Method, system and equipment for site selection of battery swap station and storage medium

Abstract

The invention belongs to the field of battery swap stations, and particularly relates to a battery swap station site selection method, system and device, a medium and a program product.The battery swap station site selection method comprises the steps that a site selection database is constructed; based on various data in the site selection database, processing through an ensemble learning calculation method to obtain regional power demand distribution; selecting a pre-selected battery swap station address based on the regional power demand distribution; collecting vehicle data of the area in real time based on the preselected battery swap station address; and processing the vehicle data based on data analysis, and adjusting the address of a pre-selected battery swap station based on a data analysis result.

Description

Background Art

[0002] With the application of new energy vehicles, more and more battery swap stations are being set up to solve the endurance problem of new energy vehicles. Battery swap stations can quickly replace batteries, effectively shorten the recharging time, improve the convenience of new energy vehicles, and promote the popularization and development of new energy vehicles.

[0003] The existing battery swap station site selection is based on historical data or fixed rules for static site selection, and the battery swap station is built according to the static site selection. For example, based on the vehicle distribution and power demand data of a certain area in the past year, the area with high power demand is selected as the location of the battery swap station.

[0004] The traditional site selection method is based on static data to select the location of battery swap stations, which easily ignores other factors that may affect the location of battery swap stations. There is a possibility of missing out on potential high-demand areas, resulting in a lack of systematic and comprehensive layout of battery swap stations, affecting user experience. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a method, system, device, medium and program product for selecting a site for a battery swap station.

[0006] A first aspect of the present invention provides a method for selecting a site for a battery swap station, comprising: Build a site selection database; Based on the data in the site selection database, the regional power demand distribution is obtained by processing through an integrated learning calculation method; Based on the regional power demand distribution, select a pre-selected power station address; Based on the pre-selected battery swap station address, real-time vehicle data in the area is collected; The vehicle data is processed based on data analysis, and the pre-selected battery swap station address is adjusted based on the data analysis result to obtain the selected battery swap station address.

[0007] In one embodiment, the data analysis method includes: Setting a time window, wherein the time window has a certain time length; When it is determined that the vehicle data collection time length reaches the set time window, the change amount of the vehicle data in the time window is calculated; Comparing the change amount with a preset change amount threshold; If the change exceeds the preset change threshold, adjusting the pre-selected battery swap station address; If the change does not exceed the preset change threshold, the pre-selected battery swap station address remains unchanged.

[0008] In one embodiment, the change includes the change in battery replacement demand, the change in traffic flow, and the change in vehicle running trajectory; The preset change amount threshold includes an electricity demand change amount threshold corresponding to the change amount of the battery swapping demand, a traffic flow change amount threshold corresponding to the change amount of the traffic flow, and a vehicle operation trajectory change amount threshold corresponding to the change amount of the vehicle operation trajectory.

[0009] In one embodiment, the method for selecting a preliminary battery swapping station address includes: Extracting the preliminary selection points of the battery swapping station from the regional electricity demand distribution; Based on the preliminary selection points of the battery swapping station, setting multiple sets of site selection factors; Performing weighted calculation based on each site selection factor in the multiple sets of site selection factors; Selecting the preliminary battery swapping station address based on the weighted calculation result.

[0010] In one embodiment, the formula for weighted calculation in the weighted calculation based on each site selection factor in the multiple sets of site selection factors is:

[0011] Wherein, is the number of site selection factors; is the weight of each site selection factor; is the score after standardization of each site selection factor.

[0012] In one embodiment, the set of site selection factors includes: The first factor, including the number of electric vehicles and the battery swapping demand within the coverage area of the battery swapping station; The second factor, including the cost of building the battery swapping station; The third factor, including the operating cost and expected revenue of the battery swapping station; The fourth factor, including the synergy effect between the battery swapping station and other traffic facilities; The fifth factor, including the rationality of the network topology.

[0013] In one embodiment, the site selection database includes: User battery swapping data obtained through a relational database; Vehicle operation trajectories obtained through a time series database; The existing distribution of battery swapping stations obtained through a geographic information system database; Traffic flow data, population density data, and geographical information of public parking lots obtained through an HTTP request interface.

