A charging station site selection aided decision-making method and system based on visual analysis

Abstract

This application discloses a method and system for assisting decision-making in charging station site selection based on visual analytics, belonging to the field of computer technology. The method includes: analyzing the existing charging station layout based on traffic and power grid data; determining the charging station site selection task and constructing an objective function based on optimization target weights, charging demand coverage constraints, service time constraints, and resource input constraints; generating initial charging demand through a dual-network fusion model based on charging station demand parameters, and determining several recommended schemes based on a genetic algorithm; determining the time-varying charging demand coverage of the recommended schemes based on overall indicators and assessing their impact on the road network and power grid, obtaining the evaluation results, and visualizing them to assist user decision-making. This application can automatically generate charging station site selection schemes and evaluate their impact on the road network and power grid, providing visual representation and thus improving decision-making reliability and efficiency.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method and system for assisting decision-making in the site selection of charging stations based on visual analysis. Background Technology

[0002] With social development, new energy vehicles are gradually being put into use, and the number of existing charging piles is steadily increasing. However, the current site selection of charging stations relies heavily on manual decision-making, lacking a site selection support system. The manual site selection process lacks quantitative indicators and data support, relies heavily on experience, is time-consuming and labor-intensive, inefficient, and makes it difficult to summarize and analyze past cases.

[0003] While some charging station operators have developed their own visualization platforms, these software programs primarily monitor the operational status and revenue of charging stations, offering few dedicated decision-making support solutions. In charging station site selection, commonly used models often employ simplified assumptions to reduce computational complexity, failing to consider all real-world factors. Therefore, existing site selection models cannot completely replace expert decision-making. Furthermore, while existing models emphasize generating site selection schemes, their evaluations are incomplete. Although some spatiotemporal constraints are considered, the models still lack analysis of the potential impact of site selection results, limiting a comprehensive assessment of the outcomes. Summary of the Invention

[0004] To address at least one of the aforementioned problems, this application proposes a highly efficient and reliable method and system for assisting charging station site selection based on visual analytics.

[0005] To achieve the above objectives, one aspect of this application proposes a charging station site selection auxiliary decision-making method based on visual analytics, the method comprising:

[0006] Based on traffic data and power grid data, the existing charging station layout is analyzed, and the layout analysis results are obtained.

[0007] Based on the layout analysis results, the task of selecting charging station locations is determined, and the charging station demand parameters, algorithm efficiency parameters, and optimization target weights are obtained.

[0008] Based on the optimization target weights, charging demand coverage constraints, service time constraints, and resource investment constraints, an objective function is constructed.

[0009] Based on the charging station demand parameters, the initial charging demand is generated through the dual-network fusion model.

[0010] Based on the charging station site selection task, the objective function, the algorithm efficiency parameters, and the initial charging demand, several recommended solutions are determined using a genetic algorithm.

[0011] Based on the overall metrics of the recommended scheme, determine how the charging demand coverage of the recommended scheme changes over time;

[0012] The impact on the road network and the power grid of all the recommended schemes are assessed, and the assessment results are obtained.

[0013] The existing charging station layout, the layout analysis results, the overall indicators of the recommended scheme, the changes in charging demand coverage over time, and the evaluation results are displayed through visual charts to assist users in site selection decisions.

[0014] In some embodiments, the analysis of the existing charging station layout based on traffic data and power grid data to obtain the layout analysis results includes the following steps:

[0015] Acquire urban road network data, power grid model parameters, origin-destination travel data, and charging station data;

[0016] Based on the road network data, the power grid model parameters, the origin-destination travel data, and the charging station data, the road network traffic volume and charging demand are determined;

[0017] Calculate the grid node voltage based on the grid model parameters, the charging demand, and the upper and lower limits of voltage and current;

[0018] Traffic hotspots are determined based on the road network traffic volume, the charging demand, and the grid node voltage.

[0019] Based on the traffic hotspots, the urban road network data, the power grid model parameters, the origin-destination travel data, and the charging station data, the layout analysis results of the existing charging station layout are determined.

