An electricity meter carrier wave communication module adaptive frequency adjustment method, medium and system

Abstract

This invention provides an adaptive frequency adjustment method, medium, and system for a power line carrier communication module, belonging to the field of smart meter technology. The method includes: firstly, acquiring and sampling the power line carrier communication signal; secondly, obtaining the communication time distribution characteristics over 24 hours through time-domain analysis; and thirdly, performing frequency-domain analysis to obtain noise spectrum characteristics, establishing a time-frequency feature correlation matrix. Based on this matrix, the 24 hours are divided into multiple transmission time windows, determining the optimal frequency range for each window and dividing it into sub-frequency bands. Channel quality is assessed for each sub-frequency band, and the assessment data is substituted into a set of noise characteristic equations to obtain the channel transmission characteristic curve. Based on this, the optimal transmission sub-frequency band is selected for communication. The system monitors communication quality in real time, and when the quality parameter falls below a threshold, it re-analyzes the time-frequency characteristics and re-selects the optimal sub-frequency band, achieving adaptive frequency adjustment and solving the problem that current power line carrier communication is difficult to adapt to complex power line channel environments.

Description

Technical Field

[0001] This invention belongs to the field of smart meter technology, and specifically relates to an adaptive frequency adjustment method, medium, and system for a meter carrier communication module. Background Technology

[0002] Power line carrier communication (PLC) utilizes power lines as the transmission medium, achieving data transmission by superimposing a high-frequency carrier signal onto a power frequency (50 / 60Hz). It is commonly used for data transmission in smart meters. However, PLC systems face severe noise interference problems. Various electrical noise sources exist on power lines, such as switching power supplies and motors, generating interference signals such as broadband white noise, narrowband harmonic noise, and short-time impulse noise. These interference signals severely affect communication quality, leading to time-varying channel environments and non-stationary noise characteristics, posing a significant challenge to the performance of the communication system. How to adapt to the complex power line channel environment and improve the reliability and stability of communication is a key technical problem that urgently needs to be solved in the field of PLC. Summary of the Invention

[0003] In view of this, the present invention provides an adaptive frequency adjustment method, medium and system for a power meter carrier communication module, which can solve the technical problem that the carrier communication of power meters in the prior art is difficult to adapt to the complex power line channel environment.

[0004] This invention is implemented as follows:

[0005] The first aspect of this invention provides an adaptive frequency adjustment method for an electricity meter carrier communication module, comprising: acquiring a power line carrier communication signal and sampling it to obtain a sampled data sequence; performing time-domain analysis on the sampled data sequence to obtain time distribution characteristics; performing frequency-domain analysis on the sampled data sequence to obtain power line noise spectrum characteristics; establishing a time-frequency characteristic correlation matrix; dividing each 24-hour period into multiple transmission time windows based on the time-frequency characteristic correlation matrix; dividing the optimal frequency range within each transmission time window into sub-bands and performing channel quality assessment; substituting the channel quality assessment data into a noise characteristic equation set to solve for a channel transmission characteristic curve; selecting the optimal transmission sub-band for carrier communication based on the channel transmission characteristic curve, and re-analyzing and selecting when the communication quality parameters are lower than a preset threshold.

[0006] The time distribution features include periods of high communication density and periods of low communication density; the power line noise spectrum features include frequency interference distributions at different times; and the time-frequency feature correlation matrix contains the correspondence between the time distribution features and the power line noise spectrum features.

[0007] Each of the transmission time windows has a corresponding optimal frequency range; the channel quality assessment data includes signal-to-noise ratio, bit error rate, and channel attenuation.

[0008] The noise characteristic equation set includes the frequency response equation, the channel attenuation equation, and the signal integrity equation.

[0009] The frequency response equation characterizes the amplitude variation characteristics of signals in different frequency bands.

[0010] The channel attenuation equation describes the energy loss of the signal during transmission.

[0011] The signal integrity equation represents the degree of waveform distortion during signal transmission.

[0012] Specifically, when the communication quality parameter is lower than a preset threshold, the process returns to the steps of time-domain analysis, frequency-domain analysis, establishing a time-frequency feature correlation matrix, dividing the transmission time window, performing sub-band division and channel quality assessment, solving the characteristic equation set, and selecting the optimal transmission sub-band, and then re-performs the time-frequency feature analysis and optimal sub-band selection. Specifically, this includes the following steps:

[0013] S10. Acquire the power line carrier communication signal and sample the power line carrier communication signal to obtain a sampled data sequence. Specifically, the power line carrier communication signal is sampled using a sampling device (such as a high-speed ADC). The sampling rate needs to be more than twice the communication bandwidth to satisfy the Nyquist sampling theorem. During the sampling process, windowing techniques such as Hanning windows or Hamming windows can be used to suppress spectral leakage. After obtaining the sampled data sequence x(n), subsequent time-domain analysis can be performed. The main purpose of this step is to acquire the original power line carrier communication signal to be analyzed.

[0014] S20. Perform time-domain analysis on the sampled data sequence to obtain the time distribution characteristics of power line carrier communication within 24 hours each day. The time distribution characteristics include communication-intensive periods and communication-sparse periods. Specifically, calculate the number of communication requests N(t) within each time window (e.g., 300 seconds); record the actual communication duration L of each type of service. i (t); according to the formula for calculating communication density Calculate the communication density for each time window, where T is the statistical time window, α and β are weighting coefficients, n is the total number of service types, and M... i (t) represents the maximum allowed communication duration for the i-th type of service; based on the changing trend of communication density D(t), the 24 hours of each day are divided into communication-intensive periods and communication-sparse periods; the purpose of this step is to obtain the time distribution characteristics of power line carrier communication, providing a basis for subsequent frequency domain analysis and time-frequency correlation.

