Charging station constant volume method and device based on multi-dimensional characteristics

Abstract

The invention relates to the technical field of charging station constant volume, and discloses a charging station constant volume method and device based on multi-dimensional features. The method comprises the following steps: acquiring a driving record table and a monthly feature record table of electric vehicles in a city, determining a hexagonal graph representing a service area of a charging station based on the driving record table, and dividing districts in the city according to the hexagonal graph; performing potential charging customer judgment and charging demand judgment on the electric vehicles in the district, and determining charging demand vehicles and charging demand quantity corresponding to each time period in the district; and determining a constant volume scheme corresponding to the charging station based on the charging demand vehicle, the charging demand electric quantity and the future vehicle increment predicted value, so that the constant volume scheme of the charging station can be determined in a time-space combination mode, the constant volume process is more comprehensive and fine, and the constant volume effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging station capacity determination, and in particular to a charging station capacity determination method and device based on multi-dimensional features. Background Art

[0002] With the rapid growth of electric vehicle ownership, the demand for electric vehicle charging is also increasing. Due to the unreasonable and imperfect layout of charging piles in the early stage of development, there are currently problems with electric vehicle charging difficulties and poor charging experience. To solve this problem, there are currently some methods for sizing charging piles based on sampling, simulation and other methods. However, these traditional charging station sizing methods are difficult to meet the market background of a large-scale increase in electric vehicles, and there is a problem of incomplete and imprecise decision-making basis in the sizing process. A better solution is needed. Summary of the invention

[0003] In view of this, the present invention provides a charging station capacity determination method and device based on multi-dimensional features to solve the problem that the decision-making basis is not comprehensive during the charging station capacity determination process, resulting in poor capacity determination effect.

[0004] On the one hand, the present invention provides a charging station capacity determination method based on multidimensional features, the method comprising obtaining a driving record sheet and a monthly feature record sheet of electric vehicles in a city, determining a hexagonal figure representing a service area of ​​the charging station based on the driving record sheet, and dividing the city into areas according to the hexagonal figure; judging potential charging customers and charging demand of electric vehicles in the area, and determining the charging demand vehicles and charging power demand corresponding to each time period in the area; and determining the corresponding capacity determination plan for the charging station based on the charging demand vehicles, the charging power demand and the future vehicle increment forecast value.

[0005] On the other hand, the present invention also provides a charging station capacity determination device based on multi-dimensional features, the device comprising: a division module, used to obtain a driving record sheet and a monthly feature record sheet of electric vehicles in a city, determine a hexagonal figure representing a service area of ​​a charging station based on the driving record sheet, and divide the city into districts according to the hexagonal figure; a demand judgment module, used to judge potential charging customers and charging demand of electric vehicles in the district, and determine the charging demand vehicles and charging power demand corresponding to each time period in the district; a capacity determination module, used to determine the corresponding capacity determination plan for the charging station based on the charging demand vehicles, the charging power demand and the future vehicle increment forecast value.

[0006] On the other hand, the present invention also provides a computer device, which includes: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor implements the above-mentioned charging station capacity determination method based on multi-dimensional features by executing the computer instructions.

[0007] On the other hand, the present invention further provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to implement the above-mentioned charging station capacity determination method based on multi-dimensional features.

[0008] On the other hand, the present invention also provides a computer program product, including computer instructions, which are used to enable a computer to execute the charging station capacity determination method based on multi-dimensional features of the above-mentioned first aspect or any corresponding embodiment.

[0009] In this process, the service area of ​​the charging station is determined based on the driving record sheet and monthly characteristic record sheet of the electric vehicle, the city is divided into districts based on the service area, the charging demand vehicles and charging power demand in different time periods in each district and the future vehicle increment forecast value are determined, and the corresponding capacity plan of the charging station in the district is determined. Combined with the service area of ​​the charging station and the charging demand in different time periods in the service area, the capacity plan of the charging station is determined in a time-space combination manner, making the capacity determination process more comprehensive and precise, improving the capacity determination effect, avoiding the construction of too many or too few charging piles, and thus improving the utilization rate of charging piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 It is a flowchart of a charging station capacity determination method based on multi-dimensional features provided by an embodiment of the present invention;

[0012] Figure 2 It is a schematic diagram of a switching process of a charging station capacity determination method based on multi-dimensional features provided in an embodiment of the present invention;

[0013] Figure 3 It is a schematic diagram of the area and adjacent areas of the charging station capacity determination method based on multi-dimensional features provided by an embodiment of the invention;

[0014] Figure 4This is a specific flow chart of a charging station capacity determination method based on multi-dimensional features provided by an embodiment of the present invention.

[0015] Figure 5 It is a structural schematic diagram of a charging station capacity determination device based on multi-dimensional features provided by an embodiment of the present invention;

[0016] Figure 6 It is another structural schematic diagram of a charging station capacity determination device based on multi-dimensional features provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In recent years, the number of electric vehicles has grown rapidly, and with it comes a huge demand for charging. On the one hand, limited by the development of current power battery technology, defects such as the long charging process and inconvenient charging of electric vehicles have become one of the main factors restricting the promotion and application of electric vehicles and consumers' purchase of electric vehicles; on the other hand, some old residential communities were developed without considering the demand for electric vehicle charging due to transportation electrification, resulting in owners not having the basic conditions to install private charging piles; in addition, the layout of charging piles was unreasonable and imperfect in the early stage of development, resulting in a shortage of charging piles in most areas and idle charging piles in remote areas, the unreasonable layout of fast charging piles and slow charging piles, and the insufficient utilization of charging piles has become the biggest problem in the industry; these problems have caused the current difficulties for consumers in charging, poor charging experience, and poor investment returns and profitability for operators, and have become a key obstacle to the healthy and rapid development of my country's new energy vehicle market.

[0018] In order to solve the above-mentioned problems, the prior art often adopts a method based on sampling, simulation or traditional fuel vehicle data to predict charging demand and determine the capacity of charging piles.

