120 results about "Noise reduction algorithm" patented technology

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 submarine cable fault positioning simulation verification system and a calibration method thereof, and belongs to the technical field of submarine cable detection. According to the system, a multi-source sensor array is deployed for data acquisition, and a multi-stage adaptive noise reduction algorithm, a time-frequency domain feature fusion technology and a land-sea environment electromagnetic field mapping model are adopted, so that magnetic field distribution features of a submarine cable in an actual marine environment can be simulated with high fidelity, and accurate positioning of a fault position is realized. By establishing a standardized calibration process, effective migration of simulation data and marine measured data is ensured, and the technical problems of complex marine noise interference and extrapolation distortion of a laboratory environment verification result are effectively solved. The scheme has the remarkable advantages of high positioning precision, strong anti-interference capability, accurate environment mapping and the like.

The invention provides a full-spectrum water quality multi-parameter dynamic inversion method and a model construction method thereof, and belongs to the technical field of spectral analysis based on machine learning. By constructing a closed-loop optimization architecture of noise reduction, wavelength optimization, turbidity correction and adaptive modeling, high-precision detection of four water quality parameters of chemical oxygen demand, total organic carbon, total nitrogen and nitrate nitrogen is realized. According to the method, a variational mode decomposition and improved threshold translation invariant wavelet combined noise reduction algorithm is provided, a kurtosis-correlation coefficient dual index is adopted to screen noise components, a multi-translation average strategy is combined to suppress a pseudo-Gibbs phenomenon, and meanwhile, a global parameter collaborative tuning mechanism based on Bayesian optimization is designed; a cross-module hyper-parameter coupling space is established in the whole process of noise reduction, wavelength optimization, turbidity correction and modeling. Through a multi-dimensional feature enhancement mechanism, the model feature representation capability is improved by 35%, and an innovative solution is provided for multi-parameter online detection in a complex water quality scene.

The invention belongs to the technical field of earphone noise reduction, and provides a noise reduction regulation and control method for an earphone and a noise reduction earphone. The method comprises the following steps: acquiring a first environment sound signal of an environment area, and extracting human voice features from the first environment sound signal to construct a human voice feature template library; in response to a noise reduction regulation and control instruction, collecting a second environment sound signal of the environment area, converting the time domain signal into a frequency domain signal through Fourier transform, and extracting a frequency spectrum feature from the frequency domain signal; performing matching calculation on the spectrum features and a human voice feature template library to determine environment voice signals, human voice signals and noise signals; an active noise reduction algorithm is adopted to generate offset sound waves opposite to the noise signals in phase, the human voice signals are amplified, and the offset sound waves and the amplified human voice signals are output through a loudspeaker of the earphone. According to the invention, effective noise filtering and clear transparent transmission of specific human voice are realized, smooth communication can be realized without taking off the earphone, and the use experience is improved.

The invention provides a GIS equipment withstand voltage test system and method, and relates to the technical field of GIS equipment withstand voltage test. Comprising the steps of obtaining parameter information and installation environment data of GIS equipment, constructing a three-dimensional digital twinborn model of the GIS equipment in combination with an electric field simulation engine, generating a withstand voltage test scheme and planning an optimal path. Acquiring partial discharge data and insulation resistance data under different voltage stresses by using an AI noise reduction algorithm according to the optimal path, carrying out multi-modal fusion processing on the partial discharge data and the insulation resistance data, extracting features by using a deep learning model, obtaining insulation defect and performance attenuation data, and carrying out multi-modal fusion processing on the insulation defect and performance attenuation data. And carrying out space mapping association with the three-dimensional digital twinborn model to generate a GIS equipment withstand voltage test evaluation report, thereby providing a powerful basis for equipment operation and maintenance. According to the invention, the three-dimensional digital twinborn model is constructed to generate the GIS equipment voltage withstanding test evaluation report, so that the accuracy of the GIS equipment voltage withstanding test is improved.