[0014] A second aspect of the present invention provides a system for battery swapping station site selection, which is characterized by including: A construction unit for constructing a site selection database; A learning and calculation unit processes data in a site selection database through an integrated learning and calculation method to obtain the regional power demand distribution. A selection unit selects preselected battery swapping station addresses based on the regional power demand distribution. A collection unit collects vehicle data in the area where the preselected battery swapping station is located in real time based on the preselected battery swapping station address. A processing and adjustment unit processes the vehicle data based on data analysis and adjusts the preselected battery swapping station address based on the data analysis results.

[0015] A third aspect of the present invention provides an electronic device, including: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, which is one of the processors of the electronic device, for the method of battery swapping station site selection described above.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method of battery swapping station site selection described above.

[0017] The beneficial effects of the present invention compared with the prior art are as follows: The battery swapping station site selection method provided by the present invention obtains preselected battery swapping station addresses based on existing data analysis. On the basis of the preselected battery swapping station addresses, it collects vehicle data in the area where the preselected battery swapping station is located in real time, and adjusts the preselected battery swapping station addresses based on the data analysis results. Compared with the static site selection method that only relies on historical data, the method provided by the present invention combines the real-time vehicle data in the area where the preselected battery swapping station is located on the basis of existing data to select the battery swapping station address, ensuring that the battery swapping stations can be systematically and comprehensively arranged, reducing resource waste, improving operation efficiency, ensuring that the selected battery swapping station addresses are closer to the user needs in terms of battery swapping station layout, and improving the user's battery swapping experience and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, so they should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 According to an embodiment of the present invention, a flowchart of a method for battery swapping station site selection is shown.

[0020] Figure 2 According to an embodiment of the present invention, a flowchart of a data analysis method is shown.

[0021] Figure 3 According to an embodiment of the present invention, a schematic flowchart of a method for selecting a preselected battery swapping station address is shown.

[0022] Figure 4 According to an embodiment of the present invention, a schematic structural diagram of a battery swapping station site selection system is shown.

[0023] Figure 5 According to an embodiment of the present invention, a schematic structural diagram of an electronic device is shown.

[0024] Figure 6 According to an embodiment of the present invention, a schematic structural diagram of a computer-readable storage medium is shown. Detailed implementation manners

[0025] The following uses specific specific examples to illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed by the present invention. The present invention can also be implemented or applied through other different specific implementation manners. Various details in the present invention can also be modified or changed according to different viewpoints and application systems without departing from the spirit of the present invention. It should be noted that, without conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0026] The following takes the accompanying drawings as a reference and details the embodiments of the present invention so that those skilled in the technical field to which the present invention belongs can easily implement it. The present invention can be embodied in many different forms and is not limited to the embodiments described herein.

[0027] In the description of the present invention, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics represented in connection with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials, or characteristics represented can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples represented in the present invention and the features of different embodiments or examples.

[0028] In addition, the terms "first" and "second" are only used for the purpose of indication and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of the features. In the description of the present invention, "a plurality" means two or more unless otherwise specifically defined.

[0029] In order to clearly illustrate the present invention, devices irrelevant to the description are omitted, and the same or similar components throughout the specification are given the same reference signs.

[0030] Throughout the specification, when it is said that a certain device is "connected" to another device, this includes not only the case of "direct connection", but also the case of "indirect connection" with other elements placed therebetween. In addition, when it is said that a certain device "includes" a certain component, unless there is a particularly contrary record, it does not exclude other components, but means that other components may also be included.

[0031] When it is said that a certain device is "above" another device, this may be directly above the other device, but there may also be other devices therebetween. When it is said contrastively that a certain device is "directly" "above" another device, there are no other devices therebetween.

[0032] Although in some instances the terms first, second, etc. are used herein to denote various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first interface and the second interface, etc. are indicated. Furthermore, as used herein, the singular forms "a", "an" and "the" are intended to also include the plural forms unless the context clearly dictates otherwise. It should be further understood that the terms "comprising", "including" indicate the presence of the features, steps, operations, elements, components, items, kinds, and / or groups, but do not preclude the presence, occurrence or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms "or" and "and / or" used herein are to be construed as inclusive, or meaning any one or any combination. Thus, "A, B or C" or "A, B and / or C" means "any one of the following: A; B; C; A and B; A and C; B and C; A, B and C". An exception to this definition occurs only when the combination of elements, functions, steps or operations is inherently mutually exclusive in some manner.