[0020] In some embodiments, determining the charging station site selection task based on the layout analysis results and obtaining charging station demand parameters, algorithm efficiency parameters, and optimization target weights includes the following steps:

[0021] Based on the layout analysis results, the task of selecting charging station locations is determined to be either adding new charging stations or optimizing the scale of existing charging stations.

[0022] Obtain the number of charging stations and the range of charging piles to be added, and get the charging station demand parameters;

[0023] Obtain the constraints on the number of iterations and the number of offspring to obtain the algorithm efficiency parameters;

[0024] By obtaining the weights of charging demand coverage, service time, and resource investment, the optimization target weights can be obtained.

[0025] In some embodiments, determining several recommended schemes based on a genetic algorithm according to the charging station site selection task, the objective function, the charging station demand parameters, the algorithm efficiency parameters, and the initial charging demand includes the following steps:

[0026] Based on the charging station site selection task and the initial charging demand, a crossover operation is performed using a genetic algorithm to generate the first generation;

[0027] The first offspring is mutated to obtain mutated offspring;

[0028] A selection operation is performed on the mutated offspring to obtain candidate solutions;

[0029] Calculate the objective function value for each candidate solution based on the objective function;

[0030] The process of generating the first generation by performing crossover operations using a genetic algorithm based on the charging station site selection task and the initial charging demand is repeated until the number of iterations reaches the algorithm efficiency parameter constraint. From the last iteration, several candidate solutions whose objective function value ranks greater than a predetermined ranking threshold are obtained to generate a recommended solution.

[0031] In some embodiments, the assessment of the road network impact and power grid impact of all recommended schemes to obtain the assessment results includes the following steps:

[0032] Based on the location and number of charging stations in the recommended scheme, a dual-network hybrid model is used to calculate the expected traffic flow distribution and expected power grid load.

[0033] The difference between the expected traffic flow distribution and the existing traffic flow distribution is used to obtain the degree of impact on the road network.

[0034] The degree of impact on the power grid is obtained by subtracting the expected power grid load from the current power grid load.

[0035] The assessment results are obtained based on the degree of impact on the road network and the degree of impact on the power grid.

[0036] In some embodiments, the existing charging station layout, the overall indicators of the recommended scheme, the changes in charging demand coverage over time, and the evaluation results are displayed through visual charts, including the following steps:

[0037] An interactive map is used to display the existing charging station layout and the layout analysis results;

[0038] The overall metrics of the recommended scheme are displayed using a first bar chart.

[0039] A line graph is used to illustrate how the charging demand coverage changes over time.

[0040] The evaluation results are presented using a second bar chart; the higher the second bar chart, the greater the impact.

[0041] In some embodiments, the expression of the objective function is:

[0042]

[0043] Where Obj represents the objective function value; It is the charging demand coverage of location i; This refers to the service time at location i. Resource allocation at position i.

[0044] To achieve the above objective, another aspect of this application proposes a charging station site selection auxiliary decision-making system based on visual analytics, the system comprising:

[0045] The first module is used to analyze the existing charging station layout based on traffic data and power grid data, and obtain the layout analysis results.

[0046] The second module is used to determine the charging station site selection task based on the layout analysis results, and to obtain charging station demand parameters, algorithm efficiency parameters and optimization target weights.

[0047] The third module is used to construct an objective function based on the optimization target weights, charging demand coverage constraints, service time constraints, and resource investment constraints.

[0048] The fourth module is used to generate initial charging demand based on the charging station demand parameters using a dual-network fusion model.

[0049] The fifth module is used to determine several recommended schemes based on a genetic algorithm, according to the charging station site selection task, the objective function, the algorithm efficiency parameters, and the initial charging demand.

[0050] The sixth module is used to determine the change in the charging demand coverage of the recommended scheme over time based on the overall indicators of the recommended scheme.

[0051] The seventh module is used to assess the impact of all the recommended schemes on the road network and the power grid, and to obtain the assessment results;

[0052] The eighth module is used to display the existing charging station layout, the layout analysis results, the overall indicators of the recommended scheme, the changes in charging demand coverage over time, and the evaluation results through visual charts to assist users in site selection decisions.

