Charging station site selection auxiliary decision-making method and system based on visual analysis

Abstract

The invention discloses an auxiliary decision-making method and system for charging station site selection based on visual analysis, and belongs to the technical field of computers, and the method comprises the steps: analyzing the layout of an existing charging station according to traffic data and power grid data; determining a charging station site selection task, and constructing a target function according to the optimization target weight, the charging demand coverage rate constraint, the service time constraint and the resource investment constraint; according to the charging station demand parameters, an initial charging demand is generated through a dual-network fusion model, and a plurality of recommendation schemes are determined based on a genetic algorithm; and according to the overall index of the recommended scheme, determining the change condition of the charging demand coverage rate of the recommended scheme along with time, evaluating the influence degree of the charging demand coverage rate on the road network and the influence degree of the charging demand coverage rate on the power grid, obtaining an evaluation result, and performing visual display to assist a user in decision making. The charging station site selection scheme can be automatically generated, the influence degree of the scheme on the road network and the influence degree of the scheme on the power grid can be evaluated, visual display is carried out, and decision reliability and decision efficiency are improved in an auxiliary mode.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method and system for auxiliary decision-making for charging station site selection based on visual analysis. Background Art

[0002] With the development of society, new energy vehicles are gradually put into use, and the number of existing charging piles has steadily increased. However, the current site selection of charging stations is highly dependent on manual decision-making, and there is a lack of a charging station site selection auxiliary decision-making system. The manual site selection process lacks quantitative indicators and data support, is highly dependent on experience, and site selection is time-consuming and labor-intensive, inefficient, and difficult to summarize and analyze past cases.

[0003] Although there are some visualization platforms developed by charging station practitioners in the related technology, these software only view the operation status and benefits of charging stations, and there are few dedicated auxiliary decision-making solutions. In the problem of charging station site selection, the commonly used charging station site selection models often use simplified assumptions to reduce the computational complexity and cannot consider all factors in reality. Therefore, the existing site selection models cannot completely replace expert decisions. In addition, the existing charging station site selection models focus on generating site selection plans, but the evaluation is not comprehensive. Although some spatiotemporal constraints are taken into account in the model, there is still a lack of analysis of the possible impact on the site selection results, which limits the comprehensive evaluation of the site selection results. Summary of the invention

[0004] In order to solve at least one of the above problems, an embodiment of the present application proposes a method and system for auxiliary decision-making for charging station site selection based on visual analysis with high efficiency and reliability.

[0005] To achieve the above-mentioned purpose, one aspect of an embodiment of the present application proposes a charging station site selection auxiliary decision method based on visual analysis, the method comprising:

[0006] Analyze the existing charging station layout based on traffic data and power grid data to obtain layout analysis results;

[0007] According to 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;

[0008] Constructing an objective function according to the optimization target weight, charging demand coverage constraint, service time constraint and resource input constraint;

[0009] According to the charging station demand parameters, generating initial charging demand through a dual-network fusion model;

[0010] Determining a plurality of recommended solutions based on a genetic algorithm according to the charging station site selection task, the objective function, the algorithm efficiency parameter and the initial charging demand;

[0011] Determine, based on the overall indicators of the recommended solution, how the charging demand coverage of the recommended solution changes over time;

[0012] Evaluate the road network impact and power grid impact of all the recommended solutions to obtain evaluation results;

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

[0014] In some embodiments, analyzing the existing charging station layout according to the traffic data and the power grid data to obtain the layout analysis result includes the following steps:

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

[0016] Determine the road network traffic volume and charging demand according to the road network data, the power grid model parameters, the start-end point travel data and the charging station data;

[0017] Calculating the grid node voltage according to the grid model parameters, the charging demand and the voltage and current upper and lower limit constraints;

[0018] Determining a traffic hotspot based on the road network traffic volume, the charging demand, and the grid node voltage;

[0019] The layout analysis result of the existing charging station layout is determined according to the traffic hotspots, the urban road network data, the power grid model parameters, the start-end point travel data and the charging station data.

