Intelligent site selection method and system for charging stations based on grid

Abstract

The present invention relates to the technical field of charging station site selection, and provides a method and system for realizing intelligent site selection of charging stations based on gridding, including: gridding each area in the potential site selection area to obtain a grid site selection area; analyzing the regional development potential corresponding to the grid site selection area; collecting vehicle driving data corresponding to the grid site selection area, counting the regional traffic flow corresponding to the grid site selection area, and calculating the road load index corresponding to the grid site selection area; calculating the distance density value of pure electric vehicles in the grid site selection area; reading the reserve power of pure electric vehicles in the grid site selection area, calculating the energy storage cycle corresponding to the pure electric vehicles, evaluating the annual energy consumption expenditure corresponding to the grid site selection area, and selecting the optimal site selection location corresponding to the charging station from the grid site selection area. The present invention aims to improve the rationality of intelligent site selection of charging stations.

Description

Technical Field

[0001] The present invention relates to the technical field of charging station site selection, and in particular to a grid-based intelligent site selection method and system for charging stations. Background Art

[0002] Charging stations are special places that provide electric energy for electric vehicles. They are equipped with a series of charging equipment and related facilities. They can transmit electricity to the batteries of electric vehicles to replenish the energy required for vehicle driving. With the rapid popularization of electric vehicles, the demand for the construction of charging infrastructure is becoming increasingly urgent.

[0003] Traditional charging station site selection uses manual experience and single variable analysis. However, due to certain errors in manual experience, the layout of charging stations will be unreasonable and unable to fully meet the charging needs of users. In addition, in urban and regional development, geographical space is diverse and complex. There will be multiple factors affecting site selection in different regions. Single-side analysis will ignore the impact of other factors on site selection, which will lead to poor rationality of the final site selection. Summary of the Invention

[0004] The present invention provides a method and system for realizing intelligent site selection of charging stations based on gridding, the main purpose of which is to improve the rationality of intelligent site selection of charging stations.

[0005] To achieve the above objectives, the present invention provides a grid-based intelligent site selection method for charging stations, comprising:

[0006] Obtaining a potential site selection area for a charging station, and performing grid processing on each area in the potential site selection area to obtain a grid site selection area;

[0007] Querying important landmarks in the potential site selection area, calculating landmark prosperity indexes corresponding to the important landmarks, and analyzing regional development potential corresponding to the grid site selection area based on the landmark prosperity indexes and the important landmarks;

[0008] collecting vehicle travel data corresponding to the grid location area, calculating regional traffic flow corresponding to the grid location area based on the vehicle travel data, and calculating a road load index corresponding to the grid location area based on the regional traffic flow;

[0009] Identifying a pure electric vehicle in the vehicle driving data, collecting vehicle-mounted GPS data corresponding to the pure electric vehicle, and calculating a distance density value of the pure electric vehicle in the grid site selection area based on the vehicle-mounted GPS data;

[0010] Reading the reserve power of the pure electric vehicle within the grid location area, calculating the energy storage period corresponding to the pure electric vehicle based on the reserve power, and evaluating the annual energy consumption expenditure corresponding to the grid location area based on the energy storage period, the vehicle driving data, and the distance density value;

[0011] In combination with the regional development potential, the road load index and the annual energy consumption expenditure, the optimal site location corresponding to the charging station is selected from the grid site selection area.

[0012] Optionally, calculating the landmark prosperity index corresponding to the important landmark includes:

[0013] Collecting landmark economic data corresponding to the important landmarks, and identifying economic indicators corresponding to the landmark economic data;

[0014] Calculating the indicator weight coefficient corresponding to the economic indicator, and extracting the economic performance value corresponding to the economic indicator from the landmark economic data;

[0015] performing standardization processing on the economic performance value to obtain a target performance value;

[0016] The landmark prosperity index corresponding to the important landmark is calculated by combining the indicator weight coefficient and the target performance value.

[0017] Optionally, analyzing the regional development potential corresponding to the grid site selection area based on the landmark prosperity index and the important landmarks includes:

[0018] Calculate the spatial distance value between each area in the grid site selection area and the important landmark;

[0019] Based on the spatial distance value, calculating the attenuation coefficient of the landmark prosperity index in the grid site selection area;

[0020] Calculating the regional development potential value corresponding to the grid site selection area based on the attenuation coefficient and the landmark prosperity index;

[0021] Based on the regional development potential value, the regional development potential corresponding to the grid site selection area is analyzed.

[0022] Optionally, the calculating of the road load index corresponding to the grid site selection area based on the regional traffic flow includes:

[0023] Determine a traffic artery in the grid site selection area, and query the road traffic capacity corresponding to the traffic artery;

[0024] Based on the regional traffic flow, counting the traffic flow of the time period corresponding to the grid location area;

[0025] Calculating the road saturation corresponding to the traffic road by combining the traffic flow in the time period and the traffic capacity of the road;

[0026] The road load index corresponding to the grid site selection area is calculated by combining the road saturation and a preset load index classification criterion.

[0027] Optionally, the calculating the road saturation corresponding to the traffic road by combining the traffic flow in the time period and the road traffic capacity includes:

[0028] Based on the road traffic capacity, determining the traffic lanes and road capacity corresponding to the traffic road, and querying the types of vehicles corresponding to the traffic lanes;

[0029] Determining the number of vehicles corresponding to the traveling vehicle category based on the traffic flow during the time period;

[0030] Calculating the lane contribution corresponding to the traffic lane and collecting the road environment corresponding to the traffic road;

[0031] Analyzing environmental factors in the road environment and calculating the influence coefficient of the environmental factors on the traffic of the traffic lane;

[0032] Based on the number of vehicles, the lane contribution, the road capacity, and the traffic impact coefficient, the road saturation corresponding to the traffic road is calculated using the following formula:

[0033] ;

[0034] Among them, D represents the road saturation corresponding to the traffic road, represents the number of vehicles in the bth lane, represents the lane contribution of the b-th traffic lane, represents the traffic impact coefficient of the bth traffic lane, b represents the serial number of the traffic lane, q represents the number of traffic lanes, and F represents the road capacity.