[0053] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0054] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0055] The embodiments of this application include at least the following beneficial effects: This application provides a charging station site selection auxiliary decision-making method and system based on visual analysis. This scheme analyzes the existing charging station layout and determines the charging station site selection task based on the layout analysis results. It obtains charging station demand parameters, algorithm efficiency parameters, and optimization target weights. Then, it constructs an objective function based on the optimization weights, charging demand coverage constraints, service time constraints, and resource input constraints. Finally, it determines several recommended schemes based on a genetic algorithm, which can automatically generate charging station site selection schemes and reduce the workload of manual decision-making. Furthermore, this application determines the charging demand coverage of the recommended schemes over time and evaluates the degree of impact of the schemes on the road network and the power grid, and displays the results visually, which helps to improve the reliability and efficiency of decision-making. Attached Figure Description

[0056] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0057] Figure 1 This is a flowchart of a charging station site selection auxiliary decision-making method based on visual analysis provided in an embodiment of this application;

[0058] Figure 2 This is a schematic diagram of the data processing process of the dual-network fusion model provided in the embodiments of this application;

[0059] Figure 3 This is a simplified flowchart of the recommendation generation scheme provided in the embodiments of this application;

[0060] Figure 4 This is a visualization diagram provided in the embodiments of this application, reflecting the impact of the site selection scheme on the dual network;

[0061] Figure 5 This is a schematic diagram of the spatiotemporal visualization analysis process provided in the embodiments of this application;

[0062] Figure 6 This is a visual chart illustrating the traffic hotspot evolution process provided in the embodiments of this application;

[0063] Figure 7 This is a schematic diagram of the charging station information sub-diagram provided in an embodiment of this application;

[0064] Figure 8 This is a schematic diagram of the interactive road network traffic volume area map provided in the embodiments of this application;

[0065] Figure 9 This is a schematic diagram highlighting the road sections related to the charging station provided in an embodiment of this application;

[0066] Figure 10 This is a schematic diagram of a charging station site selection auxiliary decision-making system based on visual analysis provided in an embodiment of this application;

[0067] Figure 11 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0069] Although functional modules are divided in the system diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first / S100," "second / S200," etc., in the specification, claims, and the aforementioned figures are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0070] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0071] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0072] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0073] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0074] In related technologies, the charging station site selection model lacks analysis of the potential impact of the site selection results, which limits the comprehensive evaluation of the site selection results and makes it difficult to assess the actual effect and potential risks of new facilities.

[0075] In view of this, this application provides a charging station site selection auxiliary decision-making method and system based on visual analysis. This solution introduces a data-driven approach to automatically generate charging station site selection schemes, reducing the workload of manual decision-making. In addition, it provides data visualization and interactive functions, providing users with multi-level data analysis tools, which can help improve decision-making efficiency and reliability.

[0076] This application provides a method for assisting in the site selection of charging stations based on visual analytics, relating to the field of computer technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle-mounted terminal, but is not limited thereto. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the method for assisting in the site selection of charging stations based on visual analytics, but is not limited to the above forms.

[0077] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0078] Figure 1 This is an optional flowchart of a charging station site selection auxiliary decision-making method based on visual analytics provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S100 to S800.

[0079] Step S100: Analyze the existing charging station layout based on traffic data and power grid data to obtain the layout analysis results.

[0080] Step S200: Based on the layout analysis results, determine the charging station site selection task and obtain the charging station demand parameters, algorithm efficiency parameters, and optimization target weights.

[0081] Step S300: Construct an objective function based on the optimization target weight, charging demand coverage constraint, service time constraint, and resource investment constraint.

[0082] Step S400: Based on the charging station demand parameters, generate initial charging demand through the dual-network fusion model.

[0083] Step S500: Based on the charging station site selection task, the objective function, the algorithm efficiency parameters, and the initial charging demand, determine several recommended schemes using a genetic algorithm.

[0084] Step S600: Based on the overall indicators of the recommended scheme, determine the change in the charging demand coverage of the recommended scheme over time.

[0085] Step S700: Evaluate the impact on the road network and the power grid of all the recommended schemes to obtain the evaluation results.

[0086] Step S800: The existing charging station layout, the layout analysis results, the overall indicators of the recommended scheme, the changes in charging demand coverage over time, and the evaluation results are displayed through visual charts to assist users in site selection decisions.

