Battery swapping operation method and system, and computer storage medium

Abstract

The application discloses a battery swap operation method and system, and a storage medium. The method comprises the following steps: acquiring user data information; determining user portrait information according to the user data information; acquiring state information of a battery swap station; inputting the user portrait information and the state information of the battery swap station into a preset target model to determine a target battery swap strategy based on the target model. In this way, the target battery swap strategy is determined corresponding to the user portrait information, the personalized needs of users can be met, and the user experience is improved.

Description

Technical Field

[0001] This application relates to the field of battery swapping technology, and in particular to a battery swapping operation method, system, and computer storage medium. Background Technology

[0002] With the rapid development of the new energy vehicle industry, issues such as long charging time and range anxiety have become key factors restricting its popularization. Battery swapping mode has gradually become an important energy replenishment solution because it can achieve "vehicle-battery separation" and rapid energy replenishment.

[0003] In existing technologies, the coverage of battery swapping services is not comprehensive enough. Most operating solutions focus on a single user group, making it difficult to simultaneously meet the large-scale vehicle use of travel and urban distribution companies as well as the personalized battery swapping needs of private car users, resulting in a poor user experience. Summary of the Invention

[0004] The purpose of this application is to provide a battery swapping operation method, a battery swapping system, and a storage medium that can meet users' personalized needs and help improve user experience.

[0005] To achieve the above objectives: In a first aspect, embodiments of this application provide a battery swapping operation method, including: Obtain user data information; Based on the user data information, determine the user profile information; Obtain the status information of the battery swapping station; The user profile information and the status information of the battery swapping station are input into a preset target model to determine the target battery swapping strategy based on the target model.

[0006] In one embodiment, obtaining user data information includes: The first data information of the first type of users is obtained through the first port; the first type of users are vehicle users used for operation. Second data information of the second type of users is obtained through the second port; the second type of users are individual users who are not used for operation.

[0007] In one embodiment, when the user data information is first data information corresponding to a first type of user, determining the user profile information based on the user data information includes: Based on the order data and operational data in the first data information, the first basic characteristic information of the first type of user is determined; Based on the battery swapping behavior information and battery information in the first data information, the first behavior prediction information of the first type of user is determined. Based on the first basic feature information and the first behavior prediction information, the user profile information of the first type of user is determined.

[0008] In one embodiment, when the user data information is second data information corresponding to a second type of user, determining the user profile information based on the user data information includes: Based on the user information and historical travel data in the second data information, the second basic characteristic information of the second type of users is determined; Based on the battery swapping behavior information and battery information in the second data information, determine the second behavior prediction information of the second type of users; Based on the second basic feature information and the second behavior prediction information, the user profile information of the second type of user is determined.

[0009] In one embodiment, when the user profile information is that of a first-type user, the step of inputting the user profile information and the status information of the battery swapping station into a preset target model to determine a target battery swapping strategy based on the target model includes: Based on the status information of the battery swapping station, determine the periods of high supply and demand and the periods of low availability of the battery swapping station; Based on the user profile information and the status information of the battery swapping station, a first target battery swapping strategy is determined for charging the vehicle during the idle period, and / or a second target battery swapping strategy is determined for charging the vehicle during the period of supply and demand tension.

[0010] In one embodiment, determining a first target battery swapping strategy for charging the vehicle during the idle period includes: Based on the user profile information, calculate the battery swapping potential value of the vehicle during the idle period; When the battery swapping potential value meets preset conditions, discount information and restrictions for charging during the idle period are determined; The discount information and the restrictions are determined as the first target battery swapping strategy; The second target battery swapping strategy for determining when to charge the vehicle during periods of supply and demand tension includes: When the battery swapping potential value does not meet the preset conditions, the charging package information for the vehicle during the period of supply and demand tension is determined and identified as the second target battery swapping strategy.

[0011] In one embodiment, when the user profile information is that of a second type of user, the step of inputting the user profile information and the status information of the battery swapping station into a preset target model to determine a target battery swapping strategy based on the target model includes: Based on the user profile information, calculate the vehicle's guidance score; Based on the guiding score, the charging of the vehicle is diverted, and the charging time and charging discount for diverting are determined. A third target battery swapping strategy is determined based on the charging time and the charging incentives.

[0012] In one embodiment, it further includes: Based on the status information of the battery swapping station, determine the current availability of the battery swapping station; The charging of the vehicle is distributed according to the user type and availability rate in the user data information.

[0013] In one embodiment, determining the current availability of the battery swapping station based on its status information includes: The availability of the current battery swapping station is determined based on the status information of at least one charging pile in the status information of the battery swapping station. The status information of the at least one charging pile includes at least one of the following: charging power, battery state of charge, and number of available batteries.

[0014] Secondly, embodiments of this application provide a battery swapping system, specifically including: a processor and a memory for storing executable instructions; wherein the processor is configured to execute the instructions for performing the battery swapping operation method as described in the first aspect.

[0015] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, wherein when the instructions in the computer-readable storage medium are executed by a processor of a battery swapping system, the battery swapping system is able to implement the battery swapping operation method as described in the first aspect.

[0016] This application provides a battery swapping operation method, system, and storage medium. The method includes: acquiring user data information; determining user profile information based on the user data information; acquiring the status information of the battery swapping station; and inputting the user profile information and the status information of the battery swapping station into a preset target model to determine a target battery swapping strategy based on the target model. Thus, by determining the target battery swapping strategy according to the user profile information, personalized user needs can be met, which helps to improve the user experience. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the battery swapping operation method provided in an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the specific architecture of the battery swapping system provided in an embodiment of the present invention.

[0019] Figure 3This is a schematic diagram illustrating the specific process for determining user profile information provided in an embodiment of the present invention.

