Detection method and system for ammeter load optimization

Abstract

The invention relates to the technical field of ammeter monitoring, in particular to a detection method and system for ammeter load optimization, and the method comprises the following steps: collecting voltage and current data based on a load access terminal, judging synchronous offset, recognizing a dominant channel change mode, adjusting sampling rhythm configuration, and constructing amplitude mapping between voltage and power; and analyzing a multi-cycle response trend and classifying load states to obtain a load feature distribution label. According to the invention, by constructing a time sequence comparison structure of voltage and current signals in the same period, the identification capability of channel synchronization characteristics is enhanced, the interference of periodic rhythm drift on data sampling is avoided, the channel dominant trend is judged according to the spacing change proportion, the difference source has clear guidance, and the fluctuation identification precision is improved. In combination with a dynamic regulation and control strategy of a sampling rhythm, adaptive distribution of sampling frequency bands of key channels is realized, the detection granularity is improved, linkage expression among multi-channel signals is completed, and the depiction depth of a load response structure is enhanced.

Description

Technical Field

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

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

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

[0004] The existing technologies mainly use a unified sampling frequency in the detection strategy, ignoring the phenomenon that the rhythm changes of the voltage and current channels are asynchronous during actual operation, and unable to capture the timing misalignment characteristics within a period. As a result, abnormal rhythm segments are misidentified as normal states. There is a lack of a mechanism to distinguish the main causes of channel changes, resulting in ambiguous attribution of fluctuations. The sampling parameter configuration is independent of the detection feedback, and it is unable to dynamically increase the sampling density of key channels during load fluctuations, resulting in insufficient sampling efficiency. The response feature recognition method relies on a single-variable amplitude threshold segment and lacks the ability to construct the joint response relationship of multiple channels, making it difficult to express the non-linear characteristics of complex load behaviors. The classification basis is mainly based on the periodic static state, lacking a continuous trend classification standard, which easily leads to label breaks or classification deviations between periods, affecting the continuity of detection and the stability of operating status assessment. Summary of the Invention

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

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A detection method for optimizing the load of an electric meter, comprising the following steps, S1: Based on the load access terminals, collect the voltage peak value and current zero point data, judge the time alignment offset of the two channels in the same period, screen the key synchronization segments, map the proportional relationship of the spacing between channels, and obtain the periodic synchronization offset data; S2: Based on the periodic synchronization offset data, compare the change trends of the voltage and current channels within adjacent periods, identify the channels where the differences originate, screen the sections with concentrated differences, generate the classification and identification features of the dominant channels, and obtain the change patterns of the dominant channels. S3: According to the change patterns of the dominant channels, adjust the sampling intervals of the corresponding channels, optimize the density of the sampling frequency bands, determine whether the non-dominant channels maintain their original states, and synchronize the parameter configurations before the cycle conversion to obtain the sampling rhythm regulation configuration. S4: Based on the sampling rhythm regulation configuration, calculate the relative proportion of the change in the peak response of the voltage channel waveform, compare it with the amplitude response change of the active power at the synchronization time node, judge the amplitude mapping situation of the two channels within the period, and obtain the periodic amplitude response characteristics.

[0007] The improvements of the present invention are that the periodic synchronization offset data includes the channel pairing time structure, the offset trend expression section, and the rhythm difference identification field; the change patterns of the dominant channels include the channel classification information, the dominant guiding coding information, and the change amplitude identification label; the sampling rhythm regulation 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 result, and the channel amplitude corresponding mapping group.

[0008] The improvements of the present invention are that the steps for obtaining the periodic synchronization offset data are specifically as follows: S111: Based on the load access terminals, analyze the positions of the maximum amplitude points in each period of the voltage sequence and the signal zero-crossing points in adjacent periods of the current sequence, calculate the time index difference between the two under the same period, judge the change trend of the difference in consecutive periods, and obtain the periodic rhythm difference amount. S112: Based on the periodic rhythm difference amount, screen the segments that fluctuate continuously in time and have an increasing amplitude, calculate the synchronization degree between the voltage change rate and the current change rate within this segment, and analyze the mutation concentration region formed by its change density to obtain the offset change trend combination amount. S113: Based on the offset change trend combination amount, judge the time position difference between channels in each sampling sequence segment, compare the relative interval ratio of the difference channels within the synchronization segment, and analyze the fluctuation frequency of the ratio in adjacent time intervals to obtain the periodic synchronization offset data.

[0009] The improvements of the present invention are that the steps for obtaining the change patterns of the dominant channels are specifically as follows: S211: Based on the periodic synchronization offset data, compare the change directions and amplitude changes of the voltage channel and the current channel within adjacent detection periods, judge whether the offset trends of the two are consistent, analyze the offset performance differences between consecutive periods, and screen the periodic segments with inconsistent changes to obtain the offset difference trend data. S212: Based on the offset difference trend data, analyze the matching situation between the channel offset direction and the change rate, determine whether the voltage channel or the current channel is dominant in each cycle, and calculate the attribution distribution of the dominant channel in the cycle to obtain the channel attribution distribution segment; S213: Based on the channel attribution distribution segment, analyze the relationship between the distribution characteristics and the change trend of the dominant channel to obtain the change mode of the dominant channel.

[0010] The improvement of the present invention is that the obtaining step of the sampling rhythm regulation configuration is specifically as follows: S311: According to the change mode of the dominant channel, adjust the sampling interval and the time distribution configuration of the channel, analyze the change of the frequency band distribution and the frequency activation combination 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 in the sampling to obtain the interval density regulation amount; S312: Invoke the interval density regulation amount, judge the difference amplitude between the state spectrum and the static reference spectrum of the other channel in each frequency band in the current cycle, compare whether the change direction of the spectrum difference remains stable in consecutive cycles, screen the frequency band behaviors without mutations as the judgment basis, and perform discriminant analysis in combination with the interval density regulation situation to obtain the channel holding state identification amount; S313: According to the channel holding state identification amount, analyze the offset relationship between the frequency band switching amplitude and the sampling parameters, and calculate the sampling rhythm combination deviation amount , and perform synchronous adjustment on the sampling rhythm setting to obtain the sampling rhythm regulation configuration.

