268 results about "Noise spectrum" patented technology

The noise online monitoring system comprises a multi-source sensing module, a positioning analysis module, a baseline generation module, a state association module and an execution control module. The multi-source sensing module synchronously collects target area parameter signals and generates noise identification signals through coherence analysis; the positioning analysis module receives the noise identification signal and generates a positioning signal containing a noise source position and a motion trail; the baseline generation module calls historical noise spectrum data of a corresponding scene according to a noise source position in the positioning signal to generate an adaptive noise baseline threshold signal; the state association module carries out space-time alignment on the baseline threshold signal and the equipment working condition data; the execution control module receives the positioning signal and the correlation degree signal. The noise on-line monitoring system can solve the problems that a traditional noise monitoring system is inaccurate in positioning, poor in base line adaptability and disjunction between noise and the equipment state.

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

The invention relates to the technical field of organic display data processing, in particular to a touch data processing method based on an organic display, which comprises the following steps of: monitoring a flexible deformation acceleration component and an environmental parameter change rate in real time through a dynamic trigger function, and triggering high-priority baseline calibration when a threshold value exceeds a limit; and generating a self-adaptive compensation amount matched with the current touch signal noise spectrum. And constructing a dielectric constant offset prediction model by using a long short-term memory neural network or a Kalman filter, deducing the dynamic change trend of the dielectric property of the material, and correcting a touch signal compensation coefficient in a grading manner according to the predicted offset. And a closed-loop error correction mechanism is formed based on the spatial distribution entropy reverse optimization model weight parameter and the trigger threshold boundary of the contact coordinate residual matrix. According to the method, the problem of time domain mismatch of fixed period calibration and organic material nonlinear offset is solved, the accumulation of touch coordinate analysis errors in a high-curvature deformation scene is effectively inhibited, and the touch positioning precision and the interaction response real-time performance are improved.

The invention discloses a self-adaptive noise reduction method for a positive pressure type air respirator based on airflow spectrum characteristics, and relates to the technical field of positive pressure type air respirators.The self-adaptive noise reduction method comprises the steps that a respirator air path pressure signal and an environment noise signal are synchronously collected through an airflow sensor and a microphone array; performing time-frequency analysis on the collected signals, and extracting airflow spectrum features; and establishing a mapping relation between the working state of the breathing machine and the noise spectrum based on the historical record according to the mapping relation between the working state of the breathing machine and the noise spectrum. Through real-time acquisition and analysis of airflow frequency spectrum characteristics, accurate identification of useful airflow signals and environmental noise signals, dynamic adjustment of noise reduction parameters, effective suppression of high-frequency noise and low-frequency vibration, and improvement of noise reduction effect, compared with a traditional fixed frequency band filtering or passive sound insulation material, the method can better cope with a non-stationary noise environment, and has good application prospects. The noise reduction strategy is adjusted in real time, clear auditory information is provided in a complex noise environment, and therefore the auditory perception ability of firemen is improved.

A method and apparatus for acquiring aerodynamic noise of a compressor, a medium, and a product are provided. The method includes: performing inverse Fourier transform on a known frequency-domain noise spectrum to obtain known time-domain noise data, then predicting unknown time-domain noise using a time series neural network, obtaining finer time-frequency noise data in combination with the known time-frequency noise data, and finally, performing Fourier transform on the finer time-frequency noise data to obtain new frequency-domain noise data. Limitations of a limited time step and a total simulation time on the acquisition of aerodynamic noise data in traditional numerical calculation of aerodynamic noise can be overcome. Finer aerodynamic noise data can be acquired rapidly and accurately. The frequency resolution of the noise spectrum can be increased, and reducing the consumption of computing resources and saving manpower and material resources can be achieved.

The invention provides a high-frequency welded pipe roller wear state monitoring method and device, and relates to the technical field of artificial intelligence and data processing, a holographic sensing system of working condition parameters and physical signals is constructed through synchronous monitoring of a multi-source sensor, and noise spectrum offset is tracked in real time based on time-frequency expression of data in different modes, so that the real-time monitoring of the wear state of a high-frequency welded pipe roller is realized. And an anti-error filtering protection channel is constructed, so that the inherent characteristics of the damage can be effectively captured. In addition, multi-physical field damage characterization information is fused from multiple complementary dimensions of transient impact (instantaneous frequency vector), spectrum complexity (spectral entropy) and amplitude distribution deflection (skewness), more comprehensive and richer state description can be provided, accurate quantitative evaluation of the roller wear level is achieved, and the capacity of real-time monitoring and accurate recognition of the roller wear state is remarkably improved.