[0020] Figure 4 This is a schematic diagram illustrating the specific process of traffic splitting provided in an embodiment of the present invention.

[0021] Figure 5 This is a schematic diagram of the battery swapping system provided in an embodiment of the present invention.

[0022] Processor 210, memory 211, network interface 212, bus system 213. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0024] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0025] It should be understood that although the terms first, second, third, etc., may be used herein to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this document, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if," as used herein, can be interpreted as "when," "when," or "in response to determination." Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms "comprising," "including," indicate the presence of the stated feature, step, operation, element, component, item, kind, and / or group, but do not exclude 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" as used herein are to be interpreted as inclusive, or mean any one or any combination thereof. Therefore, "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". Exceptions to this definition will only occur if the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0026] It should be understood that although the steps in the flowcharts of this application's embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0027] It should be noted that step designations such as S101 and S102 are used in this document for the purpose of more clearly and concisely describing the corresponding content, and do not constitute a substantial limitation on the order. In specific implementation, those skilled in the art may execute S102 first and then S101, etc., but these should all be within the protection scope of this application.

[0028] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0029] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0030] like Figure 1 As shown, the battery swapping operation method provided in this application embodiment can be implemented in software and / or hardware. In this embodiment, the battery swapping operation method is applied to a server as an example. The battery swapping operation method provided in this application embodiment includes the following steps: Step S101: Obtain user data information.

[0031] In one embodiment, obtaining user data information includes: The first data information of the first type of users is obtained through the first port; the first type of users are vehicle users used for operation. Second data information of the second type of users is obtained through the second port; the second type of users are individual users who are not used for operation.

[0032] Optionally, the first category of users refers to vehicle users used for operational purposes, including vehicles in large-scale operational scenarios such as ride-hailing, taxis, and urban logistics vehicles. The second category of users refers to individual users not used for operational purposes, namely private car owners and other users whose primary travel needs are personal.

[0033] Optionally, when acquiring the first data information, the first port can be a preset interface, such as accessing the operating vehicle platform through an API interface to collect order start and end times, transportation distance, capacity location, average daily operating time, battery swapping history, battery swapping period, number of battery swaps, preferred battery type, etc., in order to obtain user data information of the first type of user.

[0034] Optionally, when acquiring the second data information, the second port can be a preset platform software, such as collecting data on age, occupation, vehicle model, commuting route, travel frequency, frequently used battery swapping stations, battery swapping time preferences, waiting tolerance time, single battery swapping payment amount, and coupon sensitivity through the car manufacturer's APP.

[0035] Step S102: Determine user profile information based on user data information.

[0036] Optionally, when acquiring the first data information of the first type of users, the first data information is analyzed and processed to determine the user profile information corresponding to the first type of users; when acquiring the second data information of the second type of users, the second data information is analyzed and processed to determine the user profile information corresponding to the second type of users. In this way, based on the user data collected by category, core features are extracted and behavioral tendencies are predicted according to user type, and finally, accurate and differentiated user profiles are constructed, providing a basis for subsequent battery swapping strategy matching.

[0037] In one embodiment, when the user data information is the first data information corresponding to the first type of user, user profile information is determined based on the user data information, including: Based on the order data and operational data in the first data information, the first basic characteristic information of the first type of users is determined; Based on the battery swapping behavior information and battery information in the first data information, determine the first behavior prediction information of the first type of users; Based on the first basic feature information and the first behavior prediction information, the user profile information of the first type of user is determined.

[0038] Optionally, order data specifically refers to the business operation order-related data of the first type of user, which is a key basis for defining their operational scenarios and demand base. This data includes: basic order information, order operation dimensions, and order-related information. Basic order information includes: order start and end times, order number (unique identifier), and order status (e.g., completed, pending, canceled). Order operation dimensions include: transportation distance (actual mileage from the order's origin to its destination, in km), vehicle location trajectory (real-time vehicle location data during order execution, used to locate core operational areas), and cargo type (e.g., fresh produce / express delivery / industrial products in urban distribution scenarios, affecting timeliness requirements). Order-related information includes: order-receiving platform and settlement amount (indirectly reflecting operational intensity and revenue needs).

[0039] Optionally, operational data refers to data related to the large-scale operation of the battery swapping process, reflecting the linkage between battery swapping and operation. Specifically, this includes: battery swapping frequency data, battery swapping efficiency data, and operation-related data. Battery swapping frequency data includes: average daily / monthly battery swapping frequency, single battery swapping interval duration, and fluctuations in battery swapping frequency during different operating cycles (peak / off-peak seasons). Battery swapping efficiency data includes: total single battery swapping time (including the entire process of vehicle entry, battery replacement, and departure), and average battery swapping waiting time. Operation-related data includes: average daily operating time (the actual number of hours vehicles of the first type of user are put into operation), correlation data between operating mileage and battery swapping frequency, and data on the impact of battery swapping on operational order completion rate.

[0040] Optionally, the battery swapping behavior information records the selection preferences and behavioral habits of the first type of users during the battery swapping process, which is used to explore the patterns of battery swapping demand. Specifically, this includes: battery swapping spatiotemporal preferences, battery swapping decision characteristics, and battery swapping frequency characteristics. Among them, battery swapping spatiotemporal preferences include: historical battery swapping station selection records, battery swapping time period distribution, and the matching relationship between battery swapping locations and operating / travel routes; battery swapping decision characteristics include: battery swapping trigger conditions and preferences for battery swapping services; battery swapping frequency characteristics include: the average number of battery swaps per month for individual users, battery swapping cycle, and differences in battery swapping behavior between holidays and weekdays.

