Method, device and equipment for determining charging map and storage medium

Abstract

Embodiments of the present application provide a charging map determination method, device, equipment and storage medium. The method first acquires charging state information corresponding to a plurality of historical charging vehicles respectively. For the charging state information of each historical charging vehicle, the charging state information is used to indicate time data, position data and charging data of the historical charging vehicle in a charging state within a preset time length. Then, according to a preset filtering condition, the plurality of charging state information is filtered to obtain a plurality of target position data, wherein the filtering condition is determined based on a preset charging time length and a preset charging position error. Finally, according to the plurality of target position data and the charging data corresponding to the plurality of target position data, a charging map suitable for vehicle charging is determined. The method of the present application realizes the determination of the charging map suitable for vehicle charging, and effectively reduces the charging failure rate of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle charging technology, and in particular to a method, apparatus, device and storage medium for determining a charging map. Background Technology

[0002] With the transformation of the energy structure, electric heavy-duty trucks have become a key vehicle for emission reduction. However, their large-scale promotion is severely constrained by the basic charging infrastructure. On the one hand, the basic charging infrastructure limits vehicle height and high power requirements, and only a small number of public charging stations are compatible with electric heavy-duty trucks; on the other hand, the unbalanced layout of charging facilities and lack of information lead to low operational efficiency, with some electric heavy-duty trucks spending a long time finding available charging piles and charging inefficiently.

[0003] Existing charging maps are mainly based on historical charging data of electric vehicles. By obtaining the location coordinates of basic charging facilities recorded in the historical charging data, these basic charging facilities are marked on the map to generate a charging map marked with basic charging facilities. Alternatively, charging maps can also be obtained by directly marking the installation location information of basic charging facilities.

[0004] However, existing charging maps cannot meet the charging needs of electric heavy trucks, and are prone to problems such as a high charging failure rate. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for determining a charging map, in order to reduce the charging failure rate of electric heavy trucks.

[0006] In a first aspect, embodiments of this application provide a method for determining a charging map, comprising:

[0007] Obtain charging status information corresponding to multiple historical charging vehicles. For each historical charging vehicle, the charging status information is used to indicate the time data, location data, and charging data of the historical charging vehicle in the charging state within a preset time period.

[0008] According to preset filtering conditions, the multiple charging status information is filtered to obtain multiple target location data. The filtering conditions are determined based on preset charging time and preset charging location error.

[0009] Based on the multiple target location data and the corresponding charging data, a charging map suitable for vehicle charging is determined.

[0010] In one or more embodiments, determining a charging map suitable for vehicle charging based on the plurality of target location data and the charging data corresponding to the plurality of target location data includes:

[0011] Determine the spatial similarity and textual similarity between any two target location data points;

[0012] A charging map suitable for vehicle charging is determined based on the spatial similarity between every two target location data points, the textual similarity between every two target location data points, and the charging data corresponding to the multiple target location data points.

[0013] In one or more embodiments, determining a charging map suitable for vehicle charging based on the spatial similarity between every two target location data points, the textual similarity between every two target location data points, and the charging data corresponding to the plurality of target location data points includes:

[0014] Determine the spatial similarity and textual similarity values ​​for each pair of target location data;

[0015] Two target location data with a similarity value greater than the first similarity threshold are identified as the same first target location data;

[0016] Two target location data with a similarity sum less than or equal to the first similarity threshold are identified as different second target location data;

[0017] Based on all the first target location data, all the second target location data, the charging data corresponding to each of the first target location data, and the charging data corresponding to each of the second target location data, a charging map suitable for vehicle charging is determined.

[0018] In one or more embodiments, determining the spatial similarity between every two target location data includes:

[0019] Based on the latitude and longitude information in the data of each pair of target locations, determine the straight-line distance and spherical distance corresponding to each pair of target locations;

[0020] The spatial similarity between each pair of target location data is determined based on the straight-line distance, spherical distance, and cluster radius. The cluster radius is determined based on the physical region type of each pair of target location data and the longitude information in all target location data.

[0021] In one or more embodiments, determining the text similarity between every two target location data points includes:

[0022] Based on the latitude and longitude information in each pair of target location data, determine the address description information corresponding to each pair of target location data;

[0023] Based on the address description information corresponding to each pair of target location data, determine the edit distance corresponding to each pair of target location data;

[0024] The text similarity between two target location data points is determined based on the edit distance between each pair of target location data points and the address description information between each pair of target location data points.

[0025] In one or more embodiments, determining a charging map suitable for vehicle charging based on all first target location data, all second target location data, charging data corresponding to each of the first target location data, and charging data corresponding to each of the second target location data includes:

[0026] Based on all the first target location data and all the second target location data, the locations of multiple charging piles in the charging map are determined;

[0027] Based on the charging data corresponding to all first target location data and the charging data corresponding to all second target location data, the maximum power data of multiple charging piles in the charging map and the type of historical charging vehicle corresponding to each maximum power data are determined.

[0028] In one or more embodiments, filtering the plurality of charging status information according to preset filtering conditions to obtain plurality of target location data includes:

[0029] For the multiple charging status information, remove the charging status information whose time data is less than the charging duration to obtain multiple first charging status information;

[0030] For multiple first charging state information, remove the first charging state information whose position data is outside the charging position error to obtain multiple second charging state information;

[0031] The location data in the plurality of second charging status information is determined as the plurality of target location data.

[0032] Secondly, embodiments of this application provide a charging map determination device, comprising:

[0033] The acquisition module is used to acquire charging status information corresponding to multiple historical charging vehicles. For each historical charging vehicle, the charging status information is used to indicate the time data, location data, and charging data of the historical charging vehicle in the charging state within a preset time period.

