Balanced scheduling method and system for service life of battery of battery changing cabinet

Abstract

The invention discloses a battery service life balance scheduling method and system for a battery replacement cabinet, and belongs to the technical field of battery management and scheduling, and the method specifically comprises the steps: collecting related parameters of each battery in the battery replacement cabinet as first collection data, generating a battery service life state score based on the first collection data, collecting user behavior data as second collection data, and generating a battery service life state score based on the second collection data; generating a user portrait based on the second collected data, performing hierarchical management on the battery, generating a scheduling priority between the battery and the user according to a battery life state score and user portrait matching rule, and constructing a scheduling tension vector field based on a power conversion cabinet station topological graph, an energy tidal flow graph and an inventory dynamic tensor; generating a battery buffer ring and a pre-moving pool set, and carrying out life balance scheduling by combining the scheduling priority between the battery and the user; the aging rate of the high-frequency battery is delayed, the resource utilization rate of the low-frequency battery is improved, and the method is suitable for the electric two-wheeled vehicle and three-wheeled vehicle battery replacement network.

Description

Technical Field

[0001] This invention belongs to the field of battery management and scheduling technology, specifically a method and system for balancing battery life in battery swapping cabinets. Background Technology

[0002] With the rapid advancement of urban transportation electrification, electric two-wheelers and three-wheelers have become important modes of transportation for short-distance travel, logistics delivery, and food delivery. Compared to traditional charging methods, battery swapping services based on battery swapping stations are widely used in the shared mobility and heavy-duty operation markets due to their speed, high frequency, and independence from users' own charging equipment.

[0003] Currently, mainstream battery swapping cabinet systems generally have basic functions such as battery identification, SOC detection, and charge / discharge control, enabling automatic battery replacement and status monitoring. However, existing technologies still have the following problems: Since matching is mostly based on power or sequence, different users have different usage scenarios, resulting in an imbalance in the aging rate of the entire battery group. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a method and system for balancing battery life in battery swapping cabinets.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The battery life balancing scheduling method for battery swapping cabinets includes:

[0007] Collect relevant parameters of each battery in the battery swapping cabinet as the first data to be collected, and generate a battery life status score based on the first data.

[0008] Collect user behavior data as the second set of data, and generate user profiles based on the second set of data.

[0009] Batteries are managed in a hierarchical manner, and scheduling priorities between batteries and users are generated based on battery life status scores and user profile matching rules.

[0010] Based on the topology map of battery swapping station sites, energy tidal flow map, and inventory dynamic tensor, a scheduling pull vector field is constructed to generate a battery buffer ring and a pre-movement pool set. Combining the scheduling priority between batteries and users, the target battery is scheduled to the rising tide area or recycled to the falling tide area for lifespan balancing scheduling.

[0011] Specifically, generating a battery life status score based on the first collected data includes:

[0012] Data labels are established for each type of data in the first collection of data, and battery behavior trajectory sequences are constructed based on the frequency of parameter anomalies, trend changes, and stability.

[0013] A preset parameter fusion rule is used to weight various data tags based on the battery behavior trajectory sequence to generate an intermediate data index, which is used for battery status identification.

[0014] The intermediate data index is subjected to distributed clustering processing;

[0015] Based on the battery behavior trajectory sequence and clustering results, a dynamic lifetime status score is generated for each battery.

[0016] Specifically, generating a user profile based on the second collected data includes:

[0017] The second set of collected data is periodically archived, and user behavior factors are generated based on user behavior characteristics.

[0018] Based on the pre-defined user behavior classification system, user behavior factors are hierarchically categorized to generate a multi-dimensional set of user tags;

[0019] Based on the user tag set and their historical electricity consumption trajectory, a user profile mapping model is constructed to generate user profiles.

[0020] Specifically, the step of hierarchical management of batteries and generating scheduling priorities between batteries and users based on battery life status scores and user profile matching rules includes:

[0021] Based on the lifespan status score of each battery, multiple lifespan level ranges are defined.

[0022] Based on user profiles, a user energy consumption matching model is established, and an adaptation weight is generated. The energy consumption matching model includes a priority matching relationship between user profiles and battery life levels.

[0023] Establish a battery-user matching matrix, cross-match the lifespan level range with the energy consumption matching model, and generate a set of many-to-many scheduling adaptation scores;

[0024] The scheduling adaptation scores are dynamically sorted to generate a scheduling priority list between users and batteries.

[0025] Specifically, the process of establishing a user energy consumption matching model based on user profiles and generating adaptation weights includes:

[0026] Extract energy consumption-related tags from user profiles;

[0027] Based on the aforementioned tags, a multi-axis behavior vector model is constructed, defining user behavior tensors in the time dimension, spatial dimension, energy density dimension, and path inertia dimension, respectively.

[0028] The behavioral tensor is projected onto the energy consumption model space, and combined with the preset battery degradation factor matrix for analysis to calculate the user's matching adaptability value for different battery lifespan ranges.

[0029] All fitness values ​​are normalized to generate a set of adaptation weights, which are used to describe the relative matching strength between the user and different battery levels.