[0041] Optionally, battery information refers to the core state and attribute data of the battery related to battery swapping. This is crucial for determining battery compatibility and energy replenishment needs, and specifically includes: basic battery attributes, battery state data, and battery usage characteristics. Basic battery attributes include: battery model, battery capacity, and battery type; battery state data includes: state of charge (SOC, real-time remaining percentage of charge), state of health (SOH, initial capacity decay rate), and battery degradation rate; battery usage characteristics include: full-charge driving range, charging / battery swapping compatibility parameters, and historical fault records (such as abnormal battery overheating, sudden drop in driving range, etc.).

[0042] Optionally, when determining the first basic characteristic information based on the order data, the first order data is analyzed and processed, including at least one of the following: analyzing the distribution of order start and end times to determine the user's high-frequency operation period; statistically analyzing the average and fluctuation range of transportation distance to clarify the user's operating radius; and summarizing the transportation capacity location data to lock in the core operation area.

[0043] Optionally, when determining the first basic characteristic information based on the operational data, the operational data is analyzed and processed, including at least one of the following: calculating the average daily operating time and classifying the operational intensity level; extracting the average monthly number of battery swaps and the single battery swap interval from the battery swap history record to determine the basic battery swap frequency requirement; and judging the user's battery swap demand density based on the determined basic battery swap frequency requirement.

[0044] Optionally, when determining the first basic characteristic information based on battery swapping behavior data, the battery swapping behavior data is analyzed and processed, including at least one of the following: analyzing the correlation between historical battery swapping periods and peak operating periods to predict future periods of essential battery swapping; combining the matching relationship between the number of battery swaps and operating intensity to predict the subsequent battery swapping frequency trend; and identifying preferred battery types, such as high-power fast-charging batteries and long-range batteries, to predict the battery demand trend accordingly.

[0045] Optionally, when determining the first basic characteristic information of the first type of user by combining battery information, the battery information is analyzed and processed, including at least one of the following: based on battery degradation data, predicting future battery replenishment efficiency requirements, such as needing to swap batteries more frequently or prioritizing high-capacity batteries when degradation is severe; matching full-charge range with operating mileage, predicting the probability of emergency battery swapping in extreme scenarios, such as proactively swapping batteries before long-distance orders.

[0046] Optionally, by combining the analysis results of order data and operational data in the first data information, the first basic characteristic information of the first type of user is determined, and by combining the analysis results of battery swapping behavior information and battery information in the first data information, the first behavior prediction information of the first type of user is determined.

[0047] In this way, by analyzing the first data information and determining the user profile information of the first type of user, we can accurately match the large-scale operation of vehicles with the personalized needs of individual users, optimize the allocation of battery swapping resources, and improve the adaptability and operational efficiency of battery swapping services.

[0048] In one embodiment, when the user data information is the second data information corresponding to the second type of user, user profile information is determined based on the user data information, including: Based on the user information and historical travel data in the second data information, determine the second basic characteristic information of the second type of users; Based on the battery swapping behavior information and battery information in the second data information, determine the second behavior prediction information of the second type of users; Based on the second basic feature information and the second behavior prediction information, the user profile information of the second type of user is determined.

[0049] Optionally, user information specifically refers to the vehicle and personal information associated with the second type of user, including: basic personal information, basic vehicle information, and spending power information. Basic personal information includes: age and occupation; basic vehicle information includes: vehicle model and vehicle registration location; the spending power information package for the second type of user includes: single battery swap payment amount and basic participation in promotional activities, collected through transaction records from third-party payment platforms.

[0050] Optionally, historical travel data records the regularity of past trips of the second type of users to explore travel patterns and range requirements, specifically including: travel scenarios, travel frequency, and activity areas. Among them, travel scenarios include: commuting routes and travel frequency.

[0051] Optionally, battery swapping behavior information includes battery swapping time and space preferences, behavioral characteristics, and discount response characteristics. Specifically, battery swapping time and space preferences include: preferred battery swapping stations and preferred battery swapping times; behavioral characteristics can include battery swapping waiting tolerance, such as the longest acceptable waiting time; discount response characteristics include: coupon sensitivity, such as willingness to participate in different types of discounts, such as minimum purchase discounts, discount coupons, and package coupons, and price decision-making tendency, such as determining whether the user chooses a non-preferred battery swapping station due to discounts.

[0052] Optionally, battery information includes: basic battery attributes, battery status data, and battery compatibility data. Basic battery attributes include: battery model, rated capacity and actual usable capacity, and battery type; battery status data includes: battery health, real-time battery state of charge, and battery degradation rate; battery compatibility data includes: current actual driving range on a full charge, battery swapping interface specifications, and historical fault records.

[0053] Optionally, when determining the second basic feature information based on user information, the analysis of user information includes at least one of the following: associating age and occupational characteristics to match the stability of battery swapping demand; combining vehicle model and location information to predict the battery swapping service area; and analyzing single payment amount and discount participation to define consumption level and discount sensitivity.

[0054] Optionally, when determining the second basic feature information based on historical travel data, the historical travel data is specifically analyzed, including at least one of the following: associating commuting routes with activity areas to identify high-frequency battery swapping service areas; combining travel frequency and scenario type to predict the battery swapping cycle; and matching cross-regional travel records with range requirements to predict the probability of long-distance emergency battery swapping.

[0055] Optionally, when determining the prediction information for the second behavior based on the battery swapping behavior information, the battery swapping behavior information is analyzed in detail, including: analyzing the correlation between historical battery swapping periods and peak operating periods to predict periods of essential battery swapping demand; combining battery swapping waiting tolerance time with commonly used sites to predict the acceptance of diversion; and identifying the type of preferential response to clarify the direction of diversion incentives, etc.

