2158 results about "Sound pressure" patented technology

The invention discloses an electromechanical equipment noise reduction method based on machine learning, and relates to the field of electromechanical equipment noise reduction, and the method comprises the steps: collecting sound pressure and vibration signals during the operation of electromechanical equipment, and collecting the operation state data of the electromechanical equipment; sparse reconstruction is carried out on the noise features; carrying out sound source localization on the reconstructed noise features by using a beam forming algorithm to obtain a noise distribution diagram; constructing a noise reduction model based on deep learning to obtain noise signals after noise reduction; extracting the frequency of the noise signal in a preset frequency range through a peak search algorithm; analyzing the acquired running state data by using a time-varying linear predictive coding algorithm TVLPC to obtain a noise frequency offset caused by the abnormal running state of the electromechanical equipment; generating an optimal solution by using a particle swarm algorithm; and according to the optimal solution of noise control, noise dominant frequency components are extracted for separation, and a plurality of phase-reversal noise reduction control signals are obtained. Aiming at low noise reduction precision of electromechanical equipment in the prior art, the noise reduction control precision is improved.

The invention discloses an industrial area atmospheric environment monitoring management system and method, and relates to the technical field of industrial Internet of Things environment monitoring, and the method comprises the steps: triggering polarization imaging to scan a target region based on the time-space information of an abnormal event signal, obtaining the polarization characteristic data of a pollution plume, and generating a spatial distribution characteristic parameter; inputting the sound pressure abnormal indexes and the spatial distribution characteristic parameters into an industrial equipment leakage causal model, calculating a pollution source confidence coefficient, and when the pollution source confidence coefficient reaches a process safety dynamic threshold value, obtaining an industrial equipment traceability result and a leakage level; according to the traceability result of the industrial equipment and the leakage level, a hierarchical control instruction is obtained, meanwhile, the actually measured attenuation rate of the industrial pollutants is calculated, and the dynamic response effect is verified; according to the method, cross-modal fusion modeling is carried out on acoustic abnormal information and pollutant spatial distribution characteristics, so that the technical limitation that pollution source positioning is carried out by depending on a static empirical formula traditionally is broken through.

The invention discloses a tunnel fire air curtain smoke prevention control method and system, and relates to the technical field of intelligent fire fighting, and the method comprises the steps: building a digital twinborn prediction model, and generating a fire source position coordinate and a smoke diffusion trend prediction map through multi-physics field coupling simulation; analyzing the stability requirement of the air curtain jet flow through a Brapare-Prandtt boundary layer analysis algorithm, and generating a jet flow enhancement requirement parameter table in combination with temperature gradient correction; optimizing an acoustic vortex main frequency band by adopting a Slodhaar number, and generating an acoustic vortex control parameter set in combination with a matched sound pressure level and a configured resonator phase difference; and converting the acoustic vortex control parameter set into an execution instruction, monitoring the jet velocity, the vortex street stability and the smoke retardation effect in real time, and dynamically correcting the parameters of the digital twinborn prediction model. According to the method, physical quantity outline matching of the blocking intensity and the sound pressure level is achieved through the sound intensity-momentum equivalent method, and the optimal phase configuration is determined in combination with POD modal analysis.

A method of making an acoustic sensor includes forming or providing a mold having one or more grooves extending in a direction of the length of the mold to a distal end of the mold. The method also includes forming or depositing a structure having one or more piezoelectric layers over the top surface of the mold to define a beam with a proximal portion and a distal portion, the distal portion having a corrugated section including one or more grooves that correspond to the one or more grooves of the mold. The method also includes forming or applying an electrode to the proximal portion of the structure and releasing the structure from the mold to form one or more cantilever beams. The corrugated section inhibits bending of the corrugated section along the length of the distal portion of the structure when the acoustic sensor is subjected to sound pressure.

