constructed. The Gaussian distribution model of sub-charging capacity is used to describe the spatial distribution information of sub-charging capacity of existing charging stations in the area to be planned.

[0048] By overlaying the spatial distribution information of each sub-charging capacity, the spatial distribution information of charging capacity within the area to be planned is obtained, including:

[0049] The sub-charging capacity Gaussian distribution models used to describe the spatial distribution information of each sub-charging capacity are superimposed to obtain the charging capacity Gaussian distribution model, which is used to describe the spatial distribution information of charging capacity within the area to be planned.

[0050] In this embodiment of the disclosure, the charging station-related data follows a Gaussian distribution. Therefore, a Gaussian distribution model of the sub-charging capacity of the existing charging station can be constructed based on the charging station-related data of the existing charging station. The sub-charging capacity Gaussian distribution model can describe the spatial distribution information of the sub-charging capacity of the existing charging station in the area to be planned.

[0051] In this embodiment of the disclosure, the spatial distribution information of each sub-charging capability is superimposed, which can be achieved by superimposing the Gaussian distribution models of each sub-charging capability to obtain the Gaussian distribution model of the charging capability.

[0052] As an example, the Gaussian distribution model of charging capacity can be represented by the following formula:

[0053] (Formula 1)

[0054] in, The number of existing charging stations. For the first Gaussian distribution parameters of the sub-charging capacity Gaussian distribution model corresponding to each charging station. The Gaussian distribution parameters are for the Gaussian distribution model of charging capacity. The probability density value of the Gaussian distribution model for the charging capability of the sub-charger. This represents the probability density value of the Gaussian distribution model of charging capability.

[0055] In practical use, the ability of an existing charging station to provide charging services to different locations in a planned area can be measured by the probability density value of that location predicted by a Gaussian distribution model of charging capacity.

[0056] In one optional approach of this disclosure, determining the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles within the planned area includes:

[0057] A Gaussian distribution model of charging demand is constructed based on the parking locations of electric vehicles within the area to be planned. This model is used to describe the spatial distribution information of charging demand within the area to be planned.

[0058] In this embodiment of the disclosure, the parking positions of electric vehicles follow a Gaussian distribution; therefore, a Gaussian distribution model of charging demand can be constructed based on the parking positions of electric vehicles. Specifically, a first Gaussian distribution model of charging demand can be constructed based on parking positions where charging is not yet initiated. This first Gaussian distribution model of charging demand describes the spatial distribution information of the first charging demand. A second Gaussian distribution model of charging demand can be constructed based on parking and charging positions. This second Gaussian distribution model of charging demand describes the spatial distribution information of the second charging demand.

[0059] In practical use, the demand for electric vehicles to be charged at various locations in the planned area can be measured by the probability density value of that location predicted by the Gaussian distribution model of charging demand.

[0060] As an example, the Gaussian distribution model of charging demand can be expressed by the following formula:

[0061] (Formula 2)

[0062] Among them, the Gaussian distribution model of charging demand includes Gaussian mixture distribution model of individual charging demand Gaussian distribution model The specific value can be determined by the Bayesian information criterion. For the first Gaussian distribution parameters of the individual charging demand Gaussian distribution model The Gaussian distribution parameters are for the Gaussian distribution model of charging demand. The first parking position One observation data, This represents the probability density value of the Gaussian distribution model of charging demand.

[0063] Latent variables can be introduced , It can be defined using the following formula three:

[0064] (Formula 3)

[0065] in, That is, when the i-th observation data belongs to the n-th sub-model It is 1 in all other cases. It is 0.

[0066] The likelihood function can then be expressed by the following formula:

[0067] (Formula 4)

[0068] in, The value of the likelihood function. The Gaussian distribution parameters are for the Gaussian distribution model of charging demand. For the first One hidden variable, For the first One hidden variable, For the first Two hidden variables, For the first Hidden variables

[0069] The expectation-maximization algorithm is used to solve the problem, and the optimal solution of the likelihood function is obtained, thus yielding the Gaussian distribution model of charging demand.

[0070] In one optional approach disclosed herein, the site selection for charging stations to be built within the planned area is determined based on spatial distribution information of charging capacity and spatial distribution information of charging demand, including:

[0071] Spatial distribution information of site selection possibilities is constructed based on spatial distribution information of charging capacity and spatial distribution information of charging demand.