[0034] A filtering module is used to filter the multiple charging status information according to preset filtering conditions to obtain multiple target location data. The filtering conditions are determined based on preset charging time and preset charging location error.

[0035] The determination module is used to determine a charging map suitable for vehicle charging based on the multiple target location data and the charging data corresponding to the multiple target location data.

[0036] In one or more embodiments, the determining module is specifically used for:

[0037] Determine the spatial similarity and textual similarity between any two target location data points;

[0038] A charging map suitable for vehicle charging is determined based on the spatial similarity between every two target location data points, the textual similarity between every two target location data points, and the charging data corresponding to the multiple target location data points.

[0039] In one or more embodiments, the step of determining a charging map suitable for vehicle charging based on the spatial similarity between every two target location data points, the textual similarity between every two target location data points, and the charging data corresponding to the plurality of target location data points, specifically includes:

[0040] Determine the spatial similarity and textual similarity values ​​for each pair of target location data;

[0041] Two target location data with a similarity value greater than the first similarity threshold are identified as the same first target location data;

[0042] Two target location data with a similarity sum less than or equal to the first similarity threshold are identified as different second target location data;

[0043] Based on all the first target location data, all the second target location data, the charging data corresponding to each of the first target location data, and the charging data corresponding to each of the second target location data, a charging map suitable for vehicle charging is determined.

[0044] In one or more embodiments, the determining module, which determines the spatial similarity between every two target location data points, is specifically used for:

[0045] Based on the latitude and longitude information in the data of each pair of target locations, determine the straight-line distance and spherical distance corresponding to each pair of target locations;

[0046] The spatial similarity between each pair of target location data is determined based on the straight-line distance, spherical distance, and cluster radius. The cluster radius is determined based on the physical region type of each pair of target location data and the longitude information in all target location data.

[0047] In one or more embodiments, the determining module, which determines the text similarity between every two target location data points, is specifically used for:

[0048] Based on the latitude and longitude information in each pair of target location data, determine the address description information corresponding to each pair of target location data;

[0049] Based on the address description information corresponding to each pair of target location data, determine the edit distance corresponding to each pair of target location data;

[0050] The text similarity between two target location data points is determined based on the edit distance between each pair of target location data points and the address description information between each pair of target location data points.

[0051] In one or more embodiments, the step of determining a charging map suitable for vehicle charging based on all first target location data, all second target location data, charging data corresponding to each of the first target location data, and charging data corresponding to each of the second target location data, specifically includes:

[0052] Based on all the first target location data and all the second target location data, the locations of multiple charging piles in the charging map are determined;

[0053] Based on the charging data corresponding to all first target location data and the charging data corresponding to all second target location data, the maximum power data of multiple charging piles in the charging map and the type of historical charging vehicle corresponding to each maximum power data are determined.

[0054] In one or more embodiments, the filtering module is specifically used for:

[0055] For the multiple charging status information, remove the charging status information whose time data is less than the charging duration to obtain multiple first charging status information;

[0056] For multiple first charging state information, remove the first charging state information whose position data is outside the charging position error to obtain multiple second charging state information;

[0057] The location data in the plurality of second charging status information is determined as the plurality of target location data.

[0058] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0059] The memory stores computer-executed instructions;

[0060] The processor executes computer execution instructions stored in the memory, such that the processor, when executed, is used to implement the method described in the first aspect and any of the embodiments above.

[0061] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods described in the first aspect and any of the embodiments above.

[0062] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, is used to implement the method for determining a charging map as described in the first aspect and various possible implementations of the first aspect.

[0063] This application provides a method, apparatus, device, and storage medium for determining a charging map. The method first acquires charging status information corresponding to multiple historical charging vehicles. For each historical charging vehicle, the charging status information indicates the time data, location data, and charging data of the historical charging vehicle within a preset time period. Then, based on preset filtering conditions, the multiple charging status information are filtered to obtain multiple target location data. The filtering conditions are determined based on a preset charging time and a preset charging location error. Finally, based on the multiple target location data and the corresponding charging data, a charging map suitable for vehicle charging is determined. In the above method, by acquiring the charging status information of multiple historical charging vehicles, the time data, location data, and charging data of historical charging vehicles within a preset time period can be accurately grasped. By pre-setting filtering conditions, based on the preset charging time and preset charging location error, multiple charging status information can be filtered to obtain multiple target location data suitable for vehicle charging, reducing the impact of invalid data and thus ensuring the accuracy and reliability of the obtained multiple target location data. By combining the multiple target location data and the corresponding charging data, a charging map suitable for vehicle charging can be determined, which can help vehicles efficiently select suitable charging piles in the charging map, reduce the vehicle charging failure rate, and improve vehicle charging efficiency. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0065] Figure 1 A flowchart illustrating the method for determining the charging map provided in this application embodiment. Figure 1 ;

[0066] Figure 2 A flowchart illustrating the method for determining the charging map provided in this application embodiment. Figure 2 ;

[0067] Figure 3 A flowchart illustrating the method for determining the charging map provided in this application embodiment. Figure 3 ;

[0068] Figure 4 A schematic diagram of the structure of the charging map determination device provided in the embodiments of this application;

[0069] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0070] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0071] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0072] Before introducing the embodiments of this application, the application background of the embodiments of this application will be explained first:

[0073] With the transformation of the energy structure, electric heavy-duty trucks have become a key vehicle for emission reduction. However, their large-scale promotion is severely constrained by the basic charging infrastructure. On the one hand, the basic charging infrastructure limits vehicle height and high power requirements, and only a small number of public charging stations are compatible with electric heavy-duty trucks; on the other hand, the unbalanced layout of charging facilities and lack of information lead to low operational efficiency, with some electric heavy-duty trucks spending a long time finding available charging piles and charging inefficiently.