[0030] Specifically, the establishment of the battery-user matching matrix involves cross-matching the lifespan level range with the energy consumption matching model to generate a set of many-to-many scheduling adaptation scores, including:

[0031] A two-dimensional mapping framework between battery and user is constructed, with battery life level as the horizontal axis and the adaptation weight generated in the user profile as the vertical axis.

[0032] The two-dimensional mapping framework is extended into a high-dimensional scheduling space to form a multi-dimensional scheduling parameter tensor.

[0033] The parameter tensor is aggregated and transformed to construct a many-to-many matching score matrix, in which the scheduling adaptation score of each user for each type of battery is recorded.

[0034] Specifically, based on the battery swapping station topology map, energy tidal flow map, and inventory dynamic tensor, a scheduling pull vector field is constructed to generate a battery buffer ring and a pre-movement pool set. Combining the scheduling priorities between batteries and users, target batteries are scheduled to the rising tide region or recycled to the falling tide region for lifetime balancing scheduling, including:

[0035] Collect relevant data from all battery swapping cabinets within a preset period and construct a topology map of battery swapping cabinet sites;

[0036] Based on user behavior factors in user profiles, an energy tidal flow map is constructed to identify the trend direction of battery swapping demand and the structural deviation of energy recovery paths.

[0037] The energy tidal flow diagram and the topology map of the battery swapping station are cross-analyzed to generate a scheduling pull vector field, which is used to calibrate the energy input and output pressure of each station during the tidal cycle.

[0038] Based on the scheduling pull vector field, a cross-site battery buffer ring and a pre-moving pool set are generated;

[0039] Based on the battery buffer ring, pre-moving pool set, and scheduling priority list, target batteries are scheduled to the rising tide area or recycled to the falling tide area to achieve lifespan balancing scheduling of batteries at multiple battery swapping stations.

[0040] Specifically, the energy tidal flow diagram is cross-analyzed with the battery swapping station topology map to generate a scheduling pull vector field, which is used to calibrate the energy input and output pressure of each station during the tidal cycle, including:

[0041] The energy tidal flow map is projected onto the topology map of the battery swapping station, and the tidal state of each battery swapping station in the current tidal cycle is marked.

[0042] Based on the relevant data of the battery swapping cabinet within a preset period, a dynamic inventory tensor is constructed.

[0043] Cross-calculation is performed on the tidal state and inventory dynamic tensor of the battery swapping station to generate a scheduling pull vector field for each battery swapping station. The scheduling pull vector field represents the intensity and direction of the battery swapping station needing to absorb or release batteries of the corresponding lifespan level in the current cycle.

[0044] Specifically, based on the scheduling pull vector field, a cross-site battery buffer ring and pre-movement pool set are generated, including:

[0045] Based on the scheduling pull vector field of the battery swapping station and the first preset rule, the first battery swapping station group is selected as the candidate set of the buffer ring.

[0046] Calculate the tidal stability factor of each battery swapping station in the candidate set of the buffer ring, preset a steady-state threshold, and select battery swapping stations in the candidate set of the buffer ring whose tidal stability factor is lower than the steady-state threshold as nodes of the battery buffer ring.

[0047] In the outer region of the battery buffer ring, the second battery swapping station group is selected as the pre-moving pool set based on the second preset rule.

[0048] A battery life balancing scheduling system for battery swapping cabinets is used to implement the battery life balancing scheduling method for battery swapping cabinets, including: a life scoring unit, a user profile generation unit, a priority generation unit, and a balancing scheduling unit.

[0049] The lifespan scoring unit is used to collect relevant parameters of each battery in the battery swapping cabinet as the first collected data, and to generate a battery lifespan status score based on the first collected data.

[0050] The user profile generation unit is used to collect user behavior data as second collected data, and generate a user profile based on the second collected data.

[0051] The priority generation unit is used to perform hierarchical management of batteries and generate scheduling priorities between batteries and users based on battery life status scores and user profile matching rules.

[0052] The balanced scheduling unit constructs a scheduling pull vector field based on the topology map of the battery swapping station, the energy tidal flow map, and the inventory dynamic tensor. It generates a battery buffer ring and a pre-movement pool set, and combines the scheduling priorities between batteries and users to schedule target batteries to the rising tide area or recover them to the falling tide area for lifetime balanced scheduling.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] This invention proposes a battery life balancing scheduling method and system for battery swapping cabinets. By constructing battery health status scores and user profile behavior, and based on battery swapping tidal trends and multi-site inventory status, it dynamically generates cross-site battery migration paths and buffer ring structures. Utilizing a pull vector field, it collaboratively controls the cyclical flow of batteries in the urban network, while managing the reuse of edge batteries through battery buffer rings and pre-movement pools. This method can significantly slow down the aging rate of high-frequency used batteries, improve the resource utilization rate of low-frequency batteries, reduce misjudgments of scrapped batteries at the end of their lifespan, and improve overall operational efficiency. It can also cope with scenarios such as regional swapping pressure fluctuations, uneven distribution of user behavior, and unbalanced inventory structure, and is suitable for large-scale multi-site, multi-role user concurrent operation of electric two-wheeled and three-wheeled vehicle battery swapping networks. Attached Figure Description

[0055] Figure 1 The flowchart of the battery life balancing scheduling method for battery swapping cabinets provided by the present invention is shown below.

