PLC noise filtering method and system based on clustering theory and medium

Abstract

To provide a PLC noise filtering method and system based on a clustering theory, and a medium, that have the advantages of good noise filtering performance, very simple calculation, good filtering effect and high filtering efficiency.SOLUTION: A method comprises the steps: obtaining a to-be-processed PLC signal sequence; and calculating a noise determination threshold according to a clustering property of a PLC signal and noise, and filtering the noise in the PLC signal sequence according to the noise determination threshold. According to the method, In order to distinguish among a PLC modulation signal, pulse noise and background noise through the clustering property of the signals and the noise, a difference among the PLC modulation signal, the pulse noise and the background noise in an aspect of amplitude continuity is utilized.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to the field of communications, and more particularly to a PLC noise filtering method, system and medium based on clustering theory.

Background Art

[0002] Compared with various wired communication technologies, power line communication has advantages such as no need for rewiring and easy networking, and is expected to have a wide range of applications. Power line communication technology is divided into narrowband power line communication (Narrowband over power line, NPL) and broadband power line communication (Broadband over power line, BPL). Narrowband power line communication refers to a power line carrier communication technology with a bandwidth limited to 3k~500kHz. The power line communication technology includes the specified bandwidth of CENELEC in Europe (3148.5kHz), the specified bandwidth of the Federal Communications Commission (FCC) in the United States (9~490kHz), the specified bandwidth of the Radio Industries and Businesses Association (ARIB) in Japan (9~450kHz), and the specified bandwidth in China (3~500kHz). Narrowband power line communication technology mainly uses single-carrier modulation technologies such as PSK technology, DSSS technology, and linear frequency modulation chirp technology, and the communication speed is less than 1Mbit / s. Broadband power line communication technology refers to a power line carrier communication technology with a bandwidth limited to 1.6~30MHz and usually a communication speed of 1Mbps or more, and adopts various spectrum spreading communication technologies centered on OFDM.

[0003] Power line communication systems have a wide range of applications and relatively mature technologies. However, due to the numerous branches and electrical devices within the power line communication system, a large amount of noise is generated in the power line channel. Among them, random pulse noise is temporally irregular and has a high noise intensity, which may cause significant damage to the power line communication system. Therefore, the suppression technology of random pulse noise has always been the focus of research by scholars at home and abroad, and the noise model does not follow a Gaussian distribution. Therefore, the conventional communication systems designed for Gaussian noise are no longer suitable for the power line carrier communication system. To improve the signal-to-noise ratio of the power line communication system, reduce the bit error rate, and ensure the quality of the power line communication system, it is necessary to consider corresponding noise suppression technologies.

[0004] In practical applications, simple non-linear technologies such as Clipping, Blanking, and Clipping / Blanking technologies are often applied to remove power line channel noise. However, all of these research methods can only operate well under certain signal-to-noise ratio conditions and only consider the removal of collision noise. In power line communication systems, the characteristics of some commercial power line transmitters are low transmission power. In some special cases, the transmission power may be less than 18w. Therefore, in special cases, the signal is buried in a large amount of noise, causing a low signal-to-noise ratio in the power line communication system. With the application and popularization of non-linear electrical appliances, the background noise in medium and low voltage transmission and distribution networks exhibits relatively obvious non-stationarity and non-Gaussian characteristics. It is difficult for conventional low-pass filters to achieve an ideal filtering effect in a non-stationary and non-Gaussian noise environment. It is difficult to filter non-stationary non-Gaussian noise, which has a serious impact on the performance of the PLC communication system.

Summary of the Invention

Problems to be Solved by the Invention

[0005] An object of the present invention is to provide a PLC noise filtering method, system, and medium based on clustering theory that can better filter the noise in PLC signals and guarantee the performance of the PLC communication system.

Means for Solving the Problem

[0006] To achieve the above object, the present invention provides the following solutions. A PLC noise filtering method based on clustering theory, the method comprising: acquiring a PLC signal sequence before processing; calculating a noise determination threshold according to the clustering characteristics of the PLC signal and the noise, and filtering the noise in the PLC signal sequence by the noise determination threshold.

