173 results about "Acoustic spectrum" patented technology

The invention relates to the technical field of automatic welding control, and particularly discloses a multi-station automatic welding cooperative control method for electric iron accessories, which realizes full-closed-loop accurate control of a welding process through multi-sensor data fusion and an intelligent optimization algorithm. According to the method, an infrared thermal imager, an acoustic emission sensor and an ultrasonic thickness measuring device are adopted to collect molten pool temperature field, sound wave frequency spectrum and fusion depth data in real time, after feature extraction, the data are input into a multi-target optimization model, and a Pareto optimal solution set is generated with the fusion depth, the width of a heat affected zone and phase change latent heat as optimization targets; dynamically adjusting a segmented welding-back path and process parameters on the basis of an optimization result, and triggering model iterative correction through ultrasonic online monitoring; the central control unit adopts space voxelization monitoring and a three-level collision avoidance strategy to coordinate operation of the multiple mechanical arms, and collision emergency protection is achieved in combination with flexible pressure sensing.

The invention relates to a multi-agent cooperation strategy generation method, device and equipment and a medium, and the method comprises the steps: separating acoustic spectrum features and text semantic features of conference voice through environment perception processing, solving a cross-modal information conflict problem, and generating an accurate semantic understanding result; identifying the essence of the problem based on task analysis, associating the responsibility field, and constructing a classifiable problem point set; calling an agent capability library to dynamically match problem requirements, and generating a candidate agent list; quantifying a problem influence range and a decision time limit through weighted emergency scores, and generating a priority-sorted agent sequence; screening and confirming a core problem point and a primary agent; and finally, generating an executable cooperation scheme through multi-agent collaborative optimization. According to the method, the problems of incomplete feature extraction, task allocation delay and resource conflict in the prior art are solved, and the operability and decision-making efficiency of a cooperation strategy are remarkably improved.

The invention relates to the technical field of arc fault diagnosis, and provides an energy storage system arc fault diagnosis method and system based on voiceprint detection, and the method comprises the following steps: collecting a voiceprint signal in real time, and carrying out the anti-interference preprocessing, and obtaining a time-frequency domain spectrogram; identifying the time-frequency domain spectrogram based on a multi-branch convolutional neural network and weighting the time-frequency domain spectrogram to obtain a time sequence feature sequence; inputting the time sequence feature sequence into a time sequence convolutional network model, and outputting global context features; performing dynamic weighted pooling on the global context features to obtain key frame feature vectors representing the arc fault; and inputting the key frame feature vector into an arc fault diagnosis classification model to obtain an arc fault type, an arc fault severity grade and sound source positioning information, and generating a corresponding alarm signal and a control instruction based on the arc fault severity grade. According to the invention, the detection sensitivity and the anti-interference capability are improved, and the safety protection level and the fault early warning capability of the energy storage system are improved.

The invention relates to the technical field of gas sensing detection, and discloses a gas detection method and system based on a quartz tuning fork enhanced photoacoustic spectrum. The method comprises the following steps: constructing a standardized continuous tuning fork signal data stream; dynamically segmenting the data stream based on the gas concentration interval, and extracting time domain and frequency domain features in parallel in each segment to form an initial feature vector; dynamically adjusting an anomaly judgment threshold according to the jump amplitude of the adjacent segment feature vectors, and scanning and screening out an effective analysis interval of a stable concentration state according to the anomaly judgment threshold; performing cross-domain coupling compensation on the feature vectors in the effective interval to generate enhanced feature vectors; and finally, performing reverse traceability reasoning through a gas leakage propagation model based on the enhanced feature vector, and outputting a leakage source position identifier. The device can adapt to a dynamically changing concentration field, and the reliability and traceability positioning capability of gas detection in a complex environment are improved.

The invention discloses a dynamic encryption transmission method and system for sound-light alarm signals, and relates to the field of data encryption, and the method comprises the steps: obtaining a dynamic environment entropy value; generating a pseudo-random chaotic sequence; obtaining a helium-oxygen mixed gas density parameter in the dynamic environment entropy value; segmenting a preset standard alarm waveform into a plurality of signal waveforms in a time domain, and generating a distorted signal waveform according to the helium-oxygen mixed gas density parameter; performing encryption modulation on the distorted signal waveform to generate an encrypted distorted signal waveform; determining a sending sequence and a sending interval of the encrypted distortion signal waveform by combining a pseudo-random chaos sequence based on an impedance fluctuation value and background acoustic spectrum characteristics in the dynamic environment entropy; generating a dynamic modulation signal based on the transmission sequence and the transmission interval; the dynamic modulation signal is sent to a water surface console through an umbilical cable; and receiving the guide pulse, performing phase conjugation processing on the guide pulse, generating an echo verification signal, and transmitting the echo verification signal back to the water surface console.