The invention particularly relates to a voice noise reduction method suitable for different noise environments, and relates to the technical field of voice signal processing. Analyzing a noise environment and characteristics; adaptive selection of a noise reduction algorithm; adaptive noise reduction processing is executed; and noise reduction effect evaluation and iterative optimization are carried out. According to the invention, refined noise classification is matched with the algorithm, so that the limitation of a traditional single algorithm in a complex environment is solved; the steady-state noise is dynamically counteracted by adopting an adaptive filter, the frequency domain gain of the unsteady-state noise is adjusted in real time through Wiener filtering, and the pulse noise is subjected to dynamic threshold processing by combining median filtering and wavelet transform; especially, the design of cooperation factors in wavelet transform can accurately distinguish signal details and noise: intensively suppress pulse noise, loosely retain details such as voices and consonants, and realize dynamic balance of noise reduction intensity and signal distortion.

The invention discloses an elevator steel belt damage detection and quantitative analysis system based on eddy current and magnetic flux leakage dual-mode detection, and aims to solve the problems of single-mode information loss and serious industrial field strong noise interference in the existing steel belt detection. The system synchronously integrates an eddy current sensor, a magnetic flux leakage sensor and an encoder through a multi-probe array adapter; and multi-dimensional damage physical information in the steel strip is obtained. An improved wavelet packet transform-empirical mode decomposition (WPT-EMD) collaborative noise reduction algorithm is adopted, and in combination with a sub-band energy entropy and a self-attention mechanism, non-stationary mechanical noise is effectively filtered out. A multi-rule feature extraction engine is used for extracting smooth residual errors, derivative mutation and other features, a support vector machine (DE-SVM) model introducing physical priori knowledge weights is constructed, and the small sample recognition problem is solved in combination with a virtual sample generation technology. The method can realize high-precision positioning and quantitative evaluation of steel strip damage under complex working conditions, and has the characteristics of strong anti-interference capability, high identification accuracy and good generalization performance.

The invention discloses a PLC electric control cabinet control method, and belongs to the technical field of electrical control. The method specifically comprises the following steps: S1, multi-dimensional parameter fusion acquisition: acquiring multi-dimensional data through a multi-type sensor, setting a hierarchical acquisition period according to parameter types, storing multi-source data in a classified and layered manner, and performing traceability judgment on abnormal data; s2, parameter preprocessing and feature extraction: different noise reduction algorithms are adopted for different signal types, and depth features of data are extracted through multi-step processing; electrical parameters, physical state parameters, environmental parameters and time sequence label parameters are collected through multiple types of sensors, the equipment operation state and external influence factors are comprehensively captured, compared with traditional single parameter collection, the real working condition of the system can be reflected more accurately, and complete data support is provided for subsequent control and diagnosis. And a hierarchical acquisition period is set according to parameter types, so that resource waste or key data omission caused by one-step acquisition is avoided.

The invention relates to the technical field of time length detection, and discloses a news simulcasting time length detection alarm method based on voice recognition, which comprises the following steps: S1, noise reduction: integrating an advanced digital signal processing technology and a professional noise reduction algorithm to form a refined processing system adaptive to'news simulcasting 'audio, according to the system, aiming at collected useless components such as environment noise and equipment noise, noise characteristics are captured through a multi-dimensional signal analysis model, an effective information core identifier is defined, a differentiated characteristic database of signals and noise is constructed, and a technician sets scientific noise reduction parameters according to the differentiated characteristic database. And accurate noise identification and stripping: integrating an advanced digital signal processing technology and a professional noise reduction algorithm, capturing noise characteristics through a multi-dimensional signal analysis model, accurately stripping environmental noise (such as studio echo and external interference sound) and equipment noise (such as sound recording equipment floor noise and transmission link electromagnetic interference), and simultaneously retaining the original texture of effective audio.

