A method and system for detecting loop impedance of an electric energy meter based on port segmentation sampling

Abstract

The application discloses a kind of based on port segmentation sampling electric energy meter loop impedance detection method and system, wherein method includes: based on non-uniform sampling and weighted adaptive segmentation mechanism design electric energy meter loop segmentation sampling strategy;Real-time data of each sampling port is obtained based on the segmentation sampling strategy, and the real-time data is preprocessed;Based on the real-time data after preprocessing, calculate preliminary impedance, by modifying the preliminary impedance, obtain the final impedance value after modification.The technical scheme of the present application is based on the sampling mode of the segmentation sampling strategy of electric energy meter loop, which ensures that more sampling points are collected when the signal changes sharply in the loop, thereby effectively improving the detection accuracy.

Description

Technical Field

[0001] This invention relates to the field of impedance detection technology, and more specifically, to a method and system for detecting the circuit impedance of an energy meter based on port segmented sampling. Background Technology

[0002] Impedance testing of electricity meter loops is a crucial technology in power systems, directly impacting the measurement accuracy of electricity meters and the safe operation of power equipment. Excessive loop impedance can lead to problems such as phase angle shift, current amplitude attenuation, waveform distortion, and power measurement inaccuracies, resulting in multiple compliance risks and economic losses. Currently, most electricity meter loop impedance testing methods estimate loop impedance by calculating the voltage-to-current ratio. While simple and effective, these methods often neglect the dynamic response characteristics of the loop—the changes in current and voltage signals over time. Loop impedance is not only related to the instantaneous ratio of current and voltage but is also affected by electrical characteristics (such as inductance and capacitance) and external environmental changes (such as temperature, voltage fluctuations, and load variations). Traditional methods fail to account for these dynamic changes, leading to inaccurate impedance measurements, especially under conditions of rapid load changes or large voltage fluctuations.

[0003] The prior art (CN12098729B) provides a method for detecting the crimped impedance based on a low-voltage energy meter. The crimped wire at the end of the low-voltage energy meter is used as the circuit under test. The output of a constant current source is connected to one end of the crimped impedance Rz through a matching resistor R1 with a known resistance value. The input of the constant current source is connected to the other end of the crimped impedance Rz. An AD detection circuit is used to detect the voltage of the circuit under test. The AD detection circuit includes an ADC conversion chip AD7367. The input signals of the VA1 and VA2 ports of the AD7367 are the analog voltage output signals HLOUT+ and HLOUT- of the voltmeter across the matching resistor R1, respectively. The input signals of the VB1 and VB2 ports of the AD7367 are the analog voltage output signals CK+ and CK- of the probe of the millivoltmeter connected to the probe in the circuit, respectively. The voltage signals of the DOUTA and DOUTB ports of the AD7367 are finally output to the MCU control unit to calculate the crimped impedance Rz.

[0004] However, existing technologies suffer from inaccurate detection accuracy of electricity meter circuit impedance and poor adaptability and real-time performance. Summary of the Invention

[0005] The present invention provides a method and system for detecting the circuit impedance of an energy meter based on port segmented sampling, in order to solve the problems of inaccurate detection accuracy, poor adaptability and real-time performance of the circuit impedance of an energy meter.

[0006] To address the aforementioned problems, this invention provides a method for detecting the loop impedance of an energy meter based on port segmented sampling, the method comprising:

[0007] A segmented sampling strategy for the energy meter circuit is designed based on non-uniform sampling and weighted adaptive segmentation mechanism;

[0008] Real-time data for each sampling port is obtained based on the segmented sampling strategy, and the real-time data is preprocessed.

[0009] The initial impedance is calculated based on the preprocessed real-time data, and the final impedance value is obtained by correcting the initial impedance.

[0010] Preferably, the segmented sampling strategy for the energy meter circuit is designed based on non-uniform sampling and a weighted adaptive segmentation mechanism, including:

[0011] Obtain the raw data from the electricity meter circuit, and perform preliminary sampling and segmentation of the electricity meter circuit based on the raw data and the preliminary segmentation judgment threshold;

[0012] Based on the dynamic change statistical indicators in the original data, the comprehensive weight of the preliminary sampling segments is determined; based on the comprehensive weight, the sampling time interval of each preliminary sampling segment is determined.

[0013] Preferably, based on the dynamic change statistical indicators in the original data, the comprehensive weight of the preliminary sampling segments is determined; based on the comprehensive weight, the sampling time interval for each preliminary sampling segment is determined, including:

[0014] The formula for calculating the overall weight of the initial sampling segments is as follows:

[0015]

[0016] in, , where are the weighting factors for the rate of change, acceleration, and noise of the original data, respectively; It is the rate of change of the original data at time t; It is the acceleration of the original data at time t; It is noise in the original data at time t;

[0017] The sampling time interval with total weight The inverse relationship is as follows:

[0018]

[0019] in, This is a constant used to control the sampling time interval.

