A signal noise reduction method and system for improving power line communication quality

Abstract

The present application relates to the field of power line communication technology, and particularly relates to a signal noise reduction method and system for improving the quality of power line communication. The method includes: filtering the power line communication signals with different step sizes respectively; analyzing the volatility of the local range of each power line communication signal value; comparing the distances between the original extreme value signal sequences before and after filtering to obtain the filtering effects of each power line communication signal value based on different step sizes; obtaining the denoising selection weights of each power line communication signal value; based on the prediction results of the filtering weights of a preset number of previous signal values of each power line communication signal value, combining the signal distribution similarity within the corresponding window of the power line communication signal value and the denoising selection weights, obtaining the filtering weights of each power line communication signal value, weighting the filtered signals, and obtaining the final filtered value of each power line communication signal value. The purpose of the present application is to improve the denoising quality of power line communication signals.

Description

Technical Field

[0001] This application relates to the technical field of power line communication, and specifically relates to a signal noise reduction method and system for improving the quality of power line communication. Background Art

[0002] Power Line Communication (PLC) is a technology that uses existing power lines for data transmission, with advantages such as no need for additional wiring, low cost, and wide coverage. However, as a communication medium, the inherent noise interference and signal attenuation problems of power lines have always been technical challenges. The power line communication channel is often interfered by various noises, such as switch noise of electrical equipment, lightning interference, background noise, etc. These noises will seriously affect the communication quality and reduce the reliability of signal transmission.

[0003] With the popularization of the Internet of Things and intelligent applications, the power line communication market shows a rapid growth trend. It is expected that in the next few years, the power line carrier communication industry will continue to maintain a high growth rate. Currently, the performance of power line communication systems is still limited by noise interference. When using the traditional Gaussian filtering algorithm to reduce the noise of power line communication signals, Gaussian filtering with a fixed window size is usually adopted. However, due to the complex and variable noise interference in power line communication, Gaussian filtering with a fixed window size may not be able to adapt to the changes of different noise characteristics, and cannot effectively balance the relationship between noise and signal, thus affecting the quality of noise reduction. Summary of the Invention

[0004] In view of the above, it is necessary to provide a signal noise reduction method and system for improving the quality of power line communication to solve the above problems.

[0005] The first aspect of this application provides a signal noise reduction method for improving the quality of power line communication, and the method includes:

[0006] Filter the power line communication signals with a first preset step size and a second preset step size respectively;

[0007] Analyze the fluctuation characteristics of the envelope line of the signal in the preset window of each power line communication signal value, and combine the signal difference distribution characteristics between adjacent signal values to obtain the volatility of each power line communication signal value;

[0008] Based on the type of the extreme value point closest to each power line communication signal value, obtain the original extreme value signal sequence of each power line communication signal value; compare the distance between the original extreme value signal sequence and the filtered original extreme value signal sequence, and combine the anomaly detection result of the filtered original extreme value signal sequence to obtain the first filtering effect and the second filtering effect of each power line communication signal value; combine the volatility to obtain the denoising selection weight of each power line communication signal value;

[0009] Based on the prediction result of the filtering weights of a preset number of signal values before each power line communication signal value, combined with the signal distribution similarity within the window corresponding to the power line communication signal value and the denoising selection weights, the filtering weight of each power line communication signal value is obtained, and the filtered signal is weighted to obtain the final filtered value of each power line communication signal value.

[0010] Among them, the process of obtaining the volatility of each power line communication signal value is specifically as follows:

[0011] For the window of each power line communication signal value, obtain the upper and lower envelope lines of all signal values within the window, and respectively record the dispersion of the numerical values corresponding to all signal values on the upper envelope line and the lower envelope line as the first dispersion and the second dispersion;

[0012] Calculate the difference between the first dispersion and the second dispersion, and record it as the first difference;

[0013] Calculate the average value of the differences between each signal value within the window and all its adjacent signal values, and calculate the average value of the average values of the differences of all signal values within the window; accumulate the difference between the average value of the differences of all signal values within the window and the average value to obtain the second difference;

[0014] Perform positive fusion and normalization on the first dispersion, the second dispersion, the first difference, and the second difference to obtain the volatility of each power line communication signal value.