[0035] Optionally, the calculating the distance density value of the pure electric vehicle in the grid site selection area based on the vehicle-mounted GPS data includes:

[0036] Extracting the travel data of the pure electric vehicle from the onboard GPS data;

[0037] Determining the travel frequency and corresponding travel mileage of the pure electric vehicle in the grid location area based on the travel data;

[0038] Calculating an expected mileage value of the pure electric vehicle in the grid site selection area based on the trip frequency and the trip mileage;

[0039] The distance density value of the pure electric vehicle in the grid site selection area is calculated by combining the travel mileage and the expected mileage value.

[0040] Optionally, the calculating the distance density value of the pure electric vehicle in the grid site selection area by combining the travel mileage and the expected mileage value includes:

[0041] Calculating the mileage standard deviation of the pure electric vehicle in the grid location area according to the expected mileage value;

[0042] The distance density value of the pure electric vehicle in the grid location area is calculated by combining the travel mileage, the expected mileage value, and the mileage standard deviation using the following formula:

[0043] ;

[0044] Where H represents the distance density value of pure electric vehicles in the grid location area, Indicates the mileage corresponding to the lth trip frequency, l represents the serial number of the trip frequency, represents the standard deviation of mileage in the i-th area of ​​the grid site selection area, represents the expected mileage value of the i-th area in the grid location area, g represents the total value of the travel mileage, and i represents the serial number of the grid location area.

[0045] Optionally, the calculating, based on the reserve power, the energy storage cycle corresponding to the pure electric vehicle includes:

[0046] detecting a vehicle battery type corresponding to the pure electric vehicle, and determining a unit power consumption of the pure electric vehicle based on the vehicle battery type;

[0047] The energy storage cycle corresponding to the pure electric vehicle is calculated by combining the unit power consumption and the reserve power using the following formula:

[0048] ;

[0049] Among them, G represents the energy storage cycle corresponding to the pure electric vehicle, K represents the reserve power, and J represents the standard charging capacity of the vehicle battery type. Indicates the unit power consumption, h indicates the mileage of the previous car trip with the reserve power, It represents the charging efficiency of pure electric vehicles, and M represents the charging power of pure electric vehicles.

[0050] Optionally, the evaluating the annual energy consumption expenditure corresponding to the grid site selection area based on the energy storage cycle, the vehicle driving data, and the distance density value includes:

[0051] Obtaining construction criteria for the charging station;

[0052] Based on the construction criteria, determining the number of transformers and the number of charging devices required for the construction of the charging station within the grid site selection area, and obtaining the number of transformers and the number of charging devices;

[0053] Query the unit prices of the transformer and the charging device to obtain the unit price of the transformer and the unit price of the charging device;

[0054] Based on the number of transformers, the number of charging devices, the unit price of the transformers, the unit price of the charging devices, and the distance density value, the annual energy consumption expenditure corresponding to the grid site selection area is evaluated using the following formula:

[0055] ;

[0056] Among them, N represents the annual energy consumption expenditure corresponding to the grid site selection area, m represents the number of transformers, x represents the unit price of transformers, n represents the number of charging devices, y represents the unit price of charging devices, z represents the infrastructure cost of the charging station, P represents the discount rate of the charging station, and t represents the distance density value.

[0057] A grid-based intelligent site selection system for charging stations, characterized in that the system includes:

[0058] A regional grid processing module is used to obtain potential site selection areas for charging stations and perform grid processing on each area in the potential site selection area to obtain a grid site selection area;

[0059] a development potential analysis module, configured to query important landmarks in the potential site selection area, calculate a landmark prosperity index corresponding to the important landmark, and analyze the regional development potential corresponding to the grid site selection area based on the landmark prosperity index and the important landmark;

[0060] a road load index calculation module, configured to collect vehicle travel data corresponding to the grid location area, calculate regional traffic flow corresponding to the grid location area based on the vehicle travel data, and calculate a road load index corresponding to the grid location area based on the regional traffic flow;

[0061] a distance density calculation module, configured to identify pure electric vehicles in the vehicle driving data, collect vehicle-mounted GPS data corresponding to the pure electric vehicles, and calculate a distance density value of the pure electric vehicles in the grid site selection area based on the vehicle-mounted GPS data;

[0062] an energy consumption expenditure evaluation module, configured to read the reserve power of the pure electric vehicle within the grid location area, calculate the energy storage period corresponding to the pure electric vehicle based on the reserve power, and evaluate the annual energy consumption expenditure corresponding to the grid location area based on the energy storage period, the vehicle driving data, and the distance density value;

[0063] The regional site selection module is used to select the optimal site location corresponding to the charging station from the grid site selection area based on the regional development potential, the road load index and the annual energy consumption expenditure.

