Battery life equalization scheduling method and system for battery swap cabinet

Abstract

The application discloses a battery life equalization scheduling method and system for a battery swap cabinet, and belongs to the technical field of battery management and scheduling. The method specifically comprises the following steps: collecting relevant parameters of each battery in the battery swap cabinet as first collection data, generating a battery life state score based on the first collection data, collecting user behavior data as second collection data, generating a user portrait based on the second collection data, performing hierarchical management on the batteries, and generating a scheduling priority between the batteries and the users according to a battery life state score and user portrait matching rule, constructing a scheduling pull vector field based on a battery swap cabinet site topology atlas, an energy tidal flow diagram and a stock dynamic tensor, generating a battery buffer ring and a pre-moving pool set, and performing life equalization scheduling in combination with the scheduling priority between the batteries and the users. The application delays the aging rate of high-frequency use batteries, improves the resource utilization rate of low-frequency batteries, and is suitable for electric two-wheeled vehicle and three-wheeled vehicle battery swap networks.

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] The preset parameter fusion rule is used for weighting processing of various data labels based on the battery behavior trajectory sequence, and an intermediate data index is generated, which is used for battery state recognition.

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

[0015] Based on the battery behavior trajectory sequence and the clustering result, a dynamic life state score of each battery is generated.

[0016] Specifically, the user portrait is generated based on the second collected data, including:

[0017] The second collected data is periodically archived, and a user behavior factor is generated according to the user behavior characteristics.

[0018] According to a preset user behavior classification system, the user behavior factor is hierarchically classified to generate a multi-dimensional user label set.

[0019] According to the user label set and its historical power consumption trajectory, a user portrait mapping model is constructed to generate a user portrait.

[0020] Specifically, the battery is subjected to hierarchical management, and a scheduling priority between the battery and the user is generated according to the battery life state score and the user portrait matching rule, including:

[0021] According to the life state score of each battery, a plurality of life level intervals are divided.

[0022] Based on the user portrait, a user energy consumption matching model is established, and an adaptive weight is generated, wherein the energy consumption matching model includes a priority matching and comparison relationship between the user portrait and the battery life level.

[0023] A battery-user matching matrix is established, the life level interval and the energy consumption matching model are cross-matched, and a set of many-to-many scheduling adaptive scores are generated.

[0024] The scheduling adaptive scores are dynamically sorted to generate a scheduling priority list between the user and the battery.

[0025] Specifically, the user energy consumption matching model is established based on the user portrait, and an adaptive weight is generated, including:

[0026] The label items related to energy consumption in the user portrait are extracted.

[0027] According to the label items, a multi-axis behavior vector model is constructed, and the behavior tensor of the user in the time dimension, the space dimension, the energy consumption density dimension and the path inertia dimension is defined.

[0028] Projecting the behavior tensor to the energy consumption model space, combining with the preset battery attenuation factor matrix for analysis, the matching adaptability value of the user to different life level intervals of the battery is calculated;

[0029] All adaptability values are normalized to generate a set of adaptation weights, which are used to describe the relative matching strength between the user and the different level batteries.

[0030] Specifically, the battery-user matching matrix is established, the life level interval is cross-matched with the energy consumption matching model to generate a set of many-to-many scheduling adaptation scores, including:

[0031] Taking the battery life level as the horizontal axis and the adaptation weight generated in the user portrait as the vertical axis, a battery-user two-dimensional mapping framework is constructed;

[0032] The two-dimensional mapping framework is expanded 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 to each category of battery is recorded.

[0034] Specifically, based on the battery swap cabinet site topology map, the energy tidal flow diagram and the inventory dynamic tensor, a scheduling pull vector field is constructed to generate a battery buffer ring and a pre-moving pool set. Combined with the scheduling priority between the battery and the user, the target battery is scheduled to the tidal rising area or recycled to the tidal falling area for life balance scheduling, including:

[0035] Collecting relevant data of all battery swap cabinets within a preset period to construct a battery swap cabinet site topology map;

[0036] Based on the user behavior factors in the user portrait, an energy tidal flow diagram is constructed to identify the trend direction of battery swap demand and the structural deviation of energy recovery path;

[0037] Cross-analyzing the energy tidal flow diagram and the battery swap cabinet site topology map generates a scheduling pull vector field for calibrating the energy input and output pressure of each site in the tidal cycle;

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

[0039] Based on the battery buffer ring, the pre-moving pool set and the scheduling priority list, the target battery is scheduled to the tidal rising area or recycled to the tidal falling area for life balance scheduling of the battery in multiple battery swap cabinet sites.