The invention provides an underwater sonar target detection system and method. The underwater sonar target detection system comprises an optical fiber hydrophone array module which comprises a plurality of optical fiber hydrophone units, each unit comprises an optical fiber Bragg grating sensor and a sound pressure signal demodulation device, and the optical fiber hydrophone array module is used for collecting underwater sound wave signals in real time and converting the underwater sound wave signals into first electric signals; the sonar transmitting-receiving array module comprises a broadband sonar transmitter, a multi-beam receiver and a signal preprocessing circuit, and is used for actively transmitting a frequency modulation continuous wave signal, receiving a target reflection echo and generating a second electric signal; and the multi-mode signal fusion processing module comprises a high-speed data acquisition card, a self-adaptive noise suppression unit and a time-frequency analysis unit. The underwater sonar target detection system and method provided by the invention have the advantages of relatively high reliability, relatively high detection performance, relatively accurate cross-modal signal fusion and capability of performing intelligent target identification and tracking.

The invention relates to the technical field of structural design, and discloses a topology and size joint optimization design method and system for a hierarchical composite structure, and the method comprises the following steps: dividing a structural domain into a plurality of hierarchical sub-domains, defining the size constraint range of each sub-domain, and distributing a multi-phase material for each sub-domain; grid discretization processing is carried out on the structural domain, a topological design variable is given to each discrete unit, acoustic unit modeling is carried out on the sound field domain, sound pressure boundary conditions are defined, and coupling boundary conditions are applied to the interface of the sound field domain and the structural domain; solving an acoustic-structure coupling equation based on a finite element method, calculating an objective function and constraint conditions, analyzing the sensitivity of the objective function to topological variables by adopting an adjoint method, and iteratively updating the topological design variables through an optimization algorithm until a convergence criterion is met; and on the basis of a topological optimization result, constructing a neural network agent model of an input layer, a hidden layer and an output layer, and carrying out global optimization on sub-domain size parameters by adopting a genetic algorithm so as to minimize a target function.

The invention discloses an egg quality detection system and method based on multi-sensor fusion, and relates to the technical field of multi-dimensional data process.The method comprises the steps that texture features are extracted from visible light images, and feature wavelengths are extracted from spectrums; pressure distribution and shell deformation in the pressing process are synchronously collected; recording a pressure-time curve and a deformation-time curve, extracting characteristic parameters, and constructing a three-dimensional dynamic model; performing wavelet transformation on the response curve of each sensor, extracting time-frequency domain characteristics, and generating a smell fingerprint spectrum; scanning the surface of the egg to excite resonance response, recording a sound pressure signal, and converting the sound pressure signal into frequency domain data through fast Fourier transform; and presetting a business rule, and generating a final decision by combining the model output with a rule matching result. The pressure feature extraction module, the smell feature extraction module and the crack degree quantification module are arranged to realize data depth association, and the quality inspection credibility is improved through an interpretable decision model.

The invention discloses a dynamic noise analysis method and device for an urban elevated road and a medium, and belongs to the technical field of noise monitoring and analysis. The method comprises the following steps: acquiring a three-dimensional geographic information model and real-time traffic flow data of a target elevated road; processing the three-dimensional geographic information model based on a sound wave propagation attenuation model to generate a noise propagation path grid; fusing the real-time traffic flow data with a noise source intensity calculation model to generate a dynamic noise source intensity sequence; calculating a real-time equivalent sound pressure level of each grid unit through a noise superposition algorithm in combination with the dynamic noise source intensity sequence and the noise propagation path grid; performing rolling prediction on the real-time equivalent sound pressure level based on a time window sliding mechanism to generate a noise space-time distribution thermodynamic diagram; and according to the noise space-time distribution thermodynamic diagram, identifying a noise exceeding area, and outputting a dynamic noise evaluation report. According to the method, the technical effect of dynamically predicting and analyzing the noise of the elevated road is achieved.

