Digital twin system reflecting influence level of electric vehicle charging station and method for constructing digital twin system

Abstract

This digital twin system reflecting an influence level of an electric vehicle charging station comprises a first server that collects and processes charging data which is data related to charging from a terminal installed in each of a plurality of electric vehicle charging stations, wherein the first server uses the charging data to calculate an influence level which is the level of an influenced area from an installation position of a charging station for each of the plurality of electric vehicle charging stations.

Description

A digital twin system that reflects the impact of electric vehicle charging stations and a method for building such a digital twin system.

[0001] Embodiments disclosed herein relate to a digital twin system that reflects the influence of an electric vehicle charging station and a method for constructing the digital twin system.

[0002] A digital twin is a technology that digitally replicates actual physical objects, processes, people, places, systems, and devices in a virtual world. This technology can model and simulate real-world dynamics, enabling performance improvements, problem-solving, and innovative development.

[0003]

[0004] The key features of digital twins are:

[0005] (1) Real-time data synchronization: Digital twins continuously reflect the state of real-world objects using real-time data.

[0006] (2) Advanced simulation and modeling: Model complex systems and environments to simulate various scenarios.

[0007] (3) Predictive analysis: Predict future performance or status through data analysis.

[0008] (4) Decision support: Supports effective decision-making based on real-time data and simulation results.

[0009] (5) Improvement and Optimization: Improve and optimize systems and processes through continuous monitoring and analysis.

[0010] Digital twins are being used across a wide range of fields, including manufacturing, construction, automotive, aerospace, healthcare, and urban planning, enabling more sophisticated and efficient system design and operation.

[0011]

[0012] However, since the primary purpose of digital twins is to improve devices or processes used in specific situations, the domains in which they operate are often closed, resulting in a limited user base. For example, when utilizing digital twin technology to improve industrial production processes, only those involved in the production process need to view the results. Therefore, not only is it unnecessary to implement digital twins that can be accessed via open networks like the internet, it's also not necessary.

[0013]

[0014] However, in the case of a digital twin system related to electric vehicle charging stations, it must be easily accessible to multiple electric vehicle users, so it needs to be implemented so that it can be verified through an open network such as the Internet.

[0015] The embodiments disclosed in this specification are embodiments that aim to solve the technical problems described above, and the purpose is to provide a digital twin system that reflects the influence of an electric vehicle charging station that can be confirmed through an open network such as the Internet, and a method for constructing the digital twin system.

[0016] A digital twin system reflecting the influence of electric vehicle charging stations includes: a first server that collects and processes charging data, which is data related to charging, from terminals installed at each charging station for a plurality of electric vehicles; and a second server that receives, from the first server, influence information for each charging station and location information or address information of the charging stations for the plurality of electric vehicles, and uses the influence for each charging station to generate area information for indicating an area of ​​influence of each charging station, centered on the location of the charging station.

[0017]

[0018] Specifically, the first server calculates an influence degree, which is the degree of influence from the installation location of each charging station of the plurality of electric vehicles, using at least some of the charging data.

[0019] In addition, the second server may configure an area on the map as a grid divided into a plurality of cells, and generate recommendation information recommending an area for installing an additional charging station by using the ratio of an area not affected by the surrounding charging stations of each of the plurality of cells and the influence of the surrounding charging stations.

[0020] According to the digital twin system and the method for constructing the digital twin system that reflect the influence of electric vehicle charging stations of the embodiments disclosed in this specification, the influence of electric vehicle charging stations that can be confirmed through an open network such as the Internet can be reflected.

[0021] In addition, according to the digital twin system and the method for constructing the digital twin system that reflect the influence of electric vehicle charging stations of the embodiments disclosed in this specification, it is also possible to efficiently recommend installation areas for additional charging stations.

[0022] Figure 1 is a configuration diagram of a digital twin system reflecting the influence of an electric vehicle charging station according to an embodiment.

[0023] Figure 2 is an example of monitoring area information generated by a second server through a user terminal.

[0024] Figure 3 is an example of monitoring recommendation information generated by a second server through a user terminal.

[0025] Figure 4 is a flowchart of a method for constructing a digital twin system that reflects the influence of an electric vehicle charging station according to an embodiment.

