A multi-mode power line carrier communication method and carrier communication module

Abstract

The present application relates to the field of carrier communication technology, and specifically to a multi-mode power line carrier communication method and a carrier communication module, the method comprising: determining the frequency overlap by analyzing the overlap between the frequency corresponding to the positive amplitude value on the spectrum diagram of each modal component in the carrier signal and the signal frequency range of the carrier signal, so as to determine the characteristic modal vector, determining the difference index by analyzing the difference in frequency between each characteristic modal component and all modal components of the noise reference signal, and analyzing the concentrated distribution of the carrier signal in energy, determining the step adjustment value in combination with the signal interference degree, and performing noise reduction processing on the carrier signal in the power carrier communication process at the current moment. The present application aims to eliminate noise interference in the carrier signal and improve the reliability of power carrier communication.

Description

Technical Field

[0001] The present application relates to the field of carrier communication technology, and in particular to a multi-mode power line carrier communication method and a carrier communication module. Background Art

[0002] The multi-mode power line carrier communication method is a communication method that combines power line carrier communication (PLC) and wireless communication technology. It uses power lines as the transmission carrier, converts digital signals into high-frequency signals through carriers, and then realizes high-speed transmission of information through power lines. Compared with other communication methods, this communication method has the advantages of low cost, wide coverage, and convenient use. However, when the transmission line was originally designed, it was only for the transmission of electric energy, and the transmission of communication signals was not considered. As a result, when the communication signal is placed on the power line for transmission, it will be seriously interfered by noise, such as colored background noise and pulse noise in the power line, which in turn affects the reliability of power line carrier communication data transmission.

[0003] In the existing power line carrier communication method, in order to ensure the reliability of data transmission in power line carrier communication, the LSM (minimum mean square error) adaptive filtering algorithm is usually used to filter the carrier signal on the power line for noise removal. The step size factor has an important influence on the denoising performance of the LSM adaptive filtering algorithm. However, the noise to which the carrier signal on the power line is subjected during transmission has complex time-varying characteristics, which makes it difficult to determine the step size factor in the traditional LSM adaptive filtering algorithm. The step size factor in the traditional LSM adaptive filtering algorithm is usually a fixed value selected by experiment. This fixed value selected by experiment cannot adapt to the complex time-varying characteristics of the noise in the carrier signal, which will affect the convergence speed and steady-state performance of the algorithm, and further affect the effect of denoising the carrier signal on the power line, thereby reducing the reliability of multi-mode power line carrier communication. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a multi-mode power line carrier communication method and a carrier communication module. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a multi-mode power line carrier communication method, the method comprising the following steps:

[0006] Obtain the carrier signal on the power line at the current moment, and use the noise signal simulated and outputted by the carrier signal as its noise reference signal;

[0007] Obtain a bispectrum of the current download wave signal, extract characteristic signal points from all data points by analyzing the distribution of amplitude values ​​at all data points in the bispectrum, and cluster all characteristic signal points to obtain multiple clusters of the current download wave signal; fit the amplitude values ​​at all characteristic signal points in each cluster, and determine the signal interference degree of the current download wave signal by analyzing the fitting accuracy;

[0008] Extract the horizontal and vertical coordinate ranges of all characteristic signal points in each cluster in the bispectral graph respectively, and determine the signal frequency range of the carrier signal at the current moment by analyzing the overlap of the horizontal and vertical coordinate ranges and the area enclosed by the overlap of the horizontal and vertical coordinate ranges in the coordinate axis;

[0009] Perform modal decomposition on the carrier signal at the current moment to obtain multiple modal components, obtain the frequency spectrum of each modal component, determine the frequency overlap of each modal component in the carrier signal at the current moment by analyzing the overlap degree between the frequency corresponding to the positive amplitude value on the frequency spectrum and the signal frequency range, use the frequency overlap of all modal components as the input of the threshold segmentation algorithm, output the segmentation threshold, and record all modal components with frequency overlap less than the segmentation threshold as characteristic modal components;

[0010] The noise reference signal is decomposed into multiple modal components, and the difference between each characteristic modal component and all modal components of the noise reference signal is analyzed in frequency to determine the difference degree of each characteristic modal component. Based on the average distribution of the difference degrees of all characteristic modal components, the difference index of the carrier signal at the current moment is determined, and the concentrated distribution of the energy of the carrier signal is analyzed. In combination with the signal interference degree, the step adjustment value of the carrier signal at the current moment is determined, and the carrier signal in the power carrier communication process at the current moment is subjected to noise reduction processing.