[0019] However, the above technology may have the following problems:

[0020] 1. The decision-making basis considered during the capacity determination process is not comprehensive enough, resulting in poor capacity determination of charging piles, which in turn leads to low utilization of charging piles;

[0021] 2. The user experience of recharging the car was not considered during the capacity determination process, resulting in a poor user experience;

[0022] 3. The issue of future vehicle growth was not taken into consideration during the capacity determination process, resulting in the charging pile capacity determination results not being in line with the market background of massive growth in electric vehicles, affecting the service life of the charging stations.

[0023] To solve at least one of the above problems, various embodiments of the present invention provide a charging station capacity determination method based on multi-dimensional features, including: obtaining a driving record sheet and a monthly feature record sheet of electric vehicles in a city, determining a hexagonal figure representing the service area of ​​the charging station based on the driving record sheet, and dividing the city into areas according to the hexagonal figure; judging the potential charging customers and charging demand of electric vehicles in the area, and determining the charging demand vehicles and charging power demand corresponding to each time period in the area; determining the corresponding capacity determination plan for the charging station based on the charging demand vehicles, charging power demand and future vehicle increment forecast values.

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0025] According to an embodiment of the present invention, a charging station capacity determination method based on multi-dimensional features is provided. Figure 1 is a flow chart of a charging station capacity determination method based on multi-dimensional features provided by an embodiment of the present invention, such as Figure 1 As shown, the process includes the following steps:

[0026] Step S101, obtaining a driving record table and a monthly characteristic record table of electric vehicles in the city, determining a hexagonal figure representing a service area of ​​a charging station based on the driving record table, and dividing the city into areas according to the hexagonal figure;

[0027] Step S102, determining potential charging customers and charging needs of electric vehicles in the area, and determining the charging demand vehicles and charging power required for each time period in the area;

[0028] Step S103, determining a capacity solution corresponding to the charging station based on the number of vehicles in need of charging, the amount of charging power required, and the predicted value of future vehicle increments.

[0029] In a possible implementation, the record table may be a data structure with a fixed structure. The record table may include but is not limited to: table name, field, and record. The table name may refer to the unique name of the record table, the field may refer to the data type of each column in the record table, and the record may refer to the specific data corresponding to each row in the record table.

[0030] Specifically, the driving record table can be used to characterize the driving characteristics of each electric vehicle (hereinafter referred to as vehicle) in the city, and the monthly characteristic record table can be used to characterize the corresponding charging characteristics of each vehicle in the city in each month.

[0031] Exemplarily, the driving record table may include but is not limited to: vehicle identification code, trip start time, trip end time, trip start location, trip end location, and average trip speed; the monthly feature record table may include but is not limited to: vehicle identification code, month, total number of charging times this month, and average charging time this month.

[0032] In a possible implementation, based on the driving record table, a hexagonal figure representing the service area of ​​the charging station is determined, and the city is divided into areas according to the hexagonal figure, which is implemented based on the following steps:

[0033] The driving data in the vehicle's driving record table and the geographic location coordinates of the charging station are obtained, and based on the driving data and the geographic location coordinates, the reachable vehicles within the coverage area around each charging station are determined, and the vehicles are classified into the hexagonal area of ​​the charging station.

[0034] Among them, charging station refers to the facility or place that provides charging services for electric vehicles, including multiple charging piles and supporting power infrastructure; geographic location coordinates may refer to the latitude and longitude coordinates of the charging station.

[0035] Here, the service area of ​​the charging station is divided into hexagons with uniform adjacency, which can make the area division have tight coverage.

[0036] In a possible implementation, potential charging customers of electric vehicles in the area are judged, and vehicles with corresponding charging needs in each time period in the area are determined based on at least one of the following steps:

[0037] Determine whether the vehicle is a charging-required vehicle based on the vehicle's state of charge (SOC);

[0038] Determine whether the vehicle is a charging-demand vehicle based on the vehicle's trajectory;

[0039] Determine whether the vehicle is a charging-required vehicle based on the vehicle's charging habits and historical charging data.

[0040] Specifically, if the SOC of the vehicle is lower than the preset power threshold, it can indicate that the vehicle needs to be charged and can become a charging demand vehicle; if the starting point and the end point of the vehicle are frequently within the area, it can indicate that the activity range of the vehicle belongs to the area and can become a charging demand vehicle in the area; analyze the historical charging behavior of the vehicle. If the historical data of the vehicle indicates that charging is often carried out at night, it indicates that the vehicle can become a charging demand vehicle in the area during the night period.

[0041] Among them, potential charging customers may refer to electric vehicle users who may need to charge within the area.

[0042] In a possible implementation, the charging demand of electric vehicles in the area is judged, and the charging demand corresponding to each time period in the area is determined based on at least one of the following steps:

[0043] Based on the historical charging data of the area, identify the peak and off-peak charging periods in the area, and combine the vehicle type records and charging habits to predict the number of vehicles that need to be charged in a certain period of time;

[0044] Based on the SOC and battery capacity of each vehicle, the charging demand of each vehicle in a certain period of time is estimated.

[0045] Among them, charging demand refers to the charging demand of vehicles in the area during a specific time period.

[0046] In a possible implementation, based on the number of vehicles in need of charging, the amount of charging power required, and the predicted value of future vehicle increments, a capacity solution corresponding to the charging station is determined, including:

[0047] Based on the analysis data, the future vehicle increment forecast value is determined; based on the charging demand vehicles, the charging power requirement and the future vehicle increment forecast value, the number of charging piles and the power demand capacity corresponding to the charging station are determined; the number of charging piles may refer to the number of charging piles that need to be configured at the charging station, and the power demand capacity may refer to the power supply obtained by the charging station from the power.

[0048] Specifically, based on market growth forecasts, vehicle sales data, policy support levels, etc., the future growth trend of the number of electric vehicles in the area is predicted, and the future vehicle increment forecast value is determined.

[0049] Through the above method, the capacity plan of the charging station can be determined in a time-space combination manner, and the scale and capacity of the charging station can be reasonably planned to meet the continuously growing demand for electric vehicle charging.