[0020] Preferably, the preliminary impedance is calculated based on the preprocessed real-time data, and the final impedance value is obtained by correcting the preliminary impedance, including:

[0021] The initial impedance is calculated based on the preprocessed real-time data. The initial impedance is then corrected using dynamic response factor, adaptive correction factor, multidimensional influencing factors, and recursive correction factor to obtain the final corrected impedance value.

[0022] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using the dynamic response factor is as follows:

[0023]

[0024] in, It is the impedance value after dynamic response factor correction at time t; It is the initial impedance; It is the dynamic response factor of the electricity meter circuit; It is the adaptive correction factor for the electricity meter circuit.

[0025] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using multidimensional influencing factors is as follows:

[0026]

[0027] in, t is the impedance value after correction for multidimensional influencing factors at time t; n is the total number of multidimensional influencing factors; It represents the degree of influence of the i-th influencing factor on the circuit impedance of the energy meter. It is the change of the i-th influencing factor at time t.

[0028] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using the recursive correction factor is as follows:

[0029] Impedance value corrected based on multidimensional influencing factors at time t The impedance value after correction for multidimensional influencing factors at the previous time t-1. The absolute value of the difference determines the impedance error. According to impedance error Obtain the recursive correction factor at time t for:

[0030]

[0031] in, It is the recursive correction factor at time t; It is a moment The recursive correction factor;

[0032] Obtain the adaptive correction factor based on the recursive correction factor. Updated value:

[0033]

[0034] in, It is a moment The adaptive correction factor.

[0035] Based on another aspect of the present invention, the present invention provides a power meter loop impedance detection system based on port segmented sampling, the system comprising:

[0036] The initial unit is used to design a segmented sampling strategy for the energy meter circuit based on non-uniform sampling and a weighted adaptive segmentation mechanism.

[0037] The acquisition unit is used to acquire real-time data of each sampling port based on the segmented sampling strategy, and to preprocess the real-time data.

[0038] The result unit is used to calculate the preliminary impedance based on the preprocessed real-time data, and to obtain the corrected final impedance value by correcting the preliminary impedance.

[0039] Preferably, the segmented sampling strategy for the energy meter circuit is designed based on non-uniform sampling and a weighted adaptive segmentation mechanism, including:

[0040] Obtain the raw data from the electricity meter circuit, and perform preliminary sampling and segmentation of the electricity meter circuit based on the raw data and the preliminary segmentation judgment threshold;

[0041] Based on the dynamic change statistical indicators in the original data, the comprehensive weight of the preliminary sampling segments is determined; based on the comprehensive weight, the sampling time interval of each preliminary sampling segment is determined.

[0042] Preferably, based on the dynamic change statistical indicators in the original data, the comprehensive weight of the preliminary sampling segments is determined; based on the comprehensive weight, the sampling time interval for each preliminary sampling segment is determined, including:

[0043] The formula for calculating the overall weight of the initial sampling segments is as follows:

[0044]

[0045] in, , where are the weighting factors for the rate of change, acceleration, and noise of the original data, respectively; It is the rate of change of the original data at time t; It is the acceleration of the original data at time t; It is noise in the original data at time t;

[0046] The sampling time interval with total weight The inverse relationship is as follows:

[0047]

[0048] in, This is a constant used to control the sampling time interval.

[0049] Preferably, the preliminary impedance is calculated based on the preprocessed real-time data, and the final impedance value is obtained by correcting the preliminary impedance, including:

[0050] The initial impedance is calculated based on the preprocessed real-time data. The initial impedance is then corrected using dynamic response factor, adaptive correction factor, multidimensional influencing factors, and recursive correction factor to obtain the final corrected impedance value.

[0051] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using the dynamic response factor is as follows:

[0052]

[0053] in, It is the impedance value after dynamic response factor correction at time t; It is the initial impedance; It is the dynamic response factor of the electricity meter circuit; It is the adaptive correction factor for the electricity meter circuit.

[0054] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using multidimensional influencing factors is as follows:

[0055]

[0056] in, t is the impedance value after correction for multidimensional influencing factors at time t; n is the total number of multidimensional influencing factors; It represents the degree of influence of the i-th influencing factor on the circuit impedance of the energy meter. It is the change of the i-th influencing factor at time t.