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

[0041] The energy tidal flow diagram is projected into the battery swap cabinet site topology map, and the tidal state of each battery swap cabinet in the current tidal period is marked;

[0042] Based on the relevant data of the battery swap cabinet in the preset period, a dynamic inventory tensor is constructed;

[0043] The tidal state of the battery swap cabinet site and the dynamic inventory tensor are cross-operated to generate a scheduling pull vector field of each battery swap cabinet, which represents the strength and direction of the battery swap cabinet site in the current period.

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

[0045] Based on the scheduling pull vector field of the battery swap cabinet site and the first preset rule, a first battery swap cabinet site group is selected as a buffer ring candidate set;

[0046] The tidal stability factor of each battery swap cabinet site in the buffer ring candidate set is calculated, a preset steady-state threshold is set, and the battery swap cabinet sites with a tidal stability factor lower than the steady-state threshold in the buffer ring candidate set are selected as nodes of the battery buffer ring;

[0047] In the peripheral area of the battery buffer ring, based on the second preset rule, a second battery swap cabinet site group is selected as a pre-moving pool set.

[0048] The battery swap cabinet battery life balance scheduling system is used to implement the battery swap cabinet battery life balance scheduling method, including a life score unit, a user portrait generation unit, a priority generation unit, and a balance scheduling unit;

[0049] The life score unit is used to collect relevant parameters of each battery in the battery swap cabinet as first collection data, and generate a battery life state score based on the first collection data;

[0050] The user portrait generation unit is used to collect user behavior data as second collection data, and generate a user portrait based on the second collection data;

[0051] The priority generation unit is used to manage the batteries in a hierarchical manner, and generate a scheduling priority between the batteries and the users according to the battery life state score and the user portrait matching rule;

[0052] The equalization scheduling unit constructs a scheduling tension vector field based on the battery swap cabinet site topological atlas, energy tidal flow diagram and inventory dynamic tensor, generates a battery buffer ring and a pre-moving pool set, combines the scheduling priority between the battery and the user, and schedules the target battery to the tidal rising area or recycles it to the tidal falling area to perform life equalization scheduling.

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

[0054] The present application proposes a battery life equalization scheduling method and system for a battery swap cabinet, which constructs a battery health state score and user portrait behavior, and dynamically generates a cross-site battery migration path and buffer ring structure based on the battery swap tidal trend and multi-site inventory state; utilizes a tension vector field to cooperatively control the periodic flow of the battery in the urban network, and simultaneously manages the reuse of edge batteries through the battery buffer ring and the pre-moving pool; this method can significantly slow down the aging rate of high-frequency use batteries, improve the resource utilization rate of low-frequency batteries, reduce the scrapping misjudgment of life edge batteries, improve the overall operation efficiency, and can also cope with regional battery swap pressure fluctuations, uneven user behavior distribution, inventory structure imbalance and other scenarios, and is suitable for large-scale multi-site, multi-role user concurrent operation of electric two-wheeled and three-wheeled vehicle battery swap networks. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 A battery life equalization scheduling method flowchart for a battery swap cabinet is provided for the present application.

[0056] Figure 2 A scheduling priority list generation diagram is provided for the present application.

[0057] Figure 3 A life equalization scheduling schematic diagram is provided for the present application.

[0058] Figure 4 A battery life equalization scheduling system architecture diagram is provided for the present application. DETAILED DESCRIPTION

[0059] The present application will be described in detail below with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that, for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made. These all belong to the protection scope of the present application.

[0060] In order to make the purpose, technical scheme and advantages of the present application more clear and obvious, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0061] It should be noted that the various features of the embodiments of the present application can be combined with each other, and all within the protection scope of the present application, if there is no conflict. In addition, although the functional modules are divided in the device schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the order in the flowchart. In addition, the "first", "second", "third" and the like used in the present application do not limit the data and execution order, but only distinguish the same items or similar items with basically the same function and effect.

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

[0063] Embodiment 1

[0064] Please refer to Figures 1-3 An embodiment provided by the present application provides a battery life balancing scheduling method of a battery swap cabinet, comprising the following specific steps.

[0065] Step S1: Collecting relevant parameters of each battery in the battery swap cabinet as first collection data, and generating a battery life state score based on the first collection data.

[0066] Specifically, the relevant parameters of the battery include: current charging times, discharging times, remaining capacity, temperature fluctuation data, internal resistance change amplitude and charging and discharging time interval and the like.