[0064] The present invention performs gridding processing on each area in the potential site selection area, and can divide the area into several uniform or uneven grid units according to certain rules, which is convenient for the subsequent intelligent site selection analysis of the charging station. The present invention calculates the landmark prosperity index corresponding to the important landmark, and can understand the development prosperity level corresponding to the important landmark, thereby facilitating the subsequent analysis and processing of regional development potential. The present invention counts the regional traffic flow corresponding to the grid site selection area based on the vehicle driving data, and can understand the traffic vehicle flow information corresponding to the grid site selection area. Based on the regional traffic flow, the present invention calculates the road load index corresponding to the grid site selection area, and can understand the road congestion level of the grid site selection area, thereby facilitating the subsequent selection and processing of the optimal site location. In principle, the present invention calculates the distance density value of the pure electric vehicle in the grid site selection area based on the on-board GPS data, and can understand the distribution of the driving distance of the pure electric vehicle in the grid site selection area through the distance density value, thereby facilitating the subsequent processing of the annual energy consumption expenditure corresponding to the grid site selection area. The present invention calculates the energy storage cycle corresponding to the pure electric vehicle based on the reserve power, and can obtain the charging time corresponding to the pure electric vehicle, thereby facilitating the subsequent determination of the annual energy consumption expenditure corresponding to the grid site selection area. The present invention selects the optimal site location corresponding to the charging station from the grid site selection area by combining the regional development potential, the road load index and the annual energy consumption expenditure, thereby improving the rationality of the site selection of the charging station. Therefore, the embodiment of the present invention provides a method and system for realizing intelligent site selection of charging stations based on gridding, which can improve the rationality of intelligent site selection of charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of a flow chart of a method for implementing intelligent site selection for charging stations based on gridding, provided in one embodiment of the present invention;

[0066] Figure 2 This is a functional module diagram of a grid-based intelligent site selection system for charging stations provided by one embodiment of the present invention.

[0067] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0068] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0069] The embodiment of the present application provides a method for intelligent site selection of charging stations based on gridding. In the embodiment of the present application, the execution subject of the method for intelligent site selection of charging stations based on gridding includes but is not limited to at least one of the electronic devices such as the server, the terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for intelligent site selection of charging stations based on gridding can be executed by software or hardware installed on the terminal device or the server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0070] Reference Figure 1 FIG. 1 is a flow chart of a method for implementing intelligent site selection for charging stations based on gridding according to an embodiment of the present invention. In this embodiment, the method for implementing intelligent site selection for charging stations based on gridding includes steps S1 to S6.

[0071] S1. Obtain potential site selection areas for charging stations, and perform grid processing on each area in the potential site selection areas to obtain grid site selection areas.

[0072] The present invention performs gridding processing on each area in the potential site selection area, and can divide the area into a number of uniform or non-uniform grid units according to certain rules, so as to facilitate the subsequent intelligent site selection analysis of the charging station, wherein the charging station is a place that provides charging services for electric vehicles, the potential site selection area is an alternative location for the construction of the charging station, and the grid site selection area is a subdivided area obtained after the potential site selection area is gridded. Furthermore, the gridding processing of each area in the potential site selection area can be achieved through a grid division method based on a geographic information system (GIS). By utilizing the functions of GIS software and combining geographical elements such as terrain, roads, and buildings, the potential site selection area is gridded. GIS can generate a grid that is more in line with the actual situation according to the set rules and conditions.

[0073] S2. Query important landmarks in the potential site selection area, calculate the landmark prosperity index corresponding to the important landmark, and analyze the regional development potential corresponding to the grid site selection area based on the landmark prosperity index and the important landmark.

[0074] By calculating the landmark prosperity index corresponding to the important landmark, the present invention can understand the development prosperity level corresponding to the important landmark, thereby facilitating the subsequent analysis and processing of the regional development potential, wherein the important landmark is an important building in the potential site selection area, such as a shopping mall, and the landmark prosperity index represents a quantitative value of the development prosperity level corresponding to the important landmark. Furthermore, the important landmarks in the potential site selection area can be obtained through regional map query.

[0075] As an embodiment of the present invention, the calculation of the landmark prosperity index corresponding to the important landmark includes: collecting landmark economic data corresponding to the important landmark, identifying economic indicators corresponding to the landmark economic data, calculating the indicator weight coefficient corresponding to the economic indicator, and extracting the economic performance value corresponding to the economic indicator from the landmark economic data, standardizing the economic performance value to obtain the target performance value, and calculating the landmark prosperity index corresponding to the important landmark in combination with the indicator weight coefficient and the target performance value.

[0076] Among them, the landmark economic data is the economic data of the important landmark, such as consumer economic data, the economic indicators are the economic types corresponding to the landmark economic data, such as passenger flow and turnover, the indicator weight coefficient represents the importance of the economic indicators, the economic performance value is the specific performance value corresponding to the economic indicator, and the target performance value is the value obtained after eliminating the differences between the economic performance values.

[0077] Optionally, the landmark economic data corresponding to the important landmarks can be collected through a commercial data statistics platform, and economic indicators can be obtained by identifying the data type corresponding to the landmark economic data; the calculation of the indicator weight coefficient corresponding to the economic indicator can be achieved through the hierarchical analysis method; the standardization of the economic performance value can be achieved through the Z-score standardization method; the indicator weight coefficient and the target performance value are multiplied, and the result obtained is the landmark prosperity index corresponding to the important landmark.

[0078] The present invention analyzes the regional development potential corresponding to the grid site selection area based on the landmark prosperity index and the important landmarks, thereby understanding the future development trend of the grid site selection area and further analyzing the sustainable development trend of the charging stations in the grid site selection area, wherein the regional development potential is the potential development capacity of the grid site selection area.

[0079] As an embodiment of the present invention, analyzing the regional development potential corresponding to the grid site selection area based on the landmark prosperity index and the important landmark includes: calculating the spatial distance value between each area in the grid site selection area and the important landmark, calculating the attenuation coefficient of the landmark prosperity index in the grid site selection area based on the spatial distance value, calculating the regional development potential value corresponding to the grid site selection area based on the attenuation coefficient and the landmark prosperity index, and analyzing the regional development potential corresponding to the grid site selection area based on the regional development potential value.

[0080] Among them, the spatial distance value represents the geographical distance value between each area in the grid site selection area and the important indicator, the attenuation coefficient represents the radiation degree of the landmark prosperity index in the grid site selection area, and the regional development potential value represents the development potential score of the grid site selection area.

