3782 results about "Sound sources" patented technology

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice intention recognition method, device and equipment and a medium, and the method comprises the steps: obtaining a to-be-processed voice signal, carrying out the voice activity detection processing of the voice signal, dividing the voice signal into a plurality of voice segments, analyzing semantic contents of the plurality of voice segments, determining semantic correlation information of each voice segment, analyzing sound source attributes of the plurality of voice segments, determining sound field type information of each voice segment, screening out a target voice segment from the plurality of voice segments according to the semantic correlation information and the sound field type information, and executing intention recognition processing based on the target voice segment to generate an intention recognition result. According to the invention, through a dual analysis mechanism of semantic correlation information and sound field type information, effective screening of voice segments is realized before voice recognition, non-target voice or interference segments are effectively prevented from being sent to an intention recognition model, and the accuracy of a recognition result is improved.

The invention discloses a rescue method and system of a rescue robot for exploration, and relates to the technical field of underground space rescue, and the method comprises the following steps: obtaining multi-path acoustic echo data of a karst cave and motion track data of the robot, and constructing a three-dimensional point cloud model according to the multi-path acoustic echo data and the motion track data of the robot; and identifying unmatched data in the multi-path acoustic echo data and the robot motion trail data, and taking an area where the unmatched data is located as an abnormal area. According to the method, the spatial resolution of signal acquisition is enhanced through the multi-microphone array, robust acoustic fingerprints are extracted in combination with a noise reduction algorithm and short-time Fourier transform, high-confidence human body sound source signals are screened out by using a feature template matching mechanism, accurate extraction and recognition of human body acoustic features in a karst cave complex noise environment are realized, and the accuracy of human body acoustic feature recognition is improved. The problem of misjudgment caused by confusion of sound source features and environmental noise in a traditional method is effectively solved.

The invention relates to a microphone array sound source positioning method and system based on cross-correlation-beam forming closed-loop optimization, and belongs to the technical field of sound source positioning. The method comprises the following steps: collecting multichannel sound signals through a microphone array and preprocessing the multichannel sound signals to extract time-frequency features and suppress noise interference; time delay information among the microphones is estimated by adopting a generalized cross-correlation phase transformation algorithm, and an optimization strategy is introduced to improve estimation stability and anti-interference performance; enhancing the target sound source signal in combination with a minimum variance undistorted response beam forming algorithm and an adaptive Kalman filtering mechanism; constructing a closed-loop feedback optimization mechanism based on the beam output signal to realize feedback adjustment; and adopting a hybrid network architecture, taking the beam output signal amplitude spectrum as input, and outputting the frequency spectrum or mask of the obtained target sound source signal. The method has the advantages of high calculation efficiency, high positioning precision and strong anti-interference capability, and is suitable for real-time acoustic signal processing in a complex environment.

The invention relates to a multi-element microphone array sound source localization method based on multistage signal preprocessing and subspace spectrum optimization, and belongs to the technical field of acoustic detection. Aiming at the problems of poor noise immunity, weak multi-sound-source resolution capability and low calculation efficiency of the existing sound source positioning technology, a triple signal preprocessing and subspace collaborative optimization scheme is provided; firstly, incoherent noise is suppressed through phase coherent filtering, a signal is reconstructed through principal component analysis, and phase deviation is calibrated through fundamental frequency; then constructing a guiding matrix and decomposing a noise subspace, and extracting a coarse positioning result; and finally, high-precision angle optimization is realized based on a chaos initialization differential evolution algorithm, and the efficiency is improved by combining a dynamic search range and an early stop mechanism. According to the method, the anti-interference capability in a low signal-to-noise ratio environment is remarkably enhanced, the problems of missing detection and false detection during dense distribution of multiple sound sources are effectively solved, meanwhile, the positioning precision and the real-time performance are considered, and the method is suitable for acoustic fault detection of complex scenes such as power transmission line inspection.

