Electric power communication signal multi-stage anti-saturation amplification method, system and equipment based on pseudo feature recognition and medium

Abstract

The invention discloses an electric power communication signal multi-stage anti-saturation amplification method, system and device based on pseudo feature recognition and a medium, and belongs to the technical field of electric power communication signal multi-stage anti-saturation amplification. The signal sequence is sequentially input into a low-noise amplification module and an impedance matching filter for preprocessing; in a sliding time window with a set length, calculating a gradient sequence of signal points in the current window based on the preprocessed signal sequence, and obtaining a variance value of the gradient sequence; comparing the variance value with a dynamically updated global gradient variance reference value, and outputting a filtering signal; and the filtering signal is input to a gain structure composed of three stages of gain amplifiers for gain amplification. According to the method, the problems of false feature misjudgment and signal saturation distortion are solved, multi-path reflection, equipment noise and other false feature interferences can be accurately identified and suppressed through the gradient variance filter, the signal misjudgment rate is reduced, and the operation and maintenance cost of the power terminal in the remote area is reduced.

Description

Technical Field

[0001] This invention relates to the field of multi-stage anti-saturation amplification technology for power communication signals, specifically to a method, system, device, and medium for multi-stage anti-saturation amplification of power communication signals based on pseudo-feature recognition. Background Technology

[0002] Signal amplifiers in current power communication systems face two major technical bottlenecks when used in remote mountainous areas. First, in terms of pseudo-signal processing, traditional fixed-threshold filters struggle to distinguish between valid signals and environmental interference, often resulting in the false deletion of genuine signals or the false amplification of interfering signals, leading to persistently high bit error rates at the terminal. While frequency-domain filtering schemes can suppress steady-state noise, they are completely ineffective against sudden time-domain interference such as lightning and corona discharge. Second, in terms of signal amplification, conventional multi-stage amplifiers employ fixed-gain modes. When signal strength changes abruptly in mountainous areas, the final stage amplifier saturates due to input overload, causing signal clipping distortion. Existing automatic gain control technology has a slow response speed, cannot adapt to instantaneous signal changes, and the analog control circuit lacks precision, resulting in severe degradation of the signal-to-noise ratio after amplification. These shortcomings collectively lead to insufficient communication stability of the acquisition terminal in complex environments. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by this invention is: how existing automatic gain control technology has a slow response speed, cannot adapt to instantaneous changes in signals, and has insufficient accuracy of analog control circuits, resulting in severe degradation of the signal-to-noise ratio after amplification.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition, comprising: acquiring a discrete-time signal sequence by a dual-antenna receiving module, and sequentially inputting the signal sequence to a low-noise amplification module and an input impedance matching filter for preprocessing; calculating the gradient sequence of signal points within the current window based on the preprocessed signal sequence within a sliding time window of a set length, and obtaining the variance value of the gradient sequence; comparing the variance value with a dynamically updated global gradient variance benchmark value; if the variance exceeds a set threshold standard, determining that the current window is a pseudo-feature interference region and setting the output of the filtered signal to zero; if it does not exceed the threshold, transmitting the effective signal and outputting the filtered signal; inputting the filtered signal to a gain structure composed of three-stage gain amplifiers for gain amplification; when the output power of the final stage approaches the saturation threshold, adjusting the gain of the preceding stage step by step according to the feedback signal of the power detection circuit, and offsetting the nonlinear effect through an anti-saturation compensation circuit.

[0006] As a preferred embodiment of the multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition described in this invention, the step of acquiring a discrete-time signal sequence by a dual-antenna receiving module and sequentially inputting the signal sequence to a low-noise amplification module and an input impedance matching filter for preprocessing includes: acquiring electromagnetic signals within the target frequency band by the dual-antenna receiving module and converting the electromagnetic signals into a discrete-time signal sequence in the form of electrical signals; inputting the signal sequence to the low-noise amplification module to initially enhance the signal amplitude and suppress background noise; and sequentially passing the enhanced signal through the input impedance matching filter for impedance matching and bandwidth suppression processing to improve signal quality.

[0007] As a preferred embodiment of the multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition described in this invention, the step of calculating the gradient sequence of signal points within the current window based on the preprocessed signal sequence and obtaining the variance value of the gradient sequence within a sliding time window of a set length includes: in the signal processing module, buffering and temporarily storing the signal output from the input impedance matching filter; discretizing the signal using a sampling circuit and calculating the gradient sequence within the time window through differential operation; and performing statistical calculations on the gradient sequence to obtain the variance value within the current window, which characterizes the signal fluctuation amplitude.

