Design method and device of charging station, electronic equipment and storage medium

Abstract

The invention discloses a design method and device of a charging station, electronic equipment and a storage medium, and the method comprises the steps: determining at least one parameter value of each design parameter item according to the allowable design range of the charging station and each design parameter item, and combining the parameter values of each design parameter item to obtain a combined parameter value; obtaining at least one group of initial design data of the charging station; performing search optimization on the at least one group of initial design data by utilizing a design target of the charging station to obtain at least one group of target design data; inputting each group of target design data into a pre-trained performance analysis model for performance analysis to obtain an actual performance analysis result corresponding to each group of target design data; and generating and displaying a performance analysis report according to each group of target design data and the actual performance analysis result corresponding to each group of target design data. According to the method, the problem that the design period of the charging station is long due to more design data combinations is solved, and the overall design period efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a design method, device, electronic equipment and storage medium for a charging station. Background Art

[0002] With the rapid growth of the electric vehicle market, the demand for charging infrastructure has increased significantly. Therefore, the design and optimization of charging stations have become a focus of attention.

[0003] In the design process of charging stations, it is necessary to consider meeting the charging needs of users, and also to ensure the operating cost requirements and charging efficiency of the entire charging station. Therefore, designers usually need to combine all the design parameter items of the charging station in sequence, and the parameter values ​​of each design parameter item need to be traversed. In this way, the best design data can be selected to build the charging station, so that the charging station can not only meet the charging needs of users, but also ensure the low operating cost and high charging efficiency of the charging station.

[0004] However, since the design parameter items of the charging station include multiple items, and each design parameter item also includes multiple parameter values, the final combined design data is very large. If each design data is simulated, the design cycle of the charging station will be extended to a certain extent. Therefore, it is urgent to propose a new method to solve the above problem. Summary of the invention

[0005] The present invention provides a design method, device, electronic device and storage medium for a charging station, which solves the problem that the design cycle of a charging station is long due to the large number of design data combinations and the need to simulate each set of design data, thereby reducing some invalid design data that does not match the current design goals or requirements and improving the overall design cycle efficiency.

[0006] According to one aspect of the present invention, a method for designing a charging station is provided, the method comprising:

[0007] Determine at least one parameter value of each design parameter item according to the allowable design range of the charging station and each design parameter item, and combine the parameter values ​​of each design parameter item to obtain at least one set of initial design data of the charging station, wherein each set of initial design data includes a parameter value of each design parameter item;

[0008] Search and optimize at least one set of initial design data using the design goal of the charging station to obtain at least one set of target design data;

[0009] Input each set of target design data into a pre-trained performance analysis model for performance analysis, and obtain the actual performance analysis results corresponding to each set of target design data, wherein the performance analysis model is a model that takes the training design data as input samples and takes the training performance analysis results corresponding to the training design data as output;

[0010] Based on each set of target design data and the actual performance analysis results corresponding to each set of target design data, a performance analysis report is generated and displayed, wherein the performance analysis report is used to guide the design strategy of the charging station.

[0011] Optionally, the parameter values ​​of the various design parameter items are combined to obtain at least one set of initial design data for the charging station, including:

[0012] A scanning strategy is determined according to a preset parameter scanning type and a preset parameter distribution type; and parameter values ​​of various design parameter items are scanned and combined according to the scanning strategy to obtain at least one set of initial design data of the charging station.

[0013] Optionally, at least one set of initial design data is searched and optimized using the design goal of the charging station to obtain at least one set of target design data, including:

[0014] Input each set of initial design data into the performance analysis model for performance analysis to obtain initial performance analysis results corresponding to each set of initial design data; determine the fitness of each set of initial design data with the design goal according to the design goal of the charging station and the initial performance analysis results; use the fitness of each set of initial design data with the design goal to search the initial design data to obtain intermediate design data; swap some parameter values ​​of the same design parameter items in any two intermediate design data to obtain target design data.

[0015] Optionally, the process of training a performance analysis model includes:

[0016] According to the vehicle arrival information of the sample charging station, the charging demand of the sample charging station is determined, and the training design data is simulated with the charging demand as the simulation demand scenario to obtain the label performance analysis results corresponding to the training design data, wherein the training design data includes a training sample set and a verification sample set; the original model is trained using the training sample set, and the original model is verified using the verification sample set, wherein the original model is an artificial neural network with at least two hidden layers; if the difference between the training performance analysis results corresponding to the verification sample set output by the original model and the label performance analysis results is less than a preset threshold, the original model is used as the performance analysis model; if the difference between the training performance analysis results corresponding to the verification sample set output by the original model and the label performance analysis results is greater than or equal to the preset threshold, the model parameters of the original model are optimized.

[0017] Optionally, determining the charging demand of the sample charging station according to the vehicle arrival information of the sample charging station includes:

[0018] Based on the historical vehicle charging information of the sample charging stations, the vehicle arrival information of at least one vehicle is determined; based on the vehicle arrival information of at least one vehicle, the average vehicle arrival rate of the sample charging stations is determined, and the charging demand is determined based on the average vehicle arrival rate.

[0019] Optionally, the charging demand is determined based on the average vehicle arrival rate, including:

[0020] Based on historical vehicle charging information, determine the battery charging information corresponding to at least one vehicle; use the battery charging information of each vehicle and the average vehicle arrival rate to calculate the waiting time information and charging time information of each vehicle; use the vehicle's waiting time information, charging time information and the average vehicle arrival rate to calculate the load curve of the sample charging station, and use the load curve as the charging demand.