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

[0021] According to the layout analysis result, determining the charging station site selection task as adding a new charging station or optimizing the scale of an existing charging station;

[0022] Get the number of charging stations and charging pile range to be added, and obtain 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] The charging demand coverage weight, service time weight and resource input weight are obtained to obtain the optimization target weight.

[0025] In some embodiments, determining a plurality of recommended solutions based on a genetic algorithm according to the charging station site selection task, the objective function, the charging station demand parameter, the algorithm efficiency parameter and the initial charging demand comprises the following steps:

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

[0027] performing mutation processing on the first offspring to obtain a mutant offspring;

[0028] Performing a selection operation on the mutant offspring to obtain a candidate solution;

[0029] According to the objective function, calculating the objective function value of each candidate solution;

[0030] Repeat the step of generating a first generation by performing a crossover operation through a genetic algorithm according to the charging station site selection task and the initial charging demand until the number of iterations reaches the algorithm efficiency parameter constraint, obtain several candidate solutions whose objective function value ranking is greater than a predetermined ranking threshold from the last iteration, and generate a recommended solution.

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

[0032] According to the locations and number of charging stations in the recommended solution, a dual-grid hybrid model is used to calculate the expected traffic flow distribution and the expected grid load;

[0033] Subtract the expected traffic flow distribution from the existing traffic flow distribution to obtain the road network impact degree;

[0034] Subtract the expected grid load from the current grid load to obtain the grid impact level;

[0035] An evaluation result is obtained according to the road network impact degree and the power grid impact degree.

[0036] In some embodiments, presenting the existing charging station layout, the overall index of the recommended solution, the change of the charging demand coverage rate over time and the evaluation result through a visual chart includes the following steps:

[0037] Using an interactive map to display the existing charging station layout and the layout analysis results;

[0038] A first bar chart is used to display the overall indicators of the recommended solution;

[0039] A line graph is used to show the change of the charging demand coverage rate over time;

[0040] The evaluation result is displayed by using a second bar graph; wherein the higher the second bar graph is, the greater the impact is.

[0041] In some embodiments, the objective function is expressed as:

[0042]

[0043] Where, Obj represents the objective function value; is the charging demand coverage rate at location i; is the service time of location i; Resource investment at position i.

[0044] To achieve the above purpose, another aspect of the embodiment of the present application provides a charging station site selection auxiliary decision system based on visual analysis, the system comprising:

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

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

[0047] The third module is used to construct an objective function according to the optimization target weight, charging demand coverage constraint, service time constraint and resource input constraint;

[0048] A fourth module is used to generate an initial charging demand through a dual-network fusion model according to the charging station demand parameters;

[0049] A fifth module is used to determine a plurality of recommended solutions based on a genetic algorithm according to the charging station site selection task, the objective function, the algorithm efficiency parameter and the initial charging demand;

[0050] The sixth module is used to determine the change of the charging demand coverage rate of the recommended solution over time according to the overall indicators of the recommended solution;

[0051] The seventh module is used to evaluate the road network impact and power grid impact of all the recommended solutions to obtain evaluation results;

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

[0053] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.

[0054] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0055] The embodiments of the present application include at least the following beneficial effects: The present application provides a charging station site selection auxiliary decision-making method and system based on visual analysis, which analyzes the existing charging station layout and determines the charging station site selection task based on the layout analysis results, obtains the charging station demand parameters, algorithm efficiency parameters and optimization target weights, and then constructs an objective function based on the optimization weights, charging demand coverage constraints, service time constraints and resource input constraints, and then determines several recommended schemes based on the genetic algorithm, which can automatically generate charging station site selection schemes and reduce the workload of manual decision-making; and, the present application determines the changes in charging demand coverage over time for the recommended scheme, and the degree of impact of the evaluation scheme on the road network and the power grid, and performs a visual display, which helps improve the reliability and efficiency of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to provide further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0057] Figure 1 is a flow chart of a charging station site selection auxiliary decision-making method based on visual analysis provided in an embodiment of the present application;

