2653 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 provides a vehicle automatic sound production control method based on infrared recognition, which comprises the following steps: acquiring an infrared image in a preset range in front of a vehicle, recognizing a heat source target in the image, and judging whether the heat source target is an animal or a human according to the shape, size and temperature distribution characteristics of the heat source target; the prediction unit is used for predicting the moving track of the animal and the human in a period of time in the future according to typical moving track modes of the animal and the human so as to obtain predicted moving position and speed information, and transmitting the predicted information to the acoustic control unit; the acoustic control unit dynamically adjusts the sound pressure level and the frequency of the loudspeaker according to the received predicted moving information and the target type so as to generate an acoustic warning mode matched with the predicted moving position, the predicted moving speed and the target type; when the acoustic signal is sent out, the actual moving track of an animal or a person is continuously tracked, and the track prediction method is optimized by comparing the deviation between the actual track and the predicted track, so that the optimal avoidance effect is achieved.

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 a transformer partial discharge ultrasonic propagation simulation model construction method and system, and the method comprises the steps: defining a geometric model of a transformer, which is a three-dimensional model of an oil-filled transformer oil tank with a winding and an iron core; setting a sound field module and defining a sound source, and placing the sound source as a partial discharge source in the geometric model of the transformer; a hard boundary condition is applied to the surface of the geometric model tank of the transformer, reflection of the geometric model tank to sound pressure waves is obtained, and under the hard boundary condition, the normal derivative of pressure is zero at the boundary; performing grid division on the geometric model body of the whole transformer by adopting tetrahedral grid units; and after solving, further processing to obtain model parameters, wherein the model is used for displaying the distribution of the ultrasonic sound pressure in the transformer.

The invention relates to a sound pressure adjusting method and system based on a human body induction sound box. The method comprises the following steps: acquiring user motion data through a human body induction sensor, and performing triangulation positioning analysis on the user motion data through an MCU (Microprogrammed Control Unit) chip to obtain position coordinate data and motion track data; inputting a sound field analysis model in the DSP chip for characteristic analysis to obtain surrounding sound field characteristic parameters; the method comprises the following steps: performing multichannel separation and time domain equalization processing on an original audio signal by a DSP chip to obtain a multichannel preprocessed signal; inputting into a DSP chip for sound pressure balance calculation to obtain an initial sound channel gain control parameter; performing power amplification on the multichannel pre-processed signal, and driving a loudspeaker array to output a surround sound signal; and the DSP chip dynamically optimizes the initial sound channel gain control parameter to obtain a target sound channel gain control parameter. According to the invention, real-time optimization and dynamic updating of sound pressure parameters are realized, and the response speed of the human body induction sound box to environmental changes is improved.

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 discloses an in-structure sound field bounded uncertainty analysis method based on a physical information neural network. The method comprises the following steps: establishing a physical information loss function and establishing a physical information driven neural network; obtaining an input uncertain parameter value range of the internal sound field, obtaining a plurality of parameter levels, and training a sound pressure prediction model under the parameter levels; acquiring sound pressure of a sample point and establishing a data set; establishing a data driving residual error based on the data set, establishing an overall loss function by using the data driving residual error and a physical information loss function, and training a parameter-containing physical information driving neural network to obtain a parameter-containing internal sound field sound pressure prediction model; and establishing an interval model, and obtaining an internal sound field sound pressure uncertainty distribution range by using a global search algorithm and the parameter-containing internal sound field sound pressure prediction model. The method can solve the problem of prediction of the sound field in the structure containing uncertain parameters, and can effectively improve the calculation efficiency on the premise of ensuring that the calculation precision meets the engineering requirements.