[0015] Among them, the process of obtaining the original extreme value signal sequence of each power line communication signal value is as follows:

[0016] For each power line communication signal value, obtain the extreme value signal point closest to each power line communication signal value in the corresponding window, and further obtain all signal value points of the same type as the extreme value signal point to form the original extreme value signal sequence.

[0017] Among them, the steps of obtaining the first filtering effect and the second filtering effect of each power line communication signal value are as follows:

[0018] The original extreme value signal sequences after denoising the power line communication signal by the first preset step length and the second preset step length are respectively recorded as the first denoised extreme value signal sequence and the second denoised extreme value signal sequence;

[0019] Obtain the metric distance between the original extreme value signal sequence and the first denoised extreme value signal sequence;

[0020] Perform anomaly detection on the first denoised extreme value signal sequence, and calculate the anomaly average value of all elements;

[0021] The negative correlation mapping result after positively fusing the measured distance with the abnormal average value is used as the first filtering effect corresponding to each power line communication signal value;

[0022] Correspondingly, based on the original extreme value signal sequence and the second denoised extreme value signal sequence, the second filtering effect corresponding to each power line communication signal value is obtained.

[0023] Among them, the specific formula for obtaining the denoising selection weight of each power line communication signal value is: ; where represents the denoising selection weight of the i-th power line communication signal value; represents the volatility corresponding to the i-th power line communication signal value; represents the first filtering effect of the i-th power line communication signal value; represents the second filtering effect of the i-th power line communication signal value.

[0024] Among them, the process of obtaining the filtering weight of each power line communication signal value is specifically as follows:

[0025] Predict the filtering weights of a preset number of signal values before each power line communication signal value to obtain the preferred weights of each power line communication signal value;

[0026] Based on the filtering weights of a preset number of signal values before each power line communication signal value and the signal distribution characteristics of the corresponding window, combined with the denoising selection weight, obtain the signal filtering correction value of each power line communication signal value;

[0027] Take the mean of the signal filtering correction value and the preferred weight of each power line communication signal value as the filtering weight of each power line communication signal value.

[0028] Among them, obtaining the signal filtering correction value of each power line communication signal value is specifically as follows:

[0029] Analyze the signal distribution similarity between each power line communication signal value and the window corresponding to a preset number of signal values before it to obtain the similar window of each power line communication signal value;

[0030] Denote the signal filtering correction weight of the i-th power line communication signal value as , and its formula form is: ; where represents the number of similar windows of the i-th power line communication signal value; represents the reference coefficient; represents the signal similarity between the window corresponding to the i-th power line communication signal value and its f-th similar window; The filtering weight of the central signal of the f-th similarity window representing the i-th power line communication signal value; The denoising selection weight representing the i-th power line communication signal value.

[0031] Among them, obtaining the similarity window of each power line communication signal value specifically is:

[0032] Calculate the signal similarity between each power line communication signal value and the corresponding windows of the previous preset number of signal values, and record the windows with the signal similarity greater than the preset threshold as the similarity windows of each power line communication signal value.

[0033] Among them, the specific formula for obtaining the final filtered value of each power line communication signal value is: ; among them, Represents the final filtered value of each power line communication signal value; Represents the filtering weight of each power line communication signal value; Represents the filtered value of each power line communication signal value after using the first preset step size; Represents the filtered value of each power line communication signal value after using the second preset step size.