[0033] The technical terms used herein are only for referring to specific embodiments and are not intended to limit the present invention. The singular forms used herein also include the plural forms as long as the statement does not clearly indicate the contrary meaning. The meaning of "including" used in the specification is to embody specific characteristics, regions, integers, steps, operations, elements and / or components, and does not exclude the existence or addition of other characteristics, regions, integers, steps, operations, elements and / or components.

[0034] Although not defined differently, all terms, including technical and scientific terms used herein, have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. Terms defined in commonly used dictionaries are additionally interpreted as having meanings consistent with the content of relevant technical literature and current disclosures, and unless defined, shall not be overly interpreted as ideal or very formal meanings.

[0035] The following problems exist in the prior art: during the construction of the battery swap station, the data in the area where the battery swap station is located changes. After the construction of the battery swap station is completed, due to changes in the users in the area where the battery swap station is located, some users have difficulty in battery swapping, affecting the user experience.

[0036] The method for selecting a site for a battery swap station proposed in the present invention can use preliminary data to pre-select a site for a battery swap station, and then collect vehicle data in the area where the pre-selected battery swap station address is located in real time, and adjust the location of the pre-selected battery swap station according to the vehicle data, thereby optimizing the resource allocation of the battery swap station, reducing resource waste, and improving operational efficiency. The adjusted pre-selected battery swap station address is closer to the battery swap station layout required by users, thereby improving users' battery swap experience and satisfaction.

[0037] In some embodiments of the present invention, Figure 1 As shown, a method for selecting a site for a battery swap station includes: Step 110: Construct a site selection database; wherein the site selection database includes: user battery swapping data obtained through a relational database; vehicle operation trajectories obtained through a time series database; the distribution of existing battery swap stations obtained through a geographic information system database; and traffic flow data, population density data, and public parking lot geographic information obtained through an HTTP request interface.

[0038] Step 120: Based on the data in the site selection database, the data is processed by an integrated learning calculation method to obtain the regional power demand distribution; wherein the integrated learning calculation method adopts a long short-term memory network (LSTM) algorithm.

[0039] Step 130: Based on the regional power demand distribution, select the pre-selected battery swap station address; it can be understood that the address with high power demand can be clearly seen in the regional power demand distribution, and the address with high power demand indicates that the power demand here is high, and it can be set as the pre-selected battery swap station address.

[0040] Step 140: Based on the pre-selected battery swap station address, vehicle data of the area where the pre-selected battery swap station address is located is collected in real time; it can be understood that real-time collection of vehicle data of the area where the pre-selected battery swap station address is located can timely understand the simulated usage of the pre-selected battery swap station address.

[0041] Step 150: Process the obtained vehicle data based on data analysis, and adjust the preselected swap station address based on the data analysis results to obtain the selected swap station address.

[0042] The following will further illustrate the specific embodiments of the above steps 110 to 150: In the above embodiment, in the foregoing step 110, the site selection database includes user battery swap data obtained through a relational database. Specifically, the user battery swap data exists in the form of a data packet named UserBatterySwapData. The UserBatterySwapData packet contains the battery swap records of users, and the records include several key fields: UserID (user ID), which is used to track and analyze the battery swap behavior of individual users; SwapTime (battery swap time), which records the specific time when the battery swap occurs and is used to analyze the time distribution of battery swap requirements; StationID (swap station ID), which is used to determine the location where the battery swap occurs, thereby analyzing the usage of each swap station; BatteryModel (battery model), which records the battery model used during the battery swap and helps to understand the usage frequency and requirements of different models of batteries; SOC (state of charge), which represents the remaining power of the battery during the battery swap and is used to evaluate the usage status of the battery and the urgency of the battery swap requirement.