[0067] The specific steps of step S1 are:

[0068] Step S101: Establishing a data tag for each type of data in the first collection data, and constructing a battery behavior trajectory sequence according to the parameter abnormal frequency, trend change and stability.

[0069] In the present embodiment, data tag definition is performed for each type of parameter, including battery internal resistance parameter tag, temperature parameter tag and SOC change rate tag. The parameter abnormal frequency, trend change and stability are calculated according to the prior art. After completion, the various types of parameter tags are combined in sequence according to the time axis to construct the behavior trajectory sequence of the battery. The behavior trajectory is a logical splicing of the tagged features.

[0070] Step S102: Predefining a parameter fusion rule, weighting processing various types of data tags based on the battery behavior trajectory sequence, and generating an intermediate data index, which is used for battery state recognition.

[0071] In the 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 association. After the weight configuration is completed, the behavior trajectory sequence of each battery is processed by multiple rounds of weighted mapping; each label item in the sequence is replaced by a corresponding weight value, and is normalized and expanded on the time axis. The numerical trajectory of the multi-dimensional label forms a sparse vector set in the time dimension. Then, the trajectory vector is compressed and aggregated by a certain sliding window strategy, and an intermediate data index with fixed dimension and time order preservation is constructed.

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

[0073] Step S104: generating a dynamic life status score of each battery based on the battery behavior trajectory sequence and the clustering result.

[0074] In the embodiment, in the score generation process, each clustering cluster corresponds to a specific degradation risk label. Then, the behavior trajectory sequence of the battery individual is mapped back to the feature center in the cluster to which it belongs, and the basic score is adjusted according to the deviation degree. The deviation degree is calculated based on the combined difference between the index weight trajectory and the cluster center change rate, and specifically includes trend similarity, frequency isomorphism, and feature coupling consistency. If the score of a certain battery tends to decrease in multiple periods, the change weight coefficient will be increased, so that the score has stronger responsiveness. If the performance tends to be stable, the score update rhythm will be slowed down accordingly. Finally, the dynamic life status score of each battery is generated.

[0075] Step S2: collecting user behavior data as second collection data, and generating a user portrait based on the second collection data.

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

[0077] Step S201: periodically archiving the second collection data, and generating a user behavior factor according to the user behavior characteristics.

[0078] In the embodiment, the second collection data includes but is not limited to: user's battery replacement timestamp sequence, battery replacement frequency, riding start and end geographic points, average single ride mileage, vehicle type identification, use time period distribution, path repeatability, and short-term battery replacement failure record, etc.

[0079] Specifically, a behavior record period in a sliding window unit is set, each period covers a fixed number of days or a fixed number of battery replacement behavior events, and after each period is completed, the historical behavior sequence of the user is aggregated once, and the following three layers of information are introduced when archiving: behavior density dimension, behavior pattern dimension and behavior; on the basis of period archiving, further feature extraction is performed on the archived data to generate structured user behavior factors.

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

[0081] Step S202: According to the preset user behavior classification system, the user behavior factors are classified in layers to generate a multi-dimensional user label set.

[0082] In the embodiment, a user behavior classification system is constructed, which adopts a hierarchical structure of main label-sub-label-modification factor, specifically including: a main label dimension for dividing core behavior types of users; a sub-label dimension for refining operation preferences under the main label; and a modification factor dimension for introducing labels for describing current behavior trends or temporary states; the classification process of the behavior factor is mainly based on feature mapping, for example, the battery replacement density factor is automatically classified into the high-frequency user main label category when the user's daily battery replacement frequency exceeds the preset threshold for three consecutive archiving periods and the active days exceed the regional average.

[0083] Step S203: According to the user label set and its historical power consumption trajectory, a user portrait mapping model is constructed to generate a user portrait.

[0084] In the embodiment, first, the historical power consumption trajectory of the user is structured, the trajectory data in the original form is a discrete event sequence, which is converted into a continuous vector form through feature extraction operation; then the label set and the trajectory vector are input into the user portrait mapping model, the model is constructed by combining graph embedding and weight derivation, taking the user as the node, regarding the label and the trajectory feature as the attribute, the graph embedding algorithm converts all weight structures into a label-trajectory fusion vector, and determines the position of the user in the user portrait space through clustering and coordinate mapping; finally, the user portrait is generated, the generated user portrait is a multi-dimensional structure, mainly including portrait main category, portrait sub-feature set, portrait confidence matrix and portrait scheduling parameter binding structure.