[0050] In a specific embodiment, obtaining the driving record table and monthly characteristic record table of electric vehicles in a city is achieved based on the following steps:

[0051] Based on the Internet of Vehicles data of the electric vehicle, determine the vehicle operation data corresponding to the electric vehicle, the vehicle operation data includes: driving data, parking data and charging data;

[0052] Based on the annual monthly average temperature, the 12 months are divided into at least one monthly temperature interval, and the monthly temperature interval includes at least one month; the annual monthly average temperature is related to the energy consumption of electric vehicles;

[0053] Profiling electric vehicle users based on vehicle operation data to determine the charging and usage characteristics of electric vehicles corresponding to each monthly temperature range;

[0054] According to the charging usage characteristics, determine the electric vehicle's driving record sheet and monthly characteristic record sheet.

[0055] In a possible implementation, based on the Internet of Vehicles data of the electric vehicle, the vehicle operation data corresponding to the electric vehicle is determined, and the vehicle operation data includes: driving data, parking data and charging data, which is implemented based on the following steps:

[0056] Based on the vehicle network data of the vehicle, obtain the vehicle's charging data fragments throughout the year, the charging data fragments include but are not limited to: driving time, driving speed, cumulative mileage, longitude and latitude, voltage, and current;

[0057] Driving data, parking data, and charging data of the vehicle are determined based on the charging data segments.

[0058] In a possible implementation, based on the annual monthly average temperature, 12 months are divided into at least one monthly temperature interval, and the monthly temperature interval includes at least one month, including:

[0059] Based on the characteristics of vehicle energy consumption changing with temperature and the annual average temperature of each month, the months are divided into intervals with a temperature difference of 5 degrees Celsius in the average monthly temperature, and are divided into at least one monthly temperature interval.

[0060] Specifically, the monthly temperature ranges are shown in Table 1:

[0061]

[0062] Table 1

[0063] Each monthly temperature range includes at least one month, and the temperature difference between the monthly temperature ranges is 5 degrees Celsius.

[0064] In a possible implementation, a user profile of an electric vehicle is created based on vehicle operation data to determine charging usage characteristics corresponding to each monthly temperature range of the electric vehicle, including:

[0065] Taking a monthly temperature range as the statistical period, the monthly usage characteristic data of the vehicle is determined based on the vehicle operation data. The monthly usage characteristic data is used to form a user portrait based on the vehicle charging and driving behavior. The user portrait is used to characterize the corresponding behavior patterns, usage preferences and charging needs of the vehicle owners, so as to identify different types of vehicle user groups.

[0066] Specifically, the monthly usage characteristic data includes but is not limited to: parking area, charging area, average parking time, average charging amount and average charging starting SOC.

[0067] Among them, the parking area may refer to a geographical area where vehicles often stop or park in daily use, the charging area may refer to an area where vehicles are often charged, the average parking time may refer to the average parking time of each vehicle stop, the average charge amount may refer to the average amount of electricity replenished by the vehicle during each charging, and the average charging starting SOC in the monthly usage data characteristics may refer to the average ratio of the remaining battery power at the start and end of charging.

[0068] For example, user profiling based on monthly usage feature data may include:

[0069] When the parking areas are mainly concentrated in residential and working areas, the charging areas are slow-charged at home or work, the average parking time is long, the average charging amount is medium or small, the average starting SOC of charging is high, and the preference for fast and slow charging tends to slow charging, the vehicle owner can be a daily short-distance commuter.

[0070] In a possible implementation, a driving record table and a monthly characteristic record table of an electric vehicle are determined according to the charging usage characteristics, including:

[0071] Taking each vehicle as a unit, analyze the charging and usage characteristics of the vehicle in different monthly temperature ranges, and determine the monthly temperature range charging characteristics record table;

[0072] Based on the vehicle operation data of the vehicle, a trip chain record table and a monthly characteristic record table are determined.

[0073] Specifically, the monthly temperature range charging characteristic record table includes but is not limited to: vehicle identification code, month and temperature range, number of charges, charge amount and SOC starting value; the trip chain record table includes but is not limited to: vehicle identification code, departure point, arrival point, driving time, driving distance; the monthly characteristic record table includes but is not limited to: vehicle identification code, month, number of charges, charge amount.

[0074] Through the above method, based on the characteristics of vehicle energy consumption changing with temperature, the monthly temperature range charging feature record table, trip chain record table and monthly feature record table are determined, and feature mining and statistical analysis are performed according to different levels, time dimensions and regions. This can better predict the charging demand in different months and temperatures, thereby optimizing the layout of charging stations and power scheduling; determining the monthly feature record table is convenient for predicting the future growth trend of electric vehicles and the corresponding charging demand.

[0075] In a specific embodiment, based on the driving record table, a hexagonal figure representing the service area of ​​the charging station is determined, and the area in the city is divided according to the hexagonal figure, which is implemented based on the following steps:

[0076] Based on the driving record table, the maximum vehicle flow rate and median driving time in the city are determined; the driving time is used to represent the time required for vehicles in the city to reach the charging station;

[0077] Based on the product of the maximum vehicle flow speed and the median driving time, the length of the side of the hexagonal figure is determined, and the convex hull algorithm is used to construct the hexagonal figure based on the boundary determined by the side length;

[0078] A preset parallel line matching method is used to determine the hexagonal figure to which the electric vehicle's stop point belongs based on the coordinate position corresponding to the stop point, and the city is divided into areas based on the hexagonal figure; the areas include but are not limited to: charging stations and electric vehicles.

[0079] In a possible implementation, based on the driving record table, determining the maximum flow speed and median driving time of vehicles in the city includes:

[0080] Obtain the traffic flow speed distribution in the city based on the average vehicle speed V during period t veh,t , the following formula is used to determine the urban traffic flow velocity V during period t tra,t :

[0081] V tra,t =sum(V veh,t )

[0082] The maximum vehicle flow velocity V is calculated using the following formula: max :

[0083] V max =max(V tra,t )

[0084] Get T last,cha , the median driving time T is calculated using the following formula mid :

[0085] T mid = mid(T last,cha )

[0086] Among them, T last,cha It is used to represent the last driving time before the vehicle arrives at the charging station.

[0087] Among them, the maximum vehicle flow rate can represent the driving speed of the vehicle under the most ideal traffic conditions, and the median driving time can represent the driving time required for the vehicle to reach the charging station.

