Battery swapping cabinet charging control method and system based on user power consumption data analysis

Abstract

The application discloses a battery replacement cabinet charging control method and system based on user power consumption data analysis, relates to the technical field of battery replacement cabinet management and control, collects historical charging records of each cabinet body, constructs a historical single-cabinet battery charging expression model group to reflect battery charging states and replacement modes, analyzes battery replacement records to determine replacement frequencies in different time sections, and through equivalent classification and average calculation, extracts representative charging modes, adjusts and optimizes charging strategies to obtain an optimal reference single-cabinet battery charging expression model group, collects charging and replacement parameters of the power consumption cabinet in real time, matches the optimal model group, and realizes real-time charging regulation and control, the application deeply mines historical data, combines real-time information, dynamically adjusts charging strategies, effectively improves battery use efficiency, prolongs battery life, reduces operation and maintenance costs, and provides strong support for intelligent management of large-scale power consumption cabinets.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery swap cabinet management and control, and particularly to a battery swap cabinet charging control method and system based on user power consumption data analysis. BACKGROUND

[0002] With the popularization of electric vehicles and the development of energy internet technology, user-side power consumption data is growing exponentially. These data not only reflect the user's power consumption behavior, but also contain rich commercial value. However, traditional power data value mining mainly focuses on the internal power grid and the power supply end, and the vast and complex user-side power consumption data has not been fully utilized. In particular, in the field of electric vehicle battery swapping, traditional battery swap cabinet charging control methods are often based on fixed charging strategies and simple power detection, and cannot dynamically adjust according to the actual power consumption needs and battery status of users, resulting in low charging efficiency, poor user experience, and greater battery damage. SUMMARY

[0003] The purpose of the present application is to provide a charging control method and system that can optimize the charging strategy of the charging cabinet.

[0004] The present application discloses a battery swap cabinet charging control method based on user power consumption data analysis, comprising:

[0005] Obtain the historical single-cabinet battery charging records in each cabinet body of the power consumption cabinet, and record the combination of the historical single-cabinet battery charging records as a historical single-cabinet charging record group;

[0006] Construct a reference time cycle line, and based on each historical single-track charging record in the historical single-track charging record group, configure battery power, charging power, and battery replacement record identifier on different time nodes on the reference time cycle line to obtain a historical single-cabinet battery charging expression model, and record the combination of the historical single-cabinet battery charging expression model as a historical single-cabinet battery charging expression model group, wherein the historical single-cabinet battery charging expression model group is used to express the historical charging state of the entire power consumption cabinet;

[0007] Analyze the battery replacement record identifier in the historical single-cabinet battery charging expression model group to determine the number of identifiers corresponding to the battery replacement record identifier in different time segments, and record the combination of the number of identifiers corresponding to different time segments each day as an identifier number group;

[0008] Classify a plurality of identifier number groups equally to obtain a plurality of identifier number category sets, and perform an averaging calculation on the identifier number groups in the identifier number category sets to obtain an average identifier number group;

[0009] Based on the average number of identification groups, the historical single cabinet battery charging expression model group is adjusted to obtain an initial reference single cabinet battery charging expression model group, and the initial reference single cabinet battery charging expression model group is adjusted for several times to obtain several adjusted reference single cabinet battery charging expression model groups, the comprehensive application effect of each adjusted reference single cabinet battery charging expression model group is evaluated, and based on the evaluation result, the optimal reference single cabinet battery charging expression model group is selected from the adjusted reference single cabinet battery charging expression model group;

[0010] Real-time acquisition of a plurality of real-time single cabinet battery charging parameters and real-time battery replacement parameters of the power cabinet, and based on the real-time single cabinet battery charging parameters and the real-time battery replacement parameters, the most optimal reference single cabinet battery charging expression model group is selected, and the power cabinet is regulated and controlled by referring to the optimal reference single cabinet battery charging expression model group.

[0011] In some embodiments disclosed in the application, the method for determining the number of battery replacement record identification corresponding to different time segments comprises:

[0012] A battery replacement record identification mapping diagram is established, and the horizontal axis of the battery replacement record identification mapping diagram is the time axis;

[0013] The battery replacement record identification in the historical single cabinet battery charging expression model group is analyzed to determine the battery replacement record identification appearing at different time nodes, and the replacement record identification mapping points are mapped on the battery replacement record identification mapping diagram in a replacement record identification mapping point manner;

[0014] The aggregation characteristics of the record identification mapping points in the battery replacement record identification mapping diagram are analyzed, and based on the aggregation characteristics, the time axis is divided into segments to obtain a plurality of time segments, and the number of replacement record identification mapping points in the time segments is counted.

[0015] In some embodiments disclosed in the application, the method for dividing the time axis into segments based on the aggregation characteristics comprises:

[0016] The time axis is averaged and split to obtain a plurality of unit time segments, and the number of replacement record identification mapping points corresponding to each unit time segment on the battery replacement record identification mapping diagram is analyzed;

[0017] The number of replacement record identification mapping points axis is set in the vertical axis direction of the battery replacement record identification mapping diagram, the middle point of the unit time segment is taken as the horizontal axis positioning point, the vertical axis mapping points of the number of replacement record identification mapping points are marked in the vertical axis direction, and the vertical axis mapping points are smoothly connected to obtain the number of replacement record identification mapping points curve;

[0018] The number of replacement record identification mapping points is analyzed, and a curve section that meets preset standards in an upward trend and a downward trend is determined, which is recorded as a curve section of interest, and the number of unit time sections corresponding to the curve section of interest is determined based on the intersection area between the curve section of interest and the horizontal axis.

[0019] The expression for determining the number of unit time sections corresponding to the curve section of interest is:

[0020]

[0021] wherein D is the number of unit time sections, R is a combination number conversion adjustment coefficient, d is a preset standard combination number, S is the intersection area between the curve section of interest and the horizontal axis, S 预设 is a preset intersection area, L is an intersection area influence adjustment coefficient, and b is an intersection area influence adjustment constant.

