Sensor signal noise suppression method and system based on adjustable filter

Abstract

The invention provides a sensor signal noise suppression method and system based on an adjustable filter, and relates to the technical field of signal processing, and the method comprises the steps: carrying out the prediction of the signal entropy increase of a sensor, carrying out the impedance control decision under entropy flow control according to the signal entropy increase, the method comprises the following steps: determining an impedance control instruction and acting on an accessed impedance matching network, cooperating with operation of the impedance matching network, executing source end signal acquisition of a sensor, determining a first sensor signal, deploying a lightweight resonance verification array, performing frequency resonance detection on the first sensor signal, and performing noise suppression control according to a resonance detection result. And determining a filtering result. The technical problem that the noise suppression efficiency and suppression quality of the sensor signal are low due to the fact that the noise component of the sensor signal is complex and dynamically changes in the prior art is solved. The technical effects of efficient and self-adaptive sensor signal noise suppression and improvement of signal processing quality and efficiency are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, in particular to a sensor signal noise suppression method and system based on an adjustable filter. BACKGROUND

[0002] In the process of sensor signal acquisition and processing, the signal quality is often affected by multiple factors such as sensor body characteristics, external environmental interference, etc., so that a large amount of random noise and specific frequency interference are mixed in the original signal. In order to improve the quality of the sensor signal, the existing technology usually adopts the steps of parameter adjustment and filter separation to achieve the noise reduction target. However, in the process of parameter adjustment, the steps of parameter adjustment and filter separation need to be iterated multiple times, so the whole processing process is inefficient and it is difficult to meet the demand of real-time signal processing. Moreover, the filter cannot flexibly adjust the suppression strength, and if the parameter setting is improper, the effective signal component is filtered out, causing signal distortion or serious noise residue, thereby reducing the overall signal processing quality.

[0003] In summary, in the prior art, the noise component of the sensor signal is complex and dynamically changes, and the conventional filtering method cannot achieve adaptive regulation and control of different noise states, resulting in the technical problem of low sensor signal noise suppression efficiency and suppression quality. SUMMARY

[0004] The purpose of the present application is to provide a sensor signal noise suppression method and system based on an adjustable filter, which solves the technical problem in the prior art that the noise component of the sensor signal is complex and dynamically changes, and the conventional filtering method cannot achieve adaptive regulation and control of different noise states, resulting in low sensor signal noise suppression efficiency and suppression quality.

[0005] In view of the above problems, the present application provides a sensor signal noise suppression method and system based on an adjustable filter.

[0006] The first aspect of the present application provides a sensor signal noise suppression method based on an adjustable filter, which comprises: predicting the signal entropy increase of a sensor, making an impedance control decision under entropy flow control according to the signal entropy increase value, determining an impedance control instruction and acting on an accessed impedance matching network; in cooperation with the operation of the impedance matching network, performing source-end signal acquisition of the sensor to determine a first sensor signal; performing frequency resonance detection on the first sensor signal by deploying a lightweight resonance verification array, and performing noise suppression control according to the resonance detection result to determine a filtering result; wherein the noise suppression control mode comprises: if the resonance detection result is a non-resonance state, terminating the noise suppression process; if the resonance detection result is a resonance state, activating the filter unit to generate control.

[0007] Optionally, entropy increase rules of the read upper sensor signal sequence are read to determine a first entropy increase trend value; a second entropy increase value based on environmental interference is analyzed according to an environmental variable; impedance control analysis is performed on the first entropy increase trend value and the second entropy increase value according to an entropy flow controller to output an impedance control instruction; and the impedance matching network accessed is regulated according to the impedance control instruction, wherein the regulation parameters of the impedance matching network at least include resistance, capacitance and inductance parameters.

[0008] Optionally, the impedance control instruction is the negative entropy flow intensity and direction required to offset the signal entropy increase based on the first entropy increase trend value and the second entropy increase value, and the impedance state of the sensor signal source and the environmental internal resistance reach a conjugate matching relationship for the purpose of regulation.

[0009] Optionally, a lightweight second-order IIR filter array is deployed as a resonance verification array; filter records of the sensor are obtained to mine a noise frequency set; and the resonance verification array is tuned according to the noise frequency set, wherein each item in the resonance verification array corresponds to a noise frequency, and the resonance verification array is used to perceive the frequency component change of the corresponding noise.

[0010] Optionally, the first sensor signal is subjected to resonance verification according to the resonance verification array to determine a resonance detection result, wherein a preset tolerance interval is used as a constraint, an amplitude satisfying zero value is taken as a non-resonance condition, and an abnormally high amplitude frequency is taken as a resonance condition; and the resonance detection result is determined, and if all the resonance detection result is a non-resonance condition, a direct storage instruction is generated to store the first sensor signal.

[0011] Optionally, if there is a resonance condition in the resonance detection result, a matching verification unit in the resonance verification array is located; a frequency component vector of the matching verification unit is integrated to determine to-be-suppressed source data in combination with the first sensor signal, and a noise suppression instruction is generated.

