218 results about "Dynamic noise" patented technology

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 discloses a deep well rock burst early warning system and method based on multi-dimensional monitoring, and belongs to the technical field of deep well rock burst early warning. According to the method, multi-dimensional data such as stress, strain and microseism are collected through a monitoring network, and an aligned multi-source data set is obtained through space-time registration; after dynamic noise suppression processing matched with physical characteristics is adopted, strong correlation characteristics are screened through mutual information entropy; frequency domain, time domain and time-frequency domain features are extracted through principal component extraction and phase-space reconstruction, and a multi-dimensional state space data set is formed; and inputting the prediction model to obtain a danger level and trigger a corresponding early warning signal, and finally dynamically adjusting the monitoring network layout and prevention and control measures based on the early warning signal. According to the method, the early warning accuracy and real-time performance are improved, and effective technical support is provided for deep well rock burst prevention and control.

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 discloses a drilling multi-source sensing signal denoising method based on dynamic noise decoupling and a self-adaptive mode, and belongs to the technical field of underground processing. The method comprises the following steps of: separating common noise of a noise energy distribution matrix of a sensor and separating specific noise; carrying out adaptive noise set empirical mode decomposition on the separated signals, carrying out variational mode decomposition on residual signals in the signals, and screening effective modes through a kurtosis-entropy joint criterion to obtain signals with effective characteristic components reserved; high-frequency fluctuation of the signal is punished through total variation regularization, an improved alternating direction multiplier method algorithm is used for solving, short-time Fourier transform is carried out on the solved signal, low-frequency and high-frequency features are extracted through a multi-scale convolutional network, and a final denoised signal is output through gating weight fusion. According to the method, the signal denoising precision in a complex noise environment is remarkably improved, the processing time is shortened, and the resource consumption is reduced.

The invention relates to the technical field of face recognition, in particular to a face recognition method and system for a dynamic environment, and the method comprises the steps: collecting a face video stream through a multispectral imaging device, and carrying out the preprocessing of dynamic noise reduction, distortion correction and the like; constructing a multi-scale space-time fusion feature extraction network to extract dynamic space-time features and fuse cross-modal features; establishing an environment disturbance simulation generation model to generate a virtual sample, and performing domain adaptive alignment; designing an online incremental feature updating mechanism to optimize parameters of the feature encoder; deploying a heterogeneous graph neural network to carry out multi-modal decision fusion; and a hierarchical verification architecture is adopted to complete identity recognition. The system comprises a data acquisition and preprocessing module, a multi-scale space-time fusion feature extraction module and the like. According to the method, the problem of face recognition in a dynamic environment is effectively solved, the recognition accuracy, robustness, real-time performance and reliability can be remarkably improved in the scenes of complex illumination, posture expression change, shielding, background noise and the like, and the method has a wide application prospect.

The invention relates to the technical field of audio noise reduction, in particular to a multi-scene adaptive noise reduction method and system for audio terminal equipment. The method comprises the following steps: identifying a real-time environmental sound signal; environment noise source identification is carried out on the real-time environment sound signal, noise distribution perception modeling is carried out, and an environment noise distribution field is constructed; performing current scene noise change analysis on the environment noise distribution field, performing dynamic noise distribution rendering, and constructing a dynamic noise scene model; performing hedging suppression parameter calculation based on the dynamic noise scene model, performing adaptive noise suppression processing, and constructing an adaptive noise suppression strategy; acquiring an audio input signal of a user; and performing local audio gain processing on an audio input signal of a user, and performing multi-band decomposition to generate an audio frequency parameter of each band. The stability and the accuracy of the noise reduction effect are improved, so that the practicability and the adaptability of the audio terminal equipment are enhanced.