[0085] Step S3: The battery is managed in stages, and the scheduling priority between the battery and the user is generated according to the battery life state score and the user portrait matching rule.

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

[0087] Step S301: According to the life state score of each battery, several life level intervals are divided.

[0088] Step S302: Based on the user portrait, a user energy consumption matching model is established, and an adaptive weight is generated, wherein the energy consumption matching model includes a priority matching and comparison relationship between the user portrait and the battery life level.

[0089] The specific steps of step S302 are:

[0090] Step S3021: Extract the label items related to energy consumption in the user portrait.

[0091] Step S3022: According to the label items, a multi-axis behavior vector model is constructed, and the behavior tensor of the user in the time dimension, the space dimension, the energy consumption density dimension and the path inertia dimension is defined respectively.

[0092] In this embodiment, label items such as high-frequency battery replacement, single-point commuting, stable rhythm, high proportion of daytime use, high path repetition rate, etc. are extracted from the user portrait, and are mapped as a vector group in the multi-axis behavior space; in the time dimension, the user's battery replacement time distribution and peak frequency are combined to construct the electricity intensity vector in the 24-hour cycle; secondly, in the space dimension, the spatial activity tensor is constructed according to the geographical distribution of the user's historical riding path, the station switching range and the regional concentration, and the geographical dependence degree and activity radius are calibrated.

[0093] Further, in the energy consumption density dimension, the number of battery replacements per unit distance and the daily average power consumption are counted to construct an energy demand model per kilometer to reflect the use intensity; in the path inertia dimension, the path coincidence degree and the station selection preference of the user in the cycle are evaluated to form a path stickiness vector.

[0094] Step S3023: Project the behavior tensor to the energy consumption model space, and calculate the matching adaptability value of the user to different life level intervals of the battery by combining analysis with the preset battery attenuation factor matrix.

[0095] In this embodiment, the multi-axis behavior tensor constructed in step S3022 is projected to the energy consumption model space under a unified standard, which is preset as a feature domain constructed based on the battery performance attenuation process, specifically including the variation response curves of the battery in the maximum discharge capacity, peak heat capacity, internal resistance rising rate and available capacity range and other parameter dimensions in different life level intervals; in the tensor projection process, the structural displacement of the user's behavior in each dimension of energy consumption demand is calculated respectively, and the upper limit of the ability of the target battery level in the corresponding performance index dimension is compared by difference, to generate a critical value sequence representing the adaptive consumption pressure.

[0096] Further, the above difference sequence is combined with the attenuation factor matrix of the battery life grade to obtain a set of matching degrees.

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

[0098] In this embodiment, the adaptability value distribution boundary of the current user in all life grade intervals is counted, and a stable compression range is set to avoid interference of abnormal extreme values on the normalization result.

[0099] Step S303: establishing a battery-user matching matrix, cross-matching the life grade interval and the energy consumption matching model to generate a set of many-to-many scheduling adaptation scores.

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

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

[0102] In this embodiment, according to the preset battery life division standard, the battery is divided into several grade intervals according to the state score result, for example, slight attenuation, moderate attenuation, severe attenuation, etc., and each grade constitutes a classification node on the horizontal axis; at the same time, the adaptation weight vector in the user portrait is projected into the corresponding vertical axis coordinate system to represent the relative matching strength of the user to the battery.

[0103] In the construction process, each battery grade node corresponds to a set of adaptation weights, and the adaptation values from different user behavior models are mapped as coordinate points, thereby forming a dense or sparse weight distribution block in the two-dimensional plane.

[0104] Step S3032: expanding 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, first, the geographical dimension is introduced, that is, the geographical position code of the binding station of each battery grade-user adaptation point is associated; second, the time dimension is introduced to describe the effectiveness of the matching relationship in different time periods, solve the scheduling deviation caused by the behavior law and inventory fluctuation; third, the inventory tension dimension is introduced to reflect the degree of uneven distribution of the batteries of different life grades in the current station; fourth, the resource response sensitivity dimension is introduced to identify the convergence speed of the matching relationship to the scheduling intervention, and all the dimensions finally form a multi-dimensional coordinate system, and each dimension corresponds to a structured scheduling influence factor in the tensor structure, and each tensor unit represents the scheduling adaptation state of a specific matching relationship in a specific situation.

[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 the embodiment, the relevant data of each battery swap cabinet in a preset period is collected, including geographic coordinates, coverage service radius, historical battery swap frequency, battery inflow and outflow, inventory structure change, user residence time distribution and other multi-dimensional data.