The invention relates to a tunnel crack monitoring method, system and device and a medium, and belongs to the technical field of tunnel monitoring, and the monitoring method comprises the steps: obtaining a three-dimensional model and a geological parameter library of a to-be-monitored tunnel, and constructing a sensor network topology structure; deploying a sensor network based on the sensor network topology structure; receiving static strain data and dynamic vibration data, collected by the sensor network, of each monitoring point in the tunnel; performing space-time alignment on the static strain data and the dynamic vibration data, eliminating environmental interference based on a pre-trained noise spectrum feature library, and generating a cleaned structured data table; extracting a strain gradient characteristic value and a dynamic chaos characteristic value in the structured data table, inputting the strain gradient characteristic value and the dynamic chaos characteristic value into a multi-stage classification model for crack type identification and risk grade evaluation, and generating a crack risk grade map; and generating corresponding maintenance prompt information according to the crack risk grade map. According to the invention, the comprehensiveness and accuracy of tunnel monitoring can be improved.

The invention provides a machining cutter feeding speed control method and system.The method comprises the steps that a three-dimensional stress field, a temperature field and cutter micro-vibration characteristics of a cutter cutting area are collected through a preset sensor array, and real-time plastic deformation parameters of a workpiece material and machine tool spindle power fluctuation data are combined; forming a corresponding processing state sensing matrix; obtaining a machining constraint condition of the tool, and optimizing the initial particle swarm algorithm through the machining state sensing matrix and the constraint condition to generate a corresponding target particle swarm algorithm; a speed reference curve matched with the cutter is generated in real time through a target particle swarm algorithm, and cutting noise spectrum features are collected through a high-frequency sonic sensor; and performing conversion processing on the speed reference curve and the cutting noise spectrum characteristics to generate a feeding speed matched with the cutter in real time. The feeding speed can be accurately controlled, and the production efficiency is correspondingly improved.

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 millimeter wave signal phase noise testing method based on ultra-stable laser, and the method comprises the steps: generating the ultra-stable laser through employing a common-cavity Brillouin laser frequency stabilization technology, taking the ultra-stable laser as a carrier wave, and carrying out the double-sideband modulation of a to-be-tested millimeter wave signal; frequency shifts of the two sidebands are different, the sideband with high frequency has more downward frequency shifts, the sideband with low frequency has less downward frequency shifts, the generated common-cavity Brillouin laser and the double-optical-sideband pumping laser are coupled and separated to obtain upper and lower double-sideband optical signals with different frequency intervals, and then beat frequency and mixing frequency are performed to generate an intermediate frequency signal with MHz magnitude; the noise characteristic of the intermediate frequency signal directly carries the phase noise of the to-be-tested signal, and the phase noise analyzer carries out noise spectrum density analysis and quantitative evaluation on the intermediate frequency signal to complete the phase noise test of the to-be-tested millimeter wave signal. The collaborative optimization problem that the reference source stability, the system noise and the to-be-tested signal noise are difficult to separate in the ultrahigh frequency signal noise test is solved.

The invention relates to the technical field of conveyer belt nondestructive testing, and discloses a conveyer belt hidden damage identification method based on acoustic emission signal frequency domain migration, which comprises the following steps: collecting acoustic emission signals in the operation of a conveyer belt in real time, carrying out time-frequency analysis to construct a time-frequency energy matrix, and migrating a frequency band division rule of a historical working condition to a current signal through a domain adaptation algorithm in transfer learning to form a self-adaptive feature sensitive frequency band, comparing a noise spectrum fingerprint database to remove frequency band components overlapped with noise, and inputting residual frequency bands into the lightweight convolutional network to output a recognition result. As the acoustic emission signal frequency band of the hidden damage of the conveyor belt dynamically shifts along with damage evolution and working condition change, the method can sense signal energy distribution in real time and dynamically adjust and focus on the frequency band which can best represent damage characteristics, so that the problems of incomplete characteristic extraction and misjudgment caused by frequency band solidification are avoided.