[0022] In some embodiments of the present disclosure, the method of segmenting the time axis further comprises:

[0023] The non-interest curve section on the replacement record identification mapping point number curve is determined, and the unit time sections in the non-interest curve section are sequentially combined based on the preset standard combination number.

[0024] In some embodiments of the present disclosure, the method of equally classifying a plurality of identification number groups comprises:

[0025] The identification number groups are analyzed to determine the corresponding time section division length and sorting method, form a time section sequence, and take each time section interval in the time section sequence to which each time section belongs as a first classification condition to perform a first equal classification on the plurality of identification number groups.

[0026] The identification number groups of the first equal classification are secondly equally classified based on the identification number interval to which the identification number corresponding to each time section in the identification number groups belongs.

[0027] In some embodiments of the present disclosure, the method of adjusting the historical single cabinet battery charging expression model group comprises:

[0028] The average identification number group is configured on the reference time cycle line corresponding to the historical single cabinet battery charging expression model group based on the average identification number group, and the battery power change curve of each time section is determined, and the sudden drop point of the battery power change curve and the sudden drop point battery power corresponding to the sudden drop point are determined.

[0029] determining whether the difference between the average number of identifications of each time section and the cliff point is within a preset difference range, if it is determined that the difference is within the preset difference range, determining whether the battery capacity of each cliff point is greater than or equal to a preset value, if not, increasing the charging power of a first preset number of cabinets, and increasing the charging power at time nodes of a preset time length in the future, if the difference between the average number of identifications of each time section and the cliff point is not within the preset difference range, determining whether the average number of identifications is greater than the cliff point or less than the cliff point, if it is determined that the average number of identifications is greater than the cliff point, increasing the charging power of a second preset number of cabinets, and increasing the charging power at time nodes of a preset time length in the future, if it is determined that the average number of identifications is less than the cliff point, decreasing the charging power of a third preset number of cabinets, and decreasing the charging power at time nodes of a preset time length in the future.

[0030] After the power adjustment of the historical single-cabinet battery charging expression model group, the average number of identifications in the average number of identification group is used as the battery replacement demand, the historical cabinet charging expression model group is configured, the adjusted cliff point of different reference time cycles and the battery capacity of the adjusted cliff point are determined, and the adjusted reference single-cabinet battery charging expression model group is obtained.

[0031] In the embodiments disclosed in the present application, before the charging power of the cabinet is increased or decreased, the determination of the cabinet for which the charging power is adjusted is performed, and the method for determining the charging power adjustment includes:

[0032] The method for determining the cabinet for which the charging power is increased includes:

[0033] The time node corresponding to the cliff point of each time section is determined, and the average value of the time node is analyzed, which is recorded as an average cliff time point, the battery capacity corresponding to the average cliff time point of each cabinet is determined, and if the battery capacity is in a first preset interval, the corresponding cabinet is determined as a cabinet for which the charging power is increased.

[0034] The method for determining the cabinet for which the charging power is decreased includes:

[0035] The time node corresponding to the cliff point of each time section is determined, and the average value of the time node is analyzed, which is recorded as an average cliff time point, the battery capacity corresponding to the average cliff time point of each cabinet is determined, and if the battery capacity is in a second preset interval, the corresponding cabinet is determined as a cabinet for which the charging power is increased.

[0036] In the embodiments disclosed in the present application, the method for comprehensively applying and evaluating the effect of the adjusted reference single-cabinet battery charging expression model group includes:

[0037] The reference time cycle line corresponding to each adjusted reference single-cabinet battery charging expression model in the adjusted reference single-cabinet battery charging expression model group is analyzed to determine the adjusted sudden drop point of the battery power change curve in different time sections and the adjusted sudden drop point battery power;

[0038] The number of sudden drop points and the average number of identifiers of the adjusted sudden drop points corresponding to different time sections are compared to obtain a first effect comparison result, and the average sudden drop point battery power and the standard battery power corresponding to different time sections are compared to obtain a second effect comparison result, and a comprehensive application effect evaluation is determined based on the first effect comparison result and the second effect comparison result.

[0039] The expression for calculating the comprehensive application effect evaluation is:

[0040]

[0041] Wherein, G is the comprehensive application effect evaluation, δ(Δx i ∈x yushe ) is the first effect comparison function corresponding to the i th time section, Δx i is the first difference quantity of the number of sudden drop points and the average number of identifiers of the i th time section, x yushe is a preset first difference quantity interval, if the first difference quantity is within the first difference quantity interval, then δ(Δx i ∈x yushe outputs 1, otherwise 0, β(Δh i ∈h yushe ) is the second effect comparison function corresponding to the i th time section, Δh i is the second difference quantity of the average sudden drop point battery power and the standard battery power of the i th time section, h yushe is a preset second difference quantity interval, if the second difference quantity is within the second difference quantity interval, then β(Δh i ∈h yushe outputs 1, otherwise 0, K i is the weight coefficient corresponding to the i th time section, n is the total number of time sections, g man is the charging amount of low-power charging, g zong is the total charging amount, J is a charging feature influence adjustment coefficient, C is a charging feature influence adjustment constant, and F is a charging power effect conversion coefficient.

[0042] In some embodiments disclosed in the present application, the method for selecting the most optimal reference single-cabinet battery charging expression model that best fits includes:

[0043] The real-time single-cabinet battery charging parameters and the real-time battery replacement parameters are substituted into different optimal reference single-cabinet battery charging expression model groups, and the first difference value of the single-cabinet charging parameters and the second difference value of the battery replacement parameters are analyzed in real time. If the first difference value and the second difference value are less than or equal to a preset value for a plurality of times, it is determined that the corresponding optimal reference single-cabinet battery charging expression model is the most consistent.

[0044] In the embodiments disclosed in the present application, a battery replacement cabinet charging control system based on user power consumption data analysis is also disclosed, comprising:

[0045] The first module is configured to obtain historical single-cabinet battery charging records in each cabinet body of the power consumption cabinet, and the combination of the historical single-cabinet battery charging records is recorded as a historical single-cabinet charging record group.