[0043] Vehicle operation trajectory data is another key data source in the swap station network planning. These data are obtained through a time series database and exist in the form of a VehicleTrajectoryData packet. The VehicleTrajectoryData packet records the driving path of each vehicle over a period of time and includes the following key fields: VehicleID: vehicle ID, the unique identifier of the vehicle, which is used to track and analyze the driving trajectory of a single vehicle; Timestamp: timestamp, which records the time point when the data is collected and is used to analyze the time series of vehicle driving; Latitude: the latitude coordinate of the current position of the vehicle; Longitude: the longitude coordinate of the current position of the vehicle; Speed: the driving speed of the vehicle at this time point; Acceleration: the acceleration of the vehicle at this time point.

[0044] The distribution data of battery swapping stations is obtained from the Geographic Information System (GIS) database and exists in the form of the ChargingAndSwappingStationsDistribution data packet. Specifically, the ChargingAndSwappingStationsDistribution data packet contains information on all built or under-construction charging stations and battery swapping stations. The specific fields include: StationID: The ID of the battery swapping station, the unique identifier of the station, used to distinguish different charging and swapping stations; Type: The type of the station, which may be "charging" or "battery swapping" to distinguish different types of service facilities; Location: The geographical location of the station, usually represented by longitude and latitude, accurate to the specific geographical coordinates; Capacity: The maximum service capacity of the station, indicating the maximum number of vehicles that the station can serve at the same time; Status: The current status of the station, which may include "in operation", "under construction" or "suspended business", reflecting the operation of the station.

[0045] In the network planning of battery swapping stations, external data such as traffic flow data, population density data, and geographical information of public parking lots are crucial for site selection decisions. Among them, traffic flow data is obtained through the TrafficFlowAPI interface, and the URL of this interface is https: / / api.trafficdata.example.com / v1 / flow, and the GET method is used for requests. Two parameters need to be provided when making a request: location, used to specify the area code or geographical range; timeRange, used to specify the query time period. The JSON format data returned by the interface contains the average traffic flow statistics information of each road section within the specified area; Population density data is obtained through the PopulationDensityAPI interface, and the URL of this interface is https: / / api.populationdensity.example.org / v1 / density, and the GET method is used for requests. Two parameters need to be provided when making a request: area, used to specify the area identifier; resolution, used to specify the resolution level. The GeoJSON format data returned by the interface shows the population density distribution layer of a specific area; The geographical information of public parking lots can be obtained through the PublicParkingLotsAPI interface, and the URL of this interface is https: / / api.parkinglots.gov.cn / v1 / parks, and the GET method is used for requests. Two parameters need to be provided when making a request: city, used to specify the city name; district, used to specify the name of the district or county. The JSON format data returned by the interface contains the information of public parking lots within the specified city and district or county.

[0046] In the above embodiments, in the aforementioned step 120, the LSTM algorithm is used to process user battery swapping data, vehicle operation trajectory data, existing battery swapping station distribution data, traffic flow data, population density data, and public parking lot geographic information to generate a regional power demand distribution map; specifically, the long short-term memory (LSTM) algorithm includes: Data preprocessing: Standardize and normalize the above-mentioned user battery swapping data, vehicle operation trajectory data, existing battery swapping station distribution data, traffic flow data, population density data, and public parking lot geographic information to ensure the quality and consistency of the data; Feature extraction: Extract useful features from user battery swapping data, vehicle operation trajectory data, existing battery swapping station distribution data, traffic flow data, population density data, and public parking lot geographic information, such as user battery swapping frequency, regularity of vehicle driving paths, and peak periods of traffic flow; Model training: Use the LSTM algorithm to train the processed data to generate a model that can predict battery swapping demand; Power demand distribution map generation: Predict the battery swapping demand in different regions through the trained model to generate a power map.