The invention discloses an automatic charging processing method and device for a charging robot, and relates to the technical field of robot energy management, and the method comprises the following steps: constructing an acoustic array, collecting high-frequency background sound wave signals in a working area of the charging robot in real time, and extracting sound spectrum energy distribution; and identifying an interference frequency band coinciding with the working frequency of the ultrasonic sensor, and calculating the intensity level of the interference frequency band according to the sound spectrum energy intensity and the duration time in the interference frequency band. According to the method, a closed-loop system of interference perception-risk assessment-control response is constructed, and dynamic adjustment of perception confidence and output of blindness risk indexes are realized through multi-modal identification consistency analysis and historical deviation tracking. Once the threshold value is exceeded, the system executes frequency hopping and strategy optimization, frequency band shielding is effectively avoided, the sensing stability, path accuracy and operation safety of the charging robot in the sound interference environment are improved, and good application prospects are achieved.

The invention belongs to the technical field of geological analysis, and discloses a ground subsidence analysis method and device, and the method comprises the steps: matching a sensor layout strategy corresponding to a foundation subsidence risk and meteorological data of a monitoring region; acquiring land subsidence data of the monitoring area according to a sensor; performing time-frequency analysis on the land subsidence data to obtain a geoacoustic spectrogram corresponding to the land subsidence data; inputting the geoacoustic spectrogram into a preset neural network, and outputting a settlement semantic vector corresponding to the geoacoustic spectrogram; acquiring and analyzing historical land subsidence data, and constructing a subsidence rule corresponding to the historical land subsidence data; and inputting the settlement semantic vector and the settlement rule into a preset space-time analysis model, and predicting to obtain a future settlement trend of the monitored area. By using the method disclosed by the invention, the problem that all risks in a certain area cannot be checked when geological data is monitored based on local monitoring points can be solved, and the problem that the geological risk analysis efficiency is relatively low can be solved.

The invention relates to the field of photovoltaic equipment abnormity early warning, and provides a photovoltaic equipment temperature abnormity prediction method and device based on a digital twinborn model, and the method comprises the steps: obtaining an environment acoustic signal, meteorological data, and the actually measured temperature data of photovoltaic equipment; performing acoustic spectrum feature extraction according to the environmental acoustic signal to obtain a virtual environmental parameter set; sending the meteorological data and the virtual environment parameter set to a preset digital twinborn model for processing to obtain an initial temperature predicted value set; determining a temperature prediction value distribution diagram according to the initial temperature prediction value set and actually measured temperature data of the photovoltaic equipment, and comparing a temperature prediction result with an abnormal temperature threshold value to obtain a temperature abnormal risk point; and processing according to the temperature prediction value distribution diagram, the temperature anomaly risk points and the meteorological data to obtain temperature anomaly early warning information of the photovoltaic equipment, and improving the accuracy and foresight of temperature anomaly prediction of the photovoltaic equipment.

For training a perception algorithm to detect an emergency vehicle, respective audio datasets are received from two microphones and respective spectrograms are generated. At least one interaural difference map is generated based on the spectrograms, audio source localization data is generated, which specifies a number of audio sources in respective grid cells of a spatial grid, by applying a CRNN to first input data containing the spectrograms and the least one interaural difference map. An image is received from a camera and output data comprising a bounding box for the emergency vehicle is predicted by applying at least one further ANN to second input data containing the image and the spectrograms. Network parameters are adapted depending on the output data and the audio source localization data.

The invention discloses a current sensor pin bending processing control system and processing equipment, and relates to the technical field of current sensor pin bending processing, and the current sensor pin bending processing control system comprises the following steps: fusing and collecting three types of disturbance parameters with different physical dimensions and response mechanisms, namely bending acceleration ZWa, pin surface contact impedance disturbance ZBd and friction sound spectrum response ZMc; the sensing coverage range of the system on the multi-source abnormal phenomenon in the machining process is remarkably expanded, the recognition blind area of a traditional system depending on a single force or displacement signal is made up, and the sensitivity to non-dominant disturbance characteristics is improved. Non-linear interaction modeling is carried out on the three disturbance factors through the perturbation coupling strength index psi, and the system not only can recognize single-variable fluctuation, but also can capture the co-evolution trend among variables. According to the structure, the system has the capability of recognizing disturbance structural imbalance and coupling activation trend in advance, and has higher fault perspectiveness and active response capability.