[0057] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using the recursive correction factor is as follows:

[0058] Impedance value corrected based on multidimensional influencing factors at the current moment The impedance value after correction for multidimensional influencing factors at the previous time step. The absolute value of the difference determines the impedance error. According to impedance error Obtain the recursive correction factor at time t for:

[0059]

[0060] in, It is the recursive correction factor at time t; It is a moment The recursive correction factor;

[0061] Obtain the adaptive correction factor based on the recursive correction factor. Updated value:

[0062]

[0063] in, It is a moment The adaptive correction factor.

[0064] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the program is executed by a processor, it implements the steps of a method for detecting the circuit impedance of an energy meter based on port segmented sampling.

[0065] According to another aspect of the present invention, the present invention provides an electronic device, characterized in that it comprises:

[0066] The aforementioned computer-readable storage medium; and

[0067] One or more processors for executing a program in the computer-readable storage medium.

[0068] This invention provides a method and system for detecting the circuit impedance of an energy meter based on port segmented sampling. The method includes: designing a segmented sampling strategy for the energy meter circuit based on non-uniform sampling and a weighted adaptive segmentation mechanism; acquiring real-time data from each sampling port based on the segmented sampling strategy and preprocessing the real-time data; calculating a preliminary impedance based on the preprocessed real-time data; and obtaining a corrected final impedance value by correcting the preliminary impedance. This invention designs a segmented sampling strategy for the energy meter circuit and introduces non-uniform sampling and a weighted adaptive segmentation mechanism. The non-uniform sampling method provided by this invention, combined with a dynamic weighted adaptive sampling algorithm, can dynamically adjust the density and position of sampling points based on the local features and rate of change of the original data. Based on the sampling method of the rate of change and acceleration of the original data, this invention ensures that more sampling points are collected when the signal changes drastically in the circuit, thereby effectively improving the detection accuracy. Attached Figure Description

[0069] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0070] Figure 1 This is a flowchart of a method for detecting the loop impedance of an energy meter based on port segmented sampling according to a preferred embodiment of the present invention.

[0071] Figure 2 A flowchart illustrating a preferred embodiment of a method for detecting the loop impedance of an energy meter based on port segmented sampling according to the present invention; and

[0072] Figure 3 This is a structural diagram of a power meter loop impedance detection system based on port segmented sampling according to a preferred embodiment of the present invention. Detailed Implementation

[0073] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0074] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0075] Figure 1 This is a flowchart of a method for detecting the circuit impedance of an energy meter based on port segmented sampling according to a preferred embodiment of the present invention.

[0076] This invention provides a method for detecting the circuit impedance of an energy meter based on port segmented sampling, in order to solve the technical problems of inaccurate detection accuracy, poor adaptability, and poor real-time performance of the circuit impedance of an energy meter.

[0077] like Figure 1 As shown, this invention provides a method for detecting the loop impedance of an energy meter based on port segmented sampling. The method includes:

[0078] Step 101: Design a segmented sampling strategy for the energy meter circuit based on non-uniform sampling and weighted adaptive segmentation mechanism;

[0079] Preferably, the segmented sampling strategy for the energy meter circuit is designed based on non-uniform sampling and a weighted adaptive segmentation mechanism, including:

[0080] Obtain the raw data from the electricity meter circuit, and perform preliminary sampling and segmentation of the electricity meter circuit based on the raw data and the preliminary segmentation judgment threshold;

[0081] Based on the dynamic change statistical indicators in the original data, the comprehensive weight of the preliminary sampling segments is determined; based on the comprehensive weight, the sampling time interval of each preliminary sampling segment is determined.

[0082] Preferably, based on the dynamic change statistical indicators in the original data, the comprehensive weight of the preliminary sampling segments is determined; based on the comprehensive weight, the sampling time interval for each preliminary sampling segment is determined, including:

[0083] The formula for calculating the overall weight of the initial sampling segments is as follows:

[0084]

[0085] in, , where are the weighting factors for the rate of change, acceleration, and noise of the original data, respectively; It is the rate of change of the original data at time t; It is the acceleration of the original data at time t; It is noise in the original data at time t;

[0086] The sampling time interval with total weight The inverse relationship is as follows:

[0087]

[0088] in, This is a constant used to control the sampling time interval.