The invention discloses a teaching management resource matching method and system based on deep learning, and the method comprises the steps: obtaining the initial teaching management data through obtaining the text data, video data, audio data and Internet of Things equipment data related to teaching, carrying out the noise reduction of the initial teaching management data through a mixed noise reduction algorithm, and obtaining a noise reduction result; noise reduction teaching management data is obtained; performing feature normalization processing on the noise reduction teaching management data based on wavelet transform and BERT semantic embedding to obtain feature teaching management data; and constructing a CNN-LSTM dual-channel model, and inputting the feature teaching management data into the CNN-LSTM-GAN dual-channel model for identification to obtain a teaching resource matching scheme. Precise matching of teaching resources with student learning requirements and classroom teaching objectives is realized, personalized resource recommendation is supported, and teaching pertinence is improved.

The invention discloses an automatic detection method and system for the boundary dimension of a shield segment, and relates to the field of civil engineering, and the method comprises the steps: laying quantum dots on the surface of a to-be-detected shield segment, and exciting the quantum dots through ultraviolet laser to generate a fluorescence spectrum; the method comprises the following steps: receiving an optical signal penetrating through a medium gap in a to-be-detected shield segment through a single-pixel barrel detector to form a reflected light intensity sequence, and inputting the reflected light intensity sequence and a speckle pattern into a correlation reconstruction algorithm to generate a three-dimensional point cloud of a shielding area; and inputting the three-dimensional point cloud and the deformation compensation parameters into a deep learning network, generating a deviation thermodynamic diagram of the boundary dimension of the to-be-detected shield segment, automatically marking an area exceeding a tolerance threshold value based on the deviation thermodynamic diagram, and generating a detection result of the boundary dimension of the to-be-detected shield segment. According to the method, the internal medium gap is reconstructed through the single-pixel barrel detector and the basis tracking noise reduction algorithm, in a bolt hole oil stain shielding scene, compared with a traditional method, the reconstruction porosity error is reduced, and online analysis of the full-surface size error is achieved.

The invention relates to the technical field of data noise reduction, and discloses a safety alarm noise reduction method and system based on a data weaving technology, and the method comprises the steps: building a unified logic data view based on a data weaving architecture; deploying an AI-driven data intelligent agent component system, automatically identifying and adapting a changing API and a new data source, and fusing metadata, threat intelligence and infrastructure operation state information; constructing an attack chain panorama and a behavior causal chain model; a semantic noise reduction algorithm, time sequence clustering analysis and a confidence scoring mechanism are adopted to perform de-duplication alarm, false alarm identification and low-risk event filtering on the security alarm; and alarm de-duplication and priority adjustment are carried out to generate a linkage processing strategy. According to the invention, the problems of alarm flooding, high false alarm rate, low processing efficiency and the like in the existing security alarm system are solved.

The invention relates to the technical field of medical image processing, in particular to a dynamic ultrasonic noise reduction model training method and a dynamic ultrasonic noise reduction method and device. The method comprises the following steps: acquiring an ultrasonic video covering multiple clinical scenes such as antral pneumatosis, hydrops, solid contents, long antral antrum and cardiac anatomical occlusion; marking the precise boundary of the organ frame by frame by a clinical expert, segmenting and complementing a missing section, optimizing the contour of a fuzzy region, and inhibiting or erasing artifacts; a deep learning model is trained based on the labeled data, so that the model can call an exclusive noise reduction algorithm according to organ categories (the antral sinuses adopt morphological reconstruction filtering guided by ellipse / long axis prior, and the heart adopts time-space joint deblurring driven by a cardiac cycle and combining motion compensation and anisotropic filtering); and weighted noise reduction is carried out on the suspected lesion or the user indication area. In the reasoning stage, a high-fidelity and low-noise video stream can be output in real time by inputting any dynamic ultrasonic video, and organ boundary metadata is attached.