[0021] Optionally, the waiting time information and charging time information of each vehicle are calculated using the battery charging information of each vehicle and the average vehicle arrival rate, including:

[0022] The initial sorting sequence of each vehicle is determined according to the average vehicle arrival rate and the vehicle arrival information of each vehicle; the charging demand weight of each vehicle is determined according to the initial sorting sequence of each vehicle and the battery charging information corresponding to the vehicle; the final sorting sequence of each vehicle is determined according to the charging demand weight of each vehicle, and the waiting time information and charging time information of each vehicle are calculated according to the final sorting sequence of each vehicle.

[0023] According to another aspect of the present invention, a design device for a charging station is provided, the device comprising:

[0024] a combining module, configured to determine at least one parameter value of each design parameter item according to the allowable design range of the charging station and each design parameter item, and combine the parameter values ​​of each design parameter item to obtain at least one set of initial design data of the charging station, wherein each set of initial design data includes a parameter value of each design parameter item;

[0025] An optimization module, configured to search and optimize at least one set of initial design data using the design objectives of the charging station to obtain at least one set of target design data;

[0026] An analysis module, used to input each set of target design data into a pre-trained performance analysis model for performance analysis, and obtain actual performance analysis results corresponding to each set of target design data, wherein the performance analysis model is a model that takes the training design data as input samples and takes the training performance analysis results corresponding to the training design data as output;

[0027] The display module is used to generate and display a performance analysis report based on each set of target design data and the actual performance analysis results corresponding to each set of target design data, wherein the performance analysis report is used to guide the design strategy of the charging station.

[0028] Optionally, the device further includes a model training module for training a performance analysis model, wherein the model training module is specifically used for:

[0029] According to the vehicle arrival information of the sample charging station, the charging demand of the sample charging station is determined, and the training design data is simulated with the charging demand as the simulation demand scenario to obtain the label performance analysis results corresponding to the training design data, wherein the training design data includes a training sample set and a verification sample set; the original model is trained using the training sample set, and the original model is verified using the verification sample set, wherein the original model is an artificial neural network with at least two hidden layers; if the difference between the training performance analysis results corresponding to the verification sample set output by the original model and the label performance analysis results is less than a preset threshold, the original model is used as the performance analysis model; if the difference between the training performance analysis results corresponding to the verification sample set output by the original model and the label performance analysis results is greater than or equal to the preset threshold, the model parameters of the original model are optimized.

[0030] Optionally, the charging demand of the sample charging station is determined according to the vehicle arrival information of the sample charging station. The model training module is specifically used for:

[0031] Based on the historical vehicle charging information of the sample charging stations, the vehicle arrival information of at least one vehicle is determined; based on the vehicle arrival information of at least one vehicle, the average vehicle arrival rate of the sample charging stations is determined, and the charging demand is determined based on the average vehicle arrival rate.

[0032] Optionally, the charging demand is determined based on the average vehicle arrival rate. The model training module is specifically used for:

[0033] Based on historical vehicle charging information, determine the battery charging information corresponding to at least one vehicle; use the battery charging information of each vehicle and the average vehicle arrival rate to calculate the waiting time information and charging time information of each vehicle; use the vehicle's waiting time information, charging time information and the average vehicle arrival rate to calculate the load curve of the sample charging station, and use the load curve as the charging demand.

[0034] Optionally, the battery charging information of each vehicle and the average vehicle arrival rate are used to calculate the waiting time information and charging time information of each vehicle. The model training module is specifically used for:

[0035] The initial sorting sequence of each vehicle is determined according to the average vehicle arrival rate and the vehicle arrival information of each vehicle; the charging demand weight of each vehicle is determined according to the initial sorting sequence of each vehicle and the battery charging information corresponding to the vehicle; the final sorting sequence of each vehicle is determined according to the charging demand weight of each vehicle, and the waiting time information and charging time information of each vehicle are calculated according to the final sorting sequence of each vehicle.

[0036] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0037] at least one processor; and

[0038] a memory communicatively connected to the at least one processor; wherein,

[0039] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the charging station design method described in any embodiment of the present invention.

[0040] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the charging station design method described in any embodiment of the present invention when executed.

[0041] The design method of the charging station provided by the embodiment of the present invention determines at least one parameter value of each design parameter item according to the allowable design range of the charging station and each design parameter item, and combines the parameter values ​​of each design parameter item to obtain at least one set of initial design data of the charging station; uses the design goal of the charging station to search and optimize at least one set of initial design data to obtain at least one set of target design data; inputs each set of target design data into a pre-trained performance analysis model for performance analysis to obtain the actual performance analysis results corresponding to each set of target design data; generates and displays a performance analysis report according to each set of target design data and the actual performance analysis results corresponding to each set of target design data. In the above technical scheme, on the one hand, the initial design data is searched and optimized, and target design data that is closer to the target or demand of the current design of the charging station can be screened and obtained from the initial design data, which solves the problem that the design cycle of the charging station is long due to the large number of design data combinations and the need to simulate each set of design data. It can not only reduce some invalid design data that does not match the current design goal or demand, improve the overall design cycle efficiency, but also realize rapid optimization of design data, and provide design data that is more in line with the target demand for determining the design data later. On the other hand, the target design data is analyzed using the performance analysis model to obtain the actual performance analysis results corresponding to each set of target design data, so that the design of the charging station can be accurately and quickly simulated according to each set of target design data, and the performance indicators used to evaluate the target design data can be obtained. At the same time, based on each set of target design data and its corresponding actual performance analysis results, a performance analysis report is generated and displayed, providing users with a variety of optional and better target design data. While meeting the user's design goals and design needs, it also improves the user's experience and provides users with more options, so that the final design of the charging station returns to the user.