[0026] Hereinafter, with reference to the attached drawings, a digital twin system reflecting the influence of electric vehicle charging stations according to embodiments of the present disclosure and a method for constructing the digital twin system will be described in detail. The following embodiments of the present disclosure are intended to concretize the present disclosure and do not limit or restrict the scope of the present disclosure. Anything readily inferred by an expert in the technical field to which the present disclosure pertains from the detailed description and embodiments of the present disclosure is construed as falling within the scope of the present disclosure.

[0027]

[0028] First, FIG. 1 shows a configuration diagram of a digital twin system (100) that reflects the influence of an electric vehicle charging station according to an embodiment.

[0029] As can be seen from FIG. 1, a digital twin system (100) reflecting the influence of an electric vehicle charging station according to an embodiment is configured to include a charging station terminal (10), a first server (20), a second server (30), and a user terminal (40). For reference, in this specification, one electric vehicle charging station may be configured to include at least one charging device.

[0030] In addition, each of the charging station terminal (10), the first server (20), the second server (30), and the user terminal (40) may be configured as a type of computing device, including a memory and at least one processor.

[0031]

[0032] A charging terminal (10) is installed at each charging station, and when an electric vehicle is charged, the charging terminal (10) transmits charging data, which is data related to the charging, to the first server (20). Specifically, the charging terminal (10) can transmit all charging-related data, such as open data collectable via the Internet or a specific data dump file format, to the first server (20). The transmission of charging data from the charging terminal (10) to the first server (20) may be performed periodically or only once, depending on the type of charging data.

[0033]

[0034] For example, the charging data may include a number of information, including identification information of the charging station, location information of the charging station, address information of the charging station, charging date and time, charging power amount, charging start period, charging end time, and weather information.

[0035]

[0036] The first server (20) can be referred to as a data server and has the role of collecting and processing charging data, which is data related to charging, from charging station terminals (10) installed at each charging station for a number of electric vehicles.

[0037] The first server (20) may be configured to include a database (21), an influence calculation module (22), and a data sharing API (Application Programming Interface) module (23).

[0038] In the database (21), charging data collected from a charging station terminal (10) as a storage device and result data calculated from an influence calculation module (22) can be stored.

[0039] In addition, the influence calculation module (22) and the data sharing API module (23) can each be implemented in the form of a computer program executed by a processor.

[0040] Specifically, the influence calculation module (22) uses at least a portion of the charging data to calculate the influence, which is the extent of the area of ​​influence from the installation location of each charging station. The influence calculation can be performed using the charging data for a first preset period. That is, the influence can be updated every first period. The influence calculated by the influence calculation module (22) is transmitted to the second server (30) along with the identification information of the corresponding charging station.

[0041] The data sharing API module (23) transmits data stored in a database (21) other than the influence to the second server (30).

[0042]

[0043] The second server (30) can receive data from the first server (20) and can also receive map information, weather information, etc. from the map server and weather server. The second server (30) can provide simulations and modeling using the map information, weather information, etc. based on the data received from the first server (20). In other words, the second server (30) is a server that implements a digital twin and can provide a web service available via the Internet, including a 3D model of a specific area or process.

[0044] The web service provided by the second server (30) is connected to the first server (20) in two ways to exchange data. The first way is to connect to the data sharing API module (23) via the Internet and receive data in a RESTful manner using the http protocol. The second way is to connect to the Internet and create a two-way socket channel through which data is transmitted to and received from the influence calculation module (22). The second server (30) provides all data received from the first server (20) to the user terminal (40) via an interface including a 3D model. At this time, the user can access the second server (30) using the user terminal (40) and use the corresponding service.

[0045]

[0046] Hereinafter, a method for calculating influence by the influence calculation module (22) of the first server (20) will be described in detail.

[0047]

[0048] The influence calculation module (22) can cluster charging stations for multiple electric vehicles into multiple clusters using first data, which is data resulting from processing at least a portion of the charging data for a first period preset for each charging station. The first data can include multiple items among N items regarding the total number of charging times, the total charging power amount, and the number of charging times for each of N charging power amount sections.

[0049] Here, the number of charging times for each N charging power section is the number of times the power charged at one time is divided into N sections according to size, and then charged for each N section. In addition, N is a natural number greater than or equal to 2.