The invention discloses a three-dimensional noise mapping method and system based on point cloud, and belongs to the technical field of urban environmental noise prediction.The method comprises the steps that point cloud data driven three-dimensional modeling and parameter extraction are carried out, an urban three-dimensional model with a geometric topological structure is constructed through laser scanning point cloud data, and related parameters are extracted; based on sound field calculation of ISO 9613 standards, a multi-path propagation model including an atmospheric effect, a ground effect and obstacle diffraction is established, a green infrastructure noise reduction effect is especially considered, and propagation attenuation of noise in a three-dimensional space is simulated; and performing visual expression of three-dimensional noise, and constructing fusion expression of a sound pressure level contour surface and a three-dimensional city model. In view of difficulty in simulation of three-dimensional reconstruction and sound waves in a spatial propagation process in the prior art, the method improves the precision of quantitative prediction, and fuses a green infrastructure noise reduction effect to realize rapid and efficient regional three-dimensional reconstruction and noise mapping.

The invention relates to the technical field of sound wave detection, in particular to an intelligent detection system for a motor, which comprises a data acquisition module, an abnormality judgment module, a type judgment module, a risk determination module, an adjustment module and an alarm module. According to the invention, through combination of sound wave analysis and various real-time monitoring parameters, accurate detection of the operation state of the motor is realized, the system can acquire the sound pressure level, the vibration acceleration, the winding current, the bearing temperature, the rotating speed and the torque of the motor in real time, and rapidly determine abnormity based on a preset threshold value, and after abnormity is detected, the system is started. The system further combines current, temperature and torque information to analyze the abrasion type, and evaluates the serious risk level according to the rotating speed and the vibration acceleration, so that refined risk management is provided, and the problems of low fault judgment precision and slow response speed caused by data transmission delay and environmental interference are effectively solved.

The invention relates to an intelligent sleep adjusting method and system based on closed-loop acousto-optic brain wave entrainment, electroencephalogram (EEG) signals are collected and preprocessed in real time through a wireless dry electrode, and high-precision sleep staging is achieved through a deep learning model. The system adopts a fuzzy PID (Proportion Integration Differentiation) controller to dynamically adjust acousto-optic synergistic stimulation: an acoustic module generates binaural beat signals, an optical module outputs blue light (470nm) and amber light (590nm) pulses, and the brain wave entrainment efficiency is enhanced through phase synchronization (the phase difference is less than or equal to 10 degrees) and golden section frequency coupling (fL is equal to 1.618 fA). Parameters are updated every 30 seconds through closed-loop feedback, the sound pressure level (30-50 dB) and the light intensity (10-100 lux) are adjusted according to the Weber-Fechner law, and the response delay is lt; the time is 200 ms. A three-level safety mechanism monitors gamma wave abnormity, epilepsy sample discharge and impedance overrun in real time, and triggers graded protection (alarming, cutting off light stimulation and shutdown). Clinical verifications show that the entrainment success rates of the delta wave and the theta wave respectively reach 71% and 68%, the sleep improvement effect is good, and a safe and efficient intervention scheme is provided for sleep disorders.

The invention relates to a noise damping control system of an offshore wind turbine generator, and belongs to the technical field of offshore wind power generation. The coupling state evaluation module is used for determining a coupling state evaluation index through a preset deep neural network model based on the platform motion information, the vibration spectrum data and the sound pressure signal; the cooperative control strategy generation module is used for determining target rigidity and target damping according to the coupling state evaluation index and the vibration spectrum data; according to the invention, the coupling state evaluation module processes platform motion information, vibration spectrum data and sound pressure signals acquired by the data acquisition module through a preset deep neural network model; the preset model is endowed with strong nonlinear fitting capability through offline supervised learning of massive historical working condition data; and an accurate and quantitative risk basis which cannot be reached by the prior art is provided for subsequent control decisions.

The invention discloses an intelligent control method and system for a motor driver, and the method comprises the steps: building an acoustoelectric incidence matrix through collecting harmonic current and sound pressure fluctuation data in the operation process of a motor, and forming a fault feature library; identifying an available fault mode to determine an energy collection point, and performing phase deviation processing on the collection point to generate an auxiliary driving signal; performing spectrum analysis on the sound pressure fluctuation data to obtain an acoustic characteristic spectrum, extracting an efficiency indication tone through characteristic decomposition, mapping the efficiency indication tone into a torque optimization parameter, and establishing a noise reduction control table; performing spectrum analysis on the enhanced control domain to identify a high-energy frequency band, executing a frequency shift operation to generate a safety frequency band signal, and forming a multi-frequency band management strategy in combination with a noise reduction control table; a management strategy is decomposed into parallel control flows for sequential staggered arrangement, and an energy-saving control matrix is generated; and generating a PWM control signal based on the matrix, carrying out edge reconstruction, and obtaining a softened edge waveform through an anti-resonance injection technology to complete intelligent control of the motor driver.