[0072] The location of charging stations to be built within the planned area is determined based on the spatial distribution information of site selection possibilities.

[0073] In this embodiment of the disclosure, spatial distribution information on site selection probability can be constructed based on spatial distribution information of charging capacity and spatial distribution information of charging demand. This spatial distribution information describes the likelihood of each location within the planned area being selected as a site for a charging station to be built. After determining the spatial distribution information on site selection probability, the location of the charging station to be built can be determined based on this information.

[0074] In one optional embodiment of this disclosure, the spatial distribution information of charging demand includes first spatial distribution information of charging demand determined based on the location of a parked vehicle not charging, and second spatial distribution information of charging demand determined based on the location of a parked vehicle charging. A spatial distribution information of site selection probability is constructed based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand, including:

[0075] Based on the similarity between each pair of the spatial distribution information of charging capacity, the spatial distribution information of the first charging demand, and the spatial distribution information of the second charging demand, the first weight corresponding to the spatial distribution information of charging capacity, the second weight corresponding to the spatial distribution information of the first charging demand, and the third weight corresponding to the spatial distribution information of the second charging demand are determined respectively.

[0076] Based on the first weight, the second weight, and the third weight, the spatial distribution information of charging capacity, the spatial distribution information of first charging demand, and the spatial distribution information of second charging demand are superimposed to obtain the spatial distribution information of site selection possibility.

[0077] In this embodiment of the disclosure, the spatial distribution information of the location possibility is determined based on the spatial distribution information of the charging capacity, the spatial distribution information of the first charging demand, and the spatial distribution information of the second charging demand. The Gaussian distribution model of the location possibility can be obtained by fusing the Gaussian distribution model of the charging capacity used to describe the spatial distribution information of the charging capacity, the Gaussian distribution model of the first charging demand used to describe the spatial distribution information of the first charging demand, and the Gaussian distribution model of the second charging demand used to describe the spatial distribution information of the second charging demand.

[0078] In practical use, the probability of each location in the planned area being selected as a potential charging station can be measured by the probability density value of that location predicted by the Gaussian distribution model of the location probability.

[0079] As an example, the Gaussian distribution model of site selection probability can be calculated using the following formula:

[0080] (Formula 5)

[0081] in, This represents a Gaussian distribution model indicating the likelihood of site selection. As the first weight, , As the second weight, This represents the Gaussian distribution model of the first charging demand. As the third weight, This represents the Gaussian distribution model of the second charging demand.

[0082] As an example, the first weight, second weight, and third weight can all be calculated using the following formula six:

[0083] (Formula 6)

[0084] The Gaussian distribution model of charging capacity, the Gaussian distribution model of first charging demand, and the Gaussian distribution model of second charging demand can be referred to as the first Gaussian distribution model, the second Gaussian distribution model, and the third Gaussian distribution model, respectively. Indicates the first A Gaussian distribution model, Indicates the first The Gaussian distribution model and the first Similarity of Gaussian distribution models.

[0085] As an example, the first The Gaussian distribution model and the first The similarity between the Gaussian distribution models can be calculated using the following formula:

[0086] (Formula 7)

[0087] in, Indicates the area to be planned. This represents the grid divided from the area to be planned. Let u be the probability density value of the u-th Gaussian distribution model in grid j. For the first The probability density values ​​of a Gaussian distribution model in grid j.

[0088] In this embodiment of the disclosure, when determining the location of a charging station to be built within a planned area based on the spatial distribution information of location probability, multiple candidate locations in the planned area can be input into a Gaussian distribution model of location probability, and the probability density values ​​corresponding to these candidate locations can be output. The probability density values ​​characterize the likelihood of a candidate location being selected as the site for the charging station. In practical use, candidate locations with probability density values ​​higher than a set value can be selected as the site for the charging station, or the candidate locations can be sorted according to the probability density values, and a preset number of candidate locations with higher probability density values ​​can be selected as the site for the charging station.

[0089] In one alternative embodiment of this disclosure, the method further includes:

[0090] The service area of ​​the charging station to be built is determined based on its location.

[0091] Determine charging demand based on charging data of electric vehicles within the service area.

[0092] Configure the power supply capacity of the charging stations to be built based on the charging demand.