[0058] Figure 2 It is a schematic diagram of the data processing process of the dual-network fusion model provided in an embodiment of the present application;

[0059] Figure 3 It is a simplified flow chart of generating a recommendation solution provided in an embodiment of the present application;

[0060] Figure 4 It is a visualization diagram reflecting the impact of the site selection scheme on the dual networks provided in the embodiment of the present application;

[0061] Figure 5 It is a schematic diagram of the spatiotemporal visualization analysis process provided by an embodiment of the present application;

[0062] Figure 6 It is a visual diagram of the evolution process of traffic hotspots provided in the embodiment of the present application;

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

[0064] Figure 8 It is a schematic diagram of an interactive road network traffic volume area map provided in an embodiment of the present application;

[0065] Fig. 9 is a schematic diagram of a road section related to a charging station highlighted in an embodiment of the present application;

[0066] Fig.10 It is a module schematic diagram of a charging station site selection auxiliary decision system based on visual analysis provided in an embodiment of the present application;

[0067] Fig.11 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the 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 the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0069] Although the functional modules are divided in the system schematic diagram and the 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 above drawings 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 can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

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

[0072] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

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

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

[0075] In view of this, an embodiment of the present application provides a charging station site selection auxiliary decision-making method and system based on visual analysis. The scheme automatically generates a charging station site selection plan by introducing a data-driven method, thereby reducing the workload of manual decision-making. In addition, it provides data visualization and interactive functions, and provides users with multi-level data analysis methods, which can help improve decision-making efficiency and reliability.

[0076] The embodiment of the present application provides a charging station site selection auxiliary decision method based on visual analysis, which relates to the field of computer technology. The embodiment of the present application provides a charging station site selection auxiliary decision method based on visual analysis, which can be applied to a terminal, a server, or a software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, 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 that implements a charging station site selection auxiliary decision method based on visual analysis, etc., but is not limited to the above forms.

[0077] The present application can be used in many general or special computer system environments or configurations. For example: 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, distributed computing environments including any of the above systems or devices, etc. The present 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. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0078] Figure 1 is an optional flow chart of a charging station site selection auxiliary decision method based on visual analysis provided in an embodiment of the present 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 a layout analysis result.

[0080] Step S200, determining the charging station site selection task according to the layout analysis result, and obtaining the charging station demand parameters, algorithm efficiency parameters and optimization target weights.

[0081] Step S300, constructing an objective function according to the optimization target weight, charging demand coverage constraint, service time constraint and resource input constraint.

[0082] Step S400: generating an initial charging demand through a dual-network fusion model according to the charging station demand parameters.

[0083] Step S500, determining several recommended solutions based on a genetic algorithm according to the charging station site selection task, the objective function, the algorithm efficiency parameter and the initial charging demand.

[0084] Step S600: determining how the charging demand coverage of the recommended solution changes over time based on the overall indicators of the recommended solution.

[0085] Step S700, evaluating the road network impact and power grid impact of all the recommended solutions to obtain evaluation results.

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

[0087] Through the above steps S100 to S800, the existing charging station layout is analyzed, and the charging station site selection task is determined based on the layout analysis results, the charging station demand parameters, algorithm efficiency parameters and optimization target weights are obtained, and then the objective function is constructed according to the optimization weights, charging demand coverage constraints, service time constraints and resource input constraints, and then a number of recommended schemes are determined based on the genetic algorithm, which can automatically generate charging station site selection schemes and reduce the workload of manual decision-making; and the application determines the changes in charging demand coverage over time for the recommended scheme, as well as the impact of the evaluation scheme on the road network and the power grid, and performs a visual display, which helps 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, obtaining urban road network data, power grid model parameters, start-end point travel data and charging station data.

[0090] Step S120, determining the road network traffic volume and charging demand based on the road network data, the power grid model parameters, the start-end point travel data and the charging station data.