[0011] The improvement of the present invention is that the obtaining step of the cycle amplitude response characteristic is specifically as follows: S411: Based on the sampling rhythm regulation configuration, calculate the response amplitudes of the voltage channel and the current channel at the synchronous sampling nodes, analyze the respective fluctuation ranges and trends of the two channels in the complete cycle, judge their synchronous change situations in the cycle, and obtain the amplitude response change sequence; S412: Invoke the amplitude response change sequence, compare the peak values of the voltage channel waveforms and the amplitude changes of the active power at each node, calculate the response ratio at the same time point, and obtain the amplitude response ratio sequence; S413: According to the amplitude response ratio sequence, judge the fluctuation distribution within the cycle range, analyze the coherence and jump characteristics of the ratio change, screen the channel matching points with relevant relationships, and obtain the cycle amplitude response characteristic.

[0012] The improvement of the present invention also includes: S5: Based on the periodic amplitude response characteristics, judge the response distribution patterns in multiple periods, screen out the differential feature sections, analyze their proportions and trend continuity, classify the corresponding load status types, and obtain the load feature distribution labels. The load feature distribution labels include response level mapping labels, periodic structure coverage types, and load status classification indexes.

[0013] The improvement of the present invention is that the steps for obtaining the load feature distribution labels are specifically as follows: S511: Based on the periodic amplitude response characteristics, analyze the amplitude response arrangement order corresponding to each cycle number, calculate its sorting relationship and amplitude change range in the cycle sequence, judge the distribution aggregation state between cycle numbers, screen out the number sections with distribution differences, and obtain the periodic response distribution identifiers. S512: Based on the periodic response distribution identifiers, compare the quantity, sequence structure, and aggregation trend of response category numbers, integrate the coverage characteristics, sorting consistency, and periodic response differential characteristics of each category of numbers, classify the corresponding load response types of each category, and obtain the load feature distribution labels.

[0014] A detection system for optimizing the load of an electric meter, the system includes: The rhythm offset recognition module, based on the load access terminals, analyzes the collected voltage peak and current zero-point sampling data, judges the offset trend of two groups of time series data in the same cycle in the reference alignment, screens out the key synchronous segments with rhythm difference changes, and maps the proportional characteristics of the spacing changes between channels to obtain the periodic synchronous offset data. The dominant channel recognition module, based on the periodic synchronous offset data, compares the continuous change trends of the voltage channel and the current channel in adjacent detection cycles, judges the source channels with differential changes in each cycle, screens out the cycle segments in the differential concentration area, and identifies the dominant channel classification structure and recognition characteristics to obtain the dominant channel change pattern. The sampling rhythm regulation module, according to the dominant channel change pattern, adjusts the sampling interval structure of the channels, optimizes the sampling frequency band density of the corresponding channels in the current detection cycle, judges whether the other channel enters the holding state, and synchronizes the set configurations of the two channels before the cycle conversion to obtain the sampling rhythm regulation configuration. The response mapping construction module, based on the sampling rhythm regulation configuration, calculates the relative proportion of the peak response change of the voltage channel waveform, compares it with the amplitude response change of the active power at the synchronous time node, judges the amplitude mapping situation of the two channels in the cycle, and obtains the periodic amplitude response characteristics. The feature classification output module, based on the periodic amplitude response features, determines the distribution performance structure in multiple periods, screens out the sequence sections with differential response features, analyzes their proportion and continuous distribution trend within the detection period, and classifies the corresponding load response types for each category to obtain the load feature distribution label.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by constructing the timing comparison structure of voltage and current signals in the same period, the recognition ability of the channel synchronization characteristics is enhanced, the interference of cycle rhythm drift on data sampling is avoided, the dominant trend of the channel is judged according to the change ratio of the spacing, so that the source of the difference has a clear orientation, the fluctuation recognition accuracy is improved, combined with the dynamic regulation strategy of the sampling rhythm, the adaptive allocation of the sampling frequency band of the key channel is realized, the detection granularity is improved, the amplitude mapping relationship between voltage and power response is constructed, the linkage expression between multi-channel signals is completed, the description depth of the load response structure is enhanced, combined with the analysis of the response continuity of multiple detection periods, the load state label sequence is constructed, so that the feature change trend has recognizability and classifiability, thus supporting the accurate recognition and feature classification of the electricity meter load state. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is the main flowchart of the present invention; Figure 2 is the flowchart for obtaining the periodic synchronization offset data in the present invention; Figure 3 is the flowchart for obtaining the change mode of the dominant channel in the present invention; Figure 4 is the flowchart for obtaining the sampling rhythm regulation configuration in the present invention; Figure 5 is the flowchart for obtaining the periodic amplitude response features in the present invention; Figure 6 is the flowchart for obtaining the load feature distribution label in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, 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 used to limit the present invention.

[0018] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0019] Embodiment Please refer to Figure 1 , the present invention provides a technical solution: a detection method for optimizing the load of an electric meter, including the following steps: S1: Based on the load access terminals, analyze the collected voltage peak and current zero-point sampling data, judge the offset trend of two sets of time series data in the reference alignment in the same period, screen the key synchronous segments with different rhythm changes, and map the proportional change characteristics of the spacing between channels to obtain the periodic synchronous offset data; S2: Based on the periodic synchronous offset data, compare the continuous change trends of the voltage channel and the current channel in adjacent detection periods, judge the source channels of the different changes in each period, screen the periodic segments in the area with concentrated differences, identify the classification structure and recognition characteristics of the dominant channels, and obtain the change mode of the dominant channels; S3: According to the change mode of the dominant channels, adjust the sampling interval structure of the channels, optimize the sampling frequency band density of the corresponding channels in the current detection period, judge whether the other channel enters the holding state, and synchronize the set configurations of the two channels before the period conversion to obtain the sampling rhythm control configuration; S4: Based on the sampling rhythm control configuration, calculate the relative proportion of the peak response change of the voltage channel waveform, compare it with the amplitude response change of the active power at the synchronous time node, judge the amplitude mapping situation of the two channels in the period, and obtain the periodic amplitude response characteristics; S5: Based on the periodic amplitude response characteristics, judge the distribution performance structure in multiple periods, screen the sequence segments with different response characteristics, analyze their proportion and continuous distribution trend in the detection period, and classify each corresponding load response type to obtain the load characteristic distribution label.