[0074] Existing charging maps are mainly based on historical charging data of electric vehicles. By obtaining the location coordinates of basic charging facilities recorded in the historical charging data, these basic charging facilities are marked on the map to generate a charging map marked with basic charging facilities. Alternatively, charging maps can also be obtained by directly marking the installation location information of basic charging facilities.

[0075] However, existing charging maps cannot meet the charging needs of electric heavy trucks, and are prone to problems such as a high charging failure rate.

[0076] The charging map determination method provided in this application aims to solve the aforementioned technical problems of the prior art. The inventive concept of this application is as follows: Traditional charging maps are determined by directly marking the locations of all basic charging facilities. Charging piles determined based on traditional charging maps may have poor spatial compatibility or low charging power, leading to a high charging failure rate for electric heavy-duty trucks. If the vehicle's charging status information can be analyzed, considering the historical charging time data, location data, and charging data of the vehicle, and determining the target location data and the corresponding charging data (such as charging power), a charging map suitable for vehicle charging can be determined. Therefore, this application determines preset filtering conditions based on preset charging time and preset charging location error. According to the preset filtering conditions, multiple charging status information is filtered to obtain multiple target location data, ensuring the accuracy and reliability of the data. Then, based on the multiple target location data and the corresponding charging data, a charging map suitable for vehicle charging is determined. The charging data corresponding to multiple target location data in the charging map can help vehicles efficiently select suitable charging piles in the charging map, reducing the vehicle charging failure rate.

[0077] The execution subject of this application embodiment is an electronic device, which can be a terminal device, such as a laptop, desktop computer, or tablet computer, or a server. In practical applications, whether the electronic device is a terminal device or a server can be determined according to the actual situation, and no specific limitation is imposed on it.

[0078] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0079] Figure 1 A flowchart illustrating the method for determining the charging map provided in this application embodiment. Figure 1 .like Figure 1 As shown, the method for determining the charging map includes the following steps:

[0080] S110. Obtain the charging status information corresponding to multiple historical charging vehicles;

[0081] Among them, the charging status information for each historical charging vehicle is used to indicate the time data, location data, and charging data of the historical charging vehicle within a preset time period.

[0082] In this step, for each historical charging vehicle, the charging status information is used to indicate the time data, location data, and charging data of the historical charging vehicle within a preset time period. The charging status information corresponding to multiple historical charging vehicles can be obtained, thereby clarifying the time data, location data, and charging data of multiple historical charging vehicles in the charging state.

[0083] For example, the time data can be the time interval during which the historical charging vehicle was in a charging state within a preset time period, the location data can be the latitude and longitude data of the historical charging vehicle when it was in a charging state, and the charging data can be the current and voltage of the historical charging vehicle when it was in a charging state.

[0084] In one possible implementation, the charging status information of each historical charging vehicle includes multiple charging states corresponding to multiple time points within a preset time period. The time data of the historical charging vehicle being in the charging state can be determined based on the multiple charging states corresponding to multiple time points within the preset time period, that is, the time interval of the historical charging vehicle being in the charging state.

[0085] For example, suppose the charging state sequence corresponding to multiple time points is {S} i}, S i S represents the charging state at time point i. i =1 indicates the charging state, S i ≠1 indicates a non-charging state. The starting time index list for the charging state is α, and the ending time index list for the charging state is β.

[0086] For any time point i, if the charging state S at time point i... i =1 and (i=0 or the charging state S at the (i-1)th time point) i-1 If ≠1), then add time point i to the starting time index list α of the charging state.

[0087] If the charging state S at the i-th time point i =1 and (i = N-1 or the charging state S at the (i+1)th time point) i+1 If ≠1), then add i to the end time index list β of the charging state, where N is the length of the charging state sequence.

[0088] For example, taking the charging state sequence {1,1,1,1,0,0} and time points i = 0, 1, 2, 3, 4, 5 as an example, for time point i = 0, S0 = 1, then time point i = 0 is added to the starting time index list α of the charging state; for time point i = 1, S1 = 1, and S... i-1 =S0=1,

[0089] S i+1 If S2 = 1, then it is not necessary to add time point i = 1 to the start time index list α or the end time index list β of the charging state; for time point i = 3, S3 = 1, and S i+1 =S4≠1, then add time point i=3 to the end time index list β of the charging state.

[0090] In one possible implementation, combining the start time index list α of the charging state and the end time index list β of the charging state yields the time interval ε = {[s] where the historical charging vehicle was in the charging state. k ,e k ]}, where s k and e k These represent the start and end time indices of the k-th time interval in the charging state, respectively.

[0091] If the end time index e of the k-th time interval in the charging state is... k with start time index s k The difference e between k -s k If the value is 0, then the kth time interval in the charging state will be deleted from the historical time intervals ε in which the vehicle was in the charging state.

[0092] In addition, the merging condition for time intervals is set as follows: the starting time index s of the (k+1)th time interval in the charging state. k+1 The end time index e of the k-th time interval in the charging state k If the difference is less than the preset merging threshold, then adjacent time intervals that meet the merging conditions in the time interval ε of the historical charging vehicle being in the charging state can be merged, thus avoiding the problem of time interval fragmentation caused by charging state information noise or state switching.

[0093] S120. According to the preset filtering conditions, filter multiple charging status information to obtain multiple target location data.

