19164 results about "Noise" patented technology

Introduced here is a wearable audio system including modular ear-cup and ear-bud that can be attached and detached to the user together, or independently of each other. Further, the modular ear-cup and ear-bud can operate together, or independently of each other. The wearable audio system can perform active noise cancellation by measuring noise inside the ear-cup and / or the ear-bud, computing the noise canceling sound, and forwarding the noise canceling sound to a speaker inside the ear-cup and / or the ear-bud. The wearable audio system can be wirelessly charged while operating, thus allowing the user to continuously listen to music more than previously possible. The wearable audio system can optimize power consumption by redistributing power intensive tasks to power sources with the highest amount of power. Further, the ambient sound outside the ear-cup can be measured and played by the speakers in the earbud allowing the user to hear the surrounding environment.

An incubator can include a body, a bed supported by the body, and a canopy defining an interior chamber that includes the bed. The incubator can include any combination of various features configured to reduce sound energy inside the incubator. The incubator can include a passive radiator, a Helmholtz resonator, a meta material absorber, an active noise cancelation system, a vented door latch assembly, an acoustically permeable seal, a pressure relief valve, a lift mechanism that uses a belt drive, double paned canopy walls, a bin with insulating compliant elements, a manual pressure release valve, a pressure differential notification system, non-parallel or curved canopy walls, a mattress with reticulated foam, and / or an acoustic filter.

The invention relates to a method and a system for monitoring and analyzing regional noise. The method comprises the following steps: carrying out partition processing on a target area according to an acoustic environment function standard to obtain acoustic environment function area division data and determine a monitoring point position distribution matrix; noise data and environment parameter data are collected, timestamp alignment is carried out, and a multi-dimensional associated data set is obtained; performing spectral analysis and data compensation to obtain a noise feature vector, and performing standard exceeding judgment to generate audio recording data; inputting a voiceprint recognition model for classification to obtain a noise source type identifier, and performing positioning calculation to obtain a noise source space coordinate; carrying out space-time distribution statistics to obtain a functional area noise standard-reaching rate and calculating contribution degrees of various noise sources; and carrying out graded early warning judgment, and generating alarm information and a noise pollution prevention and control instruction. According to the embodiment of the invention, the whole-process automatic management of monitoring and early warning of regional noise is realized.

The invention provides an efficient flaw detection method of an ultrasonic flaw detector for automatic detection of a welding seam, and belongs to the technical field of welding seam detection.The efficient flaw detection method comprises the steps that firstly, a welding seam three-dimensional digital model is established, an optimal scanning path is determined, reflection echo signals are processed in real time through an adaptive beam forming algorithm, and defect features are extracted and subjected to quantitative analysis; and establishing a neural network model based on the defect characteristic spectrum, and performing deep learning training by using the sample library. A multi-angle scanning strategy is adopted to implement full-coverage detection, and cross scanning is applied in a key area to improve the detection probability. And calculating the accurate position of the defect by using a time-difference method, and constructing a defect three-dimensional distribution diagram by combining a multi-angle data fusion technology. The optimal filtering parameter is determined through the acoustic feature optimization function, and the defect signal-to-noise ratio is improved. And finally, a quality evaluation report is automatically generated based on a detection result, so that the problems of defect identification accuracy and detection efficiency in weld flaw detection are effectively solved.

The local intrinsic dimensionality (LID) for a diffusion model with respect to a particular data sample is determined by using the diffusion model's diffusion process to apply noise to a data sample and evaluate how the estimated log probability of the data sample changes at different levels of noise. Particularly, the differential of change in noise to change in log probability can be used to determine the local intrinsic dimensionality. This may be determined by evaluating the log probability at several noise levels and determining a slope of the difference. In additional examples, the differential is evaluated directly at a selected noise level. The selected noise level can be optimized by calculating the estimated LID for various data samples at a variety of noise levels and selecting the LID that corresponds to a “knee” where the estimated LID sharply changes.