The invention belongs to the technical field of underwater sound, and discloses an underwater target sound scattering modeling method and system based on a physical information neural network. The method comprises the following steps: establishing a loss function of a physical information neural network by establishing an underwater target acoustic-structure coupling mathematical model; a decoupling parallel dual-network architecture is established, a fluid domain neural network is used for learning and outputting a scattering sound pressure field of a fluid domain, an elastomer domain neural network is used for learning and outputting a displacement field of an elastomer domain, and the two networks realize physical coupling by sharing a sampling point and a corresponding boundary condition loss function at an acoustic-structure coupling boundary; and generating training data, performing iterative training on the decoupling parallel physical information neural network, and calculating a scattering sound pressure field outside the target and a displacement field inside the elastomer by using a trained model. According to the method, the physical information neural network is innovatively applied to the underwater target sound scattering field, and a new thought and a new way are provided for accurately forecasting the underwater target sound scattering field.

The embodiment of the invention discloses a noise reduction regulation and control method and device for a self-adaptive sound barrier and a medium, and relates to the technical field of noise reduction, and the method comprises the steps: collecting multi-source vehicle noise data which comprises current sound pressure signal time sequence data, preposed sound pressure signal time sequence data and preposed vehicle point cloud data, according to the front sound pressure signal time sequence data and the front vehicle point cloud data, determining a noise source position prediction result corresponding to a sound barrier front node so as to perform sound field prediction on the sound barrier node, and determining a corresponding sound field energy distribution thermodynamic diagram; according to a preset sensitive frequency band threshold value, a target noise high-frequency band needing to be offset is determined in the sound field energy distribution thermodynamic diagram, so that a reverse sound wave signal corresponding to a phase array offset module is generated, and the target noise high-frequency band is offset; and according to the current sound pressure signal time sequence data and the reinforcement learning strategy, determining dynamic sound absorption regulation and control parameters of the dynamic sound absorption module so as to absorb a low-frequency band.

The invention provides a method for testing flow-induced noise of a pipeline. The method comprises the following steps: S1, determining measurement positions at an upstream flow channel and a downstream flow channel of a measured element, carrying out normalization processing on measured signals, and calculating the pressure power spectrum density of the inner wall surface of a pipe; s2, calculating a wall surface pressure fluctuation time domain cross correlation coefficient, and analyzing correlation characteristics between wall surface pressure pulsation; s3, spectrum intrinsic orthogonal decomposition is carried out on the pressure pulsation field, and space-time decoupling is achieved through time domain-frequency domain conversion; s4, extracting a dominant flow-induced noise mode, constructing an empirical frequency-wave number spectrum, and decomposing fluctuating energy distribution into contributions of different frequency and wave number combinations; s5, analyzing the decoupled flow-induced noise characteristic result, and identifying the amplitude-frequency characteristic of the flow-induced noise; according to the invention, real-time measurement of pressure pulsation in the pipeline can be realized, decoupling analysis of flow field pulsation and sound pressure pulsation can be carried out on the measured signal, and characteristics and a generation mechanism of pipeline flow-induced noise can be identified.

