117 results about "Noise intensity" patented technology

The invention relates to the technical field of laser spectrum detection, in particular to an LIBS (Laser-induced Breakdown Spectroscopy) spectrum noise reduction method, system and equipment based on adaptive threshold wavelet transform and a storage medium. Acquiring an original spectral signal of the laser-induced breakdown spectroscopy; performing five-layer multi-layer wavelet decomposition on the original spectral signal by adopting a db4 wavelet basis function to obtain a high-frequency coefficient and a low-frequency coefficient of each layer; calculating a noise intensity standard deviation based on the detail coefficient of the highest decomposition layer; dynamically determining the optimal value of the regulation factor through a double-layer optimization strategy combining a grid search method and a golden section iterative optimization method; constructing an adaptive threshold value based on the noise intensity standard deviation and the adjustment factor; carrying out threshold value processing on the high-frequency coefficient by adopting a self-adaptive threshold value; and performing wavelet reconstruction on the processed high-frequency coefficient and low-frequency coefficient, and outputting a denoised spectral signal. While the LIBS spectral signal-to-noise ratio is remarkably improved, the spectral feature form is completely reserved, and reliable technical support is provided for laser-induced breakdown spectroscopy detection in a complex industrial environment.

The invention provides a training method of a generative denoising device and an image controllable generation and restoration method. The training method comprises the following steps: taking a pre-trained diffusion model as a teacher diffusion model; initializing a generative denoising device, a score model and a discriminator; obtaining a noiseless signal, and obtaining a noisy signal and Gaussian noise in combination with a generative denoising device; obtaining effective noise intensity and effective noise signals according to the noisy signals and the Gaussian noise; estimating a data score by using a teacher diffusion model according to the effective noise intensity, the effective noise signal and the noisy signal; according to the noisy signal, estimating a model score by using a score model; calculating and optimizing the gradient of learnable parameters of the generative denoising device; and according to the noisy signal, the data score, the model score and the optimized generative de-noising device, calculating de-noising score matching of the score model and adversarial loss of the discriminator, and optimizing the score model and the discriminator. And realizing image controllable generation and image restoration based on the generative de-noising device.

The invention discloses a dynamic visual noise monitoring method and system, and relates to the technical field of noise monitoring, and the method comprises the steps: obtaining the position coordinates of noise monitoring points in a to-be-monitored region, and collecting the noise intensity sequence of each noise monitoring point in real time; based on the noise intensity sequence, obtaining position coordinates and noise intensity of the estimated noise source; dividing a to-be-monitored area into a plurality of sub-areas, and obtaining a target area of a to-be-interpolated position; calculating interpolation weights of each noise monitoring point and each estimated noise source in the target area based on the position coordinates of the noise monitoring points and the estimated noise sources; performing bimodal interpolation on the to-be-interpolated position to obtain the noise intensity of the to-be-interpolated position; mapping the noise intensity and color of each position to be interpolated to obtain a noise intensity thermodynamic diagram of the area to be monitored; according to the invention, accurate interpolation can be carried out on the noise intensity of an unknown position, the accuracy of noise monitoring is enhanced, and a dynamic visualization effect of noise monitoring is realized.

The invention provides a real-time estimation method and system for a small-target radar adaptive signal-to-noise ratio. According to the method, a motion model is established based on a historical trajectory of a maneuvering target, and a dynamic position prediction sequence is generated; acquiring a scanning beam pointing angle sequence and a timestamp echo signal intensity sequence; starting multiple rounds of iterative detection for each target position point in the prediction sequence, and shrinking a detection window bidirectionally until the boundary is smaller than the width of a beam half-power point; a signal intensity peak value and background noise intensity in a final iteration window are extracted, a beam irradiation efficiency factor is generated in combination with the spatial matching degree of the scanning beam directional angle sequence and the position prediction sequence, and a signal-to-noise ratio dynamic correction is cooperatively calculated; and weighting and fusing the correction, the signal peak value and the noise intensity, and outputting a final signal-to-noise ratio value of the maneuvering target in real time. According to the invention, the signal-to-noise ratio real-time estimation precision and stability of a small target radar when a high-speed maneuvering target passes through a beam edge are improved.