An underwater acoustic sensor network node positioning error correction method combined with acoustic velocity inversion, which method relates to the technical field of acoustic velocity inversion. The method comprises: deploying an acoustic source point and underwater acoustic sensor nodes which are at fixed positions, the underwater acoustic sensor nodes collecting acoustic velocity data sent by means of the acoustic source point, and by means of acoustic velocity data at different underwater acoustic sensor nodes, using an acoustic velocity field inversion algorithm to construct an acoustic velocity field model; collecting real-time acoustic velocity data at the different underwater acoustic sensor nodes, and obtaining acoustic velocity characteristic information at the different underwater acoustic sensor nodes and water characteristic information at the different underwater acoustic sensor nodes; performing comprehensive analysis on the acoustic velocity characteristic information and the water characteristic information, and evaluating the positioning accuracy of the acoustic velocity field model in respect of the underwater acoustic sensor nodes; and comparing quantized positioning accuracy results for the underwater acoustic sensor nodes with a preset threshold, and when there is a relatively large error in the positioning accuracy, generating an error signal. The method is conducive to improving the precision of underwater acoustic wave propagation and the accuracy of underwater acoustic sensor network node positioning, and evaluating the positioning accuracy of an acoustic velocity field model in respect of underwater acoustic sensor nodes.

The invention discloses an immersed tunnel leakage voiceprint recognition system based on deep learning. The system comprises an acoustic signal acquisition module, a signal preprocessing module, a voiceprint feature extraction module, a deep learning recognition module and a leakage positioning and early warning module. According to the invention, the leakage water flow sound and the structural damage elastic wave signal of the immersed tunnel are comprehensively considered, the multi-dimensional voiceprint feature extraction and deep learning model are fused, and the adaptive signal noise reduction and sound source positioning technology is combined, so that the high-precision monitoring and real-time early warning of the leakage of the immersed tunnel are realized; the accuracy and real-time performance of immersed tunnel leakage nondestructive monitoring are improved, and powerful support is provided for follow-up risk prediction and early repair.

The invention provides a multi-mode sensing intelligent microphone array signal processing method and system, and belongs to the technical field of signal processing, and the method comprises the steps: obtaining a visual signal through employing a multi-directional visual sensor, and obtaining a sound signal through employing a microphone array; extracting visual features and acoustic features; constructing an audio-visual topological feature space, mapping visual features and acoustic features to the space, and establishing a sound source probability distribution model; processing the sound signal by adopting a multi-dimensional discriminant adversarial generative network, and separating out a target voice signal; the acoustic environment state is evaluated in real time, and processing parameters are dynamically adjusted; quality evaluation is carried out on the separated multiple paths of target voice signals, the voice signal with the highest quality is selected as output, audio-visual multi-mode information is deeply fused and cooperatively processed, and a topology enhanced adversarial generative network architecture and an environment self-adaptive mechanism are combined, so that the voice separation effect in a complex environment is remarkably improved; and more than 85% of speech intelligibility can still be kept in a scene that six persons speak simultaneously.

The invention discloses a lamp strip module atmosphere creating method based on scene induction control, and relates to the technical field of intelligent illumination control, and the method comprises the following steps: S1, collecting an original audio signal of a target space, extracting amplitude fluctuation, a spectrum structure and energy mutation features, recording the starting time and duration of a sound event, and constructing a sound source time sequence; according to the invention, through sound source time sequence modeling, abnormal behavior identification and environment trend analysis, in combination with a multi-dimensional consistency judgment mechanism, accurate response to an abnormal sound source is realized, safe transition is carried out through neutral color temperature lighting effect, and then light effect smooth regression is realized through dynamic recovery evaluation and a slow changing algorithm, so that the accuracy of the abnormal sound source is improved. An intelligent light control system with real-time perception, context understanding and closed-loop regulation and control capabilities is constructed, immersion experience and safety are improved, and the intelligent light control system has remarkable practical value and technical innovation.