[0015] S30. Perform frequency domain analysis on the sampled data sequence to obtain the power line noise spectrum characteristics, which include the frequency interference distribution at different time periods; specifically: perform FFT transformation on the sampled data sequence x(n) to calculate the power spectral density. Where Ξ is the number of FFT points (default 1024); 100 sets of noise data are collected during the communication idle period, and FFT analysis is performed on each set of data and the average value is taken to obtain the background noise power spectrum P. bg (f) Measure the power spectrum of a known signal source and compare it with the theoretical value, then calculate the correction coefficient γ = P. measured / P theoretical According to the modified power spectral density formula Calculate the final noise power spectrum characteristics; the purpose of this step is to obtain the spectral characteristics of power line noise, providing a basis for subsequent time-frequency correlation analysis.

[0016] S40. Establish a time-frequency feature correlation matrix, which contains the correspondence between the time distribution features and the power line noise spectrum features; specifically: construct a two-dimensional time-frequency feature correlation matrix M, where rows represent time features (time windows) and columns represent frequency features (noise power spectrum); map the time distribution features D(t) obtained in step S20 to the matrix rows to represent the communication density of each time window; map the noise power spectrum P(f) obtained in step S30 to the matrix columns to represent the noise features of different frequency bands; matrix elements M i,j The correlation between time window i and frequency band j can be represented by indicators such as correlation coefficient and mutual information. The purpose of this step is to establish a correlation mapping relationship between the two dimensions of time and frequency, so as to provide a basis for subsequent adaptive frequency modulation.

[0017] S50. Based on the time-frequency feature correlation matrix, the 24 hours of each day are divided into multiple transmission time windows, each transmission time window having a corresponding optimal frequency range; specifically: cluster analysis is performed on the time-frequency feature correlation matrix M to divide the 24 hours into K transmission time windows; for each time window k, the frequency range with the highest correlation [f] is found. k,min ,f k,max The optimal transmission frequency for this window is determined by merging and optimizing the optimal frequency range of each time window to minimize frequency overlap between adjacent windows. The purpose of this step is to rationally divide the transmission time window based on the correlation between time and frequency characteristics and determine the optimal frequency range for each window, thus providing a basis for subsequent adaptive frequency modulation.

[0018] S60. Divide the optimal frequency range within each transmission time window into sub-bands and generate channel quality assessment data, which includes signal-to-noise ratio, bit error rate, and channel attenuation; specifically, divide the optimal frequency range [f] within each transmission time window into sub-bands. k,min ,f k,max The frequency band is divided into M sub-bands at equal intervals. For each sub-band, channel quality assessment indicators are collected, including signal-to-noise ratio (SNR), bit error rate (BER), and channel attenuation (PER). The channel quality assessment data of each sub-band are recorded in a matrix, with rows representing time windows and columns representing sub-bands. The purpose of this step is to assess the channel quality of different sub-bands within each transmission time window, providing a basis for subsequent adaptive modulation frequency selection.

[0019] S70. Substitute the channel quality assessment data results into the pre-fitted noise characteristic equations and solve them to obtain the channel transmission characteristic curve for each transmission time window. The purpose of this step is to establish a mathematical model of the power line carrier communication channel, describe the characteristics of frequency response, attenuation characteristics and signal integrity, and provide a theoretical basis for subsequent adaptive modulation frequency selection.

[0020] S80. Select the optimal transmission sub-band based on the channel transmission characteristic curve, perform carrier communication within the transmission time window, and monitor communication quality parameters in real time. When the communication quality parameters are lower than a preset threshold, return to steps S20 to S80 to re-perform time-frequency characteristic analysis and optimal sub-band selection. Specifically: select the sub-band with the optimal channel transmission characteristics for carrier communication; monitor communication quality parameters in real time, including signal-to-noise ratio (SNR), bit error rate (BER), and packet loss rate (PER), and calculate the comprehensive communication quality score Q(t) = w1SNR(t) + w2BER(t) + w3PER(t); and use the historical communication quality score Q. hist (i) Perform statistical analysis to determine the preset threshold θ th =μ-kσ; when the real-time monitored communication quality score Q(t) < θ th When the current channel no longer meets the requirements, it is necessary to re-analyze the time-frequency characteristics and select the optimal sub-frequency band. At this time, return to step S20 to continue execution. The purpose of this step is to realize the adaptive frequency adjustment of power line carrier communication, and dynamically select the optimal transmission sub-frequency band according to the real-time channel conditions to ensure that the communication quality meets the expected requirements.

[0021] Based on the above technical solution, the adaptive frequency adjustment method for a meter carrier communication module of the present invention can be further improved as follows:

[0022] The noise characteristic equation set includes a frequency response equation, a channel attenuation equation, and a signal integrity equation, wherein:

[0023] The frequency response equation characterizes the amplitude variation characteristics of signals in different frequency bands.

[0024] The channel attenuation equation describes the energy loss of the signal during transmission.

[0025] The signal integrity equation represents the degree of waveform distortion during signal transmission.