[0056] Figure 2 A graph is generated for the scheduling priority list provided by this invention;

[0057] Figure 3 This is a schematic diagram of lifetime balancing scheduling provided by the present invention;

[0058] Figure 4 This is a diagram illustrating the architecture of the battery life balancing scheduling system for the battery swapping cabinet provided by the present invention. Detailed Implementation

[0059] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0061] It should be noted that, unless there is a conflict, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. In addition, the terms "first," "second," and "third" used in this application do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0062] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0063] Example 1

[0064] Please see Figures 1-3 The present invention provides an embodiment of a battery life balancing scheduling method for battery swapping cabinets, comprising the following specific steps:

[0065] Step S1: Collect relevant parameters of each battery in the battery swapping cabinet as the first data to be collected, and generate a battery life status score based on the first data.

[0066] Specifically, the relevant parameters of the battery include: current number of charging cycles, number of discharging cycles, remaining capacity, temperature fluctuation data, internal resistance change range, and charging / discharging time interval, etc.

[0067] The specific steps of step S1 are as follows:

[0068] Step S101: Establish data labels for each type of data in the first collection data, and construct a battery behavior trajectory sequence based on the abnormal frequency, trend changes and stability of parameters.

[0069] In this embodiment, data labels are defined for each type of parameter, including battery internal resistance parameter labels, temperature parameter labels, and SOC change rate labels. The abnormal frequency, trend change, and stability of the parameters are calculated according to existing technologies. After completion, the various parameter labels are combined in a sequential manner according to the time axis to construct the battery behavior trajectory sequence. The behavior trajectory is a logical concatenation of the labeled features.

[0070] Step S102: Preset parameter fusion rules, perform weighted processing on various data tags based on battery behavior trajectory sequence, and generate intermediate data index, which is used for battery status identification.

[0071] In this embodiment, the weight configuration of the parameter fusion rule includes: static weight configuration based on label importance, dynamic weight adjustment based on label activity, and cross-adjustment based on label correlation. After the weight configuration is completed, the behavioral trajectory sequence of each battery is subjected to multiple rounds of weighted mapping processing; each label item in the sequence is replaced with the corresponding weight value, and normalized and expanded on the time axis, so that the numerical trajectory of the multi-dimensional labels forms a sparse vector set in the time dimension; then, the trajectory vector is compressed and aggregated using a certain sliding window strategy to construct an intermediate data index with fixed dimensions and time order preservation.

[0072] Step S103: Perform distributed clustering processing on the intermediate data index.

[0073] Step S104: Based on the battery behavior trajectory sequence and clustering results, generate a dynamic lifetime status score for each battery.

[0074] In this embodiment, during the score generation process, the behavioral pattern corresponding to each cluster is assigned a specific degradation risk label; then, the behavioral trajectory sequence of the individual battery is remapped back to the feature center in its cluster, and the base score is adjusted according to its deviation. The deviation is calculated based on the combined difference between the index weight trajectory and the change rate of the cluster center, specifically including trend similarity, frequency isomorphism, and feature coupling consistency; if the score of a battery tends to decrease over multiple cycles, its change weight coefficient will be increased to make the score more responsive; if the performance stabilizes, the score update rhythm will be slowed down accordingly; finally, a dynamic lifetime status score for each battery is generated.

[0075] Step S2: Collect user behavior data as the second set of data, and generate a user profile based on the second set of data.

[0076] The specific steps of step S2 are as follows:

[0077] Step S201: Periodically archive the second collection data and generate user behavior factors based on user behavior characteristics.

[0078] In this embodiment, the second collected data includes, but is not limited to: the user's battery swap timestamp sequence, battery swap frequency, start and end geographical points of the ride, average one-way ride mileage, vehicle type identification, usage time distribution, route repetition, and short-term battery swap failure records, etc.

[0079] Specifically, a behavior recording cycle is set using a sliding window as the unit. Each cycle covers a fixed number of days or a fixed number of battery swapping behavior events. After each cycle is completed, the user's historical behavior sequence is aggregated once, and the following three layers of information are introduced during archiving: behavior density dimension, behavior pattern dimension, and behavior. Based on the periodic archiving, feature extraction is further performed on the archived data to generate structured user behavior factors.

[0080] It should be noted that each type of factor is extracted based on the archived historical behavior sequence, and does not directly depend on a single behavioral event.

[0081] Step S202: Based on the preset user behavior classification system, classify user behavior factors hierarchically to generate a multi-dimensional user tag set.

[0082] In this embodiment, a user behavior classification system is constructed. This system adopts a hierarchical structure of main label - sub-label - modifier factor, specifically including: main label dimension, which divides the core user behavior types; sub-label dimension, which refines the description of the operation preferences under the main label; and modifier factor dimension, which introduces labels to describe the current behavior change trend or temporary state. The classification process of behavior factors is mainly based on feature mapping. For example, the battery swapping density factor is automatically classified into the high-frequency user main label category when the user's average daily battery swapping frequency exceeds a preset threshold and the number of active days exceeds the regional average within three consecutive archiving periods.

