1086 results about "Environmental noise" patented technology

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

The invention relates to the technical field of earphone noise reduction processing, and discloses a motorcycle riding earphone noise reduction method combining different noise reduction algorithms, comprising the following steps: acquiring an original audio signal, and distributing the original audio signal to each processing module; a preprocessing signal is generated, voice activity information is determined, and environmental noise energy information is calculated; according to the environmental noise energy or the auxiliary information, adaptively adjusting a high-low noise energy threshold value; the original signals are input into the traditional and AI noise reduction module in parallel to generate two paths of noise reduction signals; based on the environment noise, the voice information and the threshold value, performing weighted fusion to generate an output signal; and carrying out equalization and compression processing on the output signal, and driving the loudspeaker to output. According to the invention, comprehensive judgment on environmental noise energy information, voice activity information and riding speed is introduced, an output signal of a traditional noise reduction module is set to be zero in a high-speed voice-free environment, and AI noise reduction is combined, so that tone quality distortion of a traditional noise reduction algorithm in a noise environment is effectively avoided.

The invention discloses a bird identification method based on sound-image multi-modal fusion. The bird identification method comprises the following steps: S1, carrying out standardized frame-level preprocessing on bird audio signals; s2, acoustic features are extracted and enhanced, and an acoustic high-level feature vector which highlights birdsong discrimination information and suppresses environmental noise is obtained; s3, visual image standardization preprocessing; s4, performing visual feature extraction and multi-scale fusion to obtain a visual high-level feature vector which enhances correspondence to the bird key form area and inhibits background interference; s5, performing dynamic weighted fusion on the decision-making layer to obtain a bird existence probability; and S6, comparing the bird existence probability with a preset threshold value of the corresponding bird, and judging whether the bird exists or not and the type of the existing bird. Through cross-modal feature enhancement and adaptive fusion, the precision, robustness and real-time performance of bird recognition in a complex orchard environment are significantly improved, and a core technical support is provided for green intelligent bird repelling.

The invention relates to the technical field of vibration monitoring, in particular to a self-powered transmission line fitting aeolian vibration damage diagnosis system and method, and the system comprises a sensing collection module, a signal decoupling module, a damage identification module, a damage association module and a risk assessment module. According to the method, stress wave velocity and acceleration data are synchronously collected, time alignment is implemented, feature coupling precision is enhanced, wave crest offset and energy density are respectively extracted by using moving average filtering and wavelet transform, effective data segments are dynamically screened, and environmental noise interference is suppressed. A stress wave propagation change rate is quantified based on a path attenuation model, a continuous energy abnormal node is matched to realize damage positioning, a breeze response abnormal region is identified by combining vibration direction change and a signal envelope offset degree, multi-dimensional features are coded and subjected to risk judgment through a neural network, a damage positioning and risk assessment closed-loop framework is formed, and the risk assessment accuracy is improved. And the spatial resolution and evaluation precision of aeolian vibration damage identification under complex working conditions are significantly improved.

The invention discloses a partial discharge online monitoring method based on an ultrahigh frequency original signal, and relates to the field of partial discharge monitoring, and the method comprises the steps: autonomously switching an ultrahigh frequency signal capturing frequency band according to the real-time electromagnetic spectrum distribution of a monitoring environment, and obtaining an original signal sequence covering a potential partial discharge feature; the method comprises the following steps: preprocessing an original signal sequence through multi-scale pulse contour extraction, and suppressing environmental noise interference to retain pulse characteristics of partial discharge signals; through real-time electromagnetic spectrum analysis, ultrahigh frequency capture frequency bands are automatically switched, environmental noise changes are dynamically adapted, original signal capture accuracy is improved, through multi-scale pulse contour extraction preprocessing, noise interference is effectively suppressed, key pulse features are reserved, and the accuracy of signal capture is improved. A multi-dimensional feature data set constructed based on the pulse peak gradient, the duration spectrum and the waveform similarity provides a comprehensive basis for partial discharge recognition.