[0026] Furthermore, periods of high communication density and periods of low communication density are distinguished based on communication density, which is calculated using the following formula:

[0027] The formula for calculating communication density is as follows:

[0028]

[0029] In the formula, D(t) is the communication density at time t (range 0-1); N(t) is the number of communications per unit time; T is the statistical time window (default 300s); L i (t) represents the actual communication duration of the i-th type of service; M i (t) represents the maximum allowed communication duration for the i-th type of service; α and β are weighting coefficients (default values ​​are 0.6 and 0.4, respectively); n is the total number of service types. A service refers to the various data streams transmitted and exchanged in data communication.

[0030] The above formula is generally used as follows: count the total number of communication requests N(t) every 300 seconds; record the communication duration L for each type of service. i (t); The value of D(t) is calculated according to the formula.

[0031] Each equation in the noise characteristic equation set is described in detail below:

[0032] 1. Frequency response equation:

[0033]

[0034] In the formula, H(f) is the frequency response function; A0 is the reference gain (default value 1); α is the frequency attenuation coefficient (range 0.001-0.1); f k Q is the k-th resonant frequency. k For quality factor; δ i (f) represents the i-th interference component; K represents the number of resonant points; and M represents the number of interference sources.

[0035] 2. Channel attenuation equation:

[0036]

[0037] In the formula, L(d,f) is the path loss (dB) at distance d and frequency f; L0 is the path loss at reference distance d0; η is the path loss exponent (range 2-4); κ is the frequency-dependent attenuation coefficient; W i μ is the attenuation weight for the i-th material; i is the attenuation coefficient of the i-th material; N is the number of material types along the transmission path.

[0038] 3. Signal integrity equation:

[0039]

[0040] In the formula, S(t,f) is the signal integrity index; V(t) is the time-domain voltage amplitude; V0 is the nominal voltage value; P(f) is the frequency-domain power; P0 is the nominal power value; x n These are the actual sampled values; λ represents the ideal sampled value; λ is the frequency domain weighting coefficient (range 0.1-1); ξ is the distortion weighting coefficient (range 0.1-1); Ψ is the number of sampling points.

[0041] Parameter acquisition method:

[0042] 1.f k and Q k Acquisition:

[0043] Step 1: Measure the system frequency response by sweeping the frequency (range 1-500kHz) using a network analyzer;

[0044] Step 2: Locate the resonant point using the peak detection algorithm:

[0045] Step 3: Calculate the 3dB bandwidth to determine the quality factor:

[0046] 2.W i and μ i Acquisition:

[0047] Step 1: Perform attenuation tests on lines with known material types;

[0048] Step 2: Use the least squares method to fit the test data to obtain the attenuation coefficient;

[0049] Step 3: Solve using matrices:

[0050]

[0051] The steps for obtaining the preset threshold specifically include:

[0052] 1. Collection of historical communication quality parameters:

[0053] Qhist (i)=w1SNR(i)+w2BER(i)+w3PER(i);

[0054] In the formula, Q hist (i) represents the overall communication quality score of the i-th historical sample point; SNR(i) represents the signal-to-noise ratio (dB); BER(i) represents the bit error rate; PER(i) represents the packet loss rate; w1, w2, and w3 are weighting coefficients (default values ​​are 0.4, 0.3, and 0.3, respectively).

[0055] 2. Sample statistical analysis:

[0056]

[0057] In the formula, θ th The final preset threshold is defined by μ; μ is the historical sample mean; σ is the standard deviation. Γ represents the confidence coefficient (default value 3); Γ represents the number of historical samples.

[0058] Where N(t) is the number of communications, obtained by setting a counter in the communication module; incrementing the counter by 1 when a communication request is detected; and resetting the counter after each statistical period.

[0059] Where T is the statistical time window, a system preset parameter used to analyze business response time requirements; a reasonable statistical period can be set, with a default of 300s.

[0060] Among them, L i (t) represents the actual communication duration, obtained by recording the start timestamp t of each communication session. start Record the communication end timestamp t end ; Calculate L i (t)=t end -t start .

[0061] Among them, P bg (f) represents the background noise power spectrum. The specific steps for obtaining it are: collecting 100 noise data during the communication idle period; performing FFT analysis on each set of data; and taking the average value as the background noise spectrum.

[0062] Wherein, γ is the correction coefficient, which is obtained by measuring the power spectrum of a known signal source and comparing it with the theoretical value; calculating the correction coefficient γ = P. measured / P theoretical .

[0063] Where A0 is the reference gain, which is obtained by: selecting a reference frequency (usually 10kHz); measuring the signal gain at that frequency; and normalizing it to obtain A0.

[0064] Wherein, α is the frequency attenuation coefficient, which is obtained by measuring signal attenuation at different frequency points; fitting the attenuation curve using the least squares method; and extracting the exponential attenuation coefficient α.

[0065] The method for obtaining L0 is as follows: measure the signal strength at a standard distance d0 (usually 1m); calculate the loss by comparing it with the transmission power; and take the average value by repeating the measurement.

[0066] The method for obtaining η is as follows: measure the signal strength at different distance points; plot the distance-loss logarithm relationship; and obtain the slope η through linear regression.

[0067] The method for obtaining κ is as follows: at a fixed distance, the attenuation at different frequencies is measured; a frequency-attenuation quadratic relationship model is established; and the coefficient κ is obtained by fitting.

[0068] Furthermore, the default value of the preset threshold is 0.6.

[0069] A second aspect of the present invention provides a computer-readable storage medium storing program instructions that, when executed in a computer, perform the aforementioned adaptive frequency adjustment method for a meter carrier communication module.

[0070] A third aspect of the present invention provides an adaptive frequency adjustment system for an electricity meter carrier communication module, wherein the system includes the aforementioned computer-readable storage medium.