[0008] As a preferred embodiment of the multi-level anti-saturation amplification method for power communication signals based on pseudo-feature recognition described in this invention, the following steps are included: comparing the variance value with the dynamically updated global gradient variance benchmark value; if the variance exceeds a set threshold standard, the current window is determined to be a pseudo-feature interference region and the filtered signal is set to zero; if it does not exceed the threshold standard, the valid signal is transmitted and the filtered signal is output. This includes comparing the gradient variance value obtained in the current time window with the global gradient variance benchmark value; if the variance value exceeds the set threshold standard, it is determined that the signal in the window contains pseudo-feature interference, and the corresponding signal is processed by the core signal processing module and then zeroed out in the signal output interface module; if the variance value does not exceed the set threshold standard, it is determined that the signal in the window is not determined to be interference and is directly output; the dynamically updated global gradient variance benchmark value is adaptively corrected based on the interference variance and benchmark value in the current window and the next window to achieve dynamic adaptation to different environmental changes.

[0009] As a preferred embodiment of the multi-level anti-saturation amplification method for power communication signals based on pseudo-feature recognition described in this invention, the dynamically updated global gradient variance benchmark value is adaptively corrected based on the interference variance and benchmark value in the current window and the next window to achieve dynamic adaptation to different environmental changes. This includes: in the signal processing module, acquiring the interference signal variance value in the current time window and reading the global gradient variance benchmark value from the storage area in the previous time window; inputting the interference signal variance value and the global gradient variance benchmark value into the update calculation logic to generate a new global gradient variance benchmark value; writing the newly generated global gradient variance benchmark value into the buffer area to provide a reference for interference determination in the next time window; the update process continues, enabling the global gradient variance benchmark value to respond to changes in signal characteristics within different time periods, thus achieving dynamic adaptation to complex communication environments.

[0010] This preferred solution achieves dynamic adjustment of the global gradient variance benchmark value by jointly updating the interference variance of the current window and the next window, avoiding misjudgment caused by sudden environmental changes or background noise changes. It still has stable interference recognition capability in complex electromagnetic environments and reduces the problem of false feature missed detection or false killing caused by static thresholds.

[0011] As a preferred embodiment of the multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition described in this invention, the step of discretizing the signal using a sampling circuit and calculating the gradient sequence within a time window through differential operations includes: the sampling circuit converting the analog signal output from the input impedance matching filter into a digital signal; the digital signal output by the sampling circuit being written into a buffer RAM; the buffer RAM organizing its storage structure in a time window manner and supporting the reading and writing of continuous signals; and the signal data stored in the buffer RAM being called by the core signal processing module according to the time window for gradient calculation and variance statistical analysis.

[0012] This preferred scheme adopts a discretized sampling and sliding window structure storage strategy, which can extract stable local features at the window granularity and continuously maintain a structured data flow in the cache, providing stable input for subsequent gradient and variance statistics, and improving the filtering algorithm's ability to distinguish sudden interference.

[0013] As a preferred embodiment of the multi-level anti-saturation amplification method for power communication signals based on pseudo-feature recognition described in this invention, the step of comparing the variance value with the dynamically updated global gradient variance benchmark value includes: sending the gradient variance value and the global gradient variance benchmark value into a dynamic threshold comparator to perform a threshold determination operation; transmitting the output result of the dynamic threshold comparator to a decision logic unit, whereby the decision logic unit performs interference region judgment; and entering a zero-setting output path or a pass-through path based on the output results of the dynamic threshold comparator and the decision logic unit.

[0014] This preferred solution decouples the judgment calculation and action execution in the interference identification process by setting an independent dynamic threshold comparator and decision logic unit, effectively reducing the probability of false triggering; at the same time, it ensures that the zeroing and transparent transmission operation response logics are distinct, enhancing the signal path control capability.

[0015] This invention provides a multi-stage anti-saturation amplification system for power communication signals based on pseudo-feature recognition.

[0016] To address the aforementioned technical problems, this invention provides the following technical solution: a multi-stage anti-saturation amplification system for power communication signals based on pseudo-feature recognition, comprising: a signal acquisition and preprocessing module, a gradient calculation module, a judgment output module, and a multi-stage saturation amplification module; the signal acquisition and preprocessing module is used to acquire discrete-time signal sequences from a dual-antenna receiving module and sequentially input the signal sequences to a low-noise amplification module and an input impedance matching filter for preprocessing; the gradient calculation module is used to calculate the gradient sequence of signal points within a set-length sliding time window based on the preprocessed signal sequence and obtain the variance value of the gradient sequence; the judgment output module is used to compare the variance value with a dynamically updated global gradient variance benchmark value. If the variance exceeds a set threshold standard, the current window is determined to be a pseudo-feature interference region, and the filtered signal is set to zero; if it does not exceed the threshold, the valid signal is transmitted, and the filtered signal is output; the multi-stage saturation amplification module is used to input the filtered signal to a gain structure composed of three gain amplifiers for gain amplification. When the output power of the final stage approaches the saturation threshold, the gain of the preceding stage is adjusted step by step according to the feedback signal of the power detection circuit, and the nonlinear effect is offset by an anti-saturation compensation circuit.

