A detection method and system for optimizing the load of an electricity meter

Abstract

The present invention relates to the field of electric meter monitoring technology, specifically a detection method and system for optimizing electric meter loads, comprising the following steps: based on the load access terminal, collecting voltage and current data to determine synchronization offset, identifying dominant channel change patterns, adjusting the sampling rhythm configuration, constructing an amplitude mapping between voltage and power, analyzing multi-cycle response trends and classifying load states, and obtaining load feature distribution labels. The present invention, by constructing a timing comparison structure for voltage and current signals in the same cycle, enhances the ability to identify channel synchronization characteristics, avoids the interference of cycle rhythm drift on data sampling, determines the dominant trend of the channel based on the spacing change ratio, provides a clear guide for the source of the difference, improves the accuracy of fluctuation identification, and combines a dynamic control strategy for the sampling rhythm to achieve adaptive allocation of key channel sampling frequency bands, improve detection granularity, complete the linkage expression between multi-channel signals, and enhance the depth of characterization of the load response structure.

Description

Technical Field

[0001] The present invention relates to the technical field of electric meter monitoring, and in particular to a detection method and system for optimizing electric meter load. Background Art

[0002] The field of electricity meter monitoring mainly involves technologies for real-time or periodic collection, analysis, and management of the operating status, electricity consumption data, and electrical parameters (such as current, voltage, power factor, etc.) of electricity metering equipment (i.e., electricity meters). This field covers a variety of detection and monitoring methods, such as electrical parameter measurement, abnormal load identification, remote meter reading, load classification analysis, intelligent alarms, and energy consumption trend assessment. It is widely used in smart grids, energy efficiency management systems, and user-end electricity supervision. With the development of technologies such as the Internet of Things and artificial intelligence, electricity meter monitoring is evolving towards automation, intelligence, and integration, which helps to improve the transparency and management efficiency of electricity use.

[0003] Among them, the detection method used for meter load optimization is mainly to effectively detect the power load conditions connected to the meter. By monitoring the power consumption behavior and load characteristics at the meter end, it is determined whether it is in an optimizable or abnormal state, providing support for load adjustment, fault warning or subsequent operation and maintenance, so as to improve the operational safety and monitoring accuracy of the power system.

[0004] Existing technologies mainly use a unified sampling frequency in detection strategies, ignoring the phenomenon of asynchronous rhythm changes between voltage and current channels during actual operation. They are unable to capture the characteristics of timing misalignment within a cycle, resulting in abnormal rhythm segments being mistaken for normal states. There is a lack of a mechanism to distinguish the main causes of channel changes, resulting in unclear attribution of responsibility for fluctuations. Sampling parameter configuration is independent of detection feedback, and it is impossible to dynamically increase the sampling density of key channels when the load fluctuates. There is a problem of insufficient sampling efficiency. The response feature identification method relies on the single variable amplitude threshold segment and lacks the ability to construct multi-channel joint response relationships. It is difficult to express the nonlinear characteristics of complex load behavior. The classification basis is mainly based on the static state of the cycle, and there is a lack of continuity trend classification standards, which can easily lead to label breaks or classification deviations between cycles, affecting the detection continuity and the stability of operation status assessment. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a detection method and system for optimizing the load of an electric meter.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a detection method for optimizing the load of an electric meter, comprising the following steps:

[0007] S1: Based on the load access terminal, the voltage peak and current zero point data are collected to determine the time alignment offset of the two channels in the same cycle. The key synchronization segments are selected and the ratio of the spacing between the channels is mapped to obtain the cycle synchronization offset data.

[0008] S2: Based on the cycle synchronization offset data, compare the change trends of the voltage and current channels in adjacent cycles, identify the source channels of the differences, screen the sections where the differences are concentrated, generate dominant channel classification and identification features, and obtain the dominant channel change pattern;

[0009] S3: According to the dominant channel change pattern, the sampling interval of the corresponding channel is adjusted, the sampling frequency band density is optimized, and it is determined whether the non-dominant channel maintains the original state. The parameter configuration before the synchronization period conversion is obtained to obtain the sampling rhythm control configuration;

[0010] S4: Based on the sampling rhythm control configuration, calculate the relative proportion of the voltage channel waveform peak response change, compare it with the amplitude response change of the active power in the synchronous time node, determine the amplitude mapping of the two channels within the cycle, and obtain the cycle amplitude response characteristics.

[0011] The improvements of the present invention are that the periodic synchronization offset data includes a channel pairing time structure, an offset trend expression segment, and a rhythm difference identification field; the dominant channel change mode includes channel classification information, dominant guide coding information, and a change amplitude identification label; the sampling rhythm control configuration includes a rhythm control parameter set, a channel synchronization setting template, and a frequency band switching control mark; the periodic amplitude response characteristics include a response ratio structure, a power amplitude stratification result, and a channel amplitude corresponding mapping group.

[0012] The present invention is improved in that the step of acquiring the periodic synchronization offset data is specifically as follows:

[0013] S111: Based on the load access terminal, analyze the maximum amplitude point of each cycle in the voltage sequence and the signal zero-crossing point position of the adjacent cycle in the current sequence, calculate the time index difference between the two in the same cycle, determine the changing trend of the difference in consecutive cycles, and obtain the cycle rhythm difference;

[0014] S112: Based on the periodic rhythm difference, a segment with continuous fluctuations and increasing amplitudes in time is selected, the degree of synchronization between the voltage change rate and the current change rate in the segment is calculated, and the mutation concentration area formed by the density of the changes is analyzed to obtain a combined amount of offset change trends;

[0015] S113: Based on the offset change trend combination, determine the time position difference between the channels of each sampling sequence, compare the relative interval ratios of the difference channels in the synchronization segment, analyze the fluctuation frequency of the ratio in adjacent time intervals, and obtain periodic synchronization offset data.

[0016] The present invention is improved in that the step of acquiring the dominant channel change pattern is specifically as follows:

[0017] S211: Based on the cycle synchronization offset data, compare the change direction and amplitude of the voltage channel and the current channel in adjacent detection cycles to determine whether the offset trends of the two are consistent, analyze the offset performance differences between consecutive cycles, filter out the periodic segments with inconsistent changes, and obtain offset difference trend data;

[0018] S212: Based on the offset difference trend data, analyzing the matching between the channel offset direction and the change rate, determining whether the voltage channel or the current channel is dominant in each cycle, and calculating the attribution distribution of the dominant channel in the cycle to obtain a channel attribution distribution fragment;

[0019] S213: Based on the channel attribution distribution fragments, the relationship between the distribution characteristics and the change trend of the dominant channel is analyzed to obtain a dominant channel change pattern.