[0056] Optionally, when determining the second basic characteristic information of the second type of user by combining battery information, the battery information is analyzed and processed, including at least one of the following: based on battery degradation data, predicting future battery replenishment efficiency requirements, such as needing to swap batteries more frequently or prioritizing high-capacity batteries when degradation is severe; matching full-charge range with operating mileage, predicting the probability of emergency battery swapping in extreme scenarios, such as proactively swapping batteries before long-distance orders.

[0057] Optionally, by combining the analysis results of user information and historical travel data in the second data information, the second basic characteristic information of the second type of users can be determined. Furthermore, by combining the analysis results of battery swapping behavior information and battery information in the second data information, the second behavior prediction information of the second type of users can be determined. This allows for precise delivery of discounts and services tailored to personalized travel scenarios and battery swapping preferences, improving user experience and enhancing the stickiness of the battery swapping service.

[0058] Step S103: Obtain the status information of the battery swapping station.

[0059] Optionally, data can be collected from the battery swapping station in multiple dimensions to understand its operation, adaptation, and external correlation status. The status information of the battery swapping station includes: real-time power of the charger, SOC (remaining power) and SOH (health) of the available batteries in the station, working status of the charging pile (idle / occupied / faulty), peak and valley electricity price division time period, grid load threshold, and current grid power supply stability.

[0060] In one embodiment, current external environmental data can also be obtained, including: real-time weather, such as rain, snow, high temperature and other factors that affect the willingness to swap batteries, holiday / working day identification, etc.

[0061] Step S104: Input the user profile information and the status information of the battery swapping station into the preset target model to determine the target battery swapping strategy based on the target model.

[0062] Optionally, the preset target model is an attention-based multi-source spatiotemporal graph neural network (ASTGNN) model, which achieves accurate prediction of the current user's needs and strategy matching through a multi-level structure.

[0063] In one embodiment, when the user profile information is that of a first-type user, the user profile information and the status information of the battery swapping station are input into a preset target model to determine a target battery swapping strategy based on the target model, including: Based on the status information of the battery swapping stations, determine the periods of high supply and demand and the periods of low availability at the battery swapping stations; Based on user profile information and battery swapping station status information, determine the first target battery swapping strategy for charging vehicles during idle periods, and / or determine the second target battery swapping strategy for charging vehicles during periods of supply and demand tension.

[0064] Optionally, the target model can be used to determine the peak and off-peak periods for battery swapping stations based on their status information. Specifically, the station's service capacity can be assessed by combining equipment operation data from the station's status information, such as charger occupancy rate and available battery bank, and by correlating grid data (peak and off-peak electricity pricing periods) and environmental data (holidays, weather) to match demand fluctuation patterns. For example, 7:00-9:00 AM and 6:00-8:00 PM on weekdays can be identified as peak battery swapping times for operating vehicles and designated as "peak periods"; while battery swapping demand is low from 0:00-6:00 AM and 12:00-2:00 PM and designated as "off-peak periods."

[0065] Optionally, based on determined periods of high supply and demand and periods of low supply and demand, a primary battery swapping strategy can be launched, allowing vehicles to charge during low supply and demand periods, and a secondary battery swapping strategy can be launched, allowing vehicles to charge during periods of high supply and demand. In this way, by combining user profiles to accurately match the two types of strategies, the dual goals of "guiding and diverting users during off-peak hours and ensuring services for users with essential needs" can be achieved, thereby optimizing the efficiency of battery swapping station resource allocation and the battery swapping experience for users of operating vehicles.

[0066] In one embodiment, determining a first target battery swapping strategy for charging the vehicle during idle periods includes: Based on user profile information, calculate the battery swapping potential value of vehicles during idle periods. When the battery swapping potential value meets the preset conditions, determine the discount information and restrictions for charging during idle periods; Discount information and restrictions were identified as the primary target for the battery swapping strategy. A secondary battery swapping strategy was identified to enable vehicle charging during periods of high supply and demand, including: When the battery swapping potential value does not meet the preset conditions, determine the charging package information for the vehicle during periods of tight supply and demand, and identify it as the second target battery swapping strategy.

[0067] Optionally, based on the core features of the user profile information of the first type of user, key data such as historical battery swapping time and average battery swapping interval are extracted, and the battery swapping potential value of the vehicle during off-peak hours is calculated. This can be expressed by the formula: Battery swapping potential value = Standard deviation of historical battery swapping time / Average battery swapping interval. Here, the standard deviation of historical battery swapping time reflects the flexibility of the user's battery swapping time fluctuations, and the average battery swapping interval reflects their basic battery swapping cycle. The higher the ratio of the two, the greater the likelihood that the user will adjust their battery swapping time to off-peak hours, i.e., the stronger the off-peak potential.

[0068] Optionally, when the battery swapping potential value exceeds a set battery swapping potential threshold, it is determined that the current user's battery swapping time is highly adjustable and meets preset conditions, meaning that charging can be performed during off-peak hours. Generally, the preset battery swapping potential threshold can be dynamically adjusted based on the battery swapping station's operational data; here, it can be set to 0.8.

[0069] Optionally, for users who meet preset conditions, differentiated content can be designed based on the characteristics of the battery swapping station's off-peak hours, including: discount information, clearly specifying the preferential treatment available for battery swapping during off-peak hours, such as tiered discounts of 40%-70%; and restrictions, setting advance booking requirements, such as requiring booking 2 hours in advance, to ensure that the battery swapping station can allocate battery resources and plan service processes in advance. Here, users can also enjoy the guarantee of reserved batteries at the battery swapping station during off-peak hours. Optionally, the discount information, restrictions, and guarantee measures can be integrated to determine the primary target battery swapping strategy.