[0012] Optionally, a filter architecture is determined, wherein the filter architecture is composed of an adjustable filter-mechanism discriminator, the adjustable filter receives noise-containing data to generate recovery data, and the mechanism discriminator performs mechanism fingerprint quality inspection based on a pure signal; for the filter architecture, supervised training convergence based on an adversarial network is performed to generate a filter unit and deploy the filter unit in the resonance verification array.

[0013] Optionally, the filter unit is activated with the sending of the noise suppression instruction; the to-be-suppressed source data is input into the adjustable filter of the filter unit to generate a filter result; and the filter result is subjected to mechanism fingerprint determination according to the mechanism discriminator to output a filter result satisfying a pure signal standard.

[0014] Optionally, the resonance detection array comprises a first operation mode and a second operation mode, wherein the first operation mode is a full operation state of the array, and the second operation mode is a low-power monitoring state, and the second operation mode reserves the resonance detection operation of the core frequency; the first operation mode is driven in a high-entropy period and a key sensing period, and the second operation mode is determined in a low-entropy period and a non-key sensing period.

[0015] In a second aspect of the present application, a sensor signal noise suppression system based on an adjustable filter is provided, which comprises: an instruction determination module configured to predict signal entropy increase of a sensor, make impedance control decisions under entropy flow control according to the signal entropy increase value, determine impedance control instructions, and act on an accessed impedance matching network; a signal acquisition module configured to perform source-end signal acquisition of the sensor in cooperation with operation of the impedance matching network, and determine a first sensor signal; a noise suppression control module configured to perform frequency resonance detection on the first sensor signal by deploying a lightweight resonance detection array, perform noise suppression control according to the resonance detection result, and determine a filtering result; wherein the noise suppression control mode in the noise suppression control module comprises: if the resonance detection result is a non-resonance state, terminating the noise suppression process; and if the resonance detection result is a resonance state, activating a filtering unit to generate control.

[0016] The one or more technical solutions provided in the present application have at least the following technical effects or advantages: The method provided in the present application predicts signal entropy increase of a sensor, makes impedance control decisions under entropy flow control according to the signal entropy increase value, determines impedance control instructions, and acts on an accessed impedance matching network; cooperates with operation of the impedance matching network to perform source-end signal acquisition of the sensor, and determines a first sensor signal; performs frequency resonance detection on the first sensor signal by deploying a lightweight resonance detection array, performs noise suppression control according to the resonance detection result, and determines a filtering result, thereby achieving efficient and adaptive sensor signal noise suppression, and improving the quality and efficiency of signal processing.

[0017] The above description is only a summary of the technical solutions of the present application. In order to enable one skilled in the art to better understand the technical means of the present application, the contents of the specification can be implemented, and in order to enable the above and other purposes, features and advantages of the present application to be more obvious and easy to understand, the specific embodiments of the present application are described below. It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the sensor signal noise suppression method based on an adjustable filter provided in this application.

[0020] Figure 2 This is a schematic diagram of the sensor signal noise suppression system based on an adjustable filter provided in this application.

[0021] Explanation of reference numerals in the attached diagram: Command determination module 11, signal acquisition module 12, noise suppression and control module 13. Detailed Implementation

[0022] This application provides a sensor signal noise suppression method and system based on an adjustable filter. It addresses the technical problem in existing technologies where sensor signal noise components are complex and dynamically changing, making it difficult for conventional filtering methods to adaptively adjust to different noise states, resulting in low noise suppression efficiency and quality. The method achieves efficient and adaptive sensor signal noise suppression, improving the quality and efficiency of signal processing.

[0023] The technical solutions of the present invention will now be clearly and completely described 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. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0024] Example 1, as Figure 1 As shown, this application provides a sensor signal noise suppression method based on an adjustable filter, the sensor signal noise suppression method based on an adjustable filter includes: The signal entropy increase of the sensor is predicted, and impedance control decision is made under entropy flow control based on the signal entropy increase. The impedance control command is determined and applied to the connected impedance matching network.

[0025] Furthermore, impedance control decisions are made based on the signal entropy increase value under entropy flow control, and impedance control commands are determined and applied to the connected impedance matching network. This includes: reading the entropy increase pattern of the upper-level sensor signal sequence and determining a first entropy increase trend value; analyzing a second entropy increase value based on environmental interference according to environmental variables; performing impedance control analysis on the first entropy increase trend value and the second entropy increase value according to the entropy flow controller, and outputting an impedance control command; and regulating the connected impedance matching network according to the impedance control command, wherein the regulation parameters of the impedance matching network include at least resistance, capacitance, and inductance parameters.

