81 results about "Noise intensity" patented technology

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 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 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.

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 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.

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.

The application provides a weak non-stationary signal processing method and system, and relates to the technical field of signal processing, and comprises the following steps: obtaining a weak non-stationary signal; estimating the signal frequency range of the weak non-stationary signal, and estimating the noise intensity according to the estimated signal frequency range; constructing a stochastic resonance model based on a Gaussian mixed potential function, and adaptively optimizing the Gaussian mixed potential function according to the estimated noise intensity, so as to obtain an optimal stochastic resonance model, with the maximum spectral amplification factor of the stochastic resonance model as the target; constructing a parameterized demodulation operator to demodulate the weak non-stationary signal, inputting the demodulated signal into the optimal stochastic resonance model for enhancement, and finally obtaining the enhanced non-stationary signal through inverse demodulation; and the application is based on Gaussian mixed potential function stochastic resonance, and solves the bottleneck problem of weak non-stationary signal processing through a closed-loop process of noise intensity estimation, potential function optimization, frequency estimation and signal enhancement.

This invention belongs to the field of noise-induced tinnitus protection technology, specifically disclosing a noise-induced tinnitus protection device, including protective earmuffs, a headband, and an adaptive acoustic adjustment device. It achieves "precise targeting" of harmful noise through a Helmholtz resonant cavity array and innovatively introduces a volume adaptive adjustment mechanism, allowing the silencing frequency to dynamically match the individual and the environment. Furthermore, it combines a reed-non-Newtonian fluid coupling intelligent response unit to achieve real-time changes in acoustic impedance with noise intensity, realizing dynamic and adaptive secondary silencing. The entire device requires no electricity, ensuring wideband noise reduction while effectively attenuating key frequency bands that induce tinnitus, providing a comfortable, intelligent, and reliable active hearing protection solution from the root cause.

The application discloses an indoor-oriented three-dimensional Gaussian diffusion model point cloud repairing method, expresses a missing point cloud as a three-dimensional point set, introduces a point cloud centroid as a spatial reference center, constructs a progressive-axis distance component of each point relative to the centroid, and calculates a second-order statistic in each axis direction as a directional scale signal; according to the deviation of the directional scale signal from a reference scale, the noise intensity is re-calibrated in each axis direction to form a diagonal covariance form of a directional adaptive three-dimensional Gaussian forward diffusion model; a denoising network is used to predict noise components in each diffusion step and update according to the progressive-axis diffusion intensity, while an observation consistency constraint is introduced; finally, a diffusion standard loss function is used to train the error between the real noise injected in the forward diffusion model and the predicted noise, and an optimized diffusion model is obtained. The method can be applied to indoor three-dimensional reconstruction, digital twinning, robot perception and indoor point cloud dataset enhancement scenes.

The application provides a noise identification and elimination method and system, and relates to the technical field of point cloud data processing, and comprises the following steps: obtaining point cloud data of a target area and performing standardization processing on the point cloud data to obtain standardized point cloud data, and determining layering parameters according to the vertical direction distribution characteristics of the standardized point cloud data, and then dividing the standardized point cloud data into multiple vertical layers; determining noise intensity according to the number of photons of each vertical layer; performing significance test on each vertical layer to obtain noise photons in each vertical layer, and performing an elimination operation on the noise photons to obtain preliminary screening point cloud data; performing cluster noise identification according to the area of the vertical layer and the number of photons of the preliminary screening point cloud data in each vertical layer, obtaining the remaining noise photons of the preliminary screening point cloud data in each vertical layer, and performing an elimination operation on the remaining noise photons to obtain the denoised point cloud data of the target area. The application effectively solves the precision problem of noise identification and elimination of laser radar point cloud data in complex environments such as shallow sea.

The application discloses a method and device for determining a control equation of a high-noise particle flow system, which comprises the following steps: determining the noise intensity of each data sample in a data sample set of the particle flow system; screening out high-noise data samples based on the noise intensity and a preset high-noise intensity range; randomly sampling the high-noise data samples to obtain a plurality of sub-sample sets; for each sub-sample set, firstly performing denoising processing, and then learning and mining the sub-sample set after the denoising processing based on a normalized sparse regression method to determine a control equation; and determining the control equation corresponding to the particle flow system in the high-noise intensity range based on the collective average of the control equations corresponding to the sub-sample sets. The application realizes the target of stably and accurately identifying the control equation in the high-noise particle flow system, and overcomes the problem that it is difficult to ensure the accuracy of the control equation under the condition of serious noise interference in the prior art.

The invention discloses a charging pile noise reduction device, and relates to the technical field of charging pile noise reduction devices. The noise reduction mechanism comprises a sound insulation cover fixedly connected to the side wall of the charging pile body, a plurality of mounting rods are slidably connected to the inner wall of the sound insulation cover, two mounting plates are symmetrically and rotatably connected to the side walls of the mounting rods, mounting grooves are formed in the side walls of the mounting plates, and a plurality of first springs are fixedly connected to the inner walls of the mounting grooves; the other ends of the multiple first springs are jointly and fixedly connected with a noise reduction plate. Noise can be transmitted in the sound insulation cover in a sound wave mode, when sound waves are transmitted to the surfaces of the noise reduction plates, the sound waves can be reflected on the surfaces of the noise reduction plates, the multiple noise reduction plates are arranged in an up-down staggered mode, the sound waves can be repeatedly reflected between every two adjacent noise reduction plates, and therefore the length of a sound wave propagation path is increased; the sound waves are gradually consumed in the propagation process, so that the noise intensity is reduced by increasing the length of a sound wave propagation path.