[0070] Optionally, if a user's battery swapping potential value does not meet preset conditions, for example, if it is determined that the current user's battery swapping potential value is less than a predetermined battery swapping potential threshold, then it is determined that the user has low battery swapping time flexibility and the battery swapping demand is concentrated in the period of tight supply and demand, and thus it is impossible to guide peak shifting.

[0071] Optionally, for users who need to charge during periods of high supply and demand, a corresponding guaranteed battery swapping strategy can be provided, including: battery swapping benefits, i.e., priority battery swapping during peak hours to ensure that users do not have to wait for a long time during peak demand periods; fee rules, with corresponding monthly fee payment standards, and users can enjoy the benefits after paying the monthly fee; service guarantee, with a clear service level agreement (SLA) through the service guarantee, such as providing corresponding compensation if the battery swapping waiting time during peak hours is ≤15 minutes, such as: reducing part of the monthly fee, giving battery swapping coupons, etc. Optionally, the determined battery swapping benefits, fee rules, and service guarantee can be defined as the second target battery swapping strategy.

[0072] In this way, different battery swapping strategies are determined based on the battery swapping potential value, so as to achieve accurate diversion and demand matching for the first type of users. That is, the first strategy improves the resource utilization rate of the battery swapping station during idle periods, while the second strategy ensures the operational continuity of users with rigid demand, thereby optimizing the overall battery swapping service efficiency and user experience.

[0073] In one embodiment, when the user profile information is that of a second type of user, the user profile information and the status information of the battery swapping station are input into a preset target model to determine a target battery swapping strategy based on the target model, including: Calculate the vehicle's guidance score based on user profile information; Based on the guiding score, the charging of vehicles is diverted, and the charging time and charging discounts for diverting are determined. Based on charging time and charging incentives, a third-target battery swapping strategy was determined.

[0074] Optionally, based on the user profile information of the current second type of user, the guideability score of the current vehicle is calculated, and based on the determined guideability score, it is determined whether the current vehicle can be guided to other battery swapping stations or other time points for battery swapping.

[0075] Optionally, the calculated guiding score is compared with a preset score threshold, which can generally be determined based on historical triage results, user acceptance calibration, etc.

[0076] Optionally, the guiding score can be calculated as follows: Guiding Score = 0.4 × Historical Discount Response Rate + 0.3 × Distance Attenuation Factor + 0.3 × Time Flexibility. Wherein, Historical Discount Response Rate refers to the proportion of users in the second category who actually completed battery swaps after receiving battery swap coupons in the user profile information; the Distance Attenuation Factor is calculated based on the distance between the user's current location and the current congested station and nearby available stations; the farther the user is from the current congested station and the closer they are to nearby available stations, the higher the factor value; Time Flexibility is determined by the characteristics related to battery swap time in the second category of user profile information; the higher the flexibility, the higher the score for this item.

[0077] Optionally, if the guidance score is greater than a preset threshold, it is determined that the second type of user has a high willingness and feasibility for battery swapping, and battery swapping can be implemented for them, correspondingly determining the charging time and the target battery swapping station for charging. Here, when implementing battery swapping for this vehicle, charging discounts can be configured accordingly. If the guidance score is less than or equal to the preset threshold, battery swapping will not be implemented, and the user's original battery swapping reservation or default battery swapping period will be maintained, which will be dynamically adjusted later based on changes in station utilization.

[0078] Optionally, when diverting vehicles, a third-target battery swapping strategy can be generated by combining the location information and user profile information of the second type of users. Here, the third-target battery swapping strategy may include at least one of the following: discount level, dynamically adjusted to 60-80% off based on the current congestion level and the user's historical consumption level; discount type, such as discounts corresponding to different time periods, or different fee reductions or exemptions; discount validity period; trigger time, etc.

[0079] In this way, the target model achieves precise traffic allocation based on the second type of user profile, real-time supply and demand status, and location information. While alleviating the current congestion at the battery swapping stations, it improves the battery swapping experience and coupon usage rate for the second type of users, balancing the operational efficiency of the battery swapping stations with the rights and interests of users. In summary, the battery swapping operation method provided in the above embodiments, which determines the target battery swapping strategy based on user profile information, can meet the personalized needs of users and help improve the user experience.

[0080] In one embodiment, it further includes: Determine the current availability of the battery swapping station based on its status information; Based on user data and user type and availability, vehicle charging is distributed accordingly.

[0081] Optionally, the availability of the current battery swapping station can be calculated based on the station's status information, such as the status of charging piles, batteries, and grid interaction.

[0082] Optionally, the charging resource allocation strategy can be dynamically adjusted based on user type and the availability of the battery swapping station. Optionally, based on historical operating data and constraints of the battery swapping station, a corresponding availability threshold can be set. Here, the availability threshold differs for different user types. For example, the availability threshold for the first type of user can be configured as 95%, while for the second type of user, it can be configured as 85%. Optionally, when diverting vehicles, priority is given to ensuring the needs of the first type of user, while fully accommodating the needs of the second type of user, with no restrictions on charging power allocation.

[0083] Optionally, when diverting vehicle charging, the second type of users can be guided to charge during off-peak hours based on "time-of-use pricing" (the "total electricity cost" dimension in the optimization objective), and time-of-use coupons can be issued to reduce the total electricity cost while balancing the grid load.

[0084] Optionally, charging costs can be reduced by diverting users during off-peak hours. The specific charging cost can be calculated as: Total electricity cost = α * Total electricity cost + β * User waiting time + γ * Battery health degradation + δ * Peak-to-valley difference in grid load. Here, α, β, γ, and δ are preset parameters. Optionally, by guiding the second type of user to charge during off-peak, low-price periods, the proportion of charging during high-price periods can be reduced, preventing batteries from being overcharged or stored at low charge levels for extended periods when resources are scarce, thus extending battery life and reducing grid load pressure.