[0017] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the multi-level anti-saturation amplification method for power communication signals based on pseudo-feature recognition.

[0018] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition.

[0019] The beneficial effects of this invention are as follows: This invention simultaneously solves the problems of false feature misjudgment and signal saturation distortion. Through an innovative gradient variance filter, the system can accurately identify and suppress false feature interference such as multipath reflection and equipment noise, reducing the signal misjudgment rate. The unique multi-anti-saturation amplification architecture enables the amplifier to maintain high linear output even at high gain. This technology significantly reduces the operation and maintenance costs of power terminals in remote areas and provides reliable communication support for smart grid construction. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The above is a flowchart of a multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition, provided as an embodiment of the present invention.

[0022] Figure 2 The diagram shows the algorithm for implementing the pseudo-feature recognition technology in a multi-level anti-saturation amplification method for power communication signals based on pseudo-feature recognition, as provided in an embodiment of the present invention.

[0023] Figure 3 The graph shows a comparison of signals before and after pseudo-feature filtering and the gradient variance change curves of a multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition, provided in an embodiment of the present invention.

[0024] Figure 4 The diagram shows the single-pass filtering delay time of a multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition, provided in an embodiment of the present invention.

[0025] Figure 5 The diagram shows the implementation algorithm of the multi-stage anti-saturation amplification technology for a power communication signal multi-stage anti-saturation amplification method based on pseudo-feature recognition, as provided in an embodiment of the present invention.

[0026] Figure 6 The diagram shows the multi-stage signal amplification effect of a multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition, provided in an embodiment of the present invention.

[0027] Figure 7 The diagram shows the gain control effect of each stage of the multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition, provided in an embodiment of the present invention.

[0028] Figure 8 This is a hardware structure diagram of a multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition, provided in an embodiment of the present invention. Detailed Implementation

[0029] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0030] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition, including:

[0031] S1. The discrete-time signal sequence is acquired by the dual-antenna receiving module and then sequentially input to the low-noise amplification module and the input impedance matching filter for preprocessing.

[0032] S2. Within a sliding time window of a set length, calculate the gradient sequence of signal points within the current window based on the preprocessed signal sequence, and obtain the variance value of the gradient sequence.

[0033] S3. Compare the variance value with the dynamically updated global gradient variance benchmark value. If the variance exceeds the set threshold standard, the current window is determined to be a pseudo-feature interference region and the filtered signal is set to zero. If it does not exceed the threshold, the effective signal is transmitted and the filtered signal is output.

[0034] S4. The filtered signal is input to a gain structure consisting of three gain amplifiers for gain amplification. When the output power of the final stage approaches the saturation threshold, the gain of the preceding stage is adjusted step by step according to the feedback signal of the power detection circuit, and the nonlinear effect is offset by the anti-saturation compensation circuit.

[0035] This invention addresses the challenges of real-time identification of false feature interference, high false positive rates, and susceptibility to signal amplification saturation in power communication scenarios. It constructs an interference detection mechanism based on gradient variance, combining local statistical characteristics within a sliding time window to extract signal fluctuation features for interference identification. A dynamically updated global gradient variance benchmark value is set to adaptively adjust the detection threshold, effectively adapting to different interference environments. Through the coordinated operation of preprocessing, variance comparison, dynamic correction, and output control, high-precision identification of false feature interference and stable signal output are achieved, ensuring subsequent signal amplification and signal link stability.

[0036] Example 2, refer to Figures 2-8 As an embodiment of the present invention, based on the previous embodiment, a multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition is provided, comprising:

[0037] Furthermore, in step S1, the dual-antenna receiving module acquires the discrete-time signal sequence, and the signal sequence is sequentially input to the low-noise amplification module and the input impedance matching filter for preprocessing, including the following steps A1-A3:

[0038] A1. The dual-antenna receiving module acquires electromagnetic signals within the target frequency band and converts the electromagnetic signals into a discrete-time signal sequence in the form of electrical signals.

[0039] A2. Input the signal sequence into the low-noise amplification module to initially enhance the signal amplitude and suppress background noise.

[0040] A3. The enhanced signal is then passed through an input impedance matching filter for impedance matching and bandwidth suppression to improve signal quality.