[0050] For example, the charging power range can be divided into six ranges, such as 0 to 10 kWh, 10 to 20 kWh, 20 to 30 kWh, 30 to 40 kWh, 40 to 50 kWh, and 50 kWh or more. In this case, the first data can include multiple items among the total number of charges, the total charging power, the number of charges in the 0 to 10 kWh charging power range, the number of charges in the 10 to 20 kWh charging power range, the number of charges in the 20 to 30 kWh charging power range, the number of charges in the 30 to 40 kWh charging power range, the number of charges in the 40 to 50 kWh charging power range, and the number of charges in the 50 kWh or more charging power range.

[0051] For reference, the total number of charging times is data for deriving similarity in the frequency of use of charging stations, and the total charging power is data for deriving similarity in the usage of charging stations. In addition, the number of charging times in the 0-10 kWh range and the number of charging times in the 10-20 kWh range are data for deriving similarity in the number of charging times in the low range, respectively. In addition, the number of charging times in the 20-30 kWh range and the number of charging times in the 30-40 kWh range are data for deriving similarity in the number of charging times in the medium range, respectively. In addition, the number of charging times in the 40-50 kWh range and the number of charging times in the 50 kWh or more range are data for deriving similarity in the number of charging times in the high range, respectively.

[0052]

[0053] The influence calculation module (22) uses the first data for the first period for each charging station to cluster a plurality of charging stations of electric vehicles into a plurality of clusters using a plurality of clustering algorithms, and selects and uses a clustering model, i.e., a plurality of clusters, using the clustering algorithm with the best clustering performance.

[0054]

[0055] That is, the influence calculation module (22) uses each item of the first data to perform a clustering process so as to classify charging stations with similar data between charging stations.

[0056] Clustering is performed before calculating the influence of each charging station to reduce the computational effort required to derive the influence of each charging station. For example, to calculate the influence of 500 charging stations, the same number of calculations must be performed for each station. However, if the stations are grouped into clusters that exhibit similarity and the influence of each cluster is derived, the calculation time is significantly reduced.

[0057]

[0058] Specifically, clustering can be performed through the following process:

[0059] Using each item of the first data, a clustering model capable of classifying the charging stations into M clusters is simultaneously generated for multiple clustering algorithms, where M is a natural number greater than or equal to 10.

[0060] - K-Means Clustering

[0061] - Affinity Propagation Clustering

[0062] - Mean Shift Clustering

[0063] - Spectral Clustering

[0064] - Agglomerative Clustering

[0065] - Density-Based Spatial Clustering

[0066] - OPTICS Clustering

[0067] - Birch Clustering

[0068] - K-Modes Clustering

[0069]

[0070] In addition, the influence calculation module (22) compares clustering models derived from multiple clustering algorithms, selects the clustering model with the best performance, and uses the cluster.

[0071]

[0072] The influence calculation module (22) calculates the influence using the second data, which is the average value of each item of the first data for all charging stations included in the cluster during the first period, for each cluster. For example, if cluster 1 includes the first charging station and the second charging station, the average value of each item of the first data calculated for each of the first charging station and the second charging station becomes each item of the second data.

[0073] Specifically, the influence calculation module (22) can apply preset weights to each item of the second data for all charging stations included in each cluster, for each cluster. That is, the influence calculation module (22) can multiply each item of the second data by each preset weight and sort the summed values, so that the summed values ​​can sequentially assign high influence values ​​in order from low to high, or sequentially assign low influence values ​​in order from high to low.

[0074] That is, the influence calculation module (22) calculates the average value of each item of the first data for each cluster to calculate the influence for each cluster. In addition, the influence calculation module (22) can multiply the average value of each item of the first data by each preset weight, sort the summed values ​​in order of high or low, and sequentially assign influences according to the sorted order. For example, if there are 50 clusters, influences can be assigned from 0 to 49.

[0075] For reference, the influence calculated for each cluster in the influence calculation module (22) is assigned as the influence of each charging station included in the cluster. In other words, charging stations belonging to the same cluster have the same influence value.

[0076]

[0077] The second server (30) receives data including information on the influence of each charging station and location information or address information of charging stations for multiple electric vehicles from the first server (20), and uses the influence of each charging station to generate area information for indicating an area of ​​influence of each charging station centered on the location of the charging station. The information on the influence of each charging station is transmitted to the second server (30) from the influence calculation module (22) as a data set of identification information and influence of each charging station.

[0078] In addition, from the data sharing API module (23), a data set of identification information and location information or address information of the charging station can be transmitted to the second server (30).