[0020] The present invention is improved in that the step of obtaining the sampling rhythm control configuration is specifically as follows:

[0021] S311: Adjust the sampling interval and time distribution configuration of the channel according to the dominant channel change pattern, analyze the frequency band distribution changes and frequency activation combinations in the current cycle of the dominant channel, optimize the sampling response density under each frequency band, and calculate the change trend of the frequency band switching ratio during sampling to obtain the interval density control value;

[0022] S312: The interval density control value is called to determine the difference between the state spectrum of the other channel in the current cycle and the static reference spectrum in each frequency band, and the direction of the spectrum difference change in consecutive cycles is compared to determine whether it remains stable. The frequency band behavior without sudden changes is selected as the basis for judgment, and a discriminant analysis is performed in combination with the interval density control situation to obtain the channel holding state indicator;

[0023] S313: Analyze the offset relationship between the frequency band switching amplitude and the sampling parameters according to the channel holding state identification value, and calculate the sampling rhythm combination deviation value , synchronously adjust the sampling rhythm setting to obtain the sampling rhythm control configuration.

[0024] The present invention is improved in that the steps of obtaining the periodic amplitude response characteristics are specifically as follows:

[0025] S411: Based on the sampling rhythm control configuration, calculate the response amplitudes of the voltage channel and the current channel at the synchronous sampling node, analyze the fluctuation ranges and trends of the two channels within a complete cycle, determine their synchronous changes within the cycle, and obtain an amplitude response change sequence;

[0026] S412: calling the amplitude response change sequence, comparing the amplitude changes of the voltage channel waveform peak and the active power of each node, calculating the response ratio at the same time point, and obtaining the amplitude response ratio sequence;

[0027] S413: According to the amplitude response ratio sequence, determine the fluctuation distribution within the period range, analyze the continuity and jump characteristics of the ratio change, select the channel matching points with correlation, and obtain the period amplitude response characteristics.

[0028] The present invention is improved in that the steps further include:

[0029] S5: Based on the cycle amplitude response characteristics, determine the response distribution form in multiple cycles, screen the difference feature segments, analyze their proportion and trend continuity, classify the corresponding load state type, and obtain the load feature distribution label;

[0030] The load feature distribution label includes a response level mapping label, a periodic structure coverage type, and a load state classification index.

[0031] The present invention is improved in that the steps of obtaining the load characteristic distribution label are specifically as follows:

[0032] S511: Based on the cycle amplitude response characteristics, analyze the amplitude response arrangement order corresponding to each cycle number, calculate its order relationship and amplitude variation range in the cycle sequence, determine the distribution aggregation state between the cycle numbers, filter the number segments with distribution differences, and obtain the cycle response distribution identifier;

[0033] S512: Based on the periodic response distribution identifier, compare the number, sequence structure and aggregation trend of the response category numbers, integrate the coverage characteristics, sorting consistency and periodic response difference characteristics of each category number, classify the load response type corresponding to each category, and obtain the load feature distribution label.

[0034] A detection system for optimizing electric meter load, the system comprising:

[0035] The rhythm offset identification module analyzes the collected voltage peak and current zero-point sampling data based on the load access terminals, determines the offset trend of the two sets of time series data in the same cycle in the benchmark alignment, selects the synchronization segments with key rhythm differences, and maps the spacing change ratio characteristics between channels to obtain the cycle synchronization offset data;

[0036] The dominant channel identification module compares the continuous change trends of the voltage channel and the current channel in adjacent detection cycles based on the cycle synchronization offset data, determines the source channel of the difference change in each cycle, selects the cycle segments where the difference is concentrated, identifies the dominant channel classification structure and identification features, and obtains the dominant channel change pattern;

[0037] The sampling rhythm control module adjusts the sampling interval structure of the channel according to the change pattern of the dominant channel, optimizes the sampling frequency band density of the corresponding channel in the current detection cycle, determines whether the other channel enters the hold state, synchronizes the setting configuration of the two channels before the cycle conversion, and obtains the sampling rhythm control configuration;

[0038] The response mapping construction module calculates the relative proportion of the voltage channel waveform peak response change based on the sampling rhythm control configuration, compares it with the amplitude response change of the active power at the synchronous time node, determines the amplitude mapping of the two channels within the cycle, and obtains the cycle amplitude response characteristics;

[0039] The feature classification output module determines the distribution performance structure in multiple cycles based on the periodic amplitude response characteristics, screens sequence segments with differential response characteristics, analyzes their proportion and continuous distribution trend within the detection cycle, and classifies the corresponding load response type for each category to obtain the load feature distribution label.

[0040] Compared with the prior art, the advantages and positive effects of the present invention are:

[0041] In the present invention, by constructing a timing comparison structure for voltage and current signals in the same period, the ability to identify channel synchronization characteristics is enhanced, the interference of periodic rhythm drift on data sampling is avoided, the dominant trend of the channel is judged according to the spacing change ratio, the source of the difference has a clear direction, and the accuracy of fluctuation identification is improved. Combined with the dynamic control strategy of the sampling rhythm, the adaptive allocation of the key channel sampling frequency band is realized, the detection granularity is improved, the amplitude mapping relationship between the voltage and power response is constructed, the linkage expression between multi-channel signals is completed, the characterization depth of the load response structure is enhanced, and the response continuity analysis of multiple detection cycles is combined to construct a load state label sequence, so that the characteristic change trend has identifiable and classifiable characteristics, thereby supporting the accurate identification and feature classification of the load state of the meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of the main steps of the present invention;

[0043] Figure 2 This is a flow chart for obtaining periodic synchronization offset data in the present invention;

[0044] Figure 3 This is a flow chart for obtaining the dominant channel change mode in the present invention;

[0045] Figure 4 This is a flowchart for obtaining the sampling rhythm control configuration in the present invention;

[0046] Figure 5 This is a flow chart for obtaining the periodic amplitude response characteristics in the present invention;

[0047] Figure 6 This is a flow chart for obtaining load feature distribution labels in the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0050] Example

[0051] See also Figure 1 The present invention provides a technical solution: a detection method for optimizing the load of an electric meter, comprising the following steps:

[0052] S1: Based on the load access terminals, the collected voltage peak and current zero point sampling data are analyzed to determine the offset trend of the two sets of time series data in the same cycle in the benchmark alignment. The synchronization segments with key rhythm differences are selected, and the spacing change ratio characteristics between the channels are mapped to obtain the cycle synchronization offset data.