[0041] In this embodiment, the variance value of the gradient sequence in step B2 can be a parameter calculated statistically based on the gradient sequence of discrete signal points within a set time window. This parameter is used to quantify the fluctuation intensity of the signal within a local time range, thereby determining whether spurious interference exists in the signal. In this embodiment, the signal processing module uses a fixed-length sliding time window (corresponding to a 20ms time window), uses differential operations to obtain the instantaneous gradient value of each signal point, and performs unbiased variance calculation on all gradient values ​​within the window to obtain the variance value of the gradient sequence for the current window. This variance value not only serves as a core reference indicator for interference determination but also possesses real-time performance and adaptability, reflecting the local fluctuation characteristics of the signal and supporting subsequent dynamic filtering decisions.

[0042] In an alternative implementation, the variance of the gradient sequence can also be calculated using a weighted mean strategy or a multi-window overlay method to improve the ability to identify abnormal mutations.

[0043] In another alternative implementation, the variance of the gradient sequence can also be calculated by normalizing the signal amplitude before calculating the variance, which can enhance the robustness of the algorithm under different signal amplitude conditions.

[0044] This invention significantly improves the accuracy and stability of pseudo-feature identification and reduces the false judgment rate by calculating the variance of the gradient sequence in real time and using it as the basis for interference judgment. At the same time, it provides a more reliable original signal foundation for subsequent signal amplification and transmission.

[0045] Furthermore, in step S2, within a sliding time window of a set length, the gradient sequence of signal points within the current window is calculated based on the preprocessed signal sequence, and the variance value of the gradient sequence is obtained, including the following steps B1-B3:

[0046] B1. In the signal processing module, the signal output by the input impedance matching filter is buffered and temporarily stored.

[0047] B2. The signal is discretized using a sampling circuit, and the gradient sequence within the time window is calculated through differential operation.

[0048] B3. Perform statistical calculations on the gradient sequence to obtain the variance value within the current window, which characterizes the amplitude of signal fluctuation.

[0049] Gradient variance filtering algorithm based on local statistical characteristics of signals, such as... Figure 2 As shown.

[0050] In this embodiment, the input impedance matching filter in step B1 can be placed after the low-noise amplification module and before the signal processing module in the impedance conversion and band suppression unit. This unit is used to achieve impedance matching between circuits and filter out out-of-band interference signals, thereby improving the quality and stability of the target signal. In this embodiment, the input impedance matching filter adopts a π-type topology design to achieve standard 50Ω impedance matching, while also possessing 45dB of out-of-band rejection capability. This effectively filters out interference signals in adjacent frequency bands, ensuring the accuracy and reliability of subsequent sampling and signal processing.

[0051] In one alternative implementation, the input impedance matching filter can also be constructed using an LC parallel structure to create a bandpass filter bank, which can be tuned to different frequency bands to meet the requirements of parallel reception of multi-standard communication signals.

[0052] In another alternative implementation, the input impedance matching filter can also be integrated into an integrated front-end RF module, and different impedance matching paths and filtering parameters can be switched by a programmable switch to achieve rapid deployment and adaptation for different scenarios.

[0053] This invention introduces an input impedance matching filter into the signal path, which not only ensures the power coupling efficiency of the signal when it is transmitted between different functional modules, but also significantly enhances the anti-interference capability, providing a high-fidelity, high signal-to-noise ratio input foundation for gradient variance filtering and subsequent signal analysis.

[0054] Furthermore, in step B2, a sampling circuit is used to discretize the signal, and the gradient sequence within the time window is calculated through differential operations, including the following steps B21-B24:

[0055] B21. The sampling circuit converts the analog signal output from the input impedance matching filter into a digital signal.

[0056] B22. The digital signal output by the sampling circuit is written into the buffer RAM.

[0057] B23. The cache RAM organizes its storage structure in a time window manner and supports reading and writing of continuous signals.

[0058] B24. The signal data stored in the cache RAM is called by the core signal processing module according to the time window for gradient calculation and variance statistical analysis.

[0059] Furthermore, in step B3, the discreteness of the signal gradient sequence is quantified to identify abnormal fluctuation regions. Through rigorous mathematical calculations, accurate identification of pseudo-feature interference is achieved. Let the discrete-time signal sequence be s[n] (n = 0, 1, 2, ..., N-1), and a sliding window of fixed length W (corresponding to a 20ms time window) be used. Within the window, the signal gradient is calculated as follows:

[0060] g[k]=|s[k]-s[k-1]|k∈[i-W+2,i]

[0061] Where g[k] is the gradient sequence, s[k] is the discrete-time signal sequence at point k, s[k-1] is the discrete-time signal sequence at point k-1, i is the index of the current processing point, and W is the sliding window length. The variance of the gradient sequence is calculated using unbiased estimation.

[0062]

[0063] in, Let μ be the variance of the gradient sequence. g The gradient mean within this window:

[0064]

[0065] The criteria for determining interference are:

[0066]

[0067] Where α is an adjustable threshold parameter, typically 2; This is the baseline value for the global gradient variance.