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 a transformer substation inspection method and system based on a quadruped robot, and relates to the technical field of transformer substation inspection, and the method comprises the steps: collecting topographic data and temperature data based on the quadruped robot, planning an inspection path, and carrying out the temperature monitoring; collecting sound data based on the quadruped robot, calculating a sound source direction angle, extracting a time-frequency feature and determining a feature fluctuation amplitude; constructing a sound field model based on the characteristic fluctuation amplitude, generating a simulated sound pressure distribution diagram, determining a comparison error corresponding to each grid node in the simulated sound pressure distribution diagram based on sound pressure data acquired by the quadruped robot, and determining the grid nodes with the comparison errors greater than or equal to a comparison threshold as abnormal nodes; and giving an alarm according to the abnormal node, recording early warning information, and uploading the early warning information to a cloud end to give an alarm notification. According to the method, acoustic analysis is added, abnormal sound waves or abnormal fluctuation of local noise in the substation equipment is found in time, the early failure of the equipment is effectively detected, and the inspection efficiency is improved.

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 discloses an optical fiber silicon-based microstructure underwater acoustic sensor which is a non-intrinsic F-P air microcavity probe with optical signal interference formed by an optical fiber end face and a silicon-based thin film, the characteristic that the silicon-based thin film is sensitive to sound pressure signals is used for picking up sound pressure changes in a liquid sound field environment, the sound pressure changes are converted into the variable quantity of an optical signal interference phase, and the optical fiber silicon-based microstructure underwater acoustic sensor is obtained. Reducing a sound pressure signal in the liquid sound field through a detection demodulation algorithm; the microcavity probe is packaged by using an organic material thin shell structure, so that the hydrostatic pressure resistance of the underwater acoustic sensor is improved to adapt to application in a deep water environment; the optical fiber silicon-based microstructure underwater acoustic sensor comprises an acoustic sensing silicon microstructure, a microstructure sensing probe and a sensor pressure-resistant structure, according to the acoustic characteristic design of the acoustic sensing silicon microstructure, silicon oxide / silicon nitride and other materials are used as sensing materials, and membrane external stress recovery is achieved through internal stress residues. The method is easy to implement in design process, flexible in application and high in environmental adaptability, and can be applied to noise monitoring and target detection in various complex scenes.

The utility model belongs to the technical field of loudspeakers, and discloses a vehicle-mounted loudspeaker and a vehicle-mounted audio system. It includes a rear shell, a fixed structure, a magnetic structure, a vibration structure and a front shell. Among them, the fixed structure includes a fixed basin frame and a support sheet, the fixed basin frame is detachably connected to the rear shell, and the support sheet is fixed on the fixed basin frame; the magnetic structure is embedded in the fixed basin frame, and a magnetic gap is arranged on the magnetic structure; the vibration structure includes a voice coil and a diaphragm, the voice coil is inserted on the support sheet, and the position is adjustable and inserted in the magnetic gap, and the diaphragm is designed to be integrally formed, which can meet the requirements of the sound pressure level and frequency width of the loudspeaker. The front shell is detachably connected to the fixed basin frame and is stacked on the diaphragm. A plurality of stepped sound guide holes are arranged at intervals along the circumference of the front shell, which can effectively compress and enhance the sound pressure level of the middle and high frequencies, so that the loudspeaker can meet the laws and regulations on high sound pressure levels of horn horns, and further meet the various needs of pedestrian warning sounds, horn horns, and vehicle external prompt sounds.

The invention discloses a microphone detection method and system and a storage medium. The method comprises the following steps: acquiring environment temperature and humidity data, microphone characteristic data, preset sound velocity data, an input signal of a microphone and a detection sound source signal; according to the environment temperature and humidity data, air density calculation is conducted, and the actual air density is obtained; performing data calculation operation on the actual air density and the preset sound velocity data to obtain a first mapping parameter; performing characteristic calculation on the first mapping parameter and the microphone characteristic data to obtain a second mapping parameter; performing sound source quality detection correction according to the detection sound source signal to obtain a corrected sound source distance and a corrected sound pressure level; performing correction calculation according to the input signal of the microphone, the second mapping parameter, the corrected sound source distance and the corrected sound pressure level to obtain a corrected input signal; and performing data analysis on the corrected input signal and the detection sound source signal to obtain a detection result. According to the method, the detection precision can be improved by quantifying the environmental factors.