[0026] Specifically, the signal entropy increase of the sensor is predicted to obtain the signal entropy value, which includes a first entropy increase trend value and a second entropy value. The process involves: acquiring a continuous sequence of upper-level sensor signals output by the sensor (the upper-level sensor signal sequence refers to all output data of the sensor within a certain period); normalizing the signal data; then segmenting the signal sequence according to a fixed-length sliding time window to construct a time series sample set; quantizing the signal amplitude within each window to form several discrete amplitude intervals; calculating the probability of occurrence of each interval to obtain the probability distribution of the time window; and based on the probability distribution, using an entropy calculation formula, such as the Shannon entropy formula, calculating the entropy value corresponding to each time window to obtain the signal entropy value. This signal entropy value characterizes the uncertainty and disorder of the sensor signal during that time period. The entropy increments of continuous time windows are arranged chronologically, and the difference sequence of adjacent entropy increments is calculated to obtain the growth rate of entropy increment over time, forming an entropy increment sequence. Then, using methods such as polynomial fitting or Kalman filtering, the trend of the entropy increment sequence is estimated, outputting a first entropy increment trend value. This first entropy increment trend value represents the entropy increase trend of the sensor signal itself under the absence of significant external interference. Environmental sensing units deployed in the same area as the sensor, such as temperature sensors, humidity sensors, and electromagnetic field strength meters, collect environmental information about the current sensor area, obtaining environmental variables such as temperature, humidity, and electromagnetic interference intensity. The collected environmental variables are normalized, and the characteristic probability distributions of the environmental variables, such as fluctuation frequency, amplitude variance, and sudden interference intensity, are statistically analyzed. Based on the Shannon entropy formula, the entropy value of the characteristic probability distribution is calculated. The calculated base entropy value is compared with the baseline entropy value of the sensor signal itself, and the difference is extracted as the entropy increment caused by environmental noise, obtaining a second entropy increment based on environmental interference. This second entropy increment reflects the additional disorder brought to the sensor signal by environmental interference. The first entropy increase trend value and the second entropy increase value are input into the entropy flow controller to perform impedance control analysis. The entropy flow controller determines the specific parameter values ​​of the resistor, capacitor, and inductor that need to be adjusted based on these values ​​to achieve conjugate matching between the signal source impedance and the environmental internal resistance, outputting a precise impedance control command. The entropy flow controller is a control device based on the principle of information entropy flow, used to analyze the impedance adjustment required to achieve a stable and low-noise state for the sensor signal under the current signal condition. According to the impedance control command, the connected impedance matching network is adjusted. This network is used to achieve impedance adjustment and can employ various structures, as long as it allows for fine control of the resistance, capacitance, and inductance parameters. For example, it can use a digital potentiometer to achieve continuous resistance adjustment or a MOSFET array to construct a reactive network; no specific limitations are imposed here.The control parameters of an impedance matching network include at least resistance, capacitance, and inductance parameters. The control parameters are precisely adjusted through impedance control commands to change the characteristics of the impedance matching network, so that the impedance matching network achieves a conjugate matching relationship with the impedance state of the sensor signal source and the internal resistance of the environment. This effectively reduces the reflection and loss of the sensor signal during transmission, and improves the transmission efficiency and quality of the sensor signal.

[0027] Furthermore, the impedance control command aims to counteract the negative entropy flow intensity and direction required to offset the signal entropy increase based on the first entropy increase trend value and the second entropy increase value, with the control objective being to achieve a conjugate matching relationship between the impedance state of the sensor signal source and the internal resistance of the environment.

[0028] Specifically, signal entropy increment increases the disorder or uncertainty of a signal. Entropy increase indicates increased noise interference and deterioration of signal quality. The impedance control command output by the entropy current controller is the strength and direction of the negative entropy current required to counteract the first entropy increase trend value and the second entropy increase value. Negative entropy current refers to a reverse adjustment amount applied to counteract the entropy increase caused by changes in the sensor signal's own characteristics and environmental interference. The strength of the negative entropy current represents the magnitude of the reverse adjustment amount required to reduce signal entropy increase and improve signal purity; its magnitude depends on the current degree of signal entropy increase. The direction of the negative entropy current is to reduce signal disorder and make the signal tend towards stability and purity. Based on the principle of maximum power transfer, energy transfer efficiency is highest when the signal source internal resistance and load impedance satisfy a conjugate matching relationship, while external interference from reflection and introduction is minimized. Conjugate matching refers to the condition that the signal source impedance and load impedance achieve maximum power transfer when their real parts are equal and their imaginary parts are opposites. By adjusting parameters such as resistance, capacitance, and inductance through impedance control commands, the required negative entropy current strength and direction are generated, achieving conjugate matching between the impedance state of the sensor signal source and the environmental internal resistance. By regulating the signal based on impedance control commands, not only is the signal transmission efficiency optimized, but the introduction of external interference is also reduced from a physical level, thereby obtaining a relatively pure sensing signal and improving the transmission efficiency and quality of the sensing signal.

[0029] In coordination with the operation of the impedance matching network, the source-end signal of the sensor is acquired to determine the first sensor signal.

[0030] By deploying a lightweight resonance detection array, frequency resonance detection is performed on the first sensor signal. Noise suppression and control are then performed based on the resonance detection results to determine the filtering result. The noise suppression and control methods include: if the resonance detection result is a non-resonance state, the noise suppression process is terminated; if the resonance detection result is a resonance state, the filtering unit is activated for generation control.