[0089] Step 102: Obtain real-time data from each sampling port based on the segmented sampling strategy, and preprocess the real-time data;

[0090] This invention designs a segmented sampling strategy for the energy meter circuit based on non-uniform sampling and a weighted adaptive segmentation mechanism. Real-time data is acquired at each port based on this segmented sampling strategy and preprocessed to obtain preprocessed data. Specifically, this includes:

[0091] To maximize the accuracy and efficiency of electricity meter circuit impedance detection and overcome the limitations of traditional circuit impedance detection methods, this invention designs a segmented sampling strategy for the electricity meter circuit based on non-uniform sampling and a weighted adaptive segmentation mechanism. This strategy acquires real-time data from each sampling port through a combination of dynamic segmentation and non-uniform sampling techniques. The sampling ports include current signal sampling ports and voltage signal sampling ports.

[0092] The segmented sampling strategy of the circuit in this invention specifically includes:

[0093] Step 1: Real-time assessment and preliminary segmentation of loop characteristics;

[0094] The second step is non-uniform sampling and adaptive adjustment.

[0095] In non-uniform sampling and adaptive adjustment, this invention introduces a dynamic weight adaptive sampling algorithm to allocate sampling points according to weights.

[0096] Step 103: Calculate the preliminary impedance based on the preprocessed real-time data, and obtain the corrected final impedance value by correcting the preliminary impedance.

[0097] Preferably, the final impedance value is obtained by correcting the initial impedance calculated from the preprocessed real-time data, including:

[0098] The initial impedance is calculated based on the preprocessed real-time data. The initial impedance is then corrected using dynamic response factor, adaptive correction factor, multidimensional influencing factors, and recursive correction factor to obtain the final corrected impedance value.

[0099] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using the dynamic response factor is as follows:

[0100]

[0101] in, It is the impedance value after dynamic response factor correction at time t; It is the initial impedance; It is the dynamic response factor of the electricity meter circuit; It is the adaptive correction factor for the electricity meter circuit.

[0102] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using multidimensional influencing factors is as follows:

[0103]

[0104] in, t is the impedance value after correction for multidimensional influencing factors at time t; n is the total number of multidimensional influencing factors; It represents the degree of influence of the i-th influencing factor on the circuit impedance of the energy meter. It is the change of the i-th influencing factor at time t.

[0105] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using the recursive correction factor is as follows:

[0106] Impedance value corrected based on multidimensional influencing factors at the current moment The impedance value after correction for multidimensional influencing factors at the previous time step. The absolute value of the difference determines the impedance error. According to the impedance error Obtain the recursive correction factor at time t for:

[0107]

[0108] in, It is the recursive correction factor at time t; It is a moment The recursive correction factor;

[0109] Based on the recursive correction factor, obtain the adaptive correction factor. Updated value:

[0110]

[0111] in, It is the adaptive correction factor at time t-1.

[0112] This invention calculates the initial impedance based on preprocessed data, and realizes the detection of the energy meter circuit impedance by using an adaptive detection algorithm for energy meter circuit impedance based on dynamic circuit response characteristics, combined with multi-dimensional influencing factors and adaptive feedback correction.

[0113] This invention, based on preprocessed data, uses the traditional voltage-current ratio to calculate the initial impedance at a given time. Furthermore, it introduces an adaptive detection algorithm for electricity meter circuit impedance based on dynamic circuit response characteristics. This algorithm aims to address the problems of insufficient accuracy, slow speed, and poor environmental adaptability in electricity meter circuit impedance detection. By introducing a dynamic circuit response model, multi-dimensional influence correction factors, and a recursive correction mechanism, the detection process becomes both accurate and real-time, effectively meeting the impedance measurement needs under complex power environments.

[0114] In the implementation of the adaptive detection algorithm for the circuit impedance of an energy meter based on dynamic circuit response characteristics, this invention introduces a circuit dynamic response model.

[0115] In the implementation of the adaptive detection algorithm for the circuit impedance of an energy meter based on dynamic circuit response characteristics, this invention introduces multiple influencing factors as correction parameters to ensure the accuracy of the detection results.

[0116] In the implementation of the adaptive detection algorithm for the circuit impedance of an energy meter based on dynamic circuit response characteristics, this invention adopts an adaptive feedback mechanism to improve the real-time performance and accuracy of the detection. After each calculation of a new impedance value, the measurement result for the next measurement is adjusted by a recursive correction factor.

[0117] This invention designs a segmented sampling strategy for the loop and introduces non-uniform sampling and a weighted adaptive segmentation mechanism. The non-uniform sampling method, combined with a dynamic weighted adaptive sampling algorithm, can dynamically adjust the density and position of sampling points based on the local features and rate of change of the original data. This sampling method, based on the rate of change and acceleration of the original data, ensures that more sampling points are collected when the signal changes drastically in the loop, thereby effectively improving detection accuracy.