[0007] Moreover, calculating a noise determination threshold according to the clustering characteristics of the PLC signal and the noise, and filtering the noise in the PLC signal sequence by the noise determination threshold specifically includes: calculating a first-order difference sequence and a second-order difference sequence corresponding to each signal in the PLC signal sequence; calculating a clustering sequence corresponding to each signal according to the first-order difference sequence and the second-order difference sequence corresponding to each signal; calculating a separation clustering coefficient corresponding to each signal according to the clustering sequence corresponding to each signal; calculating the noise determination threshold according to the separation clustering coefficient corresponding to each signal; and filtering the noise in the PLC signal sequence by the noise determination threshold and the separation clustering coefficient corresponding to each signal.

[0008] Moreover, filtering the noise in the PLC signal sequence by the noise determination threshold and the separation clustering coefficient corresponding to each signal specifically includes: judging whether the separation clustering coefficient corresponding to the nth signal in the PLC signal sequence is greater than or equal to the noise determination threshold; If so, noise is detected in the nth signal in the PLC signal sequence, and noise filtering is performed. If not, it may include that no noise is detected in the nth signal in the PLC signal sequence.

[0009] Also, the formula for the first difference sequence corresponding to each signal may be as follows.

[0010]

Number

[0011] The formula for the second difference sequence corresponding to each signal may be as follows.

[0012]

Number

[0013] Here, s n represents n signal elements in the PLC signal sequence S, where n = 1, 2, 3,..., N. When n = N, S N+1 , S N+2 takes the value 0.

[0014] Also, the formula for calculating the clustering sequence corresponding to each signal may be as follows.

[0015]

Number

[0016] Here, A n represents the nth adjacency matrix, and the element in the i-th row and j-th column of the adjacency matrix is a ij . JPEG2025080721000005.jpg1832 If it is ij , then a ij= 0, where i = 1, 2, 3…, N and j = 1, 2, 3…, N, JPEG2025080721000006.jpg2321 is the first-order difference sequence corresponding to the nth signal JPEG2025080721000007.jpg2726 represents the mean square error of JPEG2025080721000008.jpg2726 is the second-order difference sequence corresponding to the nth signal represents the mean square error of JPEG2025080721000009.jpg2226, I n represents an all-1 sequence of length n, E n represents an n×n dimensional identity matrix, and T represents transpose.

[0017] Also, the formula for calculating the separation clustering coefficient corresponding to each signal may be as follows.

[0018]

Number

[0019] Here, |An| represents the value of the determinant of the adjacency matrix An.

[0020] Also, the formula for calculating the noise determination threshold may be as follows.

[0021]

Number

[0022] Here, k n represents the nth eigenvalue of the clustering separation matrix C N and is as follows.

[0023]

Number

[0024] The present invention also provides a PLC noise filtering system based on clustering theory, and the system includes a signal acquisition module for acquiring a PLC signal sequence before processing, and a noise filtering module that calculates a noise determination threshold according to the clustering characteristics of the PLC signal and the noise, and filters the noise in the PLC signal sequence according to the noise determination threshold may be included.

[0025] In addition, specifically, the noise filtering module includes a difference calculation unit for calculating a first-order difference sequence and a second-order difference sequence corresponding to each signal in the PLC signal sequence, and a clustering sequence calculation unit for calculating a clustering sequence corresponding to each signal according to the first-order difference sequence and the second-order difference sequence corresponding to each signal, and a separation clustering coefficient calculation unit for calculating a separation clustering coefficient corresponding to each signal according to the clustering sequence corresponding to each signal, and a noise determination threshold calculation unit for calculating the noise determination threshold according to the separation clustering coefficient corresponding to each signal, and a noise filtering unit for filtering the noise in the PLC signal sequence according to the noise determination threshold and the separation clustering coefficient corresponding to each signal may be included.

[0026] The present invention provides a computer-readable storage medium storing a computer program for realizing a PLC noise filtering method based on clustering theory.