[0047] Furthermore, when constructing a model for predicting battery swapping demand, a multi-dimensional feature vector over a period of time is used as the input, and the LSTM model is trained to predict the battery swapping demand at a future moment. To further improve the accuracy of the model, external factors such as weather conditions and holiday effects can be introduced. These external factors can help the model better understand the demand fluctuations in specific situations, thereby improving the accuracy of the prediction. The LSTM adopted in this embodiment is an existing mature ensemble learning calculation method. In other embodiments, a random forest model can also be used. By constructing multiple decision trees and performing average prediction, it has high accuracy and robustness. The random forest can handle high-dimensional data well and has a certain tolerance for noise; or a gradient boosting decision tree (GBDT) model, which can usually provide high-precision prediction results by gradually constructing decision trees and performing gradient boosting. When the GBDT model processes time series data, it can well capture the patterns and trends in the data through gradual optimization.

[0048] In the above embodiment, in the technical solution disclosed in the present invention, the method for selecting a location for a battery swap station from step 110 to step 150 can accurately identify high-demand areas through comprehensive analysis of an integrated learning algorithm (LSTM) and multiple data sources, thereby ensuring the efficiency and accuracy of the location selection for a battery swap station. The method for selecting a location for a battery swap station provided by the present invention obtains a pre-selected battery swap station address based on existing data analysis, collects vehicle data in the area where the pre-selected battery swap station address is located in real time on the basis of the pre-selected battery swap station address, and adjusts the pre-selected battery swap station address based on the data analysis result; compared to the static location selection method that relies only on historical data, the method provided by the present invention combines the real-time vehicle data of the area where the pre-selected battery swap station is located on the basis of existing data to select the address of the battery swap station, ensure that the battery swap station can be systematically and comprehensively arranged, ensure that the selected battery swap station address is closer to the battery swap station layout required by users, improve the user's battery swap experience and satisfaction, reduce resource waste, improve operational efficiency, ensure that the selected battery swap station address is closer to the battery swap station layout required by users, and improve the user's battery swap experience and satisfaction.

[0049] In some embodiments of the present disclosure, Figure 2 A schematic flow chart of a data analysis method involved in the aforementioned step 150 is shown, as Figure 2 As shown, the data analysis method includes: Step 151: Set a time window, each time window has a certain time length; wherein the time length includes any one of a week, a month, a quarter and half a year.

[0050] Step 152: When it is determined that the vehicle data collection time length reaches a set time window, the change amount of the vehicle data in the time window is calculated.

[0051] Step 153: Compare the change amount with a preset change amount threshold; wherein the change amount includes: Change in battery replacement demand :

[0052] in, It is the battery replacement demand in the current time window.

[0053] It is the demand for battery replacement before the current time window.

[0054] Traffic flow change :

[0055] in, is the traffic flow in the current time window.

[0056] is the traffic flow before the current time window.

[0057] Vehicle running trajectory change amount (unit: meter):

[0058] Among them, represents the current vehicle running trajectory and the historical vehicle running trajectory The absolute value difference between them.

[0059] n is the specific number of vehicles.

[0060] The preset change amount thresholds include the change amount threshold of battery swapping demand, traffic flow threshold, and vehicle running trajectory change amount threshold. In this embodiment, the demand threshold is 10%, the traffic flow change amount threshold is 20%, and the vehicle running trajectory change amount threshold is 50 meters.

[0061] Step 154: If the change amount exceeds the preset change amount threshold, adjust the battery swapping station, and the adjustment content includes adjusting the operation strategy, site selection, equipment configuration, resource allocation, etc. of the battery swapping station. It can be understood that when > 10% or > 20% or > 50 meters, adjust the battery swapping station.

[0062] Step 155: If the change amount does not exceed the preset change amount threshold, keep the preselected battery swapping station address unchanged. It can be understood that when ≤ 10% or ≤ 20% or ≤ 50 meters, keep the preselected battery swapping station address unchanged.

[0063] When adjusting the battery swapping station according to the present invention, in addition to adjusting the operation strategy, site selection, equipment configuration, resource allocation, etc. of the station according to the above change amount, it also includes adjusting the distribution of the battery swapping stations according to the user satisfaction score K. User satisfaction score K:

[0064] Among them, represents the satisfaction score of the i-th user.

[0065] is the sum of all user scores.

[0066] m is the number of users.

[0067] In this embodiment, the user satisfaction score of each user is evaluated through a paper questionnaire or a scanned code electronic questionnaire for question-and-answer evaluation, and finally the question-and-answer score is calculated.