The invention relates to the technical field of intelligent conference processing, and relates to a conference summary processing method and system using AI, and the method comprises the steps: carrying out the real-time noise suppression of a collected conference audio stream and associated text data through a noise suppression algorithm, and carrying out the cross-modal alignment of the denoised data through a cross-modal alignment algorithm; a domain-specific attention head is inserted into an attention layer of the pre-trained Transform model, a domain-enhanced speech recognition model is constructed, and audio is converted into a text sequence with a speaker tag; adopting a heterogeneous graph neural network to construct a structured topic evolution graph; key decision nodes in the structured topic evolution graph are extracted based on a reinforcement learning strategy, and a final conference summary document is generated. In the decoding stage, the fusion proportion of the acoustic model and the language model is dynamically adjusted based on the real-time acoustic confidence coefficient, the recognition rate of the vocabularies in the professional field is increased, and the problems of frequent term transcription errors and poor semantic coherence in the professional conference are effectively solved.

The invention provides an unmanned aerial vehicle multi-dimensional information fusion method and system based on acousto-optic-electric composite detection. According to the method, acoustic frequency band signals, visible light image sequences and electromagnetic field intensity change data are collected, acoustic characteristic flow, optical characteristic flow and electrical characteristic flow are generated, and based on the time deviation between a low-frequency vibration mode and transient electromagnetic pulses, multi-channel dynamic calibration is carried out on an image distortion area in the optical characteristic flow. The method comprises the following steps: acquiring an acoustic feature flow, an optical feature flow and an electrical feature flow, correcting space-time coordinate parameters of the acoustic feature flow, the optical feature flow and the electrical feature flow, generating a calibration feature flow, carrying out anti-interference fusion processing on the calibration feature flow, extracting multi-dimensional coupling features, inputting the multi-dimensional coupling features into an unmanned aerial vehicle flight path planning model, and generating a three-dimensional space situation model; according to the invention, the physical space-time consistency alignment and noise suppression of the multi-modal data are realized, and the obstacle positioning precision and the dynamic trajectory prediction real-time performance are significantly improved.

The invention relates to the technical field of structural health monitoring and intelligent diagnosis, in particular to a structural fatigue damage identification method based on acoustic emission and deep learning, and the method comprises the steps: collecting a structural response signal under a fatigue load through an acoustic emission sensor array, inputting the structural response signal to a CNN-BiLSTM-Attention mixed deep learning model, and carrying out the recognition of the structural fatigue damage through the CNN-BiLSTM-Attention mixed deep learning model; the model extracts local time domain features through a dynamic adaptive convolution kernel, captures long time sequence dependence by using a bidirectional long-short-term memory network, focuses key damage features through a bimodal space-time attention mechanism, divides damage stages based on a nonlinear dynamic threshold algorithm of fracture opening amount, constructs a training data set of physical-data fusion, and performs dynamic time domain feature extraction. The learning rate is optimized by adopting a gradient sensitive cosine annealing algorithm, and the robustness of the model is improved in combination with an anti-noise and anti-loss function. The method integrates physical characteristics and an intelligent algorithm, and has the advantages of adaptive noise suppression, strong cross-domain generalization ability, high real-time performance and the like.

The invention relates to a high-altitude large steel structure corridor safety risk monitoring method and system and a medium, and belongs to the technical field of civil engineering structure health monitoring, and the monitoring method comprises the steps: collecting original monitoring data through a multi-source sensor group disposed at each node of a steel structure corridor; performing space-time alignment processing on the original monitoring data to generate a sensor data matrix; performing dynamic noise suppression on the sensor data matrix based on an environmental noise transfer function model, and outputting a pure signal matrix and a damage characteristic frequency band identifier; calculating a thermal stress sensitive factor, extracting a multi-physical field coupling feature associated with the damage feature frequency band identifier in the pure signal matrix, fusing to generate a high-dimensional damage feature tensor, inputting the high-dimensional damage feature tensor into a pre-constructed digital twinborn risk assessment model, and outputting an assessment result including a risk level label and a damage position coordinate; and matching the regulation and control strategy according to the risk level label, and generating a corresponding equipment control instruction. According to the invention, the scientificity and accuracy of risk decision can be improved.

The invention provides a wind generating set fault monitoring method and system based on voiceprint recognition, and the method comprises the steps: collecting gear box voiceprint signal data in real time, recording signal fluctuation caused by gear surface wear, and obtaining an original signal data set containing a frequency spectrum high-frequency component enhancement feature; a time-frequency analysis method is adopted for the original signal data set, non-linear interference of vibration signals is recognized, multi-scale decomposition is carried out, initial wear frequency spectrum narrow-band characteristics and medium-term harmonic components are separated out, and a frequency spectrum component set is obtained; extracting characteristic parameters related to modulation depth abnormal fluctuation from the frequency spectrum component set, identifying a gear pair meshing frequency change rule, and determining a distribution mode of a tooth surface contact noise proportion; and if the feature matching result shows that the deviation between the spectrum high-frequency component diffusion distortion feature and the reference feature library exceeds a threshold value, adding the tooth surface fatigue crack noise feature into the feature library to obtain a target feature library.