[0083] Step S203: Based on the user tag set and its historical electricity consumption trajectory, construct a user profile mapping model and generate a user profile.

[0084] In this embodiment, the user's historical electricity consumption trajectory is first structured. The original trajectory data is a discrete event sequence, which is converted into a continuous vector form through feature extraction. Then, the tag set and trajectory vector are input into the user profile mapping model. This model is constructed using a combination of graph embedding and weight derivation. With the user as the node, the tags and trajectory features are regarded as attributes. The graph embedding algorithm converts all weight structures into a tag-trajectory fusion vector, and determines the user's position in the user profile space through clustering and coordinate mapping. Finally, a user profile is generated. The generated user profile is a multi-dimensional structure, mainly including the main profile category, profile sub-feature set, profile confidence matrix, and profile scheduling parameter binding structure.

[0085] Step S3: Implement hierarchical management of batteries and generate scheduling priorities between batteries and users based on battery life status scores and user profile matching rules.

[0086] like Figure 2 As shown, the specific steps of step S3 are as follows:

[0087] Step S301: Divide the battery into several lifespan grade ranges based on the lifespan status score of each battery.

[0088] Step S302: Based on the user profile, establish a user energy consumption matching model and generate adaptation weights. The energy consumption matching model includes a priority matching relationship between the user profile and the battery life level.

[0089] The specific steps of step S302 are as follows:

[0090] Step S3021: Extract energy consumption-related tags from the user profile.

[0091] Step S3022: Based on the label items, construct a multi-axis behavior vector model and define the user's behavior tensors in the time dimension, spatial dimension, energy density dimension and path inertia dimension respectively.

[0092] In this embodiment, tags such as high-frequency battery swapping, single-point commuting, stable rhythm, high daytime usage ratio, and high route repetition rate are extracted from user profiles and mapped into vector groups in a multi-axis behavior space. In the time dimension, the power consumption intensity vector of users in a 24-hour cycle is constructed by combining the distribution of users' battery swapping time and the frequency of peak occurrence. Secondly, in the spatial dimension, a spatial activity tensor is constructed based on the geographical distribution, station switching range, and regional concentration of users' historical cycling routes to mark their geographical dependence and activity radius.

[0093] Furthermore, in terms of energy density, a per-kilometer energy demand model is constructed by statistically analyzing the number of battery swaps and average daily power consumption per unit of driving distance to reflect usage intensity; in terms of path inertia, the degree of path overlap and station selection preference of users within a cycle is evaluated to form a path stickiness vector.

[0094] Step S3023: Project the behavior tensor onto the energy consumption model space, and calculate the user's matching adaptability value for different battery lifespan ranges by combining it with the preset battery degradation factor matrix.

[0095] In this embodiment, the multi-axis behavioral tensor constructed in step S3022 is projected onto the energy consumption model space under a unified standard. This space is preset as a feature domain constructed based on the battery performance degradation process, specifically including the change response curves of the battery in different lifespan ranges in terms of parameters such as maximum discharge capacity, peak heat capacity, internal resistance rise rate, and available capacity range. During the tensor projection process, the structural displacement of user behavior in each dimension of energy consumption demand is calculated, and the difference is compared with the upper limit of the target battery level in the corresponding performance index dimension to generate a critical value sequence representing the adaptive consumption pressure.

[0096] Furthermore, by combining the above difference sequence with the attenuation factor matrix of battery life levels, a set of results reflecting the fit and matching degree is obtained.

[0097] Step S3024: Normalize all fitness values ​​to generate a set of adaptation weights, which are used to describe the relative matching strength between the user and different battery levels.

[0098] In this embodiment, the distribution boundary of the current user's fitness value across all lifespan levels is statistically analyzed, and a stable compression range is set to avoid abnormal extreme values ​​interfering with the normalization results.

[0099] Step S303: Establish a battery-user matching matrix, cross-match the lifespan level range with the energy consumption matching model, and generate a set of many-to-many scheduling adaptation scores.

[0100] The specific steps of step S303 are as follows:

[0101] Step S3031: Construct a two-dimensional mapping framework between battery and user, with battery life level as the horizontal axis and adaptation weight as the vertical axis.

[0102] In this embodiment, based on a preset battery life classification standard, the battery is divided into several level ranges according to the state score results, such as mild degradation, moderate degradation, and severe degradation. Each level constitutes a classification node on the horizontal axis. At the same time, the adaptation weight vector in the user profile is projected onto the corresponding vertical axis coordinate system to represent the relative matching strength of the user for this type of battery.

[0103] During the construction process, each battery level node corresponds to an adaptation weight set. Adaptation values ​​from different user behavior models are mapped to coordinate points, thus forming a dense or sparse weight distribution block in a two-dimensional plane.

