Charging station electric energy variable data processing method and system

Abstract

The invention provides a charging station electric energy variable data processing method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the difference processing of adjacent power measurement values in a power measurement sequence, and obtaining a power variation sequence; determining a stage boundary according to the power variation sequence, and dividing the charging process into a plurality of charging stages; for each charging stage, generating a stage sampling control parameter; performing staged sampling in each charging stage to obtain electric energy variable sampling data; in each charging stage, performing section-by-section electric energy calculation on power corresponding to each sampling time interval in the stage, and performing intra-stage accumulation on the section-by-section electric energy to obtain stage electric energy; summarizing the electric energy of each stage to generate a charging electric energy result corresponding to the charging process; according to the invention, autonomy and accuracy of charging station electric energy variable data processing are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a charging station electric energy variable data processing method and system. BACKGROUND

[0002] With the popularization of electric vehicles, as an important node of electric energy transaction and energy management, the measurement and processing technology of electric energy parameters of charging stations has gradually matured. In the prior art, for the electric energy variable data of the charging station, an electric energy metering device arranged in the charging pile or the power distribution circuit is usually used to periodically sample the voltage, current, active power, electric energy and other parameters, and generate corresponding electric energy data records according to a preset time interval. Such technology generally based on an electric energy meter or a collection terminal stores or uploads the collected original measurement values according to a fixed sampling period, and performs statistics, settlement or operation analysis on the electric energy variable data in a background system, for example, for billing settlement, electric energy consumption statistics or load monitoring application scenarios.

[0003] However, in the actual charging station operation environment, the above-mentioned method of sampling and directly processing electric energy variable data based on a fixed period may have technical defects in specific scenarios. In a direct current fast charging station, the charging process usually includes multiple stages such as starting, constant current, constant voltage and ending, and the current and power change amplitude and rate of each stage are significantly different. When a uniform and fixed sampling period (such as sampling once per second or per minute) is used to process electric energy variables, in the charging starting or power mutation stage, the sampling points may not cover the key time interval of the rapid power change, resulting in a deviation between the segmented electric energy calculated by the background system according to the discrete sampling data and the actual electric energy output by the charging device. For example, when performing time-of-use electricity price settlement for a single fast charging order, if the instantaneous power change in the high-power stage is not accurately reflected, the electric energy in the corresponding period may be underestimated or overestimated, thereby affecting the accuracy of the billing result and further affecting the electric energy settlement credibility between the operator and the user. SUMMARY

[0004] The purpose of the present application is to provide a charging station electric energy variable data processing method and system, which aims to solve the problems mentioned in the background.

[0005] To solve the above technical problems, the technical solution of the present application is as follows: In a first aspect, a charging station electric energy variable data processing method, the method comprising: obtaining continuous measurement values of voltage, current and power of a charging device in a charging process, and forming a power measurement sequence in order of sampling time; differencing adjacent power measurement values in the power measurement sequence to obtain a power change sequence; The power change in the power change sequence is traversed and compared one by one to generate a trend determination result. Based on the trend determination result, time segments with consistent trends are identified. The stage boundary is determined by dual verification of the change amplitude and stability of the time segment, and the charging process is divided into multiple charging stages with different power change characteristics. For each charging stage, the power change within the stage is extracted and the distribution characteristic data is statistically obtained. The activity level of power change in the stage is determined by combining the distribution discreteness and change frequency of the power change. The sampling control target and rule set are matched according to the activity level. After calculation correction and boundary constraint verification, the stage sampling control parameters are generated. The stage sampling control parameters include at least the energy variable sampling time interval. In each charging stage, voltage, current and power are sampled in stages according to the corresponding stage sampling control parameters to obtain the energy variable sampling data of the corresponding charging stage. Within each charging stage, based on the energy variable sampling data, the power corresponding to each sampling time interval within the stage is calculated segment by segment of energy, and the segment of energy is accumulated within the stage to obtain the stage energy corresponding to each charging stage. The electrical energy at each stage is summarized and processed to generate the charging electrical energy result corresponding to the charging process.

[0006] Preferably, the power change values ​​in the power change sequence are iterated and compared one by one to generate a trend determination result. Based on the trend determination result, time segments with consistent trends are identified. The stage boundaries are determined by dual verification of the change amplitude and stability of the time segments, and the charging process is divided into multiple charging stages with different power change characteristics, including: The power change values ​​in the power change value sequence are traversed one by one, and the current power change value is compared with the previous power change value to generate a trend determination result that characterizes the power change trend. Based on the trend determination results, identify time intervals in which multiple consecutive power changes have the same trend, and generate power change trend interval data. The power change trend segment data is judged by both the change magnitude and the continuous stability. When the power change trend segment changes from a segment whose change magnitude meets the preset stability judgment condition to a segment whose change magnitude meets the preset change judgment condition, the corresponding time position is determined as the stage boundary data. Based on the stage boundary data, the charging process is segmented to generate multiple charging stage data that are distinct from each other in the time dimension and have different power change characteristics.

[0007] Preferably, for each charging stage, the power change within the stage is extracted and its distribution characteristics are statistically analyzed. The activity level of the power change in each stage is determined by combining the dispersion and frequency of the power change distribution. Based on the activity level, a set of sampling control targets and rules is matched. After calculation correction and boundary constraint verification, stage sampling control parameters are generated, including: Extract the power change in each charging stage to generate stage power change data. Statistical processing of the power change data in each stage yields distribution characteristic data that characterizes the degree of concentration of power change over time. Based on the distribution characteristic data, combined with the distribution dispersion and frequency of power change, stage power change activity level data is generated. Based on the stage power change activity level data, the sampling control target and rule set are matched, and the sampling time interval of the power variable is determined after calculation correction and boundary constraint verification to generate stage sampling control parameter data. The stage sampling control parameter data is correlated with the corresponding charging stage, and used as the basis for the control of voltage, current and power sampling within that charging stage.

[0008] Preferably, within each charging stage, based on the energy variable sampling data, the power corresponding to each sampling time interval within the stage is calculated segment by segment of energy, and the segmented energy is accumulated within the stage to obtain the stage energy corresponding to each charging stage, including: Acquire the energy variable sampling data corresponding to each charging stage, and sort the energy variable sampling data according to the sampling time order to generate stage sampling sequence data; Based on the sampling time corresponding to adjacent sampling points in the phased sampling sequence data, determine the sampling time interval data formed between adjacent sampling points; For each sampling time interval, obtain the power measurement value corresponding to the starting position of the sampling time interval, and generate power characterization data for the sampling time interval; Based on the power characterization data of the sampling time interval and the duration of the corresponding sampling time interval, determine the interval electrical energy data corresponding to the sampling time interval; According to the sampling time sequence, the interval electrical energy data corresponding to each sampling time interval within the same charging stage are accumulated to generate the stage electrical energy data corresponding to that charging stage.