[0091] Step S130, calculating the grid node voltage according to the grid model parameters, the charging demand and the voltage and current upper and lower limit constraints.

[0092] Step S140, determining traffic hot spots according to the road network traffic volume, the charging demand and the grid node voltage.

[0093] Step S150, determining a layout analysis result of an existing charging station layout according to the traffic hotspots, the urban road network data, the power grid model parameters, the start-end point travel data and the charging station data.

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

[0095] Step S210: According to the layout analysis result, the charging station site selection task is determined as adding a new charging station or optimizing the scale of existing charging stations.

[0096] Step S220, obtaining the number of charging stations to be added and the range of charging piles, and obtaining charging station demand parameters.

[0097] Step S230, obtaining the iteration number constraint and the offspring number constraint to obtain the algorithm efficiency parameter.

[0098] Step S240, obtaining 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; is the charging demand coverage rate at location i; is the service time of location i; Resource investment at position i.

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

[0103] Step S510: generating a first generation by performing a crossover operation through a genetic algorithm according to the charging station site selection task and the initial charging demand.

[0104] Step S520, performing mutation processing on the first offspring to obtain a mutant offspring.

[0105] Step S530, performing a selection operation on the mutant offspring to obtain a candidate solution.

[0106] Step S540: Calculate the objective function value of each candidate solution according to the objective function.

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

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

[0109] Step S710, according to the charging station locations and the number of charging stations in the recommended solution, a dual-network hybrid model is used to calculate the expected traffic flow distribution and the expected power grid load.

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

[0111] Step S730, subtracting the expected grid load condition from the current grid load condition to obtain the grid impact degree.

[0112] Step S740, obtaining an evaluation result according to the road network impact degree and the power grid impact degree.

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

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

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

[0116] Step S830: Use a line graph to display the change of the charging demand coverage rate over time.

[0117] Step S840: Use a second bar graph to display the evaluation result; wherein the higher the second bar graph is, the greater the impact is.

[0118] Below, in conjunction with a specific example of a charging station site selection scenario, the solution of the embodiment of the present application is described in detail and explained:

[0119] In an embodiment of the present application, a charging station site selection auxiliary decision method based on visual analysis is provided. The method can be used to visually provide candidate solutions for charging station site selection to assist users in making decisions.

[0120] Furthermore, the embodiment of the present application constructs a dual-network fusion model to assist in the generation of charging station site selection solutions. The dual-network fusion model needs to solve two major problems: the traffic distribution problem and the optimal power flow problem. The traffic distribution problem aims to distribute OD (Orient-Destination) travel data to each section in the road network according to certain principles (such as user equilibrium, minimum travel cost, etc.) to predict the traffic volume of the section, and the vehicle flow path and charging demand can be generated when solving the traffic distribution problem. The optimal power flow problem is used to solve the state of the power grid, such as node voltage, line current, etc. In some embodiments, the IEEE14 node model can be used to construct the power grid. After the traffic distribution problem calculates the charging demand, it is necessary to match the charging demand with the charging station, determine which charging demands are connected to the power grid, convert this part of the demand connected to the power grid into the power grid load, and then update the power grid operation status.

[0121] Specifically, refer to Figure 2When solving the traffic assignment problem, according to the OD data, road network parameters and charging station data, the goal of minimizing travel costs can be solved to obtain the road network traffic volume and charging demand; among which, the travel cost includes the travel path, travel time, charging time and charging cost. When solving the optimal power flow problem, according to the power grid model, charging demand and voltage and current upper and lower limit constraints, the goal of minimizing power generation costs can be solved to obtain the grid node voltage.

[0122] Furthermore, the embodiment of the present application determines the existing layout of charging stations based on traffic data and power grid data.

[0123] Specifically, it obtains urban road network data, power grid model parameters, travel data of starting and ending points, and charging station data; determines the road network traffic volume and charging demand based on the road network data, power grid model parameters, travel data of starting and ending points, and charging station data; calculates the grid node voltage based on the power grid model parameters, charging demand, and upper and lower limits of voltage and current; determines traffic hotspots based on road network traffic volume, charging demand, and power grid node voltage; determines the existing charging station layout based on traffic hotspots, urban road network data, power grid model parameters, travel data of starting and ending points, and charging station data.