The invention provides a multi-target track tracking method based on dynamic anti-noise clustering and bidirectional verification matching. The method comprises the following steps: firstly, collecting heterogeneous data of different sensors in real time; secondly, performing dynamic space-time registration processing on the heterogeneous data to obtain space-time aligned multi-sensor measurement data; then, noise filtering and target preliminary screening are carried out on the measurement data, multi-modal data fusion is realized in combination with sensor confidence coefficient weights, and multi-target position information after multi-sensor fusion is obtained; constructing and solving a forward space-time enhancement cost matrix and a reverse space-time enhancement cost matrix, carrying out bidirectional verification on target points and a track, and carrying out track matching on obtained multi-target position information and an existing track; and finally, predicting a target state through extended Kalman filtering, and dynamically optimizing the process through a closed-loop feedback mechanism to obtain a stable track and complete multi-target track tracking. According to the method, the multi-target continuous stable tracking capability can be obviously improved, and reliable technical support is provided for sea area situation awareness.

The invention relates to the technical field of sound quality enhancement, in particular to a sound quality improvement method and device of a Bluetooth loudspeaker box, equipment and a storage medium. The method comprises the following steps: collecting sound box environment acoustic signals, and carrying out peripheral audio time-frequency spectrum analysis and three-dimensional sound field fitting to construct a peripheral three-dimensional sound field model; based on the surrounding three-dimensional sound field model, environment noise source semantic deep analysis is carried out, and user real-time scene sound quality demand prediction is carried out, so that real-time scene sound quality demand features are generated; bluetooth sound box loudspeaker monitoring parameters are collected, and adaptive noise frequency suppression adjustment is carried out based on the surrounding three-dimensional sound field model rate, so that a loudspeaker dynamic noise suppression adjustment strategy is generated; and acquiring a Bluetooth audio output signal, and performing band frequency response characteristic optimization according to the real-time scene tone quality demand characteristics to obtain a frequency response tone quality optimized audio signal. According to changes of different environments and requirements, the output tone quality of the Bluetooth loudspeaker box is optimized in real time, and high-fidelity audio output is kept.

The invention discloses a dynamic noise analysis method and device for an urban elevated road and a medium, and belongs to the technical field of noise monitoring and analysis. The method comprises the following steps: acquiring a three-dimensional geographic information model and real-time traffic flow data of a target elevated road; processing the three-dimensional geographic information model based on a sound wave propagation attenuation model to generate a noise propagation path grid; fusing the real-time traffic flow data with a noise source intensity calculation model to generate a dynamic noise source intensity sequence; calculating a real-time equivalent sound pressure level of each grid unit through a noise superposition algorithm in combination with the dynamic noise source intensity sequence and the noise propagation path grid; performing rolling prediction on the real-time equivalent sound pressure level based on a time window sliding mechanism to generate a noise space-time distribution thermodynamic diagram; and according to the noise space-time distribution thermodynamic diagram, identifying a noise exceeding area, and outputting a dynamic noise evaluation report. According to the method, the technical effect of dynamically predicting and analyzing the noise of the elevated road is achieved.

The invention relates to the technical field of Internet of Things communication, and provides a low-power-consumption semiconductor communication method based on Internet of Things, which comprises the following steps: acquiring temperature, electromagnetic interference intensity and signal attenuation coefficient data through a multi-mode sensor node, and generating an original environment data set; performing dynamic noise classification on the original environment data set, dividing noise levels and marking noise types; performing hierarchical processing on the data by adopting a multi-stage self-adaptive noise reduction algorithm to generate a preprocessed data set; extracting dynamic characteristic parameters of a communication channel, constructing an energy efficiency prediction model, and dynamically calculating a combination of optimal transmitting frequency, power and modulation order; generating a multi-system modulation signal, superposing a dynamic coding strategy, and adjusting the symbol block length and the error correction code density; and the receiving end analyzes the signal quality index, feeds back the signal quality index to the transmitting end to optimize the parameters and updates the energy efficiency prediction model. According to the invention, the energy efficiency ratio and the anti-interference capability of the Internet of Things communication system can be improved.