[0116] Specifically, taking geographic position as an initial node set, the weight relationship of edges is constructed by calculating the shortest passing path between sites, the sharing user cross rate, the battery swap behavior co-occurrence frequency and other indicators, and then the nodes are given additional labels in combination with the role attributes of the sites (such as whether it is a main site, a high-frequency use node or a boundary buffer station), and a directed weighted graph is formed by using graph structure coding.

[0117] Step S402: based on the user behavior factors in the user portrait, an energy tidal flow graph is constructed to identify the trend direction of battery swap demand and the structural deviation of energy recovery path.

[0118] In the embodiment, in the specific implementation process, first, the commuting path inertia, battery swap frequency period, site dependence, peak and off-peak use offset and other key behavior factors in the user portrait are extracted, and they are mapped to user-site interaction heat vectors in geographic space; then, through time series superposition and behavior density normalization, an energy tidal flow graph is constructed with sites as nodes and user flow direction as the guide, in which the edge weight represents the net power migration trend per unit time, the positive edge represents demand concentrated input, and the reverse edge represents the backflow or resource idle path; it should be noted that the graph not only captures instantaneous behavior, but also reflects the time-space behavior tension generated by user structure on resource distribution, which is used to determine which sites are long-term power output highlands and which paths have excessive battery concentration or scheduling breakpoints.

[0119] Step S403: cross-analyzing the energy tidal flow graph and the battery swap cabinet site topology graph to generate a scheduling pull vector field for calibrating the energy input and output pressure of each site in the tidal period.

[0120] The specific steps of step S403 are:

[0121] Step S4031: project the energy tidal flow graph into the battery swap cabinet site topology graph, and label the tidal state of each battery swap cabinet in the current tidal period.

[0122] In the embodiment, based on the mapping rules between graphs, the energy flow information driven by user behavior is projected onto specific battery swap cabinet nodes according to geographic mapping relationship and topological connectivity, realizing the local expansion of behavior information in network structure.

[0123] Specifically, first, the path flow direction in the energy tidal chart is projected to the station set covered by the connection path based on the station topological graph as the coordinate framework, and is distributed to the corresponding station in a weighted manner according to the directionality and net flow intensity of each edge in the tidal chart; then, the net energy migration value of each station is calculated according to the difference between the total inflow and outflow received by each station in a unit tidal period, and the station is labeled according to a preset classification rule, and the specific classification includes but is not limited to: input type node, net inflow greater than threshold; output type node, net outflow significant; critical type node, input and output tend to balance but volatility is strong; low participation node, tidal amplitude is insufficient.

[0124] Step S4032: Based on the relevant data of the battery swap cabinet in the preset period, a dynamic stock tensor is constructed.

[0125] In this embodiment, the battery in-out record of each battery swap cabinet in a set time period, the number of batteries of each life grade in stock, the in-stock duration, the average available capacity, and the number of battery swap behavior triggering times are obtained, and then these indicators are expanded into a multi-dimensional data vector in each time slice according to the time dimension (such as hour or half-hour granularity), and stacked along the time axis to form a three-order or four-order tensor structure, wherein the dimensions can include: time slice sequence, battery life grade, charging state interval, behavior activity level category, etc.

[0126] Step S4033: Cross operation is performed on the tidal state of the battery swap cabinet station and the dynamic stock tensor to generate a scheduling pull vector field of each battery swap cabinet, which represents the intensity and direction of the battery swap cabinet station in the current period to absorb or release a battery of a specific life grade.

[0127] In this embodiment, according to the tidal state labeled in step S4031, the behavior tension type of each station in the current period is extracted, such as net input, net output or high volatility; then, the state label is mapped to the dynamic stock tensor of the corresponding station, and the difference calculation is performed for each life grade layer in the tensor to obtain the supply and demand tension of each battery grade; if the in-stock quantity of a certain grade is much higher than the behavior guide value, the grade is marked as releasable; if it is much lower than the use frequency predicted by the behavior, it is marked as to be absorbed; according to the judgment result, a vector is constructed, wherein the direction is used to identify the resource flow direction, absorption or release, and the modulus represents the migration intensity, that is, the supply and demand deviation size.

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

[0129] The specific steps of step S404 are:

[0130] Step S4041: Based on the scheduling tension vector field of the battery swap cabinet station and the first preset rule, a first battery swap cabinet station group is selected as a buffer ring candidate set.