[0104] Step S3032: Extend the two-dimensional mapping framework into a high-dimensional scheduling space to form a multi-dimensional scheduling parameter tensor.

[0105] In this embodiment, when constructing the high-dimensional scheduling space, a geographical dimension is first introduced, which is the geographical location code of the site to which each battery level-user adaptation point is associated. Secondly, a time dimension is introduced to describe the effectiveness of the matching relationship in different time periods and to solve the scheduling offset caused by behavioral patterns and inventory fluctuations. Thirdly, an inventory tension dimension is introduced to reflect the degree of uneven distribution of batteries of different lifespan levels in the current site. A resource response sensitivity dimension is introduced to identify the convergence speed of the matching relationship to scheduling intervention. All dimensions ultimately form a multi-dimensional coordinate system. In the tensor structure, each dimension corresponds to a structured scheduling influence factor, and each tensor unit represents the scheduling adaptation state of a specific matching relationship in a specific context.

[0106] Step S3033: Perform aggregation transformation on the parameter tensor to construct a many-to-many matching score matrix, in which the scheduling adaptation score of each user for each type of battery is recorded.

[0107] In this embodiment, the non-behavioral dimensions in the parameter tensor are compressed and mapped; the behavioral dimensions are fused and weighted with the above-mentioned compression indicators to obtain the user's scheduling adaptation score under a specific battery life level; this score matrix is ​​used to express the scheduling adaptation score of each user for each battery life level category under different context conditions, thereby supporting resource matching and priority ranking in complex situations.

[0108] Step S304: Dynamically sort the scheduling adaptation scores to generate a scheduling priority list between users and batteries.

[0109] In this embodiment, the scoring matrix is ​​first segmented according to the user dimension, and a subset of battery life ratings for each user is extracted. This subset is then adjusted by the weight redistribution module and enters the sorting logic unit. The sorting rules are composed of the rating value, fluctuation coefficient, and scheduling interference factor. For example, if a user's ratings are close between two battery levels, but there is a record of insufficient power due to excessive aging in the previous period, the scheduling mechanism will automatically reduce the priority weight of that type of battery. At the same time, if the target site currently has a shortage of that type of battery, even if the rating is high, the allocation may be delayed. It should be noted that the sorting result is a list of multiple permutations and includes a dynamic confidence label to mark the sorting stability and the reliability of the current allocation.

[0110] The final output scheduling priority list not only indicates the matching relationship of the user's optimal battery, but also reflects the adaptation of scheduling decisions to changes in time, space and resource status.

[0111] Figure 2 In this context, 'n' represents the number of users or the number of batteries, and it is a one-to-one relationship.

[0112] Step S4: Based on the topology map of the battery swapping station, the energy tidal flow map, and the inventory dynamic tensor, construct the scheduling pull vector field, generate the battery buffer ring and the pre-movement pool set, and combine the scheduling priority between the battery and the user to schedule the target battery to the rising tide area or recycle it to the falling tide area for life balance scheduling.

[0113] like Figure 3 As shown, the specific steps of step S4 are as follows:

[0114] Step S401: Collect relevant data from all battery swapping cabinets within a preset period and construct a topology map of the battery swapping cabinet sites.

[0115] In this embodiment, relevant data of each battery swapping station is collected within a preset period, including geographical coordinates, coverage service radius, historical battery swapping frequency, battery inflow and outflow flow, inventory structure changes, user dwell time distribution and other multi-dimensional data.

[0116] Specifically, using geographical location as the initial node set, the weight relationship of edges is constructed by calculating indicators such as the shortest travel path between stations, the cross rate of shared users, and the co-occurrence frequency of battery swapping behavior. Then, the nodes are given additional labels by combining the role attributes of the stations (such as whether they are backbone stations, high-frequency usage nodes, or boundary buffer stations), and a directed weighted graph is formed by using graph structure encoding.

[0117] Step S402: Based on user behavior factors in the user profile, construct an energy tidal flow map to identify the trend direction of battery swapping demand and the structural deviation of energy recovery path.

[0118] In this embodiment, during the specific implementation process, key behavioral factors such as commuting path inertia, battery swapping frequency cycle, site dependence, and peak / off-peak usage offset are first extracted from the user profile and mapped into a user-site interaction heat vector in geospatial space. Subsequently, through temporal overlay and behavioral density normalization, an energy tidal flow map is constructed with sites as nodes and user flow direction as the guide. In this map, edge weights represent the net power migration trend per unit time, positive edges represent concentrated demand input, and negative edges represent return flow or resource idle paths. It should be noted that this map not only captures instantaneous behavior but also reflects the temporal-spatial behavioral tension of user structure on resource distribution, which is used to determine which sites are in a long-term high power output position and which paths have excessive battery concentration or scheduling breakpoints.

[0119] Step S403: Perform cross-analysis of the energy tidal flow diagram and the topology map of the battery swapping station to generate a scheduling pull vector field, which is used to calibrate the energy input and output pressure of each station during the tidal cycle.

[0120] The specific steps of step S403 are as follows:

[0121] Step S4031: Project the energy tidal flow map onto the topology map of the battery swapping station and mark the tidal state of each battery swapping station in the current tidal cycle.