The invention discloses an intelligent diagnosis and maintenance system for power equipment, and relates to the technical field of power equipment detection, and the system comprises the following components: an array microphone module which is composed of a plurality of microphones, is used for accurately calculating the spacing, is arranged in a three-dimensional or plane matrix manner, and is used for collecting sound signals generated in the operation process of the power equipment; according to the invention, the array microphone module is combined with a beam forming technology, the sound signals are collected efficiently, a voiceprint topological map is constructed, sound source distribution and energy conduction paths of all parts of the equipment are represented in a visual form, and the sound source distribution and energy conduction paths of all the parts of the equipment are displayed in a visual manner. Meanwhile, voiceprint features and vibration modal features are extracted through a feature extraction module, and a joint inversion module is used for performing joint analysis on the two features based on a mapping relation model and a machine learning algorithm and in combination with a modal database, so that the damage degree of the mechanical part of the power equipment is accurately inverted.

The invention relates to the technical field of submarine cable fault positioning, in particular to a submarine cable fault positioning system based on underwater beacons. The method comprises the steps that an underwater acoustic beacon array unit monitors and collects submarine cable fault sound wave signals in real time based on underwater acoustic beacons; the fault signal processing analysis unit extracts submarine cable fault sound wave signal parameters based on a non-point source space sound source waveform analysis model, and generates multi-parameter fault sound source feature vectors; the sound source positioning analysis algorithm unit performs three-dimensional inversion positioning based on the multi-parameter fault sound source feature vector and the arrival time difference of the submarine cable fault sound wave signal to the underwater acoustic beacon, and obtains the spatial position information of the submarine cable fault point; and the data communication alarm management unit sends the spatial position information of the submarine cable fault point to a shore-based operation and maintenance system. The submarine cable fault positioning method is used for realizing a submarine cable fault positioning technology for quickly and accurately positioning and identifying spatial extension characteristics in a complex submarine environment.

The invention relates to a voice recognition processing method, system and device based on a conference scene and a medium, and belongs to the technical field of voice processing. The voice recognition processing method comprises the following steps: acquiring an original conference audio stream collected by a microphone array; performing signal preprocessing on the original conference audio stream collected by the main channel, and outputting a pure voice signal; generating a sound source orientation thermodynamic diagram based on the original conference audio stream; extracting multi-dimensional voiceprint feature vectors from the pure voice signals, performing dynamic grouping, outputting a voice fragment set marked with voiceprint IDs, and generating an initial transcription text; dynamically correcting the initial transliteration text, and outputting a transliteration text stream with an industry term tag; and performing periodic memory enhancement processing on the transliteration text stream, outputting and analyzing a long text, and generating structured conference summary data. According to the invention, the automation level and accuracy of conference voice processing can be improved.

The invention relates to a partial discharge positioning method, equipment and device and a readable storage medium. The method comprises the following steps: collecting a visible light image and an infrared image of a to-be-detected area containing a partial discharge phenomenon, fusing the visible light image and the infrared image to generate a double-spectrum positioning map, collecting a sound wave signal of the to-be-detected area through a spiral microphone array, carrying out fusion processing on the double-spectrum positioning map and a sound source thermodynamic diagram of the sound wave signal, and obtaining a sound source thermodynamic diagram of the sound wave signal. Generating a comprehensive discharge positioning map, adopting a clustering algorithm to identify a plurality of features of the visible light image, the infrared image and the sound wave signal, determining the discharge position and the discharge type of the partial discharge phenomenon in the comprehensive discharge positioning map based on the plurality of features, and outputting the comprehensive discharge positioning map. Marking the discharge position and the discharge type of the partial discharge phenomenon in the comprehensive discharge positioning map; the optical, thermal and acoustic multi-mode information of the discharge is integrated, the positioning precision of the partial discharge is greatly improved, the fault condition can be conveniently and quickly judged, and the quick inspection requirement is met.