The invention discloses a pediatric spleen and stomach dynamic monitoring method and system based on an AI chip, and belongs to the field of medical health information technology and artificial intelligence chip application. The pediatric spleen and stomach dynamic monitoring method comprises the steps that a multi-mode original data set is collected, signal preprocessing is conducted, and a feature data set is obtained; key indexes of spleen and stomach syndromes are identified from the feature data set based on the viscera differentiation theory, and a dialectical result is obtained; inputting the feature data set into a dialectical verification model to generate a spleen and stomach health assessment value; when the spleen and stomach health assessment value is smaller than an assessment threshold value, a first-level health early warning signal is generated; and when the duration of the first-stage health early warning signal is greater than a duration threshold, generating a second-stage combined early warning signal according to pre-acquired user behavior log data. A multi-modal data fusion technology, a dynamic noise reduction algorithm and a grading early warning mechanism are adopted, and doctors can be assisted in achieving precise evaluation of the spleen and stomach states of children and personalized traditional Chinese medicine intervention.

The invention relates to the technical field of mechanical fault diagnosis, and discloses a deep learning-based tapered roller bearing fault diagnosis method and system, and the method comprises the steps: collecting original vibration signals of a tapered roller bearing in different operation states through an acceleration sensor, carrying out the segmentation of the collected original vibration signals according to a preset time interval, and carrying out the segmentation of the collected original vibration signals; obtaining a plurality of vibration signal samples; carrying out noise reduction processing on the vibration signal sample by combining a self-adaptive wavelet noise reduction algorithm and a soft and hard mixed threshold value, and carrying out normalization processing on the vibration signal sample after noise reduction to obtain a preprocessed vibration signal sample; inputting the preprocessed vibration signal sample into an MSCNN-BiGRU-Attention model, using a Softmax activation function to output a fault probability, and using a fault type with the maximum probability as a diagnosis result; an early warning threshold value is dynamically adjusted based on the historical operation data and the working condition, and corresponding early warning is triggered for the tapered roller bearing according to the diagnosis result and the early warning threshold value; according to the invention, the fault diagnosis efficiency is improved.

The invention provides a driving emotion early warning method and system based on voice analysis, relates to the technical field of voice emotion recognition, and effectively solves the interference problem of vehicle-mounted dynamic strong noise by deploying a microphone array and a noise sensor in a cockpit and combining multi-channel noise reduction algorithms such as beam forming and linear beam minimum variance. A high-quality voice instruction is extracted from a source; on the basis, the driving intention of the clear voice signal is accurately recognized by using the deep neural network, so that the accuracy is remarkably improved; more importantly, according to the method, the instability Ls and the high interactive friction probability Pf are recognized innovatively through calculation instructions, real-time quantitative evaluation on the man-machine interaction fluency is achieved, when the interactive friction probability Pf exceeds a preset threshold value, the system can automatically trigger self-adaptive strategy adjustment, and therefore the situation that a driver is distracted due to recognition errors is avoided, and user experience is improved. And the interaction experience and the driving safety are greatly enhanced.

The utility model provides a speech enhancement module, belongs to the technical field of communication, and enables the speech enhancement module to adapt to more equipment interfaces by separating a core circuit from an interface circuit, thereby improving the application range of the speech enhancement module. The core board adopts an embedded system technology based on an SOC architecture, and the PS unit and the PL unit cooperatively work. And the PS unit realizes noise reduction processing on the input audio through a noise reduction algorithm. The PL unit is matched with the audio coding and decoding chip to realize digital-to-analog conversion of audio signals. And the interface board is used for conditioning input and output audio signals and providing communication and control interfaces for the outside. The voice enhancement module can be independently connected between the arranged audio equipment and the electroacoustic conversion equipment, and a set of low-cost and convenient noise reduction scheme is provided for the arranged audio equipment.