[0068] In this embodiment, the user satisfaction threshold is 80 points.

[0069] In the above embodiment, in the technical solution of the present disclosure, through the data analysis method provided in steps 151 to 155, in specific applications, the adjustment includes short-term adjustment, medium-term adjustment or long-term adjustment. The time length of the time window for short-term adjustment is one week, and the trigger condition for short-term adjustment is > 10% or > 20%, and the preselected swapping station address is adjusted according to real-time data; the time length of the time window for medium-term adjustment is one month, and the trigger condition for medium-term adjustment is > 50 meters, the site selection, equipment configuration and resource allocation of the preselected swapping station are optimized, and the method of step S130 is adopted when optimizing the site selection of the preselected swapping station; the time length of the time window for long-term adjustment is one quarter or half a year, and the trigger condition for long-term adjustment is the user satisfaction score ≤ 80 points, the overall network layout is evaluated, and the distribution of swapping stations is adjusted according to vehicle growth, traffic changes and market demands for expansion or optimization.

[0070] In some embodiments of the present disclosure, Figure 3 A flowchart showing a method for selecting a preselected swapping station address involved in the foregoing step 130 is shown. The method for selecting a preselected swapping station address includes: Step 131: Extract the primary selection points of swapping stations from the regional power demand distribution; it can be understood that the addresses with high power demands can be directly obtained through the regional power demand distribution, and the addresses with high power demands are used as the primary selection points of swapping stations.

[0071] Step 132: Based on the preliminary selected points of the battery swapping stations, set multiple sets of site selection factors; among them, the set of site selection factors includes: The first factor, including the number of electric vehicles and the battery swapping demand within the coverage area of the battery swapping station; The second factor, including the construction cost of the battery swapping station; The third factor, including the operating cost and expected revenue of the battery swapping station; The fourth factor, including the synergy effect between the battery swapping station and other transportation facilities; The fifth factor, including the rationality of the network topology. The number of electric vehicles and the battery swapping demand (D) within the coverage area of the battery swapping station: Understand the usage of electric vehicles and the battery swapping demand in the area; The construction cost (C) of the battery swapping station: including land cost, equipment investment and construction period; The operating cost and expected revenue (R) of the battery swapping station: Evaluate the operating cost and expected economic benefits of the battery swapping station; The synergy effect (S) between the battery swapping station and other transportation facilities (such as charging piles, parking lots, etc.): Consider the mutual influence and synergy between the battery swapping station and other transportation facilities; The rationality (T) of the network topology: Ensure the convenience of connection between battery swapping stations and the battery transfer efficiency. The method of the present invention incorporates more factors affecting the site selection of battery swapping stations and conducts multi-dimensional comprehensive analysis through an integrated learning method. Compared with the limitations of traditional methods that ignore multi-dimensional factors, it can achieve a more scientific and systematic layout of battery swapping stations and avoid missing potential high-demand areas.

[0072] Step 133: Conduct weighted calculations based on each site selection factor in the multiple sets of site selection factors; specifically, the formula for weighted calculation is:

[0073] Among them, is the number of site selection factors.

[0074] is the weight of each site selection factor.

[0075] is the standardized score of each site selection factor.

[0076] It can be understood that the preliminary selected address of the battery swapping station is determined according to the result of the weighted calculation.

[0077] Step 134: Select the preliminary selected address of the battery swapping station based on the weighted calculation result.

[0078] The following specifically describes Steps 131 to 134: In the above embodiment, in the technical solution of the present disclosure, through the method for selecting the preliminary selected address of the battery swapping station provided in Steps 131 to 134, by comprehensively considering multiple site selection factors and conducting standardized processing and weighted calculation, the optimal site selection point of the battery swapping station can be obtained; at the same time, by combining the actual site selection factors to determine the preliminary preset site selection and layout of the battery swapping station, the effectiveness and efficiency of the battery swapping station network can be ensured.