[0088] In a possible implementation, the length of the side of the hexagonal figure is determined based on the product of the maximum vehicle flow speed and the median driving time, and the convex hull algorithm is used to construct the hexagonal figure based on the boundary determined by the side length, including:

[0089] The length L of the side of the hexagon is calculated using the following formula:

[0090] L=V max ×T mid

[0091] The longitude and latitude in the vehicle's charging data segment are fitted with the hexagonal graphic area using a convex hull algorithm to determine whether the vehicle's charging position is within the hexagonal image area.

[0092] Specifically, the charging position of the vehicle is fitted into the corresponding hexagonal area through the convex hull algorithm and the division of regular hexagonal areas to determine the area where the vehicle charging behavior occurs; the fitting process of the convex hull algorithm includes: drawing a parallel line between the charging position and the hexagon, checking whether the line coincides with the three intersection points of the hexagon, and if so, the charging position belongs to the area.

[0093] For example, the hexagonal graphic representing the service area of ​​the charging station is as follows: Figure 2 As shown, Figure 2 It is a schematic diagram of an area of ​​a charging station capacity determination method based on multidimensional features provided in an embodiment of the present invention, wherein the side length of the hexagonal figure is determined based on the product of the maximum vehicle flow rate and the median driving time, and the boundary of the hexagonal image is determined based on a convex hull algorithm.

[0094] Through the above method, by dividing the regular hexagonal area, the service area of ​​the charging station can be evenly covered, reducing the blind area of ​​the charging station service, and improving the convenience of vehicle owners to find the charging station; setting and calculating the boundary length according to the flow rate changes in the area can effectively control the time taken to reach the charging station during the congested period in the area, and improve the user charging experience; dividing the area based on the vehicle's driving speed and the median driving time allows most vehicles to reach the nearest charging station within a reasonable time.

[0095] In a specific embodiment, potential charging customers and charging demand judgments are performed on electric vehicles in the area, and the charging demand vehicles and charging power required corresponding to each time period in the area are determined, including:

[0096] Based on the preset resident assessment standard and the preset charging habit standard, determine the resident area and the non-resident area in at least one area, and determine the ownership of private charging piles in the resident area;

[0097] Based on the ownership of private charging piles in the resident area, determine the potential customers and non-potential customers in the resident area at different time periods;

[0098] Based on the real-time charging status in the monthly characteristic record table of potential customers, determine the vehicles with corresponding charging needs in each period in the area;

[0099] Based on the average charging status in the monthly characteristic record table of potential customers, determine the charging power required for each time period in the area.

[0100] In a possible implementation, based on a preset resident evaluation standard and a preset charging habit standard, determining a resident area and a non-resident area in at least one area, and determining the ownership of private charging piles in the resident area include:

[0101] When the parking time and parking frequency of the vehicle in the area meet the preset permanent evaluation criteria, the area is determined to be the permanent area of ​​the vehicle;

[0102] When the parking time and parking frequency of the vehicle in the area do not meet the preset permanent evaluation criteria, the area is determined to be a non-resident area for the vehicle;

[0103] When the charging frequency of a vehicle at a point of interest within the resident area is greater than the preset frequency, the charging power is less than or equal to the preset power, and there is no charging record in the database of the charging facility operator, it indicates that the vehicle has a private charging device;

[0104] When the charging frequency of a vehicle at a point of interest within the resident area is greater than the preset frequency and there is a charging record for the point of interest in the database of the charging facility operator, it indicates that the vehicle has no private charging equipment and has a habitual charging station.

[0105] Specifically, the preset permanent evaluation criteria may include: the parking frequency is greater than 33% and the parking time is greater than the average charging time; the permanent area may refer to the area where the vehicle is parked for a long time and with a high frequency. The permanent area may include but is not limited to: residence and workplace. The non-resident area may refer to the area where the vehicle occasionally stays; wherein the permanent area and the non-resident area are determined based on the parking area in the monthly usage characteristic data.

[0106] A point of interest (POI) may refer to a method of expressing a specific geographical location in a geographic information system.

[0107] Furthermore, no customary charging station means that the judgment conditions of having private charging equipment and having no private charging equipment but having customary charging stations are not met. The car owner has neither private charging equipment nor a customary public charging place.

[0108] In a possible implementation, based on the ownership of private charging piles in the resident area, potential customers and non-potential customers in each period of time in the resident area are determined, including:

[0109] When the vehicle is parked in the permanent area, there is no private charging equipment in the area, and the number of times the vehicle has been charged in the area is greater than or equal to 1, the vehicle is determined to be a potential customer in the area;

[0110] When the vehicle is parked in a permanent area and there is no private charging equipment in the area, the vehicle is determined to be a suspected potential customer in the area;

[0111] When the vehicle is parked in a permanent area and the vehicle has a private charging device in the permanent area, or the vehicle is parked in a non-resident area, the vehicle is determined to be a non-potential customer in the area.

[0112] Among them, potential customers may refer to vehicles that rely on public charging piles for charging, suspected potential customers may refer to vehicles that may use public charging piles for charging in the future, and non-potential customers may refer to vehicles that do not need to use public charging piles for charging or do not rely on public charging piles in a specific area for charging.

[0113] Here, by accurately identifying potential customers with charging needs as one of the bases for determining capacity, we can avoid over-construction of unnecessary charging piles.

[0114] In a possible implementation, based on the real-time charging status in the monthly characteristic record table of potential customers, the charging demand vehicles corresponding to each time period in the area are determined based on the following steps:

[0115] Obtain the number of vehicles whose real-time SOC is less than the average charging start SOC of N months among the potential customers and suspected potential customers in each period, and use the number of vehicles as the charging demand vehicles corresponding to the period.

[0116] Among them, the real-time SOC may refer to the real-time charging status of the vehicle, that is, the current remaining power of the battery; the average charging start SOC may refer to the battery charging status at the average charging start in N months, and N can be set according to specific needs.

[0117] In a possible implementation, based on the average charging status in the monthly characteristic record table of potential customers, the charging power required for each time period in the area is determined, which can be implemented based on the following steps:

[0118] Obtain the historical average SOC change of the vehicle in each month, and the average SOC consumption of the vehicle per trip during driving days in N months;

[0119] The minimum value between the historical average SOC change and the process average SOC consumption is determined as the corresponding charging power requirement of the vehicle.