[0085] In one implementation, determining the current availability of the battery swapping station based on its status information includes: The availability of the current battery swapping station is determined based on the status information of at least one charging pile in the station's status information. The status information of at least one charging station includes at least one of the following: charging power, battery state of charge, and number of available batteries.

[0086] Optionally, the charging power is the real-time output power of each charging pile in the battery swapping station, as well as the total charging power of the battery swapping station. The charging power of the battery swapping station can be collected in real time to ensure that power changes are reflected in real time.

[0087] Optionally, when detecting the availability of the current battery swapping station based on the charging power, a power constraint on the availability based on the charging power is expressed as ΣP_i(t) ≤ P_max(t). At any time t, the total charging power of all charging piles cannot exceed the maximum allowable power P_max(t) of the battery swapping station. P_max(t) may change over time, for example due to grid limitations or the contracted capacity of the battery swapping station.

[0088] Optionally, the total charging power of the battery swapping station can be subject to grid interaction constraints, specifically expressed as: P_i(t) ∈ [0, P_grid_limit(t)], meaning that the charging power of each charger i at time t must be between 0 and the grid-allowed upper limit P_grid_limit(t). Here, P_grid_limit(t) may vary over time, for example, based on grid-limited electricity consumption during peak hours.

[0089] Optionally, the battery state of charge is the real-time SOC value of all batteries in the battery swapping station (including batteries that are charging, fully charged, and waiting to be charged). Here, the detection accuracy of SOC should be less than or equal to ±2%.

[0090] Optionally, when detecting the availability of the current battery swapping station based on the battery state of charge, the battery state constraint for availability is based on the battery state of charge, expressed as SOC_min≤SOC_i(t)≤SOC_max. For each battery i, at any time t, its state of charge (SOC) must be between the minimum value SOC_min and the maximum value SOC_max. Usually, SOC_min is to prevent over-discharge of the battery, and SOC_max is to prevent over-charge. SOC_min and SOC_max are generally preset values.

[0091] Optionally, the number of available batteries should satisfy: N_available(t)≥ D_B(t)*1.2 + D_C(t)*1.1. At any time t, the number of batteries available at the battery swapping station (i.e., batteries that are fully charged and available) must be greater than or equal to 1.2 times the predicted demand of the first type of user D_B(t) plus 1.1 times the predicted demand of the second type of user D_C(t). Here, 1.2 and 1.1 are safety factors used to cope with prediction errors and sudden demand. This ensures that the battery swapping station can meet user needs in most cases and improve service reliability.

[0092] Based on the same inventive concept as the foregoing embodiments, this application proposes a specific processing method for battery swapping operation, taking the first type of user as B-end users and the second type of user as C-end users as an example. Figure 2 As shown, this can be achieved through a battery swapping system, which includes an application layer, an intelligent engine layer, a data layer, a data procurement and integration layer, and an infrastructure layer, specifically including: Optionally, for the data procurement and integration layer: A unified data API interface specification can be established to support standardized integration with transportation capacity platforms, vehicle management platforms, power grid systems, and meteorological services. Simultaneously, pre-set data cleaning and fusion algorithms address issues such as timestamp alignment, coordinate transformation, and data loss from different data sources. A unified stream and batch data processing architecture is adopted to support the unified processing of real-time data streams and batch historical data.

[0093] Optionally, data source collection includes: Collect B-end data, i.e., user data information of B-end users: Through API interface, access the vehicle management platform to collect order data (order start and end time, transportation distance, capacity location) and operation data (average daily operating time, battery swapping history); synchronize B-end user battery swapping behavior data (battery swapping time period, number of battery swaps, preferred battery type) in the battery swapping station system.

[0094] Collect C-end data, namely C-end user data information: collect basic user information (age, occupation, vehicle model) and travel scenario data (commuting route, travel frequency) through car manufacturer APP; collect C-end user battery swapping habits (frequently used battery swapping stations, preferred battery swapping time, waiting tolerance time) through battery swapping platform; obtain consumption capacity data (single battery swapping payment amount, coupon sensitivity) through third-party payment platform.

[0095] Collect auxiliary data: Collect real-time data on battery swapping station equipment (charger power, battery SOC / SOH value), power grid data (peak and valley electricity price periods, load thresholds), and environmental data (weather, holidays).

[0096] Optionally, after data collection is completed, user profile information can be determined based on the collected B-end and C-end data, such as... Figure 3 As shown: First, feature engineering and extraction are performed on the collected B-end and C-end data to obtain key features, such as the industry attributes and scale of B-end enterprises and the age and consumption habits of C-end users. Then, user cluster analysis is performed. Based on the extracted features, clustering algorithms are used to divide B-end enterprises or C-end users into different groups, such as dividing C-end users into high, medium and low consumption groups according to their consumption capacity.

[0097] Optionally, a tagging system can be automatically generated to assign representative tags to different clusters. For example, high-spending C-end users can be labeled "high-end consumption," while B-end enterprises can be labeled according to their industry, such as "manufacturing" or "internet." Differentiated profiles can be constructed for B / C-end users, creating profiles based on their different characteristics and needs. For B-end users, the focus is on business models and market influence, while for C-end users, the focus is on individual behavioral preferences and lifestyles.

[0098] Optionally, user profile quality assessment and optimization can be performed. By setting indicators such as accuracy and coverage, the quality of the user profile can be evaluated, and adjustments and optimizations can be made based on the assessment results. Finally, the optimized user profile is stored in the user profile database, thus completing the user profile information determination process and providing strong support for subsequent precision marketing, product optimization, and other tasks.

[0099] In one embodiment, user profile information and the status information of the battery swapping station are input into a preset target model to determine a target battery swapping strategy based on the target model. Optionally, an attention-based multi-source spatiotemporal graph neural network (ASTGNN) model is proposed as the target model.