[0020] 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 guiding coding information, and change amplitude identification tags. The sampling rhythm regulation configuration includes a rhythm control parameter set, a channel synchronization setting template, and a frequency band switching control flag. The periodic amplitude response characteristics include a response ratio structure, a power amplitude stratification result, and a channel amplitude corresponding mapping group. The load characteristic distribution tag includes a response level mapping tag, a periodic structure coverage type, and a load status classification index.

[0021] The offset trend in the reference alignment refers to the change trend of the time series of the voltage and current channels showing a time lag or inconsistent pace in the synchronous relationship under the unified time axis, which is used to judge whether there is a rhythmic offset between the channels; the synchronous segment refers to the continuous segment with strong data synchronization between the voltage and current in the detection period. The segment meets the requirement of waveform trend consistency and can be used as a reliable reference for subsequent comparison and rhythm analysis; the ratio characteristic of the spacing change between channels refers to the ratio relationship between the time interval between adjacent peaks in the voltage channel and the interval between adjacent zeros in the current channel. Through this characteristic, the dynamic rhythm change between the two channels can be quantified. 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 causing differential changes; the dominant channel classification structure refers to the status marking system that classifies the source channel as voltage-dominant, current-dominant, or non-dominant after identifying the source channel, which is used for subsequent dynamic adjustment of the sampling strategy; the identification feature refers to the set of parameter features used to characterize the dominant state of the channel, including but not limited to detection feature items with discrimination ability such as fluctuation continuity, change amplitude, and rhythm trend direction. The sampling interval structure refers to the time interval arrangement between consecutive sampling points configured for a certain channel by the detection system, 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 samplings configured on a specific frequency band within a unit period and is an important indicator reflecting the signal monitoring accuracy and data attention priority; before the period conversion refers to the time node between the end of the current sampling period and the start of the next period, which is a critical moment for the system to execute sampling parameter switching and channel configuration synchronization. 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, which reflects the real-time feedback characteristic of the load to the electrical parameter change; the amplitude mapping situation refers to the synchronous relationship characteristic between the voltage response and the power response among multiple sampling points, specifically manifested as the one-to-one correspondence degree and change mode between the amplitude changes of the two channels. The distribution performance structure refers to the distribution method and change form of the response ratio data in each period in the continuous period, which is the basic information structure for judging whether the load operation state is stable, regular, or abnormal.

[0022] Please refer to Figure 2 , and the specific steps for obtaining the periodic synchronization offset data are as follows: S111: Based on the load access terminal, analyze the maximum amplitude points in each cycle of the voltage sequence and the signal zero-crossing positions in adjacent cycles of the current sequence, calculate the time index difference between the two in the same cycle, judge the change trend of the difference in consecutive cycles, and obtain the cycle rhythm difference amount; First, identify the point with the largest amplitude in each cycle of the voltage channel. 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, if the sampling system collects a certain number of data points per second, then each cycle contains a fixed number of sampling points. Determine the index of the maximum amplitude point by comparing the voltage values point by point. Subsequently, find the first point that crosses the zero value within the corresponding cycle range in the current channel. This point usually occurs at the moment when the current signal changes from negative to positive or vice versa. The identification method is to detect the sign change of the current value between the current sampling point and the previous point. When the sign changes, it is determined that this point is the zero-crossing point, and it is also represented by the position number in the time series to indicate the index of the sampling point where it is located. Next, compare the time position difference between the voltage peak point and the current zero-crossing point, that is, the difference between the sampling point numbers where the two are located in the same cycle. This difference is expressed in time units or sampling point intervals. When the difference value is positive, it means that the voltage peak appears after the current zero-crossing, and vice versa if it is negative. Repeat this process for multiple consecutive cycles to form a cycle difference sequence, which is used to reflect the synchronous offset change between the two channels in each cycle. Then analyze the change trend of the difference values between consecutive cycles in this sequence. By comparing whether the difference values between adjacent cycles increase, decrease, or change repeatedly, judge whether its rhythm has changed. For example, if the difference values in consecutive cycles gradually increase, it is regarded as an expansion of the rhythm lag; if they gradually decrease, it is regarded as an enhancement of the early trend; if the difference values frequently switch between positive and negative, it is considered that there is a rhythm oscillation. In this way, the cycle rhythm difference amount is formed.

[0023] S112: Based on the cycle rhythm difference amount, screen out the segments that fluctuate continuously in time and have an increasing amplitude, calculate the synchronization degree between the voltage change rate and the current change rate within this segment, analyze the mutation concentration area formed by its change density, and obtain the offset change trend combination amount; Compare each item in the data sequence sequentially in chronological order to analyze whether there is a situation where the difference value continuously changes within a continuous period of time. When it is monitored that the difference value continuously increases or decreases and the duration exceeds the set number of time periods, mark this segment as a rhythm fluctuation area. For example, if the difference value continuously increases within three or more cycles, it is determined that there is a significant fluctuation trend in this section. Subsequently, analyze the voltage waveform change speed within the marked section. The method is to compare the voltage change amplitude between two adjacent sampling points, and process the current waveform in sequence to obtain the change speed of the current. Compare the change speed sequences of voltage and current respectively within this section, and calculate the degree of proximity between the two, that is, evaluate whether their changes tend to be consistent in time. When the change trends of voltage and current are highly consistent, it indicates that there is a synchronization relationship between the two. If the change amplitudes are different, the directions are opposite, or they are not synchronized, the synchronization is considered weak. To further identify the area where changes are concentrated, it is necessary to count the positions where significant jumps occur in the voltage and current change rates within this section, such as the number of positions where the change speed increases or decreases significantly in a short period of time. If the jump points are concentrated in a certain time sub-section, it indicates that this sub-section is the area where the voltage and current fluctuate most intensively. Such sub-sections will be extracted as mutation concentration areas. Combine the rhythm fluctuation trend and the mutation concentration area and record them as the offset change trend combined quantity.