[0120] Specifically, the historical average SOC change may refer to the overall trend of the vehicle's charging and power consumption over a period of one month, and the one-way average SOC consumption may refer to the battery power consumption of the vehicle during a one-way trip; the historical average SOC change is used to characterize the vehicle's power change trend in a month, and the one-way average SOC consumption is used to characterize the vehicle's daily power consumption.

[0121] Here, the minimum value of the historical average SOC change and the one-way average SOC consumption is taken. When the historical average SOC change is large and the one-way average SOC consumption is small, it can be characterized that the vehicle's recent usage is relatively light. Therefore, it is more conservative to use the smaller consumption as the estimate of the minimum charging demand. When the historical average SOC change is small and the one-way average SOC consumption is large, the smaller change is still used as the basis for estimating the charging demand.

[0122] In a possible implementation, a data table of potential charging data of vehicles is determined based on the date type, charging demand vehicles in each time period, and charging power requirement, as described in Table 2 below:

[0123]

[0124]

[0125] Table 2

[0126] Among them, the ID of the area where a station can be built is used to determine the unique identifier of each area; the coordinates of the latitude and longitude center point of the area where a station can be built are used to determine the latitude and longitude coordinates of the geographic center point of the area, which are used to determine the specific location of the area on the map; the latitude and longitude electronic fence of the area where a station can be built is used to determine the set of latitude and longitude coordinates of the boundary points of the area, which are used to determine the geographical scope of the area; the city name is used to determine the data recorded and classified in different cities; the date type is used to identify whether the period is a holiday; the time period can be a period of 15 minutes; the maximum value of potential customers starting to charge in this period is used to determine how many vehicles need to be charged in this period; the maximum value of suspected potential customers starting from this period is used to determine the maximum number of suspected potential customers expected to be charged; the maximum value of total charging starting from this period is used to determine the total number of all customers expected to charge, including the sum of potential customers and suspected potential customers; the mean charging power of potential customers starting to charge at this time is used to determine the average charging power of each potential charging customer; the standard deviation of the charging power of potential customers starting to charge at this time is used to determine the standard deviation of the charging power of potential charging customers, reflecting the difference in charging power between different vehicles.

[0127] Through the above method, by determining the charging demand vehicles and charging power required for each time period, it is possible to identify areas and time periods with high charging demand, so as to more effectively allocate charging resources; by predicting charging demand and rationally allocating resources, the waiting time of users at charging stations is reduced and the charging experience of users is improved; ensuring that there are enough charging piles during high-demand periods can improve user satisfaction and encourage more users to choose public charging facilities.

[0128] In a specific embodiment, based on the number of vehicles in need of charging, the amount of charging power required, and the predicted value of future vehicle increment, a capacity determination scheme corresponding to the charging station is determined, including:

[0129] Obtain the traffic volume in each time period in the area, determine the number of electric vehicles in the traffic volume according to a preset ratio, and determine the estimated demand for charging piles in each time period in the area based on the number of electric vehicles; the estimated demand for charging piles is used to indicate that there is a gap in the demand for charging piles in the area during the corresponding time period;

[0130] When the estimated demand for charging piles in the first period in the area is greater than the gap threshold, the average number of charging vehicles of potential customers in the first period and the adjacent second period is determined;

[0131] Determine the difference between the average number of charging vehicles in the area and the number of public charging piles in the adjacent preset area, and use the difference as the number of charging piles corresponding to the charging station;

[0132] Obtain the minimum charging power of the charging piles in the area and the charging power with the best charging experience, and determine the rated power of the charging piles based on the minimum charging power and the charging power with the best charging experience;

[0133] Based on the number of charging piles and the rated power of the charging piles, determine the corresponding capacity plan for the charging station.

[0134] In a possible implementation, the traffic volume in each time period in the area is obtained, the number of electric vehicles in the traffic volume is determined according to a preset ratio, and the estimated demand for charging piles in each time period in the area is determined based on the number of electric vehicles, including:

[0135] Based on the data of motor vehicle ownership and electric vehicle ownership in the city, determine the corresponding traffic volume in each time period in the area;

[0136] The fuel vehicles and electric vehicles are converted at a preset ratio of A:1 to determine the number of electric vehicles in each time period, thereby determining the estimated demand for charging piles in each time period; wherein A can be specifically determined based on actual data.

[0137] In a possible implementation, when the estimated demand for charging piles in a first time period in the area is greater than the gap threshold, determining the average number of charging vehicles of potential customers in the first time period and an adjacent second time period includes:

[0138] Determine the average number of charging vehicles of potential customers and suspected potential customers in the first time period and the adjacent second time period in the area.

[0139] Further, determining the difference between the average number of charging vehicles in the area and the number of public charging piles in the adjacent preset number of areas, and using the difference as the number of charging piles corresponding to the charging station, includes:

[0140] Determine the average number of charging vehicles in the area and the difference in the number of public charging piles in the six adjacent areas, and determine the difference as the number of charging piles corresponding to the charging station.

[0141] Here, the number of charging piles can be used to represent the recommended number of charging piles to be installed at the charging station.

[0142] For example, the number of charging piles can be calculated using the following formula:

[0143]

[0144] Among them, N all It can represent the average number of charging vehicles in the area. It can represent the number of charging piles in the charging stations in the six adjacent areas.

[0145] Furthermore, the area and the six adjacent areas can be Figure 3 As shown, Figure 3 It is a schematic diagram of the areas and adjacent areas of the charging station capacity determination method based on multi-dimensional features provided in an embodiment of the invention, wherein the target station refers to the charging station to be capacity determined, and stations 1-4 may refer to charging stations in six adjacent areas.

[0146] In a possible implementation, the minimum charging power may represent the minimum charging power that a charging pile can provide at any time, and the optimal charging experience power may represent the power required to provide users with the fastest and most comfortable charging experience.