Advantages of the Invention

[0027] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects. The present invention provides a PLC noise filtering method, system, and medium based on clustering theory. The proposed method utilizes the differences in the amplitude continuity of PLC modulation signals, pulse noise, and background noise to distinguish between PLC modulation signals, pulse noise, and background noise through the clustering characteristics of signals and noise. The proposed method has excellent noise filtering performance, is very simple to calculate, has a good filtering effect, and high filtering efficiency.

Brief Description of the Drawings

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings necessary for use in the embodiments are briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative efforts.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Modes for Carrying Out the Invention

[0029] The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts are included within the protection scope of the present invention.

[0030] The object of the present invention is to provide a PLC noise filtering method, system and medium based on clustering theory. The proposed method utilizes the differences in the amplitude continuity of PLC modulation signals, pulse noise and background noise, and distinguishes PLC modulation signals, pulse noise, and background noise through the clustering characteristics of signals and noise. The proposed method has excellent noise filtering performance and very simple calculations.

[0031] In order to make the above objects, features and advantages of the present invention clearer and easier to understand, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

Embodiment

[0032] As shown in FIGS. 1 and 2, this embodiment provides a PLC noise filtering method based on clustering theory, and the method includes the following steps. Step 1: Obtain the PLC signal sequence S before processing.

[0033] Step 2: Calculate a noise determination threshold according to the clustering characteristics of PLC signals and noise, and filter the noise in the PLC signal sequence according to the noise determination threshold.

[0034] Here, as shown in FIG. 3, step 2 specifically includes the following steps.

[0035] Step 21: Calculate the first-order difference sequence and the second-order difference sequence corresponding to each signal in the PLC signal sequence.

[0036] Here, the formula for the first-order difference sequence corresponding to each signal is as follows.

[0037]

Number

[0038] The formula for the second-order difference sequence corresponding to each signal is as follows.

[0039]

Number

[0040] In the formula, JPEG2025080721000015.jpg2726 represents the first-order difference sequence corresponding to the nth signal in the PLC signal sequence S, JPEG2025080721000016.jpg2226 represents the second-order difference sequence corresponding to the nth signal in the PLC signal sequence S, s n represents n signal elements (amplitudes) in the PLC signal sequence S, where n = 1, 2, 3, …, N. When the subscript n of the element is > N, the corresponding element s n = 0, that is, when n = N, S N+1 and S N+2 take the value of 0.

[0041] Step 22: Calculate the clustering sequence corresponding to each signal based on the first-order difference sequence and the second-order difference sequence corresponding to each signal.

[0042] Here, the formula for calculating the clustering sequence corresponding to each signal is as follows.

[0043]

Number

[0044] In the formula, An represents the nth adjacency matrix, and the element in the ith row and jth column of the adjacency matrix is a ij and, JPEG2025080721000018.jpg1832 if it is the case of a ij = 1, and if not, a ij = 0, where i = 1, 2, 3…, N and j = 1, 2, 3…, N, JPEG2025080721000019.jpg2321 is the first - order difference sequence corresponding to the nth signal JPEG2025080721000020.jpg2726 represents the mean - square error of, JPEG2025080721000021.jpg2726 is the second - order difference sequence corresponding to the nth signal JPEG2025080721000022.jpg2226 represents the mean - square error of, I n represents an all - 1 sequence with length n, E n represents an n×n - dimensional identity matrix, and T represents transpose.

[0045] Step 23: Calculate the separation clustering coefficient corresponding to each signal according to the clustering sequence corresponding to each signal.

[0046] Here, the formula for calculating the separation clustering coefficient corresponding to each signal is as follows.

[0047]

Equation

[0048] In the formula, |A n | represents the value of the determinant of the adjacency matrix A n

[0049] Step 24: Calculate the noise determination threshold according to the separation clustering coefficient corresponding to each signal.

[0050] ​ Here, the formula for calculating the noise determination threshold is as follows.

[0051]

Number

[0052] In the formula, k n represents the n-th eigenvalue of the clustering separation matrix C N and is as follows.

[0053]

Number

[0054] Step 25: Filter the noise in the PLC signal sequence according to the noise determination threshold and the separation clustering coefficient corresponding to each signal.

[0055] Here, specifically, Step 25 includes the following. Determine whether the separation clustering coefficient corresponding to the n-th signal in the PLC signal sequence is greater than or equal to the noise determination threshold.