[0034] In a second aspect, an embodiment of the present application further provides a signal denoising system for improving the quality of power line communication, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0035] The present application has at least the following beneficial effects:

[0036] In the embodiments of the present application, the power line communication signals are first filtered, and the signal fluctuations of each power line communication signal value and the surrounding signals are analyzed to obtain the volatility. The beneficial effect is to analyze the signal fluctuation characteristics around each power line communication signal value, which is convenient for obtaining the adaptive weights of the two filtering values based on the fluctuation characteristics in the subsequent process; further, the distribution characteristics of the same type of extreme points within the local range of each power line communication signal value before and after filtering are analyzed to obtain the filtering effect corresponding to each step size, which is convenient for comparing the filtering effects of different step sizes in the subsequent process, and the most suitable filtering weight can be found, so as to maximize the clarity of the signal and reduce noise interference; combined with the volatility, the denoising selection weight of each power line communication signal value is obtained, and the beneficial effect is to flexibly adjust the denoising intensity according to the fluctuation characteristics of the signal, so as to optimize the clarity and stability of the signal; based on the prediction results of the filtering weights of a preset number of signal values before each power line communication signal value, combined with the signal distribution similarity within the corresponding window of the power line communication signal value and the denoising selection weight, the filtering weight of each power line communication signal value is obtained, and the beneficial effect is that by combining historical signal information, signal similarity and denoising selection weight, the filtering weight of each signal value can be predicted more accurately, avoiding relying solely on a certain characteristic; the filtered signal is weighted to obtain the final filtered value of each power line communication signal value, and the filtering weight can be dynamically adjusted according to different situations of the signal, effectively removing noise and retaining useful signals, greatly increasing the flexibility of signal denoising and improving the signal denoising effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of the steps of a signal denoising method for improving the quality of power line communication provided by an embodiment of the present application;

[0038] Figure 2 It is a flowchart for obtaining the filtering weight provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary", "or", "for example" is intended to present relevant concepts in a specific manner.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0041] In addition, it should be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. For the methods disclosed in the embodiments of this application or the methods shown in the flowcharts, which include one or more steps for implementing the methods, without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0043] The following specifically describes the specific solutions of a signal denoising method and system for improving the power line communication quality provided by this application with reference to the accompanying drawings.

[0044] Please refer to Figure 1 , which shows a flowchart of the steps of a signal denoising method for improving the power line communication quality provided by an embodiment of this application. The method includes the following steps:

[0045] Step 1: Denoise the power line communication signals with a first preset step size and a second preset step size respectively.

[0046] In smart grids and PLC technologies, filtering techniques are widely used in the signal processing and transmission processes to overcome problems such as signal attenuation, interference, and noise. Therefore, this application considers performing Gaussian filtering on the power line communication signals. Since there are problems in the existing Gaussian filtering algorithms that different step sizes also have different effects on the filtering and denoising algorithms, after processing it with a filtering algorithm with a fixed step size, there may still be an unsatisfactory denoising effect. Therefore, the denoising effects of the first preset step size and the second preset step size can be analyzed to better adapt to the complex characteristics of the power line communication signals. In this embodiment, the first preset step size is 51, and the second preset step size is 11. Implementers can adjust the step size in the filtering algorithm according to the actual situation, and this application does not limit this.

[0047] Step 2: Analyze the fluctuation characteristics of the envelope of the signal in the preset window of each power line communication signal value, and combine the signal difference distribution characteristics between adjacent signal values to obtain the volatility of each power line communication signal value.

[0048] This application analyzes based on each power line communication signal value to obtain its corresponding signal filtering selection weight. Specifically, with each power line communication signal value as the center, a preset window is set. In this embodiment, the length of the preset window is 1001, and the implementer can adjust it according to the actual situation; analyze the fluctuation characteristics of the envelope line of all power line communication signal values in the window, and combine the signal difference distribution characteristics between adjacent power line communication signal values to obtain the volatility of each power line communication signal value:

[0049] For the window of each power line communication signal value, obtain the upper and lower envelope lines of all signal values in the window, and record the dispersion of the corresponding numerical values of all signal values on the upper envelope line and the lower envelope line as the first dispersion and the second dispersion respectively; calculate the difference between the first dispersion and the second dispersion, and record it as the first difference; calculate the average value of the differences between each signal value in the window and all its adjacent signal values, and calculate the average value of the average values of the differences of all signal values in the window; accumulate the difference between the average value of the differences of all signal values in the window and the average value to obtain the second difference; perform positive fusion and normalization on the first dispersion, the second dispersion, the first difference, and the second difference to obtain the volatility of each power line communication signal value.