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 relates to the technical field of audio noise reduction, and discloses a Bluetooth headset environment noise adaptive adjustment method and system, and the method comprises the steps: collecting multi-band sound data of headset environment noise through a preset acoustic sensor array, generating a noise spectrogram, extracting a noise masking threshold through time-frequency joint analysis, and collecting a headset audio signal; next, noise suppression is carried out on the audio signal by using a multi-order digital filter bank to obtain a primary noise reduction signal, the primary noise reduction signal is optimized into an optimized noise reduction signal based on a signal-to-noise ratio, and the frequency response matching degree of the optimized noise reduction signal and the original audio signal is calculated; then, performing phase correction and amplitude-frequency equalization processing according to the frequency response matching degree to obtain an audio equalization parameter; and finally, monitoring residual noise energy based on the parameter, generating a noise regulation and control instruction, driving adaptive sampling, and generating an adaptive noise reduction scheme. According to the invention, the audio noise reduction effect of the Bluetooth earphone can be improved.

A system configured to improve audio processing by performing dereverberation and noise reduction during a communication session. In some examples, the system may include a deep neural network (DNN) configured to perform speech enhancement, which is located after an Acoustic Echo Cancellation (AEC) component. For example, the DNN may process isolated audio data output by the AEC component to jointly mitigate additive noise and reverberation. In other examples, the system may include a DNN configured to perform acoustic interference cancellation, which may jointly mitigate additive noise, reverberation, and residual echo, removing the need to perform residual echo suppression processing. The DNN is configured to process complex-valued spectrograms corresponding to the isolated audio data and / or estimated echo data generated by the AEC component.

The invention relates to an audio noise reduction method, device and system based on deep learning, and the method comprises the steps: obtaining an input audio signal with noise, and carrying out the multi-scale time-frequency decomposition, and obtaining a mixed time-frequency feature and a noise fingerprint spectrum; performing parameter parallel processing on the noise fingerprint spectrum through a preset dynamic kernel generation network, and performing preliminary noise reduction processing on the mixed time-frequency characteristics to obtain noise-reduced mixed data; performing dual-path processing structure construction on the noise reduction mixed data to obtain amplitude optimization data and phase optimization data; performing dynamic time-frequency domain cross fusion on the amplitude optimization data and the phase optimization data to obtain fused audio data; and carrying out differentiable acoustic equation constraint adversarial training on the fused audio data, and carrying out inverse time-frequency transformation processing to obtain a target noise-reduced audio signal. According to the invention, the overall efficiency and effect of audio signal processing can be effectively improved.

The invention relates to an intelligent microphone pickup and speech enhancement method, system and device, and the method comprises the steps: obtaining an audio signal collected by a dual-silicon microphone array, and carrying out the cross-correlation analysis of the audio signal, and obtaining a corresponding human voice signal correlation feature; performing sound source positioning analysis on the audio signal based on the human voice signal correlation feature to obtain a corresponding target human voice signal; performing frequency characteristic analysis on the target human voice signal, and performing segmented dynamic gain processing on the signal according to a preset frequency band range to obtain a corresponding voice enhancement signal; performing noise component adaptive filtering processing on the target human voice signal to obtain a corresponding noise suppression parameter; and inputting the speech enhancement signal and the noise suppression parameter into a preset echo cancellation model for joint optimization to obtain a corresponding output human voice signal. According to the invention, the coupling problem of multiple acoustic interferences can be effectively solved.

The invention discloses a shield tunnel active and passive source combined geological detection method and system. The method comprises the following steps: acquiring an active source seismic wave reflection signal and a passive source acoustic wave reflection signal; according to the active source seismic wave reflection signal, obtaining an active source geological detection imaging result by adopting a longitudinal and transverse wave combined imaging principle; according to the passive source sound wave reflection signal, a frequency dispersion extraction-shear wave velocity inversion method fusing spatial autocorrelation and a half-wavelength model is adopted to obtain a passive source geological exploration imaging result; and taking an active source geological detection imaging result as a main result, taking a passive source geological detection imaging result as an auxiliary result, and combining adaptive adjustment coefficients between the two results to obtain an unfavorable geological risk level of the to-be-detected area. Shield tunneling vibration noise with high energy and wide frequency band is introduced as a passive detection source, the detection depth and resolution are improved, and support is provided for accurate geological imaging.