The invention discloses a method for extracting a self-adaptive uniform region and reducing phase noise based on a GNSS-based InSAR (Global Navigation Satellite System-Based InSAR) system. The method mainly aims at the problem that a traditional noise reduction algorithm based on a uniform region under a GNSS-based InSAR system is not applicable, and firstly, an improved Canny edge detection algorithm is used for carrying out self-adaptive uniform region extraction on an SAR image according to a resolution unit center and a gradient size. Then, a coherence matrix is introduced, a main phase component is obtained through eigenvalue decomposition, and the phase change of a deformation area is obtained; the method overcomes the limitation of a traditional InSAR imaging algorithm, is suitable for a GNSS-based InSAR system, can reduce phase noise interference, and lays a foundation for deformation monitoring application.

The invention discloses an off-line estimation method for an automobile active noise reduction nonlinear transfer function based on RNN (Recurrent Neural Network). Specific used models are LSTM (Long Short Term Memory) and GRU (Generalized Range Unit). For a specific vehicle model, offline acquisition and preprocessing of data, model training and deployment of the model to a specific audio DSP are carried out, a traditional vehicle active noise reduction algorithm (LMS, APA, LS and the like) assumes an acoustic path as a linear system (MA / ARMA model) which cannot adapt to the nonlinear characteristics of an actual acoustic path, and two RNN models of LSTM / GRU are adopted to carry out nonlinear transfer function estimation, so that the nonlinear characteristics of the acoustic path cannot be adapted to the nonlinear characteristics of the actual acoustic path. The nonlinear features of the acoustic path can be accurately represented, the in-vehicle noise suppression effect is effectively improved, and the acoustic experience of a driver and passengers is improved.

The embodiment of the invention provides a damage position identification method of an underwater production system pipeline, electronic equipment, a storage medium and a program product. The preset noise reduction algorithm is adopted to carry out noise reduction processing on the collected first reflection ultrasonic guided wave signal, noise interference in the signal can be removed, the quality and reliability of the signal can be improved, a more accurate data basis is provided for subsequent damage feature extraction and analysis, and improvement of the accuracy of damage identification is facilitated. The one-dimensional convolutional neural network model can extract at least one of the time domain damage feature and the frequency domain damage feature from the second reflection ultrasonic guided wave signal, the damage position of the pipeline is judged by comprehensively utilizing the multiple features, and the damage position of the pipeline can be reflected more comprehensively. The pipeline is divided into a plurality of pipeline position sections, and the damage probability of each pipeline position section is calculated by using the one-dimensional convolutional neural network model, so that the damage position in the pipeline of the underwater production system can be positioned more accurately.

The application discloses a kind of low-illumination scene-oriented adaptive image noise reduction processing method, it is related to digital image processing technical field.The steps include: step S100: low-illumination image is collected and preprocessed, preprocessing includes dark current correction, bad point repair and lens distortion correction;By using back-illuminated CMOS sensor to collect image and execute dark current correction, bad point repair, the preprocessing operation of lens distortion correction, in combination with average brightness and gray standard deviation division low-illumination grade, according to noise intensity and distribution characteristics identify noise type, again based on preset parameter mapping table adaptive matching filter window size, noise reduction intensity weight and detail retention threshold technical means, it is difficult to eliminate in the process of low-illumination image acquisition fixed noise and system error, traditional noise reduction algorithm parameter fixed cannot adapt to different low-illumination grade and noise type, the problem of over-reduction or noise reduction deficiency, image edge detail loss is serious in noise reduction process.