[0087] Through the steps S100 to S800 above, the existing charging station layout is analyzed, and the charging station site selection task is determined based on the layout analysis results. Charging station demand parameters, algorithm efficiency parameters, and optimization target weights are obtained. Then, an objective function is constructed based on the optimization weights, charging demand coverage constraints, service time constraints, and resource input constraints. Subsequently, several recommended schemes are determined based on a genetic algorithm, which can automatically generate charging station site selection schemes and reduce the workload of manual decision-making. Furthermore, this application determines the charging demand coverage of the recommended schemes over time and evaluates the degree of impact of the schemes on the road network and the power grid, and visualizes the results, which helps to improve the reliability and efficiency of decision-making.

[0088] In some embodiments, step S100 includes, but is not limited to, the following steps S100 to S150:

[0089] Step S110: Obtain urban road network data, power grid model parameters, origin-destination travel data, and charging station data.

[0090] Step S120: Determine the road network traffic volume and charging demand based on the road network data, the power grid model parameters, the origin-destination travel data, and the charging station data.

[0091] Step S130: Calculate the grid node voltage based on the grid model parameters, the charging demand, and the upper and lower voltage and current limits.

[0092] Step S140: Determine traffic hotspots based on the road network traffic volume, the charging demand, and the grid node voltage.

[0093] Step S150: Based on the traffic hotspots, the urban road network data, the power grid model parameters, the origin-destination travel data, and the charging station data, determine the layout analysis results of the existing charging station layout.

[0094] In some embodiments, step S200 includes, but is not limited to, the following steps S210 to S240:

[0095] Step S210: Based on the layout analysis results, the task of selecting a charging station location is determined to be adding a new charging station or optimizing the scale of an existing charging station.

[0096] Step S220: Obtain the number of charging stations to be added and the range of charging piles to obtain the charging station requirement parameters.

[0097] Step S230: Obtain the iteration count constraint and the number of offspring constraint to obtain the algorithm efficiency parameters.

[0098] Step S240: Obtain the charging demand coverage weight, service time weight, and resource investment weight to obtain the optimization target weight.

[0099] In some embodiments, the expression of the objective function constructed in step S300 is:

[0100]

[0101] Where Obj represents the objective function value; It is the charging demand coverage of location i; This refers to the service time at location i. Resource allocation at position i.

[0102] In some embodiments, step S500 includes, but is not limited to, the following steps S510 to S550:

[0103] Step S510: Based on the charging station site selection task and the initial charging demand, a crossover operation is performed using a genetic algorithm to generate the first generation.

[0104] Step S520: Perform mutation processing on the first offspring to obtain mutated offspring.

[0105] Step S530: Select the mutated offspring to obtain candidate solutions.

[0106] Step S540: Calculate the objective function value for each candidate solution based on the objective function.

[0107] Step S550: Repeat the step of generating the first generation by performing crossover operation through a genetic algorithm based on the charging station site selection task and the initial charging demand, until the number of iterations reaches the algorithm efficiency parameter constraint. From the last iteration, obtain several candidate solutions whose objective function value ranking is greater than a predetermined ranking threshold, and generate a recommended solution.

[0108] In some embodiments, step S700 includes, but is not limited to, the following steps S710 to S740:

[0109] Step S710: Based on the location and number of charging stations in the recommended scheme, the expected traffic flow distribution and expected power grid load are calculated using a dual-network hybrid model.

[0110] Step S720: Subtract the expected traffic flow distribution from the existing traffic flow distribution to obtain the degree of road network impact.

[0111] Step S730: Subtract the expected grid load from the existing grid load to obtain the degree of grid impact.

[0112] Step S740: Obtain the evaluation result based on the degree of impact on the road network and the degree of impact on the power grid.

[0113] In some embodiments, step S800 includes, but is not limited to, the following steps S810 to S840:

[0114] Step S810: Use an interactive map to display the existing charging station layout and the layout analysis results.

[0115] Step S820: Use a first bar chart to display the overall indicators of the recommended scheme.

[0116] Step S830: Use a line graph to show how the charging demand coverage changes over time.

[0117] Step S840: The evaluation results are displayed using a second bar chart; wherein, the higher the second bar chart, the greater the influence.