The invention provides an audio noise reduction method and system based on local attention and feature fusion in the technical field of crossing of audio signal processing and artificial intelligence, and the method comprises the steps: S1, building an audio noise reduction model based on an encoder, a noise reduction module and a decoder, and setting a loss function of the audio noise reduction model; s2, acquiring a large amount of historical audio data, preprocessing each piece of historical audio data, and labeling each piece of preprocessed historical audio data at least including clean audio, noise type, noise intensity, audio scene and audio content to construct a data set; s3, dividing the data set into a training set, a verification set and a test set to train, verify and test the audio noise reduction model; and S4, deploying the audio noise reduction model passing the test, and executing audio noise reduction operation through the deployed audio noise reduction model. The method has the advantages that the quality, generalization and efficiency of audio noise reduction are greatly improved.

The invention relates to the technical field of geological survey, in particular to a rapid stratum disease risk detection method based on a ground penetrating radar, and solves the technical problem of depth positioning misalignment caused by propagation parameter fluctuation due to heterogeneity of an underground medium in stratum disease detection of the ground penetrating radar in the prior art. The method comprises the following steps: acquiring a reflected wave signal at each acquisition moment through a ground penetrating radar; performing signal decomposition processing on the reflected wave signal, separating an intrinsic mode component representing dielectric noise, and performing feature extraction on the intrinsic mode component to determine a dielectric noise intensity index; constructing a dielectric noise intensity sequence according to the dielectric noise intensity index, and performing spatial-temporal characteristic analysis to determine the confidence coefficient of stratum diseases; and according to the dielectric noise intensity index and the stratum disease confidence coefficient, in combination with a pre-calibrated stratum average dielectric constant, constructing a depth correction weight, carrying out adaptive correction on the original stratum disease depth, and outputting the corrected stratum disease depth.

The invention provides a multi-redundancy gyroscope weighted fusion method and device based on real-time noise estimation, and relates to the technical field of inertial navigation and multi-sensor fusion. The method comprises the following steps: firstly, carrying out sliding window sampling and multi-scale wavelet decomposition on measurement signals of a plurality of redundant gyroscopes, and extracting wavelet coefficients under different frequency scales; then, applying a noise variance estimation method based on mutual difference observation on each scale, and estimating the noise intensity of each channel in real time; and finally, constructing a fusion weight according to a minimum variance criterion, and realizing weighted fusion of redundant signals and signal output of the virtual gyroscope. The system is simple in structure and efficient in calculation, can improve the measurement precision and robustness of an inertial navigation system in a dynamic environment, and is suitable for an embedded high-precision navigation system in a dynamic complex working condition.

The invention discloses a noise detection method and device for a dual-mode communication network of a power system. The noise detection method comprises the following steps: acquiring an analog signal transmitted by the dual-mode communication network of the power system in a current detection period and filtering the analog signal; sequentially carrying out signal amplification and analog-to-digital conversion on the filtered analog signal according to a preset sequence of a plurality of preset gain values to obtain a digital signal, carrying out validity verification on the digital signal, and terminating after obtaining a first preset gain value which is verified to be valid; and calculating a noise intensity value of the dual-mode communication network of the power system based on the preset gain value which is verified to be valid for the first time. Through orderly traversal of a preset gain sequence and a first effective immediate stop strategy, orderly matching of effective gains is realized, an ADC signal overflow risk is thoroughly avoided, and complete waveform capture in a strong noise environment is ensured; and meanwhile, traversal times are compressed, and detection time consumption is remarkably reduced compared with a traditional multi-stage gain scheme.

The invention relates to an AMT signal denoising method and device based on self-adaptive multi-level U-Net. According to the method, noise segment classification is performed on a noisy AMT signal, a corresponding position mask of the clean AMT signal is recorded, and noise positioning information is provided for training of a denoising model, so that the training effect of the denoising model is improved, and loss of non-noise segments can be avoided; the method also uses two U-Net networks to form a U-Net cascade structure, the first network performs preliminary denoising, the second network processes fragment data output by the first network, generates a noise intensity probability by using a residual error, uses the noise intensity probability as a driving signal, distributes a corresponding weight value, and outputs the driving signal to the U-Net cascade structure. According to the method and the system, the second network is dynamically controlled to selectively absorb and fuse fragment data output by the first network, an area with remarkable residual noise can be adaptively concerned, complementation and optimization of a feature level are realized, and finally, the denoising effect of the denoising model on the noisy AMT signal is improved.