[0071] Compared with existing technologies, the beneficial effects of the adaptive frequency adjustment method, medium, and system for an electricity meter carrier communication module provided by this invention are:

[0072] 1. Comprehensive analysis of the time-frequency characteristics of power line channels: This invention performs dual analysis of power line carrier communication signals in both the time and frequency domains, extracting communication time distribution characteristics and noise spectrum characteristics, and constructing an accurate time-frequency correlation model. This time-frequency characteristic analysis method reflects the dynamic changes of power line channels better than existing single-dimensional adaptive schemes.

[0073] 2. Optimization Strategy for Adaptive Frequency Modulation: Based on the constructed time-frequency correlation model, this invention proposes an optimization strategy for adaptive frequency modulation. Within each transmission time window, the optimal transmission frequency band is dynamically selected to minimize noise interference and significantly improve communication reliability. This adaptive frequency modulation mechanism is more adaptable to the complex and variable power line channel environment than existing single fixed frequency band schemes.

[0074] 3. Low-power design for small embedded devices: The algorithm implementation of this invention employs mathematical modeling techniques such as noise characteristic equations and matrix calculations, enabling efficient operation on small chips (such as microcontrollers) and featuring low power consumption and low cost. This makes the method particularly suitable for small embedded devices such as smart meters, meeting the stringent requirements of power grid automation for communication systems.

[0075] In summary, the adaptive frequency adjustment method for the power line carrier communication module proposed in this invention significantly improves the performance of the power line carrier communication system through key technological innovations such as time-frequency feature analysis and adaptive frequency modulation. This method fully utilizes the time-frequency characteristics of the power line channel, dynamically adjusting the transmission bandwidth to adapt to complex noise environments, thus greatly improving communication reliability and stability. Furthermore, this method uses mathematical models and matrix operations for processing, without relying on complex neural networks or deep learning algorithms. Its implementation employs a low-power, low-cost design, making it highly suitable for small embedded applications such as smart meters. This solves the technical problem in existing technologies where the carrier communication of power meters struggles to adapt to complex power line channel environments. Attached Figure Description

[0076] Figure 1 A flowchart of the method provided by the present invention;

[0077] Figure 2 This is the communication density variation curve for 24 hours in the example;

[0078] Figure 3 The power spectrum characteristics of power line noise are shown in the example.

[0079] Figure 4 This is a graph showing the channel transmission characteristics at different time periods. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0081] like Figure 1 The diagram shown is a flowchart of an adaptive frequency adjustment method for a meter carrier communication module provided by the present invention. This method includes the following steps:

[0082] S10. Acquire the power line carrier communication signal, sample the power line carrier communication signal, and obtain the sampled data sequence;

[0083] S20. Perform time-domain analysis on the sampled data sequence to obtain the time distribution characteristics of power line carrier communication within 24 hours each day. The time distribution characteristics include communication-intensive periods and communication-sparse periods.

[0084] S30. Perform frequency domain analysis on the sampled data sequence to obtain the power line noise spectrum characteristics, which include the frequency interference distribution at different time periods.

[0085] S40. Establish a time-frequency feature correlation matrix, which contains the correspondence between time distribution features and power line noise spectrum features;

[0086] S50. Based on the time-frequency characteristic correlation matrix, the 24 hours of each day are divided into multiple transmission time windows, and each transmission time window has a corresponding optimal frequency range.

[0087] S60. Divide the optimal frequency range within each transmission time window into sub-bands and generate channel quality assessment data, which includes signal-to-noise ratio, bit error rate, and channel attenuation.

[0088] S70. Substitute the channel quality assessment data results into the pre-fitted noise characteristic equations and solve them to obtain the channel transmission characteristic curve for each transmission time window.

[0089] S80. Select the optimal transmission sub-band based on the channel transmission characteristic curve, perform carrier communication within the transmission time window, and monitor the communication quality parameters in real time. When the communication quality parameters are lower than the preset threshold, return to steps S20 to S80 to re-perform time-frequency characteristic analysis and optimal sub-band selection.

[0090] The specific implementation methods of the above steps are described in detail below:

[0091] The specific implementation of step S10 is as follows: First, the power line carrier communication signal is sampled using a sampling device (such as a high-speed ADC). The sampling rate f s It needs to be more than twice the communication bandwidth B to satisfy the Nyquist sampling theorem, i.e., f s ≥2B. During sampling, windowing techniques such as the Hanning window w(n) or the Hamming window h(n) can be used to suppress spectral leakage. The mathematical expression for the Hanning window is: Where NL is the window length. The mathematical expression for a Hamming window is: After windowing the sampled data sequence x(n), the original power line carrier communication signal to be analyzed can be obtained.

[0092] The specific implementation of step S20 is as follows: Time-domain statistical analysis is performed on the sampled data sequence x(n), with the goal of extracting the temporal distribution characteristics of power line carrier communication over a 24-hour period each day. The specific method is as follows:

[0093] 1) Calculate the number of communication requests N(t) within each time window (e.g., 300 seconds). Set a counter in the communication module; increment the counter by 1 when a communication request is detected, and reset the counter at the end of each statistical period.

[0094] 2) Record the actual communication duration L for each type of service. i (t). By recording the start timestamp t of each communication. start and end timestamp t end Calculate L i (t)=t end -t start .

[0095] 3) According to the formula for calculating communication density:

[0096]

[0097] Calculate the communication density for each time window. Where T is the statistical time window (default 300 seconds), α and β are weighting coefficients (default values ​​are 0.6 and 0.4 respectively), n is the total number of service types, and M... i (t) represents the maximum allowed communication duration for the i-th type of service.