[0009] Preferably, the power change trend segment data undergoes dual judgment on the change amplitude and continuous stability. When the power change trend segment changes from a segment whose change amplitude meets the preset stability judgment condition to a segment whose change amplitude meets the preset change judgment condition, the corresponding time position is determined as the stage boundary data, including: Based on the power change trend segment data, extract the corresponding power change amount within each power change trend segment to generate segment power change set data. Based on the power change data set of the segment, statistical processing of the change amplitude within each power change trend segment is performed to generate segment change amplitude characterization data to represent the power change amplitude level within the power change trend segment. Based on the segment variation amplitude characterization data, the stability and continuity of the power variation amplitude within the power variation trend segment are evaluated and processed to generate segment stability characterization data for characterizing the stability of the power variation trend segment. The stability characterization data of the section is compared with the preset stability judgment conditions. When the stability characterization data of the section meets the preset stability judgment conditions, stable section identification data is generated. The segment change amplitude representation data is compared with the preset change judgment conditions. When the segment change amplitude representation data meets the preset change judgment conditions, the changed segment identification data is generated. The continuity of stable segment identifier data and changed segment identifier data in time sequence is verified. When stable segment identifier data appears continuously in time dimension and then changed segment identifier data appears continuously, the boundary time position between stable segment and changed segment is determined. The boundary time position is output as stage boundary data to characterize the change in power state.

[0010] Preferably, based on the distribution characteristic data, and combined with the dispersion and frequency of power change, stage power change activity level data is generated, including: Based on the distribution characteristic data, the time distribution of power change during the charging phase is analyzed and processed to generate distribution discreteness characterization data to characterize the degree of dispersion of power change distribution. Based on the discreteness characterization data, the frequency of power change during the charging phase is evaluated and processed to generate frequency characterization data to characterize the frequency of power change. Based on the discreteness characterization data and the frequency characterization data, active state combination data is constructed to characterize the active state of power change, wherein the active state combination data is used to reflect the joint state of power change in two dimensions: time distribution and frequency of change. The active state combination data is processed by state mapping, and the active state combination data is mapped to a preset activity level category to determine the power change activity level category corresponding to the charging stage. Based on the determined category of power change activity, generate stage power change activity level data corresponding to the charging stage.

[0011] Preferably, based on the stage power change activity level data, the sampling control target and rule set are matched, and after calculation correction and boundary constraint verification, the energy variable sampling time interval is determined to generate stage sampling control parameter data, including: Based on the power change activity level data of the stage, the sampling control target corresponding to the charging stage is determined. The sampling control target is used to characterize the balance requirement between the sampling accuracy of the power variable and the consumption of sampling resources. Based on the sampling control objective, the sampling control rules corresponding to different power change activity levels are filtered to generate a set of sampling control rules that match the current charging stage. Based on the set of sampling control rules, the sampling time interval of electrical energy variables is calculated and corrected to generate candidate sampling time interval data that meet the sampling control objectives; Boundary constraint verification is performed on the candidate sampling time interval data to generate energy variable sampling time interval data that meets the preset sampling time constraint conditions; The sampling time interval data of the electrical energy variable is output as stage sampling control parameter data, and the stage sampling control parameter data is used to control the sampling process of voltage, current and power within the charging stage.

[0012] Secondly, a charging station energy variable data processing system, the system comprising: The measurement data acquisition module is used to acquire continuous measurement values ​​of voltage, current and power of the charging device during the charging process, and form a power measurement sequence according to the sampling time order; The power change generation module is used to perform difference processing on adjacent power measurement values ​​in the power measurement sequence to obtain the power change sequence. The charging stage segmentation module is used to traverse and compare the power change in the power change sequence one by one, generate the trend determination result, identify the time segment with a consistent trend based on the trend determination result, determine the stage boundary through the dual verification of the change amplitude and stability of the time segment, and divide the charging process into multiple charging stages with different power change characteristics. The sampling control parameter generation module is used to extract the power change within each charging stage and statistically obtain the distribution characteristic data. It determines the power change activity level of the stage by combining the distribution discreteness and change frequency of the power change. Based on the activity level, it matches the sampling control target and rule set, and generates the stage sampling control parameters after calculation correction and boundary constraint verification. The stage sampling control parameters include at least the energy variable sampling time interval. The phased sampling module is used to sample voltage, current and power in stages according to the corresponding phased sampling control parameters in each charging stage, so as to obtain the energy variable sampling data of the corresponding charging stage. The stage energy calculation module is used to calculate the energy of each segment of power corresponding to each sampling time interval within each charging stage based on the energy variable sampling data, and to accumulate the energy of each segment within the stage to obtain the stage energy corresponding to each charging stage. The electrical energy aggregation module is used to aggregate and process the electrical energy at each stage to generate the charging electrical energy result corresponding to the charging process.

[0013] The above-described solution of the present invention has at least the following beneficial effects: First, by continuously acquiring measurements of voltage, current, and power during the charging process and forming a power measurement sequence according to the sampling time order, the power change over time during charging can be continuously recorded. Based on this, the difference between adjacent power measurements in the power measurement sequence is processed to obtain a power change sequence. This transforms the power data, which originally only reflected the instantaneous state, into a data form that can characterize the trend and magnitude of power changes, providing fundamental data support for identifying power changes during charging and avoiding the information loss problem caused by relying solely on data sampled at fixed time points.

[0014] Secondly, by systematically comparing the power changes in the power change sequence, a trend determination result is generated, and time segments with consistent trends are identified based on this result. Furthermore, by performing dual verification on the magnitude and stability of these time segments, the boundary of the power change state is determined, thus dividing the charging process into multiple charging stages with different power change characteristics. This stage division method prevents the charging process from being treated as a single, homogeneous time period, but rather as a staged process based on the actual evolution of the power change state, providing clear time boundaries for subsequent differentiated data processing for different charging stages.

[0015] Furthermore, within different charging stages, by extracting and statistically processing the power change within each stage, distribution characteristic data characterizing the power change distribution state is obtained. Combining the dispersion and frequency of the power change distribution, the activity level of power change in each stage is determined. Based on this, corresponding sampling control targets and rule sets are matched according to the activity level, and after calculation correction and boundary constraint verification, stage sampling control parameters are generated. This allows the sampling time intervals of voltage, current, and power to be adjusted according to the activity level of power change within the charging stage, thereby avoiding insufficient sampling during stages of frequent power changes and redundant sampling during stages of gradual power changes.

[0016] Based on phased sampling, energy variable sampling data is obtained according to the corresponding phase sampling control parameters in each charging phase. Then, the energy is calculated segment by segment according to the sampling time interval, and finally accumulated within each phase, ensuring that the energy calculation process is consistent with the sampling time interval. This segmented calculation method accurately reflects the actual contribution of power changes to energy in different charging phases, reducing calculation errors introduced by variations in sampling time intervals.