The invention relates to the technical field of livestock and poultry product safety detection, in particular to a temperature-controllable crushing device for livestock and poultry product detection, which is characterized in that sound wave signals generated in a homogenizing process are acquired through a sound sensor arranged outside a detection cup, and spectrum drift and energy attenuation characteristics of the sound wave signals are analyzed by adopting a sound spectrum temperature inversion algorithm; inverting the temperature and distribution of the sample in real time; and meanwhile, a cooling air curtain is formed through nitrogen injection, the gas flow rate and the injection angle are intelligently adjusted through a control unit according to a temperature inversion result, directional focusing cooling of a local overheating area is achieved, and a closed-loop temperature control system driven by acoustic signals is formed. According to the device and the method, cross contamination and sensor adsorption effect caused by contact type temperature measurement are thoroughly avoided, the problems that the sensor is easy to pollute and difficult to maintain in a high-humidity and fat-rich environment are solved, heat-sensitive veterinary drug decomposition caused by friction temperature rise in the homogenizing process is effectively inhibited, and the accuracy, reliability and repeatability of veterinary drug residue detection are remarkably improved.

The invention relates to the field of mechanical automation, in particular to a drilling machine intelligent operation system and a deviation correction control method.According to the system, through multi-dimensional signal fusion and dynamic risk assessment, drill jamming recognition and disposal response time is remarkably shortened, and efficiency loss caused by traditional shutdown protection is avoided; the imaging and obstacle positioning technology is adopted to replace manual drill lifting inspection, and the no-load operation time and mechanical loss of equipment are reduced. The multi-physical field signal fusion technology breaks through the sensing limitation of a single sensor, and is beneficial to accurately identifying underground obstacle space distribution and geological interface mutation features; acoustic spectrum and thermal field distribution analysis can enhance the ability to analyze drill bit-stratum interaction dynamics, and multi-dimensional input is provided for intelligent decision making. And dynamic optimization of a control strategy is realized in cooperation with a virtual-real interaction mechanism to adapt to demand changes of different stratum conditions and construction stages.

The invention relates to the technical field of data processing, in particular to an energy efficiency optimization method and system for a wind-liquid homologous data center, and the method comprises the steps: obtaining an acoustic spectrum signal during the operation of a cooling system through a sensor, and obtaining an original acoustic spectrum data set; performing frequency domain conversion on the original acoustic spectrum data set to obtain a frequency band distribution feature set; acoustic features are extracted according to the frequency band distribution feature set, and the system energy consumption state is determined in combination with a pre-established mapping model; judging the mode switching requirement of the cooling system; predicting an acoustic characteristic change trend according to a mode switching demand, and determining a mode switching time point; adjusting operation parameters of the cooling system according to the mode switching time point, and executing mode switching to obtain an updated system operation state; and updating the mapping model according to the updated system operation state to obtain an optimized energy consumption state prediction model. According to the invention, the problem of low precision of energy efficiency optimization of the data center is solved, and the precision of energy efficiency optimization is improved.

The invention relates to the technical field of unmanned aerial vehicle detection and tracking, in particular to a low-altitude unmanned aerial vehicle positioning and tracking method and system. The method comprises the following steps: acquiring sound spectrum time sequence data and radar plot data of an unmanned aerial vehicle; extracting a first sound spectrum characteristic parameter and a second sound spectrum characteristic parameter; extracting a first radar characteristic parameter and a second radar characteristic parameter; determining initial position estimation of the unmanned aerial vehicle through a first judgment condition; determining optimized position estimation of the unmanned aerial vehicle through a second judgment condition; and tracking the unmanned aerial vehicle by using an improved particle filtering algorithm to obtain a final position and a tracking trajectory of the unmanned aerial vehicle. According to the invention, through heterogeneous sensor data fusion, environment adaptive feature extraction, multistage intelligent judgment optimization and improved particle filter tracking, the precision, robustness and real-time performance of unmanned aerial vehicle detection in a complex environment are effectively improved, and the regional monitoring and countering capability is significantly enhanced.