[0042] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 A flow chart of a design method for a charging station provided by an embodiment of the present invention;

[0045] Figure 2 A flow chart of another charging station design method provided by an embodiment of the present invention;

[0046] Figure 3 An example diagram of the training process of the performance analysis model provided in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the structure of a design device for a charging station provided by an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0050] It should be noted that the terms "initial", "target", etc. in the specification and claims of the present invention 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 interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0051] Figure 1 This is a flow chart of a charging station design method provided by an embodiment of the present invention. This embodiment is applicable to the situation of quickly designing a low-cost charging station. The method can be executed by a charging station design device. The charging station design device can be implemented in the form of hardware and / or software. The charging station design device can be configured in an electronic device. In this embodiment, the electronic device can be a computer or a server. Figure 1 As shown, the method includes:

[0052] S101. Determine at least one parameter value of each design parameter item according to an allowable design range of the charging station and various design parameter items, and combine the parameter values ​​of the various design parameter items to obtain at least one set of initial design data of the charging station.

[0053] Each set of initial design data includes a parameter value of each design parameter item. The allowed design range is the optional parameter value range of each design parameter item set by the user.

[0054] Specifically, the design parameter items of a charging station may include the number of modules, the power of each module, the number of charging piles and the number of charging blocks, etc. Each design parameter item has a parameter value range. For example, the parameter value range of the number of charging piles can be 1-20. Based on this, a parameter value corresponding to each design parameter item can be determined with the allowable design range of the charging station as a restriction, and the parameter values ​​of each design parameter item can be combined to obtain at least one set of initial design data for the charging station.

[0055] For example, assume that the parameter values ​​of the number of modules are 2, 4, 6, 8, and 10; the parameter values ​​of the number of charging piles are 1, 2, 3, 4, and 5. The allowable design range limits the number of modules to be less than or equal to 4, and the number of charging piles to be less than or equal to 3. Then, a set of initial design data: (number of modules, number of charging piles) can be (2, 1), (2, 2), (2, 3), (4, 1), (4, 2), and (4, 3).

[0056] S102: Search and optimize at least one set of initial design data using the design objectives of the charging station to obtain at least one set of target design data.

[0057] Specifically, since the requirements for designing different charging stations are different, when obtaining the initial design data, the initial design data can be searched and optimized according to the goal or requirement of the current design of the charging station to obtain design data that is closer to the goal or requirement of the current design of the charging station, that is, the target design data. For example, a particle swarm optimization algorithm or a genetic algorithm can be used to search and optimize the initial design data to obtain at least one set of target design data.

[0058] In this embodiment, the initial design data is searched and optimized, and target design data that is closer to the goal or demand of the current design of the charging station can be screened and obtained from the initial design data, which solves the problem of a long charging station design cycle due to the large number of design data combinations and the need to simulate each set of design data. It can not only reduce some invalid design data that does not match the current design goal or demand and improve the overall design cycle efficiency, but also achieve rapid optimization of the design data and provide design data that is more in line with the target demand for later determination of the design data.

[0059] S103, inputting each set of target design data into a pre-trained performance analysis model for performance analysis, and obtaining actual performance analysis results corresponding to each set of target design data.

[0060] The performance analysis model is a model that uses the training design data as input samples and uses the training performance analysis results corresponding to the training design data as output. The actual performance analysis results are used to reflect the performance of the charging station designed with the target design data during the actual operation process.

[0061] Specifically, each set of target design data can be input into a pre-trained performance analysis model, which will perform performance analysis on each set of target design data and output the actual performance analysis results corresponding to each set of target design data after analysis. In addition, the actual performance analysis results include performance evaluation indicators such as system time ratio, efficiency, and capital expenditure.

[0062] S104. Generate and display a performance analysis report based on each set of target design data and the actual performance analysis results corresponding to each set of target design data.

[0063] Among them, the performance analysis report is used to guide the design strategy of charging stations.

[0064] Specifically, since each set of target design data has corresponding actual performance analysis results, in order to facilitate users to select the required target design data, a performance analysis report including each set of target design data and the actual performance analysis results corresponding to each set of target design data can be generated and displayed to the user, so that the user can select a set of target design data required by the user based on the actual performance analysis results of each set of target design data, thereby guiding the user to design a charging station.

[0065] In this embodiment, the target design data is analyzed using a performance analysis model to obtain the actual performance analysis results corresponding to each set of target design data, which solves the problem of lack of flexibility in the current design of charging stations that only rely on manual experience, and realizes accurate and rapid simulation of the design of charging stations according to each set of target design data to obtain performance indicators for evaluating the target design data. At the same time, based on each set of target design data and its corresponding actual performance analysis results, a performance analysis report is generated and displayed, providing users with a variety of optional and better target design data, while meeting the user's design goals and design requirements, it also improves the user's experience, and provides users with more options, so that the final design of the charging station is returned to the user.