[0068] Furthermore, in step S3, the variance value is compared with the dynamically updated global gradient variance benchmark value. If the variance exceeds the set threshold standard, the current window is determined to be a pseudo-feature interference region and the filtered signal is set to zero. If it does not exceed the threshold, the effective signal is transmitted and the filtered signal is output, including the following steps C1-C4:

[0069] C1. Compare the gradient variance value obtained within the current time window with the global gradient variance benchmark value to make a judgment.

[0070] C2. If the variance value exceeds the set threshold standard, it is determined that the signal within the window contains spurious feature interference. The corresponding signal is processed by the core signal processing module and then zeroed out in the signal output interface module.

[0071] C3. If the variance value does not exceed the set threshold standard, then the signal within the window is determined not to be an interference signal and is output directly.

[0072] C4. The dynamically updated global gradient variance benchmark value is adaptively corrected based on the disturbance variance and benchmark value in the current window and the next window, so as to achieve dynamic adaptation to different environmental changes.

[0073] In this embodiment, the dynamically updated global gradient variance benchmark value in step C4 can be a reference value used to determine whether a signal contains spurious feature interference. Its value is continuously and adaptively adjusted as the time window progresses. In this embodiment, the dynamically updated global gradient variance benchmark value is obtained in real-time by the signal processing module from the interference signal variance value within the current time window. This value is then combined with the global benchmark value from the previous window and input into the update calculation logic to generate a new value. This new value is stored in a buffer and used as a threshold reference for interference judgment in the next time window, thereby achieving dynamic adaptation to signal fluctuation characteristics under different environmental conditions.

[0074] In one alternative implementation, the dynamically updated global gradient variance benchmark can also be weighted and averaged based on the statistical characteristics of multiple historical windows to form a more stable and representative dynamic threshold, thereby improving the accuracy of identifying abnormal disturbances in slow-changing channel environments.

[0075] In another alternative implementation, the dynamically updated global gradient variance benchmark value can also be smoothed in real time by an embedded filtering algorithm (such as exponentially weighted moving average) to smooth out abrupt changes during the update process, thereby enhancing the system's ability to suppress drastic disturbance changes and avoiding frequent misjudgments.

[0076] This invention introduces a dynamically updated global gradient variance benchmark mechanism, which enables the interference identification algorithm to be adaptive, flexibly adapting to different interference characteristics in complex dynamic electromagnetic environments, and improving the accuracy of pseudo-feature interference identification.

[0077] Furthermore, the globally updated gradient variance benchmark value in step C4 is adaptively corrected based on the disturbance variance and benchmark value within the current window and the next window, achieving dynamic adaptation to different environmental changes, including the following steps C41-C43:

[0078] C41. In the signal processing module, obtain the variance value of the interference signal in the current time window, and read the global gradient variance benchmark value in the previous time window from the storage area.

[0079] C42. Input the interference signal variance value and the global gradient variance benchmark value into the update calculation logic to generate a new global gradient variance benchmark value.

[0080] C43. Write the newly generated global gradient variance benchmark value into the buffer to provide a reference for interference determination in the next time window.

[0081] C44. The update process continues, enabling the global gradient variance benchmark value to respond to changes in signal characteristics over different time periods, thus achieving dynamic adaptation to complex communication environments.

[0082] Environmental signals are received via dual antennas, initially amplified by a low-noise amplifier, and then fed into a gradient variance filtering module. This module calculates the signal gradient sequence in real time within a 20ms window: first, it calculates the real-time gradient sequence g[k] for discrete signal points, forming a gradient sequence that is then input into a statistical processor; subsequently, it calculates the variance of the gradient values ​​within the window. and mean μ g The output is sent to the dynamic threshold comparator. The dynamic threshold comparator executes... The judgment logic, in which, Adaptive update mechanism is adopted:

[0083]

[0084] in, and These are the global gradient variance baseline values ​​for the next time window and the current time window, respectively. This represents the variance of the interference signal within this time window. This mechanism enables the system to dynamically adapt to environmental changes. The decision logic unit processes the comparison results: when an area is determined to be interference, the output is set to zero; otherwise, valid signals are directly transmitted.

[0085] When variance exceeds the dynamic threshold When the window is found to contain false features, the output is set to zero.

[0086] like Figure 3 The image shows the actual filtering effect of the gradient variance filtering algorithm based on the local statistical characteristics of the signal. It is particularly suitable for filtering out pulse spikes caused by power equipment switching noise and signal replicas caused by mountain reflections, with a processing delay of only 0.3ms (filtering delay time is as follows). Figure 4 As shown in the figure, the gradient variance filtering algorithm improves the impulse interference suppression rate by 42% and reduces the bit error rate from 10%. -2 Reduced to 10 -5 .