[0094] The filtering conditions are determined based on the preset charging time and the preset charging position error.

[0095] In this step, filtering conditions can be determined based on preset charging time and preset charging position error. Multiple charging status information is filtered according to the preset filtering conditions, and multiple target position data are obtained based on the position data in the filtered multiple charging status information.

[0096] In one possible implementation, multiple charging status information is filtered according to preset filtering conditions. This can be done by filtering based on a preset charging duration and the time data in the multiple charging status information, deleting charging status information whose time data is less than the preset charging duration. Then, filtering is done based on a preset charging position error and the position data in the multiple charging status information, deleting charging status information whose position data is outside the charging position error. Finally, multiple target position data are obtained based on the position data in the filtered multiple charging status information.

[0097] S130. Based on multiple target location data and the charging data corresponding to the multiple target location data, determine a charging map suitable for vehicle charging.

[0098] In this step, a charging map is determined by marking multiple target location data and corresponding charging data based on multiple target location data, and this map is used as a charging map suitable for vehicle charging.

[0099] For example, the charging data corresponding to the target location data could be the maximum charging power of a vehicle in the past when it was charging.

[0100] In one possible implementation, the charging data corresponding to multiple target location data can be determined based on the charging data in multiple charging status information, and the charging data in the charging status information of each historical charging vehicle includes the current and voltage corresponding to multiple time points within a preset time period.

[0101] Based on the current and voltage at multiple time points within a preset time period, multiple charging powers at multiple time points can be obtained, and the maximum charging power among the multiple charging powers is used as the charging data corresponding to the target location data.

[0102] In one possible implementation, each historical charging vehicle may include a vehicle type identifier, which includes heavy-duty trucks and light-duty trucks. Multiple target location data may carry vehicle type identifiers. When determining a charging map suitable for vehicle charging based on multiple target location data and the charging data corresponding to the multiple target location data, the vehicle type identifiers carried by the multiple target location data can be marked on the charging map.

[0103] For example, a charging map applicable to vehicle charging is marked with multiple target location data, the maximum charging power corresponding to the multiple target location data, and the vehicle type identifier carried by the multiple target location data.

[0104] The charging map determination method provided in this application first obtains charging status information corresponding to multiple historical charging vehicles. For each historical charging vehicle, the charging status information is used to indicate the time data, location data, and charging data of the historical charging vehicle within a preset time period. Then, the multiple charging status information is filtered according to preset filtering conditions to obtain multiple target location data. The filtering conditions are determined based on a preset charging time and a preset charging location error. Finally, a charging map suitable for vehicle charging is determined based on the multiple target location data and the charging data corresponding to the multiple target location data. In this embodiment, by acquiring the charging status information of multiple historical charging vehicles, the time data, location data, and charging data of the historical charging vehicles within a preset time period can be accurately grasped. By pre-setting filtering conditions, based on a preset charging time and a preset charging location error, multiple charging status information can be filtered to obtain multiple target location data suitable for vehicle charging, reducing the impact of invalid data and ensuring the accuracy and reliability of the obtained multiple target location data. By combining the multiple target location data and the corresponding charging data, a charging map suitable for vehicle charging can be determined, which can help vehicles efficiently select suitable charging piles in the charging map, reduce the vehicle charging failure rate, and improve vehicle charging efficiency.

[0105] Based on the above embodiments, Figure 2 A flowchart illustrating the method for determining the charging map provided in this application embodiment. Figure 2 .like Figure 2 As shown, a possible implementation of step S130 above also includes the following steps:

[0106] S210. Determine the spatial similarity and textual similarity between every two target location data points.

[0107] In this step, the target location data includes spatial location data and the corresponding descriptive text. Based on the spatial location data of each pair of target location data, the spatial similarity between each pair of target location data is determined. Based on the descriptive text corresponding to the spatial location data of each pair of target location data, the textual similarity between each pair of target location data is determined.

[0108] For example, spatial location data represents the latitude and longitude information in the target location data, and the descriptive text corresponding to the spatial location data represents the address description information corresponding to the latitude and longitude information in the target location data.

[0109] In one possible implementation, determining the spatial similarity between every two target location data points further includes the following steps:

[0110] Step 1: Based on the latitude and longitude information in the data of each pair of target locations, determine the straight-line distance and spherical distance corresponding to each pair of target locations.

[0111] For example, latitude and longitude information includes longitude data and latitude data.

[0112] In one possible implementation, based on the latitude and longitude information (including longitude and latitude) in the data from each pair of target locations, the straight-line distance between each pair of target locations can be calculated using the following formula:

[0113]

[0114] in, The straight-line distance between target position data p and target position data q. and These are the longitude data corresponding to target location data p and target location data q, respectively. and These are the latitude data corresponding to the target location data p and the target location data q, respectively.

[0115] The spherical distance between any two target location data points can be calculated using the following formula:

[0116]

[0117] in, Let p and q be the spherical distances corresponding to the target position data. This represents the absolute value of the difference in latitude between target location data p and target location data q. R is the absolute value of the difference in longitude between target location data p and target location data q, where R is the Earth's radius, usually taken as R = 6371 km.

[0118] Step 2: Determine the spatial similarity between each pair of target location data based on the straight-line distance, spherical distance, and cluster radius.

[0119] The cluster radius is determined based on the physical region type of each pair of target location data and the longitude information in all target location data.

[0120] For example, the physical area type of each pair of target location data can include urban area type and suburban area type. A smaller cluster radius can be set in urban areas and a larger cluster radius can be set in suburban areas to adapt to the charging pile density distribution of different physical areas.