[0066] The design method of the charging station provided by the embodiment of the present invention determines at least one parameter value of each design parameter item according to the allowable design range of the charging station and each design parameter item, and combines the parameter values ​​of each design parameter item to obtain at least one set of initial design data of the charging station; uses the design goal of the charging station to search and optimize at least one set of initial design data to obtain at least one set of target design data; inputs each set of target design data into a pre-trained performance analysis model for performance analysis to obtain the actual performance analysis results corresponding to each set of target design data; generates and displays a performance analysis report according to each set of target design data and the actual performance analysis results corresponding to each set of target design data. In the above technical scheme, on the one hand, the initial design data is searched and optimized, and target design data that is closer to the target or demand of the current design of the charging station can be screened and obtained from the initial design data, which solves the problem that the design cycle of the charging station is long due to the large number of design data combinations and the need to simulate each set of design data. It can not only reduce some invalid design data that does not match the current design goal or demand, improve the overall design cycle efficiency, but also realize rapid optimization of design data, and provide design data that is more in line with the target demand for determining the design data later. On the other hand, the target design data is analyzed using the performance analysis model to obtain the actual performance analysis results corresponding to each set of target design data, which solves the problem of lack of flexibility in the current design of charging stations that only rely on manual experience, and realizes accurate and rapid simulation of the design of charging stations according to each set of target design data, and obtains performance indicators for evaluating the target design data. At the same time, based on each set of target design data and its corresponding actual performance analysis results, a performance analysis report is generated and displayed, providing users with a variety of optional and better target design data. While meeting the user's design goals and design needs, it also improves the user's experience and provides users with more options, so that the final design of the charging station returns to the user.

[0067] Figure 2 This is a flow chart of another charging station design method provided by an embodiment of the present invention. Based on the above embodiment, this embodiment focuses on the steps of obtaining initial design data and target design data. Figure 2 Prior to this, the training process of the performance analysis model provided by the embodiment of the present invention is introduced in detail. Figure 3 An example diagram of the training process of the performance analysis model provided in the embodiment of the present invention is shown in FIG. Figure 3 As shown, the following steps are included:

[0068] S301. Determine the charging demand of the sample charging station based on the vehicle arrival information of the sample charging station, and use the charging demand as a simulation demand scenario to simulate the training design data to obtain a label performance analysis result corresponding to the training design data.

[0069] The training design data includes a training sample set and a verification sample set. The vehicle arrival information is the time information when the vehicle arrives at the sample charging station. The charging demand is the demand for charging of different vehicles after arriving at the sample charging station.

[0070] Specifically, the vehicle arrival information of one or more sample charging stations can be determined based on the vehicle arrival information of one or more sample charging stations, and based on the vehicle arrival information, the charging process of each vehicle after arriving at the sample charging station can be simulated. The simulation process can determine the charging demand of the sample charging station. In addition, the training design data can be obtained according to a method such as S101, or according to a method such as S101-S102, or the Monte Carlo method can be used to randomly sample the parameter values ​​of each design parameter item to obtain the training design data. After obtaining the training design data, the charging demand can be used as the simulation demand scenario to simulate the training design data, and the performance analysis results corresponding to each training design data obtained after the simulation are used as the label of each training design data.

[0071] Exemplarily, determining the charging demand of the sample charging station according to the vehicle arrival information of the sample charging station may include:

[0072] (i) Determine the vehicle arrival information of at least one vehicle based on the historical vehicle charging information of the sample charging stations.

[0073] Specifically, the vehicle arrival time of each vehicle entering the sample charging station in the historical process, that is, the vehicle arrival information, can be determined based on the historical vehicle charging information of the sample charging station.

[0074] (ii) Determine an average vehicle arrival rate at the sample charging stations based on vehicle arrival information of at least one vehicle, and determine the charging demand based on the average vehicle arrival rate.

[0075] Specifically, according to the vehicle arrival information of at least one vehicle, the arrival time of each vehicle can be sorted, and the average vehicle arrival rate of the sample charging station can be determined according to the vehicle arrival information. For example, the Poisson distribution formula can be used to calculate the average vehicle arrival rate.

[0076] Exemplarily, determining the charging demand according to the average vehicle arrival rate may include:

[0077] 1) Determine battery charging information corresponding to at least one vehicle based on historical vehicle charging information.

[0078] Specifically, the historical vehicle charging information of the sample charging station not only records the arrival time of the vehicles that came to the sample charging station for charging in the historical process, that is, the vehicle arrival information, but also records the battery capacity, remaining battery power, and expected charging capacity of each vehicle, that is, the battery charging information. Therefore, based on the historical vehicle charging information, the battery charging information corresponding to at least one vehicle that arrived at the sample charging station for charging in the historical process can be determined.

[0079] 2) Using the battery charging information of each vehicle and the average vehicle arrival rate, calculate the waiting time information and charging time information of each vehicle.

[0080] Specifically, the waiting time information and charging time information of each vehicle may be calculated using a First-Come First-Served (FCFS) strategy and a Supply Minimum strategy.

[0081] For example, the calculation can be performed according to the following steps:

[0082] (1) Determine the initial sorting sequence of each vehicle based on the average vehicle arrival rate and the vehicle arrival information of each vehicle.

[0083] Specifically, the average vehicle arrival rate can reflect the arrival of vehicles at the sample charging station in a certain period of time, and the vehicle arrival information can clearly indicate the time when each vehicle arrives at the sample charging station. Therefore, based on the average vehicle arrival rate and the vehicle arrival information of each vehicle, the arrival of each vehicle can be initially sorted to obtain an initial sorting sequence.

[0084] For example, if in the first cycle, according to the average vehicle arrival rate, it is determined that there are 5 vehicles arriving at the sample charging station, and the arrival time of these 5 vehicles can be determined according to the vehicle arrival information of each vehicle, then these 5 vehicles can be sorted according to the vehicle arrival time to obtain the initial sorting sequence.