[0122] In this embodiment, based on the graph mapping rules, the energy flow information driven by user behavior is projected onto specific battery swapping cabinet nodes according to geographical mapping relationships and topological connectivity, thereby realizing the local expansion of behavioral information within the network structure.

[0123] Specifically, firstly, using the site topology map as the basic coordinate framework, the path flow direction in the energy tidal map is projected onto the set of sites covered by the connecting path. Then, based on the directionality and net flow intensity of each edge in the tidal map, the flow is weighted and allocated to the corresponding sites. Subsequently, based on the difference between the total inflow and outflow received by each site within a unit tidal cycle, its net energy migration value is calculated. The sites are then labeled with tidal status according to preset classification rules. Specific classifications include, but are not limited to: input nodes, with net inflow greater than a threshold; output nodes, with significant net outflow; critical nodes, with inflow and outflow tending to be balanced but with strong fluctuations; and low participation nodes, with insufficient tidal amplitude.

[0124] Step S4032: Construct a dynamic inventory tensor based on the relevant data of the battery swapping cabinet within a preset period.

[0125] In this embodiment, each battery swapping cabinet is used as the basic unit to obtain data such as battery entry and exit records, the number of batteries in the warehouse for each lifespan level, the warehouse storage time, the average available power, and the number of times battery swapping behavior is triggered within a set time period. Then, the data is sliced ​​according to the time dimension (such as hourly or half-hourly granularity). These indicators are expanded into multi-dimensional data vectors in each time slice and stacked along the time axis to form a third-order or fourth-order tensor structure. The dimensions may include: time slice sequence, battery lifespan level, charging state interval, behavior activity category, etc.

[0126] Step S4033: Perform cross-calculation on the tidal state and inventory dynamic tensor of the battery swapping station to generate a scheduling pull vector field for each battery swapping station. The scheduling pull vector field represents the intensity and direction of the battery swapping station needing to absorb or release batteries of a specific lifespan level in the current cycle.

[0127] In this embodiment, based on the tidal state marked in step S4031, the behavioral tension type of each station in the current cycle is extracted, such as net input, net output, or high volatility. Subsequently, the state mark is mapped to the inventory dynamic tensor of the corresponding station, and differential calculation is performed on each lifetime level layer in the tensor to obtain the supply and demand tension of each battery level. If the inventory quantity of a certain level is much higher than the behavioral guidance value, the level is marked as releaseable; if it is much lower than the usage frequency predicted by the behavior, it is marked as needing to be absorbed. A vector is constructed based on the judgment result, where the direction is used to identify the resource flow direction, absorption or release, and the magnitude represents the migration intensity, that is, the size of the supply and demand offset.

[0128] Step S404: Based on the scheduling pull vector field, generate a cross-site battery buffer ring and a pre-moving pool set.

[0129] The specific steps of step S404 are as follows:

[0130] Step S4041: Based on the battery swapping station scheduling pull vector field and the first preset rule, select the first battery swapping station group as the candidate set of the buffer ring.

[0131] In this embodiment, the vector characteristics of each station in the scheduling pull vector field are analyzed to extract its resource flow directionality (direction) and tension level (magnitude) within a specific tidal cycle. The preset rules limit the screening range on the one hand, such as: the magnitude is located in the global upper quartile, the vector direction forms a stable radiation relationship within a specified angle range, and the shortest connection path between stations is less than a preset spatial threshold. On the other hand, it also requires that the station has inventory buffer capacity indicators in historical cycles, such as a certain proportion of unreplaced battery redundancy or low-frequency high-energy-level battery retention phenomenon in multiple cycles.

[0132] After satisfying the above constraints, the sub-graphs of stations that form a relatively closed structure are selected as the candidate set of buffer rings. Each member station must have a positive or negative tensile coupling relationship with at least two adjacent stations in two directions to ensure the continuity of the buffer flow in which it participates.

[0133] Step S4042: Calculate the tidal stability factor of each battery swapping station in the candidate set of the buffer ring, preset a steady-state threshold, and select battery swapping stations in the candidate set of the buffer ring whose tidal stability factor is lower than the steady-state threshold as nodes of the battery buffer ring.

[0134] In this embodiment, the tension vector of each station in the candidate set within a set tidal cycle is processed by time slicing, and its vector direction and magnitude in each time period are extracted to construct a time-series vector sequence. Subsequently, the dispersion index of the sequence in multiple dimensions such as the rate of change of direction angle, the variance of magnitude, and the frequency of trend reversal is calculated to generate a composite index characterizing the volatility of behavior, namely the tidal stability factor. This factor essentially reflects the predictability and behavioral inertia of the station's scheduling tension state.

[0135] After obtaining the stability factor, a steady-state threshold is set according to the strategy objective. This threshold can be set in stages based on historical global average, site level, regional load structure, etc. If the tidal stability factor of a site is lower than the threshold, it indicates that its behavior changes smoothly, the pull direction is relatively fixed, and it is not easy to form a drastic reverse migration demand, making it suitable as a buffer point. Conversely, it indicates that the site itself has strong periodic load peak and valley characteristics and is not suitable to bear additional scheduling fluctuation burden. Through this screening process, a cluster of sites with high stability is finally extracted from the candidate set, which builds a structural transition channel for the allocation cycle of batteries in the multi-site network, improves the balance of resource rotation rhythm and the overall redundancy adjustment capability.