[0050] In this embodiment, variance is used to measure the dispersion of signal values on the upper and lower envelope lines; the difference between dispersions is calculated using the absolute value of the difference; multiplying is used to perform positive fusion on multiple variables; the normalization method uses the maximum-minimum normalization method, and the implementer can select a suitable normalization method according to the actual situation.

[0051] It should be understood that the first dispersion and the second dispersion are used to measure the stability characteristics of the signal distribution in the window of each power line communication signal value, the first difference is used to measure the stability difference between the upper and lower envelope lines, and the second difference is used to measure the difference distribution between the signal in the window and adjacent signal values; further, when the difference between adjacent signal values of the signal curve in the window is smaller, and the stability of the upper and lower envelope curves is stronger, and the stability difference between the upper and lower envelope lines is smaller, it indicates that the signal volatility in the window is smaller, and a longer filter should be used to denoise the signal; otherwise, a shorter filter should be used to denoise the signal.

[0052] Step 3: Based on the type of the extreme value point closest to each power line communication signal value, obtain the original extreme value signal sequence of each power line communication signal value; compare the distance between the original extreme value signal sequence and the filtered original extreme value signal sequence, and combine the anomaly detection result of the filtered original extreme value signal sequence to obtain the first filtering effect and the second filtering effect of each power line communication signal value; combine the volatility to obtain the denoising selection weight of each power line communication signal value.

[0053] For each power line communication signal value, obtain the extreme value signal point closest to each power line communication signal value in the corresponding window, and further obtain all signal value points of the same type as the extreme value signal point to form an original extreme value signal sequence, where the types include maximum value points and minimum value points; for ease of understanding, an example is given below. If the extreme value signal point closest to the \(i\)-th power line communication signal value is a maximum value point, then obtain the sequence composed of all maximum value points in the window of the \(i\)-th power line communication signal value, which is denoted as the original extreme value signal sequence of the \(i\)-th power line communication signal value. Denote the original extreme value signal sequence after denoising the \(i\)-th power line communication signal value with the first preset step and the second preset step as the first denoised extreme value signal sequence and the second denoised extreme value signal sequence, respectively.

[0054] Obtain the metric distance between the original extreme value signal sequence and the first denoised extreme value signal sequence; perform outlier detection on the first denoised extreme value signal sequence and calculate the outlier average value of all elements; use the negative correlation mapping result after positively fusing the metric distance and the outlier average value as the first filtering effect corresponding to each power line communication signal value. Based on the original extreme value signal sequence and the second denoised extreme value signal sequence, use the same calculation method as the first filtering effect to obtain the second filtering effect corresponding to each power line communication signal value.

[0055] In this embodiment, the dynamic time warping (DTW) distance is used to measure the metric distance between two sequences, denoted as \(A\); the local outlier factor (LOF) outlier detection algorithm is used for outlier detection of the sequence, and the outlier average value is denoted as \(B\); the calculation method of positive fusion for multiple variables is multiplication, and the formula form of the first filtering effect is: ; where represents the exponential function with the natural constant as the base. Among them, both the DTW distance and the LOF outlier detection algorithm are well-known technologies, and this application will not elaborate on them.

[0056] It should be understood that the closer the distance metric between the extreme value signal sequences before and after filtering, and the smaller the outlier average value of all signal values after filtering, the better the filtering effect obtained by filtering with the corresponding length.