[0011] Preferably, the process of extracting characteristic signal points from all data points is:

[0012] The amplitude values ​​of all data points in the bispectrum of the download signal at the current moment are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output, which is recorded as the amplitude threshold. The data points with amplitude values ​​greater than the amplitude threshold are taken as characteristic signal points.

[0013] Preferably, the method for determining the signal interference degree of the carrier signal at the current moment is:

[0014] In the bispectrum of the wave signal at the current moment, the amplitude values ​​at all characteristic signal points in each cluster are fitted to obtain a fitting surface, and the sum of squares of errors between the amplitude values ​​at all characteristic signal points in each cluster and the corresponding fitting values ​​on the fitting surface are calculated;

[0015] The mean of the sum of squared errors of all clusters is used as the signal interference degree of the download signal at the current moment.

[0016] Preferably, the method for determining the signal frequency range of the download signal at the current moment is:

[0017] In the bispectrum of the download wave signal at the current moment, the union of the horizontal axis ranges and the union of the vertical axis ranges in all clusters are calculated respectively, and the area enclosed by the union of the horizontal axis ranges and the union of the vertical axis ranges in the plane where the horizontal and vertical axes are located is used as the signal frequency range of the download wave signal at the current moment.

[0018] Preferably, the method for determining the frequency coincidence of each modal component in the carrier signal at the current moment is:

[0019] The frequencies corresponding to all positive amplitude values ​​in the spectrum of each modal component are combined into a frequency set of each modal component;

[0020] Frequency coincidence of modal component i in the current download wave signal The expression is: ; In the formula, It represents the number of frequencies in the frequency set of modal component i in the carrier signal at the current moment that are within the signal frequency range of the carrier signal; Represents the number of all frequencies in the frequency set of modal component i in the download wave signal at the current moment.

[0021] Preferably, the method for determining the difference of each characteristic modal component is:

[0022] The center frequencies of each characteristic modal component and all modal components in the noise reference signal are obtained respectively, and the absolute value of the difference between the center frequencies of each characteristic modal component and each modal component in the noise reference signal is calculated, and recorded as the component difference between each characteristic modal component and each modal component in the noise reference signal;

[0023] Among the component differences between each characteristic modal component and all modal components in the noise reference signal, the modal component in the noise reference signal corresponding to the minimum value is recorded as the comparison component of each characteristic modal component, and the difference between each characteristic modal component and its comparison component is taken as the difference degree of each characteristic modal component.

[0024] Preferably, the difference index of the download wave signal at the current moment is the mean value of the difference of all characteristic mode components in the download wave signal at the current moment.

[0025] Preferably, the expression of the step adjustment value of the download signal at the current moment is: ; In the formula, Indicates the step adjustment value of the download wave signal at the current moment; Indicates the difference index of the download wave signal at the current moment; Indicates the signal interference degree of the download wave signal at the current moment; It represents the inverse of the largest eigenvalue among all eigenvalues ​​of the autocorrelation matrix of the download signal at the current moment; norm() represents the normalization function; Indicates a preset constant greater than 0.

[0026] Preferably, the noise reduction processing of the carrier signal in the power carrier communication process at the current moment includes:

[0027] The carrier signal at the current moment is used as the input of the LMS adaptive filtering algorithm, the step adjustment value of the carrier signal at the current moment is used as the step factor in the LMS adaptive filtering algorithm, and the denoised carrier signal is output.

[0028] In a second aspect, an embodiment of the present application further provides a multi-mode power line carrier communication module, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the multi-mode power line carrier communication methods described above are implemented.

