A signal enhancement system

Abstract

The application discloses a signal enhancement system, comprising: a signal intelligent exploration module, an on-site side antenna module, a signal noise reduction module, a signal gain adjustment module, a large-capacity battery module and an edge computing module; wherein the signal intelligent exploration module is used for detecting the surrounding signals of the power grid operation environment and determining the best signal receiving direction through rotating action and scanning action; the on-site side antenna module is used for receiving signals and transmitting the signals to the signal noise reduction module; the signal noise reduction module is used for transmitting the signals to the signal gain adjustment module after noise reduction processing of the received signals through a filter and a noise reduction algorithm; and the signal gain adjustment module is used for automatically adjusting the signal gain through a PID control algorithm according to the strength of the received signals. The application realizes efficient signal processing and gain adjustment, and improves the signal transmission quality and the performance of the system.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to a signal enhancement system. Background Technology

[0002] Currently, power grid companies are continuously improving their standardized, streamlined, and lean management levels, aiming to become highly efficient and information-driven enterprises characterized by business integration, data sharing, superior user experience, and innovation leadership. In particular, power safety supervision has largely replaced personnel with intelligent safety monitoring equipment. By equipping frontline workers with intelligent safety monitoring terminals, remote one-to-many safety supervision and control can be achieved, reducing safety hazards in on-site operations. However, the use of intelligent safety monitoring terminals has a drawback: it has certain requirements for the on-site network communication environment. Operational control cannot be carried out in communication blind spots, and the power grid operating environment is often very complex, with power equipment deployed across mountains and valleys, where existing communication base stations cannot provide effective coverage, resulting in low data transmission efficiency or even complete disconnection of the safety monitoring terminals. Furthermore, some existing signal enhancement equipment used for safety monitoring terminals cannot adjust according to real-time signal strength changes, failing to meet the optimization requirements under different signal environments. Summary of the Invention

[0003] This application provides a signal enhancement system for achieving efficient signal processing and real-time gain adjustment, thereby improving signal transmission quality and system performance.

[0004] In view of the above, the first aspect of this application provides a signal enhancement system, the system comprising:

[0005] Signal intelligent exploration module, local antenna module, signal noise reduction module, signal gain adjustment module, high-capacity battery module, and edge computing module;

[0006] The intelligent signal exploration module is used to detect surrounding signals in the power grid operating environment and determine the optimal signal reception direction through rotation and scanning actions.

[0007] The local antenna module is used to receive signals and transmit them to the signal noise reduction module;

[0008] The signal noise reduction module is used to process the received signal through filters and noise reduction algorithms and then transmit it to the signal gain adjustment module.

[0009] The signal gain adjustment module is used to automatically adjust the signal gain according to the strength of the received signal using a PID control algorithm.

[0010] The high-capacity battery module is used to supply power to the signal enhancement system;

[0011] The edge computing module is used to provide real-time computing capabilities to the signal enhancement system through an edge server.

[0012] Optionally, the signal gain adjustment module is specifically used for:

[0013] Obtain the actual signal strength of the received signal;

[0014] A target signal strength is set according to the power grid operating environment, and the target signal strength is compared with the actual signal strength to obtain an error signal;

[0015] The PID parameters are calculated according to the Ziegler-Nichols method, and the PID parameters include: proportional gain, integral time, and derivative time.

[0016] Based on the PID control algorithm, the signal gain control quantity is calculated according to the error signal and the PID parameters;

[0017] The signal gain control quantity is:

[0018]

[0019] K i =K p / T i K d =K p / T d ;

[0020] In the formula, G is the signal gain control quantity, e is the error signal, and K... p It is the proportional gain, K i It is the integral gain, K d It is the differential gain, T i It is the integration time, T d It is differential time;

[0021] The signal gain is automatically adjusted according to the signal gain control value.