[0024] S113: Based on the offset change trend combined quantity, judge the time position difference between channels in each sampling sequence segment, compare the relative interval ratio of different channels within the synchronization segment, analyze the fluctuation frequency of the ratio in adjacent time intervals, and obtain the periodic synchronization offset data; Based on the start and end positions of the mutation concentration region, extract the sampling data sequences of the voltage and current channels during this time period, and respectively obtain the sampling time point information of each. At each sampling time point, calculate the time difference between the two channels, that is, record the time difference between each pair of voltage and current sampling points to form a time position difference sequence. Then analyze the overall change range of this difference sequence, calculate the span between the maximum value and the minimum value and the average difference, which is used to describe whether there is a long-term offset phenomenon between the voltage and current sampling times. Subsequently, divide this mutation concentration region into multiple equal-length small segments, for example, divide it at a fixed time interval. In each segment, statistically calculate the average time interval between two adjacent peaks in the voltage waveform, and at the same time statistically calculate the average interval between two adjacent zero-crossing points in the current waveform. Then compare the two with each other to obtain the relative spacing ratio within each segment, and form a ratio sequence in chronological order. Conduct a difference analysis on this ratio sequence, that is, compare the change range between the ratios segment by segment, and count the number of segments with large changes and the change frequency. If the ratio changes frequently in multiple segments and the change frequency 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 during this time period. Combining the statistical results of the time position difference and the ratio fluctuation frequency, output the periodic synchronization offset data reflecting the rhythm misalignment between the two channels.

[0025] Please refer to Figure 3 , the steps for obtaining the dominant channel change pattern are specifically as follows: S211: Based on the periodic synchronization offset data, compare the change directions and amplitude changes of the voltage channel and the current channel in adjacent detection cycles, judge whether the offset trends of the two are consistent, and analyze the offset performance differences between consecutive cycles. Screen out the periodic segment fragments with inconsistent changes to obtain the offset difference trend data; Extract the offset values and change trends of the voltage channel and the current channel in each detection cycle, and respectively compare the change directions of the offsets of the two channels in two adjacent cycles. For example, when the offset of the voltage channel rises from 5 to 8 while the current channel drops from 4 to 2, it is determined that the change directions of the two channels are inconsistent. At the same time, statistically calculate the increase and decrease amplitudes of the offset of each channel, and compare whether the difference in the change amplitudes of the two channels exceeds the set amplitude difference threshold. Set this threshold to 10% of the current average offset value to judge whether it belongs to a cycle with inconsistent trends. When there are situations of opposite directions or significant amplitude differences in consecutive multiple cycles, extract them as candidate cycle segments, and then combine the number of trend reversals and the offset value change density in the cycle segments to screen out the significant cycle segments with inconsistent change performances. For example, if the voltage continuously rises while the current continuously drops in cycle segments 6 to 11, record this segment as an offset inconsistent segment to obtain the offset difference trend data.

[0026] S212: Based on the offset difference trend data, analyze the matching situation between the channel offset direction and the change rate, determine whether the voltage channel or the current channel is dominant in each cycle, and calculate the attribution distribution of the dominant channel in the cycle to obtain the channel attribution distribution segment; Analyze the offset direction and rate values of the voltage and current channels in each cycle, calculate the rate difference of the channel offset increase and decrease for each cycle, and count the number of channels with a larger change rate value in the cycle segment. When a certain channel has a rate advantage in more than 60% of the total number of cycles, it is identified as the dominant channel in this segment. For example, if the voltage channel rate has an advantage in 5 cycles from cycle 5 to cycle 10, then the voltage channel in this segment is the dominant channel. Record the dominant attribution result according to the cycle number of the judgment result, and then label the set of consecutive cycles with the same dominant channel as an attribution distribution segment. At the same time, mark the dominant channel type, whether it is voltage or current dominant and its corresponding cycle range to form the channel attribution distribution segment data for extracting the basis for subsequent sampling adjustment.

[0027] S213: Based on the channel attribution distribution segment, analyze the relationship between the distribution characteristics of the dominant channel and the change trend, and use the formula: ; Obtain the change mode of the dominant channel , where represents the standard deviation of the channel offset in the -th cycle, which is used to measure the dispersion degree of the offset data in this cycle, represents the frequency of the channel offset change in the -th cycle, reflecting the density of the offset change, represents the average amplitude of the channel offset data in the -th cycle, which is used as a measure of the overall change level of the channel in this cycle, represents the balance factor of the channel offset in the -th cycle, which is used to judge whether the offset is evenly distributed in the cycle, represents the density of the dominant channel attribution in the -th cycle, which is used to evaluate the concentrated distribution of the dominant channel in each cycle, represents the total number of cycle segments included in the channel attribution distribution segment; Identify the attribution type for each cycle segment to determine whether the voltage channel or the current channel is the dominant channel, and then count the offset standard deviation, change frequency, average offset amplitude, balance factor and attribution density of each cycle, analyze their evolution process in the time series, and calculate the offset standard deviation by calculating the fluctuation amplitude of the channel offset amount in the cycle. For example, the offset measurement data in the first cycle is 1.15, 1.20, 1.25, 1.18, 1.22, and its standard deviation is obtained as , the change frequency can be calculated by the number of times the offset direction changes within a period. For example, if the positive and negative offset directions alternate 5 times, then , the average offset amplitude is the arithmetic mean of all offset data within the period. If the arithmetic mean of the above data is , the balance factor can be determined by the degree of left - right symmetry of the offset sequence distribution, and is measured by the absolute value of the offset difference between the average position and the center - symmetric point. For example, if the data in this period is concentrated in the first half , the attribution density represents the degree of concentration of the dominant channel attribution in the period sequence. For example, if a certain channel is dominant in 5 consecutive periods, its attribution density is 0.80. Substitute the parameters into the formula for calculation: ; ; ; .