[0029] This application has at least the following beneficial effects:

[0030] The present application constructs a signal interference degree by analyzing the concentrated distribution of amplitude values ​​at all data points in the dual spectrum of the carrier signal, and can more accurately evaluate the degree to which the useful signal components in the carrier signal are interfered with by noise during transmission in the power line; further, by analyzing the degree of overlap between the frequencies corresponding to the positive amplitude values ​​on the spectrum diagram of each modal component in the carrier signal and the signal frequency range of the carrier signal, a frequency overlap degree is constructed, which can more accurately distinguish the modal components carrying useful information in the carrier signal, and then more effectively evaluate the degree of difference between the noise components in the carrier signal and the noise components in the noise reference signal, so as to accurately filter out the interference of noise on the useful signal components in the carrier signal, thereby improving the quality of noise reduction; further, based on the frequency overlap degree, characteristic modal components are selected from all modal components of the carrier signal, the difference between the characteristic modal components and the noise reference signal is analyzed, and a step adjustment value is constructed in combination with the signal interference degree, so as to accurately filter out the noise in the carrier signal and improve the reliability of multi-mode power line carrier communication.

[0031] The present application optimizes the step size factor in the filtering algorithm by analyzing the degree to which useful information carried in the carrier signal is interfered by noise, and compares the degree of difference between the noise component in the carrier signal and the noise reference signal, thereby enhancing the filtering effect of noise interference in the carrier signal and improving the reliability of multi-mode power line carrier communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A flowchart of a multi-mode power line carrier communication method provided in one embodiment of the present application;

[0034] Figure 2 A schematic diagram of a characteristic modal component acquisition process provided for one embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the multi-mode power line carrier communication method and carrier communication module proposed in the present application, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0037] The following describes in detail a multi-mode power line carrier communication method and a specific scheme of a carrier communication module provided by the present application in conjunction with the accompanying drawings.

[0038] See also Figure 1 , which shows a step flow chart of a multi-mode power line carrier communication method provided by an embodiment of the present application, the method comprising the following steps:

[0039] Step S1: obtaining a carrier signal on the power line at the current moment, and using a noise signal simulated and outputted from the carrier signal as its noise reference signal.

[0040] The carrier communication module in this embodiment mainly includes an interface unit, a carrier chip, a filtering unit, a driving unit, a signal detection unit, a data processing unit, and a power supply. The interface unit is used to connect with external equipment (power lines and clients) to realize data input and output; the carrier chip is responsible for data modulation and demodulation; the driving unit is used to amplify the modulated carrier signal and couple it to the power line for transmission; the filtering unit is used to filter out noise in the carrier signal transmitted on the power line; the signal detection unit is used to detect the carrier signal transmitted on the power line and demodulate it to extract useful data information; the data processing unit is used to control the workflow of the entire module, process data and implement the communication protocol; the power supply is used to power the carrier communication module.

[0041] The carrier signal on the power line at the current moment is obtained, the noise in the carrier signal is simulated using the measured data of the carrier signal on the power line, and the simulated noise signal is used as the noise reference signal of the carrier signal at the current moment.

[0042] Step S2: Obtain a dual spectrum of the download wave signal at the current moment, extract characteristic signal points from all data points by analyzing the distribution of amplitude values ​​at all data points in the dual spectrum, and cluster all characteristic signal points to obtain multiple clusters of the download wave signal at the current moment; fit the amplitude values ​​at all characteristic signal points in each cluster, and determine the signal interference degree of the download wave signal at the current moment by analyzing the fitting accuracy.

[0043] In the LSM adaptive filtering algorithm, a smaller step size factor can reduce the steady-state imbalance of the algorithm and improve the convergence accuracy of the algorithm, but at the same time it will reduce the convergence speed of the algorithm and the ability to track the time-varying system; on the contrary, a larger step size factor can improve the convergence speed of the algorithm and the ability to track the time-varying noise system, but it is easy to cause the steady-state imbalance of the algorithm to increase. Therefore, when the useful signal component in the carrier signal on the power line is greatly interfered by noise, a smaller step size factor can reduce the excessive adjustment of the weight coefficient caused by noise interference in the algorithm, thereby reducing the steady-state imbalance of the algorithm; and when the noise component carried in the carrier signal is greatly different from the noise component carried in the noise reference signal, a larger step size factor can help the algorithm adapt to the changes of the noise system more quickly, improve the algorithm's ability to track irrelevant noise components and the convergence speed of the algorithm, thereby avoiding interference caused by irrelevant noise components.