The invention discloses an order noise evaluation method and device, and relates to the technical field of noise analysis. The method comprises the following steps: acquiring a noise time-domain signal and a rotating speed time-domain signal, and obtaining a three-dimensional noise spectrum signal; determining all to-be-selected frequency points corresponding to the target rotating speed based on the three-dimensional noise spectrum signal; determining the frequency points of which the intermediate critical frequency bands of the to-be-selected frequency points meet preset requirements, and obtaining a plurality of target frequency points; based on the three-dimensional noise spectrum signal, calculating protrusion ratios corresponding to the plurality of target frequency points, and determining the maximum protrusion ratio as a target protrusion ratio; and evaluating the to-be-evaluated order noise based on the target outburst ratio. According to the method and the device, the frequency points of the intermediate critical frequency band including the order noise frequency band in the to-be-selected frequency points are determined, so that the evaluation of the whole frequency band range covering the order noise is ensured, the full-band evaluation of the influence range of the order noise is realized, the comprehensiveness of order noise rating is improved, and the accuracy of the evaluation result of the order noise is improved.

The invention discloses a design method of a noise reduction fairing, which comprises the following steps: determining a performance demand parameter of the noise reduction fairing, and constructing an initial noise reduction model based on a noise spectrum of a target application scene and an air duct structure defect; structural parameters are designed according to the performance requirement parameters; establishing a fluid-solid-sound coupled parameterized model of the layered composite structure and the flow guide assembly, and performing iterative optimization to generate an optimized configuration of the noise reduction fairing; a fairing prototype is manufactured based on the optimized configuration, actual measurement data are collected, simulation errors are corrected through an empirical regression model, and boundary conditions of a parameterized model are updated; the corrected noise reduction fairing is disassembled into modular assemblies, and an assembly process based on modularization is designed; in the assembling process of the modular assembly, a digital positioning system is adopted to be matched with an assembly interface; through the collaborative design of the layered composite structure and the diversion assembly and the module assembly design of the manufacturing link, the balance of broadband noise suppression and heat dissipation efficiency is realized, and the overall performance is improved.

The invention relates to the technical field of speech enhancement, in particular to an audio optimization method and system applied to speech recognition, and the method comprises the steps: collecting audio data in real time, and uniformly dividing the audio data into audio frames; for each audio frame and each neighbor frame of the preset audio frame, evaluating whether a noise spectrum is re-selected when the audio data is enhanced by adopting a spectral subtraction method; if reselection is carried out, each mode of the audio frame is acquired; selecting a noise mode from the modes of the audio frame, and obtaining the number of lagging frames of other modes; obtaining a frequency spectrum change characteristic value and a voice characteristic value of each mode, and selecting a main voice mode; acquiring audio mode characteristic values of other modes except the main voice mode; and a noise frame and a new noise spectrum are obtained. The invention aims to improve the accuracy of voice recognition by improving the accuracy of noise frame selection and enhancing the audio enhancement effect of the spectral subtraction for the voice features of the audio frames.

The invention provides a method for testing a phased array compact range of a high-orbit communication satellite. The method comprises the following steps: unfolding and fixing a whole satellite antenna on the ground; confirming that a test coupler is installed and a cable is connected normally; uploading software to a phased array antenna and confirming that the function is normal; setting a phased array transponder path, switching on the change-over switch, and opening the active single machine and the phased array on the path; after the phased array is preheated through telemetering, system-level testing is carried out, wherein at least one performance parameter such as noise spectrum and equivalent omnidirectional radiation power and functions such as program control pointing and beam hopping are tested; program control pointing function verification is performed according to wave beam program control preset pointing, and the DUT turntable is matched to verify the precision; hopping beam function verification jumps according to a preset time slot, and a Zero Span mode of a frequency spectrograph analyzes a time domain characteristic verification time sequence; and switching the phased array to standby after verification, shutting down after telemetering is normal, and recovering the on-satellite state. In this way, it can be ensured that the integration performance of the phased-array antenna and the transponder system meets the design requirement through quantitative analysis.