The invention provides a single-microphone multi-array pickup method and system based on sound attenuation simulation, and the method comprises the steps: obtaining an original audio signal recorded by a single microphone and sound source direction information, and carrying out the sound attenuation simulation based on the sound source direction information and a preset virtual microphone array orientation parameter, calculating the included angle between the sound source direction and each virtual microphone; generating an audio intensity attenuation coefficient of each virtual microphone according to the included angle; and acting the audio intensity attenuation coefficient on the original audio signal to generate multi-channel audio data simulating the multi-microphone array. By adopting the method, the response difference of different microphone positions to the sound source can be simulated, the pickup result of the multi-microphone array is obtained, and high hardware cost and complex installation flow caused by actual deployment of a complex microphone array are avoided; and low-cost and high-diversity training data sources are provided for sound source localization, noise suppression and other models based on deep learning.

The invention relates to the field of biological medicine, and discloses a method and system for analyzing cough sound by using disease characteristics to diagnose respiratory diseases, and the method comprises the steps: deploying a six-microphone annular array to achieve the precise positioning and triggering of a sound source; self-adaptive spectral subtraction and Wiener filtering cascade are adopted to enhance the audio; segmenting a cough segment based on energy envelope; fusing the Mel-cepstrum, the linear prediction residual error, the harmonic energy ratio and the transient zero-crossing rate to construct a pathological feature matrix; extracting local, medium-range and global time sequence features through a three-branch parallel convolutional network; inputting a disease specific classifier to discriminate asthma, pneumonia and laryngitis respectively, and applying a feature decoupling regular term to improve interpretability. The system correspondingly realizes the modularized processing flow. According to the method, the cough sound collection quality and the disease subtype recognition accuracy in a complex environment are improved, meanwhile, the thermodynamic diagram is output to assist clinical decision making, and the diagnosis credibility and practicability are enhanced.

The invention relates to the technical field of building structure health monitoring, in particular to an assembly type building detection method and system, and the method comprises the steps: collecting a strong interference signal generated by an external impact source of a building, and obtaining the sound source positioning information of the strong interference signal, converging the sound source positioning information of the strong interference signal on a preset three-dimensional geometric model aligned with a coordinate system to generate a real-time interference source distribution map; in the conventional detection mode, after a to-be-distinguished target acoustic signal is detected, obtaining a theoretical acoustic signal arrival time sequence based on sound source positioning information of at least one strong interference signal in the real-time interference source distribution diagram; comparing the actual acoustic signal arrival time sequence of the target acoustic signal with the theoretical acoustic signal arrival time sequence to obtain a comparison result; and judging whether the target acoustic signal is damaged in the building or not according to a comparison result. Real damage events from the interior of the structure can be effectively separated and confirmed, and the accuracy and reliability of detection are improved.

The invention relates to the field of aviation voice signal processing, and discloses a voice recognition compensation pilot earphone hearing protection method and system, and the method comprises the following steps: collecting multi-modal data, and separating a sound source through tensor decomposition; inferring a pilot state by using a dynamic network; performing context recognition and semantic evaluation on the attention target voice; and finally, dynamically modulating the sound field based on deep reinforcement learning, enhancing the voice in a personalized manner, and outputting after noise suppression. The system comprises a multi-mode perception data acquisition module, a sound source decoupling module, a pilot state inference module, a voice processing and semantic evaluation module and a sound field modulation and output module. According to the invention, high-fidelity speech extraction is realized through multi-modal perception and tensor decomposition; evaluating priority key information in combination with attention and semantics; and deep reinforcement learning and model prediction control are adopted to dynamically optimize the sound field, so that the voice recognition accuracy and the pilot information acquisition efficiency are improved.