[0081] Furthermore, the spatial distance value between each area in the grid site selection area and the important landmark can be achieved through the Euclidean distance algorithm; the attenuation coefficient of the landmark prosperity index in the grid site selection area can be obtained by calculating the inverse square of the spatial distance value, for example, if the spatial distance value is 2, then the attenuation coefficient is 1 / 4; based on the numerical value of the regional development potential value, the regional development potential corresponding to the grid site selection area is analyzed, and the larger the regional development potential value, the higher the regional development potential.

[0082] Furthermore, as an optional embodiment of the present invention, the calculating of the regional development potential value corresponding to the grid site selection area based on the attenuation coefficient and the landmark prosperity index includes:

[0083] The regional development potential value corresponding to the grid site selection area is calculated using the following formula:

[0084] ;

[0085] Among them, A represents the regional development potential value corresponding to the grid site selection area, represents the landmark prosperity index of the a-th important landmark, It represents the attenuation coefficient of the landmark prosperity index of the a-th important landmark, and a represents the serial number corresponding to the important landmark.

[0086] S3. Collect vehicle driving data corresponding to the grid location area, count regional traffic flow corresponding to the grid location area based on the vehicle driving data, and calculate a road load index corresponding to the grid location area based on the regional traffic flow.

[0087] The present invention can understand the traffic flow information corresponding to the grid location area by counting the regional traffic flow corresponding to the grid location area based on the vehicle driving data, and calculate the road load index corresponding to the grid location area based on the regional traffic flow, so as to understand the road congestion level of the grid location area, thereby facilitating the subsequent selection and processing of the optimal location position. The vehicle driving data is the relevant data of the driving vehicles recorded in the grid location area, the regional traffic flow is the number and category information of the driving vehicles in the grid location area, and the road load index represents the traffic congestion level of the grid location area. Optionally, the vehicle driving data can be collected by monitoring equipment in the grid location area; the regional traffic flow corresponding to the grid location area can be realized by a statistical tool, and the statistical tool is compiled by a scripting language, such as JS scripting language.

[0088] As an embodiment of the present invention, the road load index corresponding to the grid site selection area is calculated based on the regional traffic flow, including: determining the traffic arteries in the grid site selection area, querying the road traffic capacity corresponding to the traffic arteries, based on the regional traffic flow, counting the time period traffic flow corresponding to the grid site selection area, combining the time period traffic flow and the road traffic capacity, calculating the road saturation corresponding to the traffic road, and combining the road saturation and a preset load index grading criterion to calculate the road load index corresponding to the grid site selection area.

[0089] The traffic arterial is a traffic road in the grid location area, the road traffic capacity is the maximum number of vehicles passing through the traffic road per unit time and the corresponding vehicle type information, the time period traffic flow is the number of vehicles passing through the traffic road per unit time in the grid location area, the road saturation represents the saturation level of the traffic road, and the load index grading criterion is a preset load index definition rule, such as when the road saturation is less than 0.6, the load index is 1 (low load), when the road saturation is between 0.6 and 0.8, the load index is 2 (moderate load), when the road saturation is between 0.8 and 1.0, the load index is 3 (high load), and when the road saturation is greater than 1.0, the load index is 4 (severe overload). Optionally, the traffic arterial can be obtained from a planning map of the grid location area; the road traffic capacity corresponding to the traffic road can be obtained by querying the corresponding road design standards and specifications; the road load index corresponding to the traffic arterial is determined by combining the road saturation and the preset load index grading criterion, and the road load index is summed in the grid location area to obtain the road load index.

[0090] Optionally, as an optional embodiment of the present invention, the calculation of the road saturation corresponding to the traffic road in combination with the traffic flow in the time period and the road traffic capacity includes: determining the passing lane and road capacity corresponding to the traffic road based on the road traffic capacity, querying the driving vehicle category corresponding to the passing lane, determining the number of vehicles corresponding to the driving vehicle category based on the traffic flow in the time period, calculating the lane contribution corresponding to the passing lane, collecting the road environment corresponding to the traffic road, analyzing the environmental factors in the road environment, calculating the traffic impact coefficient of the environmental factors on the passing lane, and calculating the road saturation corresponding to the traffic road based on the number of vehicles, the lane contribution, the road capacity and the traffic impact coefficient by the following formula:

[0091] ;

[0092] Among them, D represents the road saturation corresponding to the traffic road, represents the number of vehicles in the bth lane, represents the lane contribution of the b-th traffic lane, represents the traffic impact coefficient of the bth traffic lane, b represents the serial number of the traffic lane, q represents the number of traffic lanes, and F represents the road capacity.

[0093] Among them, the traffic lane and the road capacity are respectively the vehicle driving lane in the traffic road and the total number of vehicles accommodated by the road, the lane contribution represents the contribution of the traffic lane to traffic, the environmental factors are environmental variables in the road environment, such as weather or road maintenance and other factors, and the traffic impact coefficient represents the impact of the environmental factors on the traffic lane. Optionally, the lane contribution can be calculated by a queuing theory model; the analysis of environmental factors in the road environment can be achieved by principal component analysis; the traffic impact coefficient of the environmental factors on the traffic lane can be calculated by a linear regression model, by collecting historical data of the traffic lane, inputting historical data and environmental factors into the linear regression model, so as to calculate the traffic impact coefficient of the environmental factors on the traffic lane through the linear regression model.

[0094] S4. Identify the pure electric vehicles in the vehicle driving data, collect the vehicle-mounted GPS data corresponding to the pure electric vehicles, and calculate the distance density value of the pure electric vehicles in the grid site selection area based on the vehicle-mounted GPS data.