[0118] This invention improves the accuracy of impedance calculation by introducing a dynamic response factor and an adaptive correction factor, taking into account the dynamic characteristics of the circuit (such as current fluctuations and load changes). The dynamic response model can automatically adjust according to the electrical characteristics and timing data of the circuit, thus more accurately reflecting the actual impedance of the circuit. The adaptive feedback mechanism and recursive correction mechanism enable rapid feedback and adjustment of the correction factor after each impedance calculation, ensuring adaptive optimization of the measurement results and gradually improving accuracy in subsequent measurements. This reduces the delay in real-time feedback and enhances real-time responsiveness.

[0119] The results of this invention have been successfully applied to current loop impedance testing, achieving accurate testing of the entire meter loop impedance and effectively improving the technical problems of inaccurate loop impedance detection accuracy and poor real-time performance in energy meters.

[0120] The following is an example illustrating an implementation scheme for a power meter loop impedance detection method based on port segmented sampling provided by the present invention.

[0121] See attached document Figure 2 The diagram illustrates a flowchart of a method for detecting the loop impedance of an energy meter based on port segmented sampling, provided by the present invention. The method includes the following steps:

[0122] S1. Based on non-uniform sampling and weighted adaptive segmentation mechanism, a segmented sampling strategy for the loop is designed. Based on the segmented sampling strategy of the loop, real-time data is acquired at each sampling port and preprocessed to obtain preprocessed data.

[0123] This invention aims to maximize the accuracy and efficiency of circuit impedance detection in energy meters, overcoming the limitations of traditional circuit impedance detection methods. Based on non-uniform sampling and a weighted adaptive segmentation mechanism, a segmented sampling strategy for the circuit is designed. This strategy acquires real-time data from each sampling port through a combination of dynamic segmentation and non-uniform sampling techniques. The specific implementation process is as follows:

[0124] Step 1: Real-time assessment and preliminary segmentation of loop characteristics;

[0125] In the initial stage of data acquisition, raw data from the electricity meter circuit is acquired in real time using a high-speed data acquisition card or other high-precision electrical signal monitoring equipment. Raw data includes current and voltage. Based on this raw data, the electrical characteristics of the circuit are initially evaluated using methods such as periodic analysis, filtering, feature extraction, and slope analysis. Based on the evaluation results, preliminary segmentation is implemented: when the evaluation result of the raw data exceeds a preset threshold based on empirical methods, the segmentation range is automatically reduced and the sampling frequency is increased; when the evaluation result of the raw data is less than the preset threshold based on empirical methods, the segmentation range is expanded and the sampling frequency is reduced. The evaluation of the raw data refers to extracting quantitative indicators that reflect the dynamic changes of the signal by using techniques such as periodic analysis, slope analysis, frequency domain feature extraction, and filtering on the voltage, current, and other signals acquired in real time from the electricity meter circuit. These indicators include the first derivative (rate of change), second derivative (acceleration), periodic stability coefficient, and spectral energy density change per unit time. A comprehensive evaluation value is calculated by weighted fusion of these indicators; this evaluation value is the "evaluation result," used to quantify the signal fluctuation intensity and non-stationarity within the current sampling segment. For example, when the raw data exhibits a relatively stable state (such as slow and periodic changes in current and voltage signals), the circuit segment is divided into a larger sampling interval, meaning the time interval between each sampling point is longer (e.g., a sampling interval of 1 ms), and a lower sampling frequency is set (e.g., twice the fundamental frequency of the signal, i.e., 200 Hz). Conversely, for raw data with large fluctuations or rapid changes (such as large and rapid fluctuations in voltage or current), a smaller sampling area is used, meaning the time interval between each sampling point is shorter (e.g., a sampling interval of 0.1 ms), and the sampling frequency is increased (e.g., 10 times the fundamental frequency of the signal, reaching 1 kHz). This dynamically changing segmentation strategy allows for flexible adaptation to different characteristics of the raw circuit data, maximizing sampling accuracy and avoiding redundant data.

[0126] The second step is non-uniform sampling and adaptive adjustment.

[0127] After initial segmentation, this invention performs non-uniform sampling on the original data within each segment. By introducing a dynamic weighted adaptive sampling algorithm, the sampling points are allocated according to their weights. The specific implementation process is as follows:

[0128] First, regions of abrupt change in the original data are identified by calculating the rate of change (i.e., slope) and acceleration (i.e., the second derivative of the change in the original data) of the original data in each time period. Regions of abrupt change require more sampling points. Then, the quantization noise of each sampling point is calculated based on the standard deviation of the original data. Finally, a comprehensive weighting for the sampling point regions is defined.