[0118] The following section provides a detailed introduction and explanation of the solutions in this application embodiment, using specific examples of charging station site selection scenarios:

[0119] In this embodiment of the application, a method for assisting decision-making in charging station site selection based on visual analysis is provided. This method can be applied to visually providing candidate solutions for charging station site selection to assist users in making decisions.

[0120] Furthermore, this application embodiment constructs a dual-network fusion model to assist in generating charging station site selection schemes. The dual-network fusion model needs to solve two major problems: traffic assignment and optimal power flow. The traffic assignment problem aims to allocate OD (Orient-Destination) travel data to various road segments in the road network according to certain principles (such as user balance, minimum travel cost, etc.) to predict road segment traffic volume. Furthermore, it can generate traffic flow patterns and charging demand while solving the traffic assignment problem. The optimal power flow problem is used to solve for the state of the power grid, such as node voltage and line current. In some embodiments, the IEEE 14-node model can be used to construct the power grid. After calculating the charging demand from the traffic assignment problem, the charging demand needs to be matched with charging stations to determine which charging demands are connected to the power grid. These connected demands are then converted into grid loads, and the grid operating state is updated.

[0121] Specifically, refer to Figure 2In solving the traffic assignment problem, based on OD data, road network parameters, and charging station data, the objective of minimizing travel costs can be obtained, yielding road network traffic volume and charging demand. Travel costs include travel routes, travel duration, charging time, and charging costs. In solving the optimal power flow problem, based on the power grid model, charging demand, and voltage and current upper and lower limits, the objective of minimizing generation costs can be obtained, yielding the grid node voltages.

[0122] Furthermore, in this embodiment of the application, the layout of existing charging stations is determined based on traffic data and power grid data.

[0123] Specifically, this involves acquiring urban road network data, power grid model parameters, origin-destination travel data, and charging station data; determining road network traffic volume and charging demand based on this data; calculating power grid node voltages based on power grid model parameters, charging demand, and upper and lower voltage and current constraints; identifying traffic hotspots based on this data; and determining the existing charging station layout based on these traffic hotspots, urban road network data, power grid model parameters, origin-destination travel data, and charging station data.

[0124] Furthermore, referring to Figure 3 This application embodiment determines the charging station site selection task based on the existing charging station layout, and then uses a genetic algorithm to generate a site selection scheme. The specific detailed steps are as follows:

[0125] Step 1: Determine the site selection task: Choose to add new charging stations or optimize the scale of existing charging stations based on the needs.

[0126] Step 2: Setting Parameters: Further refine the site selection objective based on the parameters. These parameters fall into three categories: The first category is the specific task settings, such as the number of charging stations to be added and the range of each charging station's charging piles; the second category is algorithm efficiency settings, including the number of iterations and the number of offspring. More offspring and more iterations help the genetic algorithm generate more candidate solutions, which helps to obtain the optimal solution, but correspondingly, the algorithm's running time will also increase. Therefore, the parameters need to be set to balance these relationships; the third category is the weight of the optimization objective. The objective function in this embodiment is:

[0127]

[0128] This includes the charging demand coverage at location i. Service Hours and resource investment Three indicators, ω1, ω2, and ω3 are the weights of the three indicators, and CD is the weight of the three indicators. i This represents the charging requirement at position i.

[0129] The calculation process for each indicator is as follows:

[0130]

[0131] Users can adjust the weights of the three indicators according to specific circumstances to reflect the importance of different indicators. Charging demand coverage represents the proportion of charging demand in the generated site selection plan relative to the total charging demand of the road network. Service time reflects the overall operational efficiency of the charging station. Service time is calculated as the ratio of the charging demand at location i to the number of charging piles. Resource input depends on the number of new charging piles added to the plan. While deploying more charging piles can reduce waiting time when charging is needed, it may lead to reduced utilization of individual charging piles, resulting in resource waste.

[0132] Step 3: Solve the genetic algorithm. The specific solution process is as follows: First, generate initial charging demand data based on the dual-network fusion model; then, the genetic algorithm generates diverse candidate solutions through operations such as crossover, mutation, and selection. The objective function value of each solution is then calculated, and solutions with larger objective function values ​​are more likely to be retained in the next iteration. This process is repeated until the maximum number of iterations is reached.