The invention discloses a transformer partial discharge detection method and system based on multi-modal feature combination, and relates to the technical field of transformer detection, and the key points of the technical scheme are that the method comprises the steps: collecting an electromagnetic wave signal, a pulse current signal and a sound wave signal of a target transformer; respectively extracting an electromagnetic wave time-frequency matrix, a pulse current waveform parameter and a sound wave space coordinate; performing multi-modal feature fusion on the electromagnetic wave time-frequency matrix, the pulse current waveform parameters and the sound wave space coordinates based on a dynamic weighting strategy to generate a joint feature tensor; and inputting the joint feature tensor into a pre-constructed neural network model, identifying a discharge type and optimizing a positioning coordinate. According to the method, multi-modal features are subjected to tensor fusion, cross-modal correlation learning is realized through the neural network model, information islands can be eliminated, and the misjudgment rate is effectively reduced; and electromagnetic wave-current-sound wave signal adaptive fusion is realized through a dynamic weight coefficient, and the signal-to-noise ratio is obviously improved through characteristic complementation in an oil paper insulation discharge scene.

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 feature processing method, device, equipment and medium. Performing time resolution analysis based on the fused Mel band energy to generate a multi-scale Mel spectrum amplitude value, and performing nonlinear transformation on the multi-scale Mel spectrum amplitude value according to the noise intensity parameter to generate a noise suppression Mel component; and generating a perception weighting coefficient according to an auditory perception model, and executing frequency domain energy adjustment on the noise suppression Mel component to generate Mel spectrum representation. On the basis of frequency resolution self-adaption, time resolution dynamic adjustment and auditory perception modeling, nonlinear transformation and perception weighting processing are applied to the multi-scale Mel spectrum amplitude value, the influence of noise interference on voice features can be effectively reduced, and the key information retention capacity of voice signals is enhanced.

The invention relates to the technical field of audio processing, and discloses an audio processing method and system for speech spectrum reconstruction, and the method comprises the steps: carrying out the preprocessing of an original speech signal collected by a vehicle-mounted microphone, and obtaining a speech signal time-frequency matrix; performing singular value decomposition on the voice signal time-frequency matrix to obtain a sparse matrix representing voice components and a low-rank matrix representing noise components, and establishing a frequency band characteristic incidence matrix through a mutual information entropy calculation method; inputting the sparse matrix into a selection forgetting extreme learning machine for voice feature prediction to obtain predicted voice feature data; and based on the predicted voice feature data and the frequency band feature incidence matrix, performing voice probability calculation and feature interpolation on each time frequency point by adopting a logarithmic frequency spectrum amplitude calculation method to obtain reconstructed frequency spectrum data, thereby improving the accuracy of voice recognition and meeting the requirement of real-time processing in a vehicle-mounted environment at the same time.

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 enhancement method, device and equipment based on noise perception and a medium. Environment feature information is extracted and input into an audio enhancement model to generate an enhanced audio signal; obtaining a reference audio sample, extracting a personalized feature vector, and carrying out personalized processing on the enhanced audio signal; and collecting playing feedback data, determining a playing time domain adjustment parameter and a playing frequency domain adjustment parameter, adjusting the personalized enhanced audio signal, and generating an optimized audio signal. According to the method, dynamic adjustment is realized in combination with the feedback parameters in the playing process by fusing the environmental perception information and the personalized speaker characteristics, clear and natural optimized audio output with personalized styles can be generated in a complex environment, and the voice interaction quality and adaptability are improved.

The invention belongs to the technical field of bearing fault diagnosis, and particularly relates to a transmission casing typical small bearing fault weak vibration signal extraction method, which comprises the following steps: reconstructing a bearing vibration signal to obtain a modulation impact signal, and calculating a weighted kurtosis value based on the modulation impact signal to improve a Protugram algorithm; noise interference is added to a bearing fault signal, and extraction of a small bearing fault weak impact signal is realized by optimizing a filtering frequency band based on a weighted kurtosis value improved Protugram algorithm; bearing impact characteristics can be enhanced based on a simulation sensor resonance enhancement algorithm (SPM); the impact quantification of the bearing can eliminate the influence of different rotating speeds and load working conditions, and realizes the quantitative diagnosis of bearing faults. And finally, the fault data of the rolling body, the inner ring and the outer ring of the flight attachment case small bearing part test bed are verified, and effective extraction and enhancement of bearing fault weak signals can be realized.