[0053] S2: Based on the cycle synchronization offset data, the continuous change trends of the voltage channel and the current channel in adjacent detection cycles are compared to determine the source channel of the difference change in each cycle. The cycle segments with concentrated differences are selected to identify the classification structure and recognition features of the dominant channel, and the dominant channel change pattern is obtained.

[0054] S3: According to the dominant channel change pattern, adjust the channel sampling interval structure, optimize the sampling frequency band density of the corresponding channel in the current detection cycle, determine whether the other channel enters the hold state, synchronize the setting configuration of the two channels before the cycle conversion, and obtain the sampling rhythm control configuration;

[0055] S4: Based on the sampling rhythm control configuration, calculate the relative proportion of the voltage channel waveform peak response change, compare it with the amplitude response change of the active power at the synchronous time node, determine the amplitude mapping of the two channels within the cycle, and obtain the cycle amplitude response characteristics;

[0056] S5: Based on the cycle amplitude response characteristics, determine the distribution performance structure in multiple cycles, screen sequence segments with differential response characteristics, analyze their proportion and continuous distribution trend within the detection cycle, and classify the corresponding load response type for each category to obtain the load feature distribution label.

[0057] The periodic synchronization offset data includes the channel pairing time structure, the offset trend expression segment, and the rhythm difference identification field. The dominant channel change mode includes the channel classification information, the dominant guide coding information, and the change amplitude identification label. The sampling rhythm control configuration includes the rhythm control parameter set, the channel synchronization setting template, and the frequency band switching control mark. The periodic amplitude response characteristics include the response ratio structure, the power amplitude stratification results, and the channel amplitude corresponding mapping group. The load feature distribution label includes the response level mapping label, the periodic structure coverage type, and the load state classification index.

[0058] The offset trend in baseline alignment refers to the trend of misalignment or inconsistent pacing exhibited by the time series of the voltage and current channels in a synchronous relationship on a unified time axis. This trend is used to determine whether there is a rhythmic offset between the channels. A synchronous segment refers to a continuous segment of strong synchronization between the voltage and current data during the detection cycle. This segment meets the waveform trend consistency requirements and can be used as a reliable reference for subsequent comparison and rhythm analysis. The inter-channel spacing change ratio feature refers to the proportional relationship between the time intervals between adjacent peaks in the voltage channel and the intervals between adjacent zero points in the current channel. This feature can be used to quantify the dynamic rhythm changes between the two channels. The source channel refers to the electrical parameter channel that plays a dominant role in the rhythm offset or fluctuation characteristics in the periodic change trend and is the key data source for differential changes. The dominant channel classification structure refers to a state labeling system that classifies the source channel as voltage-dominant, current-dominant, or indistinguishable after identifying it, which is used for dynamic adjustment of the subsequent sampling strategy. The identification feature refers to a set of parameter features used to characterize the dominant state of the channel, including but not limited to detection feature items with distinguishing capabilities such as fluctuation continuity, change amplitude, and rhythm trend direction. The sampling interval structure refers to the time interval between consecutive sampling points configured by the detection system for a specific channel, which is used to determine the granularity and frequency strategy of signal capture. The sampling frequency band density refers to the density of the number of samples configured in a specific frequency band within a unit cycle and is an important indicator of signal monitoring accuracy and data priority. The period before the cycle transition refers to the time node between the end of the current sampling cycle and the beginning of the next cycle, which is the critical moment for the system to switch sampling parameters and synchronize channel configuration. The amplitude response change refers to the comparison of the peak change in the voltage channel and the energy output in the active power channel at the same time node, reflecting the load's real-time feedback characteristics to changes in electrical parameters. The amplitude mapping situation refers to the synchronization relationship between the voltage response and power response between multiple sampling points, specifically manifested in the degree of one-to-one correspondence and change pattern between the amplitude changes of the two channels. The distribution representation structure refers to the distribution and change pattern of the response ratio data within each cycle in a continuous cycle. It is the basic information structure for determining whether the load operating status is stable, regular, or abnormal.

[0059] See also Figure 2 The specific steps for obtaining periodic synchronization offset data are as follows:

[0060] S111: Based on the load access terminal, analyze the maximum amplitude point of each cycle in the voltage sequence and the signal zero-crossing point position of the adjacent cycle in the current sequence, calculate the time index difference between the two in the same cycle, determine the changing trend of the difference in consecutive cycles, and obtain the cycle rhythm difference;

[0061] First, in each cycle of the voltage channel, identify the point with the largest amplitude. This point is located at the peak position of the waveform, and use the sampling sequence number to mark its specific position in the entire time series. For example, the sampling system collects a certain number of data points per second, and each cycle contains a fixed number of sampling points. The index of the maximum amplitude point is determined by comparing the voltage value point by point. Then, the first zero-crossing point that appears within the corresponding cycle range is searched in the current channel. This point is usually located at the moment when the current signal changes from negative to positive or reverse. The identification method is to detect the change in the sign of the current value between the current sampling point and the previous point. When the sign jumps, the point is identified as the zero-crossing point. The index of the sampling point is also represented by the position number in the time series. Next, the time when the voltage peak point and the current cross the zero point are compared. The position difference between the two channels is the difference between the sampling point numbers in the same cycle. The difference is expressed in time units or sampling point intervals. When the difference value is positive, it means that the voltage peak occurs after the current crosses zero. If it is negative, the opposite is true. This process is repeated for multiple consecutive cycles to form a cycle difference sequence, which is used to reflect the change in synchronization offset between the two channels in each cycle. Then, the changing trend of the difference value between consecutive cycles in the sequence is analyzed. By comparing whether the difference values of adjacent cycles increase, decrease, or change repeatedly, it is determined whether the rhythm has changed. For example, if the difference value in consecutive cycles gradually increases, it is considered that the rhythm lag is expanding. If it gradually decreases, it is considered that the advance trend is strengthening. If the difference value frequently switches between positive and negative, it is considered that rhythm oscillation exists. In this way, the cycle rhythm difference value is formed.