[0017] Finally, by summarizing and processing the stage-specific electrical energy obtained at each charging stage, a charging electrical energy result corresponding to the entire charging process is generated. This allows the final electrical energy data to more accurately reflect the electrical energy output of the charging device during the complete charging process. In charging application scenarios with significant power variations, the above processing method helps reduce the deviation between stage-specific electrical energy and the actual output electrical energy, thereby providing a reliable data foundation for subsequent applications such as electrical energy statistics and metering analysis. Attached Figure Description

[0018] Figure 1 This is a flowchart of a charging station energy variable data processing method provided in an embodiment of the present invention. Detailed Implementation

[0019] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0020] like Figure 1 As shown, an embodiment of the present invention proposes a method for processing electrical energy variable data in charging stations, the method comprising: Acquire continuous measurements of voltage, current, and power of the charging device during the charging process, and form a power measurement sequence according to the sampling time order; By performing difference processing on adjacent power measurement values ​​in the power measurement sequence, a power change sequence is obtained; The power change in the power change sequence is traversed and compared one by one to generate a trend determination result. Based on the trend determination result, time segments with consistent trends are identified. The stage boundary is determined by dual verification of the change amplitude and stability of the time segment, and the charging process is divided into multiple charging stages with different power change characteristics. For each charging stage, the power change within the stage is extracted and the distribution characteristic data is statistically obtained. The activity level of power change in the stage is determined by combining the distribution discreteness and change frequency of the power change. The sampling control target and rule set are matched according to the activity level. After calculation correction and boundary constraint verification, the stage sampling control parameters are generated. The stage sampling control parameters include at least the energy variable sampling time interval. In each charging stage, voltage, current and power are sampled in stages according to the corresponding stage sampling control parameters to obtain the energy variable sampling data of the corresponding charging stage. Within each charging stage, based on the energy variable sampling data, the power corresponding to each sampling time interval within the stage is calculated segment by segment of energy, and the segment of energy is accumulated within the stage to obtain the stage energy corresponding to each charging stage. The electrical energy at each stage is summarized and processed to generate the charging electrical energy result corresponding to the charging process.

[0021] In this embodiment of the invention, by continuously acquiring measurements of voltage, current, and power during the charging process and forming a power measurement sequence according to the sampling time order, the state of power change over time during the charging process can be continuously recorded. Based on this, the difference between adjacent power measurements is processed to obtain a power change sequence, thereby transforming the original power measurement data into a data form reflecting the trend and magnitude of power change, providing basic data support for subsequent analysis of power change states during the charging process.

[0022] Furthermore, by systematically comparing the power changes in the power change sequence, a trend determination result is generated, and time segments with consistent trends are identified based on this result. On this basis, by performing dual verification on the magnitude and stability of the changes within these time segments, the boundary of the stage where the power change state changes is determined, thereby dividing the charging process into multiple charging stages with different power change characteristics. This method bases the division of charging stages on the overall evolution of power changes, rather than instantaneous changes at a single point in time, which is beneficial for accurately reflecting the power change characteristics of different stages during the charging process.

[0023] Within different charging stages, power changes within each stage are extracted and statistically processed to obtain distribution characteristic data characterizing the power change distribution. Combining the dispersion and frequency of power changes, the activity level of power changes in each stage is determined. Based on this, corresponding sampling control targets and rule sets are matched according to the activity level. After calculation correction and boundary constraint verification, stage sampling control parameters are generated. This ensures that the sampling time intervals for voltage, current, and power are adapted to the activity level of power changes in that charging stage, thereby avoiding insufficient sampling in stages of frequent power changes or redundant sampling in stages of gradual power changes.

[0024] Based on phased sampling, the energy variable sampling data obtained in each charging phase are used to calculate the corresponding power energy segment by segment according to the sampling time interval, and then accumulated within each phase to obtain the phase energy corresponding to each charging phase. Since the sampling time interval has been adjusted according to the phase power change characteristics, this segmented calculation method can more accurately reflect the actual contribution of power changes to energy in different charging phases. Subsequently, the phase energy obtained from each charging phase is summarized to generate the charging energy result corresponding to the entire charging process, making the final energy result more consistent with the actual energy output of the charging equipment.

[0025] For example, in a charging station equipped with DC fast charging equipment, the charging process of a vehicle typically goes through a phase of rapid power increase, a phase of relatively stable power, and a phase of gradual power decrease. Using the method described above, during phases with more drastic power changes, shorter sampling intervals are generated based on the activity level of the power change to obtain more frequent power sampling data; during phases with relatively gentle power changes, longer sampling intervals are generated to reduce unnecessary sampling. The stage energy is calculated based on the data collected at each charging stage and then summarized. The resulting charging energy result more accurately reflects the energy output of the vehicle throughout the entire charging process, facilitating subsequent energy statistics, analysis, or related application processing.

[0026] In a preferred embodiment of the present invention, the electrical energy at each stage is summarized and processed to generate a charging electrical energy result corresponding to the charging process, specifically including: After calculating the stage energy corresponding to each charging stage, the stage energy is organized according to the time sequence of the charging process, and the order of each stage energy in the entire charging process is determined. Then, the stage energy belonging to the same charging process is sequentially accumulated to form a charging energy result reflecting the energy output of the entire charging process. During the summarization process, stage energy can be read as an independent data unit and written into the summary result after reading to avoid data confusion between different stages. Through this method, while retaining the stage energy calculation results, a charging energy result corresponding to a complete charging process of the charging device can be obtained for subsequent use.

[0027] In a preferred embodiment of the present invention, the power change amounts in the power change sequence are sequentially compared to generate a trend determination result. Based on the trend determination result, time segments with consistent trends are identified. The stage boundaries are determined through dual verification of the change amplitude and stability of the time segments, dividing the charging process into multiple charging stages with different power change characteristics, including: The power change values ​​in the power change value sequence are traversed one by one, and the current power change value is compared with the previous power change value to generate a trend determination result that characterizes the power change trend. Based on the trend determination results, identify time intervals in which multiple consecutive power changes have the same trend, and generate power change trend interval data. The power change trend segment data is judged by both the change magnitude and the continuous stability. When the power change trend segment changes from a segment whose change magnitude meets the preset stability judgment condition to a segment whose change magnitude meets the preset change judgment condition, the corresponding time position is determined as the stage boundary data. Based on the stage boundary data, the charging process is segmented to generate multiple charging stage data that are distinct from each other in the time dimension and have different power change characteristics.

[0028] In this embodiment of the invention, by traversing the power change sequence one by one and comparing the current power change with its previous neighbor, the trend of power change over time can be determined, and a trend determination result can be generated to characterize the power change trend. This processing method allows the evolution of power change to be clearly expressed in the form of a trend determination result, thereby avoiding the instability caused by relying solely on a single power change value for judgment.

[0029] Based on this, according to the trend determination results, situations where multiple consecutive power changes show consistent trends are identified, and the corresponding time ranges are grouped into continuous power change trend segments. By organizing discrete trend determination results into time-continuous trend segments, the power change state can be described in a segmented form, thus providing a clear time structure for subsequent overall judgment of the power change state.

[0030] Furthermore, by performing a dual assessment of the power change trend segment's amplitude and sustained stability, and introducing preset stability and change criteria, the transition process from a stable to a changing power change state is identified. When the power change trend segment changes from a segment whose amplitude meets the preset stability criteria to a segment whose amplitude meets the preset change criteria, the corresponding time position is determined as the stage boundary data. This dual verification method ensures that the determination of the stage boundary is based on the overall characteristics of the trend segment, effectively reducing the possibility of stage misjudgment due to short-term abnormal fluctuations or noise changes.

[0031] Finally, the charging process is segmented based on the determined stage boundary data, dividing the entire charging process into multiple charging stages that are distinct from each other in the time dimension and have different power change characteristics. Through the above processing, the resulting charging stage segmentation can more realistically reflect the actual evolution of the power change state during charging, providing a clear and reliable stage basis for subsequent staged sampling control and staged energy calculation based on different charging stages.