[0079] Figure 2 is an example diagram of monitoring area information generated by a second server (30) through a user terminal (40). However, in Figure 2, map information is omitted.

[0080] The area information for indicating the area affected by the charging station can be expressed in the form of a circle, and depending on the size of the area, the color or type of the circumference of the circle, the color or pattern inside the circle, etc. can be expressed differently. At this time, the radius of the circle can be calculated as {value of the influence of the charging station × A} + B. Here, A and B are each preset coefficients.

[0081]

[0082] The second server (30) can transmit weather information received from the weather server to the influence calculation module (22) of the first server (20). Here, the weather information can include multiple items such as temperature, humidity, wind direction, wind speed, precipitation, fine dust, ultrafine dust, and ultraviolet ray level. In addition, the weather information can be current weather information, or weather information for a past or future point of interest.

[0083] The first server (20) can further utilize weather information to calculate the influence. Specifically, the first server (20) can calculate the influence by utilizing the similarity between weather information and the weather information at the charging station. For example, the first server (20) can calculate the influence only using charging data for charging events where the similarity between the weather information and the weather information at the charging station falls within a certain range.

[0084] The second server (30) can further transmit traffic conditions, infrastructure conditions, etc. to the influence calculation model of the first server (20). The first server (20) can further calculate the influence using the traffic conditions, infrastructure conditions, etc. Specifically, the first server (20) can calculate the influence using the similarity between the traffic conditions, infrastructure conditions, etc. and the traffic conditions, infrastructure conditions, etc. at the time of charging at the corresponding charging station.

[0085] That is, the second server (30) can replicate and implement weather information, traffic environment, infrastructure status, etc., and derive the optimal installation area for an electric vehicle charging station within the environment.

[0086]

[0087] Specifically, the second server (30) can configure an area on the map as a grid divided into a plurality of cells of a preset size, and generate recommendation information recommending an area for installing an additional charging station by using the ratio of an area not affected by the surrounding charging stations and the influence of the surrounding charging stations for each of the plurality of cells.

[0088] Figure 3 is an example diagram of monitoring recommendation information generated by a second server (30) through a user terminal (40). However, in Figure 3, map information is omitted.

[0089]

[0090] Specifically, if the influence of surrounding charging stations is greater than or equal to a first value and the cell area is not influenced by surrounding charging stations beyond a preset area, the second server (30) may generate recommendation information recommending an installation area of ​​an additional charging station for an area of ​​the cell area not influenced by surrounding charging stations. Alternatively, the second server (30) may generate recommendation information recommending an installation area of ​​an additional charging station for an area of ​​the cell area not influenced by surrounding charging stations if the influence of surrounding charging stations is less than the second value and the cell area is not completely influenced by surrounding charging stations. Here, the first value will be a value having a relatively high influence, and the second value will be a value having a relatively low influence. That is, the first value is greater than the second value.

[0091]

[0092] The user terminal (40) can connect to the second server (30) and monitor various charging data, charging station information, influence information, area information according to influence information, recommendation information, etc. on map information in a digital twin environment.

[0093]

[0094] Below, the overall operation of the digital twin system (100) reflecting the influence of an electric vehicle charging station according to an embodiment will be summarized.

[0095]

[0096] The first server (20) collects and stores charging data in a database (21). If the data is periodically updated, the collection cycle is stored at regular intervals. If the data is not periodically updated, the first server (20) stores the data only once.

[0097] Additionally, the influence of each charging station is calculated through the influence calculation module (22). The influence can be updated and calculated at pre-specified times on a daily basis, using charging data from all electric vehicles at the charging station within the pre-specified period. Once the influence is calculated, it can be considered that the preparation of all data to be provided to the second server (30) has been completed by the first server (20).

[0098] The next step involves connecting to a second server (30) via a user terminal (40), which is a web browser-compatible terminal, and using a web service. At this time, the second server (30) performs two processes simultaneously. First, data is provided to the user via the user terminal (40) through an intuitive interface, along with a 3D model. Next, all weather information, traffic conditions, infrastructure conditions, etc., adjusted by the user on the digital twin of the second server (30) via the user terminal (40) are transmitted in real time to the first server (20) and can be utilized as data for calculating impact.