[0121] In one possible implementation, the cluster radius ∈ can be calculated using the following formula:

[0122]

[0123] Where ∈ is the cluster radius, ∈0 is the baseline radius, and σ λ τ represents the standard deviation of the longitude data corresponding to the target location data, and τ is the standard deviation threshold.

[0124] The mixed distance between any two target location data points can be determined based on the straight-line distance and spherical distance corresponding to each pair of target location data points. The calculation formula is as follows:

[0125]

[0126] in, The mixed distance corresponding to target location data p and target location data q. The straight-line distance between target position data p and target position data q. Let p be the spherical distance between the target position data and q.

[0127] The spatial similarity between any two target location data can be calculated using the following formula:

[0128]

[0129] Among them, S geo (p,q) represents the spatial similarity between target location data p and target location data q. Let ω(θ) be the mixed distance between target location data p and target location data q, ω(θ) be the compensation weight, and ∈ be the cluster radius.

[0130] In one possible implementation, determining the text similarity between every two target location data points further includes the following steps:

[0131] Step 1: Based on the latitude and longitude information in each pair of target location data, determine the address description information corresponding to each pair of target location data.

[0132] For example, a geocoding service can be used to perform location conversion on the longitude and latitude data of the latitude and longitude information in each pair of target location data to obtain the address description information corresponding to each pair of target location data.

[0133] In one possible implementation, the address description information may include different levels of description information, such as city level, road level, and landmark reference level.

[0134] Step 2: Determine the edit distance between each pair of target location data based on the address description information corresponding to each pair of target location data.

[0135] For example, edit distance is used to indicate the degree of difference between two address descriptions, representing the minimum number of operations required to convert one address description into another.

[0136] In one possible implementation, the edit distance between every two target location data points can be calculated using the following formula:

[0137]

[0138] Among them, ED * (p,q) represents the edit distances corresponding to target location data p and target location data q, L p L is the address description information corresponding to the target location data p. q δ represents the address description information corresponding to the target location data q. l (L p ) is the address description information L p The content of the lth layer after being split according to the preset level m, δ l (L p ) is the address description information L q The content of the l-th layer after being split according to the preset level m, ED(δ) l (L p ),δ l (L q ) represents the edit distance of the l-th layer of address description information corresponding to every two target location data. The hierarchy weights represent the importance of different levels in address matching.

[0139] Step 3: Determine the text similarity between each pair of target location data based on the edit distance and the address description information corresponding to each pair of target location data.

[0140] In one possible implementation, the text similarity between any two target location data points can be calculated using the following formula:

[0141]

[0142] Among them, S text (p,q) represents the text similarity between target location data p and target location data q, ED * (p,q) represents the edit distances corresponding to target location data p and target location data q, |L p | and | L q |These are address description information Lp and address description information L q The length of ED is such that when the target position data p and the target position data q are exactly the same. * (p,q)=0, S text (p,q)=1.

[0143] S220. Based on the spatial similarity between every two target location data points, the textual similarity between every two target location data points, and the charging data corresponding to multiple target location data points, determine a charging map suitable for vehicle charging.

[0144] For example, the spatial similarity and textual similarity between each pair of target location data can reflect the degree of similarity between each pair of target location data. When the similarity is too high, the two target location data can be merged into the same target location data to remove redundant target location data.

[0145] In one possible implementation, step S220 may further include the following steps:

[0146] S1, determine the similarity and value of spatial similarity and textual similarity for each pair of target location data.

[0147] For example, by adding the spatial similarity and textual similarity of two target location data, we can obtain the similarity sum for each pair of target location data.

[0148] In one possible implementation, the similarity score between any two target location data points can be calculated using the following formula:

[0149] S pq =ξ1·S geo (p,q)+ξ2·S text (p,q)

[0150] Among them, S pq S represents the similarity and value between target location data p and target location data q. geo (p,q) represents the spatial similarity between target location data p and target location data q, S text (p,q) represents the text similarity between target location data p and target location data q, and ξ1 and ξ2 are the weight coefficients of spatial similarity and text similarity, respectively.

[0151] S2, two target location data with a similarity value greater than the first similarity threshold are identified as the same first target location data.

[0152] For example, the first similarity threshold is used to indicate the maximum value of the similarity sum of two target location data. The similarity sum is compared with the first similarity threshold, and two target location data with a similarity sum greater than the first similarity threshold are determined to be the same first target location data.

[0153] In one possible implementation, when the similarity sum is greater than the first similarity threshold, it indicates that the latitude and longitude information of the two target location data are adjacent or consistent, and the address description information of the two target location data is highly consistent. In this case, the two target location data can be identified as the same first target location data.

[0154] For example, if the first similarity threshold is 0.8, and the sum of the similarities between target location data p1 and target location data q1 is 0.9, which is greater than the first similarity threshold of 0.8, then target location data p1 and target location data q1 can be identified as the same first target location data x1.

[0155] S3, two target location data with a similarity sum less than or equal to the first similarity threshold are identified as different second target location data.

[0156] For example, the similarity sum is compared with a first similarity threshold, and two target location data with a similarity sum less than or equal to the first similarity threshold are identified as different second target location data.

[0157] In one possible implementation, when the similarity sum is less than or equal to the first similarity threshold, it indicates that the latitude and longitude information of the two target location data is significantly different, and the address description information of the two target location data is inconsistent. In this case, the two target location data can be identified as different second target location data.

[0158] For example, if the first similarity threshold is 0.8, and the sum of the similarities between target location data p2 and target location data q2 is 0.7, which is less than the first similarity threshold of 0.8, then target location data p2 and target location data q2 can be identified as different second target location data y1 and y2.