The invention relates to the technical field of acoustic imaging, and relates to a phase holographic imaging device based on an acoustic metamaterial, which comprises a sound wave generator, an LAM metasurface and a control unit, the sound generator is used for generating plane sound waves; the LAM metasurface is used for receiving sound waves and modulating phase and amplitude; the control unit is used for controlling the structure and the geometrical shape of the cells in the LAM metasurface; enabling sound waves to reach a decoupling point in the internal structure space of the LAM metasurface under the irradiation of front sound; decoupling modulation of the phase and amplitude of surface reflection sound waves by the cells is realized; and the processor is used for calculating the sound pressure projected to the holographic surface phase position according to the input image, and adjusting the size parameters of the cells in the LAM metasurface through the controller. After the incident sound wave interacts with the LAM metasurface, the incident sound wave is demodulated and re-modulated and is reflected at an angle perpendicular to the LAM subsurface. The LAM metasurface does not apply any angle constraint to incident sound waves; the angle of the reflected wave is always perpendicular to the LAM surface. And the imaging quality of the required phase acoustic hologram after amplitude compensation is met.

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 relates to the technical field of sound effect optimization, and discloses a sound effect optimization method and device based on a coaxial loudspeaker echo wall sound box, and the method comprises the steps: carrying out the acoustic response measurement and acoustic feature analysis of the coaxial loudspeaker echo wall sound box, and obtaining a frequency domain acoustic response characteristic curve; performing acoustic partition calculation on the internal space of the coaxial loudspeaker echo wall sound box to obtain a spatial arrangement parameter of an acoustic processing unit; carrying out acoustic center positioning calculation on the coaxial loudspeaker to obtain a space coordinate parameter and an axial inclination angle parameter of the coaxial loudspeaker; carrying out sound reflection angle calculation and material combination optimization on the inner wall structure of the echo wall sound box to obtain an inner wall geometric curvature parameter and a box body material lamination parameter; sound pressure level frequency response curve analysis and sound phase positioning precision measurement are carried out on the inner wall geometric curvature parameters and the box body material lamination parameters, sound box acoustic performance verification data are obtained, the uniformity of the sound field in the sound box is guaranteed, and then the acoustic performance of the sound box is improved.

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 diaphragm type EFPI optical fiber sonic sensor with a sensibilization structure, and relates to the field of optical fiber sensing and underwater acoustic signal communication. The sensor is composed of an optical fiber unit, a threaded sleeve and a sensitization shell which are connected in sequence. The single-mode optical fiber is embedded into the optical fiber ceramic ferrule as a first reflecting surface and inserted into the threaded sleeve in a combined manner, and the chromium-gold composite film is attached to the outer side of the threaded sleeve as a second reflecting surface through epoxy resin glue; the sensitization shell is combined with the threaded sleeve in a screwed mode, the chromium-gold composite film is protected, sound signals are gathered, and the sensitization effect is achieved. The invention has the following advantages and positive effects: (1) the FZP sensitization structure improves the minimum detectable sound pressure of the hydrophone and enhances the capability of identifying weak small signals; (2) the designed water pressure balance hole contributes to the water pressure in the F-P cavity and the outside, and can be suitable for deepwater operation; and (3) the nano-scale transduction film of the film type EFPI sensor can be protected to a certain extent, and the film type EFPI sensor can be suitable for changing environments.

The invention belongs to the technical field of underwater target radiation noise measurement, and particularly relates to a sparse combination array measurement system and method for underwater target radiation noise measurement, a program and a storage medium. A sparse combined array formed by a sound pressure spiral double-cone volume array with a sparse structure and a sound pressure vector nested vertical linear array is designed, a combined array frequency-division testing scheme of a high-frequency volume array and a low-frequency vertical linear array is adopted, a combined measurement method of a sound pressure hydrophone and a vector hydrophone is utilized, a measurement frequency band is extended, and the measurement precision is improved. And effective measurement of the full-band radiation noise of the underwater target is realized. Through a sparse array arrangement structure and an improved constant-beam-width beam forming method, a two-dimensional steady constant-beam-width beam is obtained while the number of array elements is reduced and the array complexity is reduced, and an appropriate main lobe width and a lower side lobe level can be adjusted according to a measured target and a measurement distance. According to the method, the array element redundancy of the measurement array is reduced, and the method can be flexibly applied to various application scenes of underwater radiation noise measurement.

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.