[0079] The present invention selects three preliminary sites for battery swapping stations: S1, S2, and S3. Specific examples of the weighted calculation for the three preliminary sites for battery swapping stations are as follows: Set the weight of each site selection factor: the number of electric vehicles and the battery swapping demand within the coverage area of the battery swapping station = 0.25, the cost of building the battery swapping station = 0.20, the operating cost and expected revenue of the battery swapping station = 0.30, the synergistic effect between the battery swapping station and other transportation facilities = 0.15, the rationality of the network topology = 0.10.

[0080] Set the standardized values of the three site selection points as follows: : = 0.9, = 0.6, = 0.7, = 0.8, = 0.7.

[0081] : = 0.7, = 0.4, = 0.8, = 0.6, = 0.9.

[0082] : = 0.8, = 0.5, = 0.6, = 0.7, = 0.6.

[0083] Calculate the weighted comprehensive score of each site selection point: = 0.25×0.9 + 0.2×0.6 + 0.3×0.7 + 0.15×0.8 + 0.1×0.7 = 0.745.

[0084] = 0.25×0.7 + 0.2×0.4 + 0.3×0.8 + 0.15×0.6 + 0.1×0.9 = 0.68.

[0085] = 0.25×0.8 + 0.2×0.5 + 0.3×0.6 + 0.15×0.7 + 0.1×0.6 = 0.645.

[0086] According to the above calculations, > > Therefore, the most preferred pre-selected power station address is the site selection point . Through comprehensive evaluation and spatial analysis, the specific construction location and coverage of the battery swap station are determined to ensure that the layout of the battery swap station network can maximize user needs and operational efficiency. In other embodiments, during the site selection process of the battery swap station, the convenience of regional transportation is considered to ensure that the location of the battery swap station is convenient for users to arrive and leave; user battery swap convenience: select a location that is easy for users to reach to improve the convenience of battery swap services; land resource utilization: consider land costs and utilization efficiency, and select a cost-effective location.

[0087] In some embodiments of the present disclosure, Figure 4 A schematic diagram of the structure of a system for selecting a site for a battery swap station is provided. Figure 4 As shown, the system for selecting a site for a battery swap station is used to implement the method for selecting a site for a battery swap station provided in the above-mentioned embodiment, which may specifically include: The construction unit 501 is used to construct a site selection database.

[0088] The learning calculation unit 502 processes the data in the site selection database through an integrated learning calculation method to obtain the regional power demand distribution.

[0089] The selection unit 503 selects a pre-selected battery swap station address based on the regional power demand distribution.

[0090] The collection unit 504 collects vehicle data in the area in real time based on the pre-selected battery swap station address.

[0091] The processing and adjusting unit 505 processes the vehicle data based on data analysis, and adjusts the pre-selected battery swap station address based on the data analysis result.

[0092] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0093] Specifically, Figure 5 According to an embodiment of the present disclosure, a schematic diagram of the structure of an electronic device is shown. Figure 5 The electronic device 600 according to this embodiment of the present disclosure is described. Figure 5 The electronic device 600 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0094] As shown Figure 5 in FIG. 1, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0095] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present disclosure. For example, the processing unit 610 may execute the relevant steps of the method for selecting a location for an electric vehicle swapping station as shown Figure 1 in FIG. 2.

[0096] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only memory (ROM) 6203.

[0097] The storage unit 620 may further include a program / utilities 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0098] The bus 630 may represent one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0099] The electronic device 600 can also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 650. Moreover, the electronic device 600 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 through the bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0100] Embodiments of the present disclosure also provide a computer-readable storage medium for storing a program, and the steps of the method for site selection of a battery swapping station are implemented when the program is executed. In some possible implementation manners, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments described in the above text generation method part of this specification.

[0101] Specifically, Figure 6 According to an embodiment of the present disclosure, a schematic structural diagram of a computer-readable storage medium is shown. As Figure 6 shown, a program product 800 for implementing the above method for site selection of a battery swapping station according to an embodiment of the present disclosure is described. It can adopt a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited to this. In this document, the readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, a system, or a device.