The invention relates to the technical field of industrial machine vision detection, in particular to an intelligent grading and feedback system based on image recognition. Comprising a multi-modal image acquisition unit used for synchronously acquiring a visible light image, an infrared thermal imaging image and a hyperspectral image of a target object; the dynamic self-adaptive preprocessing unit is in communication connection with the multi-modal image acquisition unit, performs combined preprocessing on the acquired multi-modal image, and comprises a dynamic noise reduction algorithm based on target edge features, a self-adaptive contrast enhancement algorithm and a multi-modal image registration algorithm; and the closed-loop feedback unit is respectively in communication connection with the multi-modal image acquisition unit and the multi-dimensional dynamic threshold grading unit. Through multi-modal image acquisition and attention-multi-scale feature fusion design, the limitation of traditional single-modal detection is effectively broken through, the recognition capability of micro bubbles and hidden cracks of the quartz crucible is remarkably improved, surface stains and internal impurities are accurately distinguished, and the problem of subjective difference of manual visual detection is solved.

The present application relates to carbon emission source inversion calculation technical field, especially to a kind of construction site carbon emission source inversion calculation method based on atmospheric diffusion model.The technical scheme includes the following steps: constructing space diffusion model: the meteorological parameter of construction site is obtained by meteorological monitoring equipment, pollutant concentration distribution is calculated based on atmospheric diffusion model, the model is corrected vertical diffusion by introducing mirror source term;Data dynamic noise reduction: the original monitoring data are filtered, and the balance between noise suppression and real fluctuation reservation is balanced by dynamic adjustment algorithm;Iterative inversion calculation;Comprehensive verification: by theory simulation and actual detection combination.The present application realizes the accurate, efficient and low-cost inversion of construction site carbon emission source by dynamically coupling meteorological-emission model, adaptive noise reduction algorithm and multi-source verification mechanism, which provides reliable technical support for real-time carbon management, carbon trading and green construction.

The application discloses a current signal denoising method and system for fault arc detection and a storage medium, comprising: collecting the working current of the load at the current time, and analyzing to obtain the noise estimation spectrum of the working current at the current time; wavelet decomposing the noise estimation spectrum and the working current of the load at the subsequent time respectively to obtain first wavelet coefficients and second wavelet coefficients; correcting the second wavelet coefficients based on the first wavelet coefficients to obtain third wavelet coefficients; and wavelet reconstructing the third wavelet coefficients to obtain a time domain enhanced signal. The improved wavelet threshold function improves the defects of poor continuity and constant deviation of the traditional wavelet threshold function, and the current signal processed by the denoising algorithm not only suppresses the existence of noise, but also improves the arc characteristics of the signal, so that the detection performance of the fault arc detection algorithm is significantly improved.

The invention discloses a wireless remote control and data return device of a coal bunker cleaning machine, relates to the technical field of wireless remote control of coal bunker cleaning machines, aims to solve the technical problem that the type and residual quantity of coal accumulated in a traditional bunker cannot be accurately recognized, and comprises a field sensing module, a data processing module, a wireless transmission module and a remote control module. In a noise reduction unit of a data processing module, a wavelet noise reduction algorithm is used for processing image data acquired by an AI visual camera. Mist noise generated by dust interference can be effectively filtered out, key features such as edges and textures of coal deposits can be clearly reserved, the problem that viscous coal and wet powdery coal as well as blocky coal and viscous coal are difficult to distinguish manually is solved, the type difference and distribution condition of the coal deposits in the bin can be clearly captured, a high-quality image basis is provided for subsequent accurate recognition of the types of the coal deposits, and the coal deposits in the bin can be accurately recognized. And the coal deposit characteristics are judged no longer depending on subjective experience of operators. The problem that the type and residual quantity of accumulated coal in a bin cannot be accurately recognized is solved.

The invention relates to the technical field of earphone noise reduction methods, in particular to a collaborative optimization method for earphone hybrid active-passive noise reduction, which comprises the following steps: firstly, constructing a passive acoustic unit database; secondly, establishing a transfer function neural network model of the passive acoustic unit based on the database; thirdly, constructing a multi-objective optimization problem of the passive acoustic unit structure; then, constructing a hybrid active and passive noise reduction system; and finally, based on the constructed linear matrix model, targets inside and outside the noise frequency band in the hybrid noise reduction system are determined, multi-target collaborative optimization is completed, and respective advantages of active noise reduction and passive noise reduction are fully exerted. The optimization of the passive noise reduction structure improves the sound insulation effect of middle and high frequency bands, and makes up for the deficiency of active noise reduction in the frequency band; the optimization of the active noise reduction algorithm further enhances the noise reduction effect of the low-frequency band, and the active noise reduction algorithm and the low-frequency band form good complementation; through collaborative optimization, the overall noise reduction performance is far better than the effect of a single technology.