[0147] Through the above method, by obtaining the traffic flow in each time period in the area and determining the number of electric vehicles therein according to a preset ratio, the charging demand of electric vehicles in each time period can be accurately predicted, ensuring that the charging piles after capacity determination can meet the charging needs of electric vehicles; the rated power of the charging piles is determined based on the minimum charging power and the optimal experience charging power, which can meet the user's fast charging needs while ensuring that the cost of the equipment and the grid load are controlled within a reasonable range.

[0148] In a specific embodiment, obtaining the minimum charging power of the charging piles in the area and the charging power with the best charging experience is achieved based on the following steps:

[0149] Obtain the number of potential customers in each time period in the area. When the number of potential customers is equal to the maximum number of charging piles that can be built at the charging station, determine the corresponding time period as the peak time period;

[0150] Determine the duration between two peak hours as the non-congested charging duration, obtain the average charging power requirement corresponding to the non-congested charging duration, and determine the minimum charging power of the charging pile based on the average charging power requirement and the non-congested charging duration;

[0151] The shortest vehicle charging time during the non-congested charging time within a preset number of months is obtained, and the charging power with the best charging experience is determined based on the average charging power requirement and the shortest vehicle charging time.

[0152] In a possible implementation, the number of potential customers and suspected potential customers in each time period is obtained, and when the sum of the potential customers and the suspected potential customers is equal to the maximum number of charging piles that can be built, the corresponding time period is determined to be a peak period.

[0153] Here, the maximum number of charging piles that can be built is used to represent the default maximum number of charging piles that can be built when the charging station is initially built. When the sum of potential customers and suspected potential customers is equal to the maximum number of charging piles that can be built, it represents that the charging demand of the charging station during this period has reached the maximum capacity of the charging facilities.

[0154] In a possible implementation, the duration between two peak hours is determined as the non-congested charging duration, the average charging power requirement corresponding to the non-congested charging duration is obtained, and the minimum charging power of the charging pile is determined based on the average charging power requirement and the non-congested charging duration, including:

[0155] Based on the number of vehicles during the peak hours of the day in the area and the corresponding charging power demand, determine the time interval to the next peak hour of the day, and use this time interval as the congestion charging time;

[0156] Use the following formula to calculate the minimum charging power of the charging pile:

[0157] P ide =Q need / T allo

[0158] Among them, Q need is the average value of the estimated charging power required during the congested charging time, T allo It is the charging time in congestion; the minimum charging power of the charging pile is used to characterize the minimum power of the charging pile required to reduce congestion and waiting time at the charging station.

[0159] In a possible implementation, the shortest vehicle charging time in the non-crowded charging time within a preset number of months is obtained, and the optimal charging experience charging power is determined based on the average charging power requirement and the shortest vehicle charging time, including:

[0160] Use the following formula to calculate the optimal charging experience charging power:

[0161] P idu =Q need / T min

[0162] Among them, T min The shortest charging time for the vehicle during this period within N months.

[0163] Here, the shortest charging time is T min Used to characterize the ideal charging time, the optimal charging experience charging power is used to characterize the optimal charging power required to ensure that potential customers can complete charging within the optimal experience time.

[0164] Through the above method, the power configuration of the charging station can be optimized when the charging station is sized to maximize charging efficiency, improve user experience, and reduce resource waste between peak and off-peak hours.

[0165] In a specific embodiment, based on the Internet of Vehicles data of the electric vehicle, the vehicle operation data corresponding to the electric vehicle is determined, and the vehicle operation data includes: driving data, parking data and charging data, including:

[0166] Obtain vehicle speed and current data from Internet of Vehicles data;

[0167] When the vehicle speed is greater than 0 or the vehicle speed is equal to 0 and the current data is greater than 0, it is determined that the electric vehicle is in a driving state, and driving data corresponding to the driving state is obtained;

[0168] When the vehicle speed is equal to 0 and the current data is equal to 0, it is determined that the electric vehicle is in a parking and ignition-off state, and parking data corresponding to the parking and ignition-off state is obtained;

[0169] When the vehicle speed is equal to 0 and the current data is less than 0, it is determined that the electric vehicle is in a parking charging state, and charging data corresponding to the parking charging state is acquired.

[0170] Specifically, the Internet of Vehicles data is obtained, which includes but is not limited to: time data, speed data, cumulative mileage data, latitude and longitude data, voltage data and current data; driving data includes but is not limited to: start time, end time, driving duration, start position, end position; parking data includes but is not limited to: start time, end time, parking duration, parking position; charging data includes but is not limited to: start time, end time, charging duration, charging position, start SOC, end SOC, average charging power, and charging amount.

[0171] For example, the vehicle operation data is divided according to the following table:

[0172]

[0173] Through the above method, the operating data corresponding to different operating states of the vehicle are determined based on the vehicle's Internet of Vehicles data, providing a data basis for the capacity determination of the charging station, so that the capacity determination result is more in line with the vehicle's movement characteristics.

[0174] For example, Figure 4 is a schematic diagram of a specific flow chart of a charging station capacity determination method based on multi-dimensional features provided by an embodiment of the present invention, such as Figure 4 As shown, the figure includes the following steps:

[0175] Step S401, multi-source data fusion;

[0176] Here, the Internet of Vehicles data and the annual average temperature are integrated to determine the driving record table and the monthly characteristic record table;

[0177] Step S402, determining the station service area;

[0178] Here, the length of the side of the hexagonal figure is calculated, and the convex hull algorithm is used to determine the boundary of the hexagonal figure. The hexagonal figure is used as the service area of ​​the charging station, and the area is divided in the city according to the hexagonal figure;

[0179] Step S403, generating a vehicle user portrait;

[0180] Here, the user profile of electric vehicles is created based on the vehicle operation data, and the charging usage characteristics corresponding to the temperature range of each month of the electric vehicle are determined;

[0181] Step S404, determining charging demand;

[0182] Here, potential customers and suspected potential customers are identified;

[0183] Step S405, predicting demand increment;

[0184] Here, the expected demand for charging piles in each period within the area is determined;

[0185] Step S406, evaluating the required charging piles;

[0186] Here, the difference between the average number of charging vehicles in the area and the number of public charging piles in the adjacent preset area is determined, and the difference is used as the number of charging piles corresponding to the charging station;

[0187] Step S407, evaluating charging demand;

[0188] Here, the minimum charging power of the charging piles in the area and the charging power with the best charging experience are obtained, and the rated power of the charging piles is determined based on the minimum charging power of the charging piles and the charging power with the best charging experience.