[0129]

[0130] in, The weighting factor represents the degree of influence of the rate of change, acceleration, and noise on the total weight, satisfying the following conditions: ; It is the rate of change of the original data at time t; It is the acceleration of the original data at time t; It is the quantization noise of the original data at time t.

[0131] Based on the comprehensive weight, the density of sampling points is dynamically adjusted, and the sampling interval is adjusted within each sampling segment. with total weight The inverse relationship is as follows:

[0132]

[0133] in, It is a constant that controls the maximum and minimum values ​​of the sampling interval, which are determined according to the specific application scenario.

[0134] Finally, based on the sampling interval, non-uniform sampling is achieved for each segment.

[0135] This invention uses a segmented sampling strategy based on loops to acquire real-time data at each sampling port and perform preprocessing such as denoising (filtering), smoothing (moving average, exponential weighted average), outlier detection (thresholding), data supplementation and interpolation (linear interpolation, spline interpolation, etc.) to obtain preprocessed data.

[0136] The initial impedance is calculated based on the preprocessed data. The circuit impedance of the energy meter is then detected by an adaptive detection algorithm based on the dynamic circuit response characteristics, combined with multi-dimensional influencing factors and adaptive feedback correction.

[0137] Based on the preprocessed data, the preliminary impedance at time t is calculated using the traditional voltage-current ratio. Furthermore, an adaptive detection algorithm for energy meter loop impedance based on dynamic loop response characteristics is introduced. This algorithm aims to address the problems of insufficient accuracy, slow speed, and poor environmental adaptability in energy meter loop impedance detection. By introducing a dynamic loop response model, a multi-dimensional influence correction factor, and a recursive correction mechanism, the detection process becomes both accurate and real-time, effectively meeting the impedance measurement needs in complex power environments. The specific implementation process is as follows:

[0138] Firstly, to consider the dynamic characteristics of the loop, such as current fluctuations and transient load changes, a dynamic response model of the loop is introduced, by incorporating a dynamic response factor. This is used to describe the frequency characteristics and transient behavior during current and voltage changes. Dynamic response factor Based on the electrical characteristics and current variation patterns of the circuit, and considering that the circuit impedance is determined not only by the instantaneous ratio of voltage and current but also affected by changes over time, we perform dynamic correction on the initial impedance to further improve calculation accuracy, obtaining the dynamically corrected impedance value. The calculation formula is as follows:

[0139]

[0140] in, It is the impedance value after dynamic correction at time t; It is the dynamic response factor of the circuit, reflecting the circuit's response characteristics to changes in current and voltage over time. It is a function that varies with time t, describing the frequency response of the circuit's electrical characteristics (such as inductance, capacitance, etc.) to the original data and its adaptability to dynamic load changes. It is obtained by modeling the circuit's physical characteristics or analyzing experimental data. It is an adaptive correction factor used to correct impedance changes caused by the inherent dynamic characteristics of the circuit (such as transient current, voltage fluctuations, etc.), and it is updated based on the measurement error.

[0141] Meanwhile, the loop impedance is affected not only by current and voltage, but also by multidimensional influencing factors such as external temperature fluctuations and load fluctuations. To ensure the accuracy of the test results, multiple influencing factors are introduced as correction parameters. A correction factor is assigned to each influencing factor. The impedance values ​​are then weighted according to their impact on the loop impedance. Calculated by the following formula:

[0142]

[0143] in, t is the impedance value after correction at time t; n is the total number of multidimensional influencing factors; This represents the degree of influence of the i-th influencing factor on the loop impedance, obtained by fitting historical data (historical measurement data of loop impedance and historical data of multidimensional influencing factors), with a reference value range of [value missing]. ; It is the change of the i-th influencing factor at time t, which is obtained by sensor measurement.

[0144] To improve the real-time performance and accuracy of the detection, this invention employs an adaptive feedback mechanism. After each calculation of a new impedance value, the next measurement result is adjusted using a recursive correction factor. Specifically, the corrected impedance value at the current moment is calculated. Corrected impedance value at the previous time step The absolute value of the difference is taken as the impedance error. According to impedance error Obtain the recursive correction factor at time t The update formula is:

[0145]

[0146] in, It is the recursive correction factor at time t, which is dynamically adjusted based on the current impedance error; It is a moment The recursive correction factor is then obtained. Furthermore, the adaptive correction factor update value is obtained, and the update formula is as follows:

[0147]

[0148] Through the aforementioned recursive mechanism, with each measurement completed, the adaptive correction factor is dynamically adjusted based on the error, thereby improving the accuracy of the next measurement. This recursive correction mechanism ensures the adaptability of impedance sensing, enabling the measurement process to be optimized based on real-time feedback.