[0136] Step S4043: In the outer area of ​​the battery buffer ring, based on the second preset rule, select the second battery swapping station group as the pre-moving pool set.

[0137] In this embodiment, the boundary structure of the outer region of the buffer ring needs to be defined first. This region can be expanded based on the geographical adjacency, behavioral correlation, or pull vector expansion direction of the buffer ring. It typically includes adjacent sites that have a direct scheduling channel with the buffer ring and whose inventory level is higher than the average. Then, a second preset rule is applied to screen the battery swapping cabinets in this region. This rule includes, but is not limited to, the following conditions: the cumulative amount of mildly or moderately aged batteries within the cycle is higher than a certain percentile; the historical battery swapping frequency shows a trough trend; the user's high compatibility label has a decreased match with the battery grade; and there is a risk of inventory saturation or allocation redundancy in the short term at the site.

[0138] Step S405: Based on the battery buffer ring, the pre-moving pool set, and the scheduling priority list, schedule the target battery to the rising tide area or reclaim it to the falling tide area, and perform lifespan balancing scheduling of the battery at multiple battery swapping stations.

[0139] Example 2

[0140] Please see Figure 4 Another embodiment of the present invention provides a battery life balancing scheduling system for battery swapping cabinets, comprising: a life scoring unit, a user profile generation unit, a priority generation unit, and a balancing scheduling unit;

[0141] The lifespan scoring unit is used to collect relevant parameters of each battery in the battery swapping cabinet as the first collected data, and to generate a battery lifespan status score based on the first collected data.

[0142] The user profile generation unit is used to collect user behavior data as second collected data, and generate a user profile based on the second collected data.

[0143] The priority generation unit is used to perform hierarchical management of batteries and generate scheduling priorities between batteries and users based on battery life status scores and user profile matching rules.

[0144] The balanced scheduling unit constructs a scheduling pull vector field based on the topology map of the battery swapping station, the energy tidal flow map, and the inventory dynamic tensor. It generates a battery buffer ring and a pre-movement pool set, and combines the scheduling priorities between batteries and users to schedule target batteries to the rising tide area or recover them to the falling tide area for lifetime balanced scheduling.

[0145] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0146] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A battery life balancing scheduling method for battery swapping cabinets, characterized in that, include: Collect relevant parameters of each battery in the battery swapping cabinet as the first data to be collected, and generate a battery life status score based on the first data. Collect user behavior data as the second set of data, and generate user profiles based on the second set of data. Batteries are managed in a hierarchical manner, and scheduling priorities between batteries and users are generated based on battery life status scores and user profile matching rules. Based on the topology map of battery swapping station sites, energy tidal flow map, and inventory dynamic tensor, a scheduling pull vector field is constructed to generate a battery buffer ring and a pre-movement pool set. Combining the scheduling priority between batteries and users, the target battery is scheduled to the rising tide area or recycled to the falling tide area for lifespan balancing scheduling.

2. The battery life balancing scheduling method for battery swapping cabinets as described in claim 1, characterized in that, The process of generating a battery life status score based on the first collected data includes: Data labels are established for each type of data in the first collection of data, and battery behavior trajectory sequences are constructed based on the frequency of parameter anomalies, trend changes, and stability. A preset parameter fusion rule is used to weight various data tags based on the battery behavior trajectory sequence to generate an intermediate data index, which is used for battery status identification. The intermediate data index is subjected to distributed clustering processing; Based on the battery behavior trajectory sequence and clustering results, a dynamic lifetime status score is generated for each battery.

3. The battery life balancing scheduling method for battery swapping cabinets as described in claim 2, characterized in that, The process of generating a user profile based on the second collected data includes: The second set of collected data is periodically archived, and user behavior factors are generated based on user behavior characteristics. Based on the pre-defined user behavior classification system, user behavior factors are hierarchically categorized to generate a multi-dimensional set of user tags; Based on the user tag set and their historical electricity consumption trajectory, a user profile mapping model is constructed to generate user profiles.

4. The battery life balancing scheduling method for battery swapping cabinets as described in claim 3, characterized in that, The process of hierarchical battery management, and generating scheduling priorities between batteries and users based on battery life status scores and user profile matching rules, includes: Based on the lifespan status score of each battery, multiple lifespan level ranges are defined. Based on user profiles, a user energy consumption matching model is established, and an adaptation weight is generated. The energy consumption matching model includes a priority matching relationship between user profiles and battery life levels. Establish a battery-user matching matrix, cross-match the lifespan level range with the energy consumption matching model, and generate a set of many-to-many scheduling adaptation scores; The scheduling adaptation scores are dynamically sorted to generate a scheduling priority list between users and batteries.