[0022] Optionally, the calculation of PID parameters according to the Ziegler-Nichols method specifically includes:

[0023] Set initial values ​​for the proportional gain, integral time, and derivative time of the PID parameters;

[0024] Gradually increase the proportional gain until the system oscillates, and record the oscillation period;

[0025] Based on the maximum proportional gain at the time of oscillation and the oscillation period, the target proportional gain, target integral time, and target derivative time are determined, wherein the target proportional gain satisfies K p =0.6*Ku The target integration time satisfies T i =0.5*T u The target differential time satisfies T d =0.125*T u , where K p For the target proportional gain, K u T represents the maximum proportional gain when oscillations occur. i Let T be the target integration time. d For the objective derivative time, T u It is an oscillation cycle.

[0026] Optionally, the intelligent signal exploration module specifically includes: a scanning calculation unit, a rotation action unit, a stepping scanning unit, and a signal detection unit;

[0027] The scanning calculation unit is used to determine the parameters for linear scanning, spiral scanning, or grid scanning.

[0028] The rotation action unit is used to control the rotation angle of the signal detection unit;

[0029] The stepping scanning unit is used to perform stepping scanning according to the parameters of linear scanning, spiral scanning or grid scanning;

[0030] The signal detection unit is used to acquire signal data of the surrounding environment through a signal sensor at each scanning position.

[0031] Optionally, the signal denoising module specifically includes: a noise acquisition unit, a noise analysis unit, a feature extraction unit, and a signal denoising unit;

[0032] The noise acquisition unit is used to separate and acquire noise data from the received signal.

[0033] The noise analysis unit is used to analyze the separated noise data using a spectrum analysis method to obtain spectrum analysis results.

[0034] The feature extraction unit is used to extract spectral features based on the spectral analysis results to obtain the spectral features of the noise data.

[0035] The signal noise reduction unit is used to determine a suitable noise reduction algorithm and filter based on the spectral characteristics, and to reduce noise in the received signal.

[0036] Optionally, the noise analysis unit is specifically used for:

[0037] The noise data obtained from the separation is segmented to obtain a set of noise data segments;

[0038] Determine the time series parameters for each noise data segment in the noise data segment set, wherein the time series parameters are discrete time series or continuous time series;

[0039] When the time series parameters of the noise data segment are discrete time series, Fourier transform algorithm, amplitude spectrum calculation formula and phase spectrum calculation formula are used to perform spectrum analysis to obtain the first spectrum analysis result;

[0040] Alternatively, when the time series parameters of the noise data segment are continuous time series, Fourier transform algorithm and power spectral density estimation algorithm are used to perform spectrum analysis to obtain the second spectrum analysis result;

[0041] The first spectrum analysis result and the second spectrum analysis result are combined to obtain the comprehensive spectrum analysis result.

[0042] Optionally, the step of performing spectrum analysis using the Fourier transform algorithm, amplitude spectrum calculation formula, and phase spectrum calculation formula to obtain the first spectrum analysis result specifically includes:

[0043] The noisy data segment is divided into multiple windows, and a window function is used to reduce spectral leakage for each window.

[0044] Based on the Fourier transform formula, the time-domain signal data of each window is converted into frequency-domain signal data to obtain a frequency-domain complex array;

[0045] The amplitude of the frequency domain complex array is calculated based on the amplitude spectrum calculation formula to obtain the amplitude at each frequency point of the frequency domain signal data. The amplitude spectrum calculation formula satisfies... In the formula, A represents the amplitude, X represents the complex number array in the frequency domain, Re(X) represents the real part of X, and Im(X) represents the imaginary part of X;

[0046] The phase angle of the frequency domain signal data at each frequency point is obtained by calculating the complex number array based on the phase spectrum calculation formula. The phase spectrum calculation formula satisfies P1=arctan2(Im(X),Re(X)), where P1 represents the phase angle, X represents the complex number array in the frequency domain, Re(X) represents the real part of X, and Im(X) represents the imaginary part of X.

[0047] The first spectral analysis result is obtained based on the amplitude and phase angle of each frequency point of the frequency domain signal data.

[0048] Optionally, the step of performing spectral analysis using the Fourier transform algorithm and the power spectral density estimation algorithm to obtain the second spectral analysis result specifically includes:

[0049] The noisy data segment is divided into multiple windows, and a window function is used to reduce spectral leakage for each window.