The application relates to a hyperspectral image denoising diffusion model construction method and system, and belongs to the technical field of computer vision. The method generates a noisy low-resolution hyperspectral image for testing by adding noise to a clean image; the number of compressed channels is dynamically determined by using an elbow point method, spectrum compression is performed, and a coefficient matrix is obtained; each band complex noise image after spectrum compression is converted into an approximate Gaussian noise image by using a noise conversion technology; the number of inverse diffusion embedding steps of each band after noise conversion is dynamically determined by using a pre-trained diffusion model, and a more accurate denoising process is performed through the determined number of inverse diffusion steps. The application can dynamically determine the number of spectrum compression channels of the noisy hyperspectral image, thereby effectively improving the recovery accuracy of the hyperspectral image, can adapt to various noisy hyperspectral image scenes, can dynamically determine the number of inverse diffusion embedding steps according to the noise intensity, can sufficiently denoise high-noise channels and moderately constrain low-noise channels, and can dynamically balance the denoising intensity and spectrum fidelity.

The invention discloses a data noise reduction method and device for magnetotelluric signals and a medium, belongs to the technical field of magnetotelluric signals, and aims to solve the problems that signal-noise separation of existing magnetotelluric signals is not accurate enough, and effective magnetotelluric signals are difficult to obtain. And main effective components of the magnetotelluric signal cannot be accurately extracted. The method comprises the following steps: performing signal superposition processing on an effective magnetotelluric signal and a simulated synthetic noise waveform to generate a magnetotelluric signal sample pair; sequentially carrying out related noise intensity perception, weight dynamic adjustment and feature noise reduction processing under self-attention weighted output on feature variables in the magnetotelluric signal sample pair; performing two-stage noise reduction feature sequence generation strategy calculation based on a convolutional layer on an original signal time sequence in the magnetotelluric signal sample pair; and performing noise reduction control and feature extraction on the current original magnetotelluric signal to obtain an effective signal feature.

The invention discloses a method and a device for positioning a low-frequency oscillation source of a wind-thermal bundling system. The method comprises the following steps: acquiring multi-source signals of the wind-thermal bundling system; carrying out adaptive wavelet threshold anti-noise preprocessing; carrying out adaptive noise intensity set empirical mode decomposition; screening a wind-fire coupling key mode; calculating a wind-fire adaptive dissipated energy flow; and oscillation source determination based on phase coupling: constructing a multi-condition fusion determination logic in combination with a phase coupling relationship between a dissipated energy flow trend and a wind-fire key mode and a phase coupling relationship between the dissipated energy flow trend and an electric energy transmission signal so as to realize oscillation source type identification. According to the invention, accurate identification of oscillation sources (a wind power side, a thermal power side and a tie line) in a frequency band of 0.1-2Hz is realized, and both positioning accuracy and engineering real-time performance are considered.

The invention provides a self-adaptive wavelet noise reduction method and system based on mass spectrum signal processing, and the method comprises the following steps: S1, collecting a data set outputted by a mass spectrometer, and inputting the data set into a noise reduction process in the form of a text document; s2, reading related data in the text document, analyzing spectral peak characteristic parameters, and initializing related parameters; s3, performing multilayer wavelet decomposition on the document data, and outputting an approximate component and a detail component of each layer; s4, calculating the noise intensity of different layers according to the output approximate component and the detail component, and adaptively calculating a wavelet threshold value based on the signal length and the noise intensity; s5, threshold processing is applied to the detail components, and wavelet signals are reconstructed; and S6, calculating signal-to-noise ratios under different decomposition layer numbers, and selecting the optimal decomposition layer number to output the mass spectrum data after noise reduction. According to the method, the related spectrogram information output by the mass spectrometer is denoised, and the signal-to-noise ratio of the mass spectrum data is improved while the spectrum peak information is reserved to a great extent.

The application relates to a fixed-pattern noise correction method of an infrared detector and belongs to the technical field of infrared imaging. The application can eliminate the fixed-pattern noise of the infrared detector in a real-time image by taking the fixed-pattern noise phenomenon of the infrared detector as a template and calculating the fixed-pattern noise intensity in the real-time image through correlation.