[0046] The second module is configured to construct a reference time cycle line, and based on each historical single-track charging record in the historical single-track charging record group, configure battery power, charging power and battery replacement record identifier on different time nodes of the reference time cycle line, obtain a historical single-cabinet battery charging expression model, and record the combination of the historical single-cabinet battery charging expression model as a historical single-cabinet battery charging expression model group. The historical single-cabinet battery charging expression model group is used to express the historical charging state of the power consumption cabinet as a whole.

[0047] The third module is configured to analyze the battery replacement record identifier in the historical single-cabinet battery charging expression model group, determine the identifier quantity of the battery replacement record identifier corresponding to different time segments, and record the combination of the identifier quantity corresponding to different time segments each day as an identifier quantity group.

[0048] The fourth module is configured to classify a plurality of identifier quantity groups equally to obtain a plurality of identifier quantity category sets, and perform an averaging calculation on the identifier quantity groups in the identifier quantity category set to obtain an average identifier quantity group.

[0049] The fifth module is configured to adjust the historical single-cabinet battery charging expression model group based on the average identifier quantity group to obtain an initial reference single-cabinet battery charging expression model group, and perform a plurality of charging strategy adjustments on the initial reference single-cabinet battery charging expression model group to obtain a plurality of adjusted reference single-cabinet battery charging expression model groups. The comprehensive application effect of each adjusted reference single-cabinet battery charging expression model group is evaluated, and based on the evaluation result, the optimal reference single-cabinet battery charging expression model group is selected from the adjusted reference single-cabinet battery charging expression model groups.

[0050] The sixth module is used for collecting a plurality of real-time single-cabinet battery charging parameters and real-time battery replacement parameters of the power consumption cabinet in real time, and selecting the most optimal reference single-cabinet battery charging expression model group most consistent with the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters, and performing charging regulation and control on the power consumption cabinet by referring to the optimal reference single-cabinet battery charging expression model group.

[0051] The application discloses a battery replacement cabinet charging control method and system based on user power consumption data analysis, relates to the technical field of battery replacement cabinet management and control, collects historical charging records of each cabinet body, constructs a historical single-cabinet battery charging expression model group to reflect battery charging states and replacement modes, analyzes battery replacement records to determine replacement frequencies in different time sections, and extracts representative charging modes through equivalent classification and average calculation, adjusts and optimizes charging strategies to obtain an optimal reference single-cabinet battery charging expression model group, and collects charging and replacement parameters of a power consumption cabinet in real time, matches the optimal model group, and realizes real-time charging regulation and control.

[0052] The technical scheme of the application will be further described in detail below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The application discloses a battery replacement cabinet charging control method based on user power consumption data analysis. DETAILED DESCRIPTION

[0054] The technical scheme of the application will be further described in detail below with reference to the drawings and embodiments.

[0055] The technical scheme of the application will be further described in detail below with reference to the drawings and embodiments.

[0056] Embodiment:

[0057] The application discloses a battery replacement cabinet charging control method based on user power consumption data analysis, relates to the technical field of battery replacement cabinet management and control, collects historical charging records of each cabinet body, constructs a historical single-cabinet battery charging expression model group to reflect battery charging states and replacement modes, analyzes battery replacement records to determine replacement frequencies in different time sections, and extracts representative charging modes through equivalent classification and average calculation, adjusts and optimizes charging strategies to obtain an optimal reference single-cabinet battery charging expression model group, and collects charging and replacement parameters of a power consumption cabinet in real time, matches the optimal model group, and realizes real-time charging regulation and control.

[0058] The application discloses a battery replacement cabinet charging control method based on user power consumption data analysis, relates to the technical field of battery replacement cabinet management and control, collects historical charging records of each cabinet body, constructs a historical single-cabinet battery charging expression model group to reflect battery charging states and replacement modes, analyzes battery replacement records to determine replacement frequencies in different time sections, and extracts representative charging modes through equivalent classification and average calculation, adjusts and optimizes charging strategies to obtain an optimal reference single-cabinet battery charging expression model group, and collects charging and replacement parameters of a power consumption cabinet in real time, matches the optimal model group, and realizes real-time charging regulation and control. Figure 1 , including:

[0059] Step S100, obtain the historical single-cabinet battery charging records in each cabinet body of the power utilization cabinet, and record the combination of the historical single-cabinet battery charging records as a historical single-cabinet charging record group.

[0060] Data collection is the foundation step of the entire power utilization cabinet battery charging management scheme. It comprehensively collects the historical charging records of the batteries in each cabinet body through the system records built-in the power utilization cabinet or external sensors. These records cover the charging start time, end time, charging amount, charging power, and possible abnormal state of the batteries. The accuracy, completeness, and time span of the data are crucial for subsequent construction of charging models and analysis of charging behavior, which provides solid data support for in-depth understanding of the charging characteristics of the batteries and optimization of charging strategies.

[0061] Step S200, construct a reference time cycle line, and based on each historical single-track charging record in the historical single-track charging record group, configure the battery power, charging power, and battery replacement record identifier on different time nodes on the reference time cycle line to obtain a historical single-cabinet battery charging expression model, and record the combination of the historical single-cabinet battery charging expression models as a historical single-cabinet battery charging expression model group, wherein the historical single-cabinet battery charging expression model group is used to express the historical charging state of the entire power utilization cabinet.

[0062] Constructing a historical single-cabinet battery charging expression model is the process of converting complex charging records into an analyzable model. Based on the collected historical charging data, this step maps the charging records in chronological order onto a reference time cycle line to form time series data. In the model, different time nodes are assigned key identifiers such as battery power, charging power, and battery replacement records, which together constitute a comprehensive description of the battery charging state. By constructing such a model, the trend of battery charging behavior over time can be observed and analyzed intuitively, providing a strong model foundation for subsequent strategy optimization.

[0063] Step S300, analyze the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determine the number of battery replacement record identifiers corresponding to different time segments, and record the combination of the number of identifiers corresponding to different time segments each day as an identifier number group.