[0062] S112: Based on the periodic rhythm difference, segments with continuous fluctuations and increasing amplitudes are selected, the degree of synchronization between the voltage change rate and the current change rate in the segment is calculated, and the mutation concentration area formed by the density of the changes is analyzed to obtain the offset change trend combination amount;

[0063] The data series are compared item by item in chronological order to analyze whether there is a situation in which the difference value changes continuously within a continuous period of time. When the difference value is monitored to increase or decrease continuously and the duration exceeds the set number of time periods, the segment is marked as a rhythm fluctuation area. For example, if the difference value increases continuously within three or more cycles, it is determined that there is a significant fluctuation trend in the segment. Then, the voltage waveform change speed is analyzed within the marked segment. The method is to compare the voltage change amplitude between two adjacent sampling points and process the current waveform in sequence to obtain the current change speed. The voltage and current change speed sequences are compared within the segment respectively, and the degree of closeness between the two is calculated, that is, the evaluation is performed. Estimate whether their changes tend to be consistent in time. When the changing trends of voltage and current remain highly consistent, it indicates that there is a synchronization relationship between the two. If the two change in different amplitudes, opposite directions or are not synchronized, it is considered that the synchronization is weak. In order to further identify the area where the changes are concentrated, it is necessary to count the locations where the voltage and current change rates show significant jumps in the segment, such as the number of locations where the change rate increases or decreases significantly in a short period of time. If the jump points are concentrated in a certain time sub-segment, it means that the sub-segment is the area where the voltage and current fluctuations are most concentrated. Such sub-segments will be extracted as mutation concentration areas, and the rhythm fluctuation trend and the mutation concentration area will be merged and recorded as the offset change trend combination.

[0064] S113: Based on the offset change trend combination, determine the time position difference between the channels of each sampling sequence, compare the relative interval ratios of the difference channels within the synchronization segment, analyze the fluctuation frequency of the ratios in adjacent time intervals, and obtain periodic synchronization offset data;

[0065] Based on the start and end positions of the mutation concentration area, the sampling data series of the voltage and current channels within the time period are extracted, and the sampling time point information of each is obtained respectively. At each sampling time point, the time difference of the two channels is calculated respectively, that is, the time difference between each pair of voltage and current sampling points is recorded to form a time position difference sequence. Then, the overall change amplitude of the difference sequence is analyzed, and the span and average difference between the maximum and minimum values are calculated to describe whether there is a long-term offset phenomenon in the voltage and current sampling time. Subsequently, this mutation concentration area is divided into multiple small segments of equal length, such as divided by fixed time intervals, and the voltage waveform in each segment is statistically analyzed. The average time interval between two adjacent peaks in the current waveform is calculated, and the average interval between two adjacent zero-crossing points in the current waveform is calculated. The two are then compared to obtain the relative spacing ratio in each segment, and a ratio sequence is formed in chronological order. The ratio sequence is subjected to difference analysis, that is, the change amplitude between the ratios is compared segment by segment, and the number of segments with large changes and the frequency of changes are counted. If the ratio changes frequently in multiple segments and the frequency of changes exceeds a certain proportion of the total number of segments, it is determined that there is a high-frequency fluctuation behavior of the relative interval in this time period. Combined with the statistical results of the time position difference and the ratio fluctuation frequency, the periodic synchronization offset data reflecting the rhythm misalignment between the two channels is output.

[0066] See also Figure 3 , the specific steps for obtaining the dominant channel change pattern are:

[0067] S211: Based on the cycle-synchronized offset data, compare the change direction and amplitude of the voltage channel and the current channel in adjacent detection cycles to determine whether the offset trends of the two are consistent, analyze the offset performance differences between consecutive cycles, filter out the periodic segments with inconsistent changes, and obtain offset difference trend data;

[0068] The offset values and change trends of the voltage and current channels in each detection cycle are extracted, and the offset direction changes of the two channels in two adjacent cycles are compared. For example, when the voltage channel offset increases from 5 to 8 and the current channel decreases from 4 to 2, it is determined that the change directions of the two channels are inconsistent. At the same time, the increase and decrease amplitudes of the offset of each channel are counted, and the difference in the amplitude change of the two channels is compared to see whether it exceeds the set amplitude difference threshold value. The threshold is set to 10% of the current average offset value to determine whether it belongs to a trend inconsistent cycle. When there are situations where the direction is opposite or the amplitude difference is significant in multiple consecutive cycles, it is extracted as a candidate cycle segment. Then, the number of trend reversals in the cycle segment and the density of offset value changes are combined to screen out significant cycle segments with inconsistent change performance. For example, in cycle segments 6 to 11, the voltage continues to rise and the current continues to decrease. This segment is recorded as an offset inconsistent segment, and the offset difference trend data is obtained.

[0069] S212: Based on the offset difference trend data, analyzing the matching between the channel offset direction and the change rate, determining whether the voltage channel or the current channel is dominant in each cycle, and calculating the attribution distribution of the dominant channel in the cycle to obtain a channel attribution distribution fragment;

[0070] Analyze the offset direction and rate value of the voltage and current channels in each cycle, calculate the rate difference of the channel offset increase or decrease for each cycle, and count the number of channels with larger rate change values within the cycle segment. When a channel has a dominant rate in cycles that exceed 60% of the total number of cycles, it is identified as the dominant channel in that segment. For example, if the voltage channel rate is dominant in 5 cycles from cycles 5 to 10, then the voltage channel in that segment is the dominant channel. The judgment result is recorded as the dominant attribution result according to the cycle number, and then the set of cycles that continuously dominate the same channel is marked as an attribution distribution segment. At the same time, mark the dominant channel type, voltage or current dominance and its corresponding cycle range to form channel attribution distribution segment data for extracting the basis for subsequent sampling adjustment.

[0071] S213: Based on the channel attribution distribution fragment, analyze the relationship between the distribution characteristics and change trends of the dominant channel using the formula: ;

[0072] Get the dominant channel change mode ,in, Indicates the The standard deviation of the channel offset within a period is used to measure the discreteness of the offset data within the period. Indicates the The frequency of channel offset changes within a cycle reflects the intensity of the offset changes. Indicates the The average amplitude of the channel offset data within a period is used as a measure of the overall change level of the channel within the period. Indicates the The channel offset balance factor within a cycle is used to determine whether the offset is evenly distributed within the cycle. Indicates the The density of the dominant channel in each cycle is used to evaluate the centralized distribution of the dominant channel in each cycle. Indicates the total number of periodic segments contained in the channel-attributed distribution segment;

[0073] Identify the attribution type of each periodic segment and determine whether the voltage channel or current channel is the dominant channel. Then, calculate the offset standard deviation, change frequency, average offset amplitude, balance factor and attribution density of each period, analyze its evolution process in the time series, and calculate the offset standard deviation. The acquisition of can be calculated by the fluctuation amplitude of the channel offset within the cycle. For example, the offset measurement data of the first cycle are 1.15, 1.20, 1.25, 1.18, and 1.22. The standard deviation is obtained , change frequency It can be calculated by the number of times the offset direction changes within a cycle. For example, if the offset direction alternates between positive and negative directions 5 times, then , average offset amplitude is the arithmetic mean of all offset data within the period. For example, the arithmetic mean of the above data is , balance factor It can be determined by the degree of symmetry of the distribution of the offset sequence, which is measured by the absolute value of the offset difference between the average position and the central symmetric point. If the data of this period is concentrated in the first half, , belonging density Indicates the concentration of the dominant channel in the periodic sequence. For example, if a channel is dominant in 5 consecutive periods, its concentration is 0.80. Substitute the parameters into the formula for calculation:

[0074] ;

[0075] ;

[0076] ;

[0077] .