The invention relates to the technical field of wind turbine generator state monitoring, in particular to a wind turbine generator state monitoring method and system based on multi-source heterogeneous data fusion, and the system comprises a data collection unit, a dynamic noise processing unit, a damage feature analysis unit and a health state output unit. A data acquisition unit synchronously obtains blade strain, unit rotating speed and environment vibration signals through hardware timestamp alignment, and a dynamic noise processing unit removes rotating speed coupling noise by using a two-parameter coupling model, a temperature-pitch angle three-dimensional correction curved surface and closed-loop feedback in combination with variable step blanking and local band elimination protection. The damage feature analysis unit extracts microcrack features by adopting multi-scale wavelet packet decomposition and kurtosis detection, and performs cross validation by fusing a vibration mode confidence factor, and the health state output unit generates a topological graph containing a crack position, an expansion trend and an alarm confidence level, so that the problems of insufficient multi-source data fusion and false and missing judgment of damage are solved, and the safety of the system is improved. And the monitoring precision is improved.

The invention discloses a torsion test method based on a Kalman filtering algorithm, and relates to the technical field of torsion test.According to the torsion test method, a dynamic noise model and a material feature library are combined to enable a filter to automatically adapt to acoustic characteristics of different substrates, and the problem of misjudgment caused by fluctuation of material parameters in a traditional method is solved; secondly, a sub-band processing mechanism can restrain high-frequency noise and meanwhile completely reserve transient characteristics before the material is broken, and effective signal loss is avoided; the transfer learning model eliminates batch differences through adversarial training, and the generalization ability of dynamic threshold judgment is ensured; the real-time compensation strategy generates a refastening scheme considering both mechanical safety and assembly precision in emergency.

The invention discloses an operation AR intelligent auxiliary management system based on voice AI interaction driving, which is applied to the field of intelligent warehousing and comprises an environment perception type voice interaction module, an AR multi-mode interaction compensation module, a dynamic noise robustness data processing module, an operation management module and a database module. The environment perception type voice interaction module is used for collecting voice signals of operating personnel and inhibiting environment noise; the dynamic noise robustness data processing module is used for carrying out noise reduction processing, semantic understanding and closed-loop optimization on the voice signal and generating feedback information and a control instruction; the AR multi-modal interaction compensation module is used for receiving feedback information of the dynamic noise robustness data processing module and providing visual and tactile interaction compensation, and the job management module is used for scheduling management equipment according to a control instruction; according to the method, the operation efficiency can be effectively improved, the accuracy is high, the real-time performance is high, and interaction experience optimization can be realized.

The invention relates to the field of adaptive noise reduction, in particular to an adaptive noise reduction method and system for a multi-mode audio SoC main control chip. The method comprises the following steps: acquiring an original audio input signal according to an SoC main control chip, performing time-frequency domain dual deconstruction and cross-frequency domain noise interference structure analysis, and constructing a full-frequency domain noise interference topology table; carrying out multi-mode interference factor separation on the full-frequency-domain noise interference topology table, carrying out multi-noise environment modeling, and constructing a real-time noise scene model; time sequence noise slope fluctuation modeling is carried out on the real-time noise scene model, dynamic noise reduction response learning optimization is carried out, and a dynamic noise reduction optimization strategy is constructed; and performing neural coding gain and audio boundary line reconstruction according to the original audio input signal to obtain a coding gain key audio signal and a weak audio optimization signal. According to the invention, by flexibly adjusting the noise reduction intensity of the audio signal, the real-time scene noise reduction performance is optimized.

The invention provides a specific unmanned aerial vehicle model rapid identification method and system based on a radio frequency fingerprint database. The method comprises the following steps: firstly, receiving a radio frequency signal generated by a target unmanned aerial vehicle during flight, carrying out dynamic noise reduction processing on the radio frequency signal to generate a noise-reduced radio frequency signal, then separating combined data comprising a signal strength fluctuation mode, a phase hopping frequency degree and a modulation distortion characteristic from the noise-reduced radio frequency signal, and finally, carrying out noise reduction processing on the combined data. The method comprises the following steps: acquiring combined data of a target unmanned aerial vehicle, performing layer-by-layer matching on the combined data and a pre-constructed radio frequency fingerprint database to generate a matching result, then extracting a model correlation parameter of the target unmanned aerial vehicle from the matching result, finally generating an identification decision containing a real-time environment adaptation factor based on the model correlation parameter, and outputting a target unmanned aerial vehicle model result corresponding to the identification decision; according to the technical scheme provided by the invention, the distinction degree of fine signal differences is greatly improved, and the stability and accuracy of unmanned aerial vehicle model identification in complex electromagnetic interference and multi-obstacle reflection scenes are realized.