[0098] 4) Based on the changing trend of communication density D(t), the 24 hours of each day are divided into communication-intensive periods and communication-sparse periods. Communication-intensive periods correspond to high communication density intervals, and communication-sparse periods correspond to low communication density intervals.

[0099] The purpose of this step is to obtain the temporal distribution characteristics of power line carrier communication, providing a foundation for subsequent frequency domain analysis and time-frequency correlation.

[0100] The specific implementation of step S30 is as follows: Frequency domain analysis is performed on the sampled data sequence x(n) to extract the spectral characteristics of power line noise. The specific method is as follows:

[0101] 1) Perform an N-point FFT transform on the sampled data sequence and calculate the power spectral density:

[0102]

[0103] Where Ξ is the number of FFT points (default 1024).

[0104] 2) Collect M sets of noise data during communication idle periods, perform FFT analysis on each set of data and take the average value to obtain the background noise power spectrum P. bg (f).

[0105] 3) Calculate the correction factor γ. Measure the power spectrum P of the known signal source. measured (f) and the theoretical value P theoretical(f) Compare and calculate γ = P measured (f) / P theoretical (f).

[0106] 4) According to the modified power spectral density formula:

[0107]

[0108] Calculate the final noise power spectrum characteristic P(f).

[0109] The purpose of this step is to obtain the spectral characteristics of power line noise, providing a basis for subsequent time-frequency correlation analysis.

[0110] The specific implementation of step S40 is as follows: A time-frequency feature correlation matrix is ​​established, and the time distribution features obtained in step S20 and the noise spectrum features obtained in step S30 are correlated and analyzed. The specific method is as follows:

[0111] 1) Construct a two-dimensional time-frequency feature correlation matrix M, where rows represent time features (time windows) and columns represent frequency features (noise power spectrum).

[0112] 2) Map the time distribution feature D(t) obtained in step S20 to matrix rows to represent the communication density of each time window.

[0113] 3) Map the noise power spectrum P(f) obtained in step S30 to a matrix column to represent the noise characteristics of different frequency bands.

[0114] 4) Matrix element M i,j This represents the correlation between time window i and frequency segment j. The correlation can be expressed using the correlation coefficient r. i,j Or mutual information I i,j These indicators are quantified. The calculation of correlation coefficients or mutual information are common existing techniques.

[0115] The purpose of this step is to establish a correlation mapping between the two dimensions of time and frequency, providing a basis for subsequent adaptive frequency modulation.

[0116] The specific implementation of step S50 is as follows: Based on the time-frequency characteristic correlation matrix M, the 24 hours of each day are divided into multiple transmission time windows, and the optimal frequency range is determined for each window. The specific steps are as follows:

[0117] 1) Perform cluster analysis on the time-frequency feature correlation matrix M, dividing 24 hours into K transmission time windows. The clustering method can be the k-means algorithm or the spectral clustering algorithm, both of which are common existing technologies.

[0118] 2) For each time window k, find the frequency range with the highest correlation [f]k,min ,f k,max The optimal transmission frequency for this window is [value]. The correlation coefficient r can be used as described above. i,j Or mutual information I i,j To measure.

[0119] 3) Merge and optimize the optimal frequency range of each time window to minimize frequency overlap between adjacent windows.

[0120] The purpose of this step is to rationally divide the transmission time window based on the correlation between time and frequency characteristics and determine the optimal frequency range for each window, so as to provide a basis for subsequent adaptive frequency modulation.

[0121] The specific implementation of step S60 is as follows: the optimal frequency range [f] for each transmission time window k,min ,f k,max The process involves dividing the frequency bands into sub-bands and then assessing the channel quality for each sub-band. The specific steps are as follows:

[0122] 1) Determine the optimal frequency range [f] for each transmission time window. k,min ,f k,max The frequency band is divided into M sub-bands at equal intervals. The sub-band width Δf can be determined according to the bandwidth requirements of the communication system, i.e.

[0123] 2) For each sub-band j, collect channel quality evaluation metrics, including signal-to-noise ratio (SNR). j Bit error rate PER j and channel attenuation PER j These metrics can be obtained by sending test signals and receiving and analyzing them.

[0124] 3) Record the channel quality assessment data for each sub-band in a matrix. Rows represent time windows, and columns represent sub-bands.

[0125] The purpose of this step is to evaluate the channel quality of different sub-bands within each transmission time window, providing a basis for subsequent adaptive modulation frequency selection.

[0126] The specific implementation of step S70 is as follows: Based on the channel quality assessment data Q obtained in step S60, fit the noise characteristic equations and solve for the channel transmission characteristic curve for each transmission time window. The specific steps are as follows:

[0127] 1) Frequency response equation:

[0128]

[0129] Where A0 is the reference gain (default value 1), α is the frequency attenuation coefficient (range 0.001-0.1), and f k Q is the k-th resonant frequency. k For quality factor, δ i (f) represents the i-th interference component, K represents the number of resonant points, and M represents the number of interference sources.

[0130] 2) Channel attenuation equation:

[0131]

[0132] Where L(d,f) is the path loss (dB) at distance d and frequency f, L0 is the path loss at reference distance d0, η is the path loss exponent (range 2-4), k is the frequency-dependent attenuation coefficient, and W i μ is the attenuation weight for the i-th material. i Let be the attenuation coefficient of the i-th material, and N be the number of material types along the transmission path.