[0078] Perform molecular operations:

[0079] ;

[0080] ;

[0081] Perform denominator operations:

[0082] ;

[0083] calculate :

[0084] ;

[0085] The results show that in the analyzed periodic series, the dominant channel has relatively stable and significant structural characteristics in terms of multiple indicators such as offset fluctuation, change frequency, distribution balance and degree of attribution concentration. This reflects that the dominant channel shows a strong trend of change aggregation in multiple periodic segments, which means that the dominance of the current channel in different periods not only dominates in terms of change amplitude and frequency, but also shows a centralized feature in distribution form. If the Represented as the strong dominant mode interval, Expressed as the neutral partial derivative interval, The weak dominant interval is mapped to the classification level of the dominant channel, and serves as a reference for subsequent behavior analysis or control logic configuration.

[0086] See also Figure 4 ,The steps for obtaining the sampling rhythm control configuration are as follows:

[0087] S311: Adjust the sampling interval and time distribution configuration of the channel according to the dominant channel change pattern, analyze the frequency band distribution changes and frequency activation combinations in the current cycle of the dominant channel, optimize the sampling response density in each frequency band, and calculate the changing trend of the frequency band switching ratio during sampling to obtain the interval density control value;

[0088] Obtain the dominant type and change characteristics of the voltage or current channel in the current cycle, extract the time interval sequence of the sampling points of the cycle in the dominant channel and adjust the sampling interval, read the time difference between every two adjacent sampling points of the dominant channel, set the basic sampling interval to 50 microseconds, and if the fluctuation frequency of the dominant channel increases, compress the time interval to 25 microseconds in the local frequency band, and retain or relax it to 100 microseconds in the stable section. Then divide the sampling points in the dominant channel according to the frequency range, for example, 0-100Hz, 100-300Hz, and 300-500Hz as low, medium, and high frequency bands respectively, count the number of sampling points in each frequency band, and divide it by the current cycle length to obtain the frequency band response density, and compare it with the same frequency band in the previous cycle. The difference in density values under the current cycle is used to determine whether the dominant channel has enhanced activation in a certain frequency band. For example, if the sampling point density of the 300-500Hz segment in the current cycle is 80 points / cycle, while it was 50 points / cycle in the previous cycle, then this frequency band is determined to be an activation-enhanced segment. Subsequently, each frequency band is analyzed to determine whether it is switched from other frequency bands in the current cycle. The switching ratio of the sampling distribution weights between frequency bands is calculated. Based on the change in the number of sampling points distributed between frequency bands within the cycle, if the low-frequency band sampling points decreases from 100 to 60 and the high-frequency band increases from 40 to 80, the frequency band switching ratio is +40 points from low to high, which is classified as a high-frequency switching trend. The changes in the sampling density and switching ratio of each frequency band are comprehensively considered to output the interval density control amount corresponding to the current cycle.

[0089] S312: Invoke the interval density control value to determine the difference between the state spectrum of the other channel in the current cycle and the static reference spectrum in each frequency band. Compare whether the direction of spectrum difference change remains stable in consecutive cycles. Select the frequency band behavior without sudden changes as the judgment basis. Combined with the interval density control situation, perform discriminant analysis to obtain the channel maintenance status indicator.

[0090] The frequency band density change value and frequency band switching trend in the interval density control amount are called to extract the frequency domain signal of the current cycle of the non-dominant channel. After the spectrum conversion operation is performed on the original time domain sampling data of the channel, the spectrum energy peaks in different frequency bands are counted and compared with the static reference spectrum. The reference spectrum is generated by averaging the spectrum of multiple cycles in the historical stable state. The difference amplitude threshold of each frequency band is set to 10%. If the energy peak difference between the current spectrum and the reference spectrum in a certain frequency band is less than this threshold, the frequency band is judged to be consistent. The difference values are compared band by band and the results are marked to form the spectrum identification vector of the current cycle state. Then, multiple cycles are extracted in chronological order. The identification vector sequence of each cycle is compared one by one in the direction of difference change of each frequency band in adjacent cycles. If the direction of change of the difference amplitude of a frequency band is the same in three or more consecutive cycles, and the absolute change value is less than the set slight change limit value of 5%, it is judged that the difference trend is stable, otherwise it is judged as a mutation, and all frequency band behaviors without mutation are screened out. Combined with the sampling density and switching trend of the frequency band in the interval density control amount, if the current frequency band behavior is stable and the high-variable segment in the uncontrolled amount is marked as the key adjustment area, the channel is marked as being in the hold state in the frequency band. After combining the judgment results of all frequency bands, the channel hold state identification amount of the current cycle is output.

[0091] S313: Analyze the offset relationship between the frequency band switching amplitude and the sampling parameter based on the channel hold status indicator, using the formula:

[0092] ;

[0093] Calculate the deviation of sampling rhythm combination , used to measure the offset strength of each frequency band in the set sampling structure, synchronously adjust the sampling rhythm setting, and obtain the sampling rhythm control configuration, where, Indicates the The frequency activation density of a frequency band reflects the frequency activation level of the channel in the frequency band. Indicates the The interval density control value of each frequency band describes the density change intensity of the sampling interval structure in this frequency band. Indicates the The frequency band switching amplitude of a frequency band measures the state switching amplitude of the channel in the frequency band in adjacent cycles. Indicates the The sampling interval parameter of each frequency band is the sampling configuration of the system for the frequency band in the current cycle. Indicates the periodic synchronization adjustment amount, which indicates the synchronization strength of the two channels configured and adjusted in the current detection cycle. Indicates the channel hold status indicator, which is used to reflect the judgment result of whether another channel maintains a stable state in the current cycle. is the total number of frequency bands;