[0095] The present invention calculates the distance density value of the pure electric vehicle in the grid location area based on the on-board GPS data, and can understand the distribution of the driving distance of the pure electric vehicle in the grid location area through the distance density value, thereby facilitating the subsequent processing of the annual energy consumption expenditure corresponding to the grid location area. The pure electric vehicle is a new energy vehicle in the vehicle driving data, and the on-board GPS data is recorded data of information such as the position, driving trajectory, and speed of the pure electric vehicle. The distance density value represents a description of the distance distribution of the pure electric vehicle in the grid location area. Furthermore, the pure electric vehicle in the vehicle driving data can be determined by identifying the vehicle license plate in the vehicle driving data.

[0096] As an embodiment of the present invention, the calculation of the distance density value of the pure electric vehicle in the grid site selection area based on the on-board GPS data includes: extracting the travel data of the pure electric vehicle from the on-board GPS data, determining the travel frequency and corresponding travel mileage of the pure electric vehicle in the grid site selection area based on the travel data, calculating the mileage expected value of the pure electric vehicle in the grid site selection area based on the travel frequency and the travel mileage, and calculating the distance density value of the pure electric vehicle in the grid site selection area in combination with the travel mileage and the mileage expected value.

[0097] Among them, the travel data is the driving record data of the pure electric vehicle in the grid area extracted from the on-board GPS data. According to the travel data, the travel mileage is the number of kilometers traveled each time by the pure electric vehicle in the grid selection area, and the mileage expectation value is the average travel mileage of the pure electric vehicle in the grid selection area.

[0098] Furthermore, as an optional embodiment of the present invention, the calculating the distance density value of the pure electric vehicle in the grid location area by combining the mileage and the expected mileage value includes: calculating the mileage standard deviation of the pure electric vehicle in the grid location area based on the expected mileage value, and calculating the distance density value of the pure electric vehicle in the grid location area by combining the mileage, the expected mileage value, and the mileage standard deviation using the following formula:

[0099] ;

[0100] Where H represents the distance density value of pure electric vehicles in the grid location area, Indicates the mileage corresponding to the lth trip frequency, l represents the serial number of the trip frequency, represents the standard deviation of mileage in the i-th area of ​​the grid site selection area, represents the expected mileage value of the i-th area in the grid location area, g represents the total value of the travel mileage, and i represents the serial number of the grid location area.

[0101] Furthermore, the trip mileage is summed to obtain the total mileage, and the total mileage is divided by the trip frequency to obtain the expected mileage of the pure electric vehicle in the grid location area. The mileage standard deviation can be achieved using a standard deviation calculation formula.

[0102] S5. Read the starting reserve power of the pure electric vehicle in the grid site selection area, calculate the energy storage cycle corresponding to the pure electric vehicle based on the starting reserve power, and evaluate the annual energy consumption expenditure corresponding to the grid site selection area based on the energy storage cycle, the vehicle driving data and the distance density value.

[0103] The present invention calculates the energy storage cycle corresponding to the pure electric vehicle based on the reserve power, and can obtain the charging time corresponding to the pure electric vehicle, thereby facilitating the subsequent determination of the annual energy consumption expenditure corresponding to the grid site selection area, wherein the reserve power is the remaining power corresponding to the pure electric vehicle's last trip within the grid site selection area, and the energy storage cycle represents the time required for the pure electric vehicle to be fully charged. Furthermore, the departure reserve power can be read through the vehicle dashboard of the pure electric vehicle.

[0104] As one embodiment of the present invention, calculating the energy storage cycle corresponding to the pure electric vehicle based on the reserve power includes: detecting the vehicle battery type corresponding to the pure electric vehicle, determining the unit power consumption of the pure electric vehicle based on the vehicle battery type, and combining the unit power consumption and the reserve power to calculate the energy storage cycle corresponding to the pure electric vehicle using the following formula:

[0105] ;

[0106] Among them, G represents the energy storage cycle corresponding to the pure electric vehicle, K represents the reserve power, and J represents the standard charging capacity of the vehicle battery type. Indicates the unit power consumption, h indicates the mileage of the previous car trip with the reserve power, It represents the charging efficiency of pure electric vehicles, and M represents the charging power of pure electric vehicles.

[0107] Among them, the car battery type indicates the battery type used by the pure electric vehicle, and the unit power consumption indicates the unit power consumption corresponding to the pure electric vehicle. Furthermore, the standard charging capacity can be obtained by querying the official website of the car battery type, and the charging efficiency and the charging power can be obtained through the historical data of the pure electric vehicle.

[0108] The present invention evaluates the annual energy consumption expenditure corresponding to the grid site selection area based on the energy storage cycle, the vehicle driving data and the distance density value, and can predict the economic expenditure of electric energy after the construction of the charging station in the grid site selection area, thereby improving the site selection accuracy of the subsequent optimal site selection, wherein the annual energy consumption expenditure is the total expenditure on electric energy in the grid site selection area for one year after the construction of the charging station.

[0109] As an embodiment of the present invention, the annual energy consumption expenditure corresponding to the grid site selection area is evaluated based on the energy storage cycle, the vehicle driving data, and the distance density value, including: obtaining the construction criteria of the charging station, determining the number of transformers and the number of charging devices required for the construction of the charging station in the grid site selection area based on the construction criteria, obtaining the number of transformers and the number of charging devices, and querying the unit prices corresponding to the transformers and the charging devices to obtain the unit price of the transformers and the unit price of the charging devices. In combination with the number of transformers, the number of charging devices, the unit price of the transformers, the unit price of the charging devices, and the distance density value, the annual energy consumption expenditure corresponding to the grid site selection area is evaluated using the following formula:

[0110] ;

[0111] Among them, N represents the annual energy consumption expenditure corresponding to the grid site selection area, m represents the number of transformers, x represents the unit price of transformers, n represents the number of charging devices, y represents the unit price of charging devices, z represents the infrastructure cost of the charging station, P represents the discount rate of the charging station, and t represents the distance density value.