[0028] Perform the numerator operation: ; ; Perform the denominator operation: ; Calculate : ; The result shows that in the analyzed period sequence, the dominant channel has relatively stable and significant structural characteristics in terms of multiple indicators such as offset fluctuation, change frequency, distribution balance, and attribution concentration. The numerical result reflects that the dominant channel shows a strong trend of change aggregation in multiple period segments, meaning that the dominance of the current channel in different periods not only has an advantage in terms of change amplitude and frequency, but also shows a concentrated feature in terms of distribution form. If it is set that represents the strong - dominant mode interval, represents the neutral - partial - derivative interval, is the weak - dominant interval, thus mapping this numerical value to the classification level of the dominant channel and serving as a reference decision - making benchmark in subsequent behavior analysis or control logic configuration.

[0029] Please refer to Figure 4 , the specific steps for obtaining the sampling rhythm regulation configuration are as follows: S311: according to the dominant channel change mode, adjust the sampling interval and time distribution configuration of the channel, analyze the frequency band distribution change and frequency activation combination 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 in sampling to obtain the interval density control amount; 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, if the fluctuation frequency of the dominant channel increases, then compress the time interval to 25 microseconds in the local frequency band, 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, 300-500Hz as low, medium, and high frequency bands, 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 activation degree of the dominant channel is enhanced in a certain frequency band. For example, the sampling point density of the 300-500Hz segment in the current cycle is 80 points / cycle, while that of the previous cycle is 50 points / cycle. 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 counted. Based on the change in the number of distribution of sampling points between frequency bands within the cycle, if the sampling points in the low-frequency band decreases from 100 to 60 and the high-frequency band increases from 40 to 80, the switching ratio of the frequency bands is +40 points from low to high, which is classified as a high-frequency switching trend. The sampling density changes and switching ratio changes of each frequency band are combined to output the interval density control amount corresponding to the current cycle.

[0030] S312: calling the interval density control amount, determining the difference amplitude between the state spectrum of another channel in the current cycle and the static reference spectrum in each frequency band, comparing whether the direction of spectrum difference change in consecutive cycles remains stable, selecting the frequency band behavior without mutation as the basis for determination, and performing discriminant analysis in combination with the interval density control situation to obtain the channel holding state identification amount; Call the frequency band density change value and the frequency band switching trend in the call interval density regulation amount, extract the frequency domain signal of the non-dominant channel in the current period, perform a spectrum conversion operation on the original time domain sampling data of this channel, and then count the peak values of the spectrum energy in different frequency bands and compare them with the static reference spectrum. The reference spectrum is generated by averaging the spectra of multiple periods in the historical steady state. Set the difference amplitude threshold for each frequency band to 10%. If the difference in the peak value of the energy between the current spectrum and the reference spectrum in a certain frequency band is less than this threshold, then this frequency band is judged to be in a consistent state. Compare the difference values for each frequency band one by one and mark the results to form the state spectrum identification vector of the current period. Subsequently, extract the identification vector sequence of multiple periods in chronological order, and compare the difference change directions of each frequency band in adjacent periods item by item. If the difference amplitude change direction of a certain frequency band is the same in three or more consecutive periods and the absolute change value is less than the set micro-variation limit value of 5%, then it is judged that the difference trend is stable, otherwise it is judged as a mutation. Screen out all the frequency band behaviors that do not show mutations, and combine the sampling density and switching trend of the frequency bands in the interval density regulation amount. If the current frequency band behavior is stable and is not marked as an adjustment key area by the high-variation section in the regulation amount, then this channel is identified as being in a maintained state within this frequency band. After synthesizing the judgment results of all frequency bands, output the channel maintenance state identification quantity of the current period.

[0031] S313: Analyze the offset relationship between the frequency band switching amplitude and the sampling parameters according to the channel maintenance state identification quantity, and use the formula: ; Calculate the sampling rhythm combination deviation amount , which is used to measure the offset intensity of each frequency band in the set sampling structure, and synchronously adjust the sampling rhythm setting to obtain the sampling rhythm regulation configuration. Among them, represents the frequency activation density of the th frequency band, reflecting the frequency activation level of the channel within this frequency band, represents the interval density regulation amount of the th frequency band, describing the density change intensity of the sampling interval structure in this frequency band, represents the frequency band switching amplitude of the th frequency band, measuring the state switching amplitude of the channel in this frequency band in adjacent periods, represents the set sampling interval parameter of the th frequency band, which is the sampling configuration of the system for this frequency band in the current period, represents the cycle synchronization adjustment amount, indicating the synchronization intensity of the configuration adjustment of the two channels in the current detection cycle, represents the channel maintenance state identification quantity, which is used to reflect the discrimination result of whether the other channel maintains a stable state in the current period, is the total number of frequency bands; Extract the frequency activation density of each frequency band, count the number of activations of each frequency band within a unit time and divide by the total time length. For example, if frequency band 1 is activated 20 times within a 100-second detection period, the frequency activation density is 0.2. Next, the sampling interval adjustment amount is jointly 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 intervals of 10 milliseconds and triggered 4 times per second, then the corresponding interval adjustment amount is 0.4. For each frequency band, calculate the frequency band switching amplitude, that is, the number of state changes of the frequency band within the current detection period. For example, if frequency band 1 changes from activation to rest and then back to activation, with 2 state switches, then the frequency band switching amplitude , set the sampling interval parameter to the constant value set in the system configuration. If the system sets it to 1.5, then the frequency band parameter , substitute the parameters into the following formula in sequence to set the current cycle synchronization adjustment amount , the channel holding state identification quantity , the total number of frequency bands , and the settings of the remaining parameters are as follows: Frequency band 1: ; Frequency band 2: ; Frequency band 3: ; Calculate each product term: The first term: ; The second term: ; The third term: ; Add the above values: ; The denominator is calculated as: ; Get: ; This calculation result It indicates that there is a certain offset in the sampling rhythm structure, mainly caused by the superposition of the high switching amplitude and high activation density in frequency band 3. This result is the sampling rhythm combination deviation amount, which is used to perform synchronization correction processing on the existing sampling rhythm setting to obtain the sampling rhythm regulation configuration for automatic optimization reference before the update of the configuration parameters in the next cycle. This formula amplifies the difference by introducing a square term among multiple frequency band parameters, enabling the parts with abnormal frequency band switching behavior to be preferentially responded to in the overall configuration optimization. Moreover, by introducing the square root normalization mechanism of the synchronization adjustment amount and the state recognition factor, a quantization method that takes into account both the frequency distribution and the channel state consistency is constructed, facilitating the formation of an adaptive regulation structure in multi-channel synchronous sampling.