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 belongs to the technical field of radio electronics and signal processing, and particularly relates to a color noise environment signal frequency domain detection method and system based on spectrum envelope extraction, and the system comprises a signal acquisition unit, a signal conversion unit, a signal judgment unit and a signal output unit. The method is a research summary of a color noise environment signal frequency domain detection algorithm based on spectrum envelope extraction, and the algorithm is used for blind signal detection under the conditions of uneven noise spectrum and broadband reception, has good robustness, can be used for low signal-to-noise ratio occasions, and is high in signal detection accuracy.

The invention belongs to the technical field of smoothness detection of glass guide rail lifting systems, and discloses a performance detection and analysis method of a guide rail glass lifting system. According to the method, the noise data, the output current and the vibration time domain signal are subjected to data analysis to construct the smoothness comprehensive evaluation matrix, the system state is comprehensively evaluated from multiple aspects of frequency band energy, current fluctuation, vibration frequency and the like, the operation anomaly characteristics are comprehensively covered, the anomaly recognition accuracy is improved, and the system reliability is improved. A scientific basis can be provided for performance quantitative evaluation, and the one-sidedness of a single parameter is avoided. When the smoothness of the guide rail glass lifting system is judged to be abnormal, the abnormal part can be accurately recognized based on the noise spectrum mapping relation, meanwhile, the specific abnormal position is positioned in combination with current, vibration signals and position-time mapping, double positioning of the part type and the physical position is achieved, manual blind troubleshooting is avoided, and the working efficiency is improved. And the fault diagnosis efficiency and the maintenance pertinence are improved.

The invention provides a self-adaptive frequency adjustment method, medium and system for an ammeter carrier communication module, and belongs to the technical field of intelligent ammeters, and the method comprises the steps: firstly obtaining and sampling a power line carrier communication signal, obtaining communication time distribution characteristics within 24 hours through time domain analysis, and carrying out frequency domain analysis to obtain noise spectrum characteristics; and establishing a time-frequency characteristic incidence matrix. 24 hours are divided into a plurality of transmission time windows based on the matrix, and the optimal frequency range of each window is determined and sub-band division is carried out. And performing channel quality evaluation on the sub-bands, substituting evaluation data into the noise characteristic equation set, solving to obtain a channel transmission characteristic curve, and selecting an optimal transmission sub-band for communication according to the channel transmission characteristic curve. The system monitors communication quality in real time, and performs time-frequency characteristic analysis and optimal sub-band selection again when a quality parameter is lower than a threshold value, so that adaptive frequency adjustment is realized, and the problem that current electric meter carrier communication is difficult to adapt to a complex power line channel environment is solved.

The invention discloses a human-computer interaction speech recognition system and method based on deep learning, relates to the technical field of human-computer interaction speech recognition, and remarkably improves the instruction recognition robustness in an industrial multi-person cooperation scene through a multi-modal signal cooperation and dynamic decision mechanism. Equipment exclusive voiceprint mapping is realized with an extremely low sample size by adopting a meta-learning adaptation layer, and the limitation that a traditional voiceprint model depends on large-scale data is broken through; a dynamic threshold value is set, noise spectrum analysis and an equipment state are combined, and misjudgment caused by burst interference is suppressed; by adopting the limited instruction syntax tree and the priority adjustment of the sensor drive, the emergency instruction is executed preferentially, and the security risk is reduced.

The invention relates to the technical field of voice recognition, and discloses a voice instruction recognition and cleaning control method for a sweeping robot, which comprises the following steps: synchronously reading a dust collection motor driving pulse width modulation value and a rolling brush motor load current value of the sweeping robot while acquiring an audio signal acquired by a microphone; retrieving and interpolating in a preset working condition noise spectrum mapping table according to the non-acoustic operation state data, determining a predicted mechanical noise power spectrum at the current moment, taking the predicted mechanical noise power spectrum as a priori reference, calculating an instantaneous signal-to-noise ratio of the predicted mechanical noise power spectrum and the frequency domain feature sequence, and constructing a frequency domain confidence mask; according to the method, a feedforward prediction mechanism is constructed by introducing non-acoustic prior information, and response lag of traditional acoustic posteriori estimation during sudden change of working conditions is avoided.