[0100] Optionally, the target model input is: Input = [historical battery swapping sequence (T-7 days to T-1 days, 15-minute granularity), user profile features (proportion of various types of users, behavioral characteristics), capacity platform order prediction (order heat map for the next 24 hours), spatiotemporal features (day of the week, whether it is a holiday, weather), marketing activity information (coupon distribution plan)].

[0101] In one implementation, a multi-objective dynamic optimization model can be established to achieve real-time decision-making for charging scheduling, and pool management can be carried out based on user priority.

[0102] Optionally, the status information of at least one charging pile in the status information of the battery swapping station is detected, and the availability of the current battery swapping station is determined based on the detection results, including: Power constraint: ΣP_i(t) ≤ P_max(t), at any time t, the total charging power of all charging piles cannot exceed the maximum allowable power P_max(t) of the battery swapping station. P_max(t) may vary over time, for example due to grid limitations or the contracted capacity of the battery swapping station.

[0103] Battery state constraints: SOC_min ≤ SOC_i(t) ≤ SOC_max. For each battery i, at any time t, its state of charge (SOC) must be between the minimum and maximum values. Typically, SOC_min is to prevent over-discharge, and SOC_max is to prevent over-charging.

[0104] Demand constraint: N_available(t) ≥ D_B(t)*1.2 + D_C(t)*1.1. At any time t, the number of batteries available at the battery swapping station (i.e., fully charged and usable batteries) must be greater than or equal to 1.2 times the predicted demand of users B plus 1.1 times the predicted demand of users C. Here, 1.2 and 1.1 are safety factors used to handle prediction errors and sudden demand. Purpose: To ensure that the battery swapping station can meet user demand in most cases, improving service reliability.

[0105] B-end priority constraint: During peak hours, the B-end user battery swapping demand satisfaction rate is ≥95%. During peak hours (within the defined peak time period), the satisfaction rate of B-end users' battery swapping needs is no less than 95%. The satisfaction rate is defined as the number of B-end users actually served divided by the total demand of B-end users.

[0106] Grid interaction constraints: P_i(t) ∈ [0, P_grid_limit(t)], the charging power of each charger i at time t must be between 0 and the grid's allowed upper limit P_grid_limit(t). P_grid_limit(t) may vary over time, for example, the grid may restrict power consumption during peak hours.

[0107] Optionally, battery swapping stations may implement a battery pool management strategy and classify battery pools, including: Peak Expectation Pool (20%): Reserved specifically for B-end users, with SOC always maintained at >90%; Flexible Supply Pool (60%): Serving all users, with SOC targets dynamically adjusted based on forecasts; Emergency Reserve Pool (10%): Low-cycle frequency batteries to cope with sudden demand; Charging / Maintenance Pool (10%): Batteries in charging or maintenance status.

[0108] like Figure 4 As shown, the charging decision algorithm steps include: 1. Real-time monitoring of all battery statuses (SOC, SOH, temperature); 2. Obtain demand forecasts for the next 24 hours (B2B / B2C); 3. Obtain the grid electricity price curve and load limits; 4. Calculate the charging urgency score for each battery: Urgency = α * User type weight + β * SOC decline rate + γ * Proximity of demand period; 5. Solve the optimization model to determine the optimal configuration for each battery: Charging start time, charging power curve, target SOC 6. Send instructions to the charging pile controller for execution.

[0109] Optionally, when determining the target strategy for current vehicle charging, a dynamic marketing strategy generation algorithm driven by supply and demand states is used to realize the real-time calculation and distribution of personalized discounts, including: B-end discount period package generation algorithm: Input: Power station ID, demand forecast for the next week, B-end user profile Output: Customized package plan Steps: 1. Identify the "peak hours" and "off-peak hours" of the battery swapping station. 2. Calculate the "peak-shaving potential value" based on the historical battery swapping time of B-end users: Peak-shifting potential = Standard deviation of historical battery swapping time / Average battery swapping interval 3. Design "high-value packages" for users with high potential for off-peak consumption: Package includes: Enjoy discounts of 40-70% on battery swaps during designated off-peak hours. Restrictions: Reservations must be made 2 hours in advance. Protection measures: Package subscribers will enjoy reserved battery during this period. 4. Design a "Peak Hour Protection Package" for users with low potential for off-peak travel: Package includes: Monthly fee payment, priority battery swapping during peak hours. Service Level Agreement (SLA): Waiting time ≤ 15 minutes C-end dynamic coupon decision algorithm: Inputs: Real-time supply and demand status, user profiles, location information Output: Whether to issue coupons, type of coupon, discount level Optionally, the current B-end / C-end customers can be segmented, such as... Figure 5 As shown, its decision-making strategies include: If the estimated availability of the current site is greater than 85% in the next 2 hours, traffic diversion is deemed necessary. For C-end users within a 10km radius, a "guidance score" is calculated for each user. This score is calculated as: Score = 0.4 * Historical Discount Response Rate + 0.3 * Distance Attenuation Factor + 0.3 * Time Flexibility. If the calculated score exceeds a set threshold, a "Time-Based Traffic Diversion Coupon" is issued to the user. This coupon offers a 60-80% discount during designated off-peak hours, is valid for the next 4 hours, and is pushed to the user when they open the app or arrive at the frequently used battery swapping station.

[0110] Optionally, if the current availability of the site is less than 50%, it means that demand needs to be stimulated. In this case, "off-peak surprise coupons" will be issued. The coupon offers a random discount, ranging from 50% to 90%. The trigger condition is that a user enters the geofence area, and this coupon is limited in quantity and available on a first-come, first-served basis.