[0112] Among them, the construction criteria are the construction requirements of the charging station, the transformer and the charging equipment are the core components of the charging station during construction, the infrastructure cost is the cost of the infrastructure of the charging station, the discount rate represents a ratio of the cash flow of the charging station during future use to the current value, and the operating cycle is the time period during which the charging station can be used. Furthermore, the scale of the charging station is determined according to the construction requirements in the construction criteria, and the number of transformers and charging equipment required for the construction of the charging station within the grid site selection area is determined according to the scale of the charging station; the infrastructure cost of the charging station can be calculated by analogy estimation, finding charging stations of similar scale and function that have been built, obtaining their infrastructure cost data, and making corresponding adjustments and estimates to the costs based on the differences between the new project and the reference project in terms of scale, geographical location, equipment configuration, etc.; the discount rate of the charging station can be determined by the industry average discount rate method; the operating cycle of the charging station can be determined by the service life of the relevant equipment.

[0113] S6. Based on the regional development potential, the road load index, and the annual energy consumption expenditure, select the optimal location corresponding to the charging station from the grid site selection area.

[0114] The present invention combines the regional development potential, the road load index and the annual energy consumption expenditure to select the optimal site location corresponding to the charging station from the grid site selection area, thereby improving the rationality of the site selection of the charging station. Furthermore, the regional development potential, the road load index and the annual energy consumption expenditure are quantified respectively to obtain corresponding quantitative values. For example, if the regional development potential has three levels of high, medium and low, the corresponding quantitative values ​​are 3, 2 and 1. The quantitative values ​​in the grid site selection area are summed to obtain a comprehensive score. According to the comprehensive score, the optimal site location corresponding to the charging station is selected from the grid site selection area.

[0115] The present invention performs gridding processing on each area in the potential site selection area, and can divide the area into several uniform or uneven grid units according to certain rules, which is convenient for the subsequent intelligent site selection analysis of the charging station. The present invention calculates the landmark prosperity index corresponding to the important landmark, and can understand the development prosperity level corresponding to the important landmark, thereby facilitating the subsequent analysis and processing of regional development potential. The present invention counts the regional traffic flow corresponding to the grid site selection area based on the vehicle driving data, and can understand the traffic vehicle flow information corresponding to the grid site selection area. Based on the regional traffic flow, the present invention calculates the road load index corresponding to the grid site selection area, and can understand the road congestion level of the grid site selection area, thereby facilitating the subsequent selection and processing of the optimal site location. In principle, the present invention calculates the distance density value of the pure electric vehicle in the grid site selection area based on the on-board GPS data, and can understand the distribution of the driving distance of the pure electric vehicle in the grid site selection area through the distance density value, thereby facilitating the subsequent processing of the annual energy consumption expenditure corresponding to the grid site selection area. The present invention calculates the energy storage cycle corresponding to the pure electric vehicle based on the reserve power, and can obtain the charging time corresponding to the pure electric vehicle, thereby facilitating the subsequent determination of the annual energy consumption expenditure corresponding to the grid site selection area. The present invention selects the optimal site location corresponding to the charging station from the grid site selection area by combining the regional development potential, the road load index and the annual energy consumption expenditure, thereby improving the rationality of the site selection of the charging station. Therefore, the embodiment of the present invention provides a method for intelligent site selection of charging stations based on gridding, which can improve the rationality of intelligent site selection of charging stations.

[0116] like Figure 2 FIG. 1 is a functional module diagram of a grid-based intelligent site selection system for charging stations according to an embodiment of the present invention.

[0117] The grid-based intelligent site selection system 100 for implementing charging stations described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the grid-based intelligent site selection system 100 for implementing charging stations can include a regional grid processing module 101, a development potential analysis module 102, a road load index calculation module 103, a distance density calculation module 104, an energy consumption expenditure assessment module 105, and the regional site selection module 106. The modules described in the present invention, also referred to as units, refer to a series of computer program segments that can be executed by an electronic device processor and can perform fixed functions, and are stored in the memory of the electronic device.

[0118] In this embodiment, the functions of each module / unit are as follows:

[0119] The regional grid processing module 101 is used to obtain potential site selection areas for charging stations, and perform grid processing on each area in the potential site selection area to obtain a grid site selection area;

[0120] The development potential analysis module 102 is used to query important landmarks in the potential site selection area, calculate the landmark prosperity index corresponding to the important landmark, and analyze the regional development potential corresponding to the grid site selection area based on the landmark prosperity index and the important landmark;

[0121] The road load index calculation module 103 is used to collect vehicle travel data corresponding to the grid location area, calculate the regional traffic flow corresponding to the grid location area based on the vehicle travel data, and calculate the road load index corresponding to the grid location area based on the regional traffic flow;

[0122] The distance density calculation module 104 is used to identify pure electric vehicles in the vehicle driving data, collect vehicle-mounted GPS data corresponding to the pure electric vehicles, and calculate the distance density value of the pure electric vehicles in the grid site selection area based on the vehicle-mounted GPS data;

[0123] The energy consumption expenditure evaluation module 105 is used to read the reserve power of the pure electric vehicle in the grid location area, calculate the energy storage cycle corresponding to the pure electric vehicle based on the reserve power, and evaluate the annual energy consumption expenditure corresponding to the grid location area based on the energy storage cycle, the vehicle driving data and the distance density value;

[0124] The regional site selection module 106 is configured to select the optimal site location corresponding to the charging station from the grid site selection area based on the regional development potential, the road load index, and the annual energy consumption expenditure.

[0125] In detail, each module described in the grid-based intelligent site selection system 100 for charging stations in the embodiment of the present application is used in the same manner as described above. Figure 1 The same technical means as the grid-based intelligent site selection method for charging stations described in and can produce the same technical effects are not described here.