[0032] In a preferred embodiment of the present invention, the power change amounts in the power change sequence are traversed one by one, and the current power change amount is compared with the previous power change amount to generate a trend determination result for characterizing the power change trend, specifically including: After obtaining the power change sequence, following the temporal order of the power changes, starting from the second power change in the sequence, the current power change and its immediate preceding power change are selected as a set of comparative data. By comparing the magnitude of the current power change with the previous power change, it is determined whether the power change at that time point is increasing, decreasing, or remaining essentially constant, and this determination is recorded as the trend judgment result. This traversal and comparison process continues until all power changes in the power change sequence have undergone trend judgment, resulting in a set of trend judgment results arranged in chronological order, used to reflect the evolution of the power change trend throughout the charging process.

[0033] In a preferred embodiment of the present invention, based on the trend determination result, time segments with consistent trends in multiple consecutive power changes are identified, and power change trend segment data is generated, specifically including: After obtaining the trend determination results, the results are scanned in chronological order. When multiple adjacent trend determination results are found to be consistent in the direction of change, the corresponding power changes are grouped into the same time segment in the time dimension, and the start and end times of this segment are recorded. If a change in trend is detected during the scanning process, the identification of the current time segment ends, and the identification of a new time segment begins. In this way, the originally discrete trend determination results are transformed into several time-continuous power change trend segments, allowing the power change trend to be expressed in a segmented form.

[0034] In a preferred embodiment of the present invention, the charging process is segmented based on stage boundary data to generate multiple charging stage data that are distinct from each other in the time dimension and have different power change characteristics, specifically including: After determining the stage boundary data, this data is used as time division points to divide the entire charging process's time axis. Specifically, the start time of the charging process is taken as the starting point of the first time period, the time interval between two adjacent stage boundaries is considered an independent charging stage, and the time interval between the last stage boundary and the end time of the charging process is considered the last charging stage. Subsequently, a corresponding time range identifier is assigned to each charging stage, and the power measurement values, power changes, and related data within that time range are included in the corresponding charging stage, thus forming multiple charging stage data that are independent in time and have different power change characteristics.

[0035] In a preferred embodiment of the present invention, for each charging stage, the power change within the stage is extracted and its distribution characteristics are statistically obtained. The activity level of the power change in the stage is determined by combining the dispersion and frequency of the power change distribution. Based on the activity level, a sampling control target and rule set are matched. After calculation, correction, and boundary constraint verification, stage sampling control parameters are generated, including: Extract the power change in each charging stage to generate stage power change data. Statistical processing of the power change data in each stage yields distribution characteristic data that characterizes the degree of concentration of power change over time. Based on the distribution characteristic data, combined with the distribution dispersion and frequency of power change, stage power change activity level data is generated. Based on the stage power change activity level data, the sampling control target and rule set are matched, and the sampling time interval of the power variable is determined after calculation correction and boundary constraint verification to generate stage sampling control parameter data. The stage sampling control parameter data is correlated with the corresponding charging stage, and used as the basis for the control of voltage, current and power sampling within that charging stage.

[0036] In this embodiment of the invention, by extracting the corresponding power change amount within each charging stage, stage power change amount data is formed. Statistical processing is then performed on this stage power change amount data to obtain distribution characteristic data characterizing the distribution of power change amount over time. This distribution characteristic data reflects the concentration of power change amount within each charging stage, providing a basis for analyzing the overall characteristics of power change within that stage.

[0037] Based on this, the activity level of power change is determined by combining the dispersion and frequency of power change distribution within the charging stage, generating a stage-specific power change activity level. By introducing the two dimensions of dispersion and frequency of change, the determination of the activity level of power change no longer relies on a single statistical result, but is based on the comprehensive performance of power change in terms of time distribution and frequency of occurrence. This provides a clear and stable basis for distinguishing the power change characteristics of different charging stages.

[0038] Furthermore, based on the activity level of power changes in a given stage, a corresponding set of sampling control objectives and rules is matched. After calculation correction and boundary constraint verification, the energy variable sampling time interval corresponding to that charging stage is determined, generating stage sampling control parameters. Since these sampling control parameters directly originate from the actual activity state of power changes within a stage, the sampling behavior of voltage, current, and power can be adjusted according to the power change characteristics of different charging stages. This avoids using a uniform sampling strategy for all charging stages, ensuring sampling continuity while maintaining consistency between the sampling density and the degree of power change within the stage, thus improving the matching degree between the energy variable sampling process and the actual charging process.

[0039] In a preferred embodiment of the present invention, statistical processing is performed on the stage power change data to obtain distribution characteristic data characterizing the degree of concentration of power change over time, specifically including: After obtaining the power change data for a specific charging stage, the data is first organized according to the chronological order of the power change within that charging stage. Then, using the time range of that charging stage as the statistical interval, the distribution of the power change at different time points is statistically analyzed. For example, it is determined whether the power change is concentrated in a few time segments or relatively evenly distributed throughout the entire time range. Through this statistical processing, distribution characteristic data reflecting the degree of concentration or dispersion of the power change over time is generated, which is used to characterize the overall distribution of power changes within that charging stage.

[0040] In a preferred embodiment of the present invention, based on the stage power change activity level data, a sampling control target and rule set are matched, and after calculation correction and boundary constraint verification, the energy variable sampling time interval is determined to generate stage sampling control parameter data, specifically including: After obtaining the power change activity level data corresponding to a certain charging stage, this activity level is used as the input for sampling control. Based on the pre-defined correspondence between the activity level and the sampling time interval, the applicable energy variable sampling time interval for that charging stage is determined. Specifically, when the power change activity level indicates frequent power changes within the stage, a relatively short sampling time interval is selected; when the power change activity level indicates relatively smooth power changes within the stage, a relatively long sampling time interval is selected. Subsequently, the determined sampling time interval is output as the stage sampling control parameter data and associated with the corresponding charging stage for later use in subsequent staged sampling processes.

[0041] In a preferred embodiment of the present invention, within each charging stage, based on the energy variable sampling data, the power corresponding to each sampling time interval within the stage is calculated segment by segment of energy, and the segmented energy is accumulated within the stage to obtain the stage energy corresponding to each charging stage, including: Acquire the energy variable sampling data corresponding to each charging stage, and sort the energy variable sampling data according to the sampling time order to generate stage sampling sequence data; Based on the sampling time corresponding to adjacent sampling points in the phased sampling sequence data, determine the sampling time interval data formed between adjacent sampling points; For each sampling time interval, obtain the power measurement value corresponding to the starting position of the sampling time interval, and generate power characterization data for the sampling time interval; Based on the power characterization data of the sampling time interval and the duration of the corresponding sampling time interval, determine the interval electrical energy data corresponding to the sampling time interval; According to the sampling time sequence, the interval electrical energy data corresponding to each sampling time interval within the same charging stage are accumulated to generate the stage electrical energy data corresponding to that charging stage.

[0042] In this embodiment of the invention, by organizing the electrical energy variable sampling data collected in each charging stage according to the sampling time sequence, the sampling time intervals formed between adjacent sampling points can be clearly defined, and a correspondence can be established between the power measurement values ​​and the corresponding time intervals. Based on this, segmented electrical energy calculation is performed on a sampling time interval basis, and the data is accumulated within each stage, ensuring that the electrical energy calculation process reflects the actual power change over time. Since the sampling time intervals originate from staged sampling control, this segmented calculation method can adapt to the electrical energy calculation requirements under different sampling time intervals, avoiding calculation deviations introduced by changes in sampling intervals. Through the above processing, the stage electrical energy corresponding to each charging stage can accurately reflect the energy output within that stage, providing a reliable data foundation for subsequently summarizing the electrical energy of multiple stages.