[0044] S201: Obtain the bispectrum of the current download wave signal.

[0045] In order to effectively evaluate the noise interference degree of the useful signal component in the carrier signal during the transmission process in the power line, the carrier signal is processed as follows, specifically:

[0046] In this embodiment, the bisp3 bispectral analysis function is used to perform bispectral estimation on the carrier signal at the current moment to obtain a bispectral graph of the carrier signal at the current moment. The horizontal (X) and vertical (Y) coordinates of the data points in the bispectral graph represent the frequency, and the vertical (Z) coordinate represents the amplitude value of the carrier signal in the frequency domain. The useful signal component and the noise component in the carrier signal will form a peak spectrum in the bispectral graph.

[0047] It should be understood that the process of using the bisp3 bispectral analysis function to perform bispectral estimation on a signal is a well-known technology, and the specific principles and steps will not be repeated here.

[0048] S202: By analyzing the distribution of amplitude values ​​at all data points in the bispectrum, characteristic signal points are extracted from all data points, and all characteristic signal points are clustered to obtain multiple clusters of the download wave signal at the current moment.

[0049] Generally speaking, the useful signal component in the carrier signal on the power line has a larger amplitude value than the corresponding peak spectrum of the noise signal component in the dual spectrum diagram of the carrier signal. This is because the useful signal components in the carrier signal on the power line are modulated signals. They carry the information that needs to be transmitted and have a higher energy concentration, while the noise signal components are interference signals on the power line. They are random and scattered signals with a relatively scattered energy distribution.

[0050] Based on the above analysis, by analyzing the distribution of amplitude values ​​at data points in the bispectrum, the data points corresponding to the useful signal components are extracted and clustered. The specific process is as follows:

[0051] The amplitude values ​​of all data points in the bispectrum of the download signal at the current moment are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output, which is recorded as the amplitude threshold. The data points with amplitude values ​​greater than the amplitude threshold are taken as characteristic signal points.

[0052] It should be noted that there are many commonly used threshold segmentation algorithms. In this embodiment, the maximum inter-class variance algorithm is used to classify all data points. In actual application, as other implementation methods, implementers may also adopt other threshold segmentation methods. Regarding the selection of threshold segmentation methods, this embodiment does not make any special restrictions.

[0053] Among them, the maximum inter-class variance algorithm is a well-known technology, and its specific principle will not be repeated here.

[0054] Furthermore, all characteristic signal points in the dual spectrum of the current download wave signal are clustered to obtain multiple clusters, which represent a set of all data points in the area where each peak spectrum corresponding to the useful signal component in the current download wave signal is located in the dual spectrum.

[0055] It should be noted that there are many commonly used clustering algorithms. In this embodiment, the DBSCAN clustering algorithm is used to cluster the characteristic signal points, wherein the Euclidean distance between the characteristic signal points in the bispectrum is used as the metric distance in the DBSCAN clustering algorithm, the neighborhood radius in the DBSCAN clustering algorithm is set to 2 unit lengths, and Minpts is set to 20; in actual application, as other implementation methods, implementers may also adopt other clustering methods such as DPC density peak clustering or k-means clustering algorithm according to specific circumstances. Regarding the selection of clustering methods, this embodiment does not impose any special restrictions.

[0056] Among them, the DBSCAN clustering algorithm and the calculation method of the Euclidean distance are both well-known technologies, and their specific principles are not repeated here.

[0057] S203: Fitting the amplitude values ​​at all characteristic signal points in each cluster, and determining the signal interference degree of the carrier signal at the current moment by analyzing the fitting accuracy.

[0058] In the signal bispectrum, the smoothness of the peak spectrum usually reflects the purity and stability of the signal. This is because noise interference will cause the signal's spectrum distribution to become more complex and dispersed, making the originally clear peak spectrum blurred. Therefore, the greater the degree to which the useful signal component in the carrier signal on the power line is interfered by noise during its transmission, the less smooth the corresponding peak spectrum of the useful signal component in the carrier signal's bispectrum will usually be.