The invention relates to the technical field of simultaneous interpretation, and discloses a simultaneous interpretation data processing method and system based on a POE microphone array. The method comprises the following steps: synchronously acquiring multi-language original audio streams and meeting place environment noise spectrum features through a distributed microphone array powered by the Ethernet; after time domain framing is carried out on the audio stream, adaptive filtering is carried out by using a dynamic noise reduction weight coefficient to obtain a primary pure voice segment; dividing the multi-language speech endpoint detection model into independent speech units with language labels through a pre-trained multi-language speech endpoint detection model, and matching a corresponding acoustic model to generate a phoneme-level time alignment sequence; comparing and outputting a term replacement instruction stream in real time in combination with a simultaneous transfer term library, and generating an intermediate semantic representation vector after fusion; and the low-delay encoder converts the voice parameter sequence into a target language voice parameter sequence, and drives the waveform synthesizer to generate final simultaneous transmission audio. The method optimizes the whole process processing, gives consideration to the simultaneous transmission accuracy and real-time performance, and is suitable for a multilingual meeting place scene.

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

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 discloses a real-time noise reduction method and system supporting Bluetooth audio interaction, and the method comprises the steps: collecting an environment audio signal and a Bluetooth interaction audio signal, and carrying out the timestamp alignment and spatial calibration of the environment audio signal and the Bluetooth interaction audio signal; an improved multivariate variational mode decomposition algorithm is adopted to decompose and extract noise features, the noise features are combined with user historical noise data, a dynamic noise model is constructed through a long-short-term memory network, parameters are updated in real time, Bluetooth interaction audio content features and a user behavior data recognition scene are analyzed, and a noise reduction strategy is determined. And according to the dynamic noise model and the scene result, reinforcing learning to optimize filter parameters, generating a directional anti-noise signal to carry out noise reduction on the Bluetooth interaction audio signal, collecting the noise reduction intensity manually adjusted by a user, calculating voice definition and total harmonic distortion, and carrying out noise reduction on the Bluetooth interaction audio signal. And inputting a feedback and evaluation result into the modeling and noise reduction steps, carrying out dynamic range adjustment, sound channel balance and power amplification on the audio after noise reduction, and outputting the audio through a loudspeaker.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a large model data analysis enhancement method and system based on dynamic noise filtering and multi-modal feature fusion, and the method comprises the following steps: obtaining user historical behavior data, and carrying out data cleaning and feature extraction; constructing a user behavior feature vector, and dividing a training set and a test set; training a machine learning model based on the training set, and optimizing model parameters; verifying the model performance by using the test set, and outputting a prediction result; the method has the beneficial effects that the dynamic noise sensing module is used for performing quality evaluation on a reference file uploaded by a user, and a self-adaptive threshold cleaning mechanism is used for eliminating redundancy and conflict information; a multi-granularity feature alignment network is designed, and a document structure analysis and semantic embedding technology is utilized to realize deep association mapping between a reference file and an input task.

The invention relates to a differential privacy position protection method based on a dynamic attenuation noise field, and the method comprises the steps: obtaining a plurality of candidate regions based on a user track, recognizing different sensitive regions from the plurality of candidate regions based on the behavior characteristics of a user, and calculating the dynamic sensitivity of each sensitive region; wherein the behavior characteristics of the user comprise a stay identifier and access time of the user in the candidate area; constructing a three-dimensional dynamic noise model for generating a dynamically attenuated space-time noise field; the method comprises the following steps: acquiring a position coordinate of a target position in a sensitive area and a corresponding timestamp, generating a corresponding noise amplitude by adopting a three-dimensional dynamic noise model, generating a two-dimensional position offset based on the noise amplitude by adopting a Laplace mechanism, and disturbing the position coordinate of the target position based on the two-dimensional position offset. According to the scheme, adaptive adjustment of the noise amplitude can be realized, and collaborative optimization of strong protection of a high-sensitivity area and high precision of a low-sensitivity area is realized.