The invention relates to the technical field of voice interaction, particularly discloses an AI intelligent robot voice interaction method and system, and aims to solve the problems of low voice interaction accuracy, insufficient reliability and lack of authority control in a complex noise environment. A dynamic noise feature library containing steady-state noise, impact noise and human voice interference features and a pre-stored gesture instruction library are constructed, audio signals are collected in real time, low-frequency-band, middle-frequency-band and high-frequency-band differential noise reduction is executed, Mel-frequency cepstral coefficient features are extracted, noise scenes are matched, corresponding voice recognition models are switched, and voice recognition is achieved. And calculating a confidence value of the voice instruction, outputting multi-modal verification data in combination with a dynamic confidence threshold, and outputting an authority control signal through voiceprint matching, authority verification and instruction consistency judgment. Through multi-modal fusion, dynamic adaptation and authority control, the voice recognition accuracy and interaction safety in a complex noise environment are remarkably improved, and the method is suitable for scenes such as factory intelligent inspection.

The invention relates to the technical field of pile foundation monitoring, and discloses a pile foundation state real-time monitoring and diagnosis system based on a digital twinborn technology. The system comprises a multi-source data acquisition module, a data confidence evaluation module and an acoustic emission monitoring decision module. The multi-source data acquisition module comprises a plurality of sensor groups deployed at different depths of a pile foundation, each group comprises a strain sensor, an acceleration sensor, an acoustic emission sensor and a temperature sensor, and pile foundation data can be acquired in multiple dimensions; the data confidence evaluation module receives original data, generates a correction data sequence through time sequence noise separation and reconstruction, and calculates data confidence according to correction data distribution dispersion; the acoustic emission monitoring decision module judges whether acoustic emission monitoring is started or not according to the data confidence coefficient, and controls the acoustic emission sensor array at the top of the pile foundation to collect acoustic emission signals during starting. The system can comprehensively obtain pile foundation data, improve data accuracy, achieve early damage recognition and guarantee pile foundation safety.

The invention discloses a leakage sound signal denoising method based on an optimized VMD combined with an improved wavelet threshold, and aims to solve the problem of low recognition accuracy when an existing noise processing method is used for processing leakage sound signals of a water supply network. According to the method, the noise component and range in the leakage sound signal are analyzed by collecting the leakage sound signal of the actual water supply network; performing parameter optimization on the decomposition modal number K and the penalty factor alpha of the VMD based on an eagle optimization algorithm to obtain optimal parameters, and constructing a variational model by adopting the optimal parameters; decomposing the leakage sound signal by using a variational model to obtain a plurality of intrinsic mode components; screening the plurality of intrinsic mode components by using a correlation coefficient method to obtain high-frequency components and low-frequency components; performing wavelet threshold de-noising processing on the high-frequency component to obtain a de-noised high-frequency component; and reconstructing the low-frequency component and the denoised high-frequency component to obtain a denoised leakage sound signal. The beneficial effects are that the signal-to-noise ratio of denoising processing is improved, and the recognition accuracy is improved.

Methods, systems, and computer programs are presented for audio-visual speech generation with multi-modal latent diffusion models. One method includes encoding raw audio signals and video frames into respective latent spaces using audio and visual autoencoders. A text transcript is processed into phoneme sequences using a text transcript processor. The audio and visual latent spaces are conditioned using the text transcript and a conditioning variable. Joint distributions of the visual and audio latent spaces, text transcript, and conditioning variable are learned using a multi-modal latent diffusion model. The model adds noise to the latent audio-visual representations and predicts the noise through denoising neural networks. An inverted diffusion process is utilized to generate diverse speech content and speaker characteristics, resulting in realistic audio-visual speech. The technology presented provides a novel approach to conditional speech generation with potential applications in speech synthesis, voice conversion, and speech recognition.