The invention discloses a video editing method and device, electronic equipment and a nonvolatile storage medium. The method comprises the following steps: acquiring an audio data stream of a target video, and segmenting the audio data stream into a plurality of audio clips; determining a classification result corresponding to the audio clip, and determining a time period corresponding to the audio clip as a candidate ball hitting time period when the classification result is that the audio clip contains the ball hitting sound; acquiring a video frame corresponding to the candidate ball hitting time period in the target video, and judging that the candidate ball hitting time period is a real ball hitting time period under the condition that the visual feature of the video frame is matched with a preset ball hitting rule; and determining an editing time point according to the real ball hitting time period, and editing the target video according to the editing time point to obtain a ball hitting round video clip. The technical problem that the accuracy of ball hitting detection segment detection is low due to the fact that ball hitting detection is conducted only through sound and is easily interfered by environmental noise in the prior art is solved.

The invention discloses a non-contact operating room intelligent voice control method supporting multi-role voiceprint recognition, and relates to the technical field of voice interaction control, and the method comprises the following steps: S100, collecting full-band noise signals of all high-frequency operation equipment in an operating room in different operation states, extracting harmonic frequency, amplitude and phase features, and carrying out the recognition of the full-band noise signals; and constructing a harmonic noise characteristic database, and establishing and dynamically updating an environmental noise baseline model. According to the invention, through the dynamic environment noise baseline model and personalized voiceprint recognition, frequency spectrum isolation and accurate voice separation of high-frequency operation equipment noise and doctor voiceprint are realized. In combination with space and frequency spectrum dual-domain decoupling and voice integrity detection, and through dual verification of voiceprint identity and non-contact action, it is ensured that an instruction sender is true and effective. And a closed-loop defense mechanism is formed through permission level comparison and context semantic verification, so that error identification and error control are effectively prevented, and the safety, accuracy and continuity of the operation process are guaranteed.

The invention discloses an auxiliary decision-making system for cognitive competence assessment of old people, which relates to the technical field of cognitive auxiliary decision-making and comprises an environmental noise acquisition and feature extraction module, a time sequence fluctuation analysis module, a dynamic threshold reconstruction and judgment module, a cross-domain consistency calibration module, a multi-modal synchronous verification module and a dynamic threshold regulation and control module. According to the method, the environmental noise is dynamically analyzed, so that the defects of a fixed noise suppression threshold and a static feature extraction model in the traditional technology are overcome. A sound field energy distribution model is constructed in real time and time sequence fluctuation analysis is combined so that a noise fluctuation interval can be identified, a voice starting point detection threshold value is dynamically adjusted and evaluation precision is enhanced. Through multi-mode synchronous verification combining a reaction time curve and a facial movement track, a time offset error is corrected, a judgment standard is adjusted in real time according to noise changes, misjudgment and virtual high risk early warning are avoided, and therefore the accuracy and reliability of cognitive ability assessment of the old are improved.

The invention discloses an Internet of Things alarm audio call scheduling method and system, and relates to the technical field of Internet of Things intelligent decision, and the method comprises the steps: receiving an alarm request message from an Internet of Things sensing terminal, and carrying out the alarm audio call scheduling according to a device identifier and a region code in the alarm request message; collecting and generating a multi-dimensional information report of real-time environment noise data, audio playing equipment state information and user state information; according to the multi-dimensional information report, generating a dynamic priority score of the alarm request message by querying a preset strategy library; and sorting the dynamic priority scores of the alarm request messages to be processed, and generating a preliminary scheduling strategy for suggesting playing equipment and broadcasting parameters for the alarm request message with the highest priority. According to the method, high-fidelity simulation is performed through the acoustic digital twin model to generate the intelligibility index report, the optimal strategy is selected based on the simulation result to generate the execution instruction, and the execution instruction is issued to the target audio equipment, so that full-process intelligent scheduling from multi-dimensional dynamic perception to acoustic effect optimization is realized.

The invention discloses an intelligent geophysical exploration geological exploration analysis system, and relates to the field of geological exploration, and the system comprises a collection and preprocessing module which is used for carrying out the adaptive collection of multiple types of geophysical field signals of a target exploration region, and synchronously carrying out the preliminary noise reduction and quality optimization of original signals through signal purification processing; the fusion module is used for receiving the preprocessed multi-type geophysical field signals and carrying out normalization and feature depth correlation fusion on coordinates of data of different dimensions through spatial-temporal feature correlation processing; the method can dynamically adapt to environmental noise optimization signal acquisition, extracts stratum recessive characteristics through multi-dimensional data fusion and intelligent mining, constructs an accurate three-dimensional geologic model, positions stratum attribute deviation and grades through multi-scale analysis, presents exploration results in combination with three-dimensional visualization, and generates target region optimization suggestions.