The invention discloses a passive synthetic aperture method and device based on R-L deconvolution beam domain processing, a medium and equipment, and the method comprises the steps: constructing a formation distortion model of a towed array under disturbance, and processing a discretized formation distortion model through a Kalman filter, so as to obtain the absolute coordinates of each array element; determining a receiving signal time domain solution of each array element based on the sound pressure expression and the absolute coordinates; based on an ETAM algorithm, obtaining beam output of overlapped sub-arrays in adjacent time samples, and performing R-L iteration on the beam output to obtain a phase compensation factor; and performing phase compensation based on the wave beam output of the phase compensation factor to obtain each phase correction wave beam and the wave beam output of the synthetic aperture. The passive synthetic aperture algorithm and the R-L deconvolution wave beam forming technology are effectively combined, so that the accuracy of the phase compensation factor after formation distortion is generated is improved to the maximum extent, and the accuracy of the phase correction wave beam is improved. Therefore, the algorithm provided by the invention still provides high-precision target positioning under the condition that the ETAM algorithm fails.

The invention discloses a voiceprint monitoring method, system, medium and equipment for rotation stall of an aero-engine compressor, and the method comprises the steps: determining the layout of a microphone array through the model parameters of the compressor of the aero-engine, and carrying out the synchronous collection of sound pressure time domain signals of multiple channels of the compressor of the aero-engine through the microphone array; arranging the multi-channel sound pressure time-domain signals in sequence to construct a time-domain signal matrix; performing spectral analysis on the sound pressure time-domain signals by adopting a fast Fourier transform algorithm to obtain a spectrogram of the sound pressure time-domain signals of each channel, monitoring whether an abnormal single-tone frequency peak value lower than one time of rotation frequency appears in the spectrogram or not, obtaining an acoustic mode spectrogram of the abnormal single-tone frequency peak value by adopting a single-frequency acoustic mode decomposition method, and obtaining an acoustic mode spectrum of the abnormal single-tone frequency peak value; and whether the rotating stall characteristic of the gas compressor occurs in the acoustic mode spectrogram is monitored to judge whether the rotating stall characteristic of the gas compressor occurs or not.

The invention discloses a bionic joint humanoid robot navigation positioning method, device and equipment, and the method comprises the steps: monitoring a landing shock wave signal and reflection echo data of a robot, carrying out the time domain expansion of the shock wave signal to form a pulse characteristic spectrum, extracting multipath propagation components, and carrying out the time difference correlation to generate an environment response value. Screening a direct wave component by using an environmental response value, positioning a sound wave propagation trajectory, and detecting impedance mutation to construct an acoustic matrix. A strong reflection zone is identified through node analysis, a sound pressure gradient field is extracted to determine an obstacle boundary, and a landmark feature bit is determined in combination with an environmental response value. And adjusting a dynamic stride frequency interval based on a landmark feature bit, forming an adaptive detection sequence, and performing sparse processing to construct path configuration. The mechanical vibration component of the shock wave signal is used for piezoelectric conversion to generate recycled electric quantity, the recycled electric quantity is matched with path configuration power to determine a sensing period, intermittent sensing is executed to obtain detection data to construct a detection convergence field, and finally an accurate positioning window is determined and converted into position coordinates.

The invention discloses a wind turbine blade operation noise real-time acquisition method, which comprises the following steps of: firstly, dividing an airspace according to the rotation characteristic of a wind turbine blade and the characteristics of a beam former so as to ensure that the acquired blade operation noise has enough sound pressure level; secondly, combining angle information of a main shaft of the wind turbine and boundary information of the divided airspace blocks, and carrying out time domain segmentation on the collected sound signals; and finally, inputting an original sound signal obtained by segmenting each block into a corresponding broadband beam former so as to obtain a blade operation noise signal enhanced according to the blocks. The method not only effectively improves the extraction quality of the target signal, but also provides reliable data support for subsequent fault diagnosis, positioning and type identification.