[0059] Based on the above analysis, in the bispectrum of the wave signal at the current moment, the amplitude values ​​at all characteristic signal points in each cluster are fitted to obtain a fitting surface, and the sum of squares of errors between the amplitude values ​​at all characteristic signal points in each cluster and the corresponding fitting values ​​on the fitting surface are calculated;

[0060] The mean of the sum of squared errors of all clusters is used as the signal interference degree of the download signal at the current moment.

[0061] It should be noted that there are many commonly used surface fitting methods. In this embodiment, a Gaussian surface fitting algorithm is used to fit the amplitude values ​​at all characteristic signal points in each cluster. In actual application, as other implementation methods, implementers may also use other surface fitting methods such as spline functions. Regarding the selection of surface fitting methods, this embodiment does not impose any special restrictions.

[0062] Among them, the Gaussian surface fitting algorithm is a well-known technology, and the specific fitting process will not be repeated here.

[0063] According to the signal interference degree of the carrier signal at the current moment, it can be understood that if the characteristic signal points in the area where the clustering point cluster is located do not have a single peak value and symmetrical data distribution, that is, the larger the sum of square errors, the less smooth the peak spectrum in the area where the clustering point cluster is located, the greater the degree to which the useful signal component in the carrier signal is interfered by noise, and the greater the obtained signal interference degree; conversely, if the peak distribution of the characteristic signal points in the area where the clustering point cluster is located is more single, and the symmetry of the area where the clustering cluster is located is higher, that is, the smaller the sum of square errors is, the smoother the peak spectrum in the area where the clustering point is located, the less the useful signal component in the carrier signal is interfered by noise, and the smaller the obtained signal interference degree is.

[0064] S3: Perform modal decomposition on the carrier signal at the current moment to obtain multiple modal components, obtain the frequency spectrum of each modal component, and determine the frequency overlap of each modal component in the carrier signal at the current moment by analyzing the overlap degree between the frequency corresponding to the positive amplitude value on the frequency spectrum and the signal frequency range. The frequency overlap of all modal components is used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. All modal components with frequency overlap less than the segmentation threshold are recorded as characteristic modal components.

[0065] In order to effectively evaluate the difference between the noise component carried in the carrier signal and the noise component carried in the noise reference signal, the carrier signal is processed as follows, specifically:

[0066] (1) Since the peak spectrum in the bispectrum of the carrier signal is symmetrical, the horizontal and vertical coordinate ranges of all characteristic signal points in each cluster in the bispectrum of the carrier signal at the current moment are extracted respectively;

[0067] (2) Furthermore, in the bispectral diagram of the carrier signal at the current moment, the union of the horizontal axis ranges and the union of the vertical axis ranges in all clusters are calculated respectively, and the area enclosed by the union of the horizontal axis ranges and the union of the vertical axis ranges in the plane where the horizontal and vertical axes are located is used as the signal frequency range of the carrier signal at the current moment, which is used to characterize the frequency distribution range of the useful signal component in the carrier signal.

[0068] (3) Furthermore, the carrier signal at the current moment is decomposed using a modal decomposition algorithm to obtain multiple modal components and the center frequency of each modal component. Each modal component corresponds to a signal component with a specific center frequency and a limited bandwidth in the carrier signal at the current moment.

[0069] It should be noted that there are many commonly used modal decomposition algorithms. In this embodiment, variational modal decomposition is used to decompose the carrier signal. In actual application, as other implementation methods, implementers can also use other decomposition methods such as empirical mode decomposition according to specific circumstances. Regarding the selection of modal decomposition algorithm, this embodiment does not impose any special restrictions.

[0070] The value of M in the variational modal component is 20, and the variational modal decomposition algorithm is a well-known technology, and its specific principle process will not be repeated here.

[0071] (4) Further, a frequency spectrum of each modal component of the carrier signal at the current moment is obtained, and all frequencies with amplitude values ​​greater than 0 in the frequency spectrum, that is, frequencies corresponding to positive amplitude values, are combined into a frequency set of each modal component to characterize the frequency distribution of the carrier signal.

[0072] (5) Further, by analyzing the degree of overlap between the frequency corresponding to the positive amplitude value on the spectrum diagram and the signal frequency range, the frequency overlap of each modal component in the download signal at the current moment is determined, specifically:

[0073] Frequency coincidence of modal component i in the current download wave signal The expression is: ; In the formula, It represents the number of frequencies in the frequency set of modal component i in the carrier signal at the current moment that are within the signal frequency range of the carrier signal; Represents the number of all frequencies in the frequency set of modal component i in the download wave signal at the current moment.