[0124] Further, refer to Figure 3 In the embodiment of the present application, the charging station site selection task is determined according to the existing charging station layout, and then a site selection plan is generated using a genetic algorithm. 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 demand.

[0126] Step 2, set parameters: further improve the site selection target based on parameters, which include three categories: the first category is the specific setting of the task, such as the number of charging stations to be added and the range of charging piles at each charging station; the second category is the algorithm efficiency setting, including the number of iterations and the number of offspring. More offspring and more iterations help the genetic algorithm to generate more candidate solutions and help to obtain the optimal solution, but the algorithm running time will increase accordingly, so the parameters need to be set to balance the relationship; the third category is the weight of the optimization target. The objective function of the embodiment of the present application is:

[0127]

[0128] Among them, the charging demand coverage rate of location i is Service Hours and resource investment Three indicators, ω1, ω2 and ω3 are the weights of the three indicators, CD i represents the charging demand at location i.

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

[0130]

[0131] Users can adjust the weights of the three indicators according to the specific situation to reflect the importance of different indicators. The charging demand coverage rate indicates the ratio of the charging demand of the generated site selection plan to the total charging demand of the road network. The service time is used to reflect the overall operating efficiency of the charging station. The calculation of the service time is defined as the ratio of the charging demand at location i to the number of charging piles. The resource input depends on the number of new charging piles added to the plan. Although deploying more charging piles can reduce the waiting time when charging is required, it may lead to a decrease in the utilization rate of individual charging piles, resulting in a waste of resources.

[0132] Step 3: Solve the genetic algorithm. The specific solution process is: first, generate the initial charging demand data according to the dual-network integration model; then, the genetic algorithm generates a variety of candidate solutions through operations such as crossover, mutation, and selection, and then calculates the objective function value of each solution. The solution with a larger objective function value is more likely to be retained in the next iteration. Repeat the above process until the number of iterations reaches the upper limit of the number of iterations set above.

[0133] Step 4: Determine the recommended solution. The solutions with the highest ranking of the objective function value from the last iteration are recommended to the decision-making user. The number of rankings can be set by the user, for example, the top three solutions or the top five solutions.

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

[0135] Step 6: Compare the differences between the expected road network status and the actual road network status, and the expected power grid status and the actual power grid status for the recommended solution. Specifically, by inputting the charging station information of the recommended solution, including the location and number of charging stations, the dual-network fusion model is run to calculate the road network and power grid status under different solutions. The new solution will calculate the new traffic flow distribution and power grid load conditions, and compare them with the original solution data to analyze the power grid changes of the new solution.

[0136] Step 7: Use visual design and interaction to show the indicators of different solutions and their possible impact on the dual grid. Specifically, first use a bar chart to show the three indicators of different solutions: resource input, charging demand coverage, and service time. In addition, based on the line chart, a new visual chart is used to analyze the impact of the charging station site selection plan on traffic and the power grid. The line chart shows the charging demand coverage of the solution over time. Figure 4When the user clicks on a plan, the corresponding line will be highlighted, and a small bar graph will be displayed at each time point to show the impact of the site selection plan on the road network and power grid. The higher the bar graph, the greater the impact and the greater the change. When the user clicks on a certain time point, the line graph will show the change position and size of the road section and power grid node in detail. In some embodiments, users can also filter the range of data changes they are interested in according to their needs. It also displays the existing charging station layout.

[0137] After viewing the above 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, reference Figure 5 The method of the embodiment of the present application can also display traffic hotspots and perform spatiotemporal visualization analysis of traffic hotspots. Users can view traffic hotspots to better make decisions on charging station site selection.