[0126] In the several embodiments provided by the present invention, it should be understood that the provided methods and systems can be implemented in other ways. For example, the method embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A grid-based intelligent site selection method for charging stations, characterized in that: The method comprises: Obtaining a potential site selection area for a charging station, and performing grid processing on each area in the potential site selection area to obtain a grid site selection area; Querying important landmarks in the potential site selection area, calculating landmark prosperity indexes corresponding to the important landmarks, and analyzing regional development potential corresponding to the grid site selection area based on the landmark prosperity indexes and the important landmarks; collecting vehicle travel data corresponding to the grid location area, calculating regional traffic flow corresponding to the grid location area based on the vehicle travel data, and calculating a road load index corresponding to the grid location area based on the regional traffic flow; Identifying a pure electric vehicle in the vehicle driving data, collecting vehicle-mounted GPS data corresponding to the pure electric vehicle, and calculating a distance density value of the pure electric vehicle in the grid site selection area based on the vehicle-mounted GPS data, wherein calculating the distance density value of the pure electric vehicle in the grid site selection area based on the vehicle-mounted GPS data includes: Extracting the travel data of the pure electric vehicle from the onboard GPS data; Determining the travel frequency and corresponding travel mileage of the pure electric vehicle in the grid location area based on the travel data; Calculating an expected mileage value of the pure electric vehicle in the grid site selection area based on the trip frequency and the trip mileage; Calculating a distance density value of the pure electric vehicle in the grid location area based on the travel mileage and the expected mileage value; The step of calculating the distance density value of the pure electric vehicle in the grid location area by combining the travel mileage and the expected mileage value includes: Calculating the mileage standard deviation of the pure electric vehicle in the grid location area according to the expected mileage value; The distance density value of the pure electric vehicle in the grid location area is calculated by combining the travel mileage, the expected mileage value, and the mileage standard deviation using the following formula: Where H represents the distance density value of pure electric vehicles in the grid location area, g l represents the mileage corresponding to the lth trip frequency, l represents the sequence number of the trip frequency, ρ i represents the standard deviation of mileage in the i-th area of ​​the grid site selection area, δ i represents the expected mileage value of the i-th area in the grid location area, g represents the total value of the travel mileage, and i represents the serial number of the grid location area; Reading the reserve power of the pure electric vehicle in the grid location area, calculating the energy storage period corresponding to the pure electric vehicle based on the reserve power, and evaluating the annual energy consumption expenditure corresponding to the grid location area based on the energy storage period, the vehicle driving data, and the distance density value, wherein calculating the energy storage period corresponding to the pure electric vehicle based on the reserve power includes: detecting a vehicle battery type corresponding to the pure electric vehicle, and determining a unit power consumption of the pure electric vehicle based on the vehicle battery type; The energy storage cycle corresponding to the pure electric vehicle is calculated by combining the unit power consumption and the reserve power using the following formula: Among them, G represents the energy storage cycle corresponding to the pure electric vehicle, K represents the reserve power, and J represents the standard charging capacity of the vehicle battery type. represents the unit power consumption, h represents the mileage of the previous car trip with reserved power, θ represents the charging efficiency of the pure electric vehicle, and M represents the charging power of the pure electric vehicle; The step of evaluating the annual energy consumption expenditure corresponding to the grid site selection area based on the energy storage cycle, the vehicle driving data, and the distance density value includes: Obtaining construction criteria for the charging station; Based on the construction criteria, determining the number of transformers and the number of charging devices required for the construction of the charging station within the grid site selection area, and obtaining the number of transformers and the number of charging devices; Query the unit prices of the transformer and the charging device to obtain the unit price of the transformer and the unit price of the charging device; Based on the number of transformers, the number of charging devices, the unit price of the transformers, the unit price of the charging devices, and the distance density value, the annual energy consumption expenditure corresponding to the grid site selection area is evaluated using the following formula: Where N represents the annual energy consumption expenditure corresponding to the grid site selection area, m represents the number of transformers, x represents the unit price of transformers, n represents the number of charging devices, y represents the unit price of charging devices, z represents the infrastructure cost of the charging station, P represents the discount rate of the charging station, and H represents the distance density value; In combination with the regional development potential, the road load index and the annual energy consumption expenditure, the optimal site location corresponding to the charging station is selected from the grid site selection area.

2. The method for realizing intelligent site selection of charging stations based on gridding as claimed in claim 1, characterized in that: Calculating the landmark prosperity index corresponding to the important landmark includes: Collecting landmark economic data corresponding to the important landmarks, and identifying economic indicators corresponding to the landmark economic data; Calculating the indicator weight coefficient corresponding to the economic indicator, and extracting the economic performance value corresponding to the economic indicator from the landmark economic data; performing standardization processing on the economic performance value to obtain a target performance value; The landmark prosperity index corresponding to the important landmark is calculated by combining the indicator weight coefficient and the target performance value.

3. The method for realizing intelligent site selection of charging stations based on gridding as claimed in claim 1, characterized in that: Analyzing the regional development potential corresponding to the grid site selection area based on the landmark prosperity index and the important landmarks includes: Calculate the spatial distance value between each area in the grid site selection area and the important landmark; Based on the spatial distance value, calculating the attenuation coefficient of the landmark prosperity index in the grid site selection area; Calculating the regional development potential value corresponding to the grid site selection area based on the attenuation coefficient and the landmark prosperity index; Based on the regional development potential value, the regional development potential corresponding to the grid site selection area is analyzed.