[0031] Specifically, when an impedance control command is applied to the connected impedance matching network, the network dynamically adjusts the resistance, capacitance, and inductance parameters to achieve a conjugate matching relationship between the equivalent output impedance of the sensor signal source and the environmental internal resistance. This results in the acquisition of the sensor's output source signal, forming the first sensor signal. This first sensor signal is then input to a pre-deployed lightweight resonance detection array for frequency resonance detection. The resonance detection array includes multiple filters used to detect specific frequency components in the signal and identify the resonant frequency, thereby determining the presence of noise interference. When the signal amplitude is close to zero, it is considered a non-resonant state; when the signal amplitude significantly increases and exceeds a preset threshold, it is considered a resonant state, meaning that the frequency component may be affected by noise interference or an external resonance source. Noise suppression is then implemented based on the resonance detection results: if the resonance detection result is a non-resonant state, i.e., the signal amplitude is close to zero or within the normal fluctuation range, it indicates that an interference-free sensor signal can be acquired under the current modulation, requiring no further processing, and the noise suppression process is terminated. If the resonance detection result indicates the presence of resonance, the filtering unit is activated. The signal component at the corresponding frequency is input as the source data to be suppressed into the filtering unit for dynamic adjustment, noise suppression control, and the filtering result is determined. Through frequency-level resonance detection, accurate identification and dynamic suppression of sensor signal noise are achieved, improving the high quality and stability of the signal during transmission and processing.

[0032] Furthermore, deploying a lightweight resonance detection array includes: deploying a lightweight second-order IIR filter array as the resonance detection array; acquiring the filter records of the sensor and mining the noise frequency set; and tuning the resonance detection array according to the noise frequency set, wherein each item in the resonance detection array corresponds to a noise frequency, and the resonance detection array is used to sense the frequency component changes of the corresponding noise.

[0033] Specifically, a resonant detection array is formed by deploying multiple lightweight second-order IIR filters. This array is used to sense changes in specific frequency components. The lightweight second-order IIR filters only retain signal frequency sensing functionality and do not possess the full-featured filtering capabilities of traditional filters. They do not require adjustment of filtering parameters; instead, they achieve real-time detection of noise frequency bands by rapidly responding to amplitude changes at specific frequencies, ensuring high efficiency and low latency in online interference detection. Filtering records from sensors under different environmental conditions are collected, including the interference frequencies, time distribution, and intensity information marked during past noise suppression processes. Clustering analysis algorithms such as DBSCAN or K-means are used to cluster the data in the filtering records, statistically analyzing the signal energy in different frequency bands to form a noise frequency set. Based on the noise frequency set, each second-order IIR filter in the resonance detection array is assigned a target frequency. That is, each item in the resonance detection array corresponds to a specific noise frequency. By adjusting its standardized center frequency and bandwidth parameters, the peak value of its resonant response is aligned with the target noise frequency, ensuring that the filter is sensitive to and responds quickly to amplitude changes in the target frequency components. Specifically, the resonant peak width is adjusted according to the noise intensity; a narrow bandwidth is used for strong interference to improve selectivity, while a wide bandwidth is used for weak interference to avoid missed detections. The tuning algorithm employs a greedy strategy, prioritizing the allocation of high-frequency noise to high-frequency filters with better anti-aliasing performance. Simultaneously, a collision detection mechanism prevents adjacent frequencies from being assigned to the same filter channel. For example, using three second-order IIR filters to form the resonance detection array, the noise frequency set includes low-frequency (0-1kHz), mid-frequency (1-5kHz), and high-frequency (5-10kHz) segments. Based on the noise frequency meter, the parameters of each second-order IIR filter are adjusted so that each filter detects within its corresponding noise frequency range, ensuring that the filter can accurately detect changes in the corresponding noise frequency components.

[0034] By deploying a lightweight second-order IIR filter array, changes in specific frequency components can be quickly detected, enabling rapid noise detection. Tuning the resonance detection array according to the noise frequency set ensures that each filter accurately detects its corresponding noise frequency, improving the filter's detection accuracy and response speed, and ensuring high quality and stability of the signal during transmission and processing.

[0035] Furthermore, frequency resonance detection is performed on the first sensor signal, and noise suppression and control are performed based on the resonance detection results, including: performing resonance detection on the first sensor signal according to the resonance detection array, determining the resonance detection results, wherein, with a preset tolerance range as a constraint, an amplitude satisfying zero value is regarded as a non-resonance state, and an abnormal increase in amplitude frequency is regarded as a resonance state; determining the resonance detection results, if all the resonance detection results are non-resonance states, generating a direct storage instruction to store the first sensor signal.