[0050] Based on the Fourier transform formula, the time-domain signal data of each window is converted into frequency-domain signal data to obtain a frequency-domain complex array;

[0051] The amplitude squared is calculated for each frequency domain complex number array based on the amplitude squared formula, to obtain the amplitude squared at each frequency point of the frequency domain signal data. The amplitude squared formula satisfies P2=[Im(X)]. 2 +[Re(X)] 2 In the formula, P2 represents the square of the amplitude, X represents the complex array in the frequency domain, Re(X) represents the real part of X, and Im(X) represents the imaginary part of X;

[0052] The power spectral density estimation result is obtained by averaging the squared amplitudes of all frequency points of the frequency domain signal data for each window.

[0053] The second spectral analysis result is obtained based on the power spectral density estimation calculation result.

[0054] Optionally, the feature extraction unit specifically includes: a noise separation unit, a high-frequency noise reduction unit, a low-frequency noise reduction unit, and a narrowband noise reduction unit;

[0055] The noise separation unit is used to determine high-frequency noise data, low-frequency noise data, and narrowband noise data in the noise data based on the spectral characteristics.

[0056] The high-frequency noise reduction unit is used to perform noise reduction processing on the high-frequency noise data according to the mean filtering algorithm and the high-pass filter;

[0057] The low-frequency noise reduction unit is used to perform noise reduction processing on the low-frequency noise data according to the median filtering algorithm and the low-pass filter.

[0058] The narrowband noise reduction unit is used to perform noise reduction processing on the narrowband noise data according to an adaptive filtering algorithm and a bandpass filter.

[0059] Optionally, the signal noise reduction unit includes: a noise separation unit, a high-frequency noise reduction unit, a low-frequency noise reduction unit, and a narrowband noise reduction unit;

[0060] The noise separation unit is used to determine high-frequency noise data, low-frequency noise data, and narrowband noise data in the noise data based on the spectral characteristics.

[0061] The high-frequency noise reduction unit is used to perform noise reduction processing on the high-frequency noise data according to the mean filtering algorithm and the high-pass filter;

[0062] The low-frequency noise reduction unit is used to perform noise reduction processing on the low-frequency noise data according to the median filtering algorithm and the low-pass filter.

[0063] The narrowband noise reduction unit is used to perform noise reduction processing on the narrowband noise data according to an adaptive filtering algorithm and a bandpass filter.

[0064] As can be seen from the above technical solutions, this application has the following advantages:

[0065] 1. By using a control algorithm to automatically adjust the signal gain and dynamically optimize the signal transmission quality based on the received signal strength, it can quickly respond to changes in signal strength and adjust the signal gain in a timely manner to ensure real-time optimization of signal transmission quality, reduce the complexity of user operation and improve the robustness of the system.

[0066] 2. Accurately calculate and adjust PID parameters using the Ziegler-Nichols method to achieve precise control of the gain.

[0067] 3. Achieve efficient signal processing and gain adjustment, improving signal transmission quality and system performance. Simultaneously, the integrated design and automated adjustment can reduce system deployment and maintenance costs. Attached Figure Description

[0068] Figure 1 This is a schematic diagram of the structure of a signal enhancement system provided in an embodiment of this application. Detailed Implementation

[0069] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0070] Please see Figure 1 The present application provides a schematic diagram of the structure of a signal enhancement system, including: a signal intelligent exploration module 101, a local antenna module 102, a signal noise reduction module 103, a signal gain adjustment module 104, a large-capacity battery module 105, and an edge computing module 106.

[0071] Among them, the signal intelligent exploration module 101 is used to detect the surrounding signals of the power grid operating environment and determine the optimal signal reception direction through rotation and scanning actions;

[0072] The local antenna module 102 is used to receive signals and transmit them to the signal noise reduction module;

[0073] The signal noise reduction module 103 is used to process the received signal through filters and noise reduction algorithms and then transmit it to the signal gain adjustment module.

[0074] The signal gain adjustment module 104 is used to automatically adjust the signal gain according to the strength of the received signal using a PID control algorithm.