[0139] Furthermore, we first screen out the OD travel data in the target area, match the starting and ending points of the travel to the road network nodes (each intersection) according to the distance, and then run the dual-network fusion model to obtain the road network traffic volume, charging demand and grid node voltage. For each day's road network traffic volume, we use the community discovery algorithm to divide the road network into several areas according to the similarity of the traffic volume, and then select the areas with the highest average traffic volume as traffic hotspots. For the traffic hotspots of two consecutive days, we calculate the geometric similarity of the traffic hotspots (the number of common intersections) to obtain the evolution law of the traffic hotspots, and then visualize them. Figure 6 The display diagram shown is used to demonstrate the evolution of traffic hotspots.

[0140] Reference Figure 7 , Figure 8 and Fig. 9 , using area maps, charging station information submaps and other forms to collect relevant data in the road network map. Specifically, a charging station information submap is displayed at the location of each charging station, showing information such as voltage and charging requirements. When the user zooms in on the map to a certain extent, the area map will show the changes in traffic volume over time on each section of the road network, using two different colors or different forms of lines to represent different driving directions of the section. When the user clicks on a charging station, the sections around the charging station may be highlighted, indicating that some vehicles on these sections may go to the charging station for charging, and the traffic area map of the corresponding section will also show the number of these vehicles and the proportion of the total traffic volume on this section of the road.

[0141] When displaying the information visually, you can rank the charging station information. Specifically, match the charging station with the road network, associate the charging demand generated by the dual-network integration with the charging station, obtain the charging demand of each charging station, list the charging demand and the scale of the charging station in the information list for display, and add sorting, clicking and other interactions.

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

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

[0144] 2. When recommending a site selection plan, the potential impact of the charging station site selection plan is comprehensively evaluated, revealing the impact of the charging station site selection results on transportation and power grid, thereby improving the reliability of decision-making.

[0145] 3. It provides rich visual data and multi-level data analysis methods to visualize the analysis results of the impact situation, making it easier for decision makers (users) to intuitively compare multiple site selection plans and improve decision-making efficiency.

[0146] See also Fig.10 The embodiment of the present application further provides a charging station site selection auxiliary decision system based on visual analysis, which can implement the above-mentioned charging station site selection auxiliary decision method based on visual analysis. The system includes:

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

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

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

[0150] The fourth module 104 is used to generate an initial charging demand through a dual network integration model according to the charging station demand parameters;

[0151] The fifth module 105 is used to determine a plurality of recommended solutions based on a genetic algorithm according to the charging station site selection task, the objective function, the algorithm efficiency parameter and the initial charging demand;

[0152] The sixth module 106 is used to determine the change of the charging demand coverage rate of the recommended solution over time according to the overall indicators of the recommended solution;

[0153] The seventh module 107 is used to evaluate the road network impact and power grid impact of all the recommended solutions to obtain evaluation 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 solution, the change of the charging demand coverage rate over time and the evaluation results through visual charts to assist users in site selection decisions.

[0155] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments 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.

[0156] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned charging station site selection auxiliary decision method based on visual analysis when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0157] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments 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] See also Fig.11 , Fig.11 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0159] The processor 201 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an 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 the present application;

[0160] The memory 202 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 202 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 202, and the processor 201 calls and executes a charging station location auxiliary decision method based on visual analysis in the embodiment of this application;

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

[0162] The communication interface 204 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0163] Bus 205 , which 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 , the memory 202 , the input / output interface 203 and the communication interface 204 are connected to each other in communication within the device via the bus 205 .

[0165] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned charging station site selection auxiliary decision method based on visual analysis.

[0166] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments 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.

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

[0168] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

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

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

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

[0172] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0173] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0174] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

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

[0176] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0177] If the integrated unit is implemented in the form of 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 the present application, 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, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0178] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present application should be within the scope of the rights of the present application.