The invention discloses a road environment semantic segmentation method based on cross-modal difference modulation and a visual basic model, and the method is based on a DINOv3 visual basic model of a frozen weight, and introduces a complementary difference fusion module and a bidirectional context flow alignment module through the design of double-flow frozen coding and difference perception interaction. According to the method, a cross attention weight is generated by calculating a local high-frequency difference, so that transverse anti-noise complementation of RGB texture information and a Depth geometric structure is realized, and single-mode noise pollution is effectively inhibited; the two-way cross-scale alignment mechanism of top-down space mask guidance and bottom-up channel feature feedback is constructed by the two-way cross-scale alignment mechanism, and the scale barrier of deep and shallow layer features is broken. Therefore, joint feature representation with deep synergy of semantics and structures is obtained, inhibition of the model to environmental noise and reservation of tiny object details are balanced in a complex road scene, and pixel-level semantic segmentation with high generalization and high precision is realized.

The invention relates to the technical field of aeronautical medicine monitoring and flight protection, in particular to a crissum health risk assessment and dynamic intervention management system for military flight personnel, which comprises a data sensing step: synchronously acquiring environmental stress data and real-time physiological sensing data; a benchmark reconstruction step: calculating ideal physiological response data based on the environmental stress; a pathological simulation step: injecting pathological rheological parameters to generate pathological simulation data; a difference extraction step: constructing a double-track difference vector, and respectively calculating a real residual vector and a theoretical residual vector; a coupling judgment step: calculating a vector similarity to judge a risk type; a dynamic intervention step: generating a physical intervention strategy in response to the risk type to adjust the equipment state; according to the invention, by dynamically stripping environmental noise, physiological hyperemia and pathological disorders are effectively distinguished, and the false alarm rate in a high dynamic environment is reduced.

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.

The invention relates to the technical field of environmental noise monitoring, and discloses a road traffic noise intelligent monitoring and three-dimensional sound field reconstruction system, which comprises an acoustic sensor module, a data collection and storage module, a data analysis and evaluation module, a three-dimensional sound field construction and display module and a traffic flow feature library. According to the system, a differential geometry principle is adopted, a sound field is regarded as a Riemannian manifold with a local microstructure, and accurate description of an irregular sound field is realized through a curvature self-adaptive sound field manifold construction technology; introducing a covariant derivative in Riemannian geometry, and constructing a sound propagation model adapted to a complex road environment; and realizing hierarchical decomposition and reconstruction of the sound field by using a multi-scale analysis theory. According to the method, the sound source positioning precision and the calculation efficiency are improved, seamless analysis from microcosmic to macroscopic is realized, an innovative solution is provided for traffic and noise collaborative management, and intelligent traffic and environmental noise management are effectively supported.

The invention relates to a bridge structure modal parameter identification method, which is based on a bridge structure tiny vibration video acquired by a camera. VMD (variational mode decomposition), PBVM (phase-based video motion amplification), Gabor filtering-based phase extraction method GBP (Gabor filtering), an improved clustering algorithm ICA (independent clustering algorithm) and an integrated covariance-driven random subspace recognition (SSI-Cov) and FDD (frequency domain decomposition) algorithm are fused to construct a set of complete non-contact structural modal parameter recognition system. According to the method, effective decoupling of multiple vibration modes of a bridge structure, frequency domain feature extraction of small-amplitude vibration and intelligent and automatic modal parameter extraction are achieved under the conditions that the target frequency bandwidth does not need to be known in advance, environmental noise interference exists and no obvious feature target exists, and the method belongs to the technical field of bridge structure health monitoring.

The invention relates to the technical field of intelligent scheduling, in particular to a steel structure weld fatigue cracking real-time monitoring system based on machine vision, which is characterized in that an image acquisition module acquires a high-stability image sequence in a strong vibration environment; the displacement field reconstruction module adopts a frequency domain-space domain combined decomposition technology to isolate environmental noise and material non-uniformity interference in a breakthrough manner, and an anti-noise deformation field and a stress distribution diagram are synchronously output through dual-channel parallel processing; a damage response module accurately locks sub-pixel-level crack initiation coordinates based on strain energy accumulation rate mutation, and dynamically predicts a yield risk area in combination with a displacement gradient diffusion model; the early warning module intelligently triggers graded response according to the crack position and the space coupling state of the risk area, performs positioning maintenance in a low risk area and automatic strengthening in a high risk area, and outputs heat treatment scheme coordinates when no crack exists; the system solves the problems of false detection caused by vibration noise and lagging response of single-parameter monitoring, and active protection from damage initiation to failure blocking is realized.