[0131] In this embodiment, the vector characteristics of each station in the scheduling tension vector field are analyzed, and the resource flow direction and tension level (module length) of each station in a specific tidal cycle are extracted. The preset rule limits the selection range on one hand, such as: the module length is located in the upper quartile of the global, the vector direction is within the specified angle range to form a stable radiation relationship, and the shortest connection path between stations is less than the preset space threshold. On the other hand, the station also requires having inventory buffering capacity indicators in historical cycles, such as having a certain proportion of unswapped battery redundancy or having low-frequency high-energy level battery retention phenomena in multiple cycles.

[0132] After meeting the above constraints, a station subgraph forming a relatively closed structure is selected as a buffer ring candidate set, wherein each member station must have a positive or negative coupling relationship with at least two directionally adjacent stations to ensure the continuity of the buffer flow it participates in.

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

[0134] In this embodiment, the time slicing processing is performed on the tension vector of each station in the candidate set in a set tidal cycle, the vector direction and module length in each time period are extracted, and a time sequence vector sequence is constructed. Then, the dispersion indexes in multiple dimensions such as the direction angle change rate, the module length variance, and the trend turning frequency of the sequence are calculated to generate a composite index representing behavior volatility, i.e., the tidal stability factor. The factor essentially reflects the predictability and behavior inertia degree of the scheduling tension state of the station.

[0135] After obtaining the stability factor, a steady state threshold is set according to the strategy target. The threshold can be set hierarchically according to the historical global mean, station level, and regional load structure. If the tidal stability factor of a station is lower than the threshold, it means that the behavior change trend is stable, the tension direction is relatively fixed, and it is not easy to form a dramatic reverse migration demand, which is suitable for being a buffer ring node. Otherwise, it means that the station itself has strong periodic load peak-valley characteristics and is not suitable for bearing additional scheduling fluctuation burden. Through this selection process, a station cluster with high stability is finally extracted from the candidate set to build a structural transition channel for battery allocation circulation in a multi-station network, and to improve the balance of resource rotation rhythm and the overall redundancy adjustment capability.

[0136] Step S4043: In the peripheral area of the battery buffer ring, based on a second preset rule, a second battery swap cabinet station group is screened out as a pre-moving pool set.

[0137] In the embodiment, first, the boundary structure of the peripheral area of the buffer ring needs to be defined. The area can be expanded based on the geographical adjacency relationship, behavior correlation or extension direction of the tension vector of the buffer ring, and generally includes adjacent stations that have a direct dispatch channel with the buffer ring and whose inventory level is higher than the average value. Then, a second preset rule is applied to screen the battery swap cabinets in the area. The rule includes but is not limited to the following conditions: the accumulated amount of slightly or moderately aged batteries in a period is higher than a certain percentage; the historical battery swap frequency presents a trough trend; the matching degree of the user high adaptation degree label with the battery grade decreases; the station has a risk of inventory saturation or allocation redundancy in a short period.

[0138] Step S405: Based on the battery buffer ring, the pre-moving pool set and the dispatch priority list, the target battery is dispatched to the tide rising area or recycled to the tide falling area for life balance scheduling of the battery in multiple battery swap cabinet stations.

[0139] Embodiment 2

[0140] Please refer to Figure 4 Another embodiment provided by the application is a battery swap cabinet life balance scheduling system, which comprises a life score unit, a user portrait generation unit, a priority generation unit and a balance scheduling unit.

[0141] The life score unit is configured to collect relevant parameters of each battery in the battery swap cabinet as first collection data, and generate a battery life state score based on the first collection data.

[0142] The user portrait generation unit is configured to collect user behavior data as second collection data, and generate a user portrait based on the second collection data.

[0143] The priority generation unit is configured to manage the batteries in a hierarchical manner, and generate a dispatch priority between the batteries and the users according to a battery life state score and a user portrait matching rule.

[0144] The balance scheduling unit is configured to construct a dispatch tension vector field based on a battery swap cabinet station topological map, an energy tide flow map and an inventory dynamic tensor, generate a battery buffer ring and a pre-moving pool set, combine the dispatch priority between the batteries and the users, dispatch a target battery to a tide rising area or recycle it to a tide falling area, and perform life balance scheduling.

[0145] In addition, the parts of the above technical solutions in the embodiments of the application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive repetition.

[0146] The specific embodiments described above are further explained in connection with the purposes, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

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 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 priority between the battery and the user, the target battery is scheduled to the rising tide area or recycled to the falling tide area to achieve lifespan balancing scheduling. 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. 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. 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.

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. 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 1-6, 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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