[0133] Step 4: Determine the recommended solutions. The solutions ranked highest based on the objective function value obtained from the last iteration are recommended to the decision-making user. The number of solutions selected can be set by the user, such as the top three or top five solutions.

[0134] Step 5: Compare the overall metrics of the recommended solutions. List the three metrics for each candidate solution: charging demand coverage, service time, and resource investment, and use a line graph to show the changes in charging demand coverage of all candidate solutions over time.

[0135] Step 6: Compare the expected and actual road network conditions and power grid conditions of the recommended schemes. Specifically, by inputting the charging station information of the recommended schemes, including the location and number of charging stations, the dual-network fusion model is run to calculate the road network and power grid conditions under different schemes. The new scheme will calculate the new traffic flow distribution and power grid load situation. By comparing the difference with the original scheme data, the changes in the power grid under the new scheme can be analyzed.

[0136] Step 7: Visualize and interactively present the metrics of different solutions and their potential impact on the dual power grid. Specifically, firstly, a bar chart is used to display three metrics for different solutions: resource input, charging demand coverage, and service time. Furthermore, based on the line chart, a new visualization chart is used to analyze the impact of charging station location schemes on traffic and the power grid. The line chart shows the charging demand coverage rate of the schemes changing over time. (Refer to...) Figure 4When a user clicks on a solution, the corresponding line graph is highlighted, and small bar charts are displayed for each time point, showing the impact of the site selection solution on the road network and power grid. Higher bars indicate greater impact and more significant changes. When a user clicks on a specific time point, the line graph shows the location and magnitude of changes in road segments and power grid nodes in detail. In some embodiments, users can also filter the range of data changes they are interested in. The existing charging station layout is also displayed.

[0137] After reviewing the above-mentioned visualized charging station site selection recommendations, users can make decisions based on the existing charging station layout and their own experience to determine the optimal charging station site selection plan.

[0138] In some embodiments, refer to Figure 5 The method in this application embodiment can also display traffic hotspots and perform spatiotemporal visualization analysis on traffic hotspots. Users can view traffic hotspots to make better decisions on charging station site selection.

[0139] Furthermore, firstly, OD (Original Departure / Origin) travel data within the target area is filtered out, and the origin and destination of each trip are matched to road network nodes (intersections) based on distance. Then, a dual-network fusion model is run to obtain road network traffic volume, charging demand, and grid node voltage. For each day's road network traffic volume, a community detection algorithm is used to divide the road network into several regions based on traffic volume similarity. Then, regions with higher average traffic volume are selected as traffic hotspots. For traffic hotspots from two consecutive days, their geometric similarity (number of shared intersections) is calculated to obtain the evolution pattern of traffic hotspots, which is then visualized. Figure 6 The diagram shown illustrates the evolution of traffic hotspots.

[0140] Reference Figure 7 , Figure 8 and Figure 9 The system uses area maps and charging station information sub-maps to aggregate relevant data into the road network map. Specifically, each charging station's location is displayed in a charging station information sub-map, showing information such as voltage and charging demand. When the user zooms in on the map to a certain extent, the area map will show the changes in traffic volume over time for each road segment, using two different colors or different line formats to represent different travel directions. When a user clicks on a charging station, the surrounding road segments may be highlighted, indicating that some vehicles on these road segments may go to that charging station. The traffic volume area map for the corresponding road segment will also show the number of these vehicles and their proportion of the total traffic volume on that road segment.

[0141] The visualization can rank charging station information. Specifically, charging stations are matched with the road network, and the charging demand generated by the integration of the two networks is associated with the charging stations to obtain the charging demand of each charging station. The charging demand and the scale of the charging stations are listed in an information list for display, and interactive elements such as sorting and clicking can be added.

[0142] In summary, the embodiments of this application have at least the following beneficial effects:

[0143] 1. By introducing a data-driven approach, charging station site selection plans can be automatically generated, reducing the workload of manual decision-making;

[0144] 2. Furthermore, when recommending site selection options, the potential impact of the charging station site selection options is comprehensively assessed, revealing the impact of the charging station site selection results on traffic and the power grid, thereby improving the reliability of decision-making.