The invention discloses a sleep aiding method and system based on brain wave data, and the method comprises the steps: synchronously collecting brain waves, electrocardiosignals, environmental noise and user voice data through a flexible electrode array, constructing a multi-mode causal graph, and dynamically recognizing the type of a noise source (electromagnetic interference or psychological noise); the method comprises the following steps of: generating a target antagonistic intervention signal (such as reverse sound wave counteracting electromagnetic noise and binaural rhythm relieving psychological pressure) according to the target antagonistic intervention signal, generating dynamically adaptive light pulse and tactile vibration parameters in combination with a psychological-physiological collaborative model, and synchronously outputting sound, light and tactile intervention signals through a multi-modal actuator. Through causal reasoning and multi-sensory cooperative regulation and control technologies, accurate inhibition of noise interference and personalized induction of the sleep state are realized, the sleep time is remarkably shortened, the deep sleep duration is prolonged, and the method is particularly suitable for complex noise environments and anxiety-related insomnia scenes.

The invention discloses a construction method of a multi-underwater robot cooperative control system, which relates to the field of underwater robots, and establishes a multi-parameter coupled data acquisition and preprocessing mechanism by using a Doppler current profiler ADCP, an inertial measurement unit IMU and controller software. A combined algorithm of sliding median filtering and Kalman filtering is adopted, and data disturbance caused by environment noise and attitude drift is effectively removed; and through abnormal point triple standard deviation determination and double adjacent point interpolation completion, stable correction of sampling data is realized. A standardized data set obtained after normalization processing unifies parameter scales of different physical magnitudes, so that the local average flow velocity, the flow velocity fluctuation root mean square and the average acceleration can be directly compared in a feature space. Therefore, according to the method, the high-consistency expression of the water flow disturbance characteristics in time and space is realized, so that the synchronous measurement precision of multiple underwater robots in a non-uniform flow field is remarkably improved.

The invention relates to the technical field of sensing data processing, and discloses a sensing data chip method based on multi-source information fusion and dynamic parameter adjustment. The method comprises the following steps: collecting original data streams of a plurality of heterogeneous sensors, and generating synchronized sensing data through timestamp alignment and format standardization processing; extracting an environment noise component, and dynamically suppressing noise by using an adaptive filtering algorithm to obtain de-noised sensing data; performing multi-dimensional feature decomposition on the de-noised data, calculating time domain, frequency domain and space domain feature vectors, and generating a multi-dimensional feature set; dynamically adjusting a feature weight distribution strategy in combination with a current system load state and a multi-dimensional feature set historical distribution rule, and generating an optimized fusion weight matrix; and carrying out weighted aggregation on the multi-dimensional feature set and the optimized fusion weight matrix, and outputting a multi-source fusion feature vector. According to the method, the problems of data synchronization, noise suppression and feature fusion suitability in multi-source heterogeneous sensing data processing can be solved, and the processing requirements of different scenes are met.

A computer-implemented method for training an anomaly detection neural network system comprising an encoder and a decoder is described. The method includes receiving training data comprising a plurality of training examples, each training example including a training audio waveform and a machine identity (ID); processing the training audio waveform to extract training audio features; receiving an environmental noise audio waveform; processing the environmental noise audio waveform to extract noise features; generating augmented features by combining the extracted training audio features and the noise features; processing, using the encoder, the augmented features to generate latent embeddings; processing, using the decoder, the latent embeddings to generate reconstructed audio features; processing, using a convolutional neural network, the augmented features to generate a predicted machine ID probability distribution; and adjusting, through backpropagation, the current values of the parameters of the encoder and the decoder to minimize an objective function.

The invention discloses a bridge expansion joint acoustic monitoring method based on a beam forming microphone array, and the method comprises the steps: synchronously collecting acoustic signals through multiple channels, and achieving the high-precision signal alignment through clock offset compensation; the noise analysis module is started by combining a pre-triggering signal of a vehicle passing through the detection unit, the optimal microphone distance is dynamically determined according to sound field coherence and a signal-to-noise ratio maximization model, and a stepping motor drives a movable microphone to achieve real-time adjustment. On the basis, a spatial filtering weight is generated by adopting a self-adaptive beam forming algorithm, and directional enhancement and interference suppression of the expansion joint sound source are realized. Through collaborative optimization of a hardware structure and a signal processing method, the sensitivity and accuracy of acoustic monitoring of the bridge expansion joint are effectively improved, the abnormal sound source recognition capability can be remarkably enhanced, the environmental noise interference is reduced, and the method is suitable for intelligent monitoring and diagnosis of the long-term service state of the bridge expansion joint.