[0036] Specifically, the first sensor signal is input into a resonance detection array. The array uses multiple filters to detect the first sensor signal in frequency bands, identifying the resonant frequency components within the signal. The output signal amplitude of each filter reflects the resonance within that frequency band. By statistically analyzing historical signal data collected by the sensor under normal operating conditions, the typical fluctuation range of the sensor signal is determined, and a preset tolerance range is set. For example, using the standard deviation method, the mean of the sensor signal ± 3 times the standard deviation is used as the tolerance range. The preset tolerance range is a pre-defined allowable error range used to determine whether the amplitude of the sensor signal meets the conditions for resonance or non-resonance. It can be dynamically adjusted according to the real-time characteristics of the sensor signal. By setting a preset tolerance range, it can accommodate small fluctuations or errors in actual conditions, ensuring that small fluctuations or errors are not misjudged as resonance or non-resonance. By analyzing the output signal amplitude of each filter, it is determined whether a resonance state exists in the signal. When the output signal amplitude is zero, it indicates a non-resonance state; when the amplitude frequency is abnormally high, it indicates a resonance state, suggesting that the signal may be affected by noise interference. The resonance detection results are obtained through resonance verification. The amplitude in the resonance detection results is judged. If all frequencies in the resonance detection results meet the zero value, it means that the first sensing signal is in a non-resonance state and there is no noise interference in the first sensing signal. A direct storage instruction is generated to store the first sensor signal without further processing, thereby saving computing resources and power consumption.

[0037] Furthermore, if resonance is detected in the resonance detection results, the matching verification unit within the resonance verification array is located; the frequency component vectors of the matching verification unit are integrated, combined with the first sensor signal, to determine the source data to be suppressed, and a noise suppression command is generated.

[0038] Specifically, if the resonance detection result indicates the presence of a resonance state in the first sensing signal, i.e., an abnormally high amplitude of the output signal of one or more filters, the amplitude of the output signal of each filter is analyzed to determine which filters have abnormally high amplitudes. Based on the amplitude analysis results, the matching verification unit within the resonance verification array is located. For example, if the output signal amplitude of the low-frequency filter is abnormally high, the low-frequency filter is located as the matching verification unit. Frequency, amplitude, and other component information are extracted from the matching verification unit and integrated to form a frequency component vector. The frequency component vector refers to the frequency and amplitude characteristics of the first sensing signal affected by noise. The frequency component vector and the first sensor signal are jointly analyzed using vector superposition or weighted fusion methods to quantify the influence intensity of interference at different frequencies. Simultaneously, the first sensor signal is mapped to the frequency domain using Fast Fourier Transform or Discrete Wavelet Transform to identify signal segments or components affected by specific frequencies, which are then determined as the source data to be suppressed. Based on the noise frequency range and intensity of the source data to be suppressed, a noise suppression command is generated to optimize the noise suppression strategy. The noise suppression command is used to guide the filtering unit to perform noise suppression operations, including parameters such as the filter cutoff frequency and gain.

[0039] By locating the matching unit within the resonance detection array and integrating frequency characteristics, the noise source can be accurately identified, enabling frequency-directed and on-demand noise suppression activation. This avoids blind filtering of the overall signal and provides high-quality, targeted noise suppression commands to the adjustable filter, optimizing the noise suppression strategy and achieving effective suppression of signal noise, thereby improving signal purity.

[0040] Furthermore, before activating the filtering unit for generation control, the construction of the filtering unit includes: determining the filtering architecture, wherein the filtering architecture consists of an adjustable filter and a mechanism discriminator, the adjustable filter receives noisy data and generates recovered data, and the mechanism discriminator performs mechanism fingerprint quality inspection based on the clean signal; for the filtering architecture, supervised training convergence based on an adversarial network is performed to generate the filtering unit and subsequently deploy it on the resonance detection array.

[0041] Specifically, the construction of the filtering unit first determines the filtering architecture, which consists of two parts: an adjustable filter and a mechanism discriminator. The adjustable filter receives noisy data and generates recovered data. Its filtering parameters are dynamically adjusted based on the noise characteristics of the input signal to achieve precise suppression of noise at specific frequencies. For example, the adjustable filter can be a band-stop filter or a notch filter to effectively suppress noise within a specific frequency range. The mechanism discriminator is the verification component of the filtering architecture. It performs quality checks on the filtered output based on the mechanism fingerprint of the clean signal. The mechanism fingerprint is a verification feature formed based on the physical characteristics and generation mechanism of the first sensing signal, including specific frequency components, amplitude distribution, phase relationships, etc., used to determine whether the recovered data meets the standard of a clean signal. A training dataset is retrieved, which includes noisy data and corresponding clean signal data. Based on the training dataset, an adversarial network, such as a generative adversarial network (GAN), is used for supervised training of the filtering architecture. The adversarial network consists of a generator, an adjustable filter, and a mechanism discriminator. The generator generates the recovered data, and the mechanism discriminator determines whether the recovered data meets the standard of a clean signal. During training, the generator continuously adjusts its parameters to generate reconstructed data that more closely approximates the pure signal, while the discriminator continuously adjusts its parameters to more accurately identify the purity of the reconstructed data. Through iterative training, the generator and discriminator compete with each other, eventually reaching a convergence state to obtain the filtering unit. The trained filtering unit is then deployed in a resonance detection array so that it can be immediately activated and noise suppression operations performed when resonance is detected, improving the response speed and processing efficiency of signal noise suppression, effectively removing interference, and improving signal purity.