[0087] Furthermore, comparing the variance value with the dynamically updated global gradient variance benchmark value includes the following steps: sending the gradient variance value and the global gradient variance benchmark value into a dynamic threshold comparator to perform a threshold determination operation; transmitting the output result of the dynamic threshold comparator to the decision logic unit, which then performs interference region judgment; and entering the zero-setting output path or the pass-through path based on the output results of the dynamic threshold comparator and the decision logic unit.

[0088] In this embodiment, the gain structure composed of the three-stage gain amplifiers in step S4 can be sequentially connected to three-stage gain amplification units, each used to amplify the filtered effective signal stage by stage to achieve signal enhancement within a wide dynamic range and prevent the final stage amplifier from entering saturation. In this embodiment, the filtered signal is first input to the first-stage gain amplifier, providing an adjustable base gain of 10-30dB; then it is passed to the second-stage fixed-gain amplifier, providing a stable gain output; finally, it enters the third-stage adjustable-gain amplifier to achieve the final signal enhancement. A power detection circuit is provided at the output of the final stage to monitor whether the amplified signal is close to the set saturation threshold. When it is close to the threshold, the control circuit will gradually reduce the gain of the preceding stage to prevent signal overload; at the same time, an anti-saturation compensation circuit is inserted between the second and third stages to cancel the amplifier's nonlinear response through predistortion technology, effectively maintaining the output signal quality. The algorithm block diagram for the multi-stage anti-saturation amplification technology is as follows: Figure 5 As shown.

[0089] In one alternative implementation, the gain structure consisting of three gain amplifiers can also be a modular structure built using the same type of programmable gain amplifier chip, which facilitates adaptation to different communication protocols or operating frequency bands.

[0090] In another alternative implementation, the gain structure consisting of a three-stage gain amplifier may also include temperature compensation and frequency response adjustment functions to adapt to the continuous and stable amplification requirements under complex high-temperature and high-frequency environments.

[0091] This invention constructs a gain structure consisting of three gain amplifiers, combined with step-by-step feedback control and nonlinear suppression mechanisms, which effectively expands the overall dynamic range of the system gain, while avoiding output saturation of the final stage, thereby improving the reliability and stability of the power communication signal processing link.

[0092] Furthermore, the filtered signal is input to a gain structure consisting of three gain amplifiers for gain amplification. When the output power of the final stage approaches the saturation threshold, the gain of the preceding stage is adjusted step by step according to the feedback signal from the power detection circuit, and the nonlinear effect is counteracted by an anti-saturation compensation circuit. This includes the following steps:

[0093] The multi-stage anti-saturation amplification system adopts a three-stage variable gain amplifier cascade architecture, and its signal transfer function can be expressed as:

[0094]

[0095] Where y(t) is the signal transfer function, G(v) c ) represents the gain function at each stage, v c To control the voltage, h c(t) represents the impulse response of the anti-saturation compensation network. When the output power approaches the saturation threshold P... sat When the gain is 16dBm, the system triggers a hierarchical control strategy: the first-stage gain amplifier provides a basic gain adjustment range of 10-30dB, the second-stage gain amplifier is configured with a fixed gain of 15dB, and the third-stage gain amplifier is set with an adjustable gain of 5-15dB, with the total gain covering a dynamic range of 30-60dB.

[0096] The amplification effect of the filtered signal is as follows: Figure 6 As shown. Each amplifier stage is equipped with an independent gain control unit, and the output of the final stage is connected to a high-precision power detection circuit. When the output power is detected to be approaching the 16dBm saturation point, the feedback loop progressively reduces the gain of the preceding stage, and the maximum threshold for each stage signal is set to 16dBm (e.g., ...). Figure 7 To prevent transient saturation in the final stage, an anti-saturation compensation circuit is inserted between the second and third stages, using predistortion technology to offset the nonlinear effects of the transistors. All control parameters are stored in the control chip and can be remotely configured to adapt to different operator frequency bands.

[0097] like Figure 8 The diagram shows the hardware structure of this invention. The hardware system employs a multi-stage cascaded architecture, and the signal processing flow strictly follows the physical path from RF reception to baseband output. The system begins with a dual-antenna receiving module, whose operating frequency band covers the electromagnetic spectrum from 900MHz to 2600MHz, employing a wideband design to adapt to mainstream global communication protocols. A low-noise amplifier, implemented using the AD8421 chip, is connected after the antenna array. This component has a noise figure of 1.2dB and a gain of 24dB, ensuring initial enhancement capability for weak signals.