[0032] Please refer to Figure 5 , and the specific steps for obtaining the periodic amplitude response characteristics are as follows: S411: Based on the sampling rhythm regulation configuration, calculate the response amplitudes of the voltage channel and the current channel at the synchronous sampling nodes, analyze the respective fluctuation ranges and trends of the two channels within a complete cycle, judge their synchronous change conditions within the cycle, and obtain the amplitude response change sequence; Based on the sampling intervals and frequency band settings of each channel, extract all the sampling point pairs marked as synchronous sampling nodes within the cycle, sequentially read the values of the voltage channel and the current channel at this sampling point as the response amplitude data points, construct the synchronous response amplitude pair sequence. Subsequently, calculate the maximum and minimum values of the voltage and current channels throughout the cycle, and define the difference between the maximum and minimum values as the fluctuation range. Then, by sequentially traversing the amplitude change directions between each sampling point, judge whether there is a continuous rising, falling, or oscillating change trend of the channel within the cycle. For example, if the voltage channel continuously increases within the cycle while the current channel decreases, then their trends are different, corresponding to a judgment of asynchronous change. If their increase and decrease directions are consistent and maintain time consistency within any time period, it is judged as a synchronous change segment. Then, record the start and end sampling point indices of each synchronous segment and asynchronous segment respectively. On this basis, form a synchronous amplitude change sequence under a complete cycle. The sequence content includes the type markers (synchronous / asynchronous) of each segment, the start and end time positions, the amplitude change rate within each segment, and the average amplitude difference. For example, if the voltage channel rises from 220 to 230 between the 100th and 150th sampling points, and the current channel rises from 1.5 to 1.8, then it is judged that this segment is a synchronous rising segment. Finally, process and summarize all the synchronous nodes within the entire cycle in this form and output to obtain the amplitude response change sequence.

[0033] S412: Invoke the amplitude response change sequence, compare the peak values of the voltage channel waveforms and the amplitude changes of the active power at each node, calculate the response ratio at the same time point, and use the formula: ; Obtain the amplitude response ratio sequence. Among them, QR represents the average ratio of the response ratio of voltage to power within a cycle, reflecting the overall corresponding relationship between the response amplitudes of the two-channel synchronization nodes. Represents the response change amount of the voltage channel waveform peak at the o-th synchronous sampling node. Represents the amplitude response change amount of the active power channel at the o-th synchronous sampling node. Is the total number of synchronous sampling nodes; Extract the voltage and power change amplitudes at each synchronous sampling node within the cycle, and set them as And respectively. Among them, the voltage channel waveform peak response change amount Can be obtained by calculating the voltage peak difference between consecutive time points. For example, at node 1, the voltage changes from 218V to 222V, then , and the power response change amount Can be calculated by the average active power change between nodes. For example, at node 1, the power changes from 2.6kW to 3.0kW, then . Perform the amplitude change extraction operation on each node once in turn, and form corresponding arrays for the two groups of data. Now, taking 5 nodes as an example, assume their voltage change amounts are in volts, and the power change amounts are in kilowatts, and substitute them into the formula for item-by-item calculation: The first item is: ; The second item is: ; The third item is: ; The fourth item is: ; The fifth item is: ; Sum up the above five items to get: ; After taking the average, we get: ; The calculation shows that among the 5 synchronous sampling nodes, the average response ratio of the voltage and power channels is 0.4201, indicating a stable correlation between the two in terms of response changes. The amplitude response ratio sequence is composed of the ratio sets of such nodes and can be used for the subsequent identification and classification of periodic response characteristics. The formula reflects the composite coupling effect of voltage and power fluctuations by introducing the square root product term, making the ratio calculation more sensitive to non-linear growth changes and having strong adaptability and generality in a multi-point synchronous monitoring environment.

[0034] S413: According to the amplitude response ratio sequence, judge the fluctuation distribution within the period range, analyze the coherence and jump characteristics of the ratio changes, screen the channel matching points with correlation relationships, and obtain the periodic amplitude response characteristics; First, calculate the amplitude ratio value sequence of all synchronous points within the period and arrange them in chronological order. For example, 50 sets of voltage-to-current ratio data are obtained among 50 synchronous nodes. Subsequently, judge whether the ratio shows a stable or mutant trend within the period. 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; if the change amplitude exceeds 20%, it is marked as a jump point. Count the number of jump points in the entire period and statistically analyze the concentrated area positions of the jump segments. Then, divide the amplitude ratio sequence within the coherent segment into intervals, find the ratio intervals that repeatedly appear within a certain numerical range, and back-check the corresponding channel values in the original synchronous amplitude sequence to judge whether there is a paragraph with the strongest synchronization of the responses of the two channels under a specific amplitude ratio, that is, when the matching rate of the voltage change direction and the current change direction within the interval reaches more than 90%, then the ratio point pair is marked as a channel matching point. Finally, screen out the ratio sections with high consistency within the period, mark the sampling point index, ratio range, and channel fluctuation rate of the area, and integrate them into the output result of the periodic amplitude response characteristics.