[0057] Based on the volatility corresponding to each power line communication signal value and the first and second filtering effects, obtain the denoising selection weight for each power line communication signal value, and its formula form is: ; where represents the denoising selection weight of the \(i\)-th power line communication signal value; represents the volatility corresponding to the \(i\)-th power line communication signal value; Represents the first filtering effect of the i-th power line communication signal value; Represents the second filtering effect of the i-th power line communication signal value.

[0058] It should be understood that when the signal value volatility within the window of each power line communication signal value is smaller, and the better the denoising effect corresponding to using the first preset step size, the more the corresponding signal denoising selection weight tends to the signal value after denoising with the first preset step size, and thus the larger the denoising selection weight.

[0059] Step 4: Based on the prediction results of the filtering weights of a preset number of signal values before each power line communication signal value, combined with the signal distribution similarity within the corresponding window of the power line communication signal value and the denoising selection weight, obtain the filtering weight of each power line communication signal value, and weight the filtered signal to obtain the final filtered value of each power line communication signal value.

[0060] This application analyzes discrete power line communication signals. It should be noted that when filtering these discrete data, the change of the weight should not be too drastic: if the weight change between adjacent signal values is too large, it may cause abnormal fluctuations in the filtering result. Especially when the signal is affected by noise interference, the filtering process may tend to over-smooth, resulting in the result tending to an abnormal stable trend. To ensure the rationality of signal changes, although it is discrete data, the adjustment of the weight during the filtering process should be as smooth and continuous as possible, avoiding sudden changes or discontinuities, so as to better reflect the change trend of the real signal.

[0061] Therefore, based on the prediction of the filtering weights of a preset number of signal values before each power line communication signal value, the preferred weight of each power line communication signal value is obtained, so as to ensure that while the weight changes dynamically, the change of the signal value is also smooth, continuous and reasonable; this embodiment uses a time series prediction algorithm. Analyze the signal distribution similarity between each power line communication signal value and the corresponding window of each previous signal value to obtain the similar window of each power line communication signal value: calculate the signal similarity between each power line communication signal value and the corresponding windows of a preset number of previous signal values, and record the window with the signal similarity greater than the preset threshold as the similar window of each power line communication signal value. In this embodiment, the preset number takes a value of 10000. It should be noted that if there are less than the preset number of signal values before a power line communication signal value, only consider all the signal values before each power line communication signal value; the signal similarity between two windows is determined by calculating the Pearson correlation coefficient, and the preset threshold takes a value of 0.8.

[0062] Further, based on the denoising selection weight of each power line communication signal value and the filtering weight of the previous signal, combined with signal similarity, the signal filtering correction value of each power line communication signal value is obtained, and its formula form is: ; where represents the signal filtering correction weight of the i-th power line communication signal value; represents the number of similarity windows of the i-th power line communication signal value; represents the reference coefficient, and the value in this embodiment is 0.5; represents the signal similarity between the window corresponding to the i-th power line communication signal value and its f-th similarity window; represents the filtering weight of the central signal of the f-th similarity window of the i-th power line communication signal value; represents the denoising selection weight of the i-th power line communication signal value.

[0063] Further, the mean value of the signal filtering correction value of each power line communication signal value and the preferred weight is used as the filtering weight of each power line communication signal value. Among them, the flowchart for obtaining the filtering weight is as shown in Figure 2 ; Based on the filtering weight of each power line communication signal value, combined with the filtering algorithm, the denoised power line communication signal is obtained. Specifically, ; where represents the final filtering value of each power line communication signal value; represents the filtering weight of each power line communication signal value; represents the filtering value of each power line communication signal value after using the first preset step size; represents the filtering value of each power line communication signal value after using the second preset step size.

[0064] Thus, the noise reduction of the power line communication signal is completed.

[0065] Based on the same inventive concept as the above method, an embodiment of the present application also provides a signal noise reduction system for improving the quality of power line communication, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for a signal noise reduction method for improving the quality of power line communication.