[0074] According to the frequency overlap of each modal component in the carrier signal at the current moment, it can be understood that if the number of frequencies in the frequency set of the modal components in the carrier signal that are located in the signal frequency range of the carrier signal is greater, the frequency overlap will be greater, and the degree of overlap between the frequency distribution of the signal in the modal component and the frequency distribution of the useful signal component in the carrier signal will be greater, indicating that the modal component is more likely to be a useful signal component in the carrier signal; conversely, if the number of frequencies in the frequency set of the modal components in the carrier signal that are located in the signal frequency range of the carrier signal is smaller, the frequency overlap will be smaller, and the degree of overlap between the frequency distribution of the signal in the modal component and the frequency distribution of the useful signal component in the carrier signal will be smaller, indicating that the modal component is more likely to be a noise signal component in the carrier signal.

[0075] (6) Furthermore, the frequency overlap of all modal components is used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. All modal components with a frequency overlap less than the segmentation threshold are recorded as characteristic modal components.

[0076] Preferably, the schematic diagram of the characteristic modal component acquisition process provided in this embodiment is as follows: Figure 2 shown.

[0077] So far, the characteristic modal components have been selected by analyzing the characteristic distribution of each modal component in the carrier signal.

[0078] Step S4: decompose the noise reference signal into multiple modal components, determine the difference between each characteristic modal component and all modal components of the noise reference signal by analyzing the difference in frequency, determine the difference index of the carrier signal at the current moment based on the average distribution of the difference of all characteristic modal components, analyze the concentrated distribution of energy of the carrier signal, and determine the step adjustment value of the carrier signal at the current moment in combination with the signal interference degree.

[0079] Based on the characteristic mode component obtained in step S3, further, by analyzing the difference between the characteristic mode component and the noise reference signal, the step adjustment value of the self-destruction signal at the current moment is determined. The specific process is as follows:

[0080] The noise reference signal is decomposed into multiple modal components using the variational mode decomposition algorithm, and the center frequency of each modal component is obtained;

[0081] Further, the center frequencies of each characteristic modal component and all modal components in the noise reference signal are obtained respectively, and the absolute value of the difference between the center frequencies of each characteristic modal component and each modal component in the noise reference signal is calculated, which is recorded as the component difference between each characteristic modal component and each modal component in the noise reference signal;

[0082] Among the component differences between each characteristic modal component and all modal components in the noise reference signal, the modal component in the noise reference signal corresponding to the minimum value is recorded as the comparison component of each characteristic modal component, and the difference between each characteristic modal component and its comparison component is taken as the difference degree of each characteristic modal component.

[0083] It should be noted that there are many methods for measuring the differences between signals. In this embodiment, the DTW distance between each characteristic modal component and its comparison component is used as the difference between each characteristic modal component and its comparison component. In actual application, as other implementation methods, the implementer may also use other methods such as Euclidean distance or Manhattan distance. Regarding the selection of the method for measuring the differences between signals, this embodiment does not impose any special restrictions.

[0084] The calculation method of the DTW distance is a well-known technology, and its specific calculation process will not be described in detail.

[0085] Furthermore, the mean value of the difference of all characteristic mode components in the download wave signal at the current moment is taken as the difference index of the download wave signal at the current moment.

[0086] Furthermore, by analyzing the concentrated distribution of the energy of the carrier signal, combined with the signal interference degree and the difference index, the step adjustment value of the carrier signal at the current moment is determined, specifically:

[0087] The expression of the step adjustment value L of the download wave signal at the current moment is: ; In the formula, Indicates the difference index of the download wave signal at the current moment; Indicates the signal interference degree of the download wave signal at the current moment; It represents the inverse of the largest eigenvalue among all eigenvalues ​​of the autocorrelation matrix of the download signal at the current moment; norm() represents the normalization function; Indicates a constant greater than 0 to prevent the denominator from being 0. The value of is artificially set. The value of is 0.01. Under the premise of ensuring that the denominator is not 0 and does not excessively affect the calculation result, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0088] Among them, the method of obtaining the autocorrelation matrix of the signal and the process of solving the matrix eigenvalues ​​are both well-known technologies, and the process of obtaining the autocorrelation matrix of the carrier signal and the process of solving the matrix eigenvalues ​​are not described in detail again.