[0094] Extract the frequency activation density of each frequency band, count the number of activations of each frequency band in unit time and divide it by the total time length. For example, if frequency band 1 is activated 20 times in a 100-second detection period, the frequency activation density is is 0.2; then, the sampling interval control amount is determined by the trigger frequency of the sampling window and the time structure reconstruction frequency. For example, in a certain frequency band, the sampling window is triggered at an interval of 10 milliseconds and 4 times per second, then the corresponding interval control amount is =0.4. For each frequency band, calculate the frequency band switching amplitude, that is, the number of times the frequency band changes state in the current detection cycle. For example, frequency band 1 switches from active to static and then back to active twice, then the frequency band switching amplitude is , set the sampling interval parameter to the constant value set in the system configuration. If the system sets it to 1.5, the frequency band parameter , substitute the parameters into the following formulas in turn to set the current cycle synchronization adjustment amount , channel hold status indicator , total number of frequency bands , and the rest of the parameters are set as follows:

[0095] Band 1: ;

[0096] Band 2: ;

[0097] Band 3: ;

[0098] Compute each product term:

[0099] Item 1: ;

[0100] Item 2: ;

[0101] Item 3: ;

[0102] Add the above values together:

[0103] ;

[0104] The denominator is calculated as:

[0105] ;

[0106] get:

[0107] ;

[0108] The calculation result It indicates that there is a certain offset in the sampling rhythm structure, which is mainly caused by the superposition of the high switching amplitude and high activation density of frequency band 3. This result is the sampling rhythm combination deviation, which is used to perform synchronization correction processing on the existing sampling rhythm setting to obtain the sampling rhythm control configuration, which is used as a reference for automatic optimization before the configuration parameters of the next cycle are updated. The formula introduces a square term between multiple frequency band parameters to amplify the difference, so that the parts with significant abnormal frequency band switching behavior can be given priority response in the overall configuration optimization. By introducing the square root normalization mechanism of the synchronous adjustment amount and the state identification factor, a quantitative method that takes into account the consistency of frequency distribution and channel state is constructed, which facilitates the formation of an adaptive control structure in multi-channel synchronous sampling.

[0109] See also Figure 5 , the steps for obtaining the periodic amplitude response characteristics are as follows:

[0110] S411: Based on the sampling rhythm control configuration, the response amplitudes of the voltage channel and the current channel at the synchronous sampling node are calculated, the fluctuation ranges and trends of the two channels within the complete cycle are analyzed, their synchronous changes within the cycle are determined, and an amplitude response change sequence is obtained;

[0111] Based on the sampling interval and frequency band settings of each channel, all sampling point pairs marked as synchronous sampling nodes are extracted within the cycle, and the values of the voltage channel and the current channel at the sampling point are read in turn as the response amplitude data points to construct a synchronous response amplitude pair sequence. Subsequently, the maximum and minimum values of the voltage and current channels in the entire cycle are calculated, and the difference between the maximum and minimum values is defined as the fluctuation range. Then, by sequentially traversing the amplitude change direction between each sampling point, it is determined whether the channel has a continuous upward, downward or oscillating change trend within the cycle. For example, if the voltage channel continuously increases within the cycle and the current channel decreases, the trends of the two are different and are correspondingly determined to be asynchronous changes. If the two are If the increase or decrease direction is consistent and time consistency is maintained within any time period, it is determined to be a synchronous change segment. The start and end sampling point indexes are then recorded for each synchronous segment and asynchronous segment. On this basis, a synchronous amplitude change sequence for a complete cycle is formed. The sequence content includes the type tag of each segment (synchronous / asynchronous), the start and end time positions, the amplitude change rate within each segment, and the average amplitude difference. For example, if the voltage channel increases from 220 to 230 between the 100th and 150th sampling points, and the current channel increases from 1.5 to 1.8, this segment is determined to be a synchronous rising segment. Finally, all synchronous nodes in the entire cycle are processed and summarized and output in this form to obtain the amplitude response change sequence.

[0112] S412: Call the amplitude response change sequence, compare the amplitude changes of the voltage channel waveform peak and active power of each node, and calculate the response ratio at the same time point using the formula:

[0113] ;

[0114] Get the amplitude response ratio sequence, where QR represents the average ratio of the voltage and power response ratio within the cycle, reflecting the overall correspondence between the response amplitudes of the two channel synchronization nodes. It represents the response change of the peak value of the voltage channel waveform at the oth synchronous sampling node, represents the amplitude response change of the active power channel at the oth synchronous sampling node, is the total number of synchronous sampling nodes;

[0115] The voltage and power variation amplitudes at each synchronous sampling node during the extraction period are set as and , where the peak response change of the voltage channel waveform is It can be calculated by the voltage peak difference at consecutive time points. For example, if the voltage at node 1 changes from 218V to 222V, then , power response change It can be calculated by the average active power change between nodes. For example, if the power at node 1 changes from 2.6kW to 3.0kW, then , perform a change amplitude extraction operation on each node respectively, and form a corresponding array of two sets of data. Now take 5 nodes as an example, and assume that their voltage changes are , the unit is volt, the power change is , in kilowatts, substitute into the formula and calculate item by item:

[0116] Item 1 is:

[0117] ;

[0118] Item 2 is:

[0119] ;

[0120] Item 3 is:

[0121] ;

[0122] Item 4 is:

[0123] ;

[0124] Item 5 is:

[0125] ;

[0126] The sum of the above five items is:

[0127] ;

[0128] After taking the average, we get:

[0129] ;

[0130] The calculation shows that among the five synchronous sampling nodes, the average response ratio of the voltage and power channels is 0.4201, indicating that there is a stable correlation between the two in terms of response changes. The amplitude response ratio sequence is composed of a set of ratios of such nodes, which can be used for the identification and classification of subsequent periodic response characteristics. The formula reflects the composite coupling effect of voltage and power fluctuations by introducing a square root product term, making the proportional calculation more sensitive to nonlinear growth changes, and has strong adaptability and versatility in multi-point synchronous monitoring environments.

[0131] S413: Based on the amplitude response ratio sequence, determine the fluctuation distribution within the period range, analyze the continuity and jump characteristics of the ratio change, select the channel matching points with correlation, and obtain the period amplitude response characteristics;

[0132] First, the amplitude ratio value sequence of all synchronization points within the cycle is calculated and arranged in chronological order. For example, 50 sets of voltage-to-current ratio data are obtained from 50 synchronization nodes. Then, whether the ratio shows a stable or sudden change trend within the cycle is determined. By comparing the change amplitudes of adjacent ratios item by item, if the change amplitude is less than the set fluctuation threshold of 5%, it is marked as a coherent segment, and if the change amplitude exceeds 20%, it is marked as a jump point. The number of jump points is accumulated throughout the entire cycle, and the location of the concentrated jump segments is counted. The amplitude ratio sequence within the coherent segment is then divided into intervals to find the ratio intervals that recur repeatedly within a certain value range. The intervals are then checked against the corresponding channel values in the original synchronization amplitude sequence to determine whether there is a segment with the strongest synchronization of the two channel responses under a specific amplitude ratio. That is, if the voltage change direction matches the current change direction within the interval at least 90%, then this ratio point pair is marked as a channel matching point. Finally, the ratio segments with high consistency within the cycle are screened out, and the regions are annotated with their sampling point index, ratio range, and channel fluctuation rate, and integrated into the output result of the cycle amplitude response feature.