The invention relates to the technical field of voice recognition, in particular to an implementation method and device of a multi-channel voiceprint recognition system, and the implementation method comprises the steps of multi-channel data acquisition and synchronization, signal preprocessing and enhancement, feature extraction and fusion, model training, real-time deployment and adaptive optimization. Compared with the problems that a traditional multichannel voiceprint recognition system depends on a fixed beam forming algorithm and an independent clock synchronization module, the synchronization error is large, manual parameter adjustment is needed for noise suppression, and generalization is poor, hardware-level clock synchronization is achieved through a PTP protocol, and the accuracy of noise suppression is improved. The method combines an end-to-end neural network to automatically learn noise distribution and a sound source space position, dynamically generates a beam forming weight, can improve the voice quality in a complex noise scene without manual intervention, remarkably reduces the interference of a synchronization error on sound source positioning, and enables the precision and stability of far-field voice enhancement to reach a new level.

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 field of gas leakage identification, and discloses a gas leakage identification method and system based on sound localization, a medium and equipment, and the method comprises the steps: collecting a leakage sound wave signal through a microphone array, and extracting acoustic features in a complex background; simulating a diffusion process of gas in a turbulence environment after gas leakage through an established leakage gas convection-diffusion model, and combining gas concentration distribution in the simulated diffusion process with acoustic characteristics to predict sound source localization at a leakage position; modeling a sound source localization search process as POMDP, and iteratively updating a confidence state of a sound source position through a particle filter; spatial distribution features are extracted from the confidence state through DBSCAN clustering to serve as input of the LSTM-DQN network, spatial and temporal features are fused through the constructed LSTM-DQN network, cross-scene generalization is achieved through transfer learning, and sound source coordinates are dynamically optimized and output through the Q network. According to the invention, the positioning accuracy and real-time performance in a complex environment are improved.

The invention belongs to the technical field of sound source processing, and provides a sound source positioning and detecting method and device. The method comprises the following steps: acquiring delay estimation of microphones I and II and delay estimation of microphones I and III based on a three-path linear uniform microphone array; based on the delay estimation sum, respectively carrying out positioning calculation under near-field and far-field conditions; according to the sound source distance under the near-field condition, comparing the sound source distance with a distance judgment threshold value, and determining a far-field / near-field output sound source position; and carrying out feature extraction on the signals of any microphone array, and carrying out event classification based on a pre-trained convolutional neural network. According to the method, far / near field model selection is carried out according to the distance judgment threshold, large deviation generated by a single model in a critical region is avoided, continuous and stable positioning from short distance to long distance is achieved, time classification can be achieved while position calculation is carried out, and integrated output is achieved.

The invention provides an up-mixing processing method, system and device for a stereo audio video, and a storage medium, and relates to the technical field of audio and video processing, and the method comprises the steps: obtaining a to-be-processed stereo audio video, carrying out the multi-stream separation of the to-be-processed stereo audio video, obtaining a plurality of independent sound source components, the to-be-processed stereo audio video comprises a to-be-processed stereo audio or a to-be-processed stereo video; performing dry and wet sound processing on the to-be-processed stereo sound video to obtain an environment sound component; performing frequency band extension on the environment sound component to obtain a target environment sound component; and carrying out space rendering on each independent sound source component and the target environment sound component by adopting a vector-based amplitude translation method, and carrying out multichannel output fusion to obtain a multichannel audio / video. According to the method, the sound source is accurately positioned and efficiently separated, the dynamic sense and the space sense of the sound source object are enhanced, and the audio and video upmix processing quality and the user experience are improved.

The invention discloses an audio data processing method and device and electronic equipment, and relates to the field of audio processing. The method comprises the following steps: acquiring original multichannel audio data through a microphone array; generating a main beam output signal pointing to the target sound source direction and at least one sidelobe suppression output signal; extracting voiceprint feature vectors; comparing the voiceprint feature vector of the current frame with the center vector of the existing voiceprint class cluster to obtain a voiceprint clustering result; if it is determined that the first voiceprint class cluster and the second voiceprint class cluster exist, extracting a first audio frame belonging to the first voiceprint class cluster and a second audio frame belonging to the second voiceprint class cluster, and performing audio reconstruction to obtain a first voice stream and a second voice stream; and carrying out residual noise elimination on the sidelobe suppression output signal to obtain a first isolated audio stream and a second isolated audio stream. By implementing the technical scheme provided by the invention, the audio processing quality is improved.