[0145] 3. It provides rich visualization data and multi-level data analysis methods, which visualize the analysis results of the impact, making it easy for decision-makers (users) to intuitively summarize and compare multiple site selection options, thereby improving decision-making efficiency.

[0146] Please see Figure 10 This application also provides a charging station site selection auxiliary decision-making system based on visual analytics, which can implement the above-mentioned charging station site selection auxiliary decision-making method based on visual analytics. The system includes:

[0147] The first module 101 is used to analyze the existing charging station layout based on traffic data and power grid data, and obtain the layout analysis results;

[0148] The second module 102 is used to determine the charging station site selection task based on the layout analysis results, and to obtain charging station demand parameters, algorithm efficiency parameters and optimization target weights.

[0149] The third module 103 is used to construct an objective function based on the optimization target weight, charging demand coverage constraint, service time constraint and resource investment constraint;

[0150] The fourth module 104 is used to generate initial charging demand based on the charging station demand parameters through a dual-network fusion model.

[0151] The fifth module 105 is used to determine several recommended schemes based on a genetic algorithm according to the charging station site selection task, the objective function, the algorithm efficiency parameters and the initial charging demand.

[0152] The sixth module 106 is used to determine the change in the charging demand coverage of the recommended scheme over time based on the overall indicators of the recommended scheme.

[0153] Module 7 107 is used to assess the impact of all the recommended schemes on the road network and the power grid, and obtain the assessment results;

[0154] The eighth module 108 is used to display the existing charging station layout, the layout analysis results, the overall indicators of the recommended scheme, the changes in charging demand coverage over time, and the evaluation results through visual charts to assist users in site selection decisions.

[0155] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0156] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned charging station site selection auxiliary decision-making method based on visual analysis. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0157] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0158] Please see Figure 11 , Figure 11 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0159] The processor 201 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0160] The memory 202 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 202 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 202 and is called and executed by the processor 201 to execute a charging station site selection auxiliary decision-making method based on visual analysis according to an embodiment of this application.

[0161] Input / output interface 203 is used to implement information input and output;

[0162] The communication interface 204 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0163] Bus 205 transmits information between various components of the device (e.g., processor 201, memory 202, input / output interface 203, and communication interface 204);

[0164] The processor 201, memory 202, input / output interface 203 and communication interface 204 are connected to each other within the device via bus 205.

[0165] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for assisting decision-making in charging station site selection based on visual analytics.

[0166] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0167] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0168] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0169] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0170] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0171] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0172] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0173] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0175] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0176] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0177] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0178] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for assisting decision-making in charging station site selection based on visual analytics, characterized in that, Includes the following steps: Based on traffic data and power grid data, the existing charging station layout is analyzed, and the layout analysis results are obtained. Based on the layout analysis results, the task of selecting charging station locations is determined, and the charging station demand parameters, algorithm efficiency parameters, and optimization target weights are obtained. Based on the optimization target weights, charging demand coverage constraints, service time constraints, and resource investment constraints, an objective function is constructed. Based on the charging station demand parameters, the initial charging demand is generated through the dual-network fusion model. Based on the charging station site selection task, the objective function, the algorithm efficiency parameters, and the initial charging demand, several recommended solutions are determined using a genetic algorithm. Based on the overall metrics of the recommended scheme, determine how the charging demand coverage of the recommended scheme changes over time; The impact on the road network and the power grid of all the recommended schemes are assessed, and the assessment results are obtained. The existing charging station layout, the layout analysis results, the overall indicators of the recommended scheme, the changes in charging demand coverage over time, and the evaluation results are displayed through visual charts to assist users in site selection decisions.

2. The method according to claim 1, characterized in that, The analysis of the existing charging station layout based on traffic data and power grid data to obtain the layout analysis results includes the following steps: Acquire urban road network data, power grid model parameters, origin-destination travel data, and charging station data; Based on the road network data, the power grid model parameters, the origin-destination travel data, and the charging station data, the road network traffic volume and charging demand are determined; Calculate the grid node voltage based on the grid model parameters, the charging demand, and the upper and lower limits of voltage and current; Traffic hotspots are determined based on the road network traffic volume, the charging demand, and the grid node voltage. Based on the traffic hotspots, the urban road network data, the power grid model parameters, the origin-destination travel data, and the charging station data, the layout analysis results of the existing charging station layout are determined.