[0075] A high-capacity battery module 105 is used to supply power to the signal enhancement system;

[0076] Edge computing module 106 is used to provide real-time computing capabilities to the signal enhancement system via an edge server.

[0077] Furthermore, in one embodiment, the signal enhancement system further includes: an intelligent safety monitoring module 107, used to monitor the power grid operating environment and upload the monitoring data to a cloud platform.

[0078] In one embodiment, the signal gain adjustment module 104 is specifically used for:

[0079] Step 201: Obtain the actual signal strength of the received signal;

[0080] Step 202: Set the target signal strength according to the power grid operating environment, compare the target signal strength with the actual signal strength, and obtain the error signal;

[0081] Step 203: Calculate the PID parameters according to the Ziegler-Nichols method. The PID parameters include: proportional gain, integral time, and derivative time.

[0082] Specifically, set initial values ​​for the proportional gain, integral time, and derivative time of the PID parameters;

[0083] Gradually increase the proportional gain until the system oscillates, and record the oscillation period;

[0084] Based on the maximum proportional gain and oscillation period when oscillation occurs, determine the target proportional gain, target integral time, and target derivative time. The target proportional gain satisfies K p =0.6*K u The objective integration time satisfies T i =0.5*T u The objective differential time satisfies T d =0.125*T u , where K p For the target proportional gain, K u T represents the maximum proportional gain when oscillations occur. i Let T be the target integration time. d For the objective derivative time, T u It is an oscillation cycle.

[0085] Step 204: Based on the PID control algorithm, calculate the signal gain control quantity according to the error signal and PID parameters;

[0086] The signal gain control quantity is:

[0087]

[0088] K i =K p / T i K d =K p / T d ;

[0089] In the formula, G is the signal gain control quantity, e is the error signal, and K... p It is the proportional gain, K i It is the integral gain, K d It is the differential gain, T i It is the integration time, T d It is differential time;

[0090] Step 205: Automatically adjust the signal gain according to the signal gain control value.

[0091] In one embodiment, the signal intelligent exploration module 101 specifically includes: a scanning calculation unit 1011, a rotation action unit 1012, a stepping scanning unit 1013, and a signal detection unit 1014;

[0092] The scanning calculation unit 1011 is used to determine the parameters for linear scanning, spiral scanning, or raster scanning.

[0093] The rotation action unit 1012 is used to control the rotation angle of the signal detection unit;

[0094] The stepping scanning unit 1013 is used to perform stepping scanning according to parameters of linear scanning, spiral scanning or grid scanning;

[0095] The signal detection unit 1014 is used to acquire signal data of the surrounding environment through a signal sensor at each scanning position.

[0096] In one embodiment, the signal noise reduction module 103 specifically includes: a noise acquisition unit 1031, a noise analysis unit 1032, a feature extraction unit 1033, and a signal noise reduction unit 1034.

[0097] The noise acquisition unit 1031 is used to separate and acquire noise data in the received signal;

[0098] The noise analysis unit 1032 is used to analyze the separated noise data using the spectrum analysis method to obtain the spectrum analysis results;

[0099] The results of the first and second spectrum analyses are combined to obtain the comprehensive spectrum analysis results.

[0100] The feature extraction unit 1033 is used to extract spectral features based on the spectral analysis results to obtain the spectral features of the noise data.

[0101] The signal noise reduction unit 1034 is used to determine a suitable noise reduction algorithm and filter based on the spectral characteristics to reduce noise in the received signal.

[0102] Furthermore, in one embodiment, the noise analysis unit 1032 is specifically used for:

[0103] Step 301: Segment the obtained noise data to obtain a set of noise data segments;

[0104] Step 302: Determine the time series parameters for each noise data segment in the noise data segment set. The time series parameters can be discrete time series or continuous time series.