[0064] Analyzing battery replacement records is an important step in evaluating battery service life, predicting maintenance needs, and optimizing charging strategies. This step analyzes the battery replacement records in the historical single-cabinet battery charging expression model to count the frequency and pattern of battery replacement in different time segments. By identifying the peak and trough periods of battery replacement and possible replacement reasons, key reference information can be provided for subsequent charging strategy adjustments, which helps to extend the overall service life of the batteries and reduce maintenance costs.

[0065] In some embodiments of the present disclosure, the method for determining the number of battery replacement record identifiers corresponding to different time segments comprises:

[0066] Step S301, a battery replacement record identifier mapping diagram is established, and the horizontal axis of the battery replacement record identifier mapping diagram is a time axis;

[0067] Step S302, the battery replacement record identifiers in the historical single-cabinet battery charging expression model group are analyzed, the battery replacement record identifiers appearing at different time nodes are determined, and the replacement record identifier mapping points are mapped on the battery replacement record identifier mapping diagram in a replacement record identifier mapping point manner;

[0068] Step S303, the aggregation characteristics of the record identifier mapping points in the battery replacement record identifier mapping diagram are analyzed, the time axis is segmented based on the aggregation characteristics, a plurality of time segments are obtained, and the number of replacement record identifier mapping points in the time segments is counted.

[0069] In some embodiments of the present disclosure, the method for segmenting the time axis based on the aggregation characteristics comprises:

[0070] Step S3031, the time axis is averaged and split to obtain a plurality of unit time segments, and the number of replacement record identifier mapping points corresponding to each unit time segment on the battery replacement record identifier mapping diagram is analyzed.

[0071] Step S3032, a replacement record identifier mapping point number axis is set in the vertical axis direction of the battery replacement record identifier mapping diagram, the middle point of the unit time segment is taken as a horizontal axis positioning point, the vertical axis mapping points of the replacement record identifier mapping point number are marked in the vertical axis direction, and the vertical axis mapping points are smoothly connected to obtain a replacement record identifier mapping point number curve.

[0072] Step S3033, the replacement record identifier mapping point number curve is analyzed, the curve segment with a rising trend meeting a preset standard and a falling trend meeting a preset standard is determined, and is recorded as a curve segment to be concerned, and the number of combinations of the unit time segments corresponding to the curve segment to be concerned is determined based on the intersection area between the curve segment to be concerned and the horizontal axis.

[0073] Wherein, the expression for determining the number of combinations of the unit time segments corresponding to the curve segment to be concerned is:

[0074]

[0075] Wherein, D is the number of combinations of unit time segments, R is a combination number conversion adjustment coefficient, d is a preset standard combination number, S is the intersection area between the curve segment to be concerned and the horizontal axis, and S预设 L is a cross-sectional area influence adjustment coefficient, and b is a cross-sectional area influence adjustment constant.

[0076] In some embodiments of the present disclosure, the method of segmenting the time axis further comprises:

[0077] In step S3034, a non-concerned curve segment on the replacement record identification mapping point quantity curve is determined, and a unit time segment in the non-concerned curve segment is sequentially combined based on a preset standard combination quantity.

[0078] In step S400, a plurality of identification quantity groups are classified equally to obtain a plurality of identification quantity category sets, and an average identification quantity group is obtained by averaging calculation on the identification quantity groups in the identification quantity category sets.

[0079] The equal classification and averaging calculation are steps for further processing and analyzing the battery replacement records. In this step, similar battery replacement records are classified together by a clustering algorithm to form different category sets. Then, averaging calculation is performed within each category set to obtain an average identification quantity group to represent the typical battery replacement mode of the category. This step aims to reduce data fluctuations and extract more representative battery replacement rules to provide more accurate and stable basis for subsequent optimization of charging strategies.

[0080] In some embodiments of the present disclosure, the method of equally classifying a plurality of identification quantity groups comprises:

[0081] In step S401, the identification quantity groups are analyzed to determine the length and order of the corresponding time segment division, form a time segment sequence, and take the time segment interval to which each time segment in the time segment sequence belongs as a first classification condition to perform a first equal classification on the plurality of identification quantity groups.

[0082] The core purpose of step S401 is to preliminarily classify the identification quantity groups through the time segment sequence. In this step, the collected identification quantity groups need to be analyzed in detail to determine the corresponding time segment of each identification quantity group, and the length of the time segment division is determined according to the actual demand or data characteristics. Subsequently, the identification quantity groups are sorted in time sequence to form an ordered time segment sequence. On this basis, the time segment interval to which each time segment in the time segment sequence belongs is taken as the first classification condition to perform a first equal classification on all identification quantity groups. This classification process is essentially a division of data in the time dimension, which helps to gather identification quantity groups with similar time characteristics together to lay a foundation for subsequent in-depth analysis.

[0083] Step S402, based on the identification quantity interval to which the identification quantity corresponding to each time segment in the identification quantity group belongs, the identification quantity group is classified for the second time.

[0084] After the first classification based on time segments is completed, step S402 further introduces the identification quantity as a classification condition to classify the identification quantity group more finely. This step first needs to set reasonable identification quantity intervals according to the distribution of the identification quantity. These intervals can be continuous or discrete, depending on the characteristics of the data and the needs of the analysis. Then, based on the results of the first classification, the identification quantity group in each time segment is classified for the second time according to the identification quantity interval to which the identification quantity belongs. This classification process not only considers the time factor, but also takes into account the quantity dimension, making the classification result more comprehensive and accurate. Through this step, we can gather identification quantity groups with similar time characteristics and quantity characteristics together, providing more powerful support for subsequent data analysis and processing.

[0085] Step S500, based on the average identification quantity group, the historical single-cabinet battery charging expression model group is adjusted to obtain an initial reference single-cabinet battery charging expression model group, and the initial reference single-cabinet battery charging expression model group is adjusted several times to obtain several adjusted reference single-cabinet battery charging expression model groups, the comprehensive application effect of each adjusted reference single-cabinet battery charging expression model group is evaluated, and based on the evaluation result, the optimal reference single-cabinet battery charging expression model group is selected from the adjusted reference single-cabinet battery charging expression model group.