[0133] 3) Signal integrity equation:

[0134]

[0135] Where S(t,f) is the signal integrity index, V(t) is the time-domain voltage amplitude, V0 is the nominal voltage value, P(f) is the frequency-domain power, P0 is the nominal power value, and x n These are the actual sampled values. For ideal sampled values, λ is the frequency domain weighting coefficient (range 0.1-1), ξ is the distortion weighting coefficient (range 0.1-1), and Ψ is the number of sampling points.

[0136] 4) By fitting the parameter values ​​of the above noise characteristic equations using the least squares method, the channel transmission characteristic curve for each transmission time window is obtained.

[0137] The purpose of this step is to establish a mathematical model of the power line carrier communication channel, describing its characteristics in terms of frequency response, attenuation, and signal integrity, so as to provide a theoretical basis for subsequent adaptive modulation frequency selection.

[0138] The specific implementation of step S80 is as follows: Based on the channel transmission characteristic curve obtained in step S70, the optimal transmission sub-frequency band is selected, and carrier communication is performed within the corresponding transmission time window. Simultaneously, communication quality parameters are monitored in real time. When the communication quality falls below a preset threshold, steps S20 to S80 are returned to perform time-frequency characteristic analysis and optimal sub-frequency band selection again. The specific method is as follows:

[0139] 1) Select the sub-band j with the optimal channel transmission characteristics * Carrier communication is performed. Preferred criteria include: signal-to-noise ratio. Highest, Bit Error Rate Minimum, channel attenuation Minimum wait.

[0140] 2) Monitor communication quality parameters in real time, including signal-to-noise ratio (SNR), bit error rate (BER), and packet loss rate (PER). A weighted average approach can be used to calculate the overall communication quality score Q.

[0141] Q(t)=w1SNR(t)+w2BER(t)+w3PER(t);

[0142] Among them, w1, w2, and w3 are the weight coefficients of the corresponding indicators, which can be set according to actual needs.

[0143] 3) Calculate the historical communication quality score Q hist (i) Perform statistical analysis to determine the preset threshold θ th :

[0144]

[0145] Where μ is the historical sample mean, σ is the standard deviation, k is the confidence coefficient (default value 3), and Γ is the number of historical samples.

[0146] 4) When the real-time monitored communication quality score Q(t) < θ th If the current channel no longer meets the requirements, it is necessary to re-analyze the time-frequency characteristics and select the optimal sub-frequency band. At this point, return to step S20 to continue execution.

[0147] The purpose of this step is to achieve adaptive frequency adjustment for power line carrier communication, dynamically selecting the optimal transmission sub-band based on real-time channel conditions to ensure that the communication quality meets the expected requirements.

[0148] In summary, the adaptive frequency adjustment method for the meter carrier communication module proposed in this invention achieves adaptive optimization of the power line carrier communication system through time-frequency characteristic analysis, noise modeling, and adaptive modulation frequency selection.

[0149] A second aspect of the present invention provides a computer-readable storage medium storing program instructions that, when executed in a computer, perform the aforementioned adaptive frequency adjustment method for a meter carrier communication module.

[0150] A third aspect of the present invention provides an adaptive frequency adjustment system for an electricity meter carrier communication module, wherein the system includes the aforementioned computer-readable storage medium.

[0151] Specifically, the principle of this invention is:

[0152] 1. Time-frequency characteristic analysis

[0153] First, by sampling and time-domain analysis of the power line carrier communication signal, the communication time distribution characteristics within a 24-hour period are obtained. Specifically, the number of communication requests N(t) and the actual communication duration L within each time window are calculated. i (t), and according to the communication density formula The communication density for each time window is obtained. This allows us to divide the 24 hours into periods of high and low communication density.

[0154] Secondly, frequency domain analysis is performed on the sampled data to extract the power spectral density features P(f) of the power line noise. Specifically, the power spectral density is first calculated using the FFT algorithm, and then the background noise P is considered. bg (f) and correction coefficient γ are used to obtain the final noise power spectrum characteristics.

[0155] Finally, a time-frequency feature correlation matrix M is established to correlate and map the time distribution feature D(t) and the noise spectrum feature P(f). Matrix element M i,j The correlation between time window i and frequency segment j can be represented by indicators such as correlation coefficient or mutual information.

[0156] 2. Adaptive frequency modulation optimization

[0157] Based on the time-frequency correlation matrix M, this invention proposes an adaptive frequency modulation optimization strategy. First, a clustering algorithm is used to divide 24 hours into K transmission time windows, and the optimal frequency range [f] for each time window is found. k,min ,f k,max ].

[0158] Then, the optimal frequency range for each transmission time window is divided into sub-bands, and channel quality metrics, including signal-to-noise ratio (SNR), are collected for each sub-band. j Bit error rate (BER) j and channel attenuation PER j Record these metrics in matrix Q.

[0159] Next, based on the channel quality assessment data in matrix Q, the parameter values ​​of the noise characteristic equation set (frequency response equation H(f), channel attenuation equation L(d,f) and signal integrity equation S(t,f)) are fitted to obtain the channel transmission characteristic curve for each transmission time window.

[0160] Finally, the sub-band with the optimal channel transmission characteristics is selected for carrier communication, and the communication quality parameter Q(t) is monitored in real time. When Q(t) is lower than a preset threshold θ, thWhen this happens, it indicates that the current channel no longer meets the requirements and it is necessary to return to perform time-frequency characteristic analysis and optimal sub-band selection again.