[0105] Step 303: When the time series parameters of the noise data segment are discrete time series, use the Fourier transform algorithm, amplitude spectrum calculation formula and phase spectrum calculation formula to perform spectrum analysis and obtain the first spectrum analysis result;

[0106] It should be noted that the Fourier transform algorithm, amplitude spectrum calculation formula, and phase spectrum calculation formula are used for spectrum analysis to obtain the first spectrum analysis result, which specifically includes:

[0107] The noisy data segment is divided into multiple windows, and a window function is used to reduce spectral leakage for each window.

[0108] Based on the Fourier transform formula, the time-domain signal data of each window is converted into frequency-domain signal data to obtain a frequency-domain complex array;

[0109] The amplitude spectrum is calculated using the amplitude spectrum calculation formula on a complex array in the frequency domain to obtain the amplitude at each frequency point of the frequency domain signal data. The amplitude spectrum calculation formula satisfies... In the formula, A represents the amplitude, X represents the complex number array in the frequency domain, Re(X) represents the real part of X, and Im(X) represents the imaginary part of X;

[0110] The phase angle of each frequency point of the frequency domain signal data is obtained by calculating the complex array in the frequency domain based on the phase spectrum calculation formula. The phase spectrum calculation formula satisfies P1=arctan2(Im(X),Re(X)), where P1 represents the phase angle, X represents the complex array in the frequency domain, Re(X) represents the real part of X, and Im(X) represents the imaginary part of X.

[0111] The first spectral analysis result is obtained based on the amplitude and phase angle of each frequency point of the frequency domain signal data.

[0112] Step 304, or, when the time series parameters of the noise data segment are continuous time series, use the Fourier transform algorithm and the power spectral density estimation algorithm to perform spectrum analysis and obtain the second spectrum analysis result;

[0113] It should be noted that the Fourier transform algorithm and the power spectral density estimation algorithm are used for spectral analysis to obtain the second spectral analysis results, which specifically include:

[0114] The noisy data segment is divided into multiple windows, and a window function is used to reduce spectral leakage for each window.

[0115] Based on the Fourier transform formula, the time-domain signal data of each window is converted into frequency-domain signal data to obtain a frequency-domain complex array;

[0116] The amplitude squared formula is used to calculate the amplitude squared for each frequency domain complex number array to obtain the amplitude squared at each frequency point of the frequency domain signal data. The amplitude squared formula satisfies P2=[Im(X)]. 2 +[Re(X)] 2 In the formula, P2 represents the square of the amplitude, X represents the complex array in the frequency domain, Re(X) represents the real part of X, and Im(X) represents the imaginary part of X;

[0117] The power spectral density estimation result is obtained by averaging the squared amplitudes of all frequency points of the frequency domain signal data for each window.

[0118] The second spectral analysis result is obtained based on the power spectral density estimation calculation result.

[0119] Step 305: Combine the first spectrum analysis results and the second spectrum analysis results to obtain the comprehensive spectrum analysis results.

[0120] Furthermore, in one embodiment, the feature extraction unit 1033 specifically includes: a noise separation unit 10331, a high-frequency noise reduction unit 10332, a low-frequency noise reduction unit 10333, and a narrowband noise reduction unit 10334.

[0121] The noise separation unit 10331 is used to determine the high-frequency noise data, low-frequency noise data and narrowband noise data in the noise data based on the spectral characteristics.

[0122] The high-frequency noise reduction unit 10332 is used to perform noise reduction processing on high-frequency noise data according to the mean filtering algorithm and the high-pass filter;

[0123] The low-frequency noise reduction unit 10333 is used to reduce the noise of low-frequency noise data according to the median filtering algorithm and the low-pass filter.

[0124] The narrowband noise reduction unit 10334 is used to perform noise reduction processing on narrowband noise data according to an adaptive filtering algorithm and a bandpass filter.

[0125] Furthermore, in one embodiment, the signal noise reduction unit 1034 specifically includes: a noise separation unit 10341, a high-frequency noise reduction unit 10342, a low-frequency noise reduction unit 10343, and a narrowband noise reduction unit 10344.

[0126] The noise separation unit 10341 is used to determine the high-frequency noise data, low-frequency noise data and narrowband noise data in the noise data based on the spectral characteristics.