[0189] Figure 5 is a schematic diagram of the structure of a charging station capacity determination device based on multi-dimensional features provided by an embodiment of the present invention, such as Figure 5 As shown, the device can be applied to intelligent electronic devices such as servers and computers; the device includes: a division module 501, a demand judgment module 502 and a capacity determination module 503;

[0190] The division module 501 is used to obtain a driving record table and a monthly characteristic record table of electric vehicles in the city, determine a hexagonal figure representing the service area of ​​the charging station based on the driving record table, and divide the area in the city according to the hexagonal figure;

[0191] Demand judgment module 502, used to judge potential charging customers and charging demand of electric vehicles in the area, and determine the charging demand vehicles and charging power required corresponding to each time period in the area;

[0192] The capacity determination module 503 is used to determine the capacity determination plan corresponding to the charging station based on the charging demand vehicles, the charging power demand and the future vehicle increment prediction value.

[0193] In a specific embodiment, the division module 501 is used to determine vehicle operation data corresponding to the electric vehicle based on the Internet of Vehicles data of the electric vehicle, where the vehicle operation data includes: driving data, parking data and charging data;

[0194] Based on the annual monthly average temperature, the 12 months are divided into at least one monthly temperature interval, and the monthly temperature interval includes at least one month; the annual monthly average temperature is related to the energy consumption of electric vehicles;

[0195] Profiling electric vehicle users based on vehicle operation data to determine the charging and usage characteristics of electric vehicles corresponding to each monthly temperature range;

[0196] According to the charging usage characteristics, determine the electric vehicle's driving record sheet and monthly characteristic record sheet.

[0197] In a specific embodiment, the partitioning module 501 is used to determine the maximum flow rate of vehicles in the city and the median driving time based on the driving record table; the driving time is used to represent the time required for vehicles in the city to reach the charging station;

[0198] Based on the product of the maximum vehicle flow speed and the median driving time, the length of the side of the hexagonal figure is determined, and the convex hull algorithm is used to construct the hexagonal figure based on the boundary determined by the side length;

[0199] A preset parallel line matching method is used to determine the hexagonal figure to which the electric vehicle's stop point belongs based on the coordinate position corresponding to the stop point, and the city is divided into areas based on the hexagonal figure; the areas include but are not limited to: charging stations and electric vehicles.

[0200] In a specific embodiment, the demand determination module 502 is used to determine the resident area and the non-resident area in at least one area based on the preset resident evaluation standard and the preset charging habit standard, and determine the ownership of private charging piles in the resident area;

[0201] Based on the ownership of private charging piles in the resident area, determine the potential customers and non-potential customers in the resident area at different time periods;

[0202] Based on the real-time charging status in the monthly characteristic record table of potential customers, determine the vehicles with corresponding charging needs in each period in the area;

[0203] Based on the average charging status in the monthly characteristic record table of potential customers, determine the charging power required for each time period in the area.

[0204] In a specific embodiment, the capacity determination module 503 is used to obtain the traffic flow in each time period in the area, determine the number of electric vehicles in the traffic flow according to a preset ratio, and determine the estimated charging pile demand in each time period in the area based on the number of electric vehicles; the estimated charging pile demand is used to characterize the existence of a charging pile demand gap in the area during the corresponding time period;

[0205] When the estimated demand for charging piles in the first period in the area is greater than the gap threshold, the average number of charging vehicles of potential customers in the first period and the adjacent second period is determined;

[0206] Determine the difference between the average number of charging vehicles in the area and the number of public charging piles in the adjacent preset area, and use the difference as the number of charging piles corresponding to the charging station;

[0207] Obtain the minimum charging power of the charging piles in the area and the charging power with the best charging experience, and determine the rated power of the charging piles based on the minimum charging power and the charging power with the best charging experience;

[0208] Based on the number of charging piles and the rated power of the charging piles, determine the corresponding capacity plan for the charging station.

[0209] In a specific embodiment, the capacity determination module 503 is used to obtain the number of potential customers in each time period in the area, and when the number of potential customers is equal to the maximum number of charging piles that can be built in the charging station, the corresponding time period is determined to be a peak period;

[0210] Determine the duration between two peak hours as the non-congested charging duration, obtain the average charging power requirement corresponding to the non-congested charging duration, and determine the minimum charging power of the charging pile based on the average charging power requirement and the non-congested charging duration;

[0211] The shortest vehicle charging time during the non-congested charging time within a preset number of months is obtained, and the charging power with the best charging experience is determined based on the average charging power requirement and the shortest vehicle charging time.

[0212] In a specific embodiment, the partitioning module 501 is used to obtain vehicle speed and current data in the Internet of Vehicles data;

[0213] When the vehicle speed is greater than 0 or the vehicle speed is equal to 0 and the current data is greater than 0, it is determined that the electric vehicle is in a driving state, and driving data corresponding to the driving state is obtained;

[0214] When the vehicle speed is equal to 0 and the current data is equal to 0, it is determined that the electric vehicle is in a parking and ignition-off state, and parking data corresponding to the parking and ignition-off state is obtained;

[0215] When the vehicle speed is equal to 0 and the current data is less than 0, it is determined that the electric vehicle is in a parking charging state, and charging data corresponding to the parking charging state is acquired.

[0216] It should be noted that: the device for determining the capacity of a charging station based on multi-dimensional features provided in the above embodiment only uses the division of the above program modules as an example when implementing the corresponding method for determining the capacity of a charging station based on multi-dimensional features. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. Figure 1 The embodiments of the method shown belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0217] The embodiment of the present invention also provides a computer device having the above Figure 5 The charging station capacity determination device based on multi-dimensional features is shown.