[0042] Furthermore, after generating the noise suppression command, the process includes: activating the filtering unit upon sending the noise suppression command; inputting the source data to be suppressed into the adjustable filter of the filtering unit to generate a filtering result; performing a mechanism fingerprint determination on the filtering result according to the mechanism discriminator, and outputting a filtering result that meets the clean signal standard.

[0043] Specifically, after generating a noise suppression command, the filtering unit deployed in the resonance detection array is activated. The data to be suppressed is input into the filtering unit. The adjustable filter in the filtering unit dynamically adjusts its center frequency, bandwidth, and gain parameters based on previous training results and noise characteristics to perform directional filtering on the interfered frequency bands in the signal and generate a filtering result. The mechanism discriminator in the filtering unit performs mechanism fingerprinting on the filtering result input by the adjustable filter. By comparing the consistency of the signal data in the filtering result with the frequency components, amplitude distribution, and phase relationship of the pure signal from the sensor, it determines whether the filtering result meets the pure signal standard. When the filtering result meets the pure signal standard, it outputs a filtering result that meets the pure signal standard. If it does not meet the standard, the filter parameters are readjusted or further processing is performed. Through the coordinated operation of the filtering unit and the mechanism discriminator, on-demand noise suppression and signal fidelity are dually guaranteed. This not only effectively removes the interference components of resonance detection and positioning but also ensures that the output signal meets physical or statistical mechanism standards, improving the accuracy and stability of the sensor signal during transmission and processing.

[0044] Furthermore, the resonance detection array includes a first operating mode and a second operating mode, wherein the first operating mode is the full operating state of the array, the second operating mode is the low-power monitoring state, and the second operating mode retains the resonance detection operation of the core frequency; the first operating mode is driven by high-entropy periods and critical sensing periods, and the second operating mode is determined by low-entropy periods and non-critical sensing periods.

[0045] Specifically, the resonant detection array operates in two modes: a first mode and a second mode. The first mode is the full-operation state, where all second-order IIR filter units in the array are active, covering all frequency bands with concentrated noise and ensuring comprehensive signal monitoring during periods of high interference or signal sensitivity. The second mode is a low-power monitoring state, retaining only the core frequency for resonant detection, reducing computational and energy consumption. The resonant detection array's operating mode is determined by entropy monitoring to assess the complexity and interference level of the sensor signal. By continuously monitoring the entropy increase, the uncertainty of the signal is evaluated in real time. When the entropy increase is greater than or equal to a preset entropy increase threshold, the sensor signal exhibits high disorder, frequent noise, and poor signal stability, indicating a high-entropy period. When the entropy increase is less than the preset threshold, the signal is stable with low noise interference, indicating a low-entropy period. When the first sensor signal is detected to be in a high-entropy period or a critical sensing period, the first mode is automatically activated to ensure maximum frequency detection coverage. The critical sensing period refers to the data acquisition cycle of the security monitoring and key control nodes. When the first sensor signal is detected to be in a low-entropy period or a non-critical sensing period, the system automatically switches to the second operating mode, retaining only the resonance verification operation of the core frequency band, thereby effectively reducing power consumption and unnecessary data processing. The low-entropy period refers to a period with stable signal and low noise interference.

[0046] By introducing a dual-mode operation mechanism, a dynamic balance between sensor signal detection accuracy and energy efficiency is achieved. This ensures noise monitoring across the entire frequency band during high-risk periods and reduces energy consumption during low-risk periods, thereby improving the flexibility of the resonance verification array under different operating conditions and achieving the best balance between performance and energy efficiency.

[0047] In summary, the sensor signal noise suppression method based on tunable filters provided in this application has the following beneficial effects: By using an impedance matching network, the energy transfer efficiency of the sensor signal is maximized under a conjugate matching relationship, effectively reducing signal reflection and suppressing the introduction of external interference, ensuring that the sensor signal is in a relatively pure state. Based on this, a resonance detection mechanism is introduced to quickly determine whether interference exists in the acquired signal. When the detection result shows no resonance interference, it indicates that the front-end modulation has brought the signal to a low-interference or interference-relaxed state, and it can be directly stored and used. When the detection result shows resonance interference, combined with the located interference frequency and the acquired source signal, a pre-trained filter unit directly outputs a pure signal based on the training results, eliminating the need for parameter tuning and separation steps under conventional filtering. This avoids the high complexity and delay problems of conventional filtering methods, achieving fast, accurate, and adaptive noise suppression. This technical effect improves the efficiency and quality of sensor signal processing while ensuring signal authenticity.