[0159] S4. Based on all first target location data, all second target location data, charging data corresponding to all first target location data, and charging data corresponding to all second target location data, determine a charging map suitable for vehicle charging.

[0160] In one possible implementation, the latitude and longitude coordinates corresponding to all the first target location data and all the second target location data can be determined based on the latitude and longitude information in all the first target location data and all the second target location data. Based on the charging data corresponding to each of the first target location data and each of the second target location data, the charging data corresponding to each latitude and longitude coordinate can be determined. By marking all the latitude and longitude coordinates and all the charging data on the map, a charging map suitable for vehicle charging can be obtained.

[0161] In one possible implementation, step S4 above may further include the following steps:

[0162] Step 1: Based on all the first target location data and all the second target location data, determine the locations of multiple charging piles in the charging map.

[0163] For example, based on the latitude and longitude information in all the first target location data and all the second target location data, the latitude and longitude coordinates corresponding to all the first target location data and all the second target location data can be determined. By using all the latitude and longitude coordinates as the latitude and longitude coordinates of multiple charging piles in the charging map, the location of multiple charging piles in the charging map can be determined.

[0164] Step 2: Based on the charging data corresponding to all first target location data and the charging data corresponding to all second target location data, determine the maximum power data of multiple charging piles in the charging map and the type of historical charging vehicle corresponding to each maximum power data.

[0165] For example, the charging data corresponding to the target location data includes maximum power data, and the target location data carries a vehicle type identifier.

[0166] In one possible implementation, the maximum power data in the charging data corresponding to all the first target location data and the charging data corresponding to all the second target location data are respectively used as the maximum power data of multiple charging piles in the charging map.

[0167] Based on the vehicle type identifiers carried in all first target location data and all second target location data, determine the type of historical charging vehicle corresponding to the maximum power data in the charging data corresponding to all first target location data and all second target location data, and use the type of historical charging vehicle corresponding to the maximum power data in all charging data as the type of historical charging vehicle corresponding to each maximum power data in multiple charging piles in the charging map.

[0168] The charging map determination method provided in this application first determines the spatial similarity and textual similarity between every two target location data. Then, based on the spatial similarity, textual similarity, and charging data corresponding to multiple target location data, a charging map suitable for vehicle charging is determined. In this embodiment, by analyzing the spatial distribution of target location data and determining the spatial similarity between any two target location data points, it is possible to effectively identify target location data points that are similar in spatial distribution. By determining the textual similarity between any two target location data points, it is possible to effectively analyze the textual similarity corresponding to the address description information in the target location data, thereby determining the similarity of the geographical location characteristics or geographical location attributes of any two target location data points. By combining the spatial similarity and textual similarity between any two target location data points, multiple target location data points can be optimized, merging two target location data points with excessively high spatial and textual similarity into one target location data point, avoiding redundancy issues caused by differences in spatial distribution and address description information, thereby determining a charging map suitable for vehicle charging. Furthermore, by combining the charging data (such as charging power) corresponding to multiple target location data points, the charging data can be marked on the charging map, enabling vehicles to efficiently select suitable charging piles on the charging map based on the marked charging data, thereby improving vehicle charging efficiency and avoiding unstable charging or charging failures due to unsuitable charging data.

[0169] Based on the above embodiments, Figure 3 A flowchart illustrating the method for determining the charging map provided in this application embodiment. Figure 3 .like Figure 3 As shown, a possible implementation of step S120 above also includes the following steps:

[0170] S310. For multiple charging status information, remove charging status information whose time data is less than the charging duration to obtain multiple first charging status information.

[0171] In this step, for multiple charging status information, the time data in the multiple charging status information is compared with the charging duration, and the charging status information corresponding to the time data being less than the charging duration is removed, resulting in a first charging status information with multiple time data greater than or equal to the charging duration.

[0172] In one possible implementation, the charging status information of each historical charging vehicle includes multiple charging states corresponding to multiple time points within a preset time period. The time data of the historical charging vehicle being in the charging state can be determined based on the multiple charging states corresponding to multiple time points within the preset time period, that is, the time interval of the historical charging vehicle being in the charging state.

[0173] By calculating the difference between the start and end times of the time interval, the duration of the time interval can be determined. This duration is then compared with the charging duration, and charging status information whose duration is less than the charging duration is removed.

[0174] S320. For multiple first charging state information, remove the first charging state information whose position data is outside the charging position error to obtain multiple second charging state information.

[0175] In this step, for multiple first charging state information, the position data in the multiple first charging state information is compared with the charging position error, and the first charging state information whose position data is outside the charging position error is removed, so as to obtain multiple second charging state information whose position data is within the charging position error.

[0176] In one possible implementation, the location data in the charging status information of each historical charging vehicle includes longitude and latitude data corresponding to multiple time points within a preset time period. Based on the longitude and latitude data corresponding to the multiple time points included in the location data of the first charging status information, the standard deviation of the longitude data and the standard deviation of the latitude data are calculated respectively. The standard deviation of the longitude data is compared with the longitude error of the charging location, and the standard deviation of the latitude data is compared with the latitude error of the charging location. The first charging status information corresponding to the location data whose standard deviation of the longitude data is greater than the longitude error of the charging location and whose standard deviation of the latitude data is greater than the latitude error of the charging location is removed. This avoids errors in the location data caused by the movement of historical charging vehicles or drift of positioning signals, and ensures the reliability of the data.