[0149] This invention, after undergoing a predetermined number of corrections and recursive feedback steps based on expert experience, or after addressing impedance errors... When the impedance is less than a threshold preset based on empirical methods, the corrected impedance value is finally obtained. To determine if the circuit is functioning correctly, this impedance value is compared with a preset standard value based on the specific application scenario. Compare the values. If the corrected impedance value exceeds the tolerance range of the standard value, it indicates that there may be a fault in the circuit, and corresponding protective measures need to be taken.

[0150] Pre-determine the error tolerance range based on empirical methods. This is used to assess whether the difference between the standard value and the detected value is within a reasonable range. The tolerance range depends on the loop's operating conditions and environmental changes, such as temperature and load. The error tolerance range is set as follows:

[0151]

[0152] If the corrected impedance value exceeds the tolerance range of the standard value, an alarm mechanism is triggered, issuing a warning signal. At this point, appropriate measures can be taken, such as disconnecting the circuit or conducting further testing.

[0153] Figure 3 This is a structural diagram of a power meter loop impedance detection system based on port segmented sampling according to a preferred embodiment of the present invention.

[0154] like Figure 3As shown, this invention provides a power meter loop impedance detection system based on port segmented sampling. The system includes:

[0155] Initial unit 301 is used to design a segmented sampling strategy for the energy meter circuit based on non-uniform sampling and a weighted adaptive segmentation mechanism;

[0156] The acquisition unit 302 is used to acquire real-time data of each sampling port based on a loop-based segmented sampling strategy and to preprocess the real-time data.

[0157] Result unit 303 is used to calculate the preliminary impedance based on the preprocessed real-time data, and obtain the corrected final impedance value by correcting the preliminary impedance.

[0158] Preferably, the segmented sampling strategy for the energy meter circuit is designed based on non-uniform sampling and a weighted adaptive segmentation mechanism, including:

[0159] Obtain the raw data from the electricity meter circuit, and perform preliminary sampling and segmentation of the electricity meter circuit based on the raw data and the preliminary segmentation judgment threshold;

[0160] Based on the dynamic change statistical indicators in the original data, the comprehensive weight of the preliminary sampling segments is determined; based on the comprehensive weight, the sampling time interval of each preliminary sampling segment is determined.

[0161] Preferably, based on the dynamic change statistical indicators in the original data, the comprehensive weight of the preliminary sampling segments is determined; based on the comprehensive weight, the sampling time interval for each preliminary sampling segment is determined, including:

[0162] The formula for calculating the overall weight of the initial sampling segments is as follows:

[0163]

[0164] in, , where are the weighting factors for the rate of change, acceleration, and noise of the original data, respectively; It is the rate of change of the original data at time t; It is the acceleration of the original data at time t; It is noise in the original data at time t;

[0165] The sampling time interval with total weight The inverse relationship is as follows:

[0166]

[0167] in, This is a constant used to control the sampling time interval.

[0168] Preferably, the preliminary impedance is calculated based on the preprocessed real-time data, and the final impedance value is obtained by correcting the preliminary impedance, including:

[0169] The initial impedance is calculated based on the preprocessed real-time data. The initial impedance is then corrected using dynamic response factor, adaptive correction factor, multidimensional influencing factors, and recursive correction factor to obtain the final corrected impedance value.

[0170] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using the dynamic response factor is as follows:

[0171]

[0172] in, It is the impedance value after dynamic response factor correction at time t; It is the initial impedance; It is the dynamic response factor of the electricity meter circuit; It is the adaptive correction factor for the electricity meter circuit.

[0173] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using multidimensional influencing factors is as follows:

[0174]

[0175] in, t is the impedance value after correction for multidimensional influencing factors at time t; n is the total number of multidimensional influencing factors; It represents the degree of influence of the i-th influencing factor on the circuit impedance of the energy meter. Is the i-th influencing factor in The change over time.

[0176] Preferably, the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor. The formula for correcting the initial impedance using the recursive correction factor is as follows:

[0177] Impedance value corrected based on multidimensional influencing factors at the current moment The impedance value after correction for multidimensional influencing factors at the previous time step. The absolute value of the difference determines the impedance error. According to the impedance error Obtain the recursive correction factor at time t for:

[0178]

[0179] in, It is the recursive correction factor at time t; It is a moment The recursive correction factor;

[0180] Based on the recursive correction factor, obtain the adaptive correction factor. Updated value:

[0181]

[0182] in, It is the adaptive correction factor at time t-1.

[0183] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for detecting the circuit impedance of an energy meter based on port segmented sampling.