[0127] The high-frequency noise reduction unit 10342 is used to perform noise reduction processing on high-frequency noise data according to the mean filtering algorithm and the high-pass filter;

[0128] The low-frequency noise reduction unit 10343 is used to reduce the noise of low-frequency noise data according to the median filtering algorithm and the low-pass filter.

[0129] The narrowband noise reduction unit 10344 is used to perform noise reduction processing on narrowband noise data based on an adaptive filtering algorithm and a bandpass filter.

[0130] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0131] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0132] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0134] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0135] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0136] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0137] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A signal enhancement system, characterized in that, include: Signal intelligent exploration module, local antenna module, signal noise reduction module, signal gain adjustment module, high-capacity battery module, and edge computing module; The intelligent signal exploration module is used to detect surrounding signals in the power grid operating environment and determine the optimal signal reception direction through rotation and scanning actions. The local antenna module is used to receive signals and transmit them to the signal noise reduction module; The signal noise reduction module is used to process the received signal through filters and noise reduction algorithms and then transmit it to the signal gain adjustment module. The signal gain adjustment module is used to automatically adjust the signal gain according to the strength of the received signal using a PID control algorithm. The high-capacity battery module is used to supply power to the signal enhancement system; The edge computing module is used to provide real-time computing capabilities to the signal enhancement system through an edge server; The signal denoising module specifically includes: a noise acquisition unit, a noise analysis unit, a feature extraction unit, and a signal denoising unit; The noise acquisition unit is used to separate and acquire noise data from the received signal. The noise analysis unit is used to analyze the separated noise data using a spectrum analysis method to obtain spectrum analysis results. The feature extraction unit is used to extract spectral features based on the spectral analysis results to obtain the spectral features of the noise data. The signal noise reduction unit is used to determine a suitable noise reduction algorithm and filter based on the spectral characteristics, and to reduce the noise in the received signal. The noise analysis unit is specifically used for: The noise data obtained from the separation is segmented to obtain a set of noise data segments; Determine the time series parameters for each noise data segment in the noise data segment set; When the time series parameters of the noise data segment are discrete time series, Fourier transform algorithm, amplitude spectrum calculation formula and phase spectrum calculation formula are used to perform spectrum analysis to obtain the first spectrum analysis result; When the time series parameters of the noise data segment are continuous time series, Fourier transform algorithm and power spectral density estimation algorithm are used to perform spectrum analysis to obtain the second spectrum analysis result; The first spectrum analysis result and the second spectrum analysis result are combined to obtain the comprehensive spectrum analysis result; The step of performing spectrum analysis using the Fourier transform algorithm, amplitude spectrum calculation formula, and phase spectrum calculation formula to obtain the first spectrum analysis result specifically includes: The noisy data segment is divided into multiple windows, and a window function is used to reduce spectral leakage for each window. Based on the Fourier transform formula, the time-domain signal data of each window is converted into frequency-domain signal data to obtain a frequency-domain complex array; The amplitude of the frequency domain complex array is calculated based on the amplitude spectrum calculation formula to obtain the amplitude at each frequency point of the frequency domain signal data. The amplitude spectrum calculation formula satisfies... In the formula, A represents the amplitude, X represents the complex number array in the frequency domain, Re(X) represents the real part of X, and Im(X) represents the imaginary part of X; The phase angle of the frequency domain signal data at each frequency point is obtained by calculating the complex number array based on the phase spectrum calculation formula, wherein the phase spectrum calculation formula satisfies... In the formula, Let X represent the phase angle, X represent the complex array in the frequency domain, Re(X) represent the real part of X, and Im(X) represent the imaginary part of X. The first spectral analysis result is obtained based on the amplitude and phase angle of each frequency point of the frequency domain signal data.

2. The signal enhancement system according to claim 1, characterized in that, The signal gain adjustment module is specifically used for: Obtain the actual signal strength of the received signal; A target signal strength is set according to the power grid operating environment, and the target signal strength is compared with the actual signal strength to obtain an error signal; The PID parameters are calculated according to the Ziegler-Nichols method, and the PID parameters include: proportional gain, integral time, and derivative time. Based on the PID control algorithm, the signal gain control quantity is calculated according to the error signal and the PID parameters; The signal gain control quantity is: ； ， ； In the formula, G is the signal gain control quantity, and e is the error signal. It is proportional gain. It is integral gain. It is the differential gain. It's the integration time. It is differential time; The signal gain is automatically adjusted according to the signal gain control value.