[0177] For example, the location data in the first charging status information includes a longitude data sequence corresponding to multiple time points as {lng1, lng2, ..., lng}. n The latitude data sequence is {lat1,lat2,…,lat}. n}, where n represents the number of time points included in the location data in the first charging state information, then the standard deviation of the longitude data and the standard deviation of the latitude data can be calculated as follows:

[0178]

[0179] Where, σ lng μ represents the standard deviation of the longitude data. lngσ is the average value of the longitude data. lat Let μ be the standard deviation of the latitude data. lat This represents the average value of the latitude data.

[0180] S330, determine the position data in multiple second charging status information as multiple target position data.

[0181] In this step, the location data in multiple second charging status information can accurately indicate the location where the historically charged vehicle is in a charging state. In order to accurately indicate the location where the vehicle can be charged, the location data in multiple second charging status information are determined as multiple target location data.

[0182] In one possible implementation, the longitude and latitude data corresponding to multiple time points included in the location data in the second charging status information can be deduplicated to obtain representative longitude and representative latitude data of the location data in the second charging status information.

[0183] For example, the position data sequence in the second charging status information is {(λ j ,φ j )|j=1,…,m}, where m is the number of time points included in the location data. Using the deduplication operator Γ, the representative longitude data of the location data in the second charging state information can be obtained. and represent latitudinal data

[0184] In addition, geocoding services can be used to convert the representative longitude and representative latitude data of the location data in the second charging status information to obtain the address description information corresponding to the location data in the second charging status information.

[0185] The charging map determination method provided in this application firstly removes charging status information whose time data is less than the charging duration from multiple charging status information, resulting in multiple first charging status information. Then, it removes first charging status information whose location data is outside the charging location error range from these first charging status information, resulting in multiple second charging status information. Finally, it determines the location data from the multiple second charging status information as multiple target location data. In this embodiment, by removing charging status information whose time data is less than a preset charging duration, multiple first charging status information with longer charging durations can be retained, improving data accuracy and avoiding invalid or inaccurate charging status information due to insufficient charging duration, thus improving the quality of the charging status information. By removing first charging status information whose location data is outside the charging location error range, the accuracy of the location data can be ensured, avoiding inaccurate location data due to excessive charging location error, thus improving the quality of the charging status information. The time and location data in the resulting multiple second charging status information are accurate and reliable. By determining the location data from the multiple second charging status information as multiple target location data, accurate target locations can be provided, providing reliable data support for subsequently determining a charging map suitable for vehicle charging.

[0186] Based on the above embodiments, the following is a charging map determination device provided in the embodiments of this application, which can execute the method provided in the above method embodiments.

[0187] Figure 4 This is a schematic diagram of the structure of the charging map determination device provided in an embodiment of this application. Figure 4 As shown, the charging map determining device 400 includes:

[0188] The acquisition module 410 is used to acquire charging status information corresponding to multiple historical charging vehicles. The charging status information for each historical charging vehicle is used to indicate the time data, location data, and charging data of the historical charging vehicle in the charging state within a preset time period.

[0189] The filtering module 420 is used to filter multiple charging status information according to preset filtering conditions to obtain multiple target location data. The filtering conditions are determined based on preset charging time and preset charging location error.

[0190] The determination module 430 is used to determine a charging map suitable for vehicle charging based on multiple target location data and the charging data corresponding to the multiple target location data.

[0191] In one or more embodiments, the determining module 430 is specifically used for:

[0192] Determine the spatial similarity and textual similarity between any two target location data points;

[0193] A charging map suitable for vehicle charging is determined based on the spatial similarity between every two target location data points, the textual similarity between every two target location data points, and the charging data corresponding to multiple target location data points.

[0194] In one or more embodiments, a charging map suitable for vehicle charging is determined based on the spatial similarity between every two target location data points, the textual similarity between every two target location data points, and the charging data corresponding to multiple target location data points. The determining module 430 is specifically used for:

[0195] Determine the spatial similarity and textual similarity values ​​for each pair of target location data;

[0196] Two target location data with a similarity value greater than the first similarity threshold are identified as the same first target location data;

[0197] Two target location data with a similarity sum less than or equal to a first similarity threshold are identified as different second target location data.

[0198] Based on all the first target location data, all the second target location data, the charging data corresponding to each of the first target location data, and the charging data corresponding to each of the second target location data, a charging map suitable for vehicle charging is determined.

[0199] In one or more embodiments, determining the spatial similarity between every two target location data points, the determining module 430 is specifically used for:

[0200] Based on the latitude and longitude information in the data of each pair of target locations, determine the straight-line distance and spherical distance corresponding to each pair of target locations;

[0201] The spatial similarity between each pair of target location data is determined based on the straight-line distance, spherical distance, and cluster radius. The cluster radius is determined based on the physical region type of each pair of target location data and the longitude information in all target location data.

[0202] In one or more embodiments, determining the text similarity between every two target location data points, the determining module 430 is specifically used for:

[0203] Based on the latitude and longitude information in each pair of target location data, determine the address description information corresponding to each pair of target location data;

[0204] Based on the address description information corresponding to each pair of target location data, determine the edit distance corresponding to each pair of target location data;

[0205] The text similarity between two target location data points is determined based on the edit distance between each pair of target location data points and the address description information between each pair of target location data points.

[0206] In one or more embodiments, a charging map suitable for vehicle charging is determined based on all first target location data, all second target location data, charging data corresponding to each of the first target location data, and charging data corresponding to each of the second target location data. The determining module 430 is specifically used for:

[0207] Based on all the first target location data and all the second target location data, determine the locations of multiple charging piles in the charging map;

[0208] Based on the charging data corresponding to all first target location data and the charging data corresponding to all second target location data, determine the maximum power data of multiple charging piles in the charging map and the type of historical charging vehicle corresponding to each maximum power data.