[0066] In summary, the present application first filters the power line communication signals, analyzes the signal fluctuations of each power line communication signal value and the signals around it to obtain the volatility. The beneficial effect is to analyze the signal fluctuation characteristics around each power line communication signal value, which is convenient for obtaining the adaptive weights of two filtering values based on the fluctuation characteristics in the subsequent process; further, analyze the distribution characteristics of the same type of extreme points within the local range of each power line communication signal value before and after filtering to obtain the filtering effect corresponding to each step size, which is convenient for comparing the filtering effects of different step sizes in the subsequent process, and the most suitable filtering weight can be found, so as to maximize the clarity of the signal and reduce noise interference; combined with the volatility, obtain the denoising selection weight of each power line communication signal value, and the beneficial effect is to flexibly adjust the denoising intensity according to the fluctuation characteristics of the signal, thereby optimizing the clarity and stability of the signal; based on the prediction results of the filtering weights of a preset number of signal values before each power line communication signal value, combined with the signal distribution similarity within the corresponding window of the power line communication signal value and the denoising selection weight, obtain the filtering weight of each power line communication signal value. The beneficial effect is that by combining historical signal information, signal similarity and denoising selection weight, the filtering weight of each signal value can be predicted more accurately, avoiding relying solely on a certain characteristic; weight the filtered signal to obtain the final filtered value of each power line communication signal value, and the filtering weight can be dynamically adjusted according to different situations of the signal, effectively removing noise and retaining useful signals, greatly increasing the flexibility of signal denoising and improving the signal denoising effect.

[0067] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description. Sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0068] For those skilled in the art, it is obvious that the present application is not limited to the details of the above-described exemplary embodiments, and the present application can be implemented in other specific forms without departing from the basic characteristics of the present application. Therefore, from any point of view, the above-described embodiments of the present application should be regarded as exemplary and non-limiting; modifying the technical solutions described in the foregoing embodiments, or equivalently replacing some of the technical features therein, does not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A signal noise reduction method for improving the quality of power line communication, characterized in that: The method comprises the following steps: filtering the power line communication signal with a first preset step size and a second preset step size respectively; Analyze the fluctuation characteristics of the envelope of the signal in the preset window of each power line communication signal value, and combine the signal difference distribution characteristics between adjacent signal values ​​to obtain the volatility of each power line communication signal value; Based on the type of the extreme point closest to each power line communication signal value, an original extreme value signal sequence of each power line communication signal value is obtained; the distance between the original extreme value signal sequence and the filtered original extreme value signal sequence is compared, and the first filtering effect and the second filtering effect of each power line communication signal value are obtained in combination with the abnormal detection result of the filtered original extreme value signal sequence; and a denoising selection weight value of each power line communication signal value is obtained in combination with the volatility; Based on the prediction results of the filtering weights of a preset number of signal values ​​before each power line communication signal value, combined with the signal distribution similarity in the window corresponding to the power line communication signal value and the denoising selection weight, the filtering weight of each power line communication signal value is obtained, and the filtered signal is weighted to obtain the final filtering value of each power line communication signal value.

2. A signal noise reduction method for improving the quality of power line communication according to claim 1, characterized in that: The volatility of each power line communication signal value is obtained as follows: For each window of power line communication signal values, obtain the upper and lower envelopes of all signal values ​​in the window, and record the discreteness of the corresponding numerical values ​​of all signal values ​​on the upper envelope and the lower envelope as the first discreteness and the second discreteness respectively; Calculating a difference between the first discreteness and the second discreteness, recorded as a first difference; Calculate the average value of the differences between each signal value in the window and all its adjacent signal values, and calculate the mean value of the average values ​​of the differences of all signal values ​​in the window; accumulate the difference between the average value of the differences of all signal values ​​in the window and the mean value to obtain a second difference; The first discreteness, the second discreteness, the first difference, and the second difference are forward fused and normalized to obtain the volatility of each power line communication signal value.