The invention discloses an all-weather sound source positioning system and method based on multi-sensor fusion, and the method comprises the steps: firstly, obtaining acoustic sensing data, millimeter wave radar data and infrared thermal imaging data, and enabling each data to have a collection timestamp; then, cross-modal time alignment processing is carried out on the multi-source data to map the multi-source data to a unified time reference, and multi-modal fusion features under a unified time axis are obtained; for each time point in the time-aligned multi-modal fusion features, according to the confidence coefficient of the data of each sensor, adaptive weighted fusion is carried out on the data of different modals, and fused common feature representation is generated; and finally, time sequence modeling and joint reasoning are carried out based on the common feature representations of a plurality of continuous time points, and continuous position information of the sound source in the three-dimensional space is regressed. According to the method, high-precision positioning of three-dimensional positions of a plurality of sound sources is realized, so that the accuracy and the stability of sound source positioning are improved in a complex environment and an all-weather condition.

The invention relates to the technical field of data processing, in particular to a three-dimensional live-action model construction system for building structure demolition and reconstruction. The system extracts event combinations with obvious transfer correlation features for localization and analysis of dangerous sound sources. Through simulation of transmission of test waves in an initial acoustic slowness field, a most probable dangerous sound source position for generating a current event combination under each test wave is obtained by using an objective function optimization solution method. And further performing statistics on simulation information of all test waves, establishing a residual curve, screening out a brittleness event position based on feature extraction, and adjusting a slowness value in a current voxel through various parameters corresponding to the brittleness event position in an initial acoustic slowness field. Through the dynamic correction method, the obtained corrected acoustic slowness field can more effectively represent the specific change of the building structure in the dismantling process, so that the internal damage can be more clearly quantified.

The invention discloses a robot voice interaction system based on multi-mode sensing fusion and a control method thereof, and belongs to the technical field of intelligent robots, and the control method comprises the steps: S1, the initial state of a robot is a dormant state; s2, awakening the robot by collecting awakening words in real time; s3, performing dynamic compensation according to a sound source and IMU data fusion algorithm, performing beam forming optimization correction on the sound source, and performing pickup to obtain voice data; s4, detecting and obtaining rule / intention voice; s5, recognizing the rule / intention voice, and performing NLU semantic understanding through the cloud ASR or the offline ASR to obtain a voice instruction; s6, the voice instruction is fused with visual data of the detection sensor group for dynamic arbitration, and the voice instruction or the obstacle avoidance instruction is executed; and S7, after the instruction in the step S6 is executed, entering a dormant state so as to achieve the purpose that a voice interaction system is formed by fusing the microphone array, the IMU sensor and edge calculation, and the intelligent robot autonomously decides in a complex environment.

A concentric circular microphone array (CCMA) may include a number of omnidirectional microphones and an equal number of directional microphones, wherein the omnidirectional microphones and the directional microphones form a plurality of concentric rings on a substantially planar platform. Each of the plurality of concentric rings includes a subset of the omnidirectional microphones and a subset of the directional microphones (e.g., arranged in mixed pairs of microphones). Responsive to a sound source, the omnidirectional microphones and the directional microphones may respectively generate first and second electronic signals. A target beampattern of Nth order may be specified for the CCMA. An Nth order beamformer for the CCMA, that is steerable in a three-dimensional space including the sound source, may be determined based on the specified target beampattern. The beamformer may be executed to calculate an estimate of the sound source based on the first electronic signals and the second electronic signals.