[0093] In this embodiment of the disclosure, the service area of ​​the charging station to be built can be determined according to the location of the charging station to be built. For example, a circular area with the location of the charging station to be built as the center and a preset length as the radius can be defined as the service area.

[0094] Charging data can be obtained from electric vehicle charging order data, which may include charging frequency, charging time, etc. Based on the charging data of electric vehicles within the service area, the charging demand can be determined. The power supply capacity of the charging stations to be built can then be configured based on this demand to ensure that the power supply capacity of the charging stations can meet the charging needs within their service areas.

[0095] In this embodiment of the disclosure, the charging demand can be obtained by predicting the charging time series based on the charging data using models such as the Autoregressive Integrated Moving Average Model (ARIMA), the Long Short-Term Memory (LSTM) model, and the Deep Autoregressive Recurrent (DeepAR) model.

[0096] Figure 2 A flowchart illustrating another method for selecting a charging station location according to an embodiment of this disclosure is shown, such as... Figure 2 As shown, the method can mainly include:

[0097] Step S210: Determine the spatial distribution information of charging capacity within the planned area based on the relevant data of existing charging stations in the planned area;

[0098] Step S220: The spatial distribution information of the first charging demand is determined based on the parking and non-charging locations of electric vehicles in the area to be planned, and the spatial distribution information of the second charging demand is determined based on the parking and charging locations of electric vehicles in the area to be planned.

[0099] Step S230: Construct spatial distribution information of site selection possibilities based on spatial distribution information of charging capacity and spatial distribution information of charging demand;

[0100] Step S240: Determine the location of the charging station to be built within the planning area based on the spatial distribution information of the site selection possibility.

[0101] The areas to be planned are those requiring the construction of charging stations. The areas to be planned also include charging stations that have already been built and are in use.

[0102] Data related to charging stations can include the location information of the charging stations, as well as data that may reflect the charging capacity of the charging stations, such as the number and type of charging piles in the charging station. The types of charging piles can include fast charging piles and slow charging piles, with fast charging piles having higher charging efficiency than slow charging piles.

[0103] The spatial distribution information of charging capacity is used to describe the ability of existing charging stations to provide charging services to different locations in the area to be planned. It can be modeled based on relevant data of charging stations, and the constructed model can describe the spatial distribution information of charging capacity.

[0104] As an example, an area adjacent to a charging station is defined as the charging station's service area. For areas outside the service area, due to their greater distance from the charging station, the station's service capacity is weaker compared to areas within the service area. Spatial distribution information of charging capacity reflects the relative strength of charging capacity both inside and outside the service area.

[0105] Spatial distribution information on charging demand is used to describe the degree of demand for charging of electric vehicles at various locations within the planned area. Since the charging time of electric vehicles is generally long, users tend to charge while parked. Therefore, a model is built based on the parking locations of electric vehicles to describe the spatial distribution information of charging demand.

[0106] In this embodiment of the disclosure, when planning the construction of charging stations in an area to be planned, the charging capacity of the area to be planned can be determined based on relevant data of the charging stations, and the charging demand of each location in the area to be planned can be analyzed in combination with the parking location of the users. Thus, the site selection of the charging station to be built can be comprehensively analyzed, making the determined site selection more reasonable.

[0107] In this embodiment of the disclosure, the parking location may include a parking location without charging and a parking location with charging. Specifically, the parking location may be a location where the electric vehicle's speed is zero. The parking location without charging may be a location where the electric vehicle's speed is zero and the remaining battery power of the electric vehicle has not increased. The parking location with charging may be a location where the electric vehicle's speed is zero and the remaining battery power of the electric vehicle has increased.

[0108] Both parked and non-charging locations and parked and charging locations can reflect the charging needs of electric vehicles. The first spatial distribution information of charging needs can be determined for parked and non-charging locations, and the second spatial distribution information of charging needs can be determined for parked and charging locations.

[0109] In this embodiment of the disclosure, spatial distribution information on site selection probability can be constructed based on spatial distribution information of charging capacity and spatial distribution information of charging demand. This spatial distribution information describes the likelihood of each location within the planned area being selected as a site for a charging station to be built. After determining the spatial distribution information on site selection probability, the location of the charging station to be built can be determined based on this information.