[0086] Model adjustment and optimization is based on the analysis results of the previous steps to improve and perfect the historical single-cabinet battery charging expression model. This step tries different charging strategies (such as adjusting charging power, charging time, charging frequency, etc.) several times, and evaluates the comprehensive application effect (such as charging efficiency, battery life, operation and maintenance cost, etc.) of each strategy, to find the optimal charging strategy combination. Optimization process may involve experimental verification, simulation and other methods to ensure that the selected strategy not only meets the user's electricity demand, but also maximizes the battery life and reduces the operation and maintenance cost.

[0087] In some embodiments disclosed in the present application, the method for adjusting the historical single-cabinet battery charging expression model group comprises:

[0088] Step S501, based on the average identification quantity group, the average identification quantity group is configured for the reference time cycle online different time segments corresponding to the historical single-cabinet battery charging expression model group, and the battery power change curve of each time segment is determined, and the step-down point of the battery power change curve and the step-down point battery power corresponding to the step-down point are determined.

[0089] This step aims to configure an average identification number for each time segment in the model group through the analysis of historical single cabinet battery charging data, which represents the average performance of the battery charging state in the time segment. First, based on the existing average identification number group, map these data to the corresponding reference time cycle line of the historical single cabinet battery charging expression model group, ensuring that each time segment has a corresponding average identification number. Then, according to these data and the charging characteristics of the battery, determine the battery power change curve of each time segment. In this process, special attention is paid to the points where the battery power drops suddenly (drop points) and the corresponding battery power values (drop point battery power), as these drop points may reflect problems or bottlenecks in the battery charging process.

[0090] In step S502, it is determined whether the difference between the average identification number and the drop point of each time segment is within the preset difference interval. If it is determined to be within the preset difference interval, it is determined whether each drop point battery power is greater than or equal to the preset value. If not, the charging power of the first preset number of cabinets is increased, and the charging power is increased at the time nodes of the preset time length in the future. If the difference between the average identification number and the drop point of each time segment is not within the preset difference interval, it is determined whether the average identification number is greater than or less than the drop point. If it is determined to be greater than the drop point, the charging power of the second preset number of cabinets is increased, and the charging power is increased at the time nodes of the preset time length in the future. If it is determined to be less than the drop point, the charging power of the third preset number of cabinets is decreased, and the charging power is decreased at the time nodes of the preset time length in the future.

[0091] The core of this step is to determine whether the charging power of the cabinet needs to be adjusted according to the difference between the average identification number and the drop point, as well as the battery power of the drop point. First, it is determined whether the difference between the average identification number and the drop point of each time segment falls within the preset difference interval. If it is within this interval and the battery power of the drop point is lower than the preset value, it indicates that the charging effect of the battery in this time segment is not good, and the charging power may need to be increased. According to the preset rules, the charging power of the first preset number of cabinets is increased, and this adjustment is implemented at the time nodes of the preset time length in the future. If the difference is not within the preset interval, it needs to be further determined whether the average identification number is greater than or less than the drop point, and accordingly it is determined whether the charging power of the cabinet is increased or decreased, as well as the specific number and time nodes of the adjustment.

[0092] In the embodiments disclosed in the present application, before the charging power of the cabinet is increased or decreased, the determination of the cabinet for charging power adjustment is performed, and the method for determining the charging power adjustment includes:

[0093] The method for determining the cabinet to perform the charging power increase comprises the following steps:

[0094] In step S5021, the time node corresponding to the step-down point of each time section is determined, and the average value of the time node is analyzed, which is recorded as the average step-down time point. The battery capacity corresponding to the average step-down time point of each cabinet is determined. If the battery capacity is in the first battery capacity preset interval, the corresponding cabinet is determined as the cabinet to perform the charging power increase.

[0095] The method for determining the cabinet to perform the charging power decrease comprises the following steps:

[0096] In step S5021, the time node corresponding to the step-down point of each time section is determined, and the average value of the time node is analyzed, which is recorded as the average step-down time point. The battery capacity corresponding to the average step-down time point of each cabinet is determined. If the battery capacity is in the second battery capacity preset interval, the corresponding cabinet is determined as the cabinet to perform the charging power increase.

[0097] In step S503, after the power adjustment of the historical single-cabinet battery charging expression model group, the average identification quantity in the average identification quantity group is used as the battery replacement demand, the historical battery charging expression model group is configured, the adjusted step-down points of different reference time cycle lines and the battery capacities of the adjusted step-down points are determined, and the adjusted reference single-cabinet battery charging expression model group is obtained.

[0098] After the adjustment of the charging power is completed, this step aims to update the historical single-cabinet battery charging expression model group to reflect the actual situation after the adjustment. First, the average identification quantity in the average identification quantity group is used as the demand index of battery replacement. Then, according to the demand and the charging characteristics of the battery, the historical battery charging expression model group is reconfigured to determine the adjusted step-down points under different reference time cycle lines and the battery capacities corresponding to these points. Finally, the adjusted reference single-cabinet battery charging expression model group is obtained, which is closer to the actual battery performance in the charging process and can provide more accurate basis for subsequent battery management and charging strategy formulation.

[0099] In the embodiments disclosed in the present application, the method for comprehensively applying the adjusted reference single-cabinet battery charging expression model group to evaluate the application effect comprises the following steps:

[0100] In step S504, the reference time cycle line corresponding to each adjusted reference single-cabinet battery charging expression model in the adjusted reference single-cabinet battery charging expression model group is analyzed, and the adjusted step-down points of the battery capacity change curve of different time sections and the battery capacities of the adjusted step-down points are determined.

[0101] In step S505, the number of the quenched points and the average number of the identification of the quenched points corresponding to different time segments after the adjustment are compared to obtain a first comparison result of the effect, the average quenched point battery power and the standard battery power corresponding to different time segments are compared to obtain a second comparison result of the effect, and the comprehensive application effect evaluation is determined based on the first comparison result of the effect and the second comparison result of the effect.