[0102] The program product may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0103] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, in which case the readable program code is carried. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable storage medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0104] The program code for performing the specific implementation operations of the method for selecting a location for a battery swapping station provided in the foregoing embodiments of the present disclosure may be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages - such as Java, C++, etc., and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, executed as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0105] In summary, through the technical solution provided by the present disclosure, the method for selecting a location for a battery swapping station obtains forward-looking data through an inter-satellite link, and can better cope with a dynamically changing communication environment; when the satellite enters the next communication arc segment, it can quickly establish a high-quality space-ground connection to ensure seamless transition between different communication arc segments of the satellite. The satellite can quickly establish a high-quality space-ground connection when entering a new communication arc segment, reduce communication interruptions, improve the continuity and stability of communication, and enhance the communication experience of users.

[0106] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A method for selecting a site for a battery swap station, characterized in that: include: Build a site selection database; Based on the data in the site selection database, the regional power demand distribution is obtained through processing through integrated learning calculation method; Based on the regional power demand distribution, select a pre-selected power station address; Based on the pre-selected battery swap station address, real-time collection of vehicle data in the area; The vehicle data is processed based on data analysis, and the pre-selected battery swap station address is adjusted based on the data analysis result to obtain the selected battery swap station address.

2. The method for selecting a site for a battery swap station according to claim 1, characterized in that: The data analysis method comprises: Setting a time window, wherein the time window has a certain time length; When it is determined that the vehicle data collection time length reaches the set time window, the change amount of the vehicle data within the time window is calculated; Comparing the change amount with a preset change amount threshold; If the change exceeds the preset change threshold, adjusting the pre-selected battery swap station address; If the change does not exceed the preset change threshold, the pre-selected battery swap station address remains unchanged.

3. The method for selecting a site for a battery swap station according to claim 2, characterized in that: The changes include changes in battery replacement demand, changes in traffic flow, and changes in vehicle running trajectories; The preset change thresholds include a battery swap demand change threshold corresponding to the battery swap demand change, a traffic flow change threshold corresponding to the traffic flow change, and a vehicle running trajectory change threshold corresponding to the vehicle running trajectory change.

4. The method for selecting a site for a battery swap station according to claim 1, characterized in that: Methods for selecting the address of the pre-selected battery swap station include: Extracting preliminary points of power swap stations from the regional power demand distribution; Based on the initial selection point of the battery swap station, multiple site selection factor sets are set; Performing weighted calculation based on each location factor in a plurality of location factor sets; The pre-selected battery swap station address is selected based on the weighted calculation results.

5. The method for selecting a site for a battery swap station as claimed in claim 4, characterized in that: The formula for weighted calculation in the weighted calculation based on each location factor in the plurality of location factor sets is: in, is the number of location factors; is the weight of each location factor; The standardized scores for each location factor.

6. The method for selecting a site for a battery swap station according to claim 4 or 5, characterized in that: The set of location factors includes: The first factor includes the number of electric vehicles and battery replacement demand in the area covered by the battery replacement station; The second factor includes the cost of building a battery swap station; The third factor includes the operating costs and expected benefits of the battery swap station; The fourth factor includes the synergy between battery swap stations and other transportation facilities; The fifth factor includes the rationality of network topology.

7. The method for selecting a site for a battery swap station according to claim 1, characterized in that: The site selection database includes: User battery replacement data obtained through relational database; Vehicle operation trajectories obtained through the time series database; The distribution of existing battery swap stations obtained through the GIS database; Obtain traffic flow data, population density data and public parking lot geographic information through the HTTP request interface.

8. A system for selecting a site for a battery swap station, characterized in that: include: A construction unit, used for constructing a site selection database; A learning calculation unit processes various data in the site selection database through an integrated learning calculation method to obtain the regional power demand distribution; A selection unit, based on the regional power demand distribution, selects a pre-selected power station address; The collection unit collects vehicle data in the area in real time based on the pre-selected battery swap station address; A processing and adjustment unit processes the vehicle data based on data analysis, and adjusts the address of a pre-selected battery swap station based on a result of the data analysis.

9. An electronic device, characterized in that: include: A memory, used to store instructions executed by one or more processors of an electronic device, and a processor, which is one of the processors of the electronic device, used to execute the method for selecting a site for a battery swap station 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-executable instructions, which, when executed by a processor, are used to implement the method for selecting a site for a battery swap station according to any one of claims 1 to 7.

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