A code error rate estimation device included in an optical transmission system using a direct detection receiver, the code error rate estimation device including: a transmission path model creation unit that creates a physical model of a transmission path for each candidate path for performing communication between user devices that perform communication; a propagation waveform calculation unit that generates an electric field signal waveform to be output from a transmitter assumed in the physical model of the transmission path and generates a reception signal waveform at the time of direct detection by using linear fiber propagation simulation; a nonlinear noise calculation unit that calculates nonlinear noise light intensity of light on the basis of the physical model of the transmission path; a noise intensity conversion unit that converts the calculated nonlinear noise light intensity of the light into noise in an electrical stage; a reception signal waveform calculation unit that calculates a Gaussian distribution of each symbol or each sample in a reception signal waveform at the time of the direct direction on the basis of the reception signal waveform at the time of the direct detection and the converted noise in the electrical stage; and a code error rate calculation unit that calculates a code error rate on the basis of the Gaussian distribution of each symbol or each sample.

The invention relates to the technical field of voice recognition, in particular to an audio device intelligent control method and system based on plant electric signals. The method comprises the following steps: acquiring a voltage signal, noise intensity and different environmental parameter values, and obtaining resting potential reference values and respective reference values; acquiring potential deviation and signal fluctuation amplitude based on the fluctuation amplitude of the voltage deviation in the time window and the overall condition; acquiring an environment gain factor and a noise suppression factor based on the difference between the environment parameter and the reference value and the difference between the noise and the reference value; pitch and tone quality are respectively adjusted through an environment gain factor, a noise suppression factor, potential deviation and signal fluctuation amplitude, and a special sound effect is determined in combination with voltage deviation; and music playing is controlled by combining musical instrument timbre recognition voice. According to the invention, the interactivity between the user and the plant is improved.

This invention relates to the field of wind power generation technology, and in particular to a method for early warning of wind turbine blade flutter based on low-frequency noise. First, the geometric model information and physical parameters of the wind turbine blade are acquired. Then, the modal frequencies and mode shapes of each order of the blade are calculated. According to the superposition principle of vibration modes, blade flutter will inevitably radiate low-frequency noise at the corresponding modal frequencies. Microphones installed on the wind turbine are used to monitor wind force and low-frequency noise generated during operation in real time. The noise intensity at the blade flutter frequency is obtained and monitored through signal processing, and an early warning intensity threshold is set. When the low-frequency noise signal at the flutter modal frequency exceeds the early warning threshold, a flutter warning is immediately issued. This allows for early warning before significant blade deformation occurs, greatly improving the safety margin.

This invention discloses a method for adaptive transmission power in ultraviolet (UV) optical communication. By simulating the frame error rate of the system's modulation and coding scheme, the signal strength required for successful decoding under different background noise intensities is obtained. The transmitter sends several data frames to the receiver and records the current link's transmission power. The receiver obtains the current link's signal strength and noise intensity data using channel estimation, quantizes it, and feeds it back to the transmitter. The transmitter judges the noise intensity; if the noise intensity is within a set noise intensity range, it determines that the background noise is in the i-th quantization interval, thus obtaining the signal strength required for successful decoding. Based on the current transmission power and the signal strength required for successful decoding, the feedback transmission power is obtained, and the corresponding UV LED power is selected to transmit the signal, achieving adjustable transmission power. This method can select different transmission powers at different transmission distances, achieving reliable data transmission with low power consumption.

The invention relates to the technical field of cable fault positioning, in particular to a cable fault accurate positioning method and system for cable health monitoring, and the method comprises the steps: obtaining a collection reflection section and an experiment reflection section; obtaining the matching degree of the experimental reflection section; other parameters except the noise intensity in the experiment parameter group are constant, and the noise intensity is quantitatively analyzed to obtain the comprehensive matching degree of the collection reflection section and the experiment reflection section; screening the parameter groups to obtain a plurality of matching parameter groups and a reflection initial search range; comprehensively analyzing the probability that the correlation degree of the reflected pulse signal and the matching parameter group reflects each moment as the starting moment, and selecting the starting moment of the reflected pulse signal; and obtaining the distance of the fault point by using the starting time of the reflected pulse signal. The invention aims to solve the interference of noise on the starting moment of the reflected pulse signal and improve the accuracy of the time domain reflection method.

The invention relates to a multi-source data fusion method, an atmospheric quality analysis method and a remote sensing vegetation monitoring method based on adaptive weighted spherical harmonic signals, and aims to realize fusion of multi-source data with spherical distribution characteristics, and the method comprises the following steps: obtaining to-be-fused multi-source space-time observation data; based on a preset time window length, calculating a residual time sequence with a current moment as a center, estimating a noise level of a data source dimension, and constructing a covariance matrix based on noise intensity and a spatial position; constructing a comprehensive normal equation based on the covariance matrix, and solving a preliminary spherical harmonic coefficient fusion solution; calculating a variance component of a data source dimension based on the current spherical harmonic coefficient fusion solution, constructing a fusion normal equation at the current moment, updating the spherical harmonic coefficient fusion solution, screening normal equation weights lower than a preset threshold value, and repeating the step; a final spherical harmonic coefficient fusion solution is obtained, and multi-source data fusion is achieved.