[0102] The expression for calculating the comprehensive application effect evaluation is:

[0103]

[0104] G is the comprehensive application effect evaluation, δ(Δx i ∈x yushe ) is the first comparison function of the effect corresponding to the i th time segment, Δx i is the first difference quantity of the number of the quenched points and the average number of the identification of the i th time segment, x yushe is the preset first difference quantity interval, if the first difference quantity is within the first difference quantity interval, then δ(Δx i ∈x yushe outputs 1, otherwise, 0, β(Δh i ∈h yushe ) is the second comparison function of the effect corresponding to the i th time segment, Δh i is the second difference quantity of the average quenched point battery power and the standard battery power of the i th time segment, h yushe is the preset second difference quantity interval, if the second difference quantity is within the second difference quantity interval, then β(Δh i ∈h yushe outputs 1, otherwise, 0, K i is the weight coefficient corresponding to the i th time segment, n is the total number of the time segments, g man is the charging quantity of the low-power charging, g zong is the total charging quantity, J is the charging feature influence adjustment coefficient, C is the charging feature influence adjustment constant, and F is the charging power effect conversion coefficient.

[0105] In step S600, the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters of the power consumption cabinet are collected in real time, the most optimal reference single-cabinet battery charging expression model group that is most consistent with the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters is selected, and the power consumption cabinet is regulated and controlled by referring to the most optimal reference single-cabinet battery charging expression model group.

[0106] Real-time charging regulation is the last step to realize intelligent battery charging management of the power consumption cabinet. Based on the real-time collected charging parameters and battery replacement parameters of the power consumption cabinet, the most optimal reference single-cabinet battery charging expression model group that is most consistent with the current state is selected through a similarity matching algorithm. Then, real-time charging regulation is performed on the power consumption cabinet according to the selected model group, including adjusting the charging power, controlling the charging time, etc. The goal of real-time charging regulation is to ensure the efficiency, safety and reliability of the charging process, and to adapt to the changing power consumption demand and battery state through dynamic adjustment of the charging strategy, so as to realize intelligent management of the power consumption cabinet battery.

[0107] In some embodiments disclosed in the present application, the method for selecting the most optimal reference single-cabinet battery charging expression model that is most consistent includes:

[0108] In step S601, real-time single-cabinet battery charging parameters and real-time battery replacement parameters are substituted into different optimal reference single-cabinet battery charging expression model groups, and real-time analysis is performed on the first difference value of the single-cabinet charging parameters and the second difference value of the battery replacement parameters. If the continuous first difference value and the second difference value are less than or equal to the preset value, it is determined that the corresponding optimal reference single-cabinet battery charging expression model is the most consistent.

[0109] In the embodiments disclosed in the present application, a battery replacement cabinet charging control system based on user power consumption data analysis is also disclosed, which includes:

[0110] The first module is used to obtain the historical single-cabinet battery charging record in each cabinet body of the power consumption cabinet, and the combination of the historical single-cabinet battery charging record is recorded as a historical single-cabinet charging record group;

[0111] The second module is used to construct a reference time cycle line, and based on each historical single-track charging record in the historical single-track charging record group, the battery capacity, the charging power and the battery replacement record identifier are configured on different time nodes on the reference time cycle line to obtain a historical single-cabinet battery charging expression model, and the combination of the historical single-cabinet battery charging expression model is recorded as a historical single-cabinet battery charging expression model group. The historical single-cabinet battery charging expression model group is used to express the historical charging state of the whole power consumption cabinet;

[0112] The third module is used to analyze the battery replacement record identifier in the historical single-cabinet battery charging expression model group, determine the identifier quantity of the battery replacement record identifier corresponding to different time sections, and record the combination of the identifier quantity corresponding to different time sections each day as an identifier quantity group;

[0113] The fourth module is used to classify the identifier quantity groups in an equivalent manner to obtain a plurality of identifier quantity category sets, and perform an averaging calculation on the identifier quantity groups in the identifier quantity category set to obtain an average identifier quantity group;

[0114] The fifth module is used for adjusting the historical single-cabinet battery charging expression model group based on the average number of identifications to obtain an initial reference single-cabinet battery charging expression model group, and adjusting the initial reference single-cabinet battery charging expression model group for several times to obtain several adjusted reference single-cabinet battery charging expression model groups, performing comprehensive application effect evaluation on each adjusted reference single-cabinet battery charging expression model group, and selecting an optimal reference single-cabinet battery charging expression model group from the adjusted reference single-cabinet battery charging expression model groups based on the evaluation results.

[0115] The sixth module is used for collecting several real-time single-cabinet battery charging parameters and real-time battery replacement parameters of the power consumption cabinet, and selecting the most optimal reference single-cabinet battery charging expression model group most consistent with the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters, and referring to the optimal reference single-cabinet battery charging expression model group to perform charging regulation and control on the power consumption cabinet.

[0116] The application discloses a battery replacement cabinet charging control method and system based on user power consumption data analysis, relates to the technical field of battery replacement cabinet management and control, collects historical charging records of cabinet bodies, constructs a historical single-cabinet battery charging expression model group to reflect battery charging states and replacement modes, analyzes battery replacement records to determine replacement frequencies in different time sections, and extracts representative charging modes through equivalent classification and average calculation, adjusts and optimizes charging strategies to obtain an optimal reference single-cabinet battery charging expression model group, collects charging and replacement parameters of the power consumption cabinet in real time, matches the optimal model group, and realizes real-time charging regulation and control.

[0117] Those skilled in the art can clearly understand the application through the description of the above embodiments that the application can be implemented by hardware or by means of software and a necessary general hardware platform. Based on such understanding, the technical solutions of the application can be embodied in the form of a software product, which can be stored in a nonvolatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk or the like) and includes a plurality of instructions for enabling a computer device (which can be a personal computer, a server or a network device) to execute the methods described in various implementation scenarios of the application.

[0118] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application rather than limit the same, although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the application can still be modified or replaced by equivalents, and these modifications or replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the application.