The invention discloses a video monitoring and AI linkage intelligent alarm verification system, and particularly relates to the technical field of video analysis, which comprises the following steps: carrying out dual anomaly preliminary screening by using a flow field structure entropy and a signal track singular value ratio, eliminating environmental noise interference, and constructing a multi-mode normal state baseline; generating a multi-modal event report containing a spatio-temporal context, uploading the multi-modal event report to a cloud, inputting the multi-modal event report into a physical perception cross attention network, configuring a physical embedding vector into a query vector and configuring a visual embedding vector into a key vector and a value vector through an asymmetric feature fusion architecture, and performing multi-modal event report analysis; actively guiding the attention weight distribution of the model on the video picture by using the change trend of the physical parameters, and outputting a confidence score based on the weighted fusion feature; performing closed-loop parameter correction on the baseline model by utilizing online incremental learning based on a verification result; the problems of high false alarm rate caused by lack of physical logic constraints and poor anti-interference capability in a complex environment in traditional monitoring are effectively solved.

The invention relates to the technical field of voice emotion recognition, in particular to a man-machine interaction voice sensing method and system based on a gradient intelligent calling subnet pool. The method comprises the steps of obtaining an emotion data set; constructing a man-machine interaction voice perception model based on a gradient intelligent dispatching sub-network pool; the system comprises an acoustic clue sensing purification module, a layered acoustic essential coding module, a gradient harmony subnet pool module, a task specific feature extraction module, a focus and confidence joint calibration module, a self-adaptive optimization strategy module and a real-time reasoning and decision fusion module. Carrying out emotion decision making by utilizing the constructed human-computer interaction voice perception model; and outputting a decision result. According to the invention, through the acoustic clue sensing purification module and the layered acoustic essential coding, the problems of emotional information distortion and identity feature confusion caused by real environmental noise are fundamentally solved.

The invention discloses a wind power blade structure health monitoring method based on multi-field decoupling optical fiber sensing, particularly relates to the technical field of wind power equipment state monitoring, and is used for solving the problem of health state misdiagnosis caused by coherent interference of environmental noise and damage signals. The method comprises the following steps: collecting vibration, strain and temperature signals and generating a background noise vector; calculating a frequency domain coherence function of the vibration signal and the noise vector to identify an interference danger frequency band; analyzing the polarization angle distribution of the vibration signal in the frequency band, and inverting the physical path topology of the noise transmitted into the blade; when the path passes through a predefined weak area, improving a damage weight coefficient of a strain signal of a corresponding frequency band and carrying out Hilbert phase shift reconstruction to generate an anti-interference strain feature vector; calculating the energy dissipation rate of the vector in a dangerous frequency band, and judging an energy leakage event caused by damage by combining a viscoelastic constitutive threshold value of the composite material; the damage type and the spatial position are judged according to the time domain correlation between the energy leakage event and the temperature signal, and the hidden damage detection reliability is improved.

The invention relates to the technical field of energy storage and noise reduction, and discloses an active and passive combined energy storage and noise reduction system which comprises a passive noise reduction subsystem and an active noise reduction subsystem which are arranged on a cabinet body, the passive noise reduction subsystem is used for prolonging an exhaust path of air at an air outlet in the cabinet body and reducing noise of the air at the air outlet, and the active noise reduction subsystem is used for reducing noise of the air at the air outlet. The active noise reduction subsystem is used for actively outputting a reverse sound wave signal for active noise reduction; according to the standard requirements of environmental noise, the exhaust noise of the cabinet body can be reduced through the passive noise reduction subsystem, or the exhaust noise of the cabinet body can be reduced through the combination of the passive noise reduction subsystem and the active noise reduction subsystem. According to the invention, passive and active combined noise reduction or passive noise reduction can be carried out on the energy storage cabinet according to the environmental noise standard, so that the energy storage cabinet can be allowed to be used in different scenes, the occupied space is saved, and the response speed is high.