The invention provides a video quality analysis method, device and equipment and a medium, and belongs to the technical field of video quality evaluation. The method comprises the following steps: acquiring original video data, and generating degraded video data with noise intensity, definition score and compression damage degree three-dimensional real labels through composite degradation processing; training a multi-task deep joint prediction network by using the data set; and inputting a real world degraded video to be analyzed into the trained network model, and outputting a corresponding noise intensity predicted value, a definition score predicted value and a compression damage degree predicted value. Compared with the prior art, the method has the advantages that high-fidelity simulation and automatic accurate evaluation of video multi-dimensional quality degradation factors can be realized, and the accuracy of a noise intensity prediction value, a definition score prediction value and a compression damage degree prediction value is improved; the method can be used for video coding and decoding optimization, network transmission quality control, video enhancement processing and other scenes.

The application provides a speech recognition method and device, a storage medium and an electronic device, and the method comprises the following steps: monitoring the noise state of the environment where the target device is located, wherein the noise state is used to represent the noise intensity of the environment; configuring a smoothing algorithm according to the noise state; performing smoothing processing on the sound signal collected by the target device according to the noise state and the smoothing algorithm to obtain a target speech signal; and identifying the target speech signal by using a speech recognition model. The technical problem of low speech recognition accuracy in the related art is solved, and the user experience is improved.

This invention discloses an active suppression method for background noise in transformer winding frequency response detection. The method includes: setting up a test circuit with the transformer de-energized, grounding all equipment grounding terminals and cable shielding layers; cutting off the excitation source output, scanning the entire frequency band using a response acquisition unit, and obtaining the background noise spectrum distribution through spectrum analysis; calculating the optimal excitation voltage and optimal sampling number for each test frequency point based on the noise intensity distribution; controlling the programmable excitation source to output the optimal excitation voltage point by point via a host computer, and controlling the response acquisition unit to collect data with the optimal sampling number; calculating the transfer function using the actual injection voltage and response voltage at each frequency point, and generating a denoised frequency response curve. This invention achieves adaptive adjustment of test parameters, suppresses interference from on-site background noise on frequency response detection, and improves the accuracy of test results.

The present application discloses a method for frequency identification of a measurement while drilling signal, which relates to the technical field of signal processing. The method comprises the following steps: s ( t ) and noise n ( t ) input a high-order bistable stochastic resonance system; transform the damping coefficient, system parameters and system output response in the high-order bistable stochastic resonance system to obtain an equivalent equation; detect the noise intensity D Is it consistent with the signal to be tested? s ( t ) matches, if it matches, the signal is output directly and the signal to be tested is restored s ( t If the frequency does not match, the parameters of the high-order bistable stochastic resonance system are adjusted according to the parameter adjustment rules. This application solves the problem that the useful signal may be damaged or even unrecognizable during filtering.

This application discloses a data retrieval method for a liquid crystal display module, comprising: connecting a difference detector, a noise injector, and a subthreshold charge pump in series between a data buffer and a two-way current disconnect switch; acquiring the difference between the real-time voltage of the current frame and the target voltage of the next frame; dividing the voltage difference signal space into a full refresh region, a critical fine-tuning region, and a holding region using dual thresholds; wherein in the critical fine-tuning region, the noise injector calculates the noise intensity based on the difference, adjusts the signal-to-noise ratio, and applies the output signal to the control terminal of the subthreshold charge pump for subthreshold charge integration; and through multiple consecutive frame cycles, the target voltage smoothly replaces the real-time voltage until the error is less than the threshold; in the full refresh region, the target voltage is input to the data line and the thin-film transistor switch is turned on to make the current voltage jump to the target voltage; and in the holding region, the original voltage is maintained unchanged, thereby achieving a continuous and smooth voltage transition, eliminating image layering and false contours, and reducing MCU wake-up frequency and power consumption.