The invention discloses a wind turbine generator voiceprint self-adaptive detection method, system and device and a storage medium, and belongs to the technical field of wind power, and the method comprises the steps: collecting a voiceprint signal, raindrop diameter, environment humidity, environment temperature and atmospheric pressure of a wind turbine generator in real time through a sensor array; temperature and humidity coupling correction parameters are calculated based on the environment humidity, and real-time air density is obtained through correction according to an air density correction formula by means of the environment humidity, the environment temperature and the atmospheric pressure; constructing a rain intensity noise spectrum mapping model based on the raindrop diameter and the real-time air density in combination with a raindrop vertical fragmentation merging effect compensation algorithm; performing rain noise suppression on the voiceprint signal of the wind turbine generator by adopting a depth spectrum subtraction algorithm in combination with the rain intensity noise spectrum mapping model and the dynamic suppression factor to obtain a clean voiceprint signal; and matching the clean voiceprint signal with a preset wind turbine generator fault voiceprint feature library to complete wind turbine generator voiceprint adaptive detection.

The invention provides a production method of a road sound barrier with light weight, high strength, corrosion resistance and strong sound absorption, and belongs to the technical field of road sound barriers, the production method comprises the following steps: collecting a traffic noise sample by using a high-precision microphone array, extracting noise spectrum characteristics by using a Noi seSpecNet model, and determining that the main noise frequency band is 500-4000 Hz; optimal perforation parameters are calculated through the model; accurately perforating the UHPC board according to the optimized parameters; the perforated UHPC board, basalt fiber cloth, a through-hole ceramic board, a sound insulation felt and other materials are bonded according to the sequence and technology to form a multi-layer composite structure; curing and curing in a constant-temperature and constant-humidity environment; carrying out acoustic performance testing; and finally, the qualified sound barrier unit is installed on a foundation structure and subjected to protection treatment. According to the method, the technical problem that a road sound barrier in the prior art is difficult to realize efficient sound absorption for traffic noise of a specific frequency band is solved.

The invention relates to a laser crystal non-radiative decay life measurement method based on relaxation oscillation frequency analysis of a solid laser, which comprises the following core steps of: firstly, acquiring a laser intensity noise spectrum in real time by adopting a self-zero beat noise detection device (2) in a stable operation state of the solid laser (1); obtaining a relaxation oscillation frequency measured value of the laser through spectral line feature analysis; secondly, a relaxation oscillation frequency theoretical model is built based on a laser rate equation, and a theoretical function curve graph with the non-radiative decay life of the laser crystal as an independent variable and the relaxation oscillation frequency as a dependent variable is built in combination with actual operation parameters (including pumping power, cavity structure parameters and crystal characteristics) of the laser; and finally, matching the actually measured relaxation oscillation frequency with the frequency value in the theoretical curve, and determining the non-radiative decay life value of the laser crystal through abscissa inversion. The method breaks through the limitation of a traditional measurement method, can realize non-intrusive high-precision measurement in a continuous working state of the laser, and is particularly suitable for in-situ characterization and life evaluation of non-radiation attenuation dynamics of the laser crystal in a solid laser system.

The invention relates to the technical field of photoacoustic measurement, and particularly provides measuring equipment and a frequency estimation method and a denoising method of equipment noise therein, the frequency estimation method comprises the following steps: controlling pump light and probe light to irradiate a first sample to be measured, the pump light generating ultrasonic waves on the surface of the first sample to be measured, the detection light is reflected by the surface of the first to-be-detected sample to form first detection signal light; acquiring a first photoacoustic time domain signal formed by the first detection signal light; removing a non-noise signal and a random noise signal in the first photoacoustic time domain signal to obtain a first time domain noise signal; performing time-frequency transformation on the first time-domain noise signal to obtain a first noise spectrum; and determining the frequency of the equipment noise according to the spectrum amplitude of the first noise spectrum. Thus, the device noise in the photoacoustic signal can be removed in a targeted manner according to the frequency of the device noise subsequently, and compared with a traditional overall noise removal manner, the signal-to-noise ratio and the measurement sensitivity can be improved.