4. The method for realizing intelligent site selection of charging stations based on gridding as claimed in claim 1, characterized in that: The calculating of the road load index corresponding to the grid site selection area based on the regional traffic flow includes: Determine a traffic artery in the grid site selection area, and query the road traffic capacity corresponding to the traffic artery; Based on the regional traffic flow, counting the traffic flow of the time period corresponding to the grid location area; Calculating the road saturation corresponding to the traffic road by combining the traffic flow in the time period and the traffic capacity of the road; The road load index corresponding to the grid site selection area is calculated by combining the road saturation and a preset load index classification criterion.

5. The method for realizing intelligent site selection of charging stations based on gridding as claimed in claim 4, characterized in that: The calculating the road saturation corresponding to the traffic road by combining the traffic flow in the time period and the road traffic capacity includes: Based on the road traffic capacity, determining the traffic lanes and road capacity corresponding to the traffic road, and querying the types of vehicles corresponding to the traffic lanes; Determining the number of vehicles corresponding to the traveling vehicle category based on the traffic flow during the time period; Calculating the lane contribution corresponding to the traffic lane and collecting the road environment corresponding to the traffic road; Analyzing environmental factors in the road environment and calculating the influence coefficient of the environmental factors on the traffic of the traffic lane; Based on the number of vehicles, the lane contribution, the road capacity, and the traffic impact coefficient, the road saturation corresponding to the traffic road is calculated using the following formula: Among them, D represents the road saturation corresponding to the traffic road, E b represents the number of vehicles in the bth lane, α b represents the lane contribution of the b-th traffic lane, β b represents the traffic impact coefficient of the bth traffic lane, b represents the serial number of the traffic lane, q represents the number of traffic lanes, and F represents the road capacity.

6. A grid-based intelligent site selection system for charging stations, characterized in that: The system comprises: A regional grid processing module is used to obtain potential site selection areas for charging stations and perform grid processing on each area in the potential site selection area to obtain a grid site selection area; a development potential analysis module, configured to query important landmarks in the potential site selection area, calculate a landmark prosperity index corresponding to the important landmark, and analyze the regional development potential corresponding to the grid site selection area based on the landmark prosperity index and the important landmark; a road load index calculation module, configured to collect vehicle travel data corresponding to the grid location area, calculate regional traffic flow corresponding to the grid location area based on the vehicle travel data, and calculate a road load index corresponding to the grid location area based on the regional traffic flow; A distance density calculation module is configured to identify pure electric vehicles in the vehicle driving data, collect vehicle-mounted GPS data corresponding to the pure electric vehicles, and calculate a distance density value of the pure electric vehicles in the grid site selection area based on the vehicle-mounted GPS data. The calculation of the distance density value of the pure electric vehicles in the grid site selection area based on the vehicle-mounted GPS data includes: Extracting the travel data of the pure electric vehicle from the onboard GPS data; Determining the travel frequency and corresponding travel mileage of the pure electric vehicle in the grid location area based on the travel data; Calculating an expected mileage value of the pure electric vehicle in the grid site selection area based on the trip frequency and the trip mileage; Calculating a distance density value of the pure electric vehicle in the grid location area based on the travel mileage and the expected mileage value; The step of calculating the distance density value of the pure electric vehicle in the grid location area by combining the travel mileage and the expected mileage value includes: Calculating the mileage standard deviation of the pure electric vehicle in the grid location area according to the expected mileage value; The distance density value of the pure electric vehicle in the grid location area is calculated by combining the travel mileage, the expected mileage value, and the mileage standard deviation using the following formula: Where H represents the distance density value of pure electric vehicles in the grid location area, g l represents the mileage corresponding to the lth trip frequency, l represents the sequence number of the trip frequency, ρ i represents the standard deviation of mileage in the i-th area of ​​the grid site selection area, δ i represents the expected mileage value of the i-th area in the grid location area, g represents the total value of the travel mileage, and i represents the serial number of the grid location area; An energy consumption expenditure evaluation module is configured to read the reserve power of the pure electric vehicle within the grid location area, calculate the energy storage period corresponding to the pure electric vehicle based on the reserve power, and evaluate the annual energy consumption expenditure corresponding to the grid location area based on the energy storage period, the vehicle driving data, and the distance density value. Calculating the energy storage period corresponding to the pure electric vehicle based on the reserve power includes: detecting a vehicle battery type corresponding to the pure electric vehicle, and determining a unit power consumption of the pure electric vehicle based on the vehicle battery type; The energy storage cycle corresponding to the pure electric vehicle is calculated by combining the unit power consumption and the reserve power using the following formula: Among them, G represents the energy storage cycle corresponding to the pure electric vehicle, K represents the reserve power, and J represents the standard charging capacity of the vehicle battery type. represents the unit power consumption, h represents the mileage of the previous car trip with reserved power, θ represents the charging efficiency of the pure electric vehicle, and M represents the charging power of the pure electric vehicle; The step of evaluating the annual energy consumption expenditure corresponding to the grid site selection area based on the energy storage cycle, the vehicle driving data, and the distance density value includes: Obtaining construction criteria for the charging station; Based on the construction criteria, determining the number of transformers and the number of charging devices required for the construction of the charging station within the grid site selection area, and obtaining the number of transformers and the number of charging devices; Query the unit prices of the transformer and the charging device to obtain the unit price of the transformer and the unit price of the charging device; Based on the number of transformers, the number of charging devices, the unit price of the transformers, the unit price of the charging devices, and the distance density value, the annual energy consumption expenditure corresponding to the grid site selection area is evaluated using the following formula: Where N represents the annual energy consumption expenditure corresponding to the grid site selection area, m represents the number of transformers, x represents the unit price of transformers, n represents the number of charging devices, y represents the unit price of charging devices, z represents the infrastructure cost of the charging station, P represents the discount rate of the charging station, and H represents the distance density value; The regional site selection module is used to select the optimal site location corresponding to the charging station from the grid site selection area based on the regional development potential, the road load index and the annual energy consumption expenditure.

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