The invention discloses a building construction equipment operation management system and method based on the Internet of Things, and relates to the technical field of building construction equipment operation management, and the method comprises the steps: obtaining the data information of building construction equipment in a building construction project, taking each parameter of each piece of building construction equipment as one piece of data, and carrying out the operation management of the building construction equipment; constructing priority data, and forming a priority data list for each piece of data; building a building construction project analysis model, forming an intelligent distribution strategy, and outputting a first building construction scheme; and according to the first building construction scheme, arranging a sensor at a building construction project site, monitoring noise intensity data of building construction equipment in real time, and feeding back the noise intensity data to the administrator port when the noise intensity data of the building construction equipment exceeds standard noise intensity data set by the system. In the invention, through analysis and recombination of noise intensity data of each part of building construction equipment, based on different intelligent distribution strategies, noise interference is reduced, and maximum construction efficiency is realized.

The invention discloses an Internet of Things equipment abnormity positioning method and system based on edge computing, and the method comprises the steps: enabling an edge gateway to control an online terminal to enter an interference monitoring mode when the system detects that a large number of street lamp terminals are offline, and collecting background noise data; the terminals which report the noise data are marked as sensing nodes, and the terminals which do not report the noise data are marked as communication blind area nodes. And determining the enhancement direction of the interference signal and generating an interference gradient vector by comparing the noise intensity values of the adjacent sensing nodes. And the system backtracks along the gradient vectors to find a topological convergence area to which all the vectors point together. If the communication blind area node exists in the area, the blind area node located in the geometric center is judged as an abnormal source; and if not, judging the sensing node with the highest noise intensity in the area as an abnormal source. The method and device are used for improving the efficiency and accuracy of determining the noise source equipment in a scene of large-area communication blocking caused by strong noise interference, thereby reducing the operation and maintenance troubleshooting noise and time cost.

The invention discloses a pulsating pressure sensor measurement signal simulation device and method, and relates to the technical field of signal processing, and the method comprises the steps: carrying out the noise recognition and classification through employing a convolutional neural network based on the preprocessed pulsating pressure data, obtaining the noise type of a pulsating pressure signal, and calculating the noise intensity; based on the noise type and intensity of the pulsating pressure signal, parameters of a Kalman filter are adjusted in real time, and noise elimination is conducted on the pulsating pressure signal; and through the pulsating pressure signal prediction model, pressure value prediction is performed on the real-time data, noise is added in a prediction result, and a pulsating pressure signal is simulated. Through an innovative noise identification and classification method and a noise elimination strategy for adjusting Kalman filter parameters in real time, the simulation precision and reliability of the pulsating pressure signals are remarkably improved, and the method is suitable for multiple fields such as industrial monitoring, medical health and environment monitoring.

The invention provides a Beidou multi-frequency positioning method and device, electronic equipment and a storage medium. The method comprises the following steps: S1, collecting observation data and satellite ephemeris data of two target frequency points of a Beidou satellite; s2, based on the satellite ephemeris data, modeling the Beidou satellite clock correction by adopting a quadratic polynomial to obtain a clock correction modeling error, and taking the clock correction modeling error as a constraint term of an original design matrix to generate a design matrix with clock correction constraint; s3, constructing an observation vector based on the observation data, and substituting the design matrix with the clock error constraint and the observation vector into a TLS resolving model for resolving to obtain a positioning parameter initial value and a corresponding resolving residual error; s4, fitting a mapping relation between the residual error and the noise intensity based on the resolving residual error, generating a weight matrix, and performing weighted updating on the TLS resolving model by using the weight matrix to obtain an updated TLS resolving model; according to the embodiment of the invention, effective constraint on the design matrix error can be realized, and the clock error modeling precision is improved.

The invention provides a noise elimination method and device, electronic equipment and a vehicle, and the method comprises the steps: collecting time series data of environment noise in the vehicle, analyzing the time series data, and determining a main noise type and noise intensity spatial distribution; activating a target noise reduction channel of a target partition in the vehicle through noise intensity spatial distribution, and determining an original reference signal of a reference microphone in the target noise reduction channel through a main noise type; performing secondary path estimation and forward path calculation of a preset adaptive filter on the original reference signal to obtain a first noise reduction driving signal for the loudspeaker; acquiring a historical reference signal of the reference microphone and a training noise signal of the error microphone, and inputting the historical reference signal and the training noise signal into a preset depth active noise control network to output a second noise reduction driving signal; and adaptive weighting is performed on the first noise reduction driving signal and the second noise reduction driving signal to obtain a target noise reduction driving signal, and elimination of the vehicle environment noise is completed through the target noise reduction driving signal, so that the user experience is improved.