[0209] In one or more embodiments, the filtering module 420 is specifically used for:

[0210] For multiple charging status information, remove charging status information whose time data is less than the charging duration to obtain multiple first charging status information;

[0211] For multiple first charging state information, remove the first charging state information whose position data is outside the charging position error to obtain multiple second charging state information;

[0212] The location data in multiple second charging status information are determined as multiple target location data.

[0213] Based on the above embodiments, Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 500 includes: a processor 510, a memory 520, and a bus 530;

[0214] The memory 520 is used to store the computer-executed instructions of the processor 510;

[0215] The processor 510 is configured to execute the technical solutions of any of the foregoing method embodiments by executing computer execution instructions.

[0216] Optionally, the memory 520 can be either standalone or integrated with the processor 510.

[0217] Optionally, memory 520 may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0218] Bus 530 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, only one thick line is used to represent a bus in the accompanying drawings of this application, but this does not imply that there is only one bus or one type of bus.

[0219] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0220] The electronic device is used to execute the technical solution of any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0221] This application also provides a computer-readable storage medium storing computer-executable instructions thereon, which, when executed by a processor, are used to implement the technical solutions provided in any of the above method embodiments.

[0222] This application also provides a computer program product, including a computer program, which includes computer instructions stored in a computer-readable storage medium. When the computer program is executed by a processor, it is used to implement the technical solutions provided in any of the above method embodiments.

[0223] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0224] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0225] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0226] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or in the form of software program modules.

[0227] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0228] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0229] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0230] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0231] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for determining a charging map, characterized in that, include: Obtain charging status information corresponding to multiple historical charging vehicles. For each historical charging vehicle, the charging status information is used to indicate the time data, location data, and charging data of the historical charging vehicle in the charging state within a preset time period. According to preset filtering conditions, the multiple charging status information is filtered to obtain multiple target location data. The filtering conditions are determined based on preset charging time and preset charging location error. Based on the multiple target location data and the corresponding charging data, a charging map suitable for vehicle charging is determined, including: The spatial and textual similarity between two target location data points determines whether they are the same target location data. The charging data corresponding to the target location data includes the maximum charging power. Each historical charging vehicle includes a vehicle type identifier. Multiple target location data points carry vehicle type identifiers. Based on the multiple target location data points and the charging data corresponding to the multiple target location data points, a charging map suitable for vehicle charging is determined.

2. The method according to claim 1, characterized in that, The method further includes: Determine the spatial similarity and textual similarity values ​​for each pair of target location data; Two target location data with a similarity value greater than the first similarity threshold are identified as the same first target location data; Two target location data with a similarity sum less than or equal to the first similarity threshold are identified as different second target location data; Based on all the first target location data, all the second target location data, the charging data corresponding to each of the first target location data, and the charging data corresponding to each of the second target location data, a charging map suitable for vehicle charging is determined.

3. The method according to claim 1, characterized in that, The method further includes: Based on the latitude and longitude information in the data of each pair of target locations, determine the straight-line distance and spherical distance corresponding to each pair of target locations; The spatial similarity between each pair of target location data is determined based on the straight-line distance, spherical distance, and cluster radius. The cluster radius is determined based on the physical region type of each pair of target location data and the longitude information in all target location data.

4. The method according to claim 1, characterized in that, The method further includes: Based on the latitude and longitude information in each pair of target location data, determine the address description information corresponding to each pair of target location data; Based on the address description information corresponding to each pair of target location data, determine the edit distance corresponding to each pair of target location data; The text similarity between two target location data points is determined based on the edit distance between each pair of target location data points and the address description information between each pair of target location data points.

5. The method according to claim 2, characterized in that, The step of determining a charging map suitable for vehicle charging based on all first target location data, all second target location data, charging data corresponding to each of the first target location data, and charging data corresponding to each of the second target location data includes: Based on all the first target location data and all the second target location data, the locations of multiple charging piles in the charging map are determined; Based on the charging data corresponding to all first target location data and the charging data corresponding to all second target location data, the maximum power data of multiple charging piles in the charging map and the type of historical charging vehicle corresponding to each maximum power data are determined.

6. The method according to any one of claims 1-5, characterized in that, The process of filtering the multiple charging status information according to preset filtering conditions to obtain multiple target location data includes: For the plurality of charging status information, the charging status information with time data shorter than the charging duration is removed to obtain a plurality of first charging status information; For multiple first charging state information, remove the first charging state information whose position data is outside the charging position error to obtain multiple second charging state information; The location data in the plurality of second charging status information is determined as the plurality of target location data.

7. A device for determining a charging map, characterized in that, include: The acquisition module is used to acquire charging status information corresponding to multiple historical charging vehicles. For each historical charging vehicle, the charging status information is used to indicate the time data, location data, and charging data of the historical charging vehicle in the charging state within a preset time period. A filtering module is used to filter the multiple charging status information according to preset filtering conditions to obtain multiple target location data. The filtering conditions are determined based on preset charging time and preset charging location error. The determination module is used to determine a charging map suitable for vehicle charging based on the multiple target location data and the charging data corresponding to the multiple target location data; The determining module is specifically used to: determine whether two target location data are the same target location data based on the spatial similarity and text similarity corresponding to each pair of target location data, the charging data corresponding to the target location data includes the maximum charging power, each historical charging vehicle includes a vehicle type identifier, multiple target location data carry vehicle type identifiers, and determine a charging map suitable for vehicle charging based on multiple target location data and the charging data corresponding to the multiple target location data.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

Images