[0043] In a preferred embodiment of the present invention, the interval electrical energy data corresponding to the sampling time interval is determined based on the power characterization data of the sampling time interval and the duration of the corresponding sampling time interval, specifically including: After determining the power characterization data for the sampling time interval, this power characterization data is associated with its corresponding sampling time interval. Specifically, the power change characterized within the sampling time interval is combined with the duration of the sampling time interval to reflect the energy output within that time interval. By quantifying the continuous effect of the power characterization data within the time interval, interval-specific electrical energy data is generated, thus ensuring that each sampling time interval has a corresponding electrical energy result.

[0044] In a preferred embodiment of the present invention, the interval energy data corresponding to each sampling time interval within the same charging stage are accumulated according to the sampling time sequence to generate the stage energy data corresponding to that charging stage, specifically including: After obtaining the interval energy data corresponding to all sampling time intervals within the same charging phase, the interval energy data is first arranged according to the chronological order of the sampling time intervals on the time axis. Then, starting from the initial sampling time interval of the charging phase, the interval energy data corresponding to subsequent sampling time intervals are sequentially accumulated until the accumulation of all sampling time intervals within the charging phase is completed. Through this sequential accumulation method, phase energy data reflecting the overall energy output of the charging phase is generated, and this phase energy data is used as input for subsequent energy aggregation processing.

[0045] In a preferred embodiment of the present invention, the power change trend segment data undergoes dual judgment of change amplitude and sustained stability. When the power change trend segment changes from a segment whose change amplitude meets a preset stability judgment condition to a segment whose change amplitude meets a preset change judgment condition, the corresponding time position is determined as stage boundary data, including: Based on the power change trend segment data, extract the corresponding power change amount within each power change trend segment to generate segment power change set data. Based on the power change data set of the segment, statistical processing of the change amplitude within each power change trend segment is performed to generate segment change amplitude characterization data to represent the power change amplitude level within the power change trend segment. Based on the segment variation amplitude characterization data, the stability and continuity of the power variation amplitude within the power variation trend segment are evaluated and processed to generate segment stability characterization data for characterizing the stability of the power variation trend segment. The stability characterization data of the section is compared with the preset stability judgment conditions. When the stability characterization data of the section meets the preset stability judgment conditions, stable section identification data is generated. The segment change amplitude representation data is compared with the preset change judgment conditions. When the segment change amplitude representation data meets the preset change judgment conditions, the changed segment identification data is generated. The continuity of stable segment identifier data and changed segment identifier data in time sequence is verified. When stable segment identifier data appears continuously in time dimension and then changed segment identifier data appears continuously, the boundary time position between stable segment and changed segment is determined. The boundary time position is output as stage boundary data to characterize the change in power state.

[0046] In this embodiment of the invention, by further processing the power change amount at the power change trend segment level, the trend information that originally only reflected the direction of change is refined into segment feature data that can characterize the level of change amplitude and stable and continuous state. Based on the statistical results of the change amplitude within the segment, whether the power change remains stable over a period of time is evaluated, and different change states are distinguished by combining the stability judgment condition and the change judgment condition. On this basis, a verification of the continuous relationship between the stable segment and the change segment in time sequence is introduced, so that the determination of the stage boundary does not depend on the judgment of a single change amplitude, but is based on the overall process of "significant change after stable continuation". This processing method can reduce misjudgments caused by instantaneous power fluctuations, and make the determined stage boundary more consistent with the real turning point of the power change state during the charging process, thereby improving the rationality of the charging stage division.

[0047] In a preferred embodiment of the present invention, based on the set of segment power change data, statistical processing of the change amplitude within each power change trend segment is performed to generate segment change amplitude characterization data for characterizing the power change amplitude level within the power change trend segment, specifically including: After obtaining the set of power change data corresponding to a certain power change trend segment, the set of data is first organized according to the time sequence of the power change within that segment. Then, a comprehensive analysis is performed on all power changes within that power change trend segment to determine the overall fluctuation level of the power changes within that segment. For example, by comparing the differences between power changes within a segment, it can be determined whether the power changes within that segment are concentrated in a small range or fluctuate over a large range. Through the above statistical processing within the segment, segment change amplitude characterization data that reflects the overall change amplitude level of the power change trend segment is generated for subsequent stability and change state assessment.

[0048] In a preferred embodiment of the present invention, based on the segment variation amplitude characterization data, the stability and continuity of the power variation amplitude within the power variation trend segment are evaluated to generate segment stability characterization data for characterizing the stability of the power variation trend segment, specifically including: After obtaining the data characterizing the power variation amplitude within a segment, this data serves as the foundational information reflecting the strength of power variation within that segment. Subsequently, the continuity of the power variation trend segment over time is analyzed to determine whether the power variation amplitude within the segment remains at a relatively consistent level over a continuous period. If the segment variation amplitude characterization data shows minimal variation across multiple consecutive time points within the power variation trend segment, the power variation within that segment is considered to have a sustained and stable characteristic, and corresponding segment stability characterization data is generated. Through this evaluation process, the stability of power variation not only reflects changes at a single point in time but also embodies the continuous nature of the variation amplitude over time.

[0049] In a preferred embodiment of the present invention, the method for setting the preset stability determination condition and the preset change determination condition specifically includes: During system initialization or configuration, the typical range of power change amplitude is analyzed based on the rated power range of the charging equipment, historical operating data, and common fluctuation characteristics of power changes. Based on this, preset stability criteria are set to characterize a period of gradual power change. When the stability data of a segment indicates that the power change amplitude consistently occurs within this condition range, the segment is determined to be a stable segment. Simultaneously, preset change criteria are set to characterize a significant increase in power change. When the change amplitude data of a segment meets this condition, the corresponding power change trend segment is determined to be a changing segment. By setting separate stability and change criteria and maintaining a distinction between them, frequent switching between stable and changing segments due to minor fluctuations or occasional changes can be avoided, thereby improving the reliability of the stage boundary identification process.

[0050] In a preferred embodiment of the present invention, the continuity of stable segment identifier data and changing segment identifier data in time sequence is verified. When stable segment identifier data is detected to appear continuously in time dimension and then changing segment identifier data appears continuously, the boundary time position between the stable segment and the changing segment is determined, specifically including: After obtaining the stable segment identification data and the changing segment identification data, the identification data are checked one by one according to the arrangement order of the power change trend segments on the time axis. First, the stable segment identification data arranged continuously in time are identified, and the end time position of the continuous stable segment is recorded. Then, it is determined whether a continuous changing segment identification data appears immediately after the end time position. If the above time sequence relationship is detected, the boundary point between the end time position of the stable segment and the start time position of the changing segment is determined as the time position where the power change state changes. Through this continuity verification method, it is possible to avoid misjudging short-term abnormal fluctuations as stage boundaries, so that the finally determined stage boundaries are more consistent with the actual process of the power change state transitioning from stable to changing during the charging process.