[0056] If so, noise is detected in the n-th signal in the PLC signal sequence, and noise filtering is performed. If not, no noise is detected in the n-th signal in the PLC signal sequence. That is, when the separation clustering coefficient H n of the n-th window satisfies the determination condition |H n |≧e 0 at the n JPEG2025080721000026.jpg1726 -th point of the PLC signal sequence S, a noise value is detected and filtered. If not, the noise value is uncertain and no noise filtering is performed.

[0057] In this embodiment, by utilizing the differences in the amplitude continuity of the PLC modulation signal, pulse noise, and background noise, the PLC modulation signal, pulse noise, and background noise are distinguished through the clustering characteristics of the signal and the noise. The proposed method has excellent noise filtering performance and is very simple to calculate.

[0058] Embodiment 2 As shown in FIG. 4, this embodiment provides a PLC noise filtering system based on clustering theory, and the system includes: A signal acquisition module 100 for acquiring the PLC signal sequence before processing; A noise filtering module 200 that calculates a noise determination threshold based on the clustering characteristics of the PLC signal and the noise, and filters the noise in the PLC signal sequence according to the noise determination threshold.

[0059] Here, as shown in FIG. 5, the noise filtering module 200 specifically includes the following: A difference calculation unit 201, which is used to calculate the first-order difference sequence and the second-order difference sequence corresponding to each signal in the PLC signal sequence.

[0060] A clustering sequence calculation unit 202, which is used to calculate the clustering sequence corresponding to each signal according to the first-order difference sequence and the second-order difference sequence corresponding to each signal.

[0061] A separation clustering coefficient calculation unit 203, which is used to calculate the separation clustering coefficient corresponding to each signal according to the clustering sequence corresponding to each signal.

[0062] A noise determination threshold calculation unit 204, which is used to calculate the noise determination threshold according to the separation clustering coefficient corresponding to each signal.

[0063] The noise filtering unit 205 is used to filter the noise in the PLC signal sequence according to the noise determination threshold and the separation clustering coefficient corresponding to each signal. Specifically, It is determined whether the separation clustering coefficient corresponding to the nth signal in the PLC signal sequence is greater than or equal to the noise determination threshold. If so, noise is detected in the nth signal in the PLC signal sequence, and noise filtering is performed. If not, no noise is detected in the nth signal in the PLC signal sequence.

[0064] Example 3 This example provides an electronic device including a memory and a processor. The memory is used to store a computer program, and the processor executes the computer program to cause the electronic device to execute the PLC noise filtering method based on the clustering theory of Example 1.

[0065] Also, the above electronic device may be a server.

[0066] This embodiment of the present invention further provides a computer-readable storage medium storing a computer program for realizing the PLC noise filtering method based on the clustering theory of Example 1.

[0067] Embodiments of the present invention provide a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware. Also, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) in which computer-usable program code is incorporated.

[0068] The present invention will be described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart diagrams and / or block diagrams, as well as combinations of processes and / or blocks in the flowchart diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices, so as to generate a device such that the instructions executed by the processor of the computer or other programmable data processing devices perform the functions specified by the processes of the flowchart or processes, and / or the boxes or boxes of the block diagram.

[0069] Each embodiment focuses on the differences from other embodiments. For the same parts and similar parts, it is sufficient to refer to each embodiment. Also, for the systems disclosed in each embodiment, since they correspond to the methods disclosed in each embodiment, the description is relatively simple. Therefore, the description is relatively simple, and the relevant parts may be referred to the section of the method.

[0070] In this specification, the principles of the present invention and specific embodiments of implementation are applied. The above description of the embodiments only helps to understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, based on the ideas of the present invention, changes will occur in specific implementations and application scopes. In summary, the content of this specification should not be construed as limiting the present invention.

Claims

1. Obtaining a sequence of PLC signals before processing; 1. A PLC noise filtering method based on clustering theory, comprising: calculating a noise judgment threshold according to clustering characteristics of a PLC signal and noise; and filtering the noise in the PLC signal sequence by using the noise judgment threshold.