[0035] Please refer to Figure 6 , and the steps for obtaining the load characteristic distribution label are specifically as follows: S511: Based on the periodic amplitude response characteristics, analyze the amplitude response arrangement order corresponding to each period number, calculate its sorting relationship and amplitude change range in the period sequence, judge the distribution aggregation state among the period numbers, screen the numbered sections with distribution differences, and obtain the periodic response distribution identifier; First, each cycle is assigned a unique number, and the representative amplitude response characteristic value in each cycle is extracted as a reference for sorting. For example, the average amplitude ratio of the voltage and current channel amplitude changes in each cycle is extracted, and the ratios corresponding to all cycles are arranged in ascending order. The position changes of the cycle numbers after sorting are recorded, and the distribution arrangement is determined by the order of the numbers in the sorted list. Then, the amplitude ratio difference between each cycle and its adjacent cycle is calculated to form an inter-cycle amplitude change sequence. The difference amplitude between the maximum and minimum values ​​in the sequence is counted, and it is determined by the period segment grouping method whether there is a group of cycle numbers with continuous distribution and a clear amplitude change range. For obvious concentration, for example, if the amplitude change from cycle 1 to 5 is less than 5% and the change from cycle 6 to 9 is greater than 20%, it is considered that there is a distribution clustering difference between the two. Then all cycle numbers are classified according to the amplitude change interval, and the cycles in the same interval are regarded as a distribution group. The number of cycle numbers and the distribution span of each group are counted. If a group of cycle numbers is highly concentrated in time series and the span is less than 20% of the total cycle sequence, it is judged to be an aggregated distribution group. If the cycle 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 cycle numbers, average amplitude, and maximum amplitude change to form a cycle response distribution identifier.

[0036] S512: Based on the periodic response distribution identifier, compare the quantity, 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 characteristic distribution label; Based on the cycle numbers marked for each distribution section and their corresponding response categories, extract the response category labels for all cycle numbers, count the quantity for each category number group, and record its number sorting structure, that is, record the position distribution order of each type of cycle number in the entire sequence. 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. Compare the number sequence with the actual sampling time series to determine whether each type of number shows a continuous distribution trend or an interval distribution characteristic. Then, compare the cycle segment lengths and cycle number spans among various types of numbers. If the cycle span of a certain 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 trend group; otherwise, it is marked as a dispersed response trend group. Subsequently, extract the mean value and the range of amplitude response characteristics recorded in the cycles corresponding to each type of number, and determine whether there are consistent response difference characteristics within the same category. For example, if the response amplitudes of a certain type of cycle are all greater than a certain set value, it is classified as a high-response category; otherwise, it is classified as a low-response category. Combining the number distribution, sorting consistency, and response characteristic situations, assign an attribution label to each type of response number, and name the load type identifier according to its dominant channel, response density characteristics, and amplitude difference situation. For example, classify cycles 3, 4, 5, and 6 as "voltage-dominated · stable type" loads, and classify cycles 7, 9, 11 as "current-dominated · irregular type" loads, and output the load characteristic distribution label.

[0037] A detection system for optimizing the load of an electric meter, the system comprising: The rhythm offset recognition module analyzes the collected voltage peak and current zero-point sampling data based on the load access terminal, judges the offset trend of the two groups of time series data in the reference alignment in the same cycle, screens the key synchronous segments with rhythm difference changes, and maps the proportional characteristics of the spacing changes between channels to obtain the cycle synchronous offset data; The dominant channel recognition module compares the continuous change trends of the voltage channel and the current channel in adjacent detection cycles based on the cycle synchronous offset data, judges the source channel of the differential changes in each cycle, screens the cycle segments in the differential concentration area, and marks the classification structure and recognition characteristics of the dominant channel to obtain the dominant channel change mode; The sampling rhythm regulation module adjusts the sampling interval structure of the channel according to the dominant channel change mode, optimizes the sampling frequency band density of the corresponding channel in the current detection cycle, judges whether the other channel enters the hold state, and synchronizes the set configuration of the two channels before the cycle conversion to obtain the sampling rhythm regulation configuration; The response mapping construction module calculates the relative proportion of the peak response change of the voltage channel waveform based on the sampling rhythm regulation configuration, compares it with the amplitude response change of the active power at the synchronous time node, judges the amplitude mapping situation of the two channels in the cycle, and obtains the cycle amplitude response characteristics; Based on the periodic amplitude response features, the feature classification output module determines the distribution performance structure in multiple periods, screens out the sequence segments with differential response features, analyzes their proportion and continuous distribution trend within the detection period, and classifies each corresponding load response type to obtain the load feature distribution label.

[0038] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A detection method for optimizing the load of an electricity meter, characterized in that, It includes the following steps: S1: Based on the load access terminal, collect the voltage peak and current zero point data, judge the time alignment offset of the two channels in the same period, screen the key synchronization segments, map the spacing ratio relationship between the channels, and obtain the periodic synchronization offset data; S2: Based on the periodic synchronization offset data, compare the change trends of the voltage and current channels in adjacent periods, identify the channels where the differences originate, screen the segments with concentrated differences, generate the classification and identification features of the dominant channels, and obtain the change mode of the dominant channels; S3: According to the change mode of the dominant channels, adjust the sampling interval of the corresponding channels, optimize the sampling frequency band density, judge whether the non-dominant channels maintain the original state, and synchronize the parameter configuration before the cycle conversion to obtain the sampling rhythm regulation configuration; S4: Based on the sampling rhythm regulation configuration, calculate the relative ratio of the change in the peak response of the voltage channel waveform, compare it with the amplitude response change of the active power at the synchronization time node, judge the amplitude mapping situation of the two channels within the cycle, and obtain the periodic amplitude response characteristics.

2. The detection method for ammeter load optimization according to claim 1, wherein The periodic synchronization offset data includes the channel pairing time structure, the offset trend expression segment, and the rhythm difference identification field. The change mode of the dominant channels includes the channel classification information, the dominant orientation coding information, and the change amplitude identification label. The sampling rhythm regulation 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 result, and the channel amplitude corresponding mapping group.