[0110] In this embodiment of the disclosure, the spatial distribution information of the location possibility is determined based on the spatial distribution information of the charging capacity, the spatial distribution information of the first charging demand, and the spatial distribution information of the second charging demand. The Gaussian distribution model of the location possibility can be obtained by fusing the Gaussian distribution model of the charging capacity used to describe the spatial distribution information of the charging capacity, the Gaussian distribution model of the first charging demand used to describe the spatial distribution information of the first charging demand, and the Gaussian distribution model of the second charging demand used to describe the spatial distribution information of the second charging demand.

[0111] In practical use, the probability of each location in the planned area being selected as a potential charging station can be measured by the probability density value of that location predicted by the Gaussian distribution model of the location probability.

[0112] The method provided in this disclosure determines the spatial distribution information of charging capacity within a planned area based on relevant data of existing charging stations; determines the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles; and determines the site selection for charging stations to be built within the planned area based on the spatial distribution information of charging capacity and charging demand. In this solution, the site selection for charging stations is obtained by analyzing the spatial distribution information of charging capacity and charging demand within the planned area, ensuring the rationality of the charging station site selection and thus improving the utilization effect of the charging stations.

[0113] Based on and Figure 1 The method shown follows the same principle. Figure 3 A schematic diagram of the structure of a site selection device for a charging station provided in an embodiment of this disclosure is shown, as follows: Figure 3 As shown, the site selection device 30 of the charging station may include:

[0114] The charging capacity spatial distribution module 310 is used to determine the charging capacity spatial distribution information within the planned area based on the relevant data of the existing charging stations in the planned area.

[0115] The charging demand spatial distribution module 320 is used to determine the charging demand spatial distribution information within the planned area based on the parking locations of electric vehicles within the planned area.

[0116] The charging station site selection module 330 is used to determine the site selection of charging stations to be built in the planned area based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand.

[0117] The apparatus provided in this disclosure determines the spatial distribution information of charging capacity within a planned area based on relevant data of existing charging stations; determines the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles; and determines the site selection for charging stations to be built within the planned area based on the spatial distribution information of charging capacity and charging demand. In this solution, the site selection for charging stations is obtained by analyzing the spatial distribution information of charging capacity and charging demand within the planned area, ensuring the rationality of the charging station site selection and thus improving the utilization effect of the charging stations.

[0118] Optionally, the parking location includes parking locations that are not charging and parking locations that are charging. The spatial distribution information of charging demand includes a first spatial distribution information of charging demand determined based on parking locations that are not charging, and a second spatial distribution information of charging demand determined based on parking locations that are charging.

[0119] Optionally, the charging capacity spatial distribution module is specifically used for:

[0120] For each existing charging station in the area to be planned, the spatial distribution information of the sub-charging capacity of the existing charging stations in the area to be planned is determined based on the relevant data of the existing charging stations.

[0121] By overlaying the spatial distribution information of each sub-charging capacity, the spatial distribution information of charging capacity within the area to be planned is obtained.

[0122] Optionally, when determining the spatial distribution information of sub-charging capacity of existing charging stations within the planned area based on relevant data of existing charging stations, the charging capacity spatial distribution module is specifically used for:

[0123] Based on the relevant data of existing charging stations, a Gaussian distribution model of sub-charging capacity is constructed. The Gaussian distribution model of sub-charging capacity is used to describe the spatial distribution information of sub-charging capacity of existing charging stations in the area to be planned.

[0124] When the charging capacity spatial distribution module overlays the spatial distribution information of each sub-charging capacity to obtain the spatial distribution information of the charging capacity within the area to be planned, it is specifically used for:

[0125] The sub-charging capacity Gaussian distribution models used to describe the spatial distribution information of each sub-charging capacity are superimposed to obtain the charging capacity Gaussian distribution model, which is used to describe the spatial distribution information of charging capacity within the area to be planned.

[0126] Optionally, the charging demand spatial distribution module is specifically used for:

[0127] A Gaussian distribution model of charging demand is constructed based on the parking locations of electric vehicles within the area to be planned. This model is used to describe the spatial distribution information of charging demand within the area to be planned.

[0128] Optionally, the charging station location module is specifically used for:

[0129] Spatial distribution information of site selection possibilities is constructed based on spatial distribution information of charging capacity and spatial distribution information of charging demand.

[0130] The location of charging stations to be built within the planned area is determined based on the spatial distribution information of site selection possibilities.