Claims

1. A battery swap cabinet charging control method based on user power consumption data analysis, characterized in that, The method comprises the following steps: acquire historical single-cabinet battery charging records in each cabinet of the power utilization cabinet, and record the combination of the historical single-cabinet battery charging records as a historical single-cabinet charging record group; construct a reference time cycle line, and configure battery capacity, charging power and battery replacement record identifier on different time nodes on the reference time cycle line based on each historical single-track charging record in the historical single-track charging record group, to obtain a historical single-cabinet battery charging expression model, and record the combination of the historical single-cabinet battery charging expression model as a historical single-cabinet battery charging expression model group, wherein the historical single-cabinet battery charging expression model group is used to express the historical charging state of the power utilization cabinet as a whole; analyze the battery replacement record identifier in the historical single-cabinet battery charging expression model group, determine the number of identifiers corresponding to the battery replacement record identifier in different time sections, and record the combination of the number of identifiers corresponding to different time sections in each day as an identifier number group; equivalent classification is performed on a plurality of identifier number groups to obtain a plurality of identifier number category sets, and average calculation is performed on the identifier number groups in the identifier number category sets to obtain an average identifier number group; based on the average identifier number group, the historical single-cabinet battery charging expression model group is adjusted to obtain an initial reference single-cabinet battery charging expression model group, and the initial reference single-cabinet battery charging expression model group is adjusted for a plurality of times to obtain a plurality of adjusted reference single-cabinet battery charging expression model groups, each adjusted reference single-cabinet battery charging expression model group is evaluated for comprehensive application effect, and based on the evaluation result, the optimal reference single-cabinet battery charging expression model group is selected from the adjusted reference single-cabinet battery charging expression model groups; real-time single-cabinet battery charging parameters and real-time battery replacement parameters of the power utilization cabinet are collected in real time, and based on the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters, the most optimal reference single-cabinet battery charging expression model group is selected, and the power utilization cabinet is regulated and controlled by referring to the optimal reference single-cabinet battery charging expression model group; the method for adjusting the historical single-cabinet battery charging expression model group comprises the following steps: based on the average identifier number group, the average identifier number group is configured on different time sections of the reference time cycle line corresponding to the historical single-cabinet battery charging expression model group, the battery capacity change curve of each time section is determined, and the step-down point of the battery capacity change curve and the step-down point battery capacity corresponding to the step-down point are determined. determining whether the difference between the average number of identifications of each time segment and the cliff point is within a preset difference range, if it is determined that the difference is within the preset difference range, determining whether the battery capacity of each cliff point is greater than or equal to a preset value, if not, increasing the charging power of a first preset number of cabinets, and increasing the charging power at time nodes of a preset time length in the future, if the difference between the average number of identifications of each time segment and the cliff point is not within the preset difference range, determining whether the average number of identifications is greater than the cliff point or less than the cliff point, if it is determined that the average number of identifications is greater than the cliff point, increasing the charging power of a second preset number of cabinets, and increasing the charging power at time nodes of a preset time length in the future, if it is determined that the average number of identifications is less than the cliff point, reducing the charging power of a third preset number of cabinets, and reducing the charging power at time nodes of a preset time length in the future; After power adjustment of the historical single-cabinet battery charging expression model group, the average number of identifications in the average identification number group is used as the battery replacement demand to configure the historical cabinet charging expression model group to determine the adjusted cliff point of different reference time cycles and the adjusted cliff point battery capacity, and obtain the adjusted reference single-cabinet battery charging expression model group. 2.The battery swap cabinet charging control method based on user power consumption data analysis of claim 1, wherein, The method for determining the number of identification of the battery replacement record identification corresponding to different time segments comprises: establishing a battery replacement record identification mapping diagram, with the time axis as the horizontal axis of the battery replacement record identification mapping diagram; analyzing the battery replacement record identification in the historical single-cabinet battery charging expression model group, determining the battery replacement record identification appearing at different time nodes, and mapping the replacement record identification mapping points on the battery replacement record identification mapping diagram; based on the aggregation characteristics of the record identification mapping points in the battery replacement record identification mapping diagram, the time axis is divided into several time segments, and the number of identification of the replacement record identification mapping points in the time segments is counted. 3.The battery swap cabinet charging control method based on user power consumption data analysis of claim 2, wherein, The method for dividing the time axis into segments based on the aggregation characteristics comprises: averaging and splitting the time axis to obtain several unit time segments, and analyzing the number of replacement record identification mapping points corresponding to each unit time segment on the battery replacement record identification mapping diagram; setting the number of replacement record identification mapping points axis in the vertical axis direction of the battery replacement record identification mapping diagram, positioning the middle point of the unit time segment as the horizontal axis, marking the vertical axis mapping points of the number of replacement record identification mapping points in the vertical axis direction, and smoothly connecting the vertical axis mapping points to obtain the number of replacement record identification mapping points curve; analyzing the number of replacement record identification mapping points curve, determining the curve segment that meets the preset standard in the rising trend and the falling trend, and recording it as the curve segment to be concerned, and based on the intersection area between the curve segment to be concerned and the horizontal axis, determining the number of combinations of the unit time segments corresponding to the curve segment to be concerned; wherein the expression for determining the number of combinations of the unit time segments corresponding to the curve segment to be concerned is: ； Wherein, D is the number of combinations in a time section, R is a combination number conversion adjustment factor, d is a preset standard combination number, S is the intersection area between the curve section of interest and the horizontal axis, is a preset intersection area, L is an intersection area influence adjustment factor, and b is an intersection area influence adjustment constant. 4.The user power consumption data analysis based battery swap cabinet charging control method according to claim 3, characterized in that, The method for dividing the time axis into segments further comprises: The non-concerned curve section on the replacement record identification mapping point quantity curve is determined, and the unit time sections in the non-concerned curve section are combined in sequence based on a preset standard combination quantity. 5.The user power consumption data analysis based battery swap cabinet charging control method according to claim 1, characterized in that, The method for equally classifying a plurality of identification quantity groups comprises: The identification quantity groups are analyzed to determine the lengths of corresponding time section divisions and the sorting manner, a time section sequence is formed, and each time section in the time section sequence belongs to a time section interval as a first classification condition, and the plurality of identification quantity groups are equally classified for the first time; The identification quantity groups classified for the first time are equally classified for the second time based on the identification quantity intervals to which the identification quantities of each time section in the identification quantity groups belong. 6.The user power consumption data analysis based battery swap cabinet charging control method according to claim 1, characterized in that, Before the charging power of the cabinet is increased or decreased, the cabinet to which the charging power adjustment is applied is determined, and the method for determining the cabinet to which the charging power adjustment is applied comprises: The method for determining the cabinet to which the charging power is increased comprises: The time nodes corresponding to the sudden drop points of each time section are determined, and the average value of the time nodes is analyzed, which is recorded as an average sudden drop time point. The battery capacity corresponding to the average sudden drop time point of each cabinet is determined. If the battery capacity is in a first battery capacity preset interval, the corresponding cabinet is determined as the cabinet to which the charging power is increased. The method for determining the cabinet to which the charging power is decreased comprises: The time nodes corresponding to the sudden drop points of each time section are determined, and the average value of the time nodes is analyzed, which is recorded as an average sudden drop time point. The battery capacity corresponding to the average sudden drop time point of each cabinet is determined. If the battery capacity is in a second battery capacity preset interval, the corresponding cabinet is determined as the cabinet to which the charging power is decreased. 7.The user power consumption data analysis based battery swap cabinet charging control method according to claim 1, characterized in that, The method for evaluating the comprehensive application effect of the adjusted reference single-cabinet battery charging expression model group comprises: The reference time cycle line corresponding to each adjusted reference single-cabinet battery charging expression model in the adjusted reference single-cabinet battery charging expression model group is analyzed to determine the adjusted sudden drop points of the battery capacity change curves of different time sections and the battery capacities of the adjusted sudden drop points. The sudden drop point quantity and the average identification quantity of the adjusted sudden drop points corresponding to different time sections are compared to obtain a first effect comparison result, and the average sudden drop point battery capacity and the standard battery capacity corresponding to different time sections are compared to obtain a second effect comparison result. Based on the first effect comparison result and the second effect comparison result, the comprehensive application effect evaluation is determined. The expression for calculating the comprehensive application effect evaluation is: ； G is a comprehensive application effect evaluation, G is a first effect comparison function corresponding to the i th time segment, G is a first difference quantity of the number of sudden drop points and the average number of marks of the i th time segment, G is a preset first difference quantity interval, if the first difference quantity is within the first difference quantity interval, then Output 1, otherwise output 0, G is a second effect comparison function corresponding to the i th time segment, G is a second difference quantity of the average sudden drop point battery power and the standard battery power of the i th time segment, G is a preset second difference quantity interval, if the second difference quantity is within the second difference quantity interval, then Output 1, otherwise output 0, G is a weight coefficient corresponding to the i th time segment, n is the total number of time segments, G is the charging amount of low-power charging, G is the total charging amount, J is the charging feature influence adjustment coefficient, C is the charging feature influence adjustment constant, and F is the charging power effect conversion coefficient. 8.The user power consumption data analysis based battery swap cabinet charging control method according to claim 1, characterized in that, The method for selecting the most optimal reference single-cabinet battery charging expression model comprises: The real-time single-cabinet battery charging parameters and real-time battery replacement parameters are substituted into different optimal reference single-cabinet battery charging expression model groups, and the first difference of the single-cabinet charging parameters and the second difference of the battery replacement parameters are analyzed in real time. If the first difference and the second difference are less than or equal to a preset value for a plurality of times, the corresponding optimal reference single-cabinet battery charging expression model is determined as the most consistent.