3. The signal enhancement system according to claim 2, characterized in that, The calculation of PID parameters according to the Ziegler-Nichols method specifically includes: Set initial values ​​for the proportional gain, integral time, and derivative time of the PID parameters; Gradually increase the proportional gain until the system oscillates, and record the oscillation period; Based on the maximum proportional gain at the time of oscillation and the oscillation period, the target proportional gain, target integral time, and target derivative time are determined, wherein the target proportional gain satisfies... The target integration time satisfies The target differential time satisfies ,in, For the target proportional gain, This represents the maximum proportional gain when oscillations occur. For the target integration time, For the objective derivative time, It is an oscillation cycle.

4. The signal enhancement system according to claim 1, characterized in that, The intelligent signal exploration module specifically includes: a scanning calculation unit, a rotation action unit, a stepping scanning unit, and a signal detection unit; The scanning calculation unit is used to determine the parameters for linear scanning, spiral scanning, or grid scanning. The rotation action unit is used to control the rotation angle of the signal detection unit; The stepping scanning unit is used to perform stepping scanning according to the parameters of linear scanning, spiral scanning or grid scanning; The signal detection unit is used to acquire signal data of the surrounding environment through a signal sensor at each scanning position.

5. The signal enhancement system according to claim 1, characterized in that, The second spectral analysis result, obtained by employing the Fourier transform algorithm and the power spectral density estimation algorithm, specifically includes: The noisy data segment is divided into multiple windows, and a window function is used to reduce spectral leakage for each window. Based on the Fourier transform formula, the time-domain signal data of each window is converted into frequency-domain signal data to obtain a frequency-domain complex array; The amplitude squared is calculated for each complex number array in the frequency domain based on the amplitude squared formula, to obtain the amplitude squared at each frequency point of the frequency domain signal data. The amplitude squared formula satisfies... In the formula, Let X represent the square of the amplitude, X represent the complex array in the frequency domain, Re(X) represent the real part of X, and Im(X) represent the imaginary part of X. The power spectral density estimation result is obtained by averaging the squared amplitudes of all frequency points of the frequency domain signal data for each window. The second spectral analysis result is obtained based on the power spectral density estimation calculation result.

6. The signal enhancement system according to claim 1, characterized in that, The feature extraction unit specifically includes: a noise separation unit, a high-frequency noise reduction unit, a low-frequency noise reduction unit, and a narrowband noise reduction unit; The noise separation unit is used to determine high-frequency noise data, low-frequency noise data, and narrowband noise data in the noise data based on the spectral characteristics. The high-frequency noise reduction unit is used to perform noise reduction processing on the high-frequency noise data according to the mean filtering algorithm and the high-pass filter; The low-frequency noise reduction unit is used to perform noise reduction processing on the low-frequency noise data according to the median filtering algorithm and the low-pass filter. The narrowband noise reduction unit is used to perform noise reduction processing on the narrowband noise data according to an adaptive filtering algorithm and a bandpass filter.

7. The signal enhancement system according to claim 1, characterized in that, include: The signal noise reduction unit includes: a noise separation unit, a high-frequency noise reduction unit, a low-frequency noise reduction unit, and a narrowband noise reduction unit; The noise separation unit is used to determine high-frequency noise data, low-frequency noise data, and narrowband noise data in the noise data based on the spectral characteristics. The high-frequency noise reduction unit is used to perform noise reduction processing on the high-frequency noise data according to the mean filtering algorithm and the high-pass filter; The low-frequency noise reduction unit is used to perform noise reduction processing on the low-frequency noise data according to the median filtering algorithm and the low-pass filter. The narrowband noise reduction unit is used to perform noise reduction processing on the narrowband noise data according to an adaptive filtering algorithm and a bandpass filter.

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