[0161] This adaptive frequency modulation optimization strategy fully utilizes the time-frequency characteristics of the power line channel, dynamically selects the optimal transmission sub-band, and minimizes noise interference, significantly improving communication reliability and stability. Furthermore, the algorithm design employs mathematical modeling techniques such as noise characteristic equations and matrix calculations, enabling efficient operation on small chips. It features low power consumption and low cost, making it highly suitable for embedded applications such as smart meters.

[0162] The following is an example of a specific application scenario of the present invention: A power grid company is using power line carrier communication technology to remotely read and monitor smart meters at the user end. This power grid is a typical low-voltage distribution network with a line length of approximately 50 kilometers, covering more than 30,000 users. Due to the uneven load distribution in the area, the varying degrees of aging of user equipment, and the large number of surrounding industrial enterprises, there is a serious noise interference problem on the power lines, which poses a great challenge to the operation of the carrier communication system.

[0163] To improve the reliability of carrier communication in low-voltage distribution networks, the power grid company decided to apply the adaptive frequency adjustment technology proposed in this invention to smart meters at the user end. The specific implementation steps are as follows:

[0164] Signal Acquisition and Time-Frequency Feature Analysis

[0165] First, the smart meter's built-in carrier communication module uses a high-speed ADC to sample the power line carrier communication signal. The sampling rate f... s The frequency is set to 200kHz, which is greater than twice the communication bandwidth B = 100kHz, satisfying the Nyquist sampling theorem. During the sampling process, the Hanning window function is used. (Where N = 1024 is the window length) Windowing is applied to the sampled data to suppress spectral leakage.

[0166] After obtaining the sampled data sequence x(n), time-domain statistical analysis is performed on it. Specifically, the number of communication requests N(t) is counted every 300 seconds (i.e., 5 minutes), and the actual communication duration L of each type of service is recorded. i (t). Taking smart meter reading service as an example, the maximum allowed communication duration M of this service is... i (t) is set to 60 seconds. Based on the communication density calculation formula:

[0167]

[0168] Calculate the communication density D(t) for each time window. Figure 2The graph shows the communication density variation curves over a 24-hour period on a given day. The horizontal axis represents time (hours), and the vertical axis represents the communication density value. The graph clearly shows the variation characteristics of the communication sparse period (0:00-6:00), the communication intensive period (6:00-22:00), and the communication sparse period (22:00-24:00). It can be seen that 0:00-6:00 is the communication sparse period, 6:00-22:00 is the communication intensive period, and 22:00-24:00 enters the communication sparse period again.

[0169] Table 1. Statistical results of communication density within 24 hours on a certain day.

[0170]

[0171]

[0172] Next, frequency domain analysis is performed on the sampled data x(n). First, the power spectral density is calculated using a 1024-point FFT algorithm. Then, during the communication off-peak period (22:00-24:00), 100 sets of noise data were collected. FFT analysis was performed on each set of data, and the average value was taken to obtain the background noise power spectrum P. bg (f). After comparative testing, the correction coefficient γ = 1.2 was calculated. Finally, based on the corrected power spectral density formula... Plot the spectral characteristics of power line noise, such as... Figure 3 The figure shows the power spectrum characteristics of power line noise in the frequency range of 50kHz-150kHz. The horizontal axis represents frequency (kHz), and the vertical axis represents power spectral density (dBm / Hz). It can be seen from the figure that the noise power is concentrated around 100kHz, indicating significant narrowband interference.

[0173] Through the dual analysis in the time and frequency domains described above, the temporal distribution characteristics and noise spectrum characteristics of power line carrier communication within a 24-hour period each day were obtained. Next, a correlation analysis was performed on these two dimensions to construct a time-frequency characteristic correlation matrix.

[0174] Time-frequency feature correlation analysis

[0175] First, the 24-hour period is divided into 12 transmission time windows, each lasting 2 hours. Using the k-means clustering algorithm, the time distribution feature D(t) is mapped to 12 rows, and the noise power spectrum P(f) is mapped to 12 columns, constructing a 12×12 time-frequency feature correlation matrix M. Matrix elements M i,j The correlation coefficient r represents the degree of correlation between time window i and frequency segment j. i,j To quantify:

[0176]

[0177] in, and D respectively i and P j The average value of the time-frequency feature correlation matrix M is shown in Table 2.

[0178] Table 2 Time-frequency feature correlation matrix M;

[0179]

[0180]

[0181] The time-frequency correlation matrix M shows that during peak communication periods (6:00-22:00), the correlation between each frequency band and the time window is high, indicating a more complex noise environment during this period. Conversely, during sparse communication periods (0:00-6:00, 22:00-24:00), the correlation between each frequency band and the time window is low, indicating a relatively better noise environment. This provides a basis for subsequent adaptive frequency modulation strategies.

[0182] Adaptive frequency modulation optimization

[0183] Based on the time-frequency feature correlation matrix M, an adaptive frequency modulation strategy is then implemented. First, each 2-hour transmission time window is divided into five equally spaced sub-bands with center frequencies of 60kHz, 80kHz, 100kHz, 120kHz, and 140kHz, each with a bandwidth of 20kHz. Then, channel quality evaluation metrics, including signal-to-noise ratio (SNR), are collected for each sub-band. j Bit error rate (BER) j and channel attenuation PER j The specific test results are shown in Table 3.