Claims

1. A charging station site selection auxiliary decision method based on visual analysis, characterized in that: The following steps are involved: Analyze the existing charging station layout based on traffic data and power grid data to obtain layout analysis results; According to 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; Constructing an objective function according to the optimization target weight, charging demand coverage constraint, service time constraint and resource input constraint; According to the charging station demand parameters, generating initial charging demand through a dual-network fusion model; Determining a plurality of recommended solutions based on a genetic algorithm according to the charging station site selection task, the objective function, the algorithm efficiency parameter and the initial charging demand; Determine, based on the overall indicators of the recommended solution, how the charging demand coverage of the recommended solution changes over time; Evaluate the road network impact and power grid impact of all the recommended solutions to obtain evaluation results; The existing charging station layout, the layout analysis results, the overall indicators of the recommended solution, the change of the charging demand coverage rate over time and the evaluation results are displayed through visual charts to assist users in making site selection decisions.

2. The method according to claim 1, characterized in that Analyzing the existing charging station layout according to the traffic data and the power grid data to obtain the layout analysis result includes the following steps: Obtain urban road network data, power grid model parameters, origin and destination travel data, and charging station data; Determine the road network traffic volume and charging demand according to the road network data, the power grid model parameters, the start-end point travel data and the charging station data; Calculating the grid node voltage according to the grid model parameters, the charging demand and the voltage and current upper and lower limit constraints; Determining a traffic hotspot based on the road network traffic volume, the charging demand, and the grid node voltage; The layout analysis result of the existing charging station layout is determined according to the traffic hotspots, the urban road network data, the power grid model parameters, the start-end point travel data and the charging station data.

3. The method according to claim 1, characterized in that Determining the charging station site selection task according to the layout analysis result, and obtaining the charging station demand parameters, algorithm efficiency parameters and optimization target weights, includes the following steps: According to the layout analysis result, determining the charging station site selection task as adding a new charging station or optimizing the scale of an existing charging station; Get the number of charging stations and charging pile range to be added, and obtain the charging station demand parameters; Obtain the constraints on the number of iterations and the number of offspring to obtain the algorithm efficiency parameters; The charging demand coverage weight, service time weight and resource input weight are obtained to obtain the optimization target weight.

4. The method according to claim 1, characterized in that: The step of determining a plurality of recommended solutions based on a genetic algorithm according to the charging station site selection task, the objective function, the charging station demand parameter, the algorithm efficiency parameter and the initial charging demand comprises the following steps: According to the charging station site selection task and the initial charging demand, a crossover operation is performed through a genetic algorithm to generate a first offspring generation; performing mutation processing on the first offspring to obtain a mutant offspring; Performing a selection operation on the mutant offspring to obtain a candidate solution; According to the objective function, calculating the objective function value of each candidate solution; Repeat the step of generating a first generation by performing a crossover operation through a genetic algorithm according to the charging station site selection task and the initial charging demand until the number of iterations reaches the algorithm efficiency parameter constraint, obtain several candidate solutions whose objective function value ranking is greater than a predetermined ranking threshold from the last iteration, and generate a recommended solution.

5. The method according to claim 1, characterized in that The step of evaluating the road network impact and power grid impact of all the recommended solutions to obtain evaluation results includes the following steps: According to the locations and number of charging stations in the recommended solution, a dual-grid hybrid model is used to calculate the expected traffic flow distribution and the expected grid load; Subtract the expected traffic flow distribution from the existing traffic flow distribution to obtain the road network impact degree; Subtract the expected grid load from the current grid load to obtain the grid impact level; An evaluation result is obtained according to the road network impact degree and the power grid impact degree.

6. The method according to claim 1, characterized in that The presenting of the existing charging station layout, the layout analysis results, the overall index of the recommended solution, the change of the charging demand coverage rate over time and the evaluation results through a visual chart includes the following steps: Using an interactive map to display the existing charging station layout and the layout analysis results; A first bar chart is used to display the overall indicators of the recommended solution; A line graph is used to show the change of the charging demand coverage rate over time; A second bar graph is used to display the evaluation result; wherein the higher the second bar graph is, the greater the impact is.

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

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

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

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

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