[0089] According to the difference index of the carrier signal at the current moment, it can be understood that the greater the degree of noise interference to the useful signal component in the carrier signal, that is, the greater the signal interference, the smaller the step factor in the algorithm should be, that is, the smaller the step adjustment value should be, in order to reduce the phenomenon of steady-state imbalance of the carrier signal in the LMS adaptive filtering algorithm; the greater the difference between the noise component carried in the carrier signal and the noise component carried in the noise reference signal, that is, the greater the difference index, the larger the step factor in the algorithm should be, that is, the larger the step adjustment value should be, in order to improve the LMS adaptive filtering algorithm's tracking ability of the noise in the carrier signal and the convergence speed of the algorithm.

[0090] Step S5: Based on the step adjustment value of the carrier signal at the current moment, the carrier signal in the power carrier communication process at the current moment is subjected to noise reduction processing, and the carrier signal after the noise reduction processing is transmitted for communication.

[0091] The carrier signal at the current moment is used as the input of the LMS adaptive filtering algorithm, the step adjustment value of the carrier signal at the current moment is used as the step factor in the LMS adaptive filtering algorithm, and the denoised carrier signal is output.

[0092] Among them, the LMS adaptive filtering algorithm is a well-known technology, and the specific process will not be repeated here.

[0093] Furthermore, the denoised carrier signal is transmitted to a signal detection unit for demodulation, the data information carried in the denoised carrier signal is output, and the interface unit is used to transmit the data information to a client connected to the interface unit, thereby completing the carrier communication of the power line.

[0094] So far, this embodiment evaluates the degree of noise interference on the useful information carried in the carrier signal, as well as the degree of difference between the noise component in the carrier signal and the noise component in the reference signal, and uses the evaluation result to dynamically adjust the step factor of the carrier signal in the LMS adaptive filtering algorithm. Compared with the step factor selected by experience in the existing method, the LMS adaptive filtering algorithm can better adapt to the time-varying characteristics of the noise interference on the carrier signal during the transmission process, better realize the filtering of the noise in the carrier signal, and thus improve the reliability of data transmission in power line carrier communication.

[0095] Based on the same inventive concept as the above method, an embodiment of the present application also provides a multi-mode power line carrier communication module, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned multi-mode power line carrier communication methods are implemented.

[0096] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0097] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0098] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A multi-mode power line carrier communication method, characterized in that: The method comprises the following steps: Obtain the carrier signal on the power line at the current moment, and use the noise signal simulated and outputted by the carrier signal as its noise reference signal; Obtain a bispectrum of the current download wave signal, extract characteristic signal points from all data points by analyzing the distribution of amplitude values ​​at all data points in the bispectrum, and cluster all characteristic signal points to obtain multiple clusters of the current download wave signal; fit the amplitude values ​​at all characteristic signal points in each cluster, and determine the signal interference degree of the current download wave signal by analyzing the fitting accuracy; Extract the horizontal and vertical coordinate ranges of all characteristic signal points in each cluster in the bispectral graph respectively, and determine the signal frequency range of the carrier signal at the current moment by analyzing the overlap of the horizontal and vertical coordinate ranges and the area enclosed by the overlap of the horizontal and vertical coordinate ranges in the coordinate axis; Perform modal decomposition on the carrier signal at the current moment to obtain multiple modal components, obtain the frequency spectrum of each modal component, determine the frequency overlap of each modal component in the carrier signal at the current moment by analyzing the overlap degree between the frequency corresponding to the positive amplitude value on the frequency spectrum and the signal frequency range, use the frequency overlap of all modal components as the input of the threshold segmentation algorithm, output the segmentation threshold, and record all modal components with frequency overlap less than the segmentation threshold as characteristic modal components; The noise reference signal is decomposed into multiple modal components, and the difference between each characteristic modal component and all modal components of the noise reference signal is analyzed in frequency to determine the difference degree of each characteristic modal component. Based on the average distribution of the difference degrees of all characteristic modal components, the difference index of the carrier signal at the current moment is determined, and the concentrated distribution of the energy of the carrier signal is analyzed. In combination with the signal interference degree, the step adjustment value of the carrier signal at the current moment is determined, and the carrier signal in the power carrier communication process at the current moment is subjected to noise reduction processing.