[0184] Table 3 Channel Quality Assessment for Each Sub-band

[0185]

[0186]

[0187] As shown in Table 3, during peak communication periods (6:00-22:00), the channel quality of each sub-band is relatively poor, with a low signal-to-noise ratio, high bit error rate, and high channel attenuation. Conversely, during sparse communication periods (0:00-6:00, 22:00-24:00), the channel quality of the sub-bands is relatively good. This verifies the results of the previous time-frequency feature correlation analysis.

[0188] Based on the sub-band channel quality data, the next step is to fit the noise characteristic equations and solve for the channel transmission characteristic curve for each transmission time window. Specifically:

[0189] 1) Frequency response equation:

[0190]

[0191] Where A0 = 1.0 is the reference gain, α = 0.05 is the frequency attenuation coefficient, f1 = 80kHz, Q1 = 40 are the parameters of the first resonant frequency, f2 = 120kHz, Q2 = 30 are the parameters of the second resonant frequency, and δ(f-100000) is the interference component at 100kHz.

[0192] 2) Channel attenuation equation:

[0193]

[0194] Where L0 = 20dB is the path loss at a reference distance of 1m, η = 3 is the path loss exponent, κ = 0.001 is the frequency-dependent attenuation coefficient, W1 = 5, and μ1 = 0.05 are the attenuation parameters of a material.

[0195] 3) Signal integrity equation:

[0196]

[0197] Where V0 = 220V is the nominal voltage value, P0 = 1W is the nominal power value, λ = 0.6, ξ = 0.2 are weighting coefficients, and N = 1024 is the number of sampling points.

[0198] Substituting the parameters of the above noise characteristic equations, we can obtain the channel transmission characteristic curve for each transmission time window. For example... Figure 4 As shown, the channel transmission characteristic curves for three typical time periods (sparse, transitional, and dense communication periods) are illustrated. The horizontal axis represents frequency (kHz), and the vertical axis represents frequency response amplitude.

[0199] Finally, based on the channel transmission characteristics of each transmission time window, the optimal sub-band is dynamically selected for carrier communication. During peak communication periods (6:00-22:00), the 120kHz and 140kHz sub-bands are preferred; during sparse communication periods (0:00-6:00, 22:00-24:00), the 60kHz and 80kHz sub-bands can be selected. Simultaneously, the real-time communication quality comprehensive score Q(t) is monitored as 0.4·SNR(t) + 0.3·BER(t) + 0.3·PER(t). When Q(t) < 28 (corresponding to an average signal-to-noise ratio of 27dB, an average bit error rate of 3e-3, and an average packet loss rate of 3%), it indicates that the current channel quality does not meet the requirements, and a return to re-analysis of time-frequency characteristics and selection of the optimal sub-band is necessary.

[0200] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An adaptive frequency adjustment method for an electricity meter carrier communication module, characterized in that, include: The power line carrier communication signal is acquired and sampled to obtain a sampled data sequence; Perform time-domain analysis on the sampled data sequence to obtain its temporal distribution characteristics; Frequency domain analysis was performed on the sampled data sequence to obtain the spectral characteristics of power line noise. Establish a time-frequency feature correlation matrix, which contains the correspondence between time distribution features and power line noise spectrum features; Specifically, this involves constructing a two-dimensional time-frequency feature correlation matrix, where rows represent time features (time windows) and columns represent frequency features (noise power spectra); mapping the time distribution features to the rows of the matrix to represent the communication density of each time window; mapping the power line noise power spectrum to the columns of the matrix to represent the noise features of different frequency bands; and defining the matrix elements of the time-frequency feature correlation matrix. Indicates time window and frequency band The correlation between them is expressed using the correlation coefficient. To quantify; Based on the time-frequency characteristic correlation matrix, each 24-hour day is divided into multiple transmission time windows; The optimal frequency range within each transmission time window is divided into sub-bands, and channel quality is evaluated for each sub-band. The optimal frequency range is obtained by performing cluster analysis on the time-frequency feature correlation matrix and dividing 24 hours into multiple transmission time windows. For each time window, the frequency range with the highest correlation is identified as the optimal frequency range for that window. The signal-to-noise ratio, bit error rate, and channel attenuation are substituted into the noise characteristic equations to solve for the parameter values ​​of each equation, thereby obtaining the channel transmission characteristic curve for each transmission time window. The optimal transmission sub-band is selected for carrier communication based on the channel transmission characteristic curve, and a comprehensive communication quality score is obtained by weighted summation of signal-to-noise ratio, bit error rate, and packet loss rate. If the score is lower than the preset threshold, the time-frequency characteristic analysis and optimal sub-band selection are performed again. The noise characteristic equation set includes a frequency response equation, a channel attenuation equation, and a signal integrity equation. The frequency response equation characterizes the amplitude variation characteristics of signals in different frequency bands, the channel attenuation equation describes the energy loss of the signal during transmission, and the signal integrity equation represents the degree of waveform distortion during signal transmission.

2. The adaptive frequency adjustment method for the meter carrier communication module according to claim 1, characterized in that, When the overall communication quality score is lower than the preset threshold, the process returns to the steps of time domain analysis, frequency domain analysis, establishing a time-frequency feature correlation matrix, dividing the transmission time window, performing sub-band division and channel quality assessment, solving the characteristic equation set, and selecting the optimal transmission sub-band, and then performs time-frequency feature analysis and optimal sub-band selection again.

3. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program instructions, which, when executed in a computer, are used to perform the adaptive frequency adjustment method for a meter carrier communication module according to any one of claims 1-2.

4. An adaptive frequency adjustment system for an electricity meter carrier communication module, characterized in that, It includes the computer-readable storage medium as described in claim 3.

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