The invention discloses a liquid metal bearing fault monitoring method and device, and belongs to the technical field of fault diagnosis. The method comprises the following steps: acquiring an acoustic signal during operation of a liquid metal bearing, processing and analyzing the acoustic signal, obtaining a plurality of singular spectrum components through a singular spectrum decomposition SSD algorithm and singular value decomposition, calculating a kurtosis value of each singular spectrum component, screening signal components according to the kurtosis values, reconstructing the signal, performing envelope demodulation on the reconstructed signal, and obtaining an envelope demodulation result of the liquid metal bearing. Feature extraction of the bearing fault is realized; and iteratively optimizing support vector machine (SVM) algorithm parameters by adopting a grey wolf optimization GWO algorithm, constructing a GWO-SVM algorithm, training the GWO-SVM algorithm through a training sample to generate a classification recognition model, and inputting a test sample to output a fault classification result. According to the liquid metal bearing fault monitoring method and device based on acoustic spectrum noise reduction analysis provided by the invention, the accuracy and efficiency of fault monitoring are improved, and the cost and risk of manual inspection are reduced.

The invention relates to the technical field of fire-fighting management and control, and particularly discloses a water level detection and fire-fighting identification method and system based on automatic control, and the method comprises the steps: obtaining the temperature and acoustic multi-modal data of a fire-fighting pipe network, building a pipeline temperature gradient model, and predicting the temperature distribution of each section through combining the environment and the medium temperature; and extracting ice crystal nucleation and fluid anomaly characteristics at low temperature. Based on the temperature gradient, the acoustic spectrum, the water level fluctuation rate and historical data, a multi-parameter fusion congelation risk assessment model is constructed, and risk indexes are calculated and graded; constructing a congelation risk and firelight early warning linkage control strategy, and adaptively adjusting a firelight detector threshold value and a sampling frequency according to a risk level; when the risk reaches an early warning value, partitioned heating is implemented, a temperature recovery curve and water level stability are collected in real time as feedback, model iterative optimization and closed-loop control are achieved, early warning accuracy is improved, energy consumption is reduced, and reliable operation of a fire fighting system is guaranteed.

The invention discloses a defibrillation decision-making method, device and equipment and a computer readable storage medium, and the defibrillation decision-making method comprises the steps: obtaining an electrocardiosignal, and carrying out the filtering and denoising of the electrocardiosignal, and obtaining an electrocardiosignal sequence; performing frequency domain analysis on the obtained electrocardiosignal sequence, and calculating frequency domain characteristic indexes including a sound spectrum flux, an optimized amplitude spectrum area and a sum of average amplitude difference functions; and according to the calculated frequency domain characteristic indexes, a comprehensive decision-making mechanism is adopted to carry out comprehensive decision-making to obtain a decision-making result of a defibrillation state, so that the problems that the defibrillation opportunity is difficult to grasp and the defibrillation effectiveness is limited are solved.

This invention provides a quantum-derived Newton-Raphson optimal fractional-order spectrogram generation method, comprising the following steps: Step 1: Acquire the original audio signal and construct a fractional-order spectrogram based on fractional Fourier transform (FRFT); Step 2: Perform nonlinear scaling compression on the fractional-order spectrogram using a Mel filter bank to generate a fractional-order Mel spectrogram; Step 3: Construct an adaptive optimization framework with information entropy minimization as the objective function to measure the information fidelity between the spectrogram and the original signal; Step 4: Use the quantum-derived Newton-Raphson optimization algorithm (QNRBO) to globally optimize the fractional-order order, frame length, and frame shift hyperparameters to generate the optimal fractional-order spectrogram; Step 5: Input the optimal fractional-order spectrogram into a downstream speech recognition model. This technical solution aims to systematically solve core problems such as insufficient traditional time-frequency representation capabilities, rigid hyperparameter configuration, limited optimization algorithm performance, and feature-task disconnect.

The invention provides an electric power circuit fault point positioning method and device based on voiceprint recognition, and relates to the technical field of electric power circuit detection, and the method comprises the steps: laying a voiceprint sensor for collecting a sound signal; preprocessing the first sound signal at the first position and then decomposing the first sound signal to obtain a first sound spectrum; traversing the first sound spectrum in a power line fault sound spectrum database to obtain a first most matched fault type corresponding to the first most matched sound spectrum; and when a first matching coefficient of the two positions accords with a preset matching threshold value, a first fault early warning instruction is sent, and fault early warning is carried out on the first position. The technical problem that the fault point positioning precision and the fault type identification accuracy are low due to the fact that the prior art depends on electrical signal perception and is easily influenced by electromagnetic interference is solved, and the technical effects that the fault positioning precision and the fault type identification capability are improved, and the fault early warning accuracy is remarkably improved are achieved.