[0133] See also Figure 6 ,The steps for obtaining the load feature distribution label are as follows:

[0134] S511: Based on the cycle amplitude response characteristics, analyze the amplitude response arrangement order corresponding to each cycle number, calculate its order relationship and amplitude variation range in the cycle sequence, determine the distribution aggregation state between the cycle numbers, filter the number segments with distribution differences, and obtain the cycle response distribution identifier;

[0135] First, assign a unique number to each cycle, and extract the representative amplitude response characteristic value in each cycle as a reference for sorting. For example, extract the average amplitude ratio of the voltage and current channel amplitude changes in each cycle, and arrange them in ascending order according to the size of the ratio corresponding to all cycles. Record the position change of each cycle number after sorting, and judge its distribution arrangement by the order of the numbers in the sorted list. Then calculate the amplitude ratio difference between each cycle and its adjacent cycles to form an inter-cycle amplitude change sequence, count the difference between the maximum and minimum values in the sequence, and judge whether there is a group of cycle numbers with continuous distribution and clear amplitude change range according to the period segment grouping method. In the obvious concentration, for example, if the amplitude change from period 1 to 5 is less than 5% and the change from period 6 to 9 is greater than 20%, it is considered that there is a distribution clustering difference between the two. Then all period numbers are classified according to the amplitude change interval, and the periods in the same interval are regarded as a distribution group. The number of period numbers and the distribution span of each group are counted. If a group of period numbers is highly concentrated in time series and the span is less than 20% of the entire period sequence, it is judged to be an aggregated distribution group. If the period numbers span a large area and the response amplitude values are discretely distributed, it is judged to be a differential distribution group. All differential distribution number segments are summarized and marked with their start and end period numbers, average amplitude, and maximum amplitude change to form a period response distribution identifier.

[0136] S512: Based on the periodic response distribution identifier, the number, sequence structure, and aggregation trend of the response category numbers are compared, the coverage characteristics, sorting consistency, and periodic response difference characteristics of each category number are integrated, and the load response type corresponding to each category is classified to obtain a load feature distribution label;

[0137] Based on the cycle numbers marked by each distribution segment and the response categories to which they belong, the response category labels of all cycle numbers are extracted, the number of each category number group is counted and its number sorting structure is recorded, that is, the position distribution order of each category cycle number in the whole sequence is recorded. For example, the voltage-dominated response category appears in cycles 3, 4, 5, and 6, and the current-dominated category appears in cycles 7, 9, 11, and 14. The number sequence is compared with the actual sampling time sequence to determine whether each category number presents a continuous distribution trend or interval distribution characteristics. Then, the cycle segment length and cycle number span in each category number are compared. If the cycle span of a category is less than 30% of the total number of cycles and the sorting continuity ratio is higher than 80%, it is recorded as an aggregated response. The load type is named according to its dominant channel, response density and amplitude difference. For example, cycles 3, 4, 5 and 6 are classified as “voltage-dominated and stable” loads, and cycles 7, 9 and 11 are classified as “current-dominated and irregular” loads. The load characteristic distribution label is then output.

[0138] A detection system for optimizing electric meter load, the system comprising:

[0139] The rhythm offset identification module analyzes the collected voltage peak and current zero-point sampling data based on the load access terminals, determines the offset trend of the two sets of time series data in the same cycle in the benchmark alignment, selects the synchronization segments with key rhythm differences, and maps the spacing change ratio characteristics between channels to obtain the cycle synchronization offset data;

[0140] The dominant channel identification module compares the continuous change trends of the voltage and current channels in adjacent detection cycles based on the cycle synchronization offset data, determines the source channel of the difference changes in each cycle, selects the cycle segments where the differences are concentrated, identifies the dominant channel classification structure and identification features, and obtains the dominant channel change pattern;

[0141] The sampling rhythm control module adjusts the sampling interval structure of the channel according to the change pattern of the dominant channel, optimizes the sampling frequency band density of the corresponding channel in the current detection cycle, determines whether the other channel enters the hold state, synchronizes the setting configuration of the two channels before the cycle conversion, and obtains the sampling rhythm control configuration;

[0142] The response mapping construction module calculates the relative proportion of the voltage channel waveform peak response change based on the sampling rhythm control configuration, compares it with the amplitude response change of the active power at the synchronous time node, determines the amplitude mapping of the two channels within the cycle, and obtains the cycle amplitude response characteristics;

[0143] The feature classification output module determines the distribution performance structure in multiple cycles based on the periodic amplitude response characteristics, screens sequence segments with differential response characteristics, analyzes their proportion and continuous distribution trend within the detection cycle, and classifies the corresponding load response type for each category to obtain the load feature distribution label.

[0144] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A detection method for optimizing electric meter load, characterized in that: The following steps are involved: S1: Based on the load access terminal, the voltage peak and current zero point data are collected to determine the time alignment offset of the two channels in the same cycle. The key synchronization segments are selected and the ratio of the spacing between the channels is mapped to obtain the cycle synchronization offset data. S2: Based on the cycle synchronization offset data, compare the change trends of the voltage and current channels in adjacent cycles, identify the source channels of the differences, screen the sections where the differences are concentrated, generate dominant channel classification and identification features, and obtain the dominant channel change pattern; S3: According to the dominant channel change pattern, the sampling interval of the corresponding channel is adjusted, the sampling frequency band density is optimized, and it is determined whether the non-dominant channel maintains the original state. The parameter configuration before the synchronization period conversion is obtained to obtain the sampling rhythm control configuration; S4: Based on the sampling rhythm control configuration, calculate the relative proportion of the voltage channel waveform peak response change, compare it with the amplitude response change of the active power at the synchronous time node, determine the amplitude mapping of the two channels within the cycle, and obtain the cycle amplitude response characteristics; S5: Based on the cycle amplitude response characteristics, determine the response distribution form in multiple cycles, screen the difference feature segments, analyze their proportion and trend continuity, classify the corresponding load state type, and obtain the load feature distribution label; The load feature distribution label includes a response level mapping label, a periodic structure coverage type, and a load state classification index; the periodic synchronization offset data includes a channel pairing time structure, an offset trend expression segment, and a rhythm difference identification field; the dominant channel change mode includes channel classification information, dominant guide coding information, and a change amplitude identification label; the sampling rhythm control configuration includes a rhythm control parameter set, a channel synchronization setting template, and a frequency band switching control mark; the periodic amplitude response feature includes a response ratio structure, a power amplitude stratification result, and a channel amplitude corresponding mapping group.