This invention discloses a method for online prediction of equipment faults with a small sample size, including the following prediction steps: S1, Data Processing: By optimizing the threshold function in the WTD denoising algorithm and introducing BIC to evaluate the impact of the number of decomposition layers on the complexity of WTD, an improved WTD algorithm is proposed for online filtering of noise in fault signals. This invention proposes an online prediction model for equipment faults with a small sample size, and verifies the effectiveness and reliability of the model using rolling bearing life cycle vibration data. BIC can accurately find the optimal number of decomposition layers in the WTD algorithm, providing a basis for improving the parameter settings of the WTD model. The improved WTD algorithm has excellent denoising effect, ensuring the reliability of fault data. The improved MEST algorithm and dual CSFI algorithm can effectively convert fault signals into fault degree indicators, providing high-quality data support for subsequent fault prediction.

The application relates to the technical field of earphone noise reduction processing, and discloses a motorcycle riding earphone different noise reduction algorithm combination noise reduction method, which comprises the following steps: obtaining an original audio signal and distributing the original audio signal to each processing module; generating a pretreatment signal, determining speech activity information, and calculating environmental noise energy information; adaptively adjusting high and low noise energy thresholds according to the environmental noise energy or auxiliary information; inputting the original signal into traditional and AI noise reduction modules in parallel to generate two-way noise reduction signals; generating an output signal through weighted fusion based on the environmental noise, speech information and thresholds; and performing equalization and compression processing on the output signal and driving a loudspeaker to output. The application introduces comprehensive judgment on environmental noise energy information, speech activity information and riding speed, the output signal of the traditional noise reduction module is set to zero in a high-speed non-speech environment, and AI noise reduction is combined, so that the sound quality distortion of the traditional noise reduction algorithm in a noise environment is effectively avoided.

The application relates to the technical field of data noise reduction, and discloses a security alarm noise reduction method and system based on a data weaving technology, which comprises the following steps: based on a data weaving architecture, a unified logical data view is constructed; an AI-driven data intelligent agent component system is deployed, API and new data sources that change automatically are recognized and adapted, metadata, threat intelligence and infrastructure operation state information are fused; an attack chain panoramic graph and a behavior causal chain model are constructed; a semantic noise reduction algorithm, time series clustering analysis and a confidence score mechanism are used to remove duplicate alarms, identify false alarms and filter low-risk events for security alarms; and alarm deduplication, priority adjustment and linkage disposal strategy generation are carried out. The application solves the problems of alarm flooding, high false alarm rate and low processing efficiency in the existing security alarm system.

The application discloses a rescue method and system of a rescue robot for exploration, and relates to the technical field of underground space rescue, and comprises the following steps: acquiring multi-path acoustic echo data and robot motion trajectory data of a karst cave, and constructing a three-dimensional point cloud model based on the data; identifying unmatched data in the multi-path acoustic echo data and the robot motion trajectory data, and taking a region where the unmatched data is located as an abnormal region. The application enhances the spatial resolution of signal collection through a multi-microphone array, extracts robust acoustic fingerprints by combining a noise reduction algorithm and a short-time Fourier transform, and screens out human body sound source signals with high confidence by using a feature template matching mechanism, so that accurate extraction and identification of human body acoustic characteristics in a complex noise environment of a karst cave are realized, and the misjudgment problem caused by confusion of sound source characteristics and environmental noise in a traditional method is effectively solved.