A training method for a speech enhancement network, a speech enhancement method, and an electronic device. The training method comprises: performing speech effectiveness classification on respective enhanced speech frames, and on the basis of the classification results of the enhanced speech frames, generating an effectiveness distribution for sample enhanced speech; by means of the effectiveness distribution, determining the speech classification accuracy of the speech enhancement network; and measuring the degree of change in speech effectiveness of each enhanced speech frame compared to that before noise reduction, and on this basis, determining speech enhancement accuracy according to noise reduction accuracy and the speech classification accuracy. In this way, the method can focus on improving the ability of the speech enhancement network to suppress noise in non-speech segments. When the trained speech enhancement network is used to perform noise reduction on speech to be processed, if the speech comprises non-speech segments, the trained speech enhancement network can effectively reduce the phenomenon of residual noise and improve the quality of speech enhancement. The invention can be widely applied in various scenarios such as cloud technology, artificial intelligence, smart transportation, and assisted driving.

A covariance accumulation-based direction of arrival estimation method for an S-mode signal at a low signal-to-noise ratio, relating to the technical field of information processing. According to the method, a delayed covariance matrix of array output data is comprehensively utilized, a weight is determined by means of a correlation feature of the S-mode signal, and the delayed covariance matrix of array output data is accumulated, so that the direction of arrival estimation precision of a MUSIC method for the S-mode signal in a low signal-to-noise ratio environment is improved, establishing more accurate angle tracking for an S-mode target in the low signal-to-noise ratio environment, effectively reducing the false alarm probability, and expanding the effective working ranges of angle measurement and a tracking system.

The invention relates to the technical field of audio, in particular to an intelligent sound effect optimization system based on dynamic frequency gain adjustment, which comprises an audio signal processing module, an environmental noise suppression module, a dynamic frequency gain adjustment module, a user interaction interface module and an intelligent learning module, through cooperation of an audio signal processing module, an environmental noise suppression module, a dynamic frequency gain adjustment module, a user interaction interface module and an intelligent learning module, the audio signal processing module is used for receiving an original audio signal and performing preliminary processing and analysis; the environmental noise suppression module is used for monitoring the environmental noise level in real time and correspondingly adjusting the audio signal so as to eliminate or reduce the influence of the noise; real-time adjustment can be carried out according to different acoustic environments and audio signal characteristics through dynamic frequency gain adjustment, it is ensured that the best sound effect experience can be provided in any environment, and the sound effect optimization effect in the complex environment is improved.

The invention discloses a noise monitoring point site selection method and system based on a noise map, and relates to the technical field of environmental noise monitoring, and the method comprises the steps: constructing a three-dimensional topology network based on a three-dimensional noise intensity matrix, calculating an optimal sound propagation path through employing a quantum heuristic topology optimization algorithm, and obtaining an optimal sound propagation path; the weights of nodes in the three-dimensional topology network are optimized in combination with quantum annealing, and an acoustic topology characteristic spectrum is generated; extracting multi-scale noise propagation features through a convolutional neural network according to the acoustic topological feature map, constructing a gridding space demand matrix in combination with a monitoring equipment parameter library and coverage constraint conditions, and generating an optimal noise monitoring network site selection scheme according to the gridding space demand matrix; according to the invention, a quantum heuristic topology optimization algorithm is combined with quantum annealing to optimize the node weight of the three-dimensional topology network, and intelligent optimization and dynamic adjustment of the sound propagation path are realized.

The invention discloses an acoustic emission intelligent detection method and system for hydrogen-induced damage of a high-pressure hydrogen system, and the method comprises the steps: collecting a system operation signal through an acoustic emission sensor, carrying out the combined preprocessing of variational mode decomposition and adaptive wavelet threshold noise reduction, restraining noise, constructing a lightweight MobileNet-TCN network, carrying out the deep feature extraction, and carrying out the detection of the hydrogen-induced damage of the high-pressure hydrogen system. GRU and a three-dimensional point cloud technology are fused to realize submillimeter-level positioning of an acoustic emission source, a big data damage case library is associated based on an acoustic emission parameter accumulation model, dynamic assessment and early warning of damage risks are realized, multi-physics field monitoring data are combined, damage classification is optimized through a GCNs (Graph Convolutional Networks), and the above processes are systematically integrated. And full-chain intelligent processing from signal acquisition to risk early warning is completed. The scheme has the advantages of strong anti-interference capability, submillimeter positioning precision, high edge end reasoning efficiency, high damage classification accuracy and dynamic early warning capability, and is suitable for safety monitoring of hydrogen energy storage and transportation equipment.