[0051] In a preferred embodiment of the present invention, based on distribution characteristic data and combining the dispersion and frequency of power change distribution, stage power change activity level data is generated, including: Based on the distribution characteristic data, the time distribution of power change during the charging phase is analyzed and processed to generate distribution discreteness characterization data to characterize the degree of dispersion of power change distribution. Based on the discreteness characterization data, the frequency of power change during the charging phase is evaluated and processed to generate frequency characterization data to characterize the frequency of power change. Based on the discreteness characterization data and the frequency characterization data, active state combination data is constructed to characterize the active state of power change, wherein the active state combination data is used to reflect the joint state of power change in two dimensions: time distribution and frequency of change. The active state combination data is processed by state mapping, and the active state combination data is mapped to a preset activity level category to determine the power change activity level category corresponding to the charging stage. Based on the determined category of power change activity, generate stage power change activity level data corresponding to the charging stage.

[0052] In this embodiment of the invention, by analyzing the temporal distribution of power changes during the charging phase, the power change is expanded from a single numerical feature to a description encompassing both discrete distribution and frequency of change, allowing for a multi-faceted characterization of the power change state within each phase. Based on this, the discrete representation data and the frequency representation data are combined to form active state combination data reflecting the joint state of power changes, which is then converted into explicit activity level categories through a state mapping method. This process avoids directly judging activity levels based on a single statistical result, ensuring that the generation of activity levels has a clear data source and processing path. This allows for the stable and repeatable identification of differences in power change activity across different charging phases, providing a reliable basis for subsequent sampling control.

[0053] In a preferred embodiment of the present invention, based on distribution characteristic data, the time distribution state of the power change during the charging phase is analyzed and processed to generate distribution discreteness characterization data for characterizing the dispersion of the power change distribution, specifically including: After obtaining the distribution characteristic data corresponding to a certain charging stage, the overall range of the charging stage in the time dimension is first determined. Then, the power changes within that stage are arranged according to their time positions, and it is observed whether the power changes are concentrated in a few time segments or dispersed throughout the entire charging stage. Through analysis of the distribution of power changes on the time axis, discrete distribution characterization data reflecting the degree of concentration or dispersion of power changes within that charging stage is generated, used to describe the discrete characteristics of power changes in the time dimension.

[0054] In a preferred embodiment of the present invention, based on the distributed discrete characterization data, the frequency of power change during the charging phase is evaluated to generate frequency characterization data for characterizing the frequency of power change, specifically including: After obtaining the discrete distribution characterization data, the frequency of power changes during the charging phase and their temporal occurrence rhythm are further analyzed. Specifically, the occurrences of power changes exceeding preset change judgment conditions during the charging phase can be statistically analyzed, and the temporal distribution of these changes can be combined to determine whether power changes occur multiple times in a short period of time or only at a few time points. Through the above evaluation process, frequency characterization data is generated to represent whether power changes occur frequently during the charging phase.

[0055] In a preferred embodiment of the present invention, based on the discreteness characterization data and the frequency characterization data, active state combination data for characterizing the active state of power change is constructed. This active state combination data reflects the joint state of power change in both time distribution and frequency dimensions, specifically including: After obtaining the discrete distribution data and the frequency of change data respectively, these two types of data are combined as two independent dimensions describing the power change state. Specifically, the discrete distribution data and the frequency of change data can be stored together to form a combined data structure that simultaneously reflects the temporal distribution characteristics and the frequency of power change. Through this combined processing, the power change state is no longer described by a single feature, but can be expressed in the form of a joint state, providing a basis for the subsequent determination of the activity level category.

[0056] In a preferred embodiment of the present invention, the active state combination data is subjected to state mapping processing to map the active state combination data to a preset activity level category, thereby determining the power change activity level category corresponding to the charging stage. Specifically, this includes: After constructing the active state combination data, it is used as input data and compared with a pre-defined activity level mapping rule. This mapping rule defines the corresponding power change activity level category under different combinations of distribution discreteness and change frequency. By determining the active state combination data, it is mapped to a specific activity level category, thus completing the transformation from continuous state description to discrete category description, enabling the power change activity level to be represented in a clear categorical form.

[0057] In a preferred embodiment of the present invention, power change activity level data corresponding to a charging stage is generated based on a determined power change activity category, specifically including: After determining the power change activity category corresponding to each charging stage, this category is used as the final representation of the power change state for that charging stage. Subsequently, the activity category is converted into stage power change activity level data consistent with the system's internal rules, and associated with the corresponding charging stage for storage. In this way, each charging stage has a clear and unique power change activity level for use in the generation of subsequent stage sampling control parameters.

[0058] In a preferred embodiment of the present invention, the method for setting the activity level category specifically includes: During the system configuration phase, based on the power change characteristics of the charging equipment and common power change patterns during actual operation, at least two power change activity categories are pre-defined to distinguish between phases with relatively smooth power changes and phases with relatively frequent power changes. Furthermore, multiple activity categories can be defined based on different combinations of the dispersion of power change over time and the frequency of change, ensuring clear distinction in power change characteristics between different activity categories. The activity categories set in this way can cover common power change states during charging and provide a clear and executable basis for subsequently generating sampling control parameters based on the activity categories.

[0059] In a preferred embodiment of the present invention, based on the stage power change activity level data, a sampling control target and rule set are matched, and after calculation correction and boundary constraint verification, the energy variable sampling time interval is determined to generate stage sampling control parameter data, including: Based on the power change activity level data of the stage, the sampling control target corresponding to the charging stage is determined. The sampling control target is used to characterize the balance requirement between the sampling accuracy of the power variable and the consumption of sampling resources. Based on the sampling control objective, the sampling control rules corresponding to different power change activity levels are filtered to generate a set of sampling control rules that match the current charging stage. Based on the set of sampling control rules, the sampling time interval of electrical energy variables is calculated and corrected to generate candidate sampling time interval data that meet the sampling control objectives; Boundary constraint verification is performed on the candidate sampling time interval data to generate energy variable sampling time interval data that meets the preset sampling time constraint conditions; The sampling time interval data of the electrical energy variable is output as stage sampling control parameter data, and the stage sampling control parameter data is used to control the sampling process of voltage, current and power within the charging stage.

[0060] In this embodiment of the invention, by introducing a process for determining the sampling control objective, the generation of the energy variable sampling time interval no longer depends solely on the power change activity level itself, but rather on a balance between sampling accuracy requirements and sampling resource consumption. Based on this, various sampling control rules are screened according to the sampling control objective to form a set of rules matching the characteristics of the current charging stage, and the sampling time interval is calculated and corrected accordingly. Subsequently, boundary constraint verification ensures that the generated sampling time interval meets the preset time range requirements. This processing mechanism provides the sampling time interval determination process with clear control logic and constraints, enabling the sampling control parameters to adapt to the power change characteristics of different charging stages, thus improving the executability and stability of staged sampling in practical applications.

[0061] In a preferred embodiment of the present invention, a sampling control target corresponding to the charging stage is determined based on the stage power change activity level data. The sampling control target is used to characterize the balance requirement between the sampling accuracy of energy variables and the consumption of sampling resources, and specifically includes: After obtaining the activity level data of power change for a specific charging stage, this activity level is used as input information reflecting the power change state within that stage. Based on this activity level, the degree of attention paid to the details of power change during the sampling process for that charging stage is determined. When the activity level indicates that power changes are frequent within that stage, the sampling control objective is set to prioritize ensuring continuous reflection of the power change process; when the activity level indicates that power changes are relatively gradual within that stage, the sampling control objective is set to reduce the number of samples while meeting basic measurement requirements. Through this method, a sampling control objective that matches the power change characteristics of the charging stage is formed.