[0131] Optionally, the spatial distribution information of charging demand includes first spatial distribution information of charging demand determined based on the location of a parked vehicle that is not charging, and second spatial distribution information of charging demand determined based on the location of a parked vehicle that is charging. When constructing the spatial distribution information of site selection possibilities based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand, the charging station site selection module is specifically used for:

[0132] Based on the similarity between each pair of the spatial distribution information of charging capacity, the spatial distribution information of the first charging demand, and the spatial distribution information of the second charging demand, the first weight corresponding to the spatial distribution information of charging capacity, the second weight corresponding to the spatial distribution information of the first charging demand, and the third weight corresponding to the spatial distribution information of the second charging demand are determined respectively.

[0133] Based on the first weight, the second weight, and the third weight, the spatial distribution information of charging capacity, the spatial distribution information of first charging demand, and the spatial distribution information of second charging demand are superimposed to obtain the spatial distribution information of site selection possibility.

[0134] Optionally, the above device further includes a power supply capability determination module, which is used for:

[0135] The service area of ​​the charging station to be built is determined based on its location.

[0136] Determine charging demand based on charging data of electric vehicles within the service area.

[0137] Configure the power supply capacity of the charging stations to be built based on the charging demand.

[0138] It is understood that the above-mentioned modules of the charging station site selection device in the embodiments of this disclosure have the ability to implement... Figure 1The embodiments shown illustrate the functionality of the corresponding steps in the charging station location selection method. This functionality can be implemented in hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions. These modules can be software and / or hardware, and each module can be implemented individually or integrated from multiple modules. For a detailed description of the functions of each module in the charging station location selection device, please refer to [link to relevant documentation]. Figure 1 The corresponding description of the charging station site selection method in the illustrated embodiments will not be repeated here.

[0139] Based on and Figure 2 The method shown follows the same principle. Figure 4 A schematic diagram of the structure of another charging station site selection device provided in an embodiment of this disclosure is shown, as follows: Figure 4 As shown, the location selection device 40 of the charging station may include:

[0140] The charging capacity spatial distribution module 410 is used to determine the charging capacity spatial distribution information within the planned area based on the relevant data of the existing charging stations in the planned area.

[0141] The charging demand spatial distribution module 420 is used to determine the first charging demand spatial distribution information based on the parking and non-charging locations of electric vehicles in the area to be planned, and the second charging demand spatial distribution information based on the parking and charging locations of electric vehicles in the area to be planned.

[0142] The site selection possibility spatial distribution module 430 is used to construct site selection possibility spatial distribution information based on charging capacity spatial distribution information and charging demand spatial distribution information.

[0143] The charging station site selection module 440 determines the site selection of the charging station to be built within the planning area based on the spatial distribution information of site selection possibilities.

[0144] The apparatus provided in this disclosure determines the spatial distribution information of charging capacity within a planned area based on relevant data of existing charging stations; determines the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles; and determines the site selection for charging stations to be built within the planned area based on the spatial distribution information of charging capacity and charging demand. In this solution, the site selection for charging stations is obtained by analyzing the spatial distribution information of charging capacity and charging demand within the planned area, ensuring the rationality of the charging station site selection and thus improving the utilization effect of the charging stations.

[0145] It is understood that the above-mentioned modules of the charging station site selection device in the embodiments of this disclosure have the ability to implement... Figure 2The embodiments shown illustrate the functionality of the corresponding steps in the charging station location selection method. This functionality can be implemented in hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions. These modules can be software and / or hardware, and each module can be implemented individually or integrated from multiple modules. For a detailed description of the functions of each module in the charging station location selection device, please refer to [link to relevant documentation]. Figure 2 The corresponding description of the charging station site selection method in the illustrated embodiments will not be repeated here.

[0146] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0147] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0148] The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a charging station location method as provided in the embodiments of this disclosure.

[0149] Compared with existing technologies, this electronic device determines the spatial distribution information of charging capacity within the planned area by analyzing relevant data from existing charging stations; it determines the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles; and it determines the site selection for charging stations to be built within the planned area based on both the spatial distribution information of charging capacity and the spatial distribution information of charging demand. This solution ensures the rationality of charging station site selection by analyzing the spatial distribution information of charging capacity and charging demand within the planned area, thereby improving the utilization efficiency of charging stations.