The invention discloses a sea surface wind speed inversion method and device based on marine environment noise, and a storage medium. The method comprises the following steps: S1, obtaining marine noise data; s2, acquiring wind speed data according to time and latitude and longitude information in the ocean noise data; s3, audio data in the ocean noise data are processed, and center frequency point noise spectrum level data are obtained; s4, summarizing the noise spectrum level data of the center frequency point and the corresponding wind speed data into a neural network data set through time and latitude and longitude information; step S5, constructing an initial Elman network; s6, performing iterative optimization on the network weight and bias of the Elman network by adopting a cuckoo search algorithm to generate optimized network parameters; step S7, initializing an Elman network by using the optimized network parameters, and generating a CS-Elman model; training a CS-Elman model by using the neural network data set; and step S8, carrying out quantitative evaluation on the CS-Elman model. The method can achieve the high-precision prediction of the sea surface wind speed.

The invention provides a space particulate matter concentration detection method and device in an industrial and mining environment, relates to the technical field of industrial and mining environment detection, and solves the technical problem of low detection accuracy in the prior art. The method comprises the following steps: sensing a charge signal of the suspended particulate matter through a charge acquisition probe; the charge acquisition probe is used for generating induction current according to electrostatic induction charge characteristics of the suspended particulate matters; carrying out amplification processing and noise processing on the charge signal, and adopting a differential signal processing mechanism to eliminate environmental noise in the noise processing; the processed charge signals are input into a particulate matter characteristic model, and the spatial particulate matter concentration is obtained through calculation; the particulate matter characteristic model is used for representing an association relationship between electrostatic induction charge characteristics and concentrations of particulate matters with different particle sizes. The method is used in the detection process of the space particulate matter concentration.

The invention discloses a baby cry recognition method for white noise equipment, and relates to the field of audio processing and intelligent acoustic recognition, and the method comprises the steps: collecting a pure reference signal, a first-path signal and a second-path signal, carrying out the synchronization and preprocessing, and obtaining a mixed audio signal based on the first-path signal; a residual signal is calculated through an adaptive echo cancellation algorithm; calculating an acoustic masking parameter based on the residual signal, the pure reference signal, and the ambient noise estimate; constructing a three-level recognition processing path including lightweight feature detection, registration voiceprint comparison and multi-modal information fusion; based on the numerical range of the acoustic masking parameter, selecting an identification processing path, and determining a crying event of the target infant from the residual signal; and triggering a corresponding grading alarm based on a crying event confirmation result. A three-level identification path is scheduled through acoustic masking parameters, and accurate and low-power-consumption baby crying monitoring under strong interference is realized by fusing multi-modal information.

The invention relates to the technical field of intelligent tourism services, and particularly discloses an AI tourism personalized explanation system and service system.The personalized explanation system comprises a positioning module used for obtaining user position information; the audio acquisition module is used for acquiring environmental noise data; the attribute acquisition module is used for acquiring user attribute data; the intelligent explanation module is used for determining explanation content based on the user position information; determining a reference speed and a reference volume based on the user attribute data; respectively compensating the reference speed and the reference volume based on the environmental noise data to generate an explanation speed and an explanation volume; and explaining the explaining content based on the explaining speed and the explaining volume. According to the method, the real-time position information of the user, the environment noise data and the attribute characteristics of the user are integrated, the interpretation content is dynamically matched, the speech speed and the volume are adaptively adjusted, and the interpretation effect and the user experience are improved.

The invention relates to the technical field of reinforcement corrosion detection, in particular to a reinforcement corrosion nondestructive testing method integrating ultrasonic and electrochemical signals, and the method comprises the following steps: calculating an environmental interference coefficient and an environmental influence evaluation value through an environmental noise signal and concrete surface impedance, and constructing an initial evaluation model; collecting and processing ultrasonic and electrochemical signals in real time to obtain characteristic parameters, and calculating an initial corrosion risk coefficient through an initial evaluation model; generating a comprehensive decision coefficient in combination with the environmental influence evaluation value, determining a feature weight distribution strategy, and generating a fusion diagnosis index; adaptively adjusting the detection point grid spacing according to the fusion diagnosis index; the diagnosis reliability is verified through the time-space consistency coefficient of the ultrasonic waves and the electrochemical active region, and a fusion diagnosis deviation value is calculated; and carrying out collaborative optimization on the detection period and the evaluation model based on the cumulative fusion diagnosis deviation value. According to the invention, the problem that a single detection means is insufficient in precision and cannot be adaptively adjusted in a complex environment is solved.