[0062] In a preferred embodiment of the present invention, based on the sampling control objective, sampling control rules corresponding to different power change activity levels are filtered to generate a set of sampling control rules matching the current charging stage, specifically including: Multiple sets of sampling control rules are pre-stored in the system, each corresponding to a power change activity level or activity level range. Once a sampling control target is determined, rules that conform to that target are selected from the pre-stored rules, while rules that do not conform to the current target are excluded. Through this filtering process, a set of sampling control rules that only matches the power change characteristics of the current charging phase is formed, providing a clear rule basis for the generation of subsequent sampling time intervals.

[0063] In a preferred embodiment of the present invention, the sampling time interval of the electrical energy variable is calculated and corrected according to the sampling control rule set to generate candidate sampling time interval data that meets the sampling control objective, specifically including: After obtaining the set of sampling control rules, the sampling time interval for electrical energy variables is initially determined based on the sampling strategy defined in the set of rules. This initially determined sampling time interval reflects the time interval requirements for voltage, current, and power sampling under the current sampling control objective. Subsequently, the initially determined sampling time interval is modified according to the actual power change during the charging phase; for example, the sampling time interval is appropriately shortened when the power change shows a short-term increasing trend. Through the above processing, candidate sampling time interval data that matches the power change state of the current charging phase is generated.

[0064] In a preferred embodiment of the present invention, boundary constraint verification processing is performed on the candidate sampling time interval data to generate energy variable sampling time interval data that meets the preset sampling time constraint conditions, specifically including: After generating candidate sampling time interval data, this data is compared with pre-set sampling time constraints. If the candidate sampling time interval is within the range defined by the sampling time constraints, it is directly used as the final sampling time interval; if the candidate sampling time interval exceeds the range defined by the sampling time constraints, it is adjusted to fall within the allowable sampling time range. Through the above boundary constraint verification process, it is ensured that the finally determined sampling time interval meets both the sampling control objective and the requirements of system operation and measurement stability.

[0065] In a preferred embodiment of the present invention, the energy variable sampling time interval data is output as stage sampling control parameter data, and the stage sampling control parameter data is used to control the sampling process of voltage, current and power during the charging stage, specifically including: After determining the final energy variable sampling time interval data, this sampling time interval is output as stage sampling control parameter data and associated with the corresponding charging stage. During voltage, current, and power sampling in the charging stage, sampling operations are performed according to the sampling time interval indicated in the stage sampling control parameter data, thus ensuring a consistent control method throughout the entire charging stage. This method ensures that the sampling control parameters can be practically applied to the energy variable sampling process within the charging stage.

[0066] In a preferred embodiment of the present invention, the method for setting the preset sampling time constraint specifically includes: During the system configuration phase, a reasonable range for the sampling time interval of electrical energy variables is set based on the measurement capabilities and data processing capabilities of the charging equipment, as well as the typical characteristics of power changes during charging. The sampling time constraint is used to limit the minimum and maximum allowable values ​​of the sampling time interval. The minimum allowable value is used to avoid excessively high sampling frequencies that could overburden data processing, while the maximum allowable value is used to avoid missing power change information due to excessively long sampling intervals. By setting these sampling time constraints, the adjustment process of the sampling time interval is always kept within a controllable range, thereby ensuring the executability and stability of the sampling process.

[0067] Embodiments of the present invention also provide a charging station energy variable data processing system, the system comprising: The measurement data acquisition module is used to acquire continuous measurement values ​​of voltage, current and power of the charging device during the charging process, and form a power measurement sequence according to the sampling time order; The power change generation module is used to perform difference processing on adjacent power measurement values ​​in the power measurement sequence to obtain the power change sequence. The charging stage segmentation module is used to traverse and compare the power change in the power change sequence one by one, generate the trend determination result, identify the time segment with a consistent trend based on the trend determination result, determine the stage boundary through the dual verification of the change amplitude and stability of the time segment, and divide the charging process into multiple charging stages with different power change characteristics. The sampling control parameter generation module is used to extract the power change within each charging stage and statistically obtain the distribution characteristic data. It determines the power change activity level of the stage by combining the distribution discreteness and change frequency of the power change. Based on the activity level, it matches the sampling control target and rule set, and generates the stage sampling control parameters after calculation correction and boundary constraint verification. The stage sampling control parameters include at least the energy variable sampling time interval. The phased sampling module is used to sample voltage, current and power in stages according to the corresponding phased sampling control parameters in each charging stage, so as to obtain the energy variable sampling data of the corresponding charging stage. The stage energy calculation module is used to calculate the energy of each segment of power corresponding to each sampling time interval within each charging stage based on the energy variable sampling data, and to accumulate the energy of each segment within the stage to obtain the stage energy corresponding to each charging stage. The electrical energy aggregation module is used to aggregate and process the electrical energy at each stage to generate the charging electrical energy result corresponding to the charging process.

[0068] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0069] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0070] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0071] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for processing electrical energy variable data in charging stations, characterized in that, The method includes: Acquire continuous measurements of voltage, current, and power of the charging device during the charging process, and form a power measurement sequence according to the sampling time order; By performing difference processing on adjacent power measurement values ​​in the power measurement sequence, a power change sequence is obtained; The power change in the power change sequence is traversed and compared one by one to generate a trend determination result. Based on the trend determination result, time segments with consistent trends are identified. The stage boundary is determined by dual verification of the change amplitude and stability of the time segment, and the charging process is divided into multiple charging stages with different power change characteristics. For each charging stage, the power change within the stage is extracted and the distribution characteristic data is statistically obtained. The activity level of power change in the stage is determined by combining the distribution discreteness and change frequency of the power change. The sampling control target and rule set are matched according to the activity level. After calculation correction and boundary constraint verification, the stage sampling control parameters are generated. The stage sampling control parameters include at least the energy variable sampling time interval. In each charging stage, voltage, current and power are sampled in stages according to the corresponding stage sampling control parameters to obtain the energy variable sampling data of the corresponding charging stage. Within each charging stage, based on the energy variable sampling data, the power corresponding to each sampling time interval within the stage is calculated segment by segment of energy, and the segment of energy is accumulated within the stage to obtain the stage energy corresponding to each charging stage. The electrical energy at each stage is summarized and processed to generate the charging electrical energy result corresponding to the charging process.

2. The charging station energy variable data processing method according to claim 1, characterized in that, Based on the power change sequence, the power changes in the sequence are iterated and compared one by one to generate a trend determination result. Based on the trend determination result, time segments with consistent trends are identified. The stage boundaries are determined through dual verification of the amplitude and stability of the time segment changes, dividing the charging process into multiple charging stages with different power change characteristics, including: The power change values ​​in the power change value sequence are traversed one by one, and the current power change value is compared with the previous power change value to generate a trend determination result that characterizes the power change trend. Based on the trend determination results, identify time intervals in which multiple consecutive power changes have the same trend, and generate power change trend interval data. The power change trend segment data is judged by both the change magnitude and the continuous stability. When the power change trend segment changes from a segment whose change magnitude meets the preset stability judgment condition to a segment whose change magnitude meets the preset change judgment condition, the corresponding time position is determined as the stage boundary data. Based on the stage boundary data, the charging process is segmented to generate multiple charging stage data that are distinct from each other in the time dimension and have different power change characteristics.