3. The method according to claim 1, characterized in that, The step of determining the charging station site selection task based on the layout analysis results and obtaining charging station demand parameters, algorithm efficiency parameters, and optimization target weights includes the following steps: Based on the layout analysis results, the task of selecting charging station locations is determined to be either adding new charging stations or optimizing the scale of existing charging stations. Obtain the number of charging stations and the range of charging piles to be added, and get the charging station demand parameters; Obtain the constraints on the number of iterations and the number of offspring to obtain the algorithm efficiency parameters; By obtaining the weights of charging demand coverage, service time, and resource investment, the optimization target weights can be obtained.

4. The method according to claim 1, characterized in that, The step of determining several recommended solutions based on a genetic algorithm according to the charging station site selection task, the objective function, the charging station demand parameters, the algorithm efficiency parameters, and the initial charging demand includes the following steps: Based on the charging station site selection task and the initial charging demand, a crossover operation is performed using a genetic algorithm to generate the first generation; The first offspring is mutated to obtain mutated offspring; A selection operation is performed on the mutated offspring to obtain candidate solutions; Calculate the objective function value for each candidate solution based on the objective function; The process of generating the first generation by performing crossover operations using a genetic algorithm based on the charging station site selection task and the initial charging demand is repeated until the number of iterations reaches the algorithm efficiency parameter constraint. From the last iteration, several candidate solutions whose objective function value ranks greater than a predetermined ranking threshold are obtained to generate a recommended solution.

5. The method according to claim 1, characterized in that, The assessment of the impact on the road network and the power grid of all recommended schemes, to obtain the assessment results, includes the following steps: Based on the location and number of charging stations in the recommended scheme, a dual-network hybrid model is used to calculate the expected traffic flow distribution and expected power grid load. The difference between the expected traffic flow distribution and the existing traffic flow distribution is used to obtain the degree of impact on the road network. The degree of impact on the power grid is obtained by subtracting the expected power grid load from the current power grid load. The assessment results are obtained based on the degree of impact on the road network and the degree of impact on the power grid.

6. The method according to claim 1, characterized in that, The process of displaying the existing charging station layout, the layout analysis results, the overall indicators of the recommended solution, the changes in charging demand coverage over time, and the evaluation results through visual charts includes the following steps: An interactive map is used to display the existing charging station layout and the layout analysis results; The overall metrics of the recommended scheme are displayed using a first bar chart. A line graph is used to illustrate how the charging demand coverage changes over time. The evaluation results are presented using a second bar chart; the higher the second bar chart, the greater the impact.

7. The method according to claim 1, characterized in that, The expression for the objective function is: Where Obj represents the objective function value; It is the charging demand coverage of location i; This refers to the service time at location i. Resource allocation at position i.

8. A charging station site selection auxiliary decision-making system based on visual analytics, characterized in that, include: The first module is used to analyze the existing charging station layout based on traffic data and power grid data, and obtain the layout analysis results. The second module is used to determine the charging station site selection task based on the layout analysis results, and to obtain charging station demand parameters, algorithm efficiency parameters and optimization target weights. The third module is used to construct an objective function based on the optimization target weights, charging demand coverage constraints, service time constraints, and resource investment constraints. The fourth module is used to generate initial charging demand based on the charging station demand parameters using a dual-network fusion model. The fifth module is used to determine several recommended schemes based on a genetic algorithm, according to the charging station site selection task, the objective function, the algorithm efficiency parameters, and the initial charging demand. The sixth module is used to determine the change in the charging demand coverage of the recommended scheme over time based on the overall indicators of the recommended scheme. The seventh module is used to assess the impact of all the recommended schemes on the road network and the power grid, and to obtain the assessment results; The eighth module is used to display the existing charging station layout, the layout analysis results, the overall indicators of the recommended scheme, the changes in charging demand coverage over time, and the evaluation results through visual charts to assist users in site selection decisions.

9. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 7.

10. A computer storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to implement the method as described in any one of claims 1 to 7.

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