[0218] See also Figure 6 , Figure 6 is another structural schematic diagram of a charging station capacity determination device based on multi-dimensional features provided in an embodiment of the present invention, such as Figure 6As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0219] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0220] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0221] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0222] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0223] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0224] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0225] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0226] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0227] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0228] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A charging station capacity determination method based on multi-dimensional features, characterized in that: The method comprises: Obtaining a driving record table and a monthly characteristic record table of electric vehicles in the city, determining a hexagonal figure representing a service area of ​​a charging station based on the driving record table, and dividing the city into areas according to the hexagonal figure; Identify potential charging customers and charging needs of electric vehicles in the area, and determine the charging demand vehicles and charging power required for each time period in the area; Based on the vehicles in need of charging, the required amount of charging electricity and the predicted value of future vehicle increment, a capacity solution corresponding to the charging station is determined.

2. The method according to claim 1, characterized in that Obtaining the driving record and monthly characteristic record of electric vehicles in the city is based on the following steps: Based on the Internet of Vehicles data of the electric vehicle, determining the vehicle operation data corresponding to the electric vehicle, the vehicle operation data including: driving data, parking data and charging data; Based on the annual monthly average temperature, 12 months are divided into at least one monthly temperature interval, and the monthly temperature interval includes at least one month; the annual monthly average temperature is associated with the energy consumption of the electric vehicle; Profiling the user of the electric vehicle based on the vehicle operation data, and determining the charging usage characteristics of the electric vehicle corresponding to each of the monthly temperature ranges; The driving record table and the monthly characteristic record table of the electric vehicle are determined according to the charging usage characteristics.

3. The method according to claim 2, characterized in that Based on the driving record table, a hexagonal figure representing the service area of ​​the charging station is determined, and the area in the city is divided according to the hexagonal figure, which is implemented based on the following steps: Based on the driving record table, determining the maximum flow rate of vehicles in the city and the median driving time; the driving time is used to represent the time required for vehicles in the city to reach the charging station; Determine the length of the side of the hexagonal figure based on the product of the maximum vehicle flow speed and the median driving time, and construct the hexagonal figure based on the boundary determined by the side length using a convex hull algorithm; A preset parallel line matching method is adopted to determine the hexagonal figure to which the electric vehicle's stop point belongs based on the coordinate position corresponding to the stop point, and the city is divided into areas according to the hexagonal figure; the areas include but are not limited to: charging stations and electric vehicles.

4. The method according to claim 3, characterized in that The potential charging customers and charging demand of electric vehicles in the area are judged, and the charging demand vehicles and charging power required corresponding to each time period in the area are determined, including: Based on the preset resident assessment standard and the preset charging habit standard, determining the resident area and the non-resident area in at least one area, and determining the ownership of private charging piles in the resident area; Based on the ownership of the private charging piles in the resident area, determining potential customers and non-potential customers in each time period in the resident area; Based on the real-time charging status in the monthly characteristic record table of the potential customers, determine the vehicles with charging demand corresponding to each time period in the area; Based on the average charging status in the monthly characteristic record table of the potential customers, the charging power requirement corresponding to each time period in the area is determined.

5. The method according to claim 4, characterized in that The determining of the capacity solution corresponding to the charging station based on the charging demand vehicle, the charging power requirement and the future vehicle increment prediction value includes: Obtaining the traffic volume in each time period in the area, determining the number of electric vehicles in the traffic volume according to a preset ratio, and determining the estimated demand for charging piles in each time period in the area based on the number of electric vehicles; the estimated demand for charging piles is used to indicate that there is a demand gap for charging piles in the area during the corresponding time period; When the estimated demand for charging piles in the first time period in the area is greater than the gap threshold, determining the average number of charging vehicles of potential customers in the first time period and an adjacent second time period; Determine the difference between the average number of charging vehicles in the area and the number of public charging piles in adjacent preset areas, and use the difference as the number of charging piles corresponding to the charging station; Obtaining the minimum charging power and the charging power with the best charging experience of the charging pile in the area, and determining the rated power of the charging pile based on the minimum charging power and the charging power with the best charging experience; Based on the number of charging piles and the rated power of the charging piles, a capacity solution corresponding to the charging station is determined.

6. The method according to claim 5, characterized in that Obtaining the minimum charging power and the optimal charging power of the charging piles in the area is achieved based on the following steps: The number of potential customers in each time period in the area is obtained, and when the number of potential customers is equal to the maximum number of charging piles that can be built in the charging station, the corresponding time period is determined to be a peak period; Determine the duration between the two peak hours as the non-congested charging duration, obtain the average charging power requirement corresponding to the non-congested charging duration, and determine the minimum charging power of the charging pile based on the average charging power requirement and the non-congested charging duration; The shortest vehicle charging time in the non-crowded charging time within a preset number of months is obtained, and the optimal charging experience charging power is determined based on the average charging power requirement and the shortest vehicle charging time.

7. The method according to claim 2, characterized in that: The vehicle operation data corresponding to the electric vehicle is determined based on the vehicle networking data of the electric vehicle, and the vehicle operation data includes: driving data, parking data and charging data, including: Obtain vehicle speed and current data from Internet of Vehicles data; When the vehicle speed is greater than 0 or the vehicle speed is equal to 0 and the current data is greater than 0, it is determined that the electric vehicle is in a driving state, and driving data corresponding to the driving state is acquired; When the vehicle speed is equal to 0 and the current data is equal to 0, it is determined that the electric vehicle is in a parking and ignition-off state, and parking data corresponding to the parking and ignition-off state is acquired; When the vehicle speed is equal to 0 and the current data is less than 0, it is determined that the electric vehicle is in a parking charging state, and charging data corresponding to the parking charging state is acquired.

8. A charging station capacity determination device based on multi-dimensional features, characterized in that: The device comprises: A division module, used to obtain a driving record table and a monthly characteristic record table of electric vehicles in the city, determine a hexagonal figure representing a service area of ​​a charging station based on the driving record table, and divide the area in the city according to the hexagonal figure; A demand judgment module is used to judge the potential charging customers and charging demand of the electric vehicles in the area, and determine the charging demand vehicles and charging power required for each time period in the area; The capacity determination module is used to determine the capacity determination plan corresponding to the charging station based on the charging demand vehicles, the charging power demand and the future vehicle increment prediction value.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the charging station capacity determination method based on multi-dimensional features as described in any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the charging station capacity determination method based on multi-dimensional features according to any one of claims 1 to 7.

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