[0150] The readable storage medium is a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the charging station site selection method provided in the embodiments of this disclosure.

[0151] Compared with existing technologies, this readable storage medium determines the spatial distribution information of charging capacity within the planned area based on relevant data of existing charging stations; it determines the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles; and it determines the site selection for charging stations to be built within the planned area based on both the spatial distribution information of charging capacity and the spatial distribution information of charging demand. In this solution, the site selection for charging stations is determined through analysis of the spatial distribution information of charging capacity and charging demand within the planned area, ensuring the rationality of the charging station site selection and thus improving the utilization effect of the charging stations.

[0152] The computer program product includes a computer program that, when executed by a processor, implements the charging station site selection method provided in the embodiments of this disclosure.

[0153] Compared with existing technologies, this computer program product determines the spatial distribution information of charging capacity within the planned area based on relevant data from existing charging stations; it determines the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles; and it determines the site selection for charging stations to be built within the planned area based on both the spatial distribution information of charging capacity and the spatial distribution information of charging demand. In this solution, the site selection for charging stations is determined through analysis of the spatial distribution information of charging capacity and charging demand within the planned area, ensuring the rationality of the charging station site selection and thus improving the utilization efficiency of the charging stations.

[0154] Figure 5 A schematic block diagram of an example electronic device 2000 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0155] like Figure 5As shown, the electronic device 2000 includes a computing unit 2010, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 2020 or a computer program loaded from a storage unit 2080 into a random access memory (RAM) 2030. The RAM 2030 may also store various programs and data required for the operation of the device 2000. The computing unit 2010, ROM 2020, and RAM 2030 are interconnected via a bus 2040. An input / output (I / O) interface 2050 is also connected to the bus 2040.

[0156] Multiple components in device 2000 are connected to I / O interface 2050, including: input unit 2060, such as keyboard, mouse, etc.; output unit 2070, such as various types of monitors, speakers, etc.; storage unit 2080, such as disk, optical disk, etc.; and communication unit 2090, such as network card, modem, wireless transceiver, etc. Communication unit 2090 allows device 2000 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0157] The computing unit 2010 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 2010 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 2010 executes the charging station addressing method provided in the embodiments of this disclosure. For example, in some embodiments, executing the charging station addressing method provided in the embodiments of this disclosure can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 2080. In some embodiments, part or all of the computer program can be loaded and / or installed on device 2000 via ROM 2020 and / or communication unit 2090. When the computer program is loaded into RAM 2030 and executed by computing unit 2010, one or more steps of the charging station addressing method provided in the embodiments of this disclosure can be performed. Alternatively, in other embodiments, the computing unit 2010 may be configured by any other suitable means (e.g., by means of firmware) to perform the charging station location method provided in the embodiments of this disclosure.

[0158] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0159] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0160] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0161] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0162] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0163] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0164] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0165] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for selecting the location of a charging station, comprising: The spatial distribution information of charging capacity in the planned area is determined based on the relevant data of the existing charging stations in the planned area. The spatial distribution information of charging demand in the planned area is determined based on the parking locations of electric vehicles in the planned area; the parking locations include parking locations that are not charging and parking locations that are charging; the spatial distribution information of charging demand includes a first spatial distribution information of charging demand determined based on the parking locations that are not charging, and a second spatial distribution information of charging demand determined based on the parking locations that are charging. Based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand, the site selection of charging stations to be built in the area to be planned is determined; The step of determining the spatial distribution information of charging capacity within the planned area based on relevant data of existing charging stations within the planned area includes: Based on the charging station-related data of the existing charging stations, a sub-charging capacity Gaussian distribution model is constructed. The sub-charging capacity Gaussian distribution model is used to describe the spatial distribution information of the sub-charging capacity of the existing charging stations in the area to be planned. The sub-charging capacity Gaussian distribution models used to describe the spatial distribution information of each sub-charging capacity are superimposed to obtain a charging capacity Gaussian distribution model, which is used to describe the spatial distribution information of charging capacity within the area to be planned. The step of determining the spatial distribution information of charging demand within the planned area based on the parking locations of electric vehicles within the planned area includes: A Gaussian distribution model of charging demand is constructed based on the parking locations of electric vehicles within the area to be planned. This model is used to describe the spatial distribution information of charging demand within the area to be planned.