The invention relates to the technical field of energy conservation and environmental protection, in particular to an industrial boiler combustion oscillation modal suppression system and a method thereof, and constructs a system which is highly intelligent, high in adaptability and remarkable in suppression effect by fusing innovative technologies such as multi-dimensional perception, intelligent identification, active suppression and adaptive optimization. The data acquisition module accurately captures hearth temperature, flame video and sound pressure signals; the signal processing module deeply analyzes and extracts oscillation modes of a temperature field and a flame area, and analyzes a sound pressure signal frequency spectrum through short-time Fourier transform; the mode identification module accurately identifies the combustion oscillation mode and determines the characteristic frequency and amplitude of the combustion oscillation mode; the suppression control module generates an infrasonic wave intervention signal to form an anti-phase sound field so as to effectively suppress oscillation; the fuel adjusting module adjusts the opening time sequence of the gas nozzle according to the instruction, and fuel supply is optimized. According to the system, the operation stability and heat efficiency of the boiler are remarkably improved, NOx emission is greatly reduced, and powerful technical support is provided for clean and efficient operation of the industrial boiler.

A calibration method comprises adjusting an operating frequency of an air-pulse generating device, such that a sound pressure level (SPL) of the air-pulse generating device is within a specific range. The sound producing module comprises the air-pulse generating device configured to produce sound via generating a plurality of air pulses at a pulse rate corresponding to the operating frequency.

The invention relates to a photoacoustic gas sensing system based on a T-type differential Helmholtz structure and a measuring method, and the system comprises a laser emission module which is used for emitting laser; the gas mixing module is used for preparing C2H2 gas with different concentrations; the MTRDH photoacoustic module is used for storing C2H2 gas into the cavity on the basis of a Helmholtz structure in an MTRDH photoacoustic cell, after the C2H2 gas absorbs the laser, an inverted photoacoustic signal is generated, and sound pressure is concentrated at the tail end of the resonance tube in combination with a T-shaped photoacoustic cell structure; and the gas detection module is used for extracting a 2f signal related to the gas concentration according to the sound pressure and carrying out concentration detection by utilizing the 2f signal. According to the invention, the gas detection of concentrated sound pressure distribution, improved SNR and reduced NNEA can be realized.

An electroacoustic transducer includes a port that guides a component of at least a partial frequency band of a sound radiated from a first diaphragm in a direction opposite to a direction in which a front surface 38A of a second diaphragm faces, to an external space on a front surface side of the second diaphragm. As the port is provided, the electroacoustic transducer is configured such that a sound pressure of a synthesized sound in the vicinity of a crossover frequency of radiated sounds from the first diaphragm and the second diaphragm in a case in which a drive unit generates vibration is equal to or higher than a sound pressure of the radiated sound from only one of the first diaphragm and the second diaphragm at the crossover frequency.

The invention belongs to the technical field of converter transformers, and particularly discloses a converter transformer vibration noise control method and system, a terminal and a medium, and the method comprises the steps: constructing a converter transformer structure geometric model, setting material attributes, and carrying out the grid division; based on the geometric model, establishing an electromagnetic field simulation model, applying excitation conditions, obtaining electromagnetic field distribution, establishing an iron core magnetostriction mathematical model, calculating vibration acceleration caused by magnetostriction and transient electromagnetic force of an iron core and a winding, and solving and extracting a displacement field and stress-strain response; establishing an acoustic coupling model on the basis of a structure vibration result, and calculating sound pressure level distribution under different frequencies by setting a structure-air coupling boundary and a matching layer; according to an acoustic simulation result, an iron core positioning structure and an oil tank reinforcing rib arrangement mode are optimized, vibration noise prediction and control of the converter transformer in a multi-harmonic excitation environment are realized, and equipment structure stability, operation reliability and environment adaptability are improved.

The invention provides a spherical complex vibration type vector hydrophone based on an MEMS accelerometer, and relates to the technical field of underwater acoustic signal detection, and the spherical complex vibration type vector hydrophone comprises an MEMS accelerometer, a built-in core, a piezoelectric ceramic spherical shell, a sound transmission sealing shell, and a suspension ring. The MEMS accelerometer is orthogonally arranged in the built-in core; the built-in core is nested in the lower half ball of the piezoelectric ceramic spherical shell; the upper hemisphere of the perforated piezoelectric ceramic spherical shell is adhered to the upper edge of the built-in core; the sound transmission sealing shell coats the piezoelectric ceramic spherical shell through an encapsulation process; the suspension ring is bonded with the sound transmission sealing shell, and the hydrophone is suspended on the fixing support for use. The composite hydrophone can detect sound pressure signals and vibration velocity signals at the same position underwater, solves the problem of omnidirectional detection of the composite hydrophone on the sound pressure signals, and has the advantages of small size, simple structure, high reliability, wide application range and the like.