[0098] The received signal, after being amplified by low-noise amplification, enters the input impedance matching filter network. This circuit employs a π-type topology to achieve a 50Ω standard impedance transformation. The filter also provides 45dB out-of-band rejection, effectively eliminating adjacent-channel interference. The signal is then transmitted to the core signal processing module, which integrates three levels of functional units: a front-end configuration with 4MB of buffer RAM for 20ms window data storage; a mid-range sampling circuit using an AD9268 ADC chip for 1MSPS high-precision sampling and quantization; and a back-end deployment of a Xilinx Artix-7 FPGA chip to implement the gradient variance filtering algorithm.

[0099] The digitally filtered baseband signal enters a three-stage amplification chain: the first stage uses an AD8336 variable gain amplifier with an adjustable gain range of 10-30dB; the second stage uses an LMH6505 amplifier with a fixed gain of 15dB; the third stage again uses an AD8339 variable gain amplifier (adjustable from 5-15dB). After final amplification, the signal is connected to a HanRun HY911130A signal output filter, which has a roll-off characteristic of 160dB / decade, and the final output impedance is stabilized at 600Ω±5%. Throughout the entire signal chain, the RF-to-baseband conversion loss is controlled within 1.2dB, the system dynamic range reaches 96dB, and the harmonic distortion is below -70dBc.

[0100] Example 3 is an embodiment of the present invention. This embodiment provides a multi-stage anti-saturation amplification system for power communication signals based on pseudo-feature recognition, including a signal acquisition and preprocessing module, a gradient calculation module, a judgment output module, and a multi-stage saturation amplification module.

[0101] The signal acquisition and preprocessing module is used to acquire discrete-time signal sequences from the dual-antenna receiving module and input the signal sequences sequentially to the low-noise amplification module and the input impedance matching filter for preprocessing.

[0102] The gradient calculation module is used to calculate the gradient sequence of signal points within the current window based on the preprocessed signal sequence within a sliding time window of a set length, and to obtain the variance value of the gradient sequence.

[0103] The determination output module compares the variance value with the dynamically updated global gradient variance benchmark value. If the variance exceeds the set threshold standard, the current window is determined to be a pseudo-feature interference region and the filtered signal is set to zero. If it does not exceed the threshold, the effective signal is transmitted and the filtered signal is output.

[0104] The multi-stage saturation amplifier module is used to input the filtered signal into a gain structure composed of three gain amplifiers for gain amplification. When the output power of the final stage approaches the saturation threshold, the gain of the preceding stage is adjusted step by step according to the feedback signal of the power detection circuit, and the nonlinear effect is offset by the anti-saturation compensation circuit.

[0105] This embodiment also provides an electronic device applicable to the multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition as proposed in the above embodiment.

[0106] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the multi-level anti-saturation amplification method for power communication signals based on pseudo-feature recognition as proposed in the above embodiments.

[0107] The storage medium proposed in this embodiment and the method for implementing multi-level anti-saturation amplification of power communication signals based on pseudo-feature recognition proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0108] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition, characterized in that: include, The discrete-time signal sequence is acquired by the dual-antenna receiving module and then sequentially input to the low-noise amplification module and the input impedance matching filter for preprocessing. Within a sliding time window of a set length, the gradient sequence of signal points within the current window is calculated based on the preprocessed signal sequence, and the variance value of the gradient sequence is obtained. The variance value is compared with the dynamically updated global gradient variance benchmark value. If the variance exceeds the set threshold standard, the current window is determined to be a false feature interference region and the filtered signal is set to zero. If it does not exceed the threshold, the effective signal is transmitted and the filtered signal is output. The filtered signal is input to a gain structure consisting of three gain amplifiers for gain amplification. When the output power of the final stage approaches the saturation threshold, the gain of the preceding stage is adjusted step by step according to the feedback signal of the power detection circuit, and the nonlinear effect is offset by the anti-saturation compensation circuit.

2. The method for multi-stage anti-saturation amplification of power communication signals based on pseudo-feature recognition as described in claim 1, characterized in that: The process involves acquiring a discrete-time signal sequence using a dual-antenna receiving module and then sequentially inputting the signal sequence into a low-noise amplification module and an input impedance matching filter for preprocessing, including: The dual-antenna receiving module acquires electromagnetic signals within the target frequency band and converts these electromagnetic signals into a discrete-time signal sequence in the form of electrical signals. The signal sequence is input to the low-noise amplification module to initially enhance the signal amplitude and suppress background noise; The enhanced signal is then passed through an input impedance matching filter for impedance matching and bandwidth suppression, thereby improving signal quality.

3. The method for multi-stage anti-saturation amplification of power communication signals based on pseudo-feature recognition as described in claim 2, characterized in that: Within a sliding time window of a set length, the gradient sequence of signal points within the current window is calculated based on the preprocessed signal sequence, and the variance value of the gradient sequence is obtained. include, In the signal processing module, the signal output from the input impedance matching filter is buffered and temporarily stored. The signal is discretized using a sampling circuit, and the gradient sequence within the time window is calculated through differential operations. Statistical calculations are performed on the gradient sequence to obtain the variance value within the current window, which characterizes the amplitude of signal fluctuations.