2. A multi-mode power line carrier communication method according to claim 1, characterized in that: The process of extracting characteristic signal points from all data points is as follows: The amplitude values ​​of all data points in the bispectrum of the download signal at the current moment are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output, which is recorded as the amplitude threshold. The data points with amplitude values ​​greater than the amplitude threshold are taken as characteristic signal points.

3. A multi-mode power line carrier communication method as claimed in claim 1, characterized in that: The method for determining the signal interference degree of the carrier signal at the current moment is: In the bispectrum of the wave signal at the current moment, the amplitude values ​​at all characteristic signal points in each cluster are fitted to obtain a fitting surface, and the sum of squares of errors between the amplitude values ​​at all characteristic signal points in each cluster and the corresponding fitting values ​​on the fitting surface are calculated; The mean of the sum of squared errors of all clusters is used as the signal interference degree of the download signal at the current moment.

4. A multi-mode power line carrier communication method as claimed in claim 1, characterized in that: The method for determining the signal frequency range of the carrier signal at the current moment is: In the bispectrum of the download wave signal at the current moment, the union of the horizontal axis ranges and the union of the vertical axis ranges in all clusters are calculated respectively, and the area enclosed by the union of the horizontal axis ranges and the union of the vertical axis ranges in the plane where the horizontal and vertical axes are located is used as the signal frequency range of the download wave signal at the current moment.

5. A multi-mode power line carrier communication method as claimed in claim 1, characterized in that: The method for determining the frequency coincidence of each modal component in the carrier signal at the current moment is: The frequencies corresponding to all positive amplitude values ​​in the spectrum of each modal component are combined into a frequency set of each modal component; Frequency coincidence of modal component i in the current download wave signal The expression is: ; In the formula, It represents the number of frequencies in the frequency set of modal component i in the carrier signal at the current moment that are within the signal frequency range of the carrier signal; Represents the number of all frequencies in the frequency set of modal component i in the download wave signal at the current moment.

6. A multi-mode power line carrier communication method according to claim 1, characterized in that: The method for determining the difference of each characteristic mode component is: The center frequencies of each characteristic modal component and all modal components in the noise reference signal are obtained respectively, and the absolute value of the difference between the center frequencies of each characteristic modal component and each modal component in the noise reference signal is calculated, and recorded as the component difference between each characteristic modal component and each modal component in the noise reference signal; Among the component differences between each characteristic modal component and all modal components in the noise reference signal, the modal component in the noise reference signal corresponding to the minimum value is recorded as the comparison component of each characteristic modal component, and the difference between each characteristic modal component and its comparison component is taken as the difference degree of each characteristic modal component.

7. A multi-mode power line carrier communication method as claimed in claim 1, characterized in that: The difference index of the current download wave signal is the average value of the difference of all characteristic mode components in the current download wave signal.

8. A multi-mode power line carrier communication method as claimed in claim 1, characterized in that: The expression of the step adjustment value of the current download signal is: ; In the formula, Indicates the step adjustment value of the download wave signal at the current moment; Indicates the difference index of the download wave signal at the current moment; Indicates the signal interference degree of the download wave signal at the current moment; It represents the inverse of the largest eigenvalue among all eigenvalues ​​of the autocorrelation matrix of the download signal at the current moment; norm() represents the normalization function; Indicates a preset constant greater than 0.

9. A multi-mode power line carrier communication method as claimed in claim 1, characterized in that: The noise reduction process of the carrier signal in the power carrier communication process at the current moment includes: The carrier signal at the current moment is used as the input of the LMS adaptive filtering algorithm, the step adjustment value of the carrier signal at the current moment is used as the step factor in the LMS adaptive filtering algorithm, and the denoised carrier signal is output.

10. A multi-mode power line carrier communication module, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the multi-mode power line carrier communication method as described in any one of claims 1-9 are implemented.

Images