3. A signal noise reduction method for improving the quality of power line communication according to claim 1, characterized in that: The process of obtaining the original extreme value signal sequence of each power line communication signal value is as follows: For each power line communication signal value, the extreme value signal point closest to each power line communication signal value is obtained in the corresponding window, and all signal values ​​of the same type as the extreme value signal point are further obtained to form an original extreme value signal sequence.

4. A signal noise reduction method for improving the quality of power line communication according to claim 1, characterized in that: The steps of obtaining the first filtering effect and the second filtering effect of each power line communication signal value are: The original extreme value signal sequences of the power line communication signal after denoising by the first preset step length and the second preset step length are recorded as the first denoised extreme value signal sequence and the second denoised extreme value signal sequence respectively; Obtaining a metric distance between the original extreme value signal sequence and the first denoised extreme value signal sequence; Perform anomaly detection on the first denoised extreme value signal sequence and calculate the anomaly average of all elements; The negative correlation mapping result after the metric distance and the abnormal average value are positively fused as the first filtering effect corresponding to each power line communication signal value; Correspondingly, based on the original extreme value signal sequence and the second denoised extreme value signal sequence, a second filtering effect corresponding to each power line communication signal value is obtained.

5. A signal noise reduction method for improving the quality of power line communication according to claim 1, characterized in that: The specific formula for obtaining the denoising selection weight of each power line communication signal value is: ;in, represents a denoising selection weight for the i-th power line communication signal value; represents the volatility corresponding to the i-th power line communication signal value; represents a first filtering effect of the i-th power line communication signal value; The second filtering effect of the i-th power line communication signal value is represented.

6. A signal noise reduction method for improving the quality of power line communication according to claim 1, characterized in that: The process of obtaining the filtering weight of each power line communication signal value is specifically as follows: Predicting the filter weights of a preset number of signal values ​​before each power line communication signal value to obtain a preferred weight for each power line communication signal value; Based on the filtering weights of a preset number of signal values ​​before each power line communication signal value and the signal distribution characteristics of the corresponding window, combined with the denoising selection weights, a signal filtering correction value for each power line communication signal value is obtained; The average of the signal filtering correction value and the preferred weight of each power line communication signal value is used as the filtering weight of each power line communication signal value.

7. A signal noise reduction method for improving the quality of power line communication according to claim 6, characterized in that: The signal filtering correction value of each power line communication signal value is obtained as follows: Analyze the signal distribution similarity between each power line communication signal value and the windows corresponding to the previously preset number of signal values ​​to obtain a similar window for each power line communication signal value; The signal filtering correction weight of the i-th power line communication signal value is recorded as , its formula form is: ;in, The number of similarity windows representing the value of the ith power line communication signal; represents the reference coefficient; represents the signal similarity between the window corresponding to the i-th power line communication signal value and its f-th similar window; The filtering weight of the center signal of the f-th similarity window representing the i-th power line communication signal value; Represents the denoising selection weight for the i-th power line communication signal value.

8. A signal noise reduction method for improving the quality of power line communication according to claim 7, characterized in that: The similarity window of each power line communication signal value is obtained as follows: The signal similarity between each power line communication signal value and windows corresponding to a preset number of signal values ​​is calculated, and windows whose signal similarity is greater than a preset threshold are recorded as similar windows of each power line communication signal value.

9. A signal noise reduction method for improving the quality of power line communication according to claim 1, characterized in that: The final filtering value of each power line communication signal value is obtained by the following specific formula: ;in, a final filtered value representing each power line communication signal value; a filter weight representing each power line communication signal value; Indicates a filtered value of each power line communication signal value after using a first preset step size; Indicates that each power line communication signal value is filtered using the second preset step size.

10. A signal noise reduction system for improving the quality of power line communication, 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 method according to any one of claims 1 to 9 are implemented.

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