[0085] (2) Determine the charging demand weight of each vehicle based on the initial sorting sequence of each vehicle and the battery charging information corresponding to the vehicle.

[0086] Specifically, since the initial sorting sequence obtained according to the method described in (1) may have a situation where the arrival times of multiple vehicles are exactly the same, that is, the initial sorting sequence of multiple vehicles may be a parallel sorting sequence, it is necessary to introduce the battery charging information corresponding to each vehicle to determine the current charging demand weight of each vehicle.

[0087] For example, the charging demand weight of the vehicle that is at the front of the initial sorting sequence is higher. In the battery charging information corresponding to the vehicle, for example, the charging demand weight of the vehicle with a smaller battery remaining power is higher, the charging demand weight of the vehicle with a higher battery capacity is lower, and the charging demand weight of the vehicle with a smaller expected charging amount is higher. In addition, the remaining battery power of the vehicle is the most important indicator, the initial sorting sequence is the second most important indicator, and the expected charging amount and the battery capacity of the vehicle can be allocated according to user needs.

[0088] Based on the above method, the charging demand weight of each vehicle can be determined according to the initial sorting sequence of each vehicle and the battery charging information corresponding to the vehicle.

[0089] (3) Determine the final sorting sequence of each vehicle according to the charging demand weight of each vehicle, and calculate the waiting time information and charging time information of each vehicle according to the final sorting sequence of each vehicle.

[0090] Specifically, after determining the charging demand weight of each vehicle, the final sorting sequence of each vehicle can be determined, that is, if each vehicle needs to be charged, the vehicles can be charged in sequence according to the sorting order indicated by the final sorting sequence. Then, according to the final sorting sequence of each vehicle, the waiting time for each vehicle to charge and the required charging time can be calculated, that is, the waiting time information and charging time information of each vehicle.

[0091] In this embodiment, based on the average vehicle arrival rate, the vehicle arrival information and battery charging information of each vehicle, the waiting time and charging time required for each vehicle to charge when arriving at the sample charging station can be clarified. Based on the first-come-first-served and minimum power supply principles, the charging demand weights of the vehicles arriving at the sample charging station can be reasonably calculated to express the degree of demand for each vehicle for the current need for charging, so as to make fine adjustments based on the demand degree and the initial sorting sequence, so that during the simulation, the sample charging station can meet the charging needs of each vehicle as much as possible and reduce the waiting time and charging time of each vehicle.

[0092] 3) Using the waiting time information, charging time information and average vehicle arrival rate of the vehicle, calculate the load curve of the sample charging station, and use the load curve as the charging demand.

[0093] Specifically, based on the waiting time information, charging time information and average vehicle arrival rate obtained above, the load curve of the sample charging station in each cycle can be calculated. The load curve is used to represent the load that the sample charging station bears in each cycle in order to provide charging needs for the arriving vehicles. Therefore, the load curve can be used as the charging demand. In the load curve, the charging demand of the sample charging station in each cycle (per hour) can be clearly stated. Optionally, the load curve can be a daily load curve.

[0094] In this embodiment, based on the arrival rate of vehicles at the sample charging station, the waiting time required for vehicle charging, and the charging time, the load curve of the sample charging station in each cycle can be determined, and the load curve is used as the charging demand of the sample charging station, thereby realizing the simulation of the load conditions of the sample charging station in the historical process, and using the simulation results as the charging demand of the sample charging station, which provides an important basis for using the charging demand as the demand of the charging station currently in the design process, thereby realizing the scenario of using the charging demand as the simulation demand, simulating the training design data, obtaining accurate label performance analysis results corresponding to the training design data, and providing labels for the sample data for the subsequent training performance analysis model.

[0095] S302: Train the original model using the training sample set, and verify the original model using the verification sample set.

[0096] Specifically, before training the original model, the training design data can be divided into a training sample set and a validation sample set. When training the original model, the training sample set can be used to train the original model, and after the training, the validation sample set can be used to verify the training results of the original model.

[0097] S303, determine whether the difference between the training performance analysis result corresponding to the verification sample set output by the original model and the label performance analysis result is less than a preset threshold; if so, execute S304; if not, execute S305.

[0098] Specifically, after the original model is trained using the training sample set, the samples in the verification sample set are input into the original model, and the original model outputs the training performance analysis results corresponding to the verification sample set. At this time, the training performance analysis results can be compared with the label performance analysis results, and the gap between the two can be determined. If the gap between the two is less than the preset threshold, it means that the training of the original model has met the user's needs, so S304 can be executed directly. If the gap between the two is greater than or equal to the preset threshold, it means that the training of the original model has not met the user's needs, so S305 needs to be executed.

[0099] S304: Use the original model as a performance analysis model.

[0100] Specifically, if the difference between the training performance analysis result and the label performance analysis result is less than a preset threshold, the original model can be directly used as the performance analysis model. At this time, the input of the performance analysis model is the design data (such as the number of modules, module power, number of charging piles, etc.), and the output is the performance analysis result (the performance analysis result includes performance indicators such as charging efficiency, cost-effectiveness ratio, waiting time, etc.).

[0101] S305: Optimize model parameters of the original model.

[0102] Specifically, if the difference between the training performance analysis results and the label performance analysis results is greater than or equal to a preset threshold, it is necessary to re-optimize the model parameters of the original model and re-train the original model until the difference between the training performance analysis results and the label performance analysis results is less than the preset threshold.