3. The detection method for meter load optimization according to claim 1, characterized in that The specific steps for obtaining the periodic synchronization offset data are as follows: S111: Based on the load access terminal, analyze the maximum amplitude point of each period in the voltage sequence and the signal zero crossing position of the adjacent period in the current sequence, calculate the time index difference between the two in the same period, and judge the change trend of the difference in consecutive periods to obtain the periodic rhythm difference amount; S112: Based on the periodic rhythm difference amount, screen the segments that fluctuate continuously in time and have an increasing amplitude, calculate the synchronization degree between the voltage change rate and the current change rate within this segment, and analyze the mutation concentration area formed by its change density to obtain the offset change trend combination amount; S113: Based on the offset change trend combination amount, judge the time position difference between the channels of each sampling sequence segment, compare the relative interval ratio of the difference channels within the synchronization segment, and analyze the fluctuation frequency of the ratio in adjacent time intervals to obtain the periodic synchronization offset data.

4. The detection method for meter load optimization according to claim 1, wherein The specific steps for obtaining the change mode of the dominant channels are as follows: S211: Based on the periodic synchronization offset data, compare the change directions and amplitude changes of the voltage channel and the current channel in adjacent detection periods, judge whether the offset trends of the two are consistent, and analyze the offset performance differences between consecutive periods. Screen the periodic segment fragments with inconsistent changes to obtain the offset difference trend data; S212: Based on the offset difference trend data, analyze the matching situation between the channel offset direction and the change rate, judge whether the voltage channel or the current channel is dominant in each period, and calculate the attribution distribution of the dominant channel in the period to obtain the channel attribution distribution segment; S213: Analyze the relationship between the distribution characteristics and the change trend of the dominant channel based on the channel attribution distribution segment to obtain the change pattern of the dominant channel.

5. The detection method for optimizing the load of an electricity meter according to claim 1, characterized in that The specific steps for obtaining the sampling rhythm regulation configuration are as follows: S311: According to the change pattern of the dominant channel, adjust the sampling interval and time distribution configuration of the channel, analyze the change of the frequency band distribution and the frequency activation combination in the current cycle of the dominant channel, optimize the sampling response density in each frequency band, and calculate the change trend of the frequency band switching ratio in the sampling to obtain the interval density regulation amount. S312: Invoke the interval density regulation amount, judge the difference amplitude of the state spectrum and the static reference spectrum of another channel in each frequency band in the current cycle, compare whether the change direction of the spectrum difference remains stable in consecutive cycles, select the frequency band behavior without mutation as the judgment basis, and perform discriminant analysis in combination with the interval density regulation situation to obtain the channel holding state identification amount. S313: Analyze the offset relationship between the frequency band switching amplitude and the sampling parameters according to the channel holding state identification quantity, and calculate the sampling rhythm combination deviation quantity , perform synchronous adjustment on the sampling rhythm setting to obtain the sampling rhythm control configuration.

6. The detection method for ammeter load optimization according to claim 1, wherein, The specific steps for obtaining the periodic amplitude response characteristics are as follows: S411: Based on the sampling rhythm regulation configuration, calculate the response amplitude of the voltage channel and the current channel at the synchronous sampling node, analyze the respective fluctuation ranges and trends of the two channels in the complete cycle, and judge their synchronous change situation in the cycle to obtain the amplitude response change sequence. S412: Invoke the amplitude response change sequence, compare the peak value of the voltage channel waveform and the amplitude change of the active power at each node, calculate the response ratio at the same time point, and obtain the amplitude response ratio sequence. S413: According to the amplitude response ratio sequence, judge the fluctuation distribution within the cycle range, analyze the coherence and jump characteristics of the ratio change, and select the channel matching points with relevant relationships to obtain the periodic amplitude response characteristics.

7. The detection method for ammeter load optimization according to claim 1, characterized in that The steps further include: S5: Based on the periodic amplitude response characteristics, judge the response distribution form in multiple cycles, select the differential characteristic sections, analyze their proportions and trend continuity, classify the corresponding load state types, and obtain the load characteristic distribution label. The load characteristic distribution label includes a response level mapping label, a periodic structure coverage type, and a load state classification index.

8. The detection method for optimizing the load of an electricity meter according to claim 7, wherein The specific steps for obtaining the load characteristic distribution label are as follows: S511: Based on the periodic amplitude response characteristics, analyze the amplitude response arrangement order corresponding to each cycle number, calculate its sorting relationship and amplitude change range in the cycle sequence, judge the distribution aggregation state between cycle numbers, and select the number sections with distribution differences to obtain the periodic response distribution identifier. S512: Based on the periodic response distribution identifier, compare the quantity, order structure and aggregation trend of the response category numbers, integrate the coverage characteristics, sorting consistency and periodic response difference characteristics of each category of numbers, classify the corresponding load response types of each category, and obtain the load characteristic distribution label.

9. A detection system for optimizing the load of an electricity meter, characterized in that, The system is used to implement the detection method for electric meter load optimization according to any one of claims 1-8, and the system includes: The rhythm offset recognition module analyzes the collected voltage peak and current zero - point sampling data based on the load access terminal, judges the offset trend of two sets of time - series data in the reference alignment in the same period, screens out the key synchronous segments with rhythm difference changes, and maps the proportional characteristics of the spacing changes between channels to obtain the period - synchronous offset data; The dominant channel recognition module compares the continuous change trends of the voltage channel and the current channel in adjacent detection periods based on the period - synchronous offset data, judges the source channels of the differential changes in each period, screens out the period segments in the concentrated area of differences, and identifies the classification structure and recognition characteristics of the dominant channel to obtain the dominant channel change pattern; The sampling rhythm regulation module adjusts the sampling interval structure of the channels according to the dominant channel change pattern, optimizes the sampling frequency band density of the corresponding channels in the current detection period, judges whether the other channel enters the holding state, and synchronizes the set configurations of the two channels before the period conversion to obtain the sampling rhythm regulation configuration; The response mapping construction module calculates the relative proportion of the peak response change of the voltage - channel waveform based on the sampling rhythm regulation configuration, compares it with the amplitude response change of the active power at the synchronous time node, judges the amplitude mapping situation of the two channels in the period to obtain the period - amplitude response characteristics; The feature classification output module judges the distribution performance structure in multiple periods based on the period - amplitude response characteristics, screens out the sequence segments with differential response characteristics, analyzes their proportion and continuous distribution trend in the detection period, and classifies each corresponding load response type to obtain the load - feature distribution label.

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