The invention relates to the technical field of audio signal processing, and discloses an industrial noise identification method and system, and the method comprises the steps: obtaining an audio signal, and carrying out the time-frequency decomposition, and obtaining preliminary decomposition data; classifying the coupling features of the preliminary decomposition data to obtain a coupling region boundary value; if the boundary value of the coupling region exceeds a preset boundary threshold value, performing filtering processing to obtain separated noise spectrum data; calculating an overlapping proportion of the separated noise spectrum data and a preset target sound; if the overlapping proportion causes extraction deviation, extracting dynamic change characteristics of the separated noise spectrum data and fusing the dynamic change characteristics into a spectrum extraction process to obtain an accurate noise spectrum; calculating an intensity value according to the accurate noise spectrum, and determining an intensity evaluation result; and comparing the intensity evaluation result with a preset pollution threshold, calculating a noise pollution degree and outputting a noise identification result. The method is suitable for industrial environment noise monitoring, and can accurately extract the noise spectrum.

The invention provides a polishing end point detection method, chemical mechanical polishing equipment and a storage medium, and belongs to the field of chemical mechanical polishing. The method comprises the steps that a reflection spectrum of the surface of a wafer in the chemical mechanical polishing process is obtained; correcting the reflection spectrum by adopting a preset correction coefficient to obtain a corrected reflection spectrum; dividing the corrected reflection spectrum into a dark noise spectrum and a to-be-measured spectrum based on a comparison result of the corrected reflection spectrum and a corresponding threshold value; determining the reflectivity of the surface of the wafer based on the to-be-measured spectrum, the dark noise spectrum, the reference spectrum and a preset reference spectrum reflectivity; the wafer thickness is determined using the reflectivity of the wafer surface to detect a polishing endpoint based on the wafer thickness. The terminal point detection accuracy is improved.

The invention concerns a method for determining a substance concentration in a sample comprising liquid containing particles, in particular glucose in blood, wherein a refractive index of the liquid is dependent on a concentration of the substance dissolved therein and a density of particles in the liquid is substantially constant. The method comprises obtaining a PPG first signal during a first time period and estimating at last a portion of a noise spectrum from the first obtained signal in particular the frequency domain. At least a portion of the noise spectrum is used to derive the substance concentration or a change in the substance concentration.

The invention relates to the technical field of intelligent power grids, and discloses a multi-electric-quantity output relay protection secondary circuit test method and system, and the method comprises the steps: collecting an environment noise time sequence, obtaining a noise spectrum, and setting a filter parameter set; outputting a test signal including the feature code signal based on the noise spectrum and the filter parameter set; executing a plurality of groups of test sequences by using the test signals to obtain segmented signals; performing real-time noise suppression on the segmented signals to obtain enhanced signals; extracting a feature vector from the enhanced signal; obtaining a fault type and a fault type position coordinate based on the feature vector; according to the invention, through noise spectrum analysis and independent component analysis, high anti-interference capability and real-time noise suppression of a test signal are realized, a composite fault is successfully separated, a fault type is identified, and accurate positioning of the fault is realized through a cross-correlation function and a coordinate optimization algorithm.

The invention belongs to the technical field of battery fault recognition, and provides a battery early fault recognition method and system based on acoustic fingerprints, and the method comprises the steps: collecting an output signal of a piezoelectric sensor on the surface of a battery at a high-frequency sampling rate, removing background noise in a mode of combining a time domain spectral subtraction method with a statistical threshold, and dynamically updating noise spectrum estimation; performing parallel calculation on short-time Fourier transform, continuous wavelet transform and Mel-frequency cepstral coefficients to form a feature tensor; learning a low-dimensional fingerprint vector through a sparse auto-encoder, wherein a loss function comprises a reconstruction error term and a sparse constraint term; and a mahalanobis distance or a single-class support vector machine is used to measure a deviation degree between the fingerprint vector and the health fingerprint database, a fault confidence coefficient is output, and when the confidence coefficient exceeds a preset threshold value, early fault early warning is triggered. According to the method, characterization processing can be carried out on the acoustic data of the battery within milliseconds, the fault confidence coefficient is given, and high-sensitivity detection, easy migration, early warning and real-time performance are achieved.