2. The detection method for optimizing the load of an electric meter according to claim 1, characterized in that: The steps for obtaining the periodic synchronization offset data are specifically as follows: S111: Based on the load access terminal, analyze the maximum amplitude point of each cycle in the voltage sequence and the signal zero-crossing point position of the adjacent cycle in the current sequence, calculate the time index difference between the two in the same cycle, determine the changing trend of the difference in consecutive cycles, and obtain the cycle rhythm difference; S112: Based on the periodic rhythm difference, a segment with continuous fluctuations and increasing amplitudes in time is selected, the degree of synchronization between the voltage change rate and the current change rate in the segment is calculated, and the mutation concentration area formed by the density of the changes is analyzed to obtain a combined amount of offset change trends; S113: Based on the offset change trend combination, determine the time position difference between the channels of each sampling sequence, compare the relative interval ratios of the difference channels in the synchronization segment, analyze the fluctuation frequency of the ratio in adjacent time intervals, and obtain periodic synchronization offset data.

3. The detection method for optimizing the load of an electric meter according to claim 1, wherein: The steps for obtaining the dominant channel change pattern are specifically as follows: S211: Based on the cycle synchronization offset data, compare the change direction and amplitude of the voltage channel and the current channel in adjacent detection cycles to determine whether the offset trends of the two are consistent, analyze the offset performance differences between consecutive cycles, filter out the periodic segments with inconsistent changes, and obtain offset difference trend data; S212: Based on the offset difference trend data, analyzing the matching between the channel offset direction and the change rate, determining whether the voltage channel or the current channel is dominant in each cycle, and calculating the attribution distribution of the dominant channel in the cycle to obtain a channel attribution distribution fragment; S213: Based on the channel attribution distribution fragments, the relationship between the distribution characteristics and the change trend of the dominant channel is analyzed to obtain a dominant channel change pattern.

4. The detection method for optimizing electric meter load according to claim 1, characterized in that: The steps for obtaining the sampling rhythm control configuration are specifically as follows: S311: Adjust the sampling interval and time distribution configuration of the channel according to the dominant channel change pattern, analyze the frequency band distribution changes and frequency activation combinations in the current cycle of the dominant channel, optimize the sampling response density under each frequency band, and calculate the change trend of the frequency band switching ratio during sampling to obtain the interval density control value; S312: The interval density control value is called to determine the difference between the state spectrum of the other channel in the current cycle and the static reference spectrum in each frequency band, and the direction of the spectrum difference change in consecutive cycles is compared to determine whether it remains stable. The frequency band behavior without sudden changes is selected as the basis for judgment, and a discriminant analysis is performed in combination with the interval density control situation to obtain the channel holding state indicator; S313: Analyze the offset relationship between the frequency band switching amplitude and the sampling parameters according to the channel holding state identification value, and calculate the sampling rhythm combination deviation value , synchronously adjust the sampling rhythm setting to obtain the sampling rhythm control configuration.

5. The detection method for optimizing the load of an electric meter according to claim 1, characterized in that: The steps for obtaining the periodic amplitude response characteristics are specifically as follows: S411: Based on the sampling rhythm control configuration, calculate the response amplitudes of the voltage channel and the current channel at the synchronous sampling node, analyze the fluctuation ranges and trends of the two channels within a complete cycle, determine their synchronous changes within the cycle, and obtain an amplitude response change sequence; S412: calling the amplitude response change sequence, comparing the amplitude changes of the voltage channel waveform peak and the active power of each node, calculating the response ratio at the same time point, and obtaining the amplitude response ratio sequence; S413: According to the amplitude response ratio sequence, determine the fluctuation distribution within the period range, analyze the continuity and jump characteristics of the ratio change, select the channel matching points with correlation, and obtain the period amplitude response characteristics.

6. The detection method for optimizing the load of an electric meter according to claim 1, characterized in that: The steps for obtaining the load characteristic distribution label are specifically as follows: S511: Based on the cycle amplitude response characteristics, analyze the amplitude response arrangement order corresponding to each cycle number, calculate its order relationship and amplitude variation range in the cycle sequence, determine the distribution aggregation state between the cycle numbers, filter the number segments with distribution differences, and obtain the cycle response distribution identifier; S512: Based on the periodic response distribution identifier, compare the number, sequence structure and aggregation trend of the response category numbers, integrate the coverage characteristics, sorting consistency and periodic response difference characteristics of each category number, classify the load response type corresponding to each category, and obtain the load feature distribution label.

7. A detection system for optimizing electric meter load, characterized in that: The system is used to implement the detection method for optimizing the load of an electric meter according to any one of claims 1 to 6, and the system includes: The rhythm offset identification module analyzes the collected voltage peak and current zero-point sampling data based on the load access terminals, determines the offset trend of the two sets of time series data in the same cycle in the benchmark alignment, selects the synchronization segments with key rhythm differences, and maps the spacing change ratio characteristics between channels to obtain the cycle synchronization offset data; The dominant channel identification module compares the continuous change trends of the voltage channel and the current channel in adjacent detection cycles based on the cycle synchronization offset data, determines the source channel of the difference change in each cycle, selects the cycle segments where the difference is concentrated, identifies the dominant channel classification structure and identification features, and obtains the dominant channel change pattern; The sampling rhythm control module adjusts the sampling interval structure of the channel according to the change pattern of the dominant channel, optimizes the sampling frequency band density of the corresponding channel in the current detection cycle, determines whether the other channel enters the hold state, synchronizes the setting configuration of the two channels before the cycle conversion, and obtains the sampling rhythm control configuration; The response mapping construction module calculates the relative proportion of the voltage channel waveform peak response change based on the sampling rhythm control configuration, compares it with the amplitude response change of the active power at the synchronous time node, determines the amplitude mapping of the two channels within the cycle, and obtains the cycle amplitude response characteristics; The feature classification output module determines the distribution performance structure in multiple cycles based on the periodic amplitude response characteristics, screens sequence segments with differential response characteristics, analyzes their proportion and continuous distribution trend within the detection cycle, and classifies the corresponding load response type for each category to obtain the load feature distribution label.

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