4. The method for multi-stage anti-saturation amplification of power communication signals based on pseudo-feature recognition as described in claim 3, characterized in that: The process involves comparing the variance value with a dynamically updated global gradient variance benchmark. If the variance exceeds a set threshold, the current window is determined to be a pseudo-feature interference region, and the filtered signal is set to zero. If the variance does not exceed the threshold, the valid signal is transmitted, and the filtered signal is output. Compare the gradient variance value obtained within the current time window with the global gradient variance benchmark value to make a judgment. If the variance value exceeds the set threshold standard, it is determined that the signal within the window contains spurious feature interference. The corresponding signal is processed by the core signal processing module and then the zeroing output operation is performed in the signal output interface module. If the variance value does not exceed the set threshold standard, it is determined that the signal within the window is not identified as an interference signal and is output directly; The dynamically updated global gradient variance benchmark value is adaptively corrected based on the disturbance variance and benchmark value in the current window and the next window, so as to achieve dynamic adaptation to different environmental changes.

5. The method for multi-stage anti-saturation amplification of power communication signals based on pseudo-feature recognition as described in claim 4, characterized in that: The dynamically updated global gradient variance baseline value is adaptively corrected based on the interference variance and baseline value within the current and next windows, achieving dynamic adaptation to different environmental changes, including: In the signal processing module, the variance value of the interference signal in the current time window is obtained, and the global gradient variance benchmark value in the previous time window is read from the storage area. The interference signal variance value and the global gradient variance benchmark value are input into the update calculation logic to generate a new global gradient variance benchmark value. The newly generated global gradient variance baseline value is written to the buffer to provide a reference for the interference determination in the next time window; The update process is ongoing, enabling the global gradient variance benchmark value to respond to changes in signal characteristics over different time periods, thus achieving dynamic adaptation to complex communication environments.

6. The method for multi-stage anti-saturation amplification of power communication signals based on pseudo-feature recognition as described in claim 5, characterized in that: The process of discretizing the signal using a sampling circuit and calculating the gradient sequence within a time window through differential operations includes: The sampling circuit converts the analog signal output from the input impedance matching filter into a digital signal; The digital signal output by the sampling circuit is written into the buffer RAM; The cache RAM organizes its storage structure using time windows and supports reading and writing of continuous signals; The signal data stored in the cache RAM is called by the core signal processing module according to the time window for gradient calculation and variance statistical analysis.

7. The method for multi-stage anti-saturation amplification of power communication signals based on pseudo-feature recognition as described in claim 6, characterized in that: The comparison of the variance value with the dynamically updated global gradient variance benchmark value includes, The gradient variance value and the global gradient variance benchmark value are fed into the dynamic threshold comparator to perform the threshold determination operation. The output of the dynamic threshold comparator is transmitted to the decision logic unit, which then performs the judgment of the interference region. Based on the output results of the dynamic threshold comparator and the decision logic unit, the system enters either the zero-set output path or the pass-through path.

8. A multi-stage anti-saturation amplification system for power communication signals based on pseudo-feature recognition, employing the multi-stage anti-saturation amplification method for power communication signals based on pseudo-feature recognition as described in any one of claims 1 to 7, characterized in that, include: Signal acquisition and preprocessing module, gradient calculation module, decision output module, and multi-stage saturation amplification module; The signal acquisition and preprocessing module is used to acquire discrete-time signal sequences from the dual-antenna receiving module and input the signal sequences sequentially to the low-noise amplification module and the input impedance matching filter for preprocessing. The gradient calculation module is used to calculate the gradient sequence of signal points within the current window based on the preprocessed signal sequence within a sliding time window of a set length, and to obtain the variance value of the gradient sequence. The determination output module is used to compare the variance value with the dynamically updated global gradient variance benchmark value. If the variance exceeds the set threshold standard, the current window is determined to be a pseudo-feature interference region and the filter signal is set to zero. If it does not exceed the threshold, the effective signal is transmitted and the filter signal is output. The multi-stage saturation amplification module is used to input the filtered signal into a gain structure composed of three gain amplifiers for gain amplification. When the output power of the final stage approaches the saturation threshold, the gain of the preceding stage is adjusted step by step according to the feedback signal of the power detection circuit, and the nonlinear effect is offset by the anti-saturation compensation circuit.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the multi-level anti-saturation amplification method for power communication signals based on pseudo-feature recognition as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-level anti-saturation amplification method for power communication signals based on pseudo-feature recognition as described in any one of claims 1 to 7.

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