5. The battery life balancing scheduling method for battery swapping cabinets as described in claim 4, characterized in that, The process of establishing a user energy consumption matching model based on user profiles and generating adaptation weights includes: Extract energy consumption-related tags from user profiles; Based on the aforementioned tags, a multi-axis behavior vector model is constructed, defining user behavior tensors in the time dimension, spatial dimension, energy density dimension, and path inertia dimension, respectively. The behavioral tensor is projected onto the energy consumption model space, and combined with the preset battery degradation factor matrix for analysis to calculate the user's matching adaptability value for different battery lifespan ranges. All fitness values ​​are normalized to generate a set of adaptation weights, which are used to describe the relative matching strength between the user and different battery levels.

6. The battery life balancing scheduling method for battery swapping cabinets as described in claim 5, characterized in that, The establishment of the battery-user matching matrix involves cross-matching the lifespan level range with the energy consumption matching model to generate a set of many-to-many scheduling adaptation scores, including: A two-dimensional mapping framework between battery and user is constructed, with battery life level as the horizontal axis and the adaptation weight generated in the user profile as the vertical axis. The two-dimensional mapping framework is extended into a high-dimensional scheduling space to form a multi-dimensional scheduling parameter tensor. The parameter tensor is aggregated and transformed to construct a many-to-many matching score matrix, in which the scheduling adaptation score of each user for each type of battery is recorded.

7. The battery life balancing scheduling method for battery swapping cabinets as described in claim 6, characterized in that, Based on the topology map of the battery swapping station, the energy tidal flow map, and the inventory dynamic tensor, a scheduling pull vector field is constructed to generate a battery buffer ring and a pre-movement pool set. Combining the scheduling priorities between batteries and users, target batteries are scheduled to the rising tide region or recycled to the falling tide region for lifetime balancing scheduling, including: Collect relevant data from all battery swapping cabinets within a preset period and construct a topology map of battery swapping cabinet sites; Based on user behavior factors in user profiles, an energy tidal flow map is constructed to identify the trend direction of battery swapping demand and the structural deviation of energy recovery paths. The energy tidal flow diagram and the topology map of the battery swapping station are cross-analyzed to generate a scheduling pull vector field, which is used to calibrate the energy input and output pressure of each station during the tidal cycle. Based on the scheduling pull vector field, a cross-site battery buffer ring and a pre-moving pool set are generated; Based on the battery buffer ring, pre-moving pool set, and scheduling priority list, target batteries are scheduled to the rising tide area or recycled to the falling tide area to achieve lifespan balancing scheduling of batteries at multiple battery swapping stations.

8. The battery life balancing scheduling method for battery swapping cabinets as described in claim 7, characterized in that, The energy tidal flow diagram is cross-analyzed with the battery swapping station topology map to generate a scheduling pull vector field, which is used to calibrate the energy input and output pressure of each station during the tidal cycle, including: The energy tidal flow map is projected onto the topology map of the battery swapping station, and the tidal state of each battery swapping station in the current tidal cycle is marked. Based on the relevant data of the battery swapping cabinet within a preset period, a dynamic inventory tensor is constructed. Cross-calculation is performed on the tidal state and inventory dynamic tensor of the battery swapping station to generate a scheduling pull vector field for each battery swapping station. The scheduling pull vector field represents the intensity and direction of the battery swapping station needing to absorb or release batteries of the corresponding lifespan level in the current cycle.

9. The battery life balancing scheduling method for battery swapping cabinets as described in claim 8, characterized in that, Based on the aforementioned scheduling pull vector field, a cross-site battery buffer loop and pre-movement pool set are generated, including: Based on the scheduling pull vector field of the battery swapping station and the first preset rule, the first battery swapping station group is selected as the candidate set of the buffer ring. Calculate the tidal stability factor of each battery swapping station in the candidate set of the buffer ring, preset a steady-state threshold, and select battery swapping stations in the candidate set of the buffer ring whose tidal stability factor is lower than the steady-state threshold as nodes of the battery buffer ring. In the outer region of the battery buffer ring, the second battery swapping station group is selected as the pre-moving pool set based on the second preset rule.

10. A battery life balancing scheduling system for battery swapping cabinets, used to implement the battery life balancing scheduling method for battery swapping cabinets as described in any one of claims 1-9, characterized in that, include: Lifespan scoring unit, user profile generation unit, priority generation unit, and load balancing unit; The lifespan scoring unit is used to collect relevant parameters of each battery in the battery swapping cabinet as the first collected data, and to generate a battery lifespan status score based on the first collected data. The user profile generation unit is used to collect user behavior data as second collected data, and generate a user profile based on the second collected data. The priority generation unit is used to perform hierarchical management of batteries and generate scheduling priorities between batteries and users based on battery life status scores and user profile matching rules. The balanced scheduling unit constructs a scheduling pull vector field based on the topology map of the battery swapping station, the energy tidal flow map, and the inventory dynamic tensor. It generates a battery buffer ring and a pre-movement pool set, and combines the scheduling priorities between batteries and users to schedule target batteries to the rising tide area or recover them to the falling tide area for lifetime balanced scheduling.

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