[0048] Example 2 is based on the same inventive concept as the sensor signal noise suppression method based on tunable filters in the previous examples, such as... Figure 2 As shown, this application provides a sensor signal noise suppression system based on an adjustable filter, wherein the sensor signal noise suppression system based on an adjustable filter includes: The instruction determination module 11 is used to predict the signal entropy increase of the sensor, make impedance control decisions under entropy flow control based on the signal entropy increase, determine the impedance control instruction, and apply it to the connected impedance matching network; the signal acquisition module 12 is used to coordinate the operation of the impedance matching network, perform source-end signal acquisition of the sensor, and determine the first sensor signal; the noise suppression and control module 13 is used to perform frequency resonance detection on the first sensor signal by deploying a lightweight resonance detection array, perform noise suppression and control based on the resonance detection result, and determine the filtering result; wherein, the noise suppression and control method in the noise suppression and control module 13 includes: if the resonance detection result is a non-resonance state, terminating the noise suppression process; if the resonance detection result is a resonance state, activating the filtering unit for generation control.

[0049] Furthermore, the instruction determination module 11 in the sensor signal noise suppression system based on the adjustable filter is also used to: read the entropy increase law of the upper-level sensor signal sequence and determine the first entropy increase trend value; analyze the second entropy increase value based on environmental interference according to environmental variables; perform impedance control analysis on the first entropy increase trend value and the second entropy increase value according to the entropy flow controller, and output an impedance control instruction; and regulate the connected impedance matching network according to the impedance control instruction, wherein the regulation parameters of the impedance matching network include at least resistance, capacitance, and inductance parameters.

[0050] Furthermore, the instruction determination module 11 in the sensor signal noise suppression system based on the adjustable filter is also used to: the impedance control instruction is the intensity and direction of the negative entropy flow required to counteract the signal entropy increase based on the first entropy increase trend value and the second entropy increase value, with the purpose of achieving a conjugate matching relationship between the impedance state of the sensor signal source and the internal resistance of the environment.

[0051] Furthermore, the noise suppression control module 13 in the sensor signal noise suppression system based on the tunable filter is also used to: deploy a lightweight second-order IIR filter array as a resonance detection array; acquire the filter records of the sensor and mine the noise frequency set; and tune the resonance detection array according to the noise frequency set, wherein each item in the resonance detection array corresponds to a noise frequency, and the resonance detection array is used to sense the frequency component changes of the corresponding noise.

[0052] Furthermore, the noise suppression control module 13 in the sensor signal noise suppression system based on the adjustable filter is also used to: perform resonance detection on the first sensor signal according to the resonance detection array, determine the resonance detection result, wherein, with a preset tolerance range as a constraint, the amplitude satisfying zero value is regarded as a non-resonance state, and the amplitude frequency abnormally increased is regarded as a resonance state; determine the resonance detection result, and if all the resonance detection results are non-resonance states, generate a direct storage instruction to store the first sensor signal.

[0053] Furthermore, the noise suppression control module 13 in the sensor signal noise suppression system based on the adjustable filter is also used to: if there is resonance in the resonance detection result, locate the matching verification unit in the resonance verification array; integrate the frequency component vector of the matching verification unit, combine it with the first sensor signal, determine the source data to be suppressed, and generate a noise suppression command.

[0054] Furthermore, the noise suppression control module 13 in the sensor signal noise suppression system based on the tunable filter is also used to: determine the filtering architecture, wherein the filtering architecture consists of a tunable filter and a mechanism discriminator, the tunable filter receives noisy data and generates recovered data, and the mechanism discriminator performs mechanism fingerprint quality inspection based on the clean signal; for the filtering architecture, supervised training convergence based on an adversarial network is performed to generate filtering units and subsequently deployed on the resonance detection array.

[0055] Furthermore, the noise suppression control module 13 in the sensor signal noise suppression system based on the adjustable filter is also used to: activate the filtering unit upon sending the noise suppression command; input the source data to be suppressed into the adjustable filter of the filtering unit to generate a filtering result; and perform mechanism fingerprint determination on the filtering result according to the mechanism discriminator to output a filtering result that meets the clean signal standard.

[0056] Furthermore, the noise suppression control module 13 in the sensor signal noise suppression system based on the adjustable filter is also used for: the resonance detection array includes a first operating mode and a second operating mode, wherein the first operating mode is the full operating state of the array, the second operating mode is the low-power monitoring state, and the second operating mode retains the resonance detection operation of the core frequency; the first operating mode is driven by high-entropy periods and critical sensing periods, and the second operating mode is determined by low-entropy periods and non-critical sensing periods.

[0057] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1The sensor signal noise suppression method and specific examples based on tunable filters in Example 1 are also applicable to the sensor signal noise suppression system based on tunable filters in this embodiment. Through the foregoing detailed description of the sensor signal noise suppression method based on tunable filters, those skilled in the art can clearly understand the sensor signal noise suppression system based on tunable filters in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0058] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0059] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A sensor signal noise suppression method based on an adjustable filter, characterized in that, The method includes: The signal entropy increase of the sensor is predicted, and impedance control decision is made under entropy flow control based on the signal entropy increase. The impedance control command is determined and applied to the connected impedance matching network. In coordination with the operation of the impedance matching network, the source-end signal of the sensor is acquired to determine the first sensor signal; By deploying a lightweight resonance detection array, frequency resonance detection is performed on the first sensor signal, and noise suppression and control are performed based on the resonance detection results to determine the filtering result; The noise suppression and control methods include: If the resonance detection result is a non-resonance state, the noise suppression process is terminated. If the resonance detection result indicates the presence of a resonance state, the filtering unit is activated for generation control.