[0103] After the introduction Figure 3 After the performance analysis model training process shown, continue to refer to Figure 2 .

[0104] like Figure 2 As shown, the method includes:

[0105] S201. Determine at least one parameter value of each design parameter item according to an allowable design range of the charging station and various design parameter items.

[0106] Specifically, a parameter value corresponding to each design parameter item may be determined with the allowable design range of the charging station as a restriction.

[0107] S202: Determine a scanning strategy according to a preset parameter scanning type and a preset parameter distribution type.

[0108] Specifically, the parameters are scanned, and the parameter scanning type can be divided into full parameter scanning or partial scanning. Full parameter scanning is to obtain all possible values ​​of each parameter by combining them with all possible values ​​of other parameters. Partial scanning is to select only a few representative values ​​within the parameter range. In addition, the parameter distribution type is divided into uniform distribution or non-uniform distribution. Uniform distribution is a parameter distribution with equal spacing. Non-uniform distribution is a parameter distribution that needs to be adjusted according to the key inspection area. The parameter distribution type can be determined according to the user's pre-settings. After selecting the parameter scanning type and parameter distribution type, the scanning strategy of the parameter scanning can be determined.

[0109] S203: Scan and combine parameter values ​​of various design parameter items according to a scanning strategy to obtain at least one set of initial design data of the charging station.

[0110] Specifically, in one implementation, if a full parameter scan is performed, a list of values ​​can be created for each parameter, and then all possible parameter combinations can be generated using a Cartesian product. That is, the parameter values ​​of each design parameter item are combined to obtain data of all combinations as initial design data. In another implementation, representative values ​​are selected to generate parameter combinations. That is, some parameter values ​​of some design parameter items are selected and combined, and the obtained data is used as initial design data.

[0111] S204 , inputting each set of initial design data into a performance analysis model to perform performance analysis, and obtaining initial performance analysis results corresponding to each set of initial design data.

[0112] Specifically, each set of initial design data is input into the performance analysis model, and the performance analysis model analyzes the data to obtain initial performance analysis results corresponding to the initial design data.

[0113] S205. Determine the adaptability of each set of initial design data to the design target according to the design target of the charging station and the initial performance analysis result.

[0114] The design goal is the design requirements of the user for the charging station when designing the charging station. For example, the user needs to design a charging station with high efficiency and low cost, so high efficiency and low cost are the design goals.

[0115] Specifically, each initial design data has a corresponding initial performance analysis result. Therefore, the similarity or matching degree between the initial performance analysis result corresponding to each initial design data and the design target can be calculated and used as the fitness of the design target.

[0116] Exemplarily, in one implementation, if it is a full parameter scan, different performance analysis results corresponding to all design parameter items under different parameter value combinations, as well as the fitness with the design goal can be obtained. Based on this, the impact of the change of each parameter value on the performance of the final charging station can be known. In another implementation, if it is a partial parameter scan, different performance analysis results corresponding to different parameter items under different parameter value combinations, as well as the fitness with the design goal can be obtained. Based on this, it can be known which parameter item will affect the performance of the final charging station under different parameter item combinations.

[0117] In this embodiment, the fitness of each set of initial design data and the design goal is determined according to the design goal of the charging station and the initial performance analysis results. The fitness can be used to quantify the degree of closeness between the final performance indicators of the charging station simulated and designed using different initial design data and the design goal. In addition, the importance of different design parameter items or different parameter values ​​to the design goal can be determined according to the fitness, providing important support for the subsequent selection of target design data.

[0118] S206 , searching the initial design data using the fitness between each group of initial design data and the design target to obtain intermediate design data.

[0119] Specifically, the initial design data may be searched according to each set of initial design data and the fitness of the design target to determine the intermediate design data.

[0120] Exemplarily, in one implementation, if it is a full parameter scan, it is possible to determine which parameter item has a greater impact on the efficiency and cost of the performance of the charging station based on the fitness of the design target. For example, the parameter item is the number of modules. The larger the number corresponding to the number of modules, the higher the efficiency of the designed charging station. Then, the parameter item corresponding to the parameter value with a larger number of modules can be selected. In addition, if the module power is large and the number of modules is small, the efficiency of the designed charging station can also be guaranteed to be high and the cost is low. Then, the initial design data of the parameter items corresponding to the parameter values ​​with a medium number of modules and a large module power can be selected as the intermediate design data. In another implementation, if it is a partial parameter scan, it is possible to directly determine which parameter item has a greater impact on the designed charging station based on the parameter items included in the initial design data. For example, if the initial design data includes module power, it will make the designed charging station efficient and low in cost to a certain extent. Then, the initial design data of the parameter item including the module power can be used as the intermediate design data.

[0121] S207 , exchanging some parameter values ​​of the same design parameter items in any two intermediate design data to obtain target design data.

[0122] Specifically, in order to ensure that the final target design data has diversity and that the performance of the charging station designed according to the design data is closer to the design target, some parameter values ​​of the same design parameter items in any two intermediate design data can be swapped to obtain the target design data.

[0123] Exemplarily, assuming there are two intermediate design data A: [number of modules 6, number of charging piles 10], and intermediate design data B: [number of modules 8, number of charging piles 6], the target design data that can be obtained are: [number of modules 8, number of charging piles 10], [number of modules 6, number of charging piles 6], and, if the computing power is sufficient, the intermediate design data can also be directly used as the target design data, that is, the final target design data is [number of modules 8, number of charging piles 10], [number of modules 6, number of charging piles 6], [number of modules 6, number of charging piles 10] and [number of modules 8, number of charging piles 6].