[0111] Based on the same inventive concept as the foregoing embodiments, this invention provides a battery swapping system, such as... Figure 5 As shown, the battery swapping system includes: a processor 210 and a memory 211 storing a computer program; wherein, Figure 5 The processor 210 shown in the diagram does not refer to a single processor 210, but rather to its positional relationship relative to other devices. In practical applications, there can be one or more processors 210. Figure 5 The memory 211 shown in the diagram has the same meaning, that is, it is only used to indicate the positional relationship of memory 211 relative to other devices. In practical applications, there can be one or more memories 211. When the processor 210 runs the computer program, the above-described battery swapping operation method is implemented.

[0112] The battery swapping system may also include at least one network interface 212. The various components in the battery swapping system are coupled together via a bus system 213. It is understood that the bus system 213 is used to achieve communication between these components. In addition to a data bus, the bus system 213 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 5 The general designated all buses as Bus System 213.

[0113] The memory 211 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 211 described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0114] The memory 211 in this embodiment of the invention is used to store various types of data to support the operation of the battery swapping system. Examples of this data include: any computer programs used to operate on the battery swapping system, such as operating systems and applications; contact data; phonebook data; messages; pictures; videos, etc. The operating system includes various system programs, such as a framework layer, core library layer, driver layer, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications, such as media players, browsers, etc., used to implement various application services. Here, the program implementing the method of this embodiment of the invention can be included in the application.

[0115] Based on the same inventive concept as the foregoing embodiments, this embodiment also provides a computer-readable storage medium storing a computer program. The computer-readable storage medium can be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it can also be various devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc. When the computer program stored in the computer-readable storage medium is executed by a processor, it implements the battery swapping operation method applied to the aforementioned battery swapping system. For the specific steps implemented when the computer program is executed by the processor, please refer to [reference needed]. Figure 1 The description of the illustrated embodiments will not be repeated here.

[0116] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] In this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, which includes not only the elements listed but also other elements not expressly listed.

[0118] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A battery swapping operation method, characterized in that, include: Obtain user data information; Based on the user data information, determine the user profile information; Obtain the status information of the battery swapping station; The user profile information and the status information of the battery swapping station are input into a preset target model to determine the target battery swapping strategy based on the target model.

2. The method according to claim 1, characterized in that, The acquisition of user data information includes: The first data information of the first type of users is obtained through the first port; the first type of users are vehicle users used for operation. Second data information of the second type of users is obtained through the second port; the second type of users are individual users who are not used for operation.

3. The method according to claim 2, characterized in that, When the user data information is the first data information corresponding to the first type of user, the step of determining the user profile information based on the user data information includes: Based on the order data and operational data in the first data information, the first basic characteristic information of the first type of user is determined; Based on the battery swapping behavior information and battery information in the first data information, the first behavior prediction information of the first type of user is determined. Based on the first basic feature information and the first behavior prediction information, the user profile information of the first type of user is determined.

4. The method according to claim 2, characterized in that, When the user data information is the second data information corresponding to the second type of user, the step of determining the user profile information based on the user data information includes: Based on the user information and historical travel data in the second data information, the second basic characteristic information of the second type of users is determined; Based on the battery swapping behavior information and battery information in the second data information, determine the second behavior prediction information of the second type of users; Based on the second basic feature information and the second behavior prediction information, the user profile information of the second type of user is determined.

5. The method according to claim 3, characterized in that, When the user profile information is that of a first-type user, the step of inputting the user profile information and the status information of the battery swapping station into a preset target model to determine the target battery swapping strategy based on the target model includes: Based on the status information of the battery swapping station, determine the periods of high supply and demand and the periods of low availability of the battery swapping station; Based on the user profile information and the status information of the battery swapping station, a first target battery swapping strategy is determined for charging the vehicle during the idle period, and / or a second target battery swapping strategy is determined for charging the vehicle during the period of supply and demand tension.

6. The method according to claim 5, characterized in that, The first target battery swapping strategy for determining when to charge the vehicle during the idle period includes: Based on the user profile information, calculate the battery swapping potential value of the vehicle during the idle period; When the battery swapping potential value meets preset conditions, discount information and restrictions for charging during the idle period are determined; The discount information and the restrictions are determined as the first target battery swapping strategy; The second target battery swapping strategy for determining when to charge the vehicle during periods of supply and demand tension includes: When the battery swapping potential value does not meet the preset conditions, the charging package information for the vehicle during the period of supply and demand tension is determined and identified as the second target battery swapping strategy.

7. The method according to claim 4, characterized in that, When the user profile information is that of a second type of user, the step of inputting the user profile information and the status information of the battery swapping station into a preset target model to determine the target battery swapping strategy based on the target model includes: Based on the user profile information, calculate the vehicle's guidance score; Based on the guiding score, the charging of the vehicle is diverted, and the charging time and charging discount for diverting are determined. A third target battery swapping strategy is determined based on the charging time and the charging incentives.

8. The method according to claim 1, characterized in that, Also includes: Based on the status information of the battery swapping station, determine the current availability of the battery swapping station; The charging of the vehicle is distributed according to the user type and availability rate in the user data information.

9. The method according to claim 8, characterized in that, Determining the availability of the current battery swapping station based on its status information includes: The availability of the current battery swapping station is determined based on the status information of at least one charging pile in the status information of the battery swapping station. The status information of the at least one charging pile includes at least one of the following: charging power, battery state of charge, and number of available batteries.

10. A battery swapping system, characterized in that, include: A processor and a memory for storing executable instructions; wherein the processor is configured to execute the instructions to implement the battery swapping operation method as described in any one of claims 1-9.

11. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by a processor, the battery swapping operation method as described in any one of claims 1-9 is implemented.

Images