[0184] This invention provides an electronic device, comprising:

[0185] The aforementioned computer-readable storage medium; and

[0186] One or more processors for executing a program in a computer-readable storage medium.

[0187] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0188] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0189] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0190] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0191] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0192] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0193] The invention has been described with reference to a few embodiments. However, as will be known to those skilled in the art, and as defined in the appended claims, other embodiments besides those disclosed above fall equivalently within the scope of the invention.

[0194] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

Claims

1. A method for detecting the loop impedance of an energy meter based on port segmented sampling, the method comprising: A segmented sampling strategy for energy meter circuits is designed based on non-uniform sampling and a weighted adaptive segmentation mechanism, including: Obtain raw data from the electricity meter circuit, and perform preliminary sampling and segmentation of the electricity meter circuit based on the raw data and the preliminary segmentation judgment threshold; Based on the dynamic change statistical indicators in the original data, the comprehensive weight of the preliminary sampling segments is determined; Based on the comprehensive weight, the sampling time interval for each preliminary sampling segment is determined; Real-time data for each sampling port is obtained based on the segmented sampling strategy, and the real-time data is preprocessed. The initial impedance is calculated based on the preprocessed real-time data, and the final impedance value is obtained by correcting the initial impedance.

2. The method according to claim 1, wherein determining the comprehensive weight of the preliminary sampling segments based on the dynamic change statistical indicators in the original data; and determining the sampling time interval for each preliminary sampling segment based on the comprehensive weight, includes: The formula for calculating the overall weight of the initial sampling segments is as follows: in, , where are the weighting factors for the rate of change, acceleration, and noise of the original data, respectively; The original data in time The rate of change at that location; It is the acceleration of the original data at time t; The original data in time Noise at the location; The sampling time interval with total weight The inverse relationship is as follows: in, This is a constant used to control the sampling time interval.

3. The method according to claim 1, wherein calculating the preliminary impedance based on preprocessed real-time data and obtaining the corrected final impedance value by correcting the preliminary impedance includes: The initial impedance is calculated based on the preprocessed real-time data. The initial impedance is then corrected using dynamic response factor, adaptive correction factor, multidimensional influencing factors, and recursive correction factor to obtain the corrected final impedance value.

4. The method according to claim 3, wherein the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor, wherein the formula for correcting the initial impedance using the dynamic response factor is: in, It is the impedance value after dynamic response factor correction at time t; It is the initial impedance; It is the dynamic response factor of the electricity meter circuit; It is the adaptive correction factor for the electricity meter circuit.

5. The method according to claim 4, wherein the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor, wherein the formula for correcting the initial impedance using multidimensional influencing factors is: in, t is the impedance value after correction for multidimensional influencing factors; n is the total number of multidimensional influencing factors; It represents the degree of influence of the i-th influencing factor on the circuit impedance of the energy meter. Is the i-th influencing factor in The change over time.

6. The method according to claim 5, wherein the initial impedance is corrected using a dynamic response factor, an adaptive correction factor, multidimensional influencing factors, and a recursive correction factor, wherein the formula for correcting the initial impedance using the recursive correction factor is: Impedance value corrected based on multidimensional influencing factors at the current moment The impedance value after correction for multidimensional influencing factors at the previous time step. The absolute value of the difference determines the impedance error. According to the impedance error Obtain the recursive correction factor at time t for: in, It is the recursive correction factor at time t; It is the recursive correction factor at time t-1; Based on the recursive correction factor, obtain the adaptive correction factor. Updated value: in, It is the adaptive correction factor at time t-1.

7. A system for detecting the loop impedance of an energy meter based on port segmented sampling, the system comprising: The initial unit, used to design a segmented sampling strategy for the energy meter circuit based on non-uniform sampling and a weighted adaptive segmentation mechanism, includes: Obtain raw data from the electricity meter circuit, and perform preliminary sampling and segmentation of the electricity meter circuit based on the raw data and the preliminary segmentation judgment threshold; Based on the dynamic change statistical indicators in the original data, the comprehensive weight of the preliminary sampling segments is determined; Based on the comprehensive weight, the sampling time interval for each preliminary sampling segment is determined; The acquisition unit is used to acquire real-time data of each sampling port based on the segmented sampling strategy, and to preprocess the real-time data. The result unit is used to calculate the preliminary impedance based on the preprocessed real-time data, and to obtain the corrected final impedance value by correcting the preliminary impedance.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-6.

9. An electronic device, characterized in that, include: The computer-readable storage medium as described in claim 8; as well as One or more processors for executing a program in the computer-readable storage medium.

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