The invention relates to the technical field of intelligent household appliances and man-machine interaction, and discloses an electric cooker anti-noise voice interaction system based on multi-mode perception and a control method, and the system comprises a multi-mode perception and collection unit which synchronously collects millimeter wave radar echo signals and acoustic signals; the signal preprocessing and feature extraction unit is used for extracting a user physiological vibration signal and a three-dimensional space position vector from the radar signal and extracting an acoustic energy envelope and a sound source direction vector from the acoustic signal; the time-space consistency verification unit and the voice gating and recognition unit are used for carrying out time synchronization verification by calculating the correlation between the physiological vibration signal and the acoustic energy envelope, and distinguishing real human voice from an environment false trigger source; meanwhile, space consistency verification is carried out by comparing the user direction of radar positioning with the sound source direction of acoustic positioning, so that non-target human voice interference is eliminated. According to the invention, the anti-interference capability and reliability of voice interaction in a real home environment are improved through double verification on a physical level.

The invention discloses a motor fault diagnosis method and system based on voiceprint analysis, and relates to the related field of motor fault diagnosis technology, and the method comprises the steps: collecting sound signals, vibration data and working condition parameters during the operation of a motor, carrying out the preprocessing, separating the voiceprint features of the motor through a harmonic vector analysis method, and removing the irrelevant sound source interference; obtaining a pre-training comparison learning model through a small amount of motor fault data in combination with data enhancement, fault feature analysis and similarity calculation; constructing and training a motor fault diagnosis model, taking the motor voiceprint features, the vibration data and the working condition parameters as input, embedding a pre-training comparison learning model to learn fault information in the motor voiceprint features, extracting fault features through a time delay neural network, inputting motor operation data which are collected and preprocessed in real time into the trained model, and performing motor fault diagnosis. And outputting a judgment result of the motor fault type. The problem that an existing motor fault diagnosis model excessively depends on labeled data is solved, and model generalization is improved.

The invention provides a deep sea convergence area characteristic forecasting method and system based on a normal wave theory. The method comprises the following steps: determining a normal wave eigenvalue equation; solving a normal wave eigenequation, and calculating a normal wave eigenvalue of each order from the first order to a set normal wave order upper limit; calculating a normal wave eigenfunction value according to the sound source and the receiving depth; superposing and synthesizing a sound field by using the normal wave eigenvalue and the normal wave eigenfunction value; and calculating the sound propagation loss of the convergence area by using the synthesized sound field so as to obtain the energy and position of the deep sea convergence area. Compared with the prior art, the method has the advantages that the analytical solution form of the normal wave eigenequation is obtained in advance, compared with pure numerical calculation, certain calculation time can be saved, in addition, some function expansion and frequency interpolation technologies can be conveniently combined, a basis is provided for broadband sound propagation and signal waveform forecasting of the deep sea convergence area, and the method has a wide application prospect. The sound field calculation speed of the convergence area is greatly improved, and the method has good calculation precision.

The invention discloses a voice data recognition method and system based on an AI voice algorithm, relates to the technical field of AI voice recognition, and solves the problem that the voice data recognition capability is low. The method comprises the following steps: S1, multi-mode cooperative triggering collection: synchronously collecting lip electromyographic signals and voiceprint features through a multi-mode sensor, an activation instruction is generated through feature fusion, and voice acquisition starting is triggered; s2, AI adaptive noise reduction processing: carrying out noise separation on the original audio signal by adopting a generative adversarial network, separating environmental noise features to generate a dynamic noise reduction mask, and keeping the integrity of human voice features; s3, beam dynamic optimization adjustment: analyzing real-time audio quality based on a reinforcement learning algorithm, dynamically adjusting beam pointing and gain parameters of a microphone array, and focusing a target sound source; and S4, semantic association cache enhancement: carrying out real-time semantic analysis on the collected voice data. According to the invention, the voice data recognition capability of an AI voice algorithm is greatly improved.