[0099]

[0100] Hereinafter, a method for constructing a digital twin system (100) reflecting the influence of an electric vehicle charging station according to an embodiment will be described. The method for constructing a digital twin system (100) reflecting the influence of an electric vehicle charging station according to an embodiment is implemented by the above-described digital twin system (100), and therefore includes all the features of the digital twin system (100) even without a separate description. In addition, each step of the method for constructing a digital twin system (100) reflecting the influence of an electric vehicle charging station is an operation method of at least one computing device, and can be implemented by at least one processor of at least one computing device. That is, the method for constructing a digital twin system (100) reflecting the influence of an electric vehicle charging station can be implemented in the form of a computer program executed by at least one processor, and each step included in the method for constructing a digital twin system (100) reflecting the influence of an electric vehicle charging station can be implemented by at least one of the processors.

[0101]

[0102] Figure 4 shows a flowchart of a method for constructing a digital twin system (100) that reflects the influence of an electric vehicle charging station according to an embodiment.

[0103] As can be seen from FIG. 4, a method for constructing a digital twin system (100) reflecting the influence of electric vehicle charging stations according to an embodiment includes a step (S10) in which a first server (20) collects charging data, which is data related to charging, from charging station terminals (10) installed at each charging station for a plurality of electric vehicles; a step (S20) in which the first server (20) calculates an influence, which is the degree of influence of an area from the installation location of each charging station, using at least a portion of the charging data, which is data related to charging, collected from the charging station terminals (10) installed at each charging station for a plurality of electric vehicles; a step (S30) in which a second server (30) receives, from the first server (20), data including influence information of each charging station and location information or address information of the charging stations for a plurality of electric vehicles, and uses the influence of each charging station to generate area information for indicating an area of ​​influence of each charging station, centered on the location of the corresponding charging station.

[0104] In addition, a method for constructing a digital twin system (100) reflecting the influence of an electric vehicle charging station according to an embodiment may further include a step (S40) in which a second server (30) configures a map area as a grid divided into a plurality of cells of a preset size, and generates recommendation information recommending an installation area of ​​an additional charging station by using the ratio of an area not affected by surrounding charging stations and the influence of surrounding charging stations in each of the plurality of cells.

[0105]

[0106] Step S20 may include a step (S21) of clustering charging stations for a plurality of electric vehicles into a plurality of clusters using first data for a first period for each charging station; a step (S22) of calculating an influence using first data for each charging station included in each cluster; and a step (S23) of assigning the calculated influence for the cluster to the influence of the charging station included in the cluster.

[0107] The first data may include multiple items among the total number of charging items, the total charging power items, and N items regarding the number of charging times for each of N charging power sections. In addition, the number of charging times for each of N charging power sections is the number of times charging is performed for each of N sections after dividing the amount of power charged at one time into N sections according to size. Here, N is a natural number greater than or equal to 2.

[0108] Specifically, step S21 may include a step of clustering charging stations for a plurality of electric vehicles into a plurality of clusters by each of a plurality of clustering algorithms; and a step of setting a clustering model by a clustering algorithm with the best clustering performance as a final plurality of clusters.

[0109]

[0110] In addition, the method for constructing a digital twin system (100) reflecting the influence of an electric vehicle charging station according to an embodiment may further include a step (S50) in which a second server (30) transmits at least one of weather information, traffic environment, and infrastructure status to a first server (20).

[0111] At step S20, the first server (20) can calculate the influence using the similarity between the weather information and the weather information at the time of charging at the corresponding charging station. Furthermore, at step S20, the first server (20) can further calculate the influence using the traffic environment, infrastructure status, etc. Specifically, the first server (20) can calculate the influence using the similarity between the traffic environment, infrastructure status, etc. and the traffic environment, infrastructure status, etc. at the time of charging at the corresponding charging station.

[0112] In addition, the method for constructing a digital twin system (100) reflecting the influence of an electric vehicle charging station according to an embodiment may further include a step (S60) in which a user terminal (40) connects to a second server (30).

[0113]

[0114] According to the digital twin system (100) reflecting the influence of electric vehicle charging stations according to embodiments and the method for constructing the digital twin system (100), it can be seen that the influence of electric vehicle charging stations that can be confirmed through an open network such as the Internet can be reflected, and recommendations for installation areas of additional charging stations can be efficiently implemented.