The invention relates to the technical field of signal demodulation, and discloses an OFDM (Orthogonal Frequency Division Multiplexing) signal demodulation method of a signal analyzer. According to the invention, through joint optimization of a synchronous detection and frequency offset compensation mechanism, the symbol positioning precision in a low signal-to-noise ratio environment is improved, and subcarrier interference caused by a cyclic prefix pseudo peak value and frequency offset phase ambiguity is effectively suppressed; a time-frequency two-dimensional adaptive interpolation strategy is adopted to enhance the robustness of channel estimation to non-uniform pilot frequency distribution and a fast time-varying channel, and data subcarrier response distortion is reduced; in combination with minimum mean square error equalization and dynamic noise reduction processing, inter-symbol interference and a noise accumulation effect are greatly weakened, and the bit error rate performance of a high-noise scene is improved; the error control technology is updated through a closed-loop channel, a demodulation error propagation path is blocked, and cooperative improvement of system demodulation stability and precision in a complex multi-path environment is achieved.

The invention relates to the technical field of sensing, and discloses a capacitive touch screen input event classification and processing system which comprises an input capture module, an event classification module, an intention prediction module, a dynamic response module, an energy efficiency optimization module and a multi-channel cooperation module. When an input event of the capacitive touch screen is identified, a dynamic noise suppression model is established, and adaptive filtering strategies are configured for different use scenes, so that the reliability of signal analysis in various electromagnetic interference environments is ensured, and meanwhile, the spatial and temporal distribution characteristics of capacitance variation are analyzed in real time; real touch operation and environmental electromagnetic noise can be effectively distinguished, the identification fidelity of basic operations such as clicking and sliding is ensured, the false triggering rate is reduced, the operation accuracy in a weak signal scene is improved, and when multi-finger operation is detected, the consistency of contact coordinates in a curved surface state and a plane state is maintained, so that the accuracy of the touch operation is improved. And the track continuity of writing and drawing type fine operation is ensured.

The invention provides a battery thermal runaway early warning method and device. The method comprises the following steps: acquiring an original acoustic signal released by a target battery pack by using an acoustic sensor; working condition information of the target battery pack is obtained, dynamic noise reduction processing is performed on the original acoustic signal based on the working condition information, multi-source interference noise is suppressed, a de-noised optical signal is obtained, and acoustic characteristics of the de-noised optical signal in a preset frequency band are extracted; constructing a thermal runaway acoustic triggering index based on the energy threshold value, the combined characteristic change threshold value and the continuous triggering time, and generating an initial early warning signal according to the thermal runaway acoustic triggering index and the acoustic characteristics; state information of the target battery pack is obtained, multi-mode fusion analysis is carried out based on the state information and the initial early warning signal, and a thermal runaway verification signal is generated; and based on the thermal runaway verification signal and the initial early warning signal, performing thermal runaway grading early warning on the target battery pack. According to the invention, the accuracy of thermal runaway early warning can be improved.

The invention discloses a household electrical appliance fault diagnosis system based on artificial intelligence, and relates to the technical field of household electrical appliance fault diagnosis and state monitoring. Working condition parameters aligned with a time window are obtained through working condition sensing, and fault features and the working condition parameters are linked in an order form to be converted into a target frequency track; fault characterization is converted into track characterization capable of being continuously tracked along with working conditions from a fixed frequency point, and disturbance to fault positioning in the speed change process is reduced; in order to solve the problem that weak fault components are covered by the same frequency band due to strong dynamic background noise jointly formed by inverter switch harmonic waves, current harmonic waves and load fluctuation, a dynamic noise reference spectrum is output under the current working condition through a condition generation model, and a current spectrum and the dynamic noise reference spectrum form a residual spectrum; the background spectrum base related to the working condition is suppressed, and the separability of the weak fault component in the residual domain is improved from the source.