Disclosed are integrated DFOS / DAS systems, methods, and structures that employ a large-scale pretrained recognition model we refer to as an “acoustic-language model”, which is pretrained with natural-language supervision (“contrastive language-audio pretraining”. The acoustic-language model comprises two primary components: an acoustic encoder and a text encoder. These encoders are pretrained using a cross-modal approach on a vast dataset of acoustic features (such as images created from log Mel spectrograms) and their corresponding textual captions. When acoustic features and / or languages are input into their respective encoders within the model, they generate corresponding embedding vectors. Both embedding vectors are then linked in a joint multimodal space using linear projections. The acoustic classification tasks using this model are executed by assessing the similarity between the acoustic and language embedding vectors, essentially evaluating the maximum similarity between the acoustic features and the events described in a specific language.

Embodiments of the present disclosure provide a singing evaluation method and device, a storage medium and a computing device. The method comprises: performing feature extraction on obtained singing audio to obtain a sound spectrum feature of the singing audio; inputting the sound spectrum feature into a multi-scale network model for calculation; wherein the multi-scale network model comprises a model based on a convolutional neural network, and a convolutional layer in the multi-scale network model comprises convolutional kernels corresponding to multiple time scales; and determining a final evaluation result based on a probability distribution of different evaluation results output by the multi-scale network model.

The present application relates to the technical field of artificial intelligence, and more particularly to an AI interaction system for ornithological scientific research, which comprises a collection unit, a trigger determination unit, a path determination unit, a dominant determination unit, a prompt generation unit and an adjustment unit. The present application constructs a multi-level behavior determination process through joint analysis of bird song spectrum, image motion characteristics, flight trajectory and environmental factors, and through statistical analysis of the time distribution and spatial distribution of these behavior indicators within a preset period, and correlation analysis with environmental factors such as temperature, humidity, wind speed and illumination, the influence of environmental changes on bird activity frequency and spatial aggregation degree can be identified, so that the acoustic trigger threshold can be dynamically adjusted, so that the system can continuously maintain the sensitive recognition ability of bird activity with the change of the habitat environment, and then realize high-reliable behavior recognition and interaction prompt for ornithological scientific research.

The invention discloses a method, a system, equipment, a medium and a program for intelligently diagnosing faults of multiple parts of a wind power generator cabin, and belongs to the technical field of wind power generation. The method comprises the following steps: collecting a multi-source acoustic signal in a cabin of the wind power generator, preprocessing the multi-source acoustic signal to obtain a preprocessed voiceprint signal, and generating a spectrogram; performing cross-domain feature extraction on the pre-processed voiceprint signal, performing feature dimension reduction and redundancy elimination through principal component analysis, and constructing a voiceprint feature vector; inputting the spectrogram and the voiceprint feature vector into a CNN-LSTM fusion diagnosis model, and performing fault diagnosis to obtain a fault diagnosis result; and matching the fault diagnosis result with the fault-voiceprint knowledge graph to generate a decision suggestion. According to the method, through non-contact voiceprint monitoring and multi-dimensional analysis, high-precision and real-time diagnosis and active decision-making of concurrent faults of multiple components of the wind power generator cabin are realized, and the defects of complex deployment and sound signal interference resistance of traditional vibration monitoring are effectively overcome.

This invention relates to the fields of animal behavior, animal welfare, and mental health conditioning, specifically a music conditioning system and method for pets and laboratory animals. It is a pure PC-based software system, including an audio editing and playback control module, a music library management module, and a music playback module. The output of the audio editing and playback control module is connected to the input of the music playback module, and the music library management module and the audio editing and playback control module have a bidirectional data connection. This invention precisely matches different animals through three parameters: pitch shifting, speed adjustment, and volume adjustment, significantly improving audio compatibility. The effects of emotional stabilization, anxiety relief, and sleep promotion are significantly better than general music. One-click templates are suitable for quick home use. Customizable visual editing is suitable for pet hospitals, laboratory animal centers, and research institutions to establish standardized programs. Waveform graphs, spectrograms, and acoustic spectrograms are displayed synchronously in real time, allowing users to intuitively judge the audio structure. Adjustment accuracy is improved by more than 80%, requiring no professional audio knowledge.