[0115] That is, according to the digital twin system (100) reflecting the influence of electric vehicle charging stations according to embodiments and the method for constructing the digital twin system (100), the user can calculate the influence of other electric vehicle charging stations in the region and, based on this, identify an efficient installation location for a new electric vehicle charging station. At this time, the user can visually check the status of electric vehicle charging stations in the region in real time through the web along with a 3D model. Since the service can be utilized through the web, anyone can utilize the information, and this can be expected to lead to a rapid expansion of electric vehicle charging stations.

Claims

1. In a digital twin system that reflects the influence of electric vehicle charging stations, A first server that collects and processes charging data, which is data related to charging, from terminals installed at each charging station for a plurality of electric vehicles; including: The above first server, A digital twin system that calculates an influence degree, which is the extent of an area of ​​influence from the installation location of each charging station of the plurality of electric vehicles, by using at least some of the above charging data.

2. In paragraph 1, The above first server, Using the first data for the first period for each of the above charging stations, the charging stations for the plurality of electric vehicles are clustered into a plurality of clusters. The above first data is, Includes a plurality of items among the total number of charging items, the total charging power items, and the N items regarding the number of charging items for each of the N charging power intervals, The number of charging times for each of the above N charging power intervals is The amount of power charged at one time is divided into N sections according to size, and the number of times charged for each of the N sections is N is a digital twin system that is a natural number greater than or equal to 2.

3. In paragraph 2, The above first server, A digital twin system that calculates the influence by using the first data for each of the charging stations included in each cluster of the above multiple clusters.

4. In paragraph 3, The above first server, A digital twin system that calculates the influence by using the average value of each item of the first data for all charging stations included in each cluster.

5. In paragraph 1, The above digital twin system, A digital twin system further comprising a second server which receives, from the first server, information on the influence of each charging station and location information or address information of charging stations for a plurality of electric vehicles, and uses the influence of each charging station to generate area information for indicating an area of ​​influence of the charging station centered on the location of the charging station.

6. In paragraph 5, The above second server, Transmit weather information to the first server, The above first server, A digital twin system that further utilizes the above weather information to calculate the above influence.

7. In paragraph 6, The above first server, A digital twin system that calculates the influence by using the similarity between the above weather information and the weather information at the time of charging at the corresponding charging station.

8. In paragraph 5, The above second server, A digital twin system that configures a map area as a grid divided into a plurality of cells, and generates recommendation information recommending an area for installing an additional charging station by using the ratio of an area not affected by a surrounding charging station and the influence of the surrounding charging station for each of the plurality of cells.

9. A method for constructing a digital twin system that reflects the influence of electric vehicle charging stations, A method for constructing a digital twin system, comprising: a step of calculating an influence degree, which is the extent of an area of ​​influence from the installation location of each charging station of the plurality of electric vehicles, by using at least some of the charging data, which is data related to charging collected from terminals installed at each charging station of the plurality of electric vehicles; 10. In paragraph 9, The steps for calculating the above influence are: A step of clustering the charging stations of the plurality of electric vehicles into a plurality of clusters using the first data for the first period for each of the charging stations; and A step of calculating the influence by using the first data for each of the charging stations included in each cluster of the above multiple clusters; including, The above first data is, Includes a plurality of items among the total number of charging items, the total charging power items, and the N items regarding the number of charging items for each of the N charging power intervals, The number of charging times for each of the above N charging power intervals is The amount of power charged at one time is divided into N sections according to size, and the number of times charged for each of the N sections is A method for constructing a digital twin system, where N is a natural number greater than or equal to 2.

11. In paragraph 9, The method for constructing the above digital twin system is as follows: A method for constructing a digital twin system, further comprising: a step in which a second server receives, from the first server, information on the influence of each charging station and location information or address information of charging stations for a plurality of electric vehicles, and, using the influence of each charging station, generates area information for indicating an area of ​​influence of the charging station centered on the location of the charging station.

12. In paragraph 11, The method for constructing the above digital twin system is as follows: The second server further includes a step of transmitting weather information to the first server; The above first server, A method for constructing a digital twin system, which calculates the influence by using the similarity between the above weather information and the weather information at the time of charging at the corresponding charging station.

13. In paragraph 11, The method for constructing the above digital twin system is as follows: A method for constructing a digital twin system, further comprising: a step of generating recommendation information for recommending installation areas of additional charging stations by using the ratio of areas not affected by surrounding charging stations and the influence of surrounding charging stations in each of the surrounding cells, wherein the second server configures an area on the map as a grid divided into a plurality of cells.

Images