The invention discloses a real-time processing method based on voice short messages, and relates to the technical field of communication, and the method comprises the specific steps of voice preprocessing, voice transcription and text specification, semantic analysis and processing, information processing and result feedback. According to the invention, through an adaptive filtering formula in voice preprocessing and a segmentation mechanism based on voice pause intervals and intonation changes, dynamic noise reduction processing and accurate segmentation of voice signals are realized, and the frequency and amplitude of noise signals and corresponding characteristics of the voice signals can be analyzed in real time. A filtering coefficient is dynamically adjusted through a self-adaptive filtering formula, non-voice signal components are effectively filtered out, meanwhile, complete voice information is reserved, continuous voice streams are accurately segmented by capturing pause intervals and intonation changes in voice signals, standardized voice segments with timestamps and sequence identifiers are formed, and therefore the voice processing efficiency is improved. And a high-quality data basis is provided for subsequent voice transcription and semantic analysis.

The invention discloses an audio signal measurement method and device, an electronic device and a storage medium, and relates to the field of audio signal measurement, and the method comprises the steps: deploying a detection microphone nearby a tested device, and arranging a reference microphone in a noise field, so as to synchronously collect a mixed signal containing a test signal and noise, and a pure ambient noise reference signal. After the noise of the two signals is subjected to correlation evaluation and the consistency of noise sources is confirmed, dynamically changing noise components are accurately stripped from the signals of the tested equipment by utilizing real-time noise data acquired by the reference microphone and through an adaptive noise cancellation algorithm, so that pure test signals are restored. Therefore, the problem of non-stationary noise pollution is solved through an active signal processing means instead of depending on imperfect physical sound insulation, and accurate and efficient measurement of the performance of the microphone in a noisy environment is realized.

The invention relates to an active noise reduction device, an active noise reduction method and a range hood, and the active noise reduction device comprises a sound signal collection device which is used for collecting a noise signal; the controller is electrically connected with the sound signal acquisition device, and the controller is configured to generate a noise reduction signal for counteracting the noise signal according to the noise signal acquired by the sound signal acquisition device; the sound signal transmitting device is electrically connected with the controller and is used for transmitting a noise reduction signal; the device is characterized in that the sound signal transmitting device comprises a plurality of plasma loudspeakers which are circumferentially arranged around the noise source, and the plasma loudspeakers are configured to be capable of generating electric arcs through high-voltage electricity so as to drive air to produce sound through the vibration of the electric arcs. The active noise reduction device can be suitable for a complex noise environment, is slightly influenced by the environment and is low in complexity; and the discharge parameters of the plasma loudspeaker can be obtained through a noise reduction method so as to meet the real-time requirement of a dynamic noise environment, and the method is suitable for active noise control, sound field regulation and control and other scenes.

The invention relates to the technical field of speech analysis, in particular to a speech enhancement method and system in a dynamic noise environment. The method comprises the following steps: acquiring a voice signal within a preset time under a fixed voice frequency band, and determining a main channel and a secondary channel; framing and windowing the voice signal to obtain a voice field, obtaining a time-frequency matrix of the voice field, determining a feature vector according to a phase difference and an amplitude ratio of each time-frequency point, and obtaining a key human voice cluster based on feature vector clustering; in the key human voice cluster, direction consistency of time-frequency points is calculated based on complex numbers of the main channel and the secondary channel, each time-frequency point is compared with other time-frequency points to obtain voiceprint feature consistency, and a human voice evaluation index is formed based on the time-frequency points and the other time-frequency points; obtaining a frequency domain binary label matrix; and training a network based on the frequency domain binary label matrix, and completing voice enhancement through the network. According to the invention, the problems of many interference persons and insufficient noise interference separation capability are solved.

The invention relates to the technical field of gas concentration detection, in particular to a gas concentration detection method and system for a ring main unit. The method comprises the following steps: acquiring a concentration time sequence of a gas sensor in the ring main unit; constructing an instantaneous fluctuation coefficient; analyzing the instantaneous fluctuation coefficient and a gas physical diffusion limit rate constant to obtain an environmental interference factor representing electromagnetic interference intensity; obtaining an environment interference curve, and combining the environment interference factor and the integral operation value of the environment interference curve to obtain a dynamic adjustment parameter; a dynamic noise covariance used for Kalman filtering is obtained; and performing Kalman filtering processing on the concentration time sequence by using the dynamic noise covariance to obtain an optimal estimated gas concentration value. The problems of false alarm and missing detection in the gas concentration detection process are solved.