[0124] In this embodiment, each set of initial design data is searched to obtain intermediate design data, and the intermediate design data is optimized to obtain target design data. This not only realizes the screening of the initial design data and selects the design data containing the performance of the charging station closer to the design target parameter items as the target design data, but also can optimize the target parameter items to make the target design data diverse.

[0125] S208: Input each set of target design data into a pre-trained performance analysis model for performance analysis to obtain actual performance analysis results corresponding to each set of target design data.

[0126] Specifically, each set of target design data may be input into a pre-trained performance analysis model, which will perform performance analysis on each set of target design data and output actual performance analysis results corresponding to each set of target design data after analysis.

[0127] S209: Generate and display a performance analysis report based on each set of target design data and the actual performance analysis results corresponding to each set of target design data.

[0128] Specifically, a performance analysis report including each set of target design data and the actual performance analysis results corresponding to each set of target design data can be generated and displayed to the user, so that the user can select a set of target design data required by the user based on the actual performance analysis results of each set of target design data, thereby guiding the user to design a charging station.

[0129] Figure 4 A schematic diagram of the structure of a design device for a charging station provided in an embodiment of the present invention.

[0130] like Figure 4 As shown, the device comprises:

[0131] A combining module 401 is used to determine at least one parameter value of each design parameter item according to the allowable design range of the charging station and each design parameter item, and combine the parameter values ​​of each design parameter item to obtain at least one set of initial design data of the charging station, wherein each set of initial design data includes a parameter value of each design parameter item;

[0132] An optimization module 402 is used to search and optimize at least one set of initial design data using the design goal of the charging station to obtain at least one set of target design data;

[0133] The analysis module 403 is used to input each set of target design data into a pre-trained performance analysis model for performance analysis, and obtain the actual performance analysis results corresponding to each set of target design data, wherein the performance analysis model is a model that takes the training design data as input samples and takes the training performance analysis results corresponding to the training design data as output;

[0134] The display module 404 is used to generate and display a performance analysis report according to each set of target design data and the actual performance analysis results corresponding to each set of target design data, wherein the performance analysis report is used to guide the design strategy of the charging station.

[0135] Optionally, the combination module 401 is specifically used for:

[0136] A scanning strategy is determined according to a preset parameter scanning type and a preset parameter distribution type; and parameter values ​​of various design parameter items are scanned and combined according to the scanning strategy to obtain at least one set of initial design data of the charging station.

[0137] Optionally, the optimization module 402 is specifically used for:

[0138] Input each set of initial design data into the performance analysis model for performance analysis to obtain initial performance analysis results corresponding to each set of initial design data; determine the fitness of each set of initial design data with the design goal according to the design goal of the charging station and the initial performance analysis results; use the fitness of each set of initial design data with the design goal to search the initial design data to obtain intermediate design data; swap some parameter values ​​of the same design parameter items in any two intermediate design data to obtain target design data.

[0139] Optionally, the device further includes a model training module for training a performance analysis model, wherein the model training module is specifically used for:

[0140] According to the vehicle arrival information of the sample charging station, the charging demand of the sample charging station is determined, and the training design data is simulated with the charging demand as the simulation demand scenario to obtain the label performance analysis results corresponding to the training design data, wherein the training design data includes a training sample set and a verification sample set; the original model is trained using the training sample set, and the original model is verified using the verification sample set, wherein the original model is an artificial neural network with at least two hidden layers; if the difference between the training performance analysis results corresponding to the verification sample set output by the original model and the label performance analysis results is less than a preset threshold, the original model is used as the performance analysis model; if the difference between the training performance analysis results corresponding to the verification sample set output by the original model and the label performance analysis results is greater than or equal to the preset threshold, the model parameters of the original model are optimized.

[0141] Optionally, the charging demand of the sample charging station is determined according to the vehicle arrival information of the sample charging station. The model training module is specifically used for:

[0142] Based on the historical vehicle charging information of the sample charging stations, the vehicle arrival information of at least one vehicle is determined; based on the vehicle arrival information of at least one vehicle, the average vehicle arrival rate of the sample charging stations is determined, and the charging demand is determined based on the average vehicle arrival rate.

[0143] Optionally, the charging demand is determined based on the average vehicle arrival rate. The model training module is specifically used for:

[0144] Based on historical vehicle charging information, determine the battery charging information corresponding to at least one vehicle; use the battery charging information of each vehicle and the average vehicle arrival rate to calculate the waiting time information and charging time information of each vehicle; use the vehicle's waiting time information, charging time information and the average vehicle arrival rate to calculate the load curve of the sample charging station, and use the load curve as the charging demand.

[0145] Optionally, the battery charging information of each vehicle and the average vehicle arrival rate are used to calculate the waiting time information and charging time information of each vehicle. The model training module is specifically used for:

[0146] The initial sorting sequence of each vehicle is determined according to the average vehicle arrival rate and the vehicle arrival information of each vehicle; the charging demand weight of each vehicle is determined according to the initial sorting sequence of each vehicle and the battery charging information corresponding to the vehicle; the final sorting sequence of each vehicle is determined according to the charging demand weight of each vehicle, and the waiting time information and charging time information of each vehicle are calculated according to the final sorting sequence of each vehicle.

[0147] The charging station design device provided in the embodiment of the present invention can execute the charging station design method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0148] Figure 5 A schematic diagram of the structure of an electronic device provided for an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0149] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0150] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0151] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the design method of the charging station.