This invention relates to the field of video image processing technology and discloses a method for denoising high-speed motion continuous frames. The invention receives a raw video sequence in Bayer format and establishes a multi-scale image pyramid with a variance-stabilized domain. At each level of the pyramid, based on local brightness statistics, local texture contrast information, and estimated motion amplitude, a motion texture discriminability index representing texture change relative to noise intensity is calculated. This index is used to dynamically adjust the data fidelity term and smoothing constraint term in the variational optical flow estimation process to obtain a motion-compensated image. Subsequently, this index is mapped to an alignment deviation estimate and a similarity measurement range, multi-frame spatiotemporal fusion weights are calculated, and a primary fusion image is generated. Finally, an adaptive filtering threshold is calculated based on this index, high-frequency texture details are extracted from the differential data and superimposed back onto the primary fusion image, and a denoised video sequence is output after inverse transformation processing.

This invention discloses a noise intensity analysis method, apparatus, electronic device, and storage medium. The method includes: acquiring seismic data; processing the seismic data to obtain data to be detected; inputting the data to be detected into a pre-trained signal recognition network model for identification; determining the location of valid signals based on the output of the signal recognition network model; determining valid signals and noise signals based on the location of valid signals; calculating the first root mean square (RMS) of the valid signals; calculating the second RMS of the noise signals; and determining the noise intensity based on the first and second RMS. This method solves the problem of inaccurately identifying noise in the data. By using a pre-trained signal recognition network model to identify the data to be detected, determining the noise intensity based on the first RMS of the valid signals and the second RMS of the noise signals, and quantifying the noise intensity using the RMS, noise in the data can be detected in a timely manner, improving the overall efficiency and reliability of seismic data acquisition.

This invention relates to the field of gas turbine technology, and more particularly to a silencing device for a gas turbine intake system. The device includes an internal pipe, a middle pipe, and an external pipe arranged sequentially from the inside out. The internal pipe forms an intake channel through which the intake channel is axially connected. A silencing plate is installed within the internal pipe to divide the intake channel into multiple ventilation channels. A sound-absorbing layer is provided between the internal and middle pipes, and this layer has multiple silencing cavities with smaller inner and outer sides and a larger middle cavity. A sound-insulating layer is provided between the middle and external pipes, and this layer has multiple sound-insulating cavities. A vibration-damping structure is provided on the outer side of the external pipe to support it. Through the silencing plate, sound-absorbing layer, sound-insulating layer, and vibration-damping structure, continuous four-layer silencing is achieved. This multi-stage silencing effectively widens the noise reduction band, eliminating noise over a wider area. Simultaneously, the multi-stage silencing can progressively weaken the noise intensity, eliminating high-intensity noise and effectively increasing the silencing capacity.

The invention discloses a two-stage sample detoxication backdoor attack defense method based on a diffusion model. The method comprises the following steps: step 1, global lightweight noise injection and diffusion detoxication; 2, carrying out structure similarity difference positioning and noise intensity mapping; 3, local high-intensity noise injection and selective denoising are carried out; and 4, accelerating reasoning and resource optimization. According to the method, a black box defense framework without model parameters and training data is constructed, any third-party service scene is adapted, and the application limitation of a traditional white box scheme is solved; a dynamic noise intensity distribution mechanism is designed, damage thresholds of backdoor triggers with different visibility are accurately matched based on trigger visibility-robustness association, and the problem of excessive or insufficient purification of an existing method is avoided; rapid detection and local purification of a high-visibility trigger are realized through a structural similarity difference positioning technology, original sample semantics are reserved to the maximum extent, and the classification precision of detoxicated samples is guaranteed.

This invention relates to the field of noise monitoring and intelligent analysis technology, and discloses a method and system for intelligent noise source tracing based on human-machine perception alignment. The method includes acquiring acoustic signals and public perception data from the acoustic environment of a social living area; performing time warping on both using a cross-similarity matrix to obtain a perception feature set; analyzing the perception feature set to obtain a deviation distribution and quantifying it into a global deviation correction coefficient; using this coefficient to calibrate the acoustic signal to obtain a signal flow; when the coefficient exceeds a threshold, reconstructing the spatial gain distribution of the microphone array based on the signal flow to locate the noise; combining the location coordinates with image data to identify the type of abnormal event; if it belongs to multi-source interference, predicting the noise intensity trend and generating a real-time parameter optimization scheme; adjusting the detection threshold accordingly and combining the signal flow to pinpoint the final noise source. This method can solve the problems of misalignment between early warning strategies and public perception, as well as the low reliability of noise source tracing.

The invention relates to the technical field of noise power generation, in particular to a honeycomb type noise power generation system and method, and the system comprises a honeycomb power generation device which comprises a plurality of honeycomb power generation units, is used for carrying out the noise reduction of the noise of a target noise region, and generates an alternating current based on the noise sound wave vibration after the noise reduction; and the intelligent control system generates a control instruction of the honeycomb power generation device according to the initial noise intensity by using a mapping model of noise distribution and generated power, so that the honeycomb power generation device generates power based on the control instruction, and performs fault diagnosis according to the power generation data of each honeycomb power generation unit. Therefore, the problems that in the prior art, a scheme for generating power through noise vibration is simple in structure, low in efficiency, lack of intelligent management and optimization and incapable of adapting to industrial scenes such as thermal power plants with complex noise sources, variable frequency spectrums and wide space are solved, and the energy utilization efficiency and the intelligent management level of the thermal power plants are improved.