The invention provides a physical model driven deep neural network sound field reconstruction method under a small sample condition. The method comprises the steps of obtaining a training sample; establishing a deep learning neural network, wherein the input and output of the neural network are spatial position coordinates and sound pressure values corresponding to the coordinate positions respectively; training a neural network model by using the training sample; and performing sound field reconstruction on the target area by using the trained neural network model. According to the invention, the physical law of sound wave propagation is introduced into the training process of the neural network, so that the neural network not only can learn the characteristics of data in the training process, but also can follow the physical characteristics of sound wave propagation, thereby realizing sound field reconstruction with higher precision.

The invention discloses a method for realizing ocean sound field prediction based on a pre-training optimized physical information neural network, which comprises the following steps of: pre-training: constructing a hypothetical ocean environment, generating simulation data in a sound pressure envelope form by using an acoustic numerical calculation tool, and pre-training the physical information neural network to learn a physical rule of sound field propagation; fine tuning: adjusting an energy scale factor according to the proportion of the sound pressure amplitude of the actual measurement point to the sound pressure amplitude of the pre-trained environment, and performing fine tuning by using the envelope amplitude data of the actual measurement sound field for the pre-trained physical information neural network to obtain an ocean sound field prediction model through a pre-training-fine tuning dual-stage strategy; and ocean sound field prediction: according to the ocean sound field prediction model, obtaining a target position envelope from the to-be-predicted range, and then converting the target position envelope into a sound pressure result to realize ocean sound field prediction. The method can improve the high-frequency sound field modeling precision and training efficiency, and can be widely applied to the fields of ocean sound field modeling, sound source localization and environment inversion.

The invention discloses a pipeline leakage monitor based on optical fiber interference and a monitoring method thereof, and belongs to the technical field of pipe network leakage monitoring. According to the invention, through the cooperation of the differential deformation of the outer cylinder and the inner cylinder and the air cavity, the two paths of sensing optical fibers are driven to generate reverse and equal-length length strain, and the improvement of the sensitivity of pipeline micro leakage sound pressure signal-optical phase signal conversion and the suppression of environmental common-mode noise are realized; a sound pressure signal generated in the leakage process of a pipeline is converted into phase change of light through a balanced optical fiber interferometer, a leakage signal is demodulated through a phase detection technology, and high-precision pickup of the tiny leakage sound pressure signal of the pipeline is achieved; the sound velocity is calibrated in real time through the temperature sensor and the sound velocity compensation module, and microsecond-level wireless signal time service is combined, so that the technical effects of large positioning error and difficulty in multi-device synchronous sampling caused by temperature drift of the sound velocity along with a transmission medium are solved.

The invention relates to the technical field of concrete bubble monitoring, in particular to an aluminum film system concrete bubble dynamic monitoring and regulating method which comprises the following steps: applying a high-frequency alternating current with a first preset parameter to an aluminum film system to induce eddy current in an aluminum film and enable the aluminum film to generate thermally induced acoustic vibration; meanwhile, an eddy current thermally induced acoustic response signal reflected by an internal interface of the concrete is obtained; based on the response signal, determining current bubble state information in the concrete, including amplitude attenuation characteristics based on the sound pressure signal, frequency spectrum characteristics based on the sound pressure signal and phase delay information; and based on the bubble state information, a regulation and control instruction of a second preset parameter is generated, the regulation and control instruction is executed to adjust the parameter of the high-frequency alternating current applied to the aluminum film system, and directional traction regulation and control are conducted on the bubbles in the concrete. According to the invention, by means of an eddy current thermally induced sound technology, dynamic distribution, including the number, the particle size, the motion trail and the like, of bubbles is captured in real time, and instantaneous changes in the pouring process can be accurately reflected.