3. The charging station energy variable data processing method according to claim 1, characterized in that, For each charging stage, the power change within that stage is extracted and its distribution characteristics are statistically analyzed. The activity level of the power change in that stage is determined by combining the dispersion and frequency of the power change distribution. Based on this activity level, a set of sampling control objectives and rules is matched. After calculation, correction, and boundary constraint verification, stage sampling control parameters are generated, including: Extract the power change in each charging stage to generate stage power change data. Statistical processing of the power change data in each stage yields distribution characteristic data that characterizes the degree of concentration of power change over time. Based on the distribution characteristic data, combined with the distribution dispersion and frequency of power change, stage power change activity level data is generated. Based on the stage power change activity level data, the sampling control target and rule set are matched, and the sampling time interval of the power variable is determined after calculation correction and boundary constraint verification to generate stage sampling control parameter data. The stage sampling control parameter data is correlated with the corresponding charging stage, and used as the basis for the control of voltage, current and power sampling within that charging stage.

4. The method for processing electrical energy variable data in charging stations according to claim 1, characterized in that, Within each charging stage, based on the sampled data of electrical energy variables, the power corresponding to each sampling time interval within the stage is calculated segment by segment of electrical energy, and the segmented electrical energy is accumulated within the stage to obtain the stage electrical energy corresponding to each charging stage, including: Acquire the energy variable sampling data corresponding to each charging stage, and sort the energy variable sampling data according to the sampling time order to generate stage sampling sequence data; Based on the sampling time corresponding to adjacent sampling points in the phased sampling sequence data, determine the sampling time interval data formed between adjacent sampling points; For each sampling time interval, obtain the power measurement value corresponding to the starting position of the sampling time interval, and generate power characterization data for the sampling time interval; Based on the power characterization data of the sampling time interval and the duration of the corresponding sampling time interval, determine the interval electrical energy data corresponding to the sampling time interval; According to the sampling time sequence, the interval electrical energy data corresponding to each sampling time interval within the same charging stage are accumulated to generate the stage electrical energy data corresponding to that charging stage.

5. The method for processing electrical energy variable data in charging stations according to claim 2, characterized in that, The power change trend data is evaluated using both the magnitude of change and the stability of the change. When a power change trend segment changes from a segment whose magnitude meets a preset stability criterion to a segment whose magnitude meets a preset change criterion, the corresponding time position is determined as the stage boundary data, including: Based on the power change trend segment data, extract the corresponding power change amount within each power change trend segment to generate segment power change set data. Based on the power change data set of the segment, statistical processing of the change amplitude within each power change trend segment is performed to generate segment change amplitude characterization data to represent the power change amplitude level within the power change trend segment. Based on the segment variation amplitude characterization data, the stability and continuity of the power variation amplitude within the power variation trend segment are evaluated and processed to generate segment stability characterization data for characterizing the stability of the power variation trend segment. The stability characterization data of the section is compared with the preset stability judgment conditions. When the stability characterization data of the section meets the preset stability judgment conditions, stable section identification data is generated. The segment change amplitude representation data is compared with the preset change judgment conditions. When the segment change amplitude representation data meets the preset change judgment conditions, the changed segment identification data is generated. The continuity of stable segment identifier data and changed segment identifier data in time sequence is verified. When stable segment identifier data appears continuously in time dimension and then changed segment identifier data appears continuously, the boundary time position between stable segment and changed segment is determined. The boundary time position is output as stage boundary data to characterize the change in power state.

6. The method for processing electrical energy variable data in charging stations according to claim 3, characterized in that, Based on the distribution characteristic data, combined with the dispersion and frequency of power change, stage power change activity level data is generated, including: Based on the distribution characteristic data, the time distribution of power change during the charging phase is analyzed and processed to generate distribution discreteness characterization data to characterize the degree of dispersion of power change distribution. Based on the discreteness characterization data, the frequency of power change during the charging phase is evaluated and processed to generate frequency characterization data to characterize the frequency of power change. Based on the discreteness characterization data and the frequency characterization data, active state combination data is constructed to characterize the active state of power change, wherein the active state combination data is used to reflect the joint state of power change in two dimensions: time distribution and frequency of change. The active state combination data is processed by state mapping, and the active state combination data is mapped to a preset activity level category to determine the power change activity level category corresponding to the charging stage. Based on the determined category of power change activity, generate stage power change activity level data corresponding to the charging stage.

7. The method for processing electrical energy variable data in charging stations according to claim 3, characterized in that, Based on the stage power change activity level data, the sampling control target and rule set are matched, and after calculation correction and boundary constraint verification, the energy variable sampling time interval is determined, generating stage sampling control parameter data, including: Based on the power change activity level data of the stage, the sampling control target corresponding to the charging stage is determined. The sampling control target is used to characterize the balance requirement between the sampling accuracy of the power variable and the consumption of sampling resources. Based on the sampling control objective, the sampling control rules corresponding to different power change activity levels are filtered to generate a set of sampling control rules that match the current charging stage. Based on the set of sampling control rules, the sampling time interval of electrical energy variables is calculated and corrected to generate candidate sampling time interval data that meet the sampling control objectives; Boundary constraint verification is performed on the candidate sampling time interval data to generate energy variable sampling time interval data that meets the preset sampling time constraint conditions; The sampling time interval data of the electrical energy variable is output as stage sampling control parameter data, and the stage sampling control parameter data is used to control the sampling process of voltage, current and power within the charging stage.

8. A charging station energy variable data processing system, characterized in that, The system, used in any one of claims 1 to 7, comprises: The measurement data acquisition module is used to acquire continuous measurement values ​​of voltage, current and power of the charging device during the charging process, and form a power measurement sequence according to the sampling time order; The power change generation module is used to perform difference processing on adjacent power measurement values ​​in the power measurement sequence to obtain the power change sequence. The charging stage segmentation module is used to traverse and compare the power change in the power change sequence one by one, generate the trend determination result, identify the time segment with a consistent trend based on the trend determination result, determine the stage boundary through the dual verification of the change amplitude and stability of the time segment, and divide the charging process into multiple charging stages with different power change characteristics. The sampling control parameter generation module is used to extract the power change within each charging stage and statistically obtain the distribution characteristic data. It determines the power change activity level of the stage by combining the distribution discreteness and change frequency of the power change. Based on the activity level, it matches the sampling control target and rule set, and generates the stage sampling control parameters after calculation correction and boundary constraint verification. The stage sampling control parameters include at least the energy variable sampling time interval. The phased sampling module is used to sample voltage, current and power in stages according to the corresponding phased sampling control parameters in each charging stage, so as to obtain the energy variable sampling data of the corresponding charging stage. The stage energy calculation module is used to calculate the energy of each segment of power corresponding to each sampling time interval within each charging stage based on the energy variable sampling data, and to accumulate the energy of each segment within the stage to obtain the stage energy corresponding to each charging stage. The electrical energy aggregation module is used to aggregate and process the electrical energy at each stage to generate the charging electrical energy result corresponding to the charging process.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

Images