[0152] In some embodiments, the design method of the charging station may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the design method of the charging station described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the design method of the charging station in any other appropriate manner (e.g., by means of firmware).

[0153] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0154] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0155] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0156] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0157] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0158] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0159] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0160] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A design method for a charging station, characterized in that: include: Determine at least one parameter value of each design parameter item according to the allowable design range of the charging station and each design parameter item, and combine the parameter values ​​of each design parameter item to obtain at least one set of initial design data of the charging station, wherein each set of initial design data includes a parameter value of each design parameter item; Search and optimize at least one set of initial design data using the design goal of the charging station to obtain at least one set of target design data; Input each set of target design data into a pre-trained performance analysis model for performance analysis, and obtain actual performance analysis results corresponding to each set of target design data, wherein the performance analysis model is a model that takes the training design data as input samples and takes the training performance analysis results corresponding to the training design data as output; A performance analysis report is generated and displayed according to each set of target design data and the actual performance analysis results corresponding to each set of target design data, wherein the performance analysis report is used to guide the design strategy of the charging station.

2. The design method of a charging station according to claim 1, characterized in that: The step of combining the parameter values ​​of the various design parameter items to obtain at least one set of initial design data for the charging station includes: Determine a scanning strategy according to a preset parameter scanning type and a preset parameter distribution type; The parameter values ​​of the various design parameter items are scanned and combined according to the scanning strategy to obtain at least one set of the initial design data of the charging station.

3. The design method of a charging station according to claim 1, characterized in that: The step of searching and optimizing at least one set of initial design data using the design goal of the charging station to obtain at least one set of target design data includes: Inputting each set of the initial design data into the performance analysis model to perform performance analysis, and obtaining initial performance analysis results corresponding to each set of the initial design data; Determining the adaptability of each set of the initial design data to the design target according to the design target of the charging station and the initial performance analysis result; Searching the initial design data using the fitness of each group of the initial design data and the design target to obtain intermediate design data; Part of the parameter values ​​of the same design parameter items in any two of the intermediate design data are exchanged to obtain the target design data.

4. The design method of a charging station according to claim 1, characterized in that: The process of training the performance analysis model includes: Determine the charging demand of the sample charging station according to the vehicle arrival information of the sample charging station, and use the charging demand as a simulation demand scenario to simulate the training design data to obtain a label performance analysis result corresponding to the training design data, wherein the training design data includes a training sample set and a verification sample set; Using the training sample set to train the original model, and using the verification sample set to verify the original model, wherein the original model is an artificial neural network with at least two hidden layers; If the difference between the training performance analysis result corresponding to the verification sample set output by the original model and the label performance analysis result is less than a preset threshold, the original model is used as the performance analysis model; If the difference between the training performance analysis result corresponding to the verification sample set output by the original model and the label performance analysis result is greater than or equal to a preset threshold, the model parameters of the original model are optimized.

5. The design method of a charging station according to claim 4, characterized in that: The determining the charging demand of the sample charging station according to the vehicle arrival information of the sample charging station includes: Determining the vehicle arrival information of at least one vehicle based on the historical vehicle charging information of the sample charging station; An average vehicle arrival rate of the sample charging stations is determined according to the vehicle arrival information of at least one of the vehicles, and the charging demand is determined according to the average vehicle arrival rate.

6. The design method of a charging station according to claim 5, characterized in that: The determining the charging demand according to the average vehicle arrival rate includes: Determining battery charging information corresponding to at least one of the vehicles according to the historical vehicle charging information; Calculate the waiting time information and charging time information of each vehicle by using the battery charging information of each vehicle and the average arrival rate of the vehicle; The load curve of the sample charging station is calculated by using the waiting time information of the vehicle, the charging time information and the average arrival rate of the vehicle, and the load curve is used as the charging demand.

7. The design method of a charging station according to claim 6, characterized in that: The method of calculating the waiting time information and charging time information of each vehicle by using the battery charging information of each vehicle and the average vehicle arrival rate includes: Determining an initial sorting sequence for each of the vehicles according to the average vehicle arrival rate and the vehicle arrival information of each of the vehicles; Determining a charging demand weight of each of the vehicles according to the initial sorting sequence of each of the vehicles and the battery charging information corresponding to the vehicle; The final sorting sequence of each of the vehicles is determined according to the charging demand weight of each of the vehicles, and the waiting time information and the charging time information of each of the vehicles are calculated according to the final sorting sequence of each of the vehicles.

8. A design device for a charging station, characterized in that: include: a combining module, configured to determine at least one parameter value of each design parameter item according to the allowable design range of the charging station and each design parameter item, and combine the parameter values ​​of each design parameter item to obtain at least one set of initial design data of the charging station, wherein each set of initial design data includes a parameter value of each design parameter item; An optimization module, configured to search and optimize at least one set of initial design data using the design objectives of the charging station to obtain at least one set of target design data; An analysis module, used for inputting each set of target design data into a pre-trained performance analysis model for performance analysis, and obtaining actual performance analysis results corresponding to each set of target design data, wherein the performance analysis model is a model that takes training design data as input samples and takes training performance analysis results corresponding to the training design data as output; The display module is used to generate and display a performance analysis report according to each set of target design data and the actual performance analysis results corresponding to each set of target design data, wherein the performance analysis report is used to guide the design strategy of the charging station.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the charging station design method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the charging station design method according to any one of claims 1 to 7 when executed.

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