2. The sensor signal noise suppression method based on an adjustable filter as described in claim 1, characterized in that, Impedance control decisions are made based on the increase in signal entropy, under entropy flow control. Impedance control commands are then determined and applied to the connected impedance matching network, including: Read the entropy increase pattern of the upper-level sensor signal sequence and determine the first entropy increase trend value; Based on environmental variables, analyze the second entropy increment based on environmental disturbances; Based on the entropy flow controller, impedance control analysis is performed on the first entropy increase trend value and the second entropy increase value, and an impedance control command is output. According to the impedance control command, the connected impedance matching network is adjusted, wherein the adjustment parameters of the impedance matching network include at least resistance, capacitance and inductance parameters.

3. The sensor signal noise suppression method based on an adjustable filter as described in claim 2, characterized in that, The impedance control command is to counteract the negative entropy flow intensity and direction required to offset the signal entropy increase based on the first entropy increase trend value and the second entropy increase value, with the aim of achieving a conjugate matching relationship between the impedance state of the sensor signal source and the internal resistance of the environment.

4. The sensor signal noise suppression method based on an adjustable filter as described in claim 1, characterized in that, Deploying a lightweight resonance detection array includes: Deploy a lightweight second-order IIR filter array as a resonance detection array; Obtain the filtered records from the sensors and mine the noise frequency set; The resonance detection array is tuned according to the noise frequency set, wherein each item in the resonance detection array corresponds to a noise frequency, and the resonance detection array is used to sense the frequency component changes of the corresponding noise.

5. The sensor signal noise suppression method based on an adjustable filter as described in claim 4, characterized in that, Frequency resonance detection is performed on the first sensor signal, and noise suppression and control are performed based on the resonance detection results, including: According to the resonance detection array, the first sensor signal is resonantly detected to determine the resonance detection result. The preset tolerance range is used as a constraint. The amplitude is zero as a non-resonance condition and the amplitude frequency is abnormally increased as a resonance condition. If the resonance detection results are all non-resonance cases, a direct storage instruction is generated to store the first sensor signal.

6. The sensor signal noise suppression method based on an adjustable filter as described in claim 5, characterized in that, If resonance is detected in the resonance detection results, locate the matching verification unit within the resonance verification array; By integrating the frequency component vectors of the matching verification unit and combining them with the first sensor signal, the source data to be suppressed is determined, and a noise suppression command is generated.

7. The sensor signal noise suppression method based on an adjustable filter as described in claim 6, characterized in that, Before activating the filtering unit for generation control, the construction of the filtering unit includes: A filtering architecture is determined, wherein the filtering architecture consists of an adjustable filter and a mechanism discriminator. The adjustable filter receives noisy data and generates recovered data, and the mechanism discriminator performs mechanism fingerprint quality inspection based on the clean signal. For the aforementioned filtering architecture, supervised training convergence based on adversarial networks is performed to generate filtering units, which are then deployed on the resonance calibrator array.

8. The sensor signal noise suppression method based on an adjustable filter as described in claim 7, characterized in that, After generating the noise suppression command, it includes: Upon receiving the noise suppression command, the filtering unit is activated; The source data to be suppressed is input into the adjustable filter of the filtering unit to generate a filtering result; Based on the mechanism discriminator, the filtering result is subjected to mechanism fingerprinting, and a filtering result that meets the clean signal standard is output.

9. The sensor signal noise suppression method based on an adjustable filter as described in claim 4, characterized in that, The resonance detection array includes a first operating mode and a second operating mode. The first operating mode is the full operating state of the array, and the second operating mode is the low-power monitoring state. The second operating mode retains the resonance detection operation of the core frequency. The first operating mode is driven by high-entropy periods and critical sensing periods, and the second operating mode is determined by low-entropy periods and non-critical sensing periods.

10. A sensor signal noise suppression system based on an adjustable filter, characterized in that, The steps for implementing the sensor signal noise suppression method based on an adjustable filter according to any one of claims 1 to 9 include: The instruction determination module is used to predict the signal entropy increase of the sensor, make impedance control decisions under entropy flow control based on the signal entropy increase, determine the impedance control instruction and apply it to the connected impedance matching network. The signal acquisition module is used to coordinate the operation of the impedance matching network, perform source-end signal acquisition of the sensor, and determine the first sensor signal; The noise suppression and control module is used to perform frequency resonance detection on the first sensor signal by deploying a lightweight resonance detection array, perform noise suppression and control based on the resonance detection results, and determine the filtering result. The noise suppression and control methods in the noise suppression and control module include: If the resonance detection result is a non-resonance state, the noise suppression process is terminated. If the resonance detection result indicates the presence of a resonance state, the filtering unit is activated for generation control.

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