The application relates to the technical field of biomedical engineering, and particularly discloses a closed-loop OSA stimulation control method and device based on multi-modal physiological signal fusion, which comprises a multi-modal physiological signal acquisition module, a control processing module, a stimulation output module and a feedback correction module; by fusing related physiological signals such as respiratory muscle electricity, blood oxygen saturation and snore sound acoustic spectrum characteristics, the blocking risk in the sleep process is evaluated in real time, and the corresponding stimulation time, stimulation channel and stimulation parameter are determined in combination with the breathing phase information and the feedback result after stimulation, so that the precise, real-time and individualized closed-loop control for OSA intervention is realized under the premise of meeting the preset safety constraint. The application can more comprehensively reflect the respiratory state and the upper airway blocking risk in the acquired data, improve the accuracy of OSA related event identification, and determine the stimulation triggering time according to the current respiratory state, thereby improving the matching degree between the stimulation intervention and the actual respiratory demand.

The present application belongs to the technical field of compressor abnormal sound detection, and particularly relates to a compressor abnormal sound detection method and system based on acoustic spectrum pattern recognition, which comprises the following steps: extracting a characteristic signal from a sound signal through wavelet packet transform and kurtosis criterion, locating the occurrence time of an impact event contained in the characteristic signal and calculating the time interval of adjacent impact events, calculating a rhythm stability index based on all time intervals in an analysis time window, intercepting a time domain signal window containing each impact event and calculating its energy envelope, calculating the intrinsic damping factor of each energy envelope according to the logarithmic difference between the energy envelope peak value and the energy value after a set time length, calculating the fault authenticity score of each impact event according to the energy value of the impact event in the characteristic signal, the rhythm stability index corresponding to the analysis time window and the intrinsic damping factor, and performing early warning determination, so as to realize high-reliability and accurate early warning of compressor early faults.

The invention provides a sound spectrum nondestructive testing device for an austenite manganese casting for ship equipment. The sound spectrum nondestructive testing device comprises a base, supporting legs are fixedly mounted at the bottom of the base, a control box is fixedly mounted at the upper end of the base, a placement plate is mounted and connected to the upper end of the base, and two sliding plates are slidably connected to the upper end of the base; a plurality of hydraulic rods are fixedly installed at the bottom of the base, and pulleys are fixedly installed at the bottoms of the hydraulic rods and provided with brake pads. According to the acoustic spectrum nondestructive testing device for the austenite manganese casting for the ship equipment, by arranging structures such as the hydraulic rod, the hydraulic rod realizes height adjustment and flexible movement of the device, and stably supports the equipment, so that the acoustic spectrum nondestructive testing device adapts to a complex detection environment of a ship; the sliding block is matched with the sliding rod to achieve high-precision transverse movement, it is guaranteed that the probe is accurately positioned, and castings in complex shapes are efficiently covered. The second motor drives the placing plate to rotate at a constant speed, diversified detection modes are provided, no dead angle of sound wave detection is guaranteed, the detection efficiency and accuracy are improved, and the durability of equipment is enhanced.

The invention discloses a brake pad quality detection system and method based on standardized mechanical excitation and sound spectrum analysis. The system consists of a programmable control standardized array excitation module, a multi-dimensional quality grading judgment algorithm and a self-adaptive dynamic threshold value judgment model, and is integrated on an embedded development board to realize one-button integrated detection. Standardized knocking is achieved through a programmable control electromagnet array, interference of strength, position and human factors is eliminated, knocking audio is collected and subjected to power spectrum analysis, and quantitative grading detection is conducted on the quality of the brake pad according to extracted characteristics in audio signals and based on a multi-dimensional quality grading judgment model. The device is reliable in detection data, high in precision, high in efficiency, low in cost and nondestructive in detection, is widely applicable to quality detection of various brake pads and mechanical parts, remarkably improves the detection level, and has a wide application prospect.

The invention discloses a voiceprint correction method for a wind turbine generator and a related device. The voiceprint correction method comprises the following steps: acquiring operation parameters of the wind turbine generator; calculating a fog drop scattering coefficient, a corrected sound velocity and a corrected sound path distance difference according to the operation parameters of the wind turbine generator; dynamically reconstructing a sound spectrum according to the fog drop scattering coefficient, the corrected sound velocity and the corrected sound path distance difference; and obtaining the corrected voiceprint according to the dynamically reconstructed sound spectrum. The method and the related device can accurately detect the voiceprint signal of the wind turbine generator.