2. The method according to claim 1, wherein, The step of determining the site selection of charging stations to be built within the planned area based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand includes: Based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand, a spatial distribution information of site selection possibility is constructed. The location of the charging station to be built in the planned area is determined based on the spatial distribution information of the site selection possibility.

3. The method according to claim 2, wherein, The spatial distribution information of charging demand includes first spatial distribution information of charging demand determined based on the parking and non-charging location, and second spatial distribution information of charging demand determined based on the parking and charging location. The step of constructing spatial distribution information of site selection possibilities based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand includes: Based on the similarity between each two items in the spatial distribution information of charging capacity, the spatial distribution information of the first charging demand, and the spatial distribution information of the second charging demand, a first weight corresponding to the spatial distribution information of charging capacity, a second weight corresponding to the spatial distribution information of the first charging demand, and a third weight corresponding to the spatial distribution information of the second charging demand are determined respectively. Based on the first weight, the second weight, and the third weight, the spatial distribution information of charging capacity, the spatial distribution information of the first charging demand, and the spatial distribution information of the second charging demand are superimposed to obtain the spatial distribution information of the site selection possibility.

4. The method according to claim 1, further comprising: The service area of ​​the proposed charging station is determined based on its location. The charging demand is determined based on the charging data of electric vehicles within the service area. Configure the power supply capacity of the charging station to be built based on the charging demand.

5. A site selection device for a charging station, comprising: The charging capacity spatial distribution module is used to determine the charging capacity spatial distribution information within the planned area based on the relevant data of the existing charging stations within the planned area. A charging demand spatial distribution module is used to determine the charging demand spatial distribution information within the planned area based on the parking locations of electric vehicles within the planned area; the parking locations include parking locations without charging and parking locations with charging; the charging demand spatial distribution information includes first charging demand spatial distribution information determined based on the parking locations without charging, and second charging demand spatial distribution information determined based on the parking locations with charging. The charging station site selection module is used to determine the site selection of the charging station to be built in the area to be planned based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand. The charging capacity spatial distribution module is specifically used for: Based on the charging station-related data of the existing charging stations, a sub-charging capacity Gaussian distribution model is constructed. The sub-charging capacity Gaussian distribution model is used to describe the spatial distribution information of the sub-charging capacity of the existing charging stations in the area to be planned. The sub-charging capacity Gaussian distribution models used to describe the spatial distribution information of each sub-charging capacity are superimposed to obtain a charging capacity Gaussian distribution model, which is used to describe the spatial distribution information of charging capacity within the area to be planned. The charging demand spatial distribution module is specifically used for: A Gaussian distribution model of charging demand is constructed based on the parking locations of electric vehicles within the area to be planned. This model is used to describe the spatial distribution information of charging demand within the area to be planned.

6. The apparatus according to claim 5, wherein, The charging station site selection module is specifically used for: Based on the spatial distribution information of charging capacity and the spatial distribution information of charging demand, a spatial distribution information of site selection possibility is constructed. The location of the charging station to be built in the planned area is determined based on the spatial distribution information of the site selection possibility.

7. The apparatus according to claim 6, wherein, The charging demand spatial distribution information includes first charging demand spatial distribution information determined based on the parking and non-charging location, and second charging demand spatial distribution information determined based on the parking and charging location. When constructing the site selection possibility spatial distribution information based on the charging capacity spatial distribution information and the charging demand spatial distribution information, the charging station site selection module is specifically used for: Based on the similarity between each two items in the spatial distribution information of charging capacity, the spatial distribution information of the first charging demand, and the spatial distribution information of the second charging demand, a first weight corresponding to the spatial distribution information of charging capacity, a second weight corresponding to the spatial distribution information of the first charging demand, and a third weight corresponding to the spatial distribution information of the second charging demand are determined respectively. Based on the first weight, the second weight, and the third weight, the spatial distribution information of charging capacity, the spatial distribution information of the first charging demand, and the spatial distribution information of the second charging demand are superimposed to obtain the spatial distribution information of the site selection possibility.

8. The apparatus according to claim 5, further comprising a power supply capability determination module, the power supply capability determination module being used for: The service area of ​​the proposed charging station is determined based on its location. The charging demand is determined based on the charging data of electric vehicles within the service area. Configure the power supply capacity of the charging station to be built based on the charging demand.

9. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

11. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-4.

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