9. The battery replacement cabinet charging control system based on user power consumption data analysis, characterized in that, The battery swapping cabinet charging control method for executing any one of claims 1-8 comprises: The first module is configured to acquire historical single-cabinet battery charging records in each cabinet body of the power utilization cabinet, and record a combination of the historical single-cabinet battery charging records as a historical single-cabinet charging record group; The second module is configured to construct a reference time cycle line, configure battery power, charging power and battery replacement record identifiers at different time nodes on the reference time cycle line based on each historical single-cabinet charging record in the historical single-cabinet charging record group, obtain a historical single-cabinet battery charging expression model, and record a combination of the historical single-cabinet battery charging expression model as a historical single-cabinet battery charging expression model group, wherein the historical single-cabinet battery charging expression model group is used to express the historical charging state of the power utilization cabinet as a whole; The third module is configured to analyze the battery replacement record identifiers in the historical single-cabinet battery charging expression model group, determine the number of identifiers corresponding to different time segments, and record a combination of the number of identifiers corresponding to different time segments in each day as an identifier number group; The fourth module is configured to classify a plurality of identifier number groups equally to obtain a plurality of identifier number category sets, and perform an averaging calculation on the identifier number groups in the identifier number category sets to obtain an average identifier number group; The fifth module is configured to adjust the historical single-cabinet battery charging expression model group based on the average identifier number group to obtain an initial reference single-cabinet battery charging expression model group, perform a plurality of charging strategy adjustments on the initial reference single-cabinet battery charging expression model group to obtain a plurality of adjusted reference single-cabinet battery charging expression model groups, perform a comprehensive application effect evaluation on each adjusted reference single-cabinet battery charging expression model group, and select an optimal reference single-cabinet battery charging expression model group from the adjusted reference single-cabinet battery charging expression model groups based on the evaluation results; The sixth module is configured to collect a plurality of real-time single-cabinet battery charging parameters and real-time battery replacement parameters of the power utilization cabinet in real time, select the most optimal reference single-cabinet battery charging expression model group based on the real-time single-cabinet battery charging parameters and the real-time battery replacement parameters, and refer to the optimal reference single-cabinet battery charging expression model group to perform charging regulation and control on the power utilization cabinet.

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