2. Calculating a noise judgment threshold according to clustering characteristics of the PLC signal and the noise, and filtering the noise in the PLC signal sequence according to the noise judgment threshold, specifically, calculating a first order differential sequence and a second order differential sequence corresponding to each signal in the PLC signal sequence; Calculate a clustering sequence corresponding to each signal according to the first-order difference sequence and the second-order difference sequence corresponding to each signal; Calculate a separation clustering coefficient corresponding to each signal according to the clustering sequence corresponding to each signal; Calculate the noise decision threshold according to the separation clustering coefficient corresponding to each signal; 2. The PLC noise filtering method according to claim 1, further comprising: filtering the noise in the PLC signal sequence according to the noise judgment threshold and a separation clustering coefficient corresponding to each signal.

3. Filtering noise in the PLC signal sequence according to the noise judgment threshold and the separation clustering coefficient corresponding to each signal specifically includes: determining whether the separation clustering coefficient corresponding to an n-th signal in the PLC signal sequence is equal to or greater than the noise determination threshold; If so, noise is detected in the n-th signal in the PLC signal sequence, and noise filtering is performed; 3. The method for filtering PLC noise based on clustering theory according to claim 2, further comprising: otherwise, no noise is detected in the n-th signal in the sequence of PLC signals.

4. The equation for the primary difference sequence for each signal is: [0010] The equation for the secondary difference sequence corresponding to each signal is: [0025] Here, s ｎ represents n signal elements in a PLC signal sequence S, where n=1, 2, 3, ..., N, and for n=N, S Ｎ＋１ , S Ｎ＋２ The PLC noise filtering method based on clustering theory according to claim 3, characterized in that:

5. The formula for calculating the clustering sequence corresponding to each signal is as follows: [0030] Here, A ｎ represents the n-th adjacent matrix, and the element in the i-th row and j-th column of the adjacent matrix is ​​a ｉｊ and 【number】 If a ｉｊ = 1, otherwise a ｉｊ = 0, i = 1, 2, 3 ..., N, j = 1, 2, 3 ..., N, 【number】 is the first-order difference sequence corresponding to the nth signal 【number】 represents the mean square error of 【number】 is the second-order difference sequence corresponding to the nth signal 【number】 represents the mean square error of ｎ represents an all-1 sequence of length n, and E ｎ 5. The PLC noise filtering method based on clustering theory as claimed in claim 4, wherein T denotes an n×n dimensional unit matrix, and T denotes the transpose.

6. The formula for calculating the separation clustering coefficient corresponding to each signal is as follows: [0045] 6. The PLC noise filtering method according to claim 5, wherein |An| represents the value of the determinant of the adjacent matrix An.

7. The formula for calculating the noise determination threshold is as follows: [0050] Here, k ｎ is the clustering separation matrix C Ｎ represents the n-th eigenvalue of [006] 7. The PLC noise filtering method based on clustering theory according to claim 6, wherein:

8. a signal acquisition module for acquiring a sequence of PLC signals before processing; A PLC noise filtering system based on clustering theory, comprising: a noise filtering module for calculating a noise judgment threshold according to clustering characteristics of a PLC signal and noise, and filtering noise in the PLC signal sequence according to the noise judgment threshold.

9. The noise filtering module specifically includes: a difference calculation unit for calculating a first-order difference sequence and a second-order difference sequence corresponding to each signal in the PLC signal sequence; a clustering sequence calculation unit for calculating a clustering sequence corresponding to each signal according to the first differential sequence and the second differential sequence corresponding to each signal; a separation clustering coefficient calculation unit for calculating a separation clustering coefficient corresponding to each signal according to the clustering sequence corresponding to each signal; a noise judgment threshold calculation unit for calculating the noise judgment threshold according to the separation clustering coefficients corresponding to each signal; 9. The PLC noise filtering system based on clustering theory as claimed in claim 8, further comprising a noise filtering unit for filtering noise in the PLC signal sequence according to the noise judgment threshold and a separation clustering coefficient corresponding to each signal.

10. A computer